diff --git a/.github/workflows/gputrain.yml b/.github/workflows/gputrain.yml
index f02be6fd..2126a117 100644
--- a/.github/workflows/gputrain.yml
+++ b/.github/workflows/gputrain.yml
@@ -50,7 +50,10 @@ jobs:
           MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }}
         run: |
           test -n "$MODAL_TOKEN_ID" || { echo "MODAL_TOKEN_ID is not set (owner-only)"; exit 1; }
-          uv run --with modal modal run scripts/modal_train.py \
+          # a call that never starts training must not wait for hours: the segment's limit
+          # plus 25 minutes for the image, the queue and saving
+          LIMIT=$(python3 -c "import json; print(json.load(open('configs/regress/${{ steps.plan.outputs.name }}.json'))['segment_limit_seconds'] + 1500)")
+          timeout "$LIMIT" uv run --with modal modal run scripts/modal_train.py \
             --plan "configs/regress/${{ steps.plan.outputs.name }}.json" \
             --out "$RUNNER_TEMP/out" --run-name "${{ steps.plan.outputs.name }}-${{ github.run_id }}" \
             --commit "${{ github.sha }}"
@@ -72,7 +75,8 @@ jobs:
           MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }}
           MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }}
         run: |
-          uv run --with modal modal run scripts/modal_train.py \
+          LIMIT=$(python3 -c "import json; print(json.load(open('configs/regress/${{ needs.segment1.outputs.name }}.json'))['segment_limit_seconds'] + 1500)")
+          timeout "$LIMIT" uv run --with modal modal run scripts/modal_train.py \
             --plan "configs/regress/${{ needs.segment1.outputs.name }}.json" \
             --out "$RUNNER_TEMP/out" --run-name "${{ needs.segment1.outputs.name }}-${{ github.run_id }}" \
             --commit "${{ github.sha }}"
diff --git a/.github/workflows/timelate.yml b/.github/workflows/timelate.yml
index fb2efcd0..97eb9266 100644
--- a/.github/workflows/timelate.yml
+++ b/.github/workflows/timelate.yml
@@ -79,6 +79,21 @@ jobs:
           EXTRA=()
           if [ "$THIN" != "plan" ]; then EXTRA+=(--thin "$THIN"); fi
           if [ "$SYSTEMS" != "plan" ]; then EXTRA+=(--systems "$SYSTEMS"); fi
+          # spec 016: a run file may name the learned model's seeds (results branch, folder,
+          # training plan); their tracks are predicted here, outside the checkout, and scored
+          # beside the controls
+          python3 - "$RUN" > "$RUNNER_TEMP/learned" <<'PY'
+          import json, sys
+          for name, v in (json.load(open(sys.argv[1])).get("learned") or {}).items():
+              print(name, v["branch"], v["dir"], v["config"])
+          PY
+          mkdir -p "$RUNNER_TEMP/ckpt" "$RUNNER_TEMP/lp"
+          while read -r LNAME LBRANCH LDIR LCONFIG; do
+            git fetch -q --depth 1 origin "$LBRANCH"
+            git show "FETCH_HEAD:$LDIR/checkpoint.safetensors" > "$RUNNER_TEMP/ckpt/$LNAME.safetensors"
+            uv run tma2-tlpredict --weights "$RUNNER_TEMP/ckpt/$LNAME.safetensors" --config "$LCONFIG" --in "$TRUTH" --plan "$PLAN" --split "${{ matrix.split }}" --start "${{ matrix.start }}" --count "${{ matrix.count }}" --out "$RUNNER_TEMP/lp/$LNAME"
+            EXTRA+=(--learned "$LNAME=$RUNNER_TEMP/lp/$LNAME")
+          done < "$RUNNER_TEMP/learned"
           uv run tma2-tlsolve --in "$TRUTH" --plan "$PLAN" --split "${{ matrix.split }}" --start "${{ matrix.start }}" --count "${{ matrix.count }}" --out "$RUNNER_TEMP/shards/${{ matrix.split }}.${{ matrix.shard }}" "${EXTRA[@]}"
       - uses: actions/upload-artifact@v4
         with:
@@ -106,7 +121,10 @@ jobs:
           PLAN="$(python3 -c "import json, sys; print(json.load(open(sys.argv[1]))['plan'])" "$RUN")"
           mkdir -p "$RUNNER_TEMP/result"
           set -o pipefail
-          uv run tma2-tlscore --shards "$RUNNER_TEMP/shards" --plan "$PLAN" --out "$RUNNER_TEMP/result" | tee "$RUNNER_TEMP/result/score.txt"
+          RECORD="$(python3 -c "import json, sys; print(json.load(open(sys.argv[1])).get('controls_record', ''))" "$RUN")"
+          EXTRA=()
+          if [ -n "$RECORD" ]; then EXTRA+=(--controls-record "$RECORD"); fi
+          uv run tma2-tlscore --shards "$RUNNER_TEMP/shards" --plan "$PLAN" --out "$RUNNER_TEMP/result" "${EXTRA[@]}" | tee "$RUNNER_TEMP/result/score.txt"
           cp "$RUN" "$RUNNER_TEMP/result/run.json"
           cp "$PLAN" "$RUNNER_TEMP/result/plan.json"
           echo "{\"run_id\": \"$GITHUB_RUN_ID\", \"commit\": \"$GITHUB_SHA\", \"cpu_model\": \"$(grep -m1 'model name' /proc/cpuinfo | cut -d: -f2 | xargs)\"}" > "$RUNNER_TEMP/result/ci.json"
diff --git a/.gitignore b/.gitignore
index 1cb10722..8691290c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -31,3 +31,4 @@ conformance/onnx/java/target/
 conformance/onnx/export720k/
 conformance/onnx/evidence/
 experiments/**/errors.npz
+.claude/worktrees/
diff --git a/AGENTS.md b/AGENTS.md
index 1c0358bf..9de141db 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -350,6 +350,89 @@ learned model. Seeds 333–335 used; 324, 325 unused; the next fresh seed is 336
 **Owner 2026-11-13: repair and re-register (spec 015)**: fix the duration volume, the edge rule
 and the drifting reference; decide the same quantities on fresh seeds (336 validation, 337 test);
 seeds 333–335 become development data. Nothing trained, no GPU.
+015 (time-late repair and re-registration, `specs/015-time-late-repair/`; done; a registered study,
+not a gate; nothing trained): prior version 3 (`prior.open_precision`: volume = 1/density for
+every factor without a Gaussian residual), `timelate/match.py` (`same_history`, `cut_flags`: one
+edge-aware rule), `run3.py` (plan key `runner: run3`; reference held to the true times and flagged
+when it drifts; refit fall-back), `study3.py`, `scripts/research/tl3_tables.py` (every record's
+`tables.md` generated from `summary.json`). Development on spent seed 335
+(`experiments/20261113-tl3-dev/`), decision `20261113-tl3-decision/` (plan
+`configs/timelate/tl-d.json` `a6556059…`, code `3b67d026…`, never edited), validation seed 336
+(`20261113-tl3-val/`, soundness passed), **test record `experiments/20261113-tl3/` (seed 337, 364
+scored periods): D1 0.33 [0.20, 0.45]; D2 first candidate right 0.64, wrong histories 0.44 / 0.13 /
+0.43; D3 headroom 1.06 (perfect search) and 1.17 [1.10, 1.25], lower limit 1.099 (perfect evidence
+as well): inconclusive; D4 held-course false manoeuvres: search 0.53 (0.80 for spec 014's search on
+the same periods; 0.09 under 30 min, 1.00 over 90), C″ 0.02, flat 0.01, mixed 0.00; D5 by the
+rule: no GPU budget. Decided by the panel, round 1** (`review.md`, five approve with findings; no
+bug changes a decided number). Narrowed: the headroom depends most on how the reference is fitted
+(1.11 to 1.19; the 19 held references carry a third of it, ten periods over half; 1.08 to 1.30
+across registered sensitivities): an oracle figure, not a limit; the rule's "no" stands and an
+override would be a departure from it (the first record's "a judgement, the owner's" is corrected);
+D1 is "between a third and a half" and mostly restates how often C″ has the history right; D2's
+ambiguous share is the threshold's (0.11 / 0.44 / 0.66 at 1 / 3 / 5); the 0.09 on short held-course
+periods is not what a correct evidence gives (integrated over where a manoeuvre might be, Annex B's
+prior favours "some manoeuvre" almost everywhere); **the evidence repair is not finished**: version
+3's pairing fails at the edges of the prior's support and the 10 s floor (1 to 3.5 nats; recorded,
+not repaired, with smaller ones in `review.md`: `_leg_tail` zero beyond 5,868 s, combined
+manoeuvres' acceleration unconstrained, speed after a change unpaired). The repair trades false
+manoeuvres for missed single ones (one-manoeuvre targets called quiet 0.13 against 0.04; mixed
+prior 0.20; re-ranked at a quiet probability of 0.01: 0.09 false, 0.14 missed). Not delivered: each
+repair's effect separated per decided quantity; unit tests for `run3`/`study3`; the fall-back
+caught one of seven runaway refits. Gaps where the simulator's assumptions fail (search ÷ oracle:
+shifted prior 1.7, correlated errors 1.4, outliers 1.5) have no classical remedy yet. Write-up
+`site/blog/2026-11-13-time-late-repair.md`, slides `2026-11-13-time-late-repair-slides.md` (figures
+`scripts/research/tl3_charts.py`). Panel's order (owner's choice): a hedged track from the stored
+candidates and a declaration margin; robust and correlated-error likelihoods and a prior that can
+be wrong, scored on the mismatch splits; then a learned model registered against those; no fourth
+Annex B identification study. Seeds 336, 337 used; 324, 325 unused; the next fresh seed is 338.
+**Owner 2026-11-13: option B (spec 016)**: classical controls on CPU and, in parallel, a learned
+whole-period model on GPU under a plan registered before any result, scored only once the controls
+exist; within the $50 total.
+016 (time-late controls and a learned whole-period model, study L, `specs/016-time-late-learned/`;
+done; SRD §5 row proposed, owner merges). **Controls** (nothing trained; `timelate/hedge.py`,
+`corr.py`, `run4.py` with plan key `runner: run4`, `study4.py`): wild bearings edited out; an
+effective bearing sd from the error correlation estimated from the bearings' second differences
+(used above a share of 0.2); the search under Annex B's prior and a broadened one
+(`configs/timelate/tl-broad.json`), pooled; a declared history (evidence margin 1, quiet probability
+0.01: `ctl-d1-q0.01`) and a hedged track (temperature 1: `ctl-h1`); the combined control `ctl` is
+not told the conditions. Tuning seed 338 (`experiments/20261113-tl4-tuning/`). **Model**
+(`regress/period_data.py`, `period_predict.py`, `model.py` with `causal=False` and a manoeuvre
+head, `timelate/learned.py`, `tma2-tlpredict`; mix `configs/regress/tlm-mix.json`, plans
+`tlm-s1..s3.json`): a bidirectional transformer over 240 tokens of six 5-second rows, three seeds of
+12.5 million simulated periods on a T4 each (pilot `experiments/20261113-tlm-pilot/`, runs
+`experiments/20261114-tlm-runs/`, $14; November's Modal total $41 of the owner's $50); reported track
+`learned-sK-arc` (the arc model fitted at the model's manoeuvre spans, probability over 0.5), own
+track beside. Decision `experiments/20261113-tl4-decision/` (plan `configs/timelate/tl-e.json`
+`82cd70ee…`, code `e5040f5e…`, never edited; two addenda); controls' validation seed 340
+(`20261113-tl4-val/`, L-a passed; `tma2-tlscore --controls-record` refuses a learned track without
+it); **test record `experiments/20261114-tl4/` (seed 341, 2,860 periods, scored once): learning
+shown on no split (declared control ÷ learned 0.80 to 0.82 shifted, 0.87 to 0.94 shifted 2, 0.83
+correlated, 0.77 to 0.81 outliers, 0.97 to 1.00 bias), harm on Annex B (0.81 to 0.82), the learned
+track ahead on held course (1.06): by the rule the classical controls stand and the learned track
+stops. Decided by the panel, round 1** (`review.md`, five approve with findings; all 42 ratios
+reproduce). Controls against the standing search: 1.68 with 2% outliers, 2.44 with 5%, 1.33
+correlated, 1.70 held course (false manoeuvres 0.08 against 0.49), 1.03 shifted, 1.00 bias, 1.09
+Annex B (0.96 on validation); replicated on three seeds. Narrowed: the 20% is a tail (median
+per-period ratio 1.00 everywhere; level where the model declares as many manoeuvres as the control,
+about twice as bad where it declares fewer): **the registered comparison measured the model's
+untuned detector against the evidence search**, so "this model with this read-out stops"; the own
+track is 0.71 of the control on Annex B and level under the shifted prior in range only (course
+0.79, speed 0.77; a win where the bound is loose cancels a loss by three where it is tight); the
+mismatch conditions lie inside the model's training ranges (it lost in-distribution); a search
+started from the model equals the control at a third of the time (tuning data, not a result); the
+control is not free (one-manoeuvre targets called quiet 0.29 against 0.05 with correlated errors),
+does nothing under bias, and knows the generator's error family; the shifted-prior gap (control ÷
+true-history reference 1.7 to 2.1 by convention) is open for both and may be identifiability. Not
+delivered: a working prior-mismatch diagnostic (`log_bayes_broad` is not a Bayes factor), a Huber
+cost, a whitened fit, prior version 4, the export check on trained models, IMM/EKF tracks beside,
+the weights' digests in the test record, unit tests for `run4`/`study4`. Not tested: a learned
+proposer inside the search, training through the arc layer, a larger budget. Write-up
+`site/blog/2026-11-14-time-late-learned.md`, slides `2026-11-14-time-late-learned-slides.md`
+(figures `scripts/research/tl4_charts.py`; tables `tl4_tables.py`, `tl4_learned_tables.py`,
+`tl4_supplement.py`). Panel's order (owner's choice): adopt `ctl` as the standing time-late system
+and study whether the shifted-prior gap is identifiability (classical, CPU); only if it is not, the
+trained models as proposers inside the search. Seeds 338 to 341 used; 324, 325 unused; the next
+fresh seed is 342.
 
 ## Stack (owner decisions, 2026-10-31)
 
@@ -675,6 +758,35 @@ seeds per carried configuration; nothing above OFFICIAL, mock data only.
   0.43). Before running an adaptive rule, run it on noise alone. "At no cost" needs the counts
   by case, not a pooled median. A fixture pinned to 1e-9 fails on another CPU family; pin the
   well-determined outputs tightly and the rest loosely.
+- Spec 015 panel: a repair to a reference changes every statistic that reads it; report the
+  decided figure under each way of fitting the reference, not only the registered one. A fit told
+  the true times knows more than which history is right. Do not register as a decision rule a
+  line you forecast as a coin's toss at the planned sample size; and when a rule returns "no" by
+  a hair, report the rule's output as the outcome and do not reword it as a judgement. Test a
+  density-volume pairing at the edges of the support and at solver bounds, not only inside.
+  Comparing one Laplace mode with "no manoeuvre" is not the evidence for "some manoeuvre":
+  integrate over where it might be. Report a class share at every registered threshold. Commit
+  the script that checks a README's numbers, or do not claim the check. A forecast written from
+  a development run minutes earlier is a restatement, not a prediction.
+- Spec 016 panel: a registered comparison is only as wide as its narrowest part: thresholding a
+  model's manoeuvre probability at an untuned 0.5 and fitting a classical layer at those spans
+  measures the detector, not the model. Tune both sides or neither, and score the learned side on
+  validation as soon as the order allows. Report the median per-period ratio and the shares over
+  twice as bad beside any geometric mean (the 20% was a tail; the median was 1.00), and split a
+  loss by whether the two sides agree on structure. Read a "level" pooled ratio by how tight the
+  bound is and on course and speed before writing "level". A held-out condition inside the
+  training ranges is not a mismatch for the model. A reference fitted to unedited bearings is no
+  yardstick under outliers. Keep the declared and undeclared variants of a control in separate
+  rows. Record the weights' digest in the scored record, not only in shard headers. A change of
+  shard size reaches every system that starts from C″. Never edit a file the decision record
+  binds while its run is in progress, even to add a field; put new readings in a new script.
+- Modal: functions must be defined at module scope with the decorator; functions built in a loop
+  with `serialized=True` cannot be imported in the container ("the 'modal_train' module is not
+  available"), and Modal retries the failed start for as long as the client waits. Bound the
+  workflow step with `timeout`. The ledger loses a replaced container's time.
+- A script that writes a record must run under `uv run python`: a plain `python3` here has no
+  torch, the script fails, and a chained `git commit` still commits whatever else was staged
+  (spec 016's decision record took two commits). Check the commit holds what its message says.
 - Modal rate-limits app creation: of five `modal run` jobs started within ten seconds, two were
   refused ("App create rate limit exceeded") before any container started. Launch GPU plans a
   minute apart, or re-run the refused workflow run (the plan and its digest are untouched).
diff --git a/configs/regress/tlm-mix.json b/configs/regress/tlm-mix.json
new file mode 100644
index 00000000..684ecc9b
--- /dev/null
+++ b/configs/regress/tlm-mix.json
@@ -0,0 +1,64 @@
+{
+  "description": "Spec 016 research R9: the training mix of the learned whole-period model. Every engagement of the stream draws its family and its bearing-error overlay from its own sub-stream of the stream seed (tma2.regress.period_data). Families: 40% Annex B plus the 25% R9 leaves unassigned (0.65 in all), 20% randomised statistics, 15% quiet. Independently, a quarter of all engagements carry an overlay: correlated error, outliers or both, a third each. Bias and the unknown-sigma factor are never in training (the absent-from-training split). Each engagement is cut to a period as Gate T does (tma2.timelate.periods.cut).",
+  "base_ranges": "configs/timelate/tl-ranges.json",
+  "quiet_ranges": "configs/timelate/tl-quiet-ranges.json",
+  "families": {
+    "annex_b": 0.65,
+    "randomised": 0.2,
+    "quiet": 0.15
+  },
+  "randomised": {
+    "leg_mean_s": {
+      "min": 600,
+      "max": 2400
+    },
+    "leg_sd_over_mean": 0.3,
+    "leg_min_s": 300,
+    "scale": {
+      "min": 0.5,
+      "max": 1.5
+    },
+    "scaled": [
+      "target_manoeuvre_angle_abs_rad (min and max, one factor; max at most pi)",
+      "target_turn_rate_rad_s.max",
+      "target_speed_change_abs_mps.max",
+      "target_speed_change_rate_mps2.max"
+    ],
+    "course_weight": {
+      "min": 0.5,
+      "max": 0.9
+    },
+    "speed_share_of_rest": {
+      "min": 0.3,
+      "max": 0.7
+    }
+  },
+  "overlay": {
+    "fraction": 0.25,
+    "kinds": {
+      "correlated": 0.3333333333333333,
+      "outliers": 0.3333333333333333,
+      "both": 0.3333333333333334
+    },
+    "corr_tau_s": {
+      "min": 20,
+      "max": 240
+    },
+    "corr_share": {
+      "min": 0.2,
+      "max": 0.7
+    },
+    "outlier_rate": {
+      "min": 0,
+      "max": 0.06
+    },
+    "outlier_spread": {
+      "min": 5,
+      "max": 15
+    }
+  },
+  "held_out": [
+    "bias",
+    "sigma_factor"
+  ]
+}
diff --git a/configs/regress/tlm-pilot.json b/configs/regress/tlm-pilot.json
new file mode 100644
index 00000000..110bf378
--- /dev/null
+++ b/configs/regress/tlm-pilot.json
@@ -0,0 +1,35 @@
+{
+  "description": "Spec 016 T009: the pilot of the whole-period model on a T4 (research R7, R10): the real architecture (8 layers, width 256, 8 heads, 240 tokens of 6 five-second rows, non-causal, manoeuvre logit), one segment of at most 50 minutes (an hour with the start-up allowance). Measures periods per second and gives a loss curve; 6,000 updates of 256 periods complete inside the limit only above about 510 periods a second, otherwise the limit stops it. Not a training run: its weights are not carried.",
+  "gpu": "T4",
+  "config": {
+    "id": "tlm-pilot",
+    "seed": 1,
+    "warmup_updates": 500,
+    "total_updates": 6000,
+    "val_every_updates": 500,
+    "limit_seconds": 3000,
+    "layers": 8,
+    "width": 256,
+    "heads": 8,
+    "feedforward": 1024,
+    "batch_scenarios": 256,
+    "learning_rate": 0.0003,
+    "weight_decay": 0.01,
+    "val_count": 512,
+    "checkpoint_seconds": 900,
+    "precision": "amp",
+    "patch_rows": 6,
+    "max_rows": 1440,
+    "causal": false,
+    "manoeuvre_head": true,
+    "manoeuvre_weight": 0.1,
+    "n_features": 9,
+    "data": "periods",
+    "mix": "configs/regress/tlm-mix.json",
+    "stream_seed": 339,
+    "val_seed": 339,
+    "stream_start": 0
+  },
+  "segments": 1,
+  "segment_limit_seconds": 3000
+}
diff --git a/configs/regress/tlm-s1.json b/configs/regress/tlm-s1.json
new file mode 100644
index 00000000..febcf2ed
--- /dev/null
+++ b/configs/regress/tlm-s1.json
@@ -0,0 +1,35 @@
+{
+  "description": "Spec 016 T011: the whole-period model, training seed 1 (research R7, R9, R10). 49,000 updates of 256 periods (12.5 million periods from stream seed 339), two segments of at most 4 h 20 min on a T4 (9 h with the start-up allowances); the pilot measured 453 periods a second while training (experiments/20261113-tlm-pilot/). Bound by digest in experiments/20261113-tl4-decision/; never edited after.",
+  "gpu": "T4",
+  "config": {
+    "id": "tlm-s1",
+    "seed": 1,
+    "warmup_updates": 2000,
+    "total_updates": 49000,
+    "val_every_updates": 1000,
+    "limit_seconds": 15600,
+    "layers": 8,
+    "width": 256,
+    "heads": 8,
+    "feedforward": 1024,
+    "batch_scenarios": 256,
+    "learning_rate": 0.0003,
+    "weight_decay": 0.01,
+    "val_count": 512,
+    "checkpoint_seconds": 900,
+    "precision": "amp",
+    "patch_rows": 6,
+    "max_rows": 1440,
+    "causal": false,
+    "manoeuvre_head": true,
+    "manoeuvre_weight": 0.1,
+    "n_features": 9,
+    "data": "periods",
+    "mix": "configs/regress/tlm-mix.json",
+    "stream_seed": 339,
+    "val_seed": 339,
+    "stream_start": 0
+  },
+  "segments": 2,
+  "segment_limit_seconds": 15600
+}
diff --git a/configs/regress/tlm-s2.json b/configs/regress/tlm-s2.json
new file mode 100644
index 00000000..c2fe1640
--- /dev/null
+++ b/configs/regress/tlm-s2.json
@@ -0,0 +1,35 @@
+{
+  "description": "Spec 016 T011: the whole-period model, training seed 2 (research R7, R9, R10). 49,000 updates of 256 periods (12.5 million periods from stream seed 339), two segments of at most 4 h 20 min on a T4 (9 h with the start-up allowances); the pilot measured 453 periods a second while training (experiments/20261113-tlm-pilot/). Bound by digest in experiments/20261113-tl4-decision/; never edited after.",
+  "gpu": "T4",
+  "config": {
+    "id": "tlm-s2",
+    "seed": 2,
+    "warmup_updates": 2000,
+    "total_updates": 49000,
+    "val_every_updates": 1000,
+    "limit_seconds": 15600,
+    "layers": 8,
+    "width": 256,
+    "heads": 8,
+    "feedforward": 1024,
+    "batch_scenarios": 256,
+    "learning_rate": 0.0003,
+    "weight_decay": 0.01,
+    "val_count": 512,
+    "checkpoint_seconds": 900,
+    "precision": "amp",
+    "patch_rows": 6,
+    "max_rows": 1440,
+    "causal": false,
+    "manoeuvre_head": true,
+    "manoeuvre_weight": 0.1,
+    "n_features": 9,
+    "data": "periods",
+    "mix": "configs/regress/tlm-mix.json",
+    "stream_seed": 339,
+    "val_seed": 339,
+    "stream_start": 0
+  },
+  "segments": 2,
+  "segment_limit_seconds": 15600
+}
diff --git a/configs/regress/tlm-s3.json b/configs/regress/tlm-s3.json
new file mode 100644
index 00000000..9a8a53e2
--- /dev/null
+++ b/configs/regress/tlm-s3.json
@@ -0,0 +1,35 @@
+{
+  "description": "Spec 016 T011: the whole-period model, training seed 3 (research R7, R9, R10). 49,000 updates of 256 periods (12.5 million periods from stream seed 339), two segments of at most 4 h 20 min on a T4 (9 h with the start-up allowances); the pilot measured 453 periods a second while training (experiments/20261113-tlm-pilot/). Bound by digest in experiments/20261113-tl4-decision/; never edited after.",
+  "gpu": "T4",
+  "config": {
+    "id": "tlm-s3",
+    "seed": 3,
+    "warmup_updates": 2000,
+    "total_updates": 49000,
+    "val_every_updates": 1000,
+    "limit_seconds": 15600,
+    "layers": 8,
+    "width": 256,
+    "heads": 8,
+    "feedforward": 1024,
+    "batch_scenarios": 256,
+    "learning_rate": 0.0003,
+    "weight_decay": 0.01,
+    "val_count": 512,
+    "checkpoint_seconds": 900,
+    "precision": "amp",
+    "patch_rows": 6,
+    "max_rows": 1440,
+    "causal": false,
+    "manoeuvre_head": true,
+    "manoeuvre_weight": 0.1,
+    "n_features": 9,
+    "data": "periods",
+    "mix": "configs/regress/tlm-mix.json",
+    "stream_seed": 339,
+    "val_seed": 339,
+    "stream_start": 0
+  },
+  "segments": 2,
+  "segment_limit_seconds": 15600
+}
diff --git a/configs/regress/tlm-toy.json b/configs/regress/tlm-toy.json
new file mode 100644
index 00000000..eb53b163
--- /dev/null
+++ b/configs/regress/tlm-toy.json
@@ -0,0 +1,33 @@
+{
+  "description": "Spec 016: the whole-period model in miniature, for tests and CPU dry runs (30 updates of 16 periods from stream seed 339).",
+  "config": {
+    "id": "tlm-toy",
+    "seed": 1,
+    "layers": 2,
+    "width": 64,
+    "heads": 4,
+    "feedforward": 128,
+    "batch_scenarios": 16,
+    "learning_rate": 0.001,
+    "warmup_updates": 5,
+    "total_updates": 30,
+    "val_count": 32,
+    "val_every_updates": 10,
+    "limit_seconds": 600,
+    "checkpoint_seconds": 60,
+    "precision": "float32",
+    "patch_rows": 6,
+    "max_rows": 1440,
+    "causal": false,
+    "manoeuvre_head": true,
+    "manoeuvre_weight": 0.1,
+    "n_features": 9,
+    "data": "periods",
+    "mix": "configs/regress/tlm-mix.json",
+    "stream_seed": 339,
+    "val_seed": 339,
+    "stream_start": 0
+  },
+  "segments": 1,
+  "segment_limit_seconds": 600
+}
diff --git a/configs/timelate/runs/tl-d-test.json b/configs/timelate/runs/tl-d-test.json
new file mode 100644
index 00000000..1c75b040
--- /dev/null
+++ b/configs/timelate/runs/tl-d-test.json
@@ -0,0 +1,33 @@
+{
+ "plan": "configs/timelate/tl-d.json",
+ "seed": 337,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift": {
+   "start": 0,
+   "periods": 300
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 400
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 100
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 100
+  },
+  "whole": {
+   "start": 0,
+   "periods": 60
+  }
+ },
+ "shard_size": 25,
+ "what": "spec 015 test: seed 337 (never generated before), scored once"
+}
diff --git a/configs/timelate/runs/tl-d-val.json b/configs/timelate/runs/tl-d-val.json
new file mode 100644
index 00000000..dd467579
--- /dev/null
+++ b/configs/timelate/runs/tl-d-val.json
@@ -0,0 +1,17 @@
+{
+ "plan": "configs/timelate/tl-d.json",
+ "seed": 336,
+ "count": 450,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 150
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 150
+  }
+ },
+ "shard_size": 25,
+ "what": "spec 015 validation: seed 336 (never generated before); the soundness stop"
+}
diff --git a/configs/timelate/runs/tl-e-test.json b/configs/timelate/runs/tl-e-test.json
new file mode 100644
index 00000000..9446bcc8
--- /dev/null
+++ b/configs/timelate/runs/tl-e-test.json
@@ -0,0 +1,67 @@
+{
+ "plan": "configs/timelate/tl-e.json",
+ "seed": 341,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift2": {
+   "start": 0,
+   "periods": 400
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-corr-mild": {
+   "start": 0,
+   "periods": 200
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-out-harsh": {
+   "start": 0,
+   "periods": 200
+  },
+  "man2-bias": {
+   "start": 0,
+   "periods": 300
+  },
+  "whole": {
+   "start": 0,
+   "periods": 60
+  }
+ },
+ "shard_size": 12,
+ "what": "spec 016 test: seed 341 (never generated before), scored once. The learned seeds' results branches and the controls' validation record are added in an addendum to the decision record before launch; the splits and sizes here are fixed.",
+ "controls_record": "experiments/20261113-tl4-val",
+ "learned": {
+  "learned-s1": {
+   "branch": "results/gputrain-tlm-s1-43507401545",
+   "dir": "experiments/gputrain-runs/tlm-s1-43507401545",
+   "config": "configs/regress/tlm-s1.json"
+  },
+  "learned-s2": {
+   "branch": "results/gputrain-tlm-s2-43507608865",
+   "dir": "experiments/gputrain-runs/tlm-s2-43507608865",
+   "config": "configs/regress/tlm-s2.json"
+  },
+  "learned-s3": {
+   "branch": "results/gputrain-tlm-s3-43507809179",
+   "dir": "experiments/gputrain-runs/tlm-s3-43507809179",
+   "config": "configs/regress/tlm-s3.json"
+  }
+ }
+}
\ No newline at end of file
diff --git a/configs/timelate/runs/tl-e-tune.json b/configs/timelate/runs/tl-e-tune.json
new file mode 100644
index 00000000..6541e26e
--- /dev/null
+++ b/configs/timelate/runs/tl-e-tune.json
@@ -0,0 +1,45 @@
+{
+ "plan": "configs/timelate/tl-e-draft.json",
+ "seed": 338,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 150
+  },
+  "shift": {
+   "start": 0,
+   "periods": 100
+  },
+  "shift2": {
+   "start": 0,
+   "periods": 100
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 150
+  },
+  "man2-corr-mild": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out-harsh": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-bias": {
+   "start": 0,
+   "periods": 60
+  }
+ },
+ "shard_size": 10,
+ "what": "spec 016 tuning: seed 338, the controls' settings (temperature, margin, quiet probability) chosen here"
+}
\ No newline at end of file
diff --git a/configs/timelate/runs/tl-e-val.json b/configs/timelate/runs/tl-e-val.json
new file mode 100644
index 00000000..dc18c066
--- /dev/null
+++ b/configs/timelate/runs/tl-e-val.json
@@ -0,0 +1,45 @@
+{
+ "plan": "configs/timelate/tl-e.json",
+ "seed": 340,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 150
+  },
+  "shift": {
+   "start": 0,
+   "periods": 100
+  },
+  "shift2": {
+   "start": 0,
+   "periods": 100
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 100
+  },
+  "man2-corr-mild": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out-harsh": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-bias": {
+   "start": 0,
+   "periods": 80
+  }
+ },
+ "shard_size": 10,
+ "what": "spec 016 validation: seed 340, the classical controls only (L-a); the learned model is not scored here"
+}
\ No newline at end of file
diff --git a/configs/timelate/tl-broad.json b/configs/timelate/tl-broad.json
new file mode 100644
index 00000000..216eff09
--- /dev/null
+++ b/configs/timelate/tl-broad.json
@@ -0,0 +1,97 @@
+{
+ "window_s": {
+  "min": 2400,
+  "max": 7200
+ },
+ "dt_s": 5,
+ "ownship_speed_mps": {
+  "min": 2.5722222222222224,
+  "max": 7.716666666666667
+ },
+ "zig_angle_abs_rad": {
+  "min": 0.5235987755982988,
+  "max": 1.5707963267948966
+ },
+ "zig_fraction": {
+  "min": 0.4,
+  "max": 0.6
+ },
+ "target_range0_m": {
+  "min": 2000,
+  "max": 20000
+ },
+ "range_distribution": "log_uniform",
+ "target_speed_mps": {
+  "min": 1.5433333333333334,
+  "max": 10.28888888888889
+ },
+ "noise_sigma_rad_values": [
+  0.017453292519943295,
+  0.03490658503988659
+ ],
+ "dropout_fraction": {
+  "min": 0,
+  "max": 0.1
+ },
+ "ownship_turn_rate_rad_s": {
+  "min": 0.008726646259971648,
+  "max": 0.05235987755982989
+ },
+ "min_range_m": 100,
+ "zig_rule": "across_los",
+ "los_offset_rad": {
+  "min": 0.5235987755982988,
+  "max": 1.5707963267948966
+ },
+ "zig2_delay_s": {
+  "min": 180,
+  "max": 300
+ },
+ "target_manoeuvre_angle_abs_rad": {
+  "min": 0.17453292519943295,
+  "max": 2.6179938779914944
+ },
+ "target_turn_rate_rad_s": {
+  "min": 0.002908882086657216,
+  "max": 0.026179938779914945
+ },
+ "target_speed_change_abs_mps": {
+  "min": 0.6859259259259259,
+  "max": 4.630000000000001
+ },
+ "target_speed_change_rate_mps2": {
+  "min": 0.006666666666666667,
+  "max": 0.075
+ },
+ "target_leg_length_s": {
+  "mean": 1320,
+  "sd": 819,
+  "min": 300
+ },
+ "target_change_kind_weights": {
+  "course": 0.5,
+  "speed": 0.25,
+  "both": 0.25
+ },
+ "ownship_wander": {
+  "sd_rad": 0.02617993877991494,
+  "tau_s": 60,
+  "clip_rad": 0.06981317007977318
+ },
+ "ownship_oscillation": {
+  "amp_rad": {
+   "min": 0.017453292519943295,
+   "max": 0.05235987755982989
+  },
+  "period_s": {
+   "min": 120,
+   "max": 360
+  }
+ },
+ "ownship_wander_weights": {
+  "none": 0.4,
+  "wander": 0.3,
+  "oscillation": 0.3,
+  "both": 0
+ }
+}
\ No newline at end of file
diff --git a/configs/timelate/tl-d.json b/configs/timelate/tl-d.json
new file mode 100644
index 00000000..8655badd
--- /dev/null
+++ b/configs/timelate/tl-d.json
@@ -0,0 +1,74 @@
+{
+ "description": "Spec 015's registered study (2026-11-13): spec 014's quantities decided again with the repaired evidence (prior version 3), one edge-aware rule for a history's identity, a reference held where it drifts and a refit control with a fall-back. Bound by digest in experiments/20261113-tl3-decision/; never edited after.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics (the search's prior is right)"
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs (mean 15 min), smaller turns (15 to 75 degrees), faster turn rates, more speed changes: the search's prior is wrong"
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course for the whole engagement (spec 014 FR-009)"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with correlated bearing errors (60 s, half the variance)",
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with 2% outlying bearings (10 sd)",
+   "condition": "out-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "whole": true,
+   "what": "Annex B engagements uncut (40 to 120 minutes)"
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 5,
+  "score_every": 6,
+  "systems": [
+   "search2",
+   "search3",
+   "search3-flat",
+   "search3-mix",
+   "c2-refit"
+  ],
+  "thinning": "none"
+ },
+ "temperature": 3.0,
+ "top": 3,
+ "runner": "run3",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "class_threshold": 3.0,
+  "match_tolerance_s": 120.0,
+  "materiality": 1.1,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "quiet_interval_halfwidth_max": 0.05
+ },
+ "reference_flagged_max": 0.25
+}
diff --git a/configs/timelate/tl-e-draft.json b/configs/timelate/tl-e-draft.json
new file mode 100644
index 00000000..c9c50b05
--- /dev/null
+++ b/configs/timelate/tl-e-draft.json
@@ -0,0 +1,158 @@
+{
+ "description": "Spec 016 DRAFT (tuning seed 338 only; never a registered result): the classical controls beside the standing search.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics, independent Gaussian errors",
+   "systems": [
+    "search3",
+    "edit",
+    "corr",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs, smaller turns, faster turn rates, more speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift2": {
+   "ranges": "configs/timelate/tl-shift2-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "longer legs (mean 35 min), larger and slower turns, few speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "man2-corr-mild": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 30 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-mild"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 60 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "2% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-central"
+  },
+  "man2-out-harsh": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "5% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-harsh"
+  },
+  "man2-bias": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "a slowly varying bias (0.5 degree, 30 min): addressed by no control and absent from the model's training",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "condition": "bias-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B engagements uncut",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "whole": true
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "prior_broad": "configs/timelate/tl-broad.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 10,
+  "score_every": 6,
+  "temperatures": [
+   1,
+   2,
+   3,
+   5
+  ],
+  "margins": [
+   0,
+   1,
+   2,
+   3,
+   5
+  ],
+  "quiets": [
+   0,
+   0.01,
+   0.1
+  ],
+  "store_tracks": 20
+ },
+ "runner": "run4",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "match_tolerance_s": 120.0,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "reference_flagged_max": 0.25
+ }
+}
\ No newline at end of file
diff --git a/configs/timelate/tl-e.json b/configs/timelate/tl-e.json
new file mode 100644
index 00000000..06253233
--- /dev/null
+++ b/configs/timelate/tl-e.json
@@ -0,0 +1,284 @@
+{
+ "description": "Spec 016's registered study (2026-11-13): the classical controls (settings from tuning seed 338) and the learned whole-period model's three seeds, scored on the same periods. Bound by digest in the decision record; never edited after.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics, independent Gaussian errors",
+   "systems": [
+    "search3",
+    "edit",
+    "corr",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs, smaller turns, faster turn rates, more speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift2": {
+   "ranges": "configs/timelate/tl-shift2-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "longer legs (mean 35 min), larger and slower turns, few speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "man2-corr-mild": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 30 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-mild"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 60 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "2% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-central"
+  },
+  "man2-out-harsh": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "5% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-harsh"
+  },
+  "man2-bias": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "a slowly varying bias (0.5 degree, 30 min): addressed by no control and absent from the model's training",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "condition": "bias-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B engagements uncut",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "whole": true
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "prior_broad": "configs/timelate/tl-broad.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 10,
+  "score_every": 6,
+  "temperatures": [
+   1
+  ],
+  "margins": [
+   1
+  ],
+  "quiets": [
+   0.01
+  ],
+  "store_tracks": 20
+ },
+ "runner": "run4",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "match_tolerance_s": 120.0,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "reference_flagged_max": 0.25
+ },
+ "comparisons": [
+  [
+   "ctl-d1-q0.01",
+   "learned-s1-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s1-arc"
+  ],
+  [
+   "search3",
+   "learned-s1-arc"
+  ],
+  [
+   "c2",
+   "learned-s1-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s2-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s2-arc"
+  ],
+  [
+   "search3",
+   "learned-s2-arc"
+  ],
+  [
+   "c2",
+   "learned-s2-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s3-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s3-arc"
+  ],
+  [
+   "search3",
+   "learned-s3-arc"
+  ],
+  [
+   "c2",
+   "learned-s3-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s1"
+  ],
+  [
+   "ctl-h1",
+   "learned-s1"
+  ],
+  [
+   "search3",
+   "learned-s1"
+  ],
+  [
+   "c2",
+   "learned-s1"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s2"
+  ],
+  [
+   "ctl-h1",
+   "learned-s2"
+  ],
+  [
+   "search3",
+   "learned-s2"
+  ],
+  [
+   "c2",
+   "learned-s2"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s3"
+  ],
+  [
+   "ctl-h1",
+   "learned-s3"
+  ],
+  [
+   "search3",
+   "learned-s3"
+  ],
+  [
+   "c2",
+   "learned-s3"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "ctl-h1"
+  ],
+  [
+   "search3",
+   "ctl-d1-q0.01"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "c2"
+  ]
+ ],
+ "criteria": {
+  "control": "ctl-d1-q0.01",
+  "hedged": "ctl-h1",
+  "learned": [
+   "learned-s1",
+   "learned-s2",
+   "learned-s3"
+  ],
+  "mismatch_splits": [
+   "shift",
+   "shift2",
+   "man2-corr",
+   "man2-out",
+   "man2-bias"
+  ],
+  "no_harm_splits": [
+   "man2",
+   "quiet"
+  ]
+ },
+ "learned_plans": {
+  "learned-s1": "configs/regress/tlm-s1.json",
+  "learned-s2": "configs/regress/tlm-s2.json",
+  "learned-s3": "configs/regress/tlm-s3.json"
+ }
+}
\ No newline at end of file
diff --git a/configs/timelate/tl-shift2-ranges.json b/configs/timelate/tl-shift2-ranges.json
new file mode 100644
index 00000000..af2ab467
--- /dev/null
+++ b/configs/timelate/tl-shift2-ranges.json
@@ -0,0 +1,97 @@
+{
+ "window_s": {
+  "min": 2400,
+  "max": 7200
+ },
+ "dt_s": 5,
+ "ownship_speed_mps": {
+  "min": 2.5722222222222224,
+  "max": 7.716666666666667
+ },
+ "zig_angle_abs_rad": {
+  "min": 0.5235987755982988,
+  "max": 1.5707963267948966
+ },
+ "zig_fraction": {
+  "min": 0.4,
+  "max": 0.6
+ },
+ "target_range0_m": {
+  "min": 2000,
+  "max": 20000
+ },
+ "range_distribution": "log_uniform",
+ "target_speed_mps": {
+  "min": 1.5433333333333334,
+  "max": 10.28888888888889
+ },
+ "noise_sigma_rad_values": [
+  0.017453292519943295,
+  0.03490658503988659
+ ],
+ "dropout_fraction": {
+  "min": 0,
+  "max": 0.1
+ },
+ "ownship_turn_rate_rad_s": {
+  "min": 0.008726646259971648,
+  "max": 0.05235987755982989
+ },
+ "min_range_m": 100,
+ "zig_rule": "across_los",
+ "los_offset_rad": {
+  "min": 0.5235987755982988,
+  "max": 1.5707963267948966
+ },
+ "zig2_delay_s": {
+  "min": 180,
+  "max": 300
+ },
+ "target_manoeuvre_angle_abs_rad": {
+  "min": 1.0471975511965976,
+  "max": 2.792526803190927
+ },
+ "target_turn_rate_rad_s": {
+  "min": 0.0035,
+  "max": 0.0105
+ },
+ "target_speed_change_abs_mps": {
+  "min": 1.028888888888889,
+  "max": 3.086666666666667
+ },
+ "target_speed_change_rate_mps2": {
+  "min": 0.01,
+  "max": 0.05
+ },
+ "target_leg_length_s": {
+  "mean": 2100,
+  "sd": 600,
+  "min": 300
+ },
+ "target_change_kind_weights": {
+  "course": 0.9,
+  "speed": 0.05,
+  "both": 0.05
+ },
+ "ownship_wander": {
+  "sd_rad": 0.02617993877991494,
+  "tau_s": 60,
+  "clip_rad": 0.06981317007977318
+ },
+ "ownship_oscillation": {
+  "amp_rad": {
+   "min": 0.017453292519943295,
+   "max": 0.05235987755982989
+  },
+  "period_s": {
+   "min": 120,
+   "max": 360
+  }
+ },
+ "ownship_wander_weights": {
+  "none": 0.4,
+  "wander": 0.3,
+  "oscillation": 0.3,
+  "both": 0
+ }
+}
\ No newline at end of file
diff --git a/docs/SRD.md b/docs/SRD.md
index e8d1ceaa..67ded36d 100644
--- a/docs/SRD.md
+++ b/docs/SRD.md
@@ -117,6 +117,7 @@ configuration; the carried weights chosen by a rule named in the decision record
 | **Gate 4 usable uncertainty** | (agent, proposed 2026-11-08; owner to merge) On a fresh MAN2 test seed, σ = 1°, G5 rows, the carried E with its uncertainty statement (points unchanged): course stated as an arc ±X (X at most 45°) with a probability P that the true course lies within it, otherwise unknown; log range ratio and speed as 95% intervals. A row without a statement states nothing (P = 0, not covered, the uniform course score). **G4-a** range and speed 95% coverage each wholly within [0.92, 0.98]; **G4-b** reliability: hit rate minus mean P wholly within ±0.03 overall, and within ±0.05 (point) in each of five equal-count bins of P; **G4-c** informativeness: the mean circular log score of the course statement wholly above the uniform's, and mean P at least 0.5; **G4-d** the learned statement's standardised summed score (course log score and the two interval scores, each divided by the temperature's mean on the same rows) below a per-slice temperature stated on sd^β per quantity, fitted on validation tuning rows (plan; paired difference wholly below zero; void if a shuffled-error head also beats the temperature on validation), the learning claim against that control (a pass does not by itself show learning beyond a richer classical rule: see §9, 2026-11-09); **G4-e** point errors identical to the uncalibrated E's on every row | As Gate 3: the write-up proposes the follow-on with a recommended default; if only G4-d fails, the temperature's statement is carried |
 | **Gate H headroom** | (agent, proposed 2026-11-10; owner to merge; threshold and the form of H-b are the owner's, 2026-11-09 and 2026-11-10) Nothing is trained. On a fresh MAN2 test seed under the primary bearing-error mismatch (correlated error 60 s on half the variance, bias 0.5° with a 30 min correlation time, true σ within a factor 1.5 of the stated 1°, 2% outliers of 10 σ; spec 010 research R2), σ = 1°, G5 rows, **every estimator on the true segmentation**: **H-a** (soundness) the attainable ceiling (the declared solve whitened by the true error covariance, outlier flags not given) has range efficiency against the Cramér–Rao bound under the true covariance within [0.9, 1.6] on settled rows, and a median range error no worse than the plain solve's; **H-b** (soundness; owner, 2026-11-10) on mismatch-off data the classical robust control (Huber weights, σ and a correlation model estimated from residuals; settings from the tuning seed) costs at most 5% in median range error over the plain solve, and the interval of that cost is not wholly above 2%; **H-c** (the verdict; owner, 2026-11-09) R, the robust control's median range error over the attainable ceiling's, minus 1: headroom is **material** if the interval is wholly above 10%, **not material** if wholly below, otherwise inconclusive with one re-score on a larger fresh seed. Intervals are paired, scenario-clustered, 97.5%. H-a and H-b are applied on the validation seed and stop the gate before the test seed if they fail; H-c is decided on the test seed only | Material: a learned component is trained under mismatch with C″ and the robust control as standing controls. Not material: the learning track on this estimator stops and the null result is published. Either way the scaled end-to-end regressor control (§9, 2026-11-09) follows |
 | **Gate T time-late** | (agent, proposed 2026-11-12 and re-registered the same day on the owner's decision after the first registration stopped at validation; owner to merge; the 10% materiality level follows the owner's for Gate H) Nothing is trained. On a fresh test seed of 5-second MAN2 engagements, σ = 1°, one period cut at random from each, every system giving the whole track over the period and scored at rows 30 s apart where the known-history bound on relative range is below 0.5: **T-a** (soundness) the true manoeuvre history, fitted to the bearings from neutral starts with its times held as the bound assumes, has range efficiency against that bound within [0.8, 1.3] on rows where the bound is below 0.05; **T-b** (the verdict) the standing classical system C″ read as a track has a median period range error over that of the evidence-scored search over manoeuvre histories (Annex B's statistics as the prior; spec 013) with a 95% period-bootstrap interval wholly above 1.10; **T-c** (the prior's reach) the same ratio on a split whose targets manoeuvre otherwise (legs of mean 15 min, turns of 15° to 75°, faster turn rates, more speed changes) while the search keeps Annex B's prior has an interval wholly above 1.00; **T-d** (the candidates) the true history is among the search's first three candidate tracks in at least 0.60 of periods, and the mean probability stated for the first candidate (evidence at temperature 3) is within 0.10 of the share of periods in which it is the true history; **T-d-shift** the same calibration gap within 0.10 on the shifted split; **T-e** (headroom, a reading) the search's median period range error over that of the true history fitted with the same prior from neutral starts: material if the interval is wholly above 1.10. One leg, the tuned IMM and EKF read as tracks, and the truth-started fits are reported on the same periods; every system is also reported by how tightly its rows are bounded. T-a is applied on the validation seed and stops the gate before the test seed if it fails; the others are decided on the test seed only | T-b passes: the search is the standing time-late system and the control any learned time-late model must beat. T-c fails: that standing is conditional on the manoeuvre statistics being known, and is reported so. T-d fails: the candidate probabilities are not shown to an analyst as probabilities. T-d-shift fails: they are shown only with the warning that they assume Annex B's statistics. T-e material: a learned full-period model (GPU, within the $50 budget) is tested against the search with the shifted split as its control; not material: the time-late learning step is not taken |
+| **Study L time-late, learned** | (agent, proposed 2026-11-13 on the owner's decision of that day, option B; owner to merge) A registered study with criteria the sample can decide, not a materiality line. Classical controls on the standing evidence-scored search (nothing trained; settings from tuning seed 338): wild bearings edited out, an effective bearing sd from the error correlation estimated from the bearings, a broadened manoeuvre prior pooled with Annex B's, a manoeuvre declared only at an evidence margin of 1 with a stated 1% chance of a quiet target (`ctl-d1-q0.01`), and the candidates' probability-weighted mean track (`ctl-h1`). A learned whole-period model (bidirectional transformer over 5-second bearings; three seeds of 12.5 million simulated periods of mixed kinds on a T4, within the $50 budget), its reported track the arc model fitted from its output (`learned-arc`), its plans bound by digest before the first training job. On a fresh test seed, periods cut as Gate T, paired per-period ratios of period median range error with 95% period-bootstrap intervals (a side *wins* when the interval is wholly above 1): **L-a** (soundness, on the validation seed, controls only; stops the study) the true history with times held is at [0.8, 1.3] of the bound on tight rows, its fits converge, at most 25% of references are held; **L-b** (the controls, reported) the standing search ÷ the control on the shifted-prior, correlated-error and outlier splits, and the share of the gap to the true-history reference closed; **L-c** (the verdict) on each of two shifted-prior splits, correlated errors, outliers and a bias absent from training: learning is *shown* if every seed wins against the declared control and against the hedged track, *hedging* if every seed wins against the declared control only, *not shown* otherwise; **L-d** (no harm) on Annex B and held-course periods: *harm* if every seed loses to the declared control. The learned model is scored once, on the test seed, and only after the controls' validation record is committed (the scorer enforces it). Reported beside: the model's direct track, single-factor controls, C″ as a track, the reference under three conventions, efficiency by slice, manoeuvre counts | Learning shown on at least two mismatch splits without harm: a gate for a learned time-late system is proposed. Shown on none: the classical controls stand as the time-late system and the learned track stops. Otherwise: reported, and the owner decides |
 
 ## 6. Architecture and tooling
 
@@ -300,6 +301,24 @@ and recorded, not asked.
   repair and re-register (spec 015). The evidence, the classing and the reference are
   repaired, and the same quantities are decided again on fresh seeds (336 validation, 337
   test), with the spent seeds 333 to 335 as development data. Nothing trained, no GPU run.
+- **2026-11-13 (agent, proposed; owner to merge):** spec 015's study is registered
+  (`experiments/20261113-tl3-decision/`): spec 014's four quantities decided again on test
+  seed 337 with the repaired evidence, one rule for manoeuvres cut by a period, a reference
+  held where it drifts and a refit control with a fall-back. The rule for a learned
+  whole-period model is unchanged; the record is to state its output and, separately, what
+  the evidence supports.
+- **2026-11-13 (owner, recorded by the agent):** spec 015's study is decided by the panel
+  (`experiments/20261113-tl3/review.md`; five approve with findings): the registered rule
+  again gives no GPU budget for identification on Annex B data, where 400 periods cannot
+  decide a 10% line; the large gaps are where the simulator's assumptions fail, with no
+  classical remedy tried. The owner chose option B: build the classical controls on CPU and
+  train a learned whole-period model on GPU in parallel under a plan registered before any
+  result, scored only once the controls exist (spec 016), within the $50 total.
+- **2026-11-13 (agent, proposed; owner to merge):** study L (spec 016) added to §5 and
+  registered (`experiments/20261113-tl4-decision/`). On tuning seed 338 the controls close
+  the outlier gap, about half the correlated-error gap and little of the shifted-prior gap
+  (`experiments/20261113-tl4-tuning/`). The criteria are sign tests against 1 with three
+  seeds agreeing, with the power of each stated; no materiality line is registered.
 
 ## Annexes
 
diff --git a/experiments/20261113-tl3-decision/README.md b/experiments/20261113-tl3-decision/README.md
new file mode 100644
index 00000000..2a8dbaf9
--- /dev/null
+++ b/experiments/20261113-tl3-decision/README.md
@@ -0,0 +1,66 @@
+# Spec 015: decision record for the re-registered study (2026-11-13)
+
+Written after the development run on spent seed 335 (`experiments/20261113-tl3-dev/`) and
+before seeds 336 and 337 exist. `decision.json` binds the plan `configs/timelate/tl-d.json`,
+the run files, the ranges files, the time-late code and the scorer by digest. The plan is
+never edited after this commit. A registered study, as spec 014 was; the panel reviews it.
+
+## What is decided on test seed 337
+
+| | Quantity (Annex B, 400 periods, unless said) | Forecast |
+| --- | --- | --- |
+| D1 | Share of the repaired search's gain over C″ that refitting C″'s own manoeuvre times obtains | 0.50; the interval includes 0.5, excludes 0.25 and 0.75 |
+| D2 | Scored periods whose first candidate is wrong (edge-aware): ambiguous, search failure, evidence failure | 0.50, 0.12, 0.38; first candidate right in 0.63 |
+| D3 | Attainable headroom against 1.10: a perfect search; a perfect evidence as well | about 1.05 to 1.08; 1.12 to 1.20, an even chance of "material" |
+| D4 | Held-course periods (400) with a manoeuvre declared: repaired search, C″, flat prior, mixed prior | 0.50, 0.02, 0.01, 0.01; under 0.15 below 30 minutes, above 0.90 over an hour |
+| D5 | Recommendation on a learned whole-period model | spec 014's rule, unchanged |
+
+## What changed from spec 014's definitions
+
+- The search uses prior version 3: every factor without a Gaussian residual has the volume
+  its own density implies (research R1).
+- One rule decides whether a found history is the true one, ignoring manoeuvres the period
+  cuts (R2). D2 is of scored periods.
+- The reference is held to the true times where it drifts into another history, and
+  flagged (R3). D3 is also given without flagged periods.
+- The refit control falls back to C″'s track when its fit is not acceptable (R4). D1 is
+  also given without fall-back periods, and by two other estimands declared in research R7.
+
+## Reported, not decided
+
+Each decided statistic at class thresholds 1, 3 and 5 and match tolerances 60, 120 and
+240 s, as a ratio of medians beside the paired figure; spec 014's search beside the repaired
+one on every split; the flat-prior search's share of the gain; false manoeuvres by period
+length; manoeuvring targets declared quiet by number of true manoeuvres; headroom on the
+shifted, correlated, outlier and whole-engagement splits, where no remedy has been built;
+counts and candidate calibration. Every table comes from `summary.json` by
+`scripts/research/tl3_tables.py`.
+
+## Soundness stop (validation seed 336)
+
+The reference with times held within [0.8, 1.3] of the bound on tightly bounded rows; at
+least 90% of true-history fits with a cost the noise explains; at most 25% of references
+held. Fail: seed 337 is not generated.
+
+## Changed after the development run, before this record
+
+- The refit's fall-back now keeps a fit whose cost the noise explains. The first form fell
+  back in 26 of 200 periods because C″'s chord track fits bearings more closely than a sound
+  arc refit. The changed rule has not been run on any data.
+- The stop's limit on held references is 25%; it was drafted at 15% and the development
+  periods gave exactly 15%.
+
+## Known weaknesses, stated beforehand
+
+- The class threshold of 3 is a convention and the evidence's own noise is of that size;
+  every threshold is reported.
+- The composites are oracles of identification, and the reference is a fit: where it is
+  worse than the search the composite understates the headroom. The "where it helps only"
+  variant is reported beside.
+- The Laplace volume can exceed a parameter's feasible range at a bound (about +0.7 at the
+  duration's floor): recorded in research R1, not repaired.
+- D5's rule reads the Annex B split, where the search holds the simulator's own model. The
+  record must state the other splits' headroom and that nothing was built against it.
+- The forecasts come from 200 periods of a spent seed and from code that differs from the
+  registered commit in the fall-back rule.
+- One test seed. Every split is drawn from the same seed with different ranges files.
diff --git a/experiments/20261113-tl3-decision/decision.json b/experiments/20261113-tl3-decision/decision.json
new file mode 100644
index 00000000..640999ee
--- /dev/null
+++ b/experiments/20261113-tl3-decision/decision.json
@@ -0,0 +1,74 @@
+{
+ "study": "spec 015: time-late, repair and re-registration",
+ "registered": "2026-11-13",
+ "digests": {
+  "configs/timelate/tl-d.json": "a65560592dd419337599d73a2442a2896e8be2696fce0ec6dd53fd81d1576d92",
+  "configs/timelate/runs/tl-d-val.json": "78709f21dc6415496bd721e1f50d27e53a585a9aade6f327b852989be39af135",
+  "configs/timelate/runs/tl-d-test.json": "a2890ecbd31db1337d952b17421ddc5894a4fda1ffa7660df7a36b3347d84a98",
+  "configs/timelate/tl-ranges.json": "cb48fcabb5a51f76685cab482e23f01cb53c504406a1654c2ec1b664216a4784",
+  "configs/timelate/tl-shift-ranges.json": "bc76abf0d5916f4a91909ebb6ef90e84438eae2ee1142e6607c21823d489081b",
+  "configs/timelate/tl-quiet-ranges.json": "a964509642c46015db6c38dc7b6c774c3cf36d992d3be954a10627be1368782b",
+  "configs/timelate/tl-heldout.json": "aeedfe10ca841d95d1cf2026382c43afe59c548aef9f5fed9fe07e16d3832634",
+  "configs/gateh/conditions.json": "d260fd7538d2fbcbc8c25cfd402a091ed9d50bf254a79bfd2c6c5bcc4b634e15",
+  "configs/gateh/gh-a.json": "26d8e8987a2f696fd72ea235c5183f9f53b1ec2a5623cbc4e646d32139d2d88a"
+ },
+ "time_late_code_sha256": "3b67d0267f2b99c75e7bea0d408989d2de5a308581d440511c63b71465585ec9",
+ "scorer_files": {
+  "src/tma2/timelate/study3.py": "c32e2a3fc11a68f5649f6ccf5dcc58089a0e4cd41b1ad1639d0f30dae662e255",
+  "src/tma2/timelate/study.py": "a17c54155cf1cf8c2a65595faf3811fa6b4ab2b979275533db33d8388c6c3883",
+  "src/tma2/timelate/gate.py": "fd31e1c82759c8a0f5a0161b504b3979e6affe813d0f3bb0ba846ab79f1030c7",
+  "src/tma2/timelate/match.py": "fff96f28bee96c75361d7f0bf97624f5437a6beeb315129a7585be0428c9a7ab",
+  "src/tma2/cli/tlscore.py": "6c7f16a9e5f25fb380ae6ae0ee3a4327846d833cf0fa2eb4c68b91d505b1c87a",
+  "src/tma2/cli/tlsolve.py": "ec80661597abd9aee2bd3ae2369168294ecd8145abd7c3d5b93bf8f968119613",
+  "scripts/research/tl3_tables.py": "ca6a1e6087121053881b10928855a6f2ed6d4fa3870ef3881534f8817c76f0df"
+ },
+ "seeds": {
+  "development (spent)": [
+   333,
+   334,
+   335
+  ],
+  "validation": 336,
+  "test": 337
+ },
+ "criteria": {
+  "class_threshold": 3.0,
+  "match_tolerance_s": 120.0,
+  "materiality": 1.1,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "quiet_interval_halfwidth_max": 0.05,
+  "reference_flagged_max": 0.25
+ },
+ "decided": {
+  "D1": "share of the log gain C″ → search3 that c2-refit (with its fall-back) obtains, Annex B test split, scored periods, 95% interval",
+  "D2": "among scored periods where search3's first candidate is not the true history by the edge-aware rule: shares ambiguous, search failure, evidence failure (threshold 3, 120 s)",
+  "D3": "attainable headroom: geometric mean per-period ratio search3 ÷ ident-search and search3 ÷ ident-all (the reference held where it drifts), each material / not material / inconclusive against 1.10",
+  "D4": "share of the 400 held-course periods on which a manoeuvre is declared, for search3, C″, search3-flat and search3-mix",
+  "D5": "recommendation on a learned whole-period model by spec 014's rule, unchanged"
+ },
+ "changed_definitions": [
+  "the search is search3 (prior version 3: every open factor's volume is the reciprocal of its density)",
+  "a found history is the true one by match.same_history (manoeuvres cut by the period are ignored), in the classing, the candidates and the counts",
+  "D2's shares are of scored periods (spec 014: of all periods)",
+  "the true-history reference is held to the true times where it fails the match rule (flagged); D3 is also reported without flagged periods",
+  "c2-refit falls back to C″'s track when its fit is not acceptable (research R4, as changed on development data); D1 is also reported without fall-back periods"
+ ],
+ "forecasts": {
+  "D1": "0.50; the interval includes 0.5 and excludes 0.25 and 0.75",
+  "D2": "first candidate right in 0.63 of scored periods; among wrong: ambiguous 0.50, search failure 0.12, evidence failure 0.38",
+  "D3": "ident-search: inconclusive or not material, point 1.05 to 1.08; ident-all: point 1.12 to 1.20, verdict a coin's toss between inconclusive and material",
+  "D4": "search3 0.50 (between 0.44 and 0.56); C″ 0.02; flat prior 0.01; mixed prior 0.01. By length for search3: under 0.15 below 30 minutes, above 0.90 over 60 minutes",
+  "soundness": "pass: reference at 1.0 to 1.15 of the bound on tight rows; every fit consistent; 12% to 18% of references held",
+  "reported": "search2 ÷ search3 1.03 on Annex B data and 1.15 to 1.20 on held course; refit fall-backs under 5% of periods; the mixed prior calls a one-manoeuvre target quiet in about 0.2 of periods against 0.1 for search3; headroom with a perfect evidence material under the shifted prior and both error conditions"
+ },
+ "recommendation_rule": "Spec 014's, unchanged: pursue a learned whole-period model only if the ident-all headroom is material on the Annex B test split. The record states the rule's output and, separately, what the evidence supports and is silent on (FR-013).",
+ "soundness_stop": "validation seed 336: history-fixed range efficiency on rows bounded below 0.05 within [0.8, 1.3]; at least 0.90 of true-history fits with a consistent cost; at most 0.25 of references held. Otherwise seed 337 is not generated.",
+ "changed_after_development": [
+  "the refit's fall-back keeps a fit whose cost the noise explains (the first form fell back in 26 of 200 periods); untested on any run",
+  "the stop's limit on held references is 0.25 (drafted at 0.15; the development periods gave 0.15)"
+ ]
+}
diff --git a/experiments/20261113-tl3-dev/README.md b/experiments/20261113-tl3-dev/README.md
new file mode 100644
index 00000000..afebf5fb
--- /dev/null
+++ b/experiments/20261113-tl3-dev/README.md
@@ -0,0 +1,73 @@
+# Spec 015, development on a spent seed: what each repair changes (2026-11-13)
+
+**Development data: seed 335 was spec 014's test seed and is spent. Nothing here is a
+registered result.** CI run 43383667575 (`timelate/tl-d-dev`, commit 1895362, clean tree):
+the first 200 Annex B periods (185 scored) and the first 200 held-course periods of seed 335,
+with spec 014's search (`search2`) beside the repaired one (`search3`). Every table is in
+`tables.md`, generated from `summary.json` by `scripts/research/tl3_tables.py`; the figures
+below are read from it.
+
+## The evidence repair
+
+| | Spec 014's evidence | Repaired |
+| --- | --- | --- |
+| Held course: periods with a manoeuvre declared | 0.76 [0.69, 0.81] | 0.50 [0.43, 0.57] |
+| ... under 30 minutes (38 periods) | 0.42 | 0.08 |
+| ... 30 to 45 minutes (60) | 0.68 | 0.22 |
+| ... 45 to 60 minutes (48) | 0.88 | 0.69 |
+| ... 60 to 90 minutes (42) | 0.95 | 0.93 |
+| ... over 90 minutes (12) | 1.00 | 1.00 |
+| Held course: track error | 0.038 | 0.032 |
+| Annex B: track error | 0.038 | 0.036 |
+| Annex B: old ÷ repaired, paired | – | 1.03 [0.93, 1.14] |
+| Annex B: false manoeuvres a period | 0.36 | 0.23 |
+| Annex B: true manoeuvres missed | 0.19 | 0.22 |
+| Annex B: first candidate right (edge-aware) | 0.605 | 0.650 |
+| Annex B: targets with one manoeuvre declared quiet (57) | 0.035 | 0.088 |
+
+- **The repair removes the false manoeuvres on short periods and leaves them on long ones.**
+  Under 30 minutes the rate falls from 0.42 to 0.08; over an hour it stays above 0.9. That
+  separates the two causes the panel named: the short-period ones were the evidence's
+  defect, the long-period ones are the prior saying a target does not hold course for an hour.
+- On Annex B data the track is no worse (1.03 [0.93, 1.14]), the search declares a third
+  fewer false manoeuvres and misses slightly more true ones.
+- The mixed prior still removes the held-course problem entirely (0 of 200) and still pays
+  for it: it calls a target with one manoeuvre quiet in 0.23 of periods, where the repaired
+  search does in 0.09. C″ does in 0.32 and the flat-prior search in 0.25, which spec 014
+  did not report.
+
+## The edge rule
+
+38 of 200 Annex B periods have a manoeuvre the period cuts. With the one rule the first
+candidate is right in 0.64 of scored periods (0.62 on periods with none cut). The wrong
+histories divide 0.49 / 0.12 / 0.39 (ambiguous / search failure / evidence failure), and
+nearly the same without the cut periods (0.51 / 0.12 / 0.37): the classes no longer depend
+on the period's edges.
+
+## The reference
+
+15% of true-history fits drifted beyond the match tolerance and were held to the true
+times (30 of 200). Every true-history fit has a cost the noise explains. The headroom with
+a perfect evidence is 1.17 [1.08, 1.27] with them and 1.11 [1.04, 1.19] without: the held
+fits are better than the drifted ones were, so the headroom is larger than spec 014's
+composite showed on these periods, and still inconclusive against 1.10.
+
+**Changed before registration**: the soundness stop's limit on held references is set at
+25%. It was drafted at 15% before this run existed; 15% is what the data give.
+
+## The refit control
+
+The fall-back fired in 26 of 200 Annex B periods, far more than the four runaways it was
+built for. Cause: the rule compared the refit's bearing cost with C″'s own track, and the
+chord track has four free unknowns a manoeuvre, so it fits the bearings more closely than a
+sound arc refit. **Changed before registration** (research R4): a refit whose cost the noise
+explains is kept whatever C″'s track costs. The refit's share of the gain on these periods:
+0.47 [0.32, 0.61] with the fall-backs, 0.54 [0.39, 0.69] without those periods. What it is
+with the changed rule is not measured here; the registered runs use the changed rule.
+
+## Cautions
+
+- These periods were seen by spec 014's panel and by this spec's design. They show the
+  repairs do what they were built to do; they are not evidence for the decided quantities.
+- The fall-back rule and the stop's limit were both changed after this run and before
+  registration. The first is untested on any run.
diff --git a/experiments/20261113-tl3-dev/ci.json b/experiments/20261113-tl3-dev/ci.json
new file mode 100644
index 00000000..9223286d
--- /dev/null
+++ b/experiments/20261113-tl3-dev/ci.json
@@ -0,0 +1 @@
+{"run_id": "43383667575", "commit": "18953626acee19ba2b3ba145d0e767823674ad5c", "cpu_model": "AMD EPYC 9V45 96-Core Processor"}
diff --git a/experiments/20261113-tl3-dev/man2.periods.json b/experiments/20261113-tl3-dev/man2.periods.json
new file mode 100644
index 00000000..bae06bcd
--- /dev/null
+++ b/experiments/20261113-tl3-dev/man2.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "first_row": 12, "rows": 865, "seconds_long": 4320.0, "bearings": 779, "true": [[0.0, 20.0], [2005.0, 2095.0], [3855.0, 4080.0]], "true_kinds": [[true, false], [true, false], [false, true]], "true_cut": [true, false, false], "ownship_turns": 2, "history_cost": 723.5511619233941, "history_converged": true, "candidates2": [{"spans": [[1870.61964839127, 2150.248444409801], [3841.3908396778183, 4055.192090268254]], "kinds": [[true, false], [false, true]], "evidence": -415.9379128171232, "theta": [11346.656353474227, 4776.82549017236, -3.637602167108142, 8.1741420749819, 1.3704469793079392, 0.0, 0.0, -3.0598112360997227]}, {"spans": [[1840.090039504003, 2167.6510240319108], [3699.7686323497705, 3920.3255264210243]], "kinds": [[true, false], [true, false]], "evidence": -417.8909809712866, "theta": [11373.674440171599, 4792.937850250381, -3.643270376929882, 8.189496033869451, 1.381040720444645, 0.0, -1.1499227811905663, 0.0]}, {"spans": [[882.0998628150978, 1119.2656881464593], [2149.9014935661635, 2203.006207384028], [3846.280815156673, 4041.525534284608]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -420.29241383550084, "theta": [14490.211933161967, 6275.966108897397, 2.4062407830301638, 9.069837305405732, 0.8327793907953144, 0.0, 0.9276848850006864, 0.0, 0.6058683703695128, 0.0]}, {"spans": [[176.86948627415927, 276.0197073495487], [1870.875162143647, 2140.6411411542485], [3841.481388181453, 4055.9249472798497]], "kinds": [[false, true], [true, false], [false, true]], "evidence": -421.27939505200897, "theta": [12636.550639689654, 5522.983627717579, 2.74029131657542, 9.117538407862055, 0.0, -0.9912696814222105, 1.2656021161024733, 0.0, 0.0, -3.0105800172076447]}, {"spans": [[781.6767964895773, 896.8579121087002], [1859.5124602540811, 2174.643616395462], [3840.8636478282237, 4049.8407398025374]], "kinds": [[true, true], [true, false], [false, true]], "evidence": -421.5173196572217, "theta": [5936.132721306797, 2330.465334227973, 1.983909981310941, 10.82889846033981, 0.6070835958017172, -2.545558040869738, 1.4471914157227173, 0.0, 0.0, -3.1366667236013472]}], "histories_evaluated": 68, "candidates": [{"spans": [[1870.61964839127, 2150.248444409801], [3841.3908396778183, 4055.192090268254]], "kinds": [[true, false], [false, true]], "evidence": -415.8067254409313, "theta": [11346.656353474227, 4776.82549017236, -3.637602167108142, 8.1741420749819, 1.3704469793079392, 0.0, 0.0, -3.0598112360997227]}, {"spans": [[1840.090039504003, 2167.6510240319108], [3699.7686323497705, 3920.3255264210243]], "kinds": [[true, false], [true, false]], "evidence": -417.8190223676984, "theta": [11373.674440171599, 4792.937850250381, -3.643270376929882, 8.189496033869451, 1.381040720444645, 0.0, -1.1499227811905663, 0.0]}, {"spans": [[882.0998628150978, 1119.2656881464593], [2149.9014935661635, 2203.006207384028], [3846.280815156673, 4041.525534284608]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -421.09864829056517, "theta": [14490.211933161967, 6275.966108897397, 2.4062407830301638, 9.069837305405732, 0.8327793907953144, 0.0, 0.9276848850006864, 0.0, 0.6058683703695128, 0.0]}, {"spans": [[176.86948627415927, 276.0197073495487], [1870.875162143647, 2140.6411411542485], [3841.481388181453, 4055.9249472798497]], "kinds": [[false, true], [true, false], [false, true]], "evidence": -421.1937504333361, "theta": [12636.550639689654, 5522.983627717579, 2.74029131657542, 9.117538407862055, 0.0, -0.9912696814222105, 1.2656021161024733, 0.0, 0.0, -3.0105800172076447]}, {"spans": [[781.6767964895773, 896.8579121087002], [1859.5124602540811, 2174.643616395462], [3840.8636478282237, 4049.8407398025374]], "kinds": [[true, true], [true, false], [false, true]], "evidence": -421.63879831656925, "theta": [5936.132721306797, 2330.465334227973, 1.983909981310941, 10.82889846033981, 0.6070835958017172, -2.545558040869738, 1.4471914157227173, 0.0, 0.0, -3.1366667236013472]}], "refit_fell_back": false, "reference_flagged": true, "true_evidence": -422.3128288414489, "true_fit_spans": [[0.0, 20.0], [2005.0, 2095.0], [3855.0, 4080.0]], "histories": {"c2": {"spans": [[2375.0, 2625.0], [3845.0, 4015.0]]}, "search2": {"spans": [[1870.61964839127, 2150.248444409801], [3841.3908396778183, 4055.192090268254]], "kinds": [[true, false], [false, true]], "evidence": -415.9379128171232}, "search3": {"spans": [[1870.61964839127, 2150.248444409801], [3841.3908396778183, 4055.192090268254]], "kinds": [[true, false], [false, true]], "evidence": -415.8067254409313}, "search3-flat": {"spans": [[1821.9267641389065, 2173.7250171790192], [3841.5587823853148, 4055.3584803808885]], "kinds": [[true, false], [false, true]], "evidence": -417.53113769019734}, "search3-mix": {"spans": [[1870.61964839127, 2150.248444409801], [3841.3908396778183, 4055.192090268254]], "kinds": [[true, false], [false, true]], "evidence": -416.49987262149125}, "c2-refit": {"spans": [[2105.646134906264, 2215.104721092626], [3837.2014233232317, 4024.7242117080136]], "kinds": [[true, true], [false, true]], "evidence": -423.33134312345567}}, "seconds": {"layer": 14.75, "reference": 22.04, "search2": 25.87, "search3": 25.83, "search3-flat": 36.55, "search3-mix": 25.83, "c2-refit": 13.58}}, {"ordinal": 1, "first_row": 130, "rows": 636, "seconds_long": 3175.0, "bearings": 581, "true": [[565.0, 620.0], [2220.0, 2280.0]], "true_kinds": [[true, false], [true, false]], "true_cut": [false, false], "ownship_turns": 2, "history_cost": 595.4836352376288, "history_converged": true, "candidates2": [{"spans": [[568.8163945230501, 605.5494534156467], [2248.3066494004615, 2277.2656124064197]], "kinds": [[true, false], [true, false]], "evidence": -327.84560252793096, "theta": [-393.589374648561, 166.18332921964506, 7.394766045534305, 3.1351724174668067, -0.6400112621679048, 0.0, 0.5005518416928771, 0.0]}, {"spans": [[249.00974403349883, 698.0098975255262], [2244.0755010083863, 2279.6965170102562], [2928.6253892386567, 2954.3827248164334]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -332.6291365399858, "theta": [103.9093930955671, 1713.7271572093912, 1.8580909982923393, 2.6907855948105595, -1.386516998623469, 0.0, 0.6197025539683841, 0.0, 0.44616299493571737, 0.0]}, {"spans": [[583.6693247505244, 617.013806441256], [2293.3136746006217, 2420.866042141794], [2868.2673788107786, 2922.932888100483]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -336.42709810201956, "theta": [-531.1582991295525, -247.53489752239878, 7.30451361229593, 3.2950387160426176, -0.5513918656326744, 0.0, 2.2370595232172867, 0.0, -0.9549761194160762, 0.0]}, {"spans": [[243.40748566477805, 689.258135365181], [2246.554204457826, 2274.0380596612677]], "kinds": [[true, true], [true, false]], "evidence": -336.56217500623706, "theta": [-178.22512796970014, 793.0204610026545, 1.8058321297447382, 2.2467345502922442, -1.3349310080645067, 0.9720920448842919, 0.47057144578882587, 0.0]}, {"spans": [[574.2659119256464, 609.838469804067], [1737.108502926189, 1797.3880894557242]], "kinds": [[true, false], [false, true]], "evidence": -336.60348754301646, "theta": [-437.3826842844459, 37.939646462415325, 7.371777168273822, 3.165246947457218, -0.6221875480222175, 0.0, 0.0, 3.0154577750207894]}], "histories_evaluated": 68, "candidates": [{"spans": [[263.4812308132766, 694.8378307536857], [2254.604170712166, 2299.0967207822387]], "kinds": [[true, false], [true, false]], "evidence": -334.6832144463969, "theta": [116.97074043823962, 1717.3345695429855, 1.8995155376723012, 2.6151971905293547, -1.4282731166043334, 0.0, 0.7767781628787107, 0.0]}, {"spans": [[582.6523302098849, 649.5051067286231], [2247.6660423508897, 2291.1111786354368]], "kinds": [[true, true], [true, false]], "evidence": -337.0161160665209, "theta": [-584.8519892429448, -365.0443283851787, 7.155221572561333, 4.3439145851270355, -0.40177545711923446, -1.4305073837652191, 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diff --git a/experiments/20261113-tl3-dev/plan.json b/experiments/20261113-tl3-dev/plan.json
new file mode 100644
index 00000000..2b2be179
--- /dev/null
+++ b/experiments/20261113-tl3-dev/plan.json
@@ -0,0 +1,73 @@
+{
+ "description": "Draft of spec 015's plan (development on spent seeds only).",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics (the search's prior is right)"
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs (mean 15 min), smaller turns (15 to 75 degrees), faster turn rates, more speed changes: the search's prior is wrong"
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course for the whole engagement (spec 014 FR-009)"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with correlated bearing errors (60 s, half the variance)",
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with 2% outlying bearings (10 sd)",
+   "condition": "out-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "whole": true,
+   "what": "Annex B engagements uncut (40 to 120 minutes)"
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 5,
+  "score_every": 6,
+  "systems": [
+   "search2",
+   "search3",
+   "search3-flat",
+   "search3-mix",
+   "c2-refit"
+  ],
+  "thinning": "none"
+ },
+ "temperature": 3.0,
+ "top": 3,
+ "runner": "run3",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "class_threshold": 3.0,
+  "match_tolerance_s": 120.0,
+  "materiality": 1.1,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "quiet_interval_halfwidth_max": 0.05
+ }
+}
diff --git a/experiments/20261113-tl3-dev/quiet.periods.json b/experiments/20261113-tl3-dev/quiet.periods.json
new file mode 100644
index 00000000..b45760a4
--- /dev/null
+++ b/experiments/20261113-tl3-dev/quiet.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "first_row": 12, "rows": 865, "seconds_long": 4320.0, "bearings": 779, "true": [], "true_kinds": [], "true_cut": [], "ownship_turns": 2, "history_cost": 732.8609754508803, "history_converged": true, "candidates2": [{"spans": [[767.7399536282196, 928.0685344197519], [3753.7736340425913, 3804.403514549554]], "kinds": [[true, true], [true, false]], "evidence": -398.6148830370524, "theta": [16450.009552282045, 7273.145682418542, 2.7072314810409717, 5.781960529742145, -0.6413087277710838, 1.5902501712288164, 0.8840044961556882, 0.0]}, {"spans": [[3838.3454576512513, 3854.9164973605866]], "kinds": [[true, false]], "evidence": -399.133067389986, "theta": [12059.976338480106, 5173.427258636007, 2.0130226116670693, 7.624899034766435, 0.2886231799592139, 0.0]}, {"spans": [[818.8856581870336, 976.0180803230928]], "kinds": [[true, true]], "evidence": -402.67315451456165, "theta": [15250.40282291362, 6715.4920598370645, 2.892755943244903, 6.073623536067601, -1.045051637945557, 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diff --git a/experiments/20261113-tl3-dev/run.json b/experiments/20261113-tl3-dev/run.json
new file mode 100644
index 00000000..083fe353
--- /dev/null
+++ b/experiments/20261113-tl3-dev/run.json
@@ -0,0 +1,17 @@
+{
+ "plan": "configs/timelate/tl-d-draft.json",
+ "seed": 335,
+ "count": 1350,
+ "splits": {
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+  "quiet": {
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+ },
+ "shard_size": 25,
+ "what": "spec 015 development on a spent seed (335): spec 014's search beside the repaired one, the held reference, the refit's fall-back"
+}
diff --git a/experiments/20261113-tl3-dev/summary.json b/experiments/20261113-tl3-dev/summary.json
new file mode 100644
index 00000000..e8588546
--- /dev/null
+++ b/experiments/20261113-tl3-dev/summary.json
@@ -0,0 +1,3817 @@
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+    "by_minutes": {
+     "0-30": {
+      "c2": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.09180986822558396,
+       "n": 38
+      },
+      "c2-refit": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.09180986822558396,
+       "n": 38
+      },
+      "search2": {
+       "rate": 0.42105263157894735,
+       "lo": 0.27852501157758713,
+       "hi": 0.5780765465632945,
+       "n": 38
+      },
+      "search3": {
+       "rate": 0.07894736842105263,
+       "lo": 0.027214595506987724,
+       "hi": 0.20799371457771454,
+       "n": 38
+      },
+      "search3-flat": {
+       "rate": 0.02631578947368421,
+       "lo": 0.004660587685768783,
+       "hi": 0.1349487611595213,
+       "n": 38
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.09180986822558396,
+       "n": 38
+      }
+     },
+     "30-45": {
+      "c2": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.06017185214208986,
+       "n": 60
+      },
+      "c2-refit": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.06017185214208986,
+       "n": 60
+      },
+      "search2": {
+       "rate": 0.6833333333333333,
+       "lo": 0.5576623297382323,
+       "hi": 0.7869413244763347,
+       "n": 60
+      },
+      "search3": {
+       "rate": 0.21666666666666667,
+       "lo": 0.13123044048190308,
+       "hi": 0.33620027573194783,
+       "n": 60
+      },
+      "search3-flat": {
+       "rate": 0.016666666666666666,
+       "lo": 0.0029481597947862495,
+       "hi": 0.08855129727590061,
+       "n": 60
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.06017185214208986,
+       "n": 60
+      }
+     },
+     "45-60": {
+      "c2": {
+       "rate": 0.041666666666666664,
+       "lo": 0.011501893489386586,
+       "hi": 0.13975655870455445,
+       "n": 48
+      },
+      "c2-refit": {
+       "rate": 0.041666666666666664,
+       "lo": 0.011501893489386586,
+       "hi": 0.13975655870455445,
+       "n": 48
+      },
+      "search2": {
+       "rate": 0.875,
+       "lo": 0.7529954171361317,
+       "hi": 0.9414294856142802,
+       "n": 48
+      },
+      "search3": {
+       "rate": 0.6875,
+       "lo": 0.5466692858410841,
+       "hi": 0.8005431655341219,
+       "n": 48
+      },
+      "search3-flat": {
+       "rate": 0.020833333333333332,
+       "lo": 0.003687110977510649,
+       "hi": 0.10899217995251861,
+       "n": 48
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 6.938893903907228e-18,
+       "hi": 0.07410012966611748,
+       "n": 48
+      }
+     },
+     "60-90": {
+      "c2": {
+       "rate": 0.0,
+       "lo": -6.938893903907228e-18,
+       "hi": 0.08379879086570391,
+       "n": 42
+      },
+      "c2-refit": {
+       "rate": 0.0,
+       "lo": -6.938893903907228e-18,
+       "hi": 0.08379879086570391,
+       "n": 42
+      },
+      "search2": {
+       "rate": 0.9523809523809523,
+       "lo": 0.8421014760304114,
+       "hi": 0.9868424750910944,
+       "n": 42
+      },
+      "search3": {
+       "rate": 0.9285714285714286,
+       "lo": 0.8099057319884169,
+       "hi": 0.9754095901266941,
+       "n": 42
+      },
+      "search3-flat": {
+       "rate": 0.0,
+       "lo": -6.938893903907228e-18,
+       "hi": 0.08379879086570391,
+       "n": 42
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": -6.938893903907228e-18,
+       "hi": 0.08379879086570391,
+       "n": 42
+      }
+     },
+     "90-1000": {
+      "c2": {
+       "rate": 0.08333333333333333,
+       "lo": 0.014865094404917095,
+       "hi": 0.35387991114111694,
+       "n": 12
+      },
+      "c2-refit": {
+       "rate": 0.08333333333333333,
+       "lo": 0.014865094404917095,
+       "hi": 0.35387991114111694,
+       "n": 12
+      },
+      "search2": {
+       "rate": 1.0,
+       "lo": 0.7575059933447592,
+       "hi": 1.0,
+       "n": 12
+      },
+      "search3": {
+       "rate": 1.0,
+       "lo": 0.7575059933447592,
+       "hi": 1.0,
+       "n": 12
+      },
+      "search3-flat": {
+       "rate": 0.0,
+       "lo": 2.7755575615628914e-17,
+       "hi": 0.24249400665524085,
+       "n": 12
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 2.7755575615628914e-17,
+       "hi": 0.24249400665524085,
+       "n": 12
+      }
+     }
+    }
+   },
+   "declared_quiet": {
+    "c2": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "c2-refit": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search2": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search3": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search3-flat": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search3-mix": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    }
+   },
+   "by_true_count": {
+    "0": {
+     "periods": 184,
+     "c2": 0.0190381589345634,
+     "c2-refit": 0.017980210483074188,
+     "history-best": 0.01702363369986415,
+     "history-fit": 0.017023584339767694,
+     "history-fixed": 0.017023574095219374,
+     "history-map": 0.017023584339767694,
+     "one-leg": 0.017767405603080988,
+     "search2": 0.037901515141129494,
+     "search3": 0.031580850481987,
+     "search3-flat": 0.017023584339767694,
+     "search3-mix": 0.017023584339767694
+    }
+   },
+   "candidates": {
+    "periods": 200,
+    "top_right": 0.5,
+    "truth_in_top3": 0.71,
+    "mean_top_probability": 0.4506203241579715,
+    "calibration_gap": -0.049379675842028514,
+    "kinds_differ_among_right": 0.0
+   },
+   "candidates2": {
+    "periods": 200,
+    "top_right": 0.245,
+    "truth_in_top3": 0.54,
+    "mean_top_probability": 0.41150724683844286,
+    "calibration_gap": 0.16650724683844287,
+    "kinds_differ_among_right": 0.0
+   },
+   "seconds_per_period": {
+    "layer": 13.127500000000005,
+    "reference": 0.10010000000000002,
+    "search2": 8.215250000000001,
+    "search3": 6.48905,
+    "search3-flat": 2.42575,
+    "search3-mix": 2.3719,
+    "c2-refit": 0.13965000000000002
+   },
+   "dataset_digest": [
+    "29ef6674ed07d5bcc92c5d01a150b183accb55b778b8cb0335b70963d2ccd8e9"
+   ],
+   "seed": [
+    335
+   ],
+   "search_code_sha256": [
+    "c5c8814dd8afadbcb7c20da90fa0d5e8e36ba42fc2d74736d0708d53b7a0ae6f"
+   ],
+   "git_commit": [
+    "18953626acee19ba2b3ba145d0e767823674ad5c"
+   ],
+   "git_dirty": false,
+   "condition": [
+    "clean"
+   ],
+   "thinning": [
+    "none"
+   ]
+  }
+ }
+}
diff --git a/experiments/20261113-tl3-dev/tables.md b/experiments/20261113-tl3-dev/tables.md
new file mode 100644
index 00000000..93b52201
--- /dev/null
+++ b/experiments/20261113-tl3-dev/tables.md
@@ -0,0 +1,196 @@
+## Systems (median period range error)
+
+| System | Annex B (185) | Held course (184) |
+| --- | --- | --- |
+| One leg | 0.376 | 0.018 |
+| C″ read as a track | 0.080 | 0.019 |
+| C″ refitted | 0.056 | 0.018 |
+| Search, flat prior | 0.042 | 0.017 |
+| Search, spec 014's evidence | 0.038 | 0.038 |
+| Search, repaired evidence | 0.036 | 0.032 |
+| Search, repaired, mixed prior | 0.036 | 0.017 |
+| True history, best evidence | 0.025 | 0.017 |
+| True history, times held | 0.023 | 0.017 |
+
+## Annex B: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 2.39 [1.95, 2.90] | 2.22 [1.73, 2.77] | 0.26 | 0.50 / 0.08 |
+| c2 ÷ c2-refit | 1.50 [1.30, 1.76] | 1.42 [1.11, 1.71] | 0.34 | 0.27 / 0.08 |
+| c2-refit ÷ search3 | 1.59 [1.36, 1.87] | 1.57 [1.28, 2.07] | 0.36 | 0.29 / 0.05 |
+| search3-flat ÷ search3 | 1.24 [1.08, 1.42] | 1.18 [0.99, 1.56] | 0.37 | 0.17 / 0.10 |
+| search2 ÷ search3 | 1.03 [0.93, 1.14] | 1.06 [0.95, 1.25] | 0.23 | 0.07 / 0.06 |
+| search3 ÷ search3-mix | 0.98 [0.94, 1.02] | 1.01 [0.91, 1.11] | 0.03 | 0.01 / 0.02 |
+| search3 ÷ history-best | 1.45 [1.27, 1.65] | 1.43 [1.19, 1.77] | 0.37 | 0.27 / 0.05 |
+| search3 ÷ history-fixed | 1.65 [1.40, 1.94] | 1.56 [1.26, 1.94] | 0.36 | 0.38 / 0.11 |
+| search3 ÷ one-leg | 0.07 [0.05, 0.09] | 0.10 [0.07, 0.13] | 0.90 | 0.04 / 0.82 |
+| c2 ÷ one-leg | 0.16 [0.11, 0.22] | 0.21 [0.15, 0.27] | 0.76 | 0.05 / 0.61 |
+| search3-mix ÷ one-leg | 0.07 [0.05, 0.09] | 0.09 [0.07, 0.13] | 0.89 | 0.03 / 0.80 |
+
+## Annex B: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 185 | 0.64 [0.57, 0.70] | 67 | 0.49 [0.38, 0.61] | 0.12 [0.06, 0.22] | 0.39 [0.28, 0.51] |
+| all | 200 | 0.65 [0.58, 0.71] | 70 | 0.49 [0.37, 0.60] | 0.11 [0.06, 0.21] | 0.40 [0.29, 0.52] |
+| scored without cut | 152 | 0.62 [0.55, 0.70] | 57 | 0.51 [0.38, 0.63] | 0.12 [0.06, 0.23] | 0.37 [0.26, 0.50] |
+
+## Annex B: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.11 [1.04, 1.20] inconclusive | 1.33 [1.17, 1.50] material | 1.37 [1.21, 1.56] | 1.43 [1.29, 1.59] |
+| threshold 1 tol 120 | 1.09 [1.02, 1.17] inconclusive | 1.27 [1.14, 1.42] material | 1.30 [1.17, 1.46] | 1.35 [1.22, 1.49] |
+| threshold 1 tol 240 | 1.10 [1.04, 1.18] inconclusive | 1.24 [1.12, 1.37] material | 1.27 [1.14, 1.41] | 1.30 [1.19, 1.43] |
+| threshold 3 tol 60 | 1.08 [1.02, 1.16] inconclusive | 1.18 [1.08, 1.30] inconclusive | 1.37 [1.21, 1.56] | 1.23 [1.13, 1.34] |
+| threshold 3 tol 120 | 1.07 [1.02, 1.14] inconclusive | 1.17 [1.08, 1.27] inconclusive | 1.30 [1.17, 1.46] | 1.19 [1.11, 1.29] |
+| registered | 1.07 [1.02, 1.14] inconclusive | 1.17 [1.08, 1.27] inconclusive | 1.30 [1.17, 1.46] | 1.19 [1.11, 1.29] |
+| registered without flagged | 1.06 [1.01, 1.13] inconclusive | 1.11 [1.04, 1.19] inconclusive | 1.17 [1.06, 1.29] | 1.12 [1.05, 1.21] |
+| threshold 3 tol 240 | 1.07 [1.02, 1.14] inconclusive | 1.15 [1.07, 1.26] inconclusive | 1.27 [1.14, 1.41] | 1.18 [1.09, 1.28] |
+| threshold 5 tol 60 | 1.08 [1.03, 1.16] inconclusive | 1.15 [1.07, 1.25] inconclusive | 1.37 [1.21, 1.56] | 1.16 [1.08, 1.26] |
+| threshold 5 tol 120 | 1.07 [1.02, 1.14] inconclusive | 1.12 [1.05, 1.20] inconclusive | 1.30 [1.17, 1.46] | 1.13 [1.06, 1.21] |
+| threshold 5 tol 240 | 1.07 [1.02, 1.14] inconclusive | 1.11 [1.05, 1.20] inconclusive | 1.27 [1.14, 1.41] | 1.12 [1.06, 1.21] |
+
+Ratio of medians at the registered setting: perfect search 1.04 [0.99, 1.18]; perfect evidence as well 1.17 [1.02, 1.36].
+
+## Annex B: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | 0.47 [0.32, 0.61] (185 periods) |
+| without fallbacks | 0.54 [0.39, 0.69] (162 periods) |
+| medians at most 1 | 0.45 [0.32, 0.60] (181 periods) |
+| search right | 0.51 [0.36, 0.65] (118 periods) |
+| search wrong | 0.28 [-0.19, 0.69] (67 periods) |
+| flat prior search | 0.75 [0.61, 0.90] (185 periods) |
+| ratio of log medians | 0.44 |
+
+Refit fall-backs: 26 of 200; refits with a period median of 1 or more: 1.
+
+## Annex B: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 357 | 192 | 0.54 | 157 | 0.79 | 52 | 0.75 / 0.34 |
+| c2-refit | 357 | 140 | 0.39 | 103 | 0.52 | 19 | 0.49 / 0.29 |
+| search3-flat | 357 | 100 | 0.28 | 43 | 0.21 | 17 | 0.36 / 0.24 |
+| search2 | 357 | 68 | 0.19 | 73 | 0.36 | 15 | 0.27 / 0.14 |
+| search3 | 357 | 78 | 0.22 | 47 | 0.23 | 15 | 0.32 / 0.17 |
+| search3-mix | 357 | 81 | 0.23 | 39 | 0.20 | 15 | 0.32 / 0.18 |
+
+## Annex B: periods with no true manoeuvre (6): share with one declared
+
+| System | All | 0-30 min | 30-45 min |
+| --- | --- | --- | --- |
+| c2 | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| c2-refit | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| search3-flat | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| search2 | 0.50 [0.19, 0.81] | 0.40 (5) | 1.00 (1) |
+| search3 | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| search3-mix | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+
+## Annex B: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.316 (57) | 0.049 (61) | 0.000 (38) |
+| c2-refit | 0.316 (57) | 0.049 (61) | 0.000 (38) |
+| search3-flat | 0.246 (57) | 0.033 (61) | 0.000 (38) |
+| search2 | 0.035 (57) | 0.000 (61) | 0.000 (38) |
+| search3 | 0.088 (57) | 0.000 (61) | 0.000 (38) |
+| search3-mix | 0.228 (57) | 0.033 (61) | 0.000 (38) |
+
+Candidates (repaired evidence, 200 periods): first right 0.650; true history in the first three 0.780; calibration gap -0.050; kinds differ among right 0.19.
+
+Candidates (spec 014's evidence, 200 periods): first right 0.605; true history in the first three 0.745; calibration gap -0.093; kinds differ among right 0.20.
+
+References held to the true times: 0.150; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 38 of 200.
+
+## Held course: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 0.61 [0.52, 0.72] | 0.60 [0.47, 0.75] | 0.65 | 0.07 / 0.30 |
+| c2 ÷ c2-refit | 1.11 [1.03, 1.21] | 1.06 [0.88, 1.21] | 0.41 | 0.10 / 0.03 |
+| c2-refit ÷ search3 | 0.55 [0.47, 0.64] | 0.57 [0.46, 0.75] | 0.53 | 0.02 / 0.28 |
+| search3-flat ÷ search3 | 0.54 [0.45, 0.63] | 0.54 [0.45, 0.74] | 0.45 | 0.02 / 0.29 |
+| search2 ÷ search3 | 1.18 [1.05, 1.33] | 1.20 [1.03, 1.40] | 0.23 | 0.15 / 0.05 |
+| search3 ÷ search3-mix | 1.88 [1.60, 2.25] | 1.86 [1.36, 2.23] | 0.09 | 0.29 / 0.02 |
+| search3 ÷ history-best | 1.88 [1.60, 2.25] | 1.86 [1.36, 2.23] | 0.16 | 0.29 / 0.02 |
+| search3 ÷ history-fixed | 1.86 [1.58, 2.24] | 1.86 [1.36, 2.23] | 0.24 | 0.29 / 0.02 |
+| search3 ÷ one-leg | 1.72 [1.47, 2.06] | 1.78 [1.40, 2.21] | 0.35 | 0.32 / 0.07 |
+| c2 ÷ one-leg | 1.06 [1.00, 1.14] | 1.07 [1.00, 1.16] | 0.00 | 0.02 / 0.00 |
+| search3-mix ÷ one-leg | 0.92 [0.85, 0.98] | 0.96 [0.85, 1.16] | 0.58 | 0.03 / 0.09 |
+
+## Held course: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 184 | 0.47 [0.40, 0.54] | 98 | 0.43 [0.34, 0.53] | 0.00 [0.00, 0.04] | 0.57 [0.47, 0.66] |
+| all | 200 | 0.50 [0.43, 0.57] | 100 | 0.44 [0.35, 0.54] | 0.00 [0.00, 0.04] | 0.56 [0.46, 0.65] |
+| scored without cut | 184 | 0.47 [0.40, 0.54] | 98 | 0.43 [0.34, 0.53] | 0.00 [0.00, 0.04] | 0.57 [0.47, 0.66] |
+
+## Held course: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.00 [1.00, 1.00] not material | 1.79 [1.53, 2.14] material | 1.88 [1.60, 2.25] | 1.86 [1.59, 2.21] |
+| threshold 1 tol 120 | 1.00 [1.00, 1.00] not material | 1.79 [1.53, 2.14] material | 1.88 [1.60, 2.25] | 1.86 [1.59, 2.21] |
+| threshold 1 tol 240 | 1.00 [1.00, 1.00] not material | 1.79 [1.53, 2.14] material | 1.88 [1.60, 2.25] | 1.86 [1.59, 2.21] |
+| threshold 3 tol 60 | 1.00 [1.00, 1.00] not material | 1.58 [1.37, 1.87] material | 1.88 [1.60, 2.25] | 1.62 [1.41, 1.92] |
+| threshold 3 tol 120 | 1.00 [1.00, 1.00] not material | 1.58 [1.37, 1.87] material | 1.88 [1.60, 2.25] | 1.62 [1.41, 1.92] |
+| registered | 1.00 [1.00, 1.00] not material | 1.58 [1.37, 1.87] material | 1.88 [1.60, 2.25] | 1.62 [1.41, 1.92] |
+| registered without flagged | 1.00 [1.00, 1.00] not material | 1.58 [1.37, 1.87] material | 1.88 [1.60, 2.25] | 1.62 [1.41, 1.92] |
+| threshold 3 tol 240 | 1.00 [1.00, 1.00] not material | 1.58 [1.37, 1.87] material | 1.88 [1.60, 2.25] | 1.62 [1.41, 1.92] |
+| threshold 5 tol 60 | 1.00 [1.00, 1.00] not material | 1.50 [1.32, 1.76] material | 1.88 [1.60, 2.25] | 1.52 [1.34, 1.78] |
+| threshold 5 tol 120 | 1.00 [1.00, 1.00] not material | 1.50 [1.32, 1.76] material | 1.88 [1.60, 2.25] | 1.52 [1.34, 1.78] |
+| threshold 5 tol 240 | 1.00 [1.00, 1.00] not material | 1.50 [1.32, 1.76] material | 1.88 [1.60, 2.25] | 1.52 [1.34, 1.78] |
+
+Ratio of medians at the registered setting: perfect search 1.00 [1.00, 1.00]; perfect evidence as well 1.59 [1.29, 2.00].
+
+## Held course: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | -0.22 [-0.49, -0.06] (184 periods) |
+| without fallbacks | -0.22 [-0.47, -0.06] (182 periods) |
+| medians at most 1 | -0.22 [-0.49, -0.06] (184 periods) |
+| search right | 0.95 [0.65, 1.02] (86 periods) |
+| search wrong | -0.10 [-0.19, -0.02] (98 periods) |
+| flat prior search | -0.27 [-0.58, -0.09] (184 periods) |
+| ratio of log medians | -0.11 |
+
+Refit fall-backs: 2 of 200; refits with a period median of 1 or more: 0.
+
+## Held course: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 0 | 0 | – | 4 | 0.02 | – | – / – |
+| c2-refit | 0 | 0 | – | 4 | 0.02 | – | – / – |
+| search3-flat | 0 | 0 | – | 3 | 0.01 | – | – / – |
+| search2 | 0 | 0 | – | 181 | 0.91 | – | – / – |
+| search3 | 0 | 0 | – | 113 | 0.56 | – | – / – |
+| search3-mix | 0 | 0 | – | 0 | 0.00 | – | – / – |
+
+## Held course: periods with no true manoeuvre (200): share with one declared
+
+| System | All | 0-30 min | 30-45 min | 45-60 min | 60-90 min | 90-1000 min |
+| --- | --- | --- | --- | --- | --- | --- |
+| c2 | 0.01 [0.01, 0.04] | 0.00 (38) | 0.00 (60) | 0.04 (48) | 0.00 (42) | 0.08 (12) |
+| c2-refit | 0.01 [0.01, 0.04] | 0.00 (38) | 0.00 (60) | 0.04 (48) | 0.00 (42) | 0.08 (12) |
+| search3-flat | 0.01 [0.01, 0.04] | 0.03 (38) | 0.02 (60) | 0.02 (48) | 0.00 (42) | 0.00 (12) |
+| search2 | 0.76 [0.69, 0.81] | 0.42 (38) | 0.68 (60) | 0.88 (48) | 0.95 (42) | 1.00 (12) |
+| search3 | 0.50 [0.43, 0.57] | 0.08 (38) | 0.22 (60) | 0.69 (48) | 0.93 (42) | 1.00 (12) |
+| search3-mix | 0.00 [0.00, 0.02] | 0.00 (38) | 0.00 (60) | 0.00 (48) | 0.00 (42) | 0.00 (12) |
+
+Candidates (repaired evidence, 200 periods): first right 0.500; true history in the first three 0.710; calibration gap -0.049; kinds differ among right 0.00.
+
+Candidates (spec 014's evidence, 200 periods): first right 0.245; true history in the first three 0.540; calibration gap 0.167; kinds differ among right 0.00.
+
+References held to the true times: 0.000; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 0 of 200.
+
+## Soundness
+
+Reference efficiency on tight rows 1.06 (band 0.8 to 1.3); consistent fits 1.000 (at least 0.9); references held 0.150 (at most 0.15): pass.
diff --git a/experiments/20261113-tl3-val/README.md b/experiments/20261113-tl3-val/README.md
new file mode 100644
index 00000000..7d9d53b1
--- /dev/null
+++ b/experiments/20261113-tl3-val/README.md
@@ -0,0 +1,45 @@
+# Spec 015, validation seed 336: the soundness stop (2026-11-13)
+
+CI run 43393847363 (`timelate/tl-d-val`, commit 30cc410, clean tree; plan and code digests
+equal the decision record's). 150 Annex B periods (134 scored) and 150 held-course periods
+(130 scored). Every table is in `tables.md`, generated from `summary.json`.
+
+## The stop: passed
+
+| Check | Value | Required |
+| --- | --- | --- |
+| Reference (true history, neutral starts, times held): range efficiency on tight rows | 1.01 | 0.8 to 1.3 |
+| True-history fits with a cost the noise explains | 1.000 | at least 0.90 |
+| References held to the true times | 0.147 | at most 0.25 |
+
+Test seed 337 was launched from the same commit after this reading and before this file
+was written.
+
+## Reported only (validation decides nothing else)
+
+Read from `tables.md`; the development column is `experiments/20261113-tl3-dev/`.
+
+| | Development (spent 335) | Validation 336 |
+| --- | --- | --- |
+| C″ ÷ repaired search, paired | 2.39 [1.95, 2.90] | 1.69 [1.37, 2.08] |
+| D1: refit's share of the gain | 0.47 [0.32, 0.61] | 0.64 [0.36, 1.01] |
+| Refit fall-backs | 26 of 200 (first rule) | 5 of 150 (registered rule) |
+| D2: first candidate right; wrong: ambiguous / search / evidence | 0.64; 0.49 / 0.12 / 0.39 | 0.63; 0.48 / 0.12 / 0.40 |
+| D3: perfect search | 1.07 [1.02, 1.14] | 1.08 [1.01, 1.19] |
+| D3: perfect evidence as well | 1.17 [1.08, 1.27] | 1.30 [1.15, 1.49] |
+| ... without held references | 1.11 [1.04, 1.19] | 1.20 [1.09, 1.36] |
+| D4: held course, repaired search | 0.50 [0.43, 0.57] | 0.53 [0.45, 0.60] |
+| ... under 30 minutes; over 60 | 0.08; 0.93 to 1.00 | 0.03; 0.90 to 1.00 |
+| D4: C″ / flat prior / mixed prior | 0.01 / 0.01 / 0.00 | 0.02 / 0.02 / 0.00 |
+| Spec 014's search ÷ repaired: Annex B; held course | 1.03; 1.18 | 1.11 [1.00, 1.25]; 1.21 [1.07, 1.38] |
+| One-manoeuvre targets declared quiet: repaired; mixed | 0.088; 0.228 | 0.077; 0.135 |
+
+- D2 and D4 repeat closely. D1 again moves between seeds (0.47, 0.64), with an interval on
+  134 periods that reaches from a third to all of the gain.
+- The headroom with a perfect evidence is **material** on this seed (1.30 [1.15, 1.49]) and
+  inconclusive without held references (1.20 [1.09, 1.36]). On the development periods it
+  was inconclusive either way. The forecast called it an even chance; the test seed, with
+  400 periods, decides.
+- The changed fall-back rule fired in 5 of 150 periods, in line with its purpose.
+- The held-course split shows the same pattern by length as development: almost no false
+  manoeuvres under 30 minutes, almost always over an hour.
diff --git a/experiments/20261113-tl3-val/ci.json b/experiments/20261113-tl3-val/ci.json
new file mode 100644
index 00000000..eff22cb5
--- /dev/null
+++ b/experiments/20261113-tl3-val/ci.json
@@ -0,0 +1 @@
+{"run_id": "43393847363", "commit": "30cc41030bce3de01d02c46dd79155fffa28a020", "cpu_model": "Intel(R) Xeon(R) 6975P-C"}
diff --git a/experiments/20261113-tl3-val/man2.periods.json b/experiments/20261113-tl3-val/man2.periods.json
new file mode 100644
index 00000000..31d26439
--- /dev/null
+++ b/experiments/20261113-tl3-val/man2.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "first_row": 100, "rows": 963, "seconds_long": 4810.0, "bearings": 905, "true": [[505.0, 590.0], [1070.0, 1205.0], [2055.0, 2170.0], [4075.0, 4170.0], [4785.0, 4810.0]], "true_kinds": [[false, true], [true, true], [true, false], [true, false], [true, false]], "true_cut": [false, false, false, false, true], "ownship_turns": 2, "history_cost": 952.9694721878419, "history_converged": true, "candidates2": [{"spans": [[1054.2532059972389, 1235.083371682692], [2053.715571640527, 2167.0061871792273], [4062.789211194816, 4162.767249722175]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -539.8104513457537, "theta": [10993.371309695134, 4365.676017245063, -2.055087626540866, 7.822670568098631, 1.2539811148202578, 0.0, 1.3006723841326073, 0.0, -1.75149255011581, 0.0]}, {"spans": [[32.54345595824103, 57.97963240751629], [1056.6911854195282, 1233.4083896999407], [2081.3554644086685, 2156.4236716618266], [4062.1552148207666, 4163.121303155446]], "kinds": [[true, false], [true, false], [true, false], [true, false]], "evidence": -540.8267484692279, "theta": [11059.689320773754, 4524.584968169787, -2.500384904776149, 7.897287293683071, 0.44363087306001137, 0.0, 1.2549653774560339, 0.0, 1.3046228945448655, 0.0, -1.7694288574967347, 0.0]}, {"spans": [[1054.6905033296607, 1235.9971758192478], [2078.095085270046, 2155.3662066421252], [3661.7280777877986, 3681.4038552297857], [4085.20907527417, 4220.376022828448]], "kinds": [[true, false], [true, false], [false, true], [true, false]], "evidence": -544.6336328041344, "theta": [12565.30251899834, 4859.404596258702, -2.097081618146787, 8.510677941921795, 1.3143317898870315, 0.0, 1.2888801677277355, 0.0, 0.0, 0.983672812918582, -1.943823504860014, 0.0]}, {"spans": [[1055.3041538336247, 1235.4471123781989], [2031.8304346485222, 2174.1713455986146], [4058.766665599312, 4203.148078177203], [4605.357239541379, 4625.047209048927]], "kinds": [[true, false], [true, false], [true, false], [false, true]], "evidence": -544.9495889003113, "theta": [11107.281178424133, 4399.84409296357, -2.058401538359997, 7.866240315409923, 1.2619533297968144, 0.0, 1.2930141159305395, 0.0, -1.6702846425461182, 0.0, 0.0, 0.9857046466717397]}, {"spans": [[1054.3904114418278, 1235.2987820001506], [2053.262118093599, 2167.614349888065], [4060.93545706991, 4160.842735521943], [4708.5782253523375, 4799.159323520099]], "kinds": [[true, false], [true, false], [true, false], [true, false]], "evidence": -545.3877724142101, "theta": [10983.47531270721, 4361.862564957653, -2.054679069915623, 7.817695411397096, 1.2538997973804562, 0.0, 1.3004205543496268, 0.0, -1.7563458565270045, 0.0, 1.5842085136131723, 0.0]}], "histories_evaluated": 191, "candidates": [{"spans": [[1054.2532059972389, 1235.083371682692], [2053.715571640527, 2167.0061871792273], [4062.789211194816, 4162.767249722175]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -540.3770972067675, "theta": [10993.371309695134, 4365.676017245063, -2.055087626540866, 7.822670568098631, 1.2539811148202578, 0.0, 1.3006723841326073, 0.0, -1.75149255011581, 0.0]}, {"spans": [[32.54345595824103, 57.97963240751629], [1056.6911854195282, 1233.4083896999407], [2081.3554644086685, 2156.4236716618266], [4062.1552148207666, 4163.121303155446]], "kinds": [[true, false], [true, false], [true, false], [true, false]], "evidence": -545.1883398553683, "theta": [11059.689320773754, 4524.584968169787, -2.500384904776149, 7.897287293683071, 0.44363087306001137, 0.0, 1.2549653774560339, 0.0, 1.3046228945448655, 0.0, -1.7694288574967347, 0.0]}, {"spans": [[1054.3904114418278, 1235.2987820001506], [2053.262118093599, 2167.614349888065], [4060.93545706991, 4160.842735521943], [4708.5782253523375, 4799.159323520099]], "kinds": [[true, false], [true, false], [true, false], [true, false]], "evidence": -547.3870192610385, "theta": [10983.47531270721, 4361.862564957653, -2.054679069915623, 7.817695411397096, 1.2538997973804562, 0.0, 1.3004205543496268, 0.0, -1.7563458565270045, 0.0, 1.5842085136131723, 0.0]}, {"spans": [[1055.2436148940126, 1235.3814653099166], [2032.7614648051313, 2174.2262985375605], [4053.0435450698615, 4215.755275905787], [4603.208687731225, 4622.82655043221]], "kinds": [[true, false], [true, false], [true, false], [false, true]], "evidence": -548.2031726279328, "theta": [11094.070164076016, 4396.576297644484, -2.058197834899268, 7.860679066543043, 1.2616043935535841, 0.0, 1.2933877481950673, 0.0, -1.670572770996539, 0.0, 0.0, 0.9820861510355263]}, {"spans": [[476.1117206778801, 520.7231271182594], [1027.3502136240118, 1248.6266042319571], [2050.1632731634368, 2172.482246541051], [4062.818424033322, 4162.492646424998]], "kinds": [[false, true], [true, false], [true, false], [true, false]], "evidence": -548.2443098858001, "theta": [9761.38617536751, 3949.028148760482, -2.0839958393472315, 5.510592741295802, 0.0, 2.231850123427589, 1.287600297145458, 0.0, 1.2941347912156893, 0.0, 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[2049.1317268251178, 2168.0360490884786], [4077.4088199033235, 4111.574847436078]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -548.0610950440126}, "search3-mix": {"spans": [[1054.2532059972389, 1235.083371682692], [2053.715571640527, 2167.0061871792273], [4062.789211194816, 4162.767249722175]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -541.0702443873274}, "c2-refit": {"spans": [[1054.0524172157184, 1232.24263229201], [2086.7237213639746, 2162.2049767110393], [3236.367464500093, 3254.865833940692], [4075.758017422779, 4222.221573296283]], "kinds": [[true, false], [true, false], [false, true], [true, false]], "evidence": -548.3981759701107}}, "seconds": {"layer": 17.14, "reference": 24.85, "search2": 55.01, "search3": 54.28, "search3-flat": 41.89, "search3-mix": 52.92, "c2-refit": 32.68}}, {"ordinal": 1, "first_row": 218, "rows": 705, "seconds_long": 3520.0, "bearings": 672, "true": [[800.0, 885.0], [3150.0, 3210.0]], "true_kinds": [[true, false], [true, false]], "true_cut": [false, false], "ownship_turns": 2, "history_cost": 656.811398961072, "history_converged": true, "candidates2": [{"spans": [[732.4345628657221, 797.7193192522398], [1832.2517154106754, 1858.8181699825593], [3144.50224598009, 3192.6253601308013]], "kinds": [[false, true], [true, false], [true, false]], "evidence": -358.70503621874735, "theta": [25834.41431740064, -6601.654692809499, 3.3758445083669812, 3.406022396960024, 0.0, 2.8219830427313375, -0.4588601129258844, 0.0, 0.675716370120141, 0.0]}, {"spans": [[786.6434785263876, 872.6440224412736], [3133.083557388573, 3217.781519809561]], "kinds": [[true, false], [true, false]], "evidence": -360.97141183001685, "theta": [25239.396926682726, -6514.824155421405, 4.295232744471422, 6.7244343080697755, -1.5038030195729095, 0.0, 0.8450491967997528, 0.0]}, {"spans": [[763.3249906993165, 883.5259530054334], [1528.6548330482472, 1590.0224612293334], [3149.0533982547877, 3198.1181675156326]], "kinds": 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diff --git a/experiments/20261113-tl3-val/plan.json b/experiments/20261113-tl3-val/plan.json
new file mode 100644
index 00000000..8655badd
--- /dev/null
+++ b/experiments/20261113-tl3-val/plan.json
@@ -0,0 +1,74 @@
+{
+ "description": "Spec 015's registered study (2026-11-13): spec 014's quantities decided again with the repaired evidence (prior version 3), one edge-aware rule for a history's identity, a reference held where it drifts and a refit control with a fall-back. Bound by digest in experiments/20261113-tl3-decision/; never edited after.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics (the search's prior is right)"
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs (mean 15 min), smaller turns (15 to 75 degrees), faster turn rates, more speed changes: the search's prior is wrong"
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course for the whole engagement (spec 014 FR-009)"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with correlated bearing errors (60 s, half the variance)",
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with 2% outlying bearings (10 sd)",
+   "condition": "out-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "whole": true,
+   "what": "Annex B engagements uncut (40 to 120 minutes)"
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 5,
+  "score_every": 6,
+  "systems": [
+   "search2",
+   "search3",
+   "search3-flat",
+   "search3-mix",
+   "c2-refit"
+  ],
+  "thinning": "none"
+ },
+ "temperature": 3.0,
+ "top": 3,
+ "runner": "run3",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "class_threshold": 3.0,
+  "match_tolerance_s": 120.0,
+  "materiality": 1.1,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "quiet_interval_halfwidth_max": 0.05
+ },
+ "reference_flagged_max": 0.25
+}
diff --git a/experiments/20261113-tl3-val/quiet.periods.json b/experiments/20261113-tl3-val/quiet.periods.json
new file mode 100644
index 00000000..f857cbef
--- /dev/null
+++ b/experiments/20261113-tl3-val/quiet.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "first_row": 100, "rows": 963, "seconds_long": 4810.0, "bearings": 905, "true": [], "true_kinds": [], "true_cut": [], "ownship_turns": 2, "history_cost": 969.5230508253624, "history_converged": true, "candidates2": [{"spans": [[1515.6488958741877, 1560.7010165767501], [3203.5518369694432, 3232.3020381238507], [4564.792122083518, 4586.31518246632]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -518.1528398450004, "theta": [11031.162085827516, 4475.629840427257, -2.6346799159316556, 3.440188631552078, 0.3092541360212129, 0.0, 0.50195139514483, 0.0, 0.37381356625384965, 0.0]}, {"spans": [[1300.2967547773426, 1314.5204898224645], [2533.676328952297, 2560.7702608074287], [3506.512422971577, 3530.5072002603692]], "kinds": [[false, true], [true, false], [true, false]], "evidence": -518.8167875984049, "theta": [9332.091825596392, 3871.7262270557426, 3.9580351387114185, 3.418485414562458, 0.0, -0.6852016631122133, -0.4739813370350574, 0.0, 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diff --git a/experiments/20261113-tl3-val/run.json b/experiments/20261113-tl3-val/run.json
new file mode 100644
index 00000000..dd467579
--- /dev/null
+++ b/experiments/20261113-tl3-val/run.json
@@ -0,0 +1,17 @@
+{
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+ "seed": 336,
+ "count": 450,
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+ "shard_size": 25,
+ "what": "spec 015 validation: seed 336 (never generated before); the soundness stop"
+}
diff --git a/experiments/20261113-tl3-val/summary.json b/experiments/20261113-tl3-val/summary.json
new file mode 100644
index 00000000..37e746d3
--- /dev/null
+++ b/experiments/20261113-tl3-val/summary.json
@@ -0,0 +1,3817 @@
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+      "missed": 0,
+      "false": 103,
+      "found": 103,
+      "missed_share": NaN,
+      "false_per_period": 0.6866666666666666,
+      "timing_median_s": NaN
+     },
+     "240": {
+      "periods": 150,
+      "true": 0,
+      "matched": 0,
+      "missed": 0,
+      "false": 103,
+      "found": 103,
+      "missed_share": NaN,
+      "false_per_period": 0.6866666666666666,
+      "timing_median_s": NaN
+     }
+    },
+    "search3-flat": {
+     "60": {
+      "periods": 150,
+      "true": 0,
+      "matched": 0,
+      "missed": 0,
+      "false": 3,
+      "found": 3,
+      "missed_share": NaN,
+      "false_per_period": 0.02,
+      "timing_median_s": NaN
+     },
+     "120": {
+      "periods": 150,
+      "true": 0,
+      "matched": 0,
+      "missed": 0,
+      "false": 3,
+      "found": 3,
+      "missed_share": NaN,
+      "false_per_period": 0.02,
+      "timing_median_s": NaN
+     },
+     "240": {
+      "periods": 150,
+      "true": 0,
+      "matched": 0,
+      "missed": 0,
+      "false": 3,
+      "found": 3,
+      "missed_share": NaN,
+      "false_per_period": 0.02,
+      "timing_median_s": NaN
+     }
+    },
+    "search3-mix": {
+     "60": {
+      "periods": 150,
+      "true": 0,
+      "matched": 0,
+      "missed": 0,
+      "false": 0,
+      "found": 0,
+      "missed_share": NaN,
+      "false_per_period": 0.0,
+      "timing_median_s": NaN
+     },
+     "120": {
+      "periods": 150,
+      "true": 0,
+      "matched": 0,
+      "missed": 0,
+      "false": 0,
+      "found": 0,
+      "missed_share": NaN,
+      "false_per_period": 0.0,
+      "timing_median_s": NaN
+     },
+     "240": {
+      "periods": 150,
+      "true": 0,
+      "matched": 0,
+      "missed": 0,
+      "false": 0,
+      "found": 0,
+      "missed_share": NaN,
+      "false_per_period": 0.0,
+      "timing_median_s": NaN
+     }
+    }
+   },
+   "quiet": {
+    "periods": 150,
+    "false_manoeuvre_rate": {
+     "c2": {
+      "rate": 0.02,
+      "lo": 0.006824751322967459,
+      "hi": 0.05714668327038608,
+      "n": 150
+     },
+     "c2-refit": {
+      "rate": 0.02,
+      "lo": 0.006824751322967459,
+      "hi": 0.05714668327038608,
+      "n": 150
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+     "search2": {
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+      "hi": 0.8388397629824609,
+      "n": 150
+     },
+     "search3": {
+      "rate": 0.5266666666666666,
+      "lo": 0.44710055355423833,
+      "hi": 0.6049010334127976,
+      "n": 150
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+     "search3-flat": {
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+      "n": 150
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+     "search3-mix": {
+      "rate": 0.0,
+      "lo": 1.734723475976807e-18,
+      "hi": 0.0249702443680766,
+      "n": 150
+     }
+    },
+    "by_minutes": {
+     "0-30": {
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+      },
+      "search3-flat": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.11351339317396876,
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+      },
+      "search3-mix": {
+       "rate": 0.0,
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+       "hi": 0.11351339317396876,
+       "n": 30
+      }
+     },
+     "30-45": {
+      "c2": {
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+       "lo": 6.938893903907228e-18,
+       "hi": 0.07269781922049637,
+       "n": 49
+      },
+      "c2-refit": {
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+       "lo": 6.938893903907228e-18,
+       "hi": 0.07269781922049637,
+       "n": 49
+      },
+      "search2": {
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+       "lo": 0.6191368101239605,
+       "hi": 0.8539765474166023,
+       "n": 49
+      },
+      "search3": {
+       "rate": 0.40816326530612246,
+       "lo": 0.28215238520386865,
+       "hi": 0.5475268060815286,
+       "n": 49
+      },
+      "search3-flat": {
+       "rate": 0.02040816326530612,
+       "lo": 0.0036116725898252405,
+       "hi": 0.10693521523391616,
+       "n": 49
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 6.938893903907228e-18,
+       "hi": 0.07269781922049637,
+       "n": 49
+      }
+     },
+     "45-60": {
+      "c2": {
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+       "lo": 0.0,
+       "hi": 0.11696979849974075,
+       "n": 29
+      },
+      "c2-refit": {
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+       "lo": 0.0,
+       "hi": 0.11696979849974075,
+       "n": 29
+      },
+      "search2": {
+       "rate": 0.8620689655172413,
+       "lo": 0.6944100627964798,
+       "hi": 0.9450256003588804,
+       "n": 29
+      },
+      "search3": {
+       "rate": 0.6551724137931034,
+       "lo": 0.47345104090476137,
+       "hi": 0.80059281473325,
+       "n": 29
+      },
+      "search3-flat": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.11696979849974075,
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+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.11696979849974075,
+       "n": 29
+      }
+     },
+     "60-90": {
+      "c2": {
+       "rate": 0.03333333333333333,
+       "lo": 0.005908590381612455,
+       "hi": 0.16670390991409173,
+       "n": 30
+      },
+      "c2-refit": {
+       "rate": 0.03333333333333333,
+       "lo": 0.005908590381612455,
+       "hi": 0.16670390991409173,
+       "n": 30
+      },
+      "search2": {
+       "rate": 0.9666666666666667,
+       "lo": 0.8332960900859082,
+       "hi": 0.9940914096183874,
+       "n": 30
+      },
+      "search3": {
+       "rate": 0.9,
+       "lo": 0.7437891742081593,
+       "hi": 0.9654001112526658,
+       "n": 30
+      },
+      "search3-flat": {
+       "rate": 0.06666666666666667,
+       "lo": 0.018477023791270378,
+       "hi": 0.2132345836261692,
+       "n": 30
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.11351339317396876,
+       "n": 30
+      }
+     },
+     "90-1000": {
+      "c2": {
+       "rate": 0.0,
+       "lo": 2.7755575615628914e-17,
+       "hi": 0.24249400665524085,
+       "n": 12
+      },
+      "c2-refit": {
+       "rate": 0.0,
+       "lo": 2.7755575615628914e-17,
+       "hi": 0.24249400665524085,
+       "n": 12
+      },
+      "search2": {
+       "rate": 1.0,
+       "lo": 0.7575059933447592,
+       "hi": 1.0,
+       "n": 12
+      },
+      "search3": {
+       "rate": 1.0,
+       "lo": 0.7575059933447592,
+       "hi": 1.0,
+       "n": 12
+      },
+      "search3-flat": {
+       "rate": 0.0,
+       "lo": 2.7755575615628914e-17,
+       "hi": 0.24249400665524085,
+       "n": 12
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 2.7755575615628914e-17,
+       "hi": 0.24249400665524085,
+       "n": 12
+      }
+     }
+    }
+   },
+   "declared_quiet": {
+    "c2": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "c2-refit": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search2": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search3": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search3-flat": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    },
+    "search3-mix": {
+     "1": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "2": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     },
+     "3+": {
+      "rate": NaN,
+      "lo": NaN,
+      "hi": NaN,
+      "n": 0
+     }
+    }
+   },
+   "by_true_count": {
+    "0": {
+     "periods": 130,
+     "c2": 0.016240331809967756,
+     "c2-refit": 0.015751727391034365,
+     "history-best": 0.015757900197058916,
+     "history-fit": 0.01623409613966942,
+     "history-fixed": 0.01623409241437912,
+     "history-map": 0.015751719009131193,
+     "one-leg": 0.016240331809967756,
+     "search2": 0.0469637019559741,
+     "search3": 0.04100420046597719,
+     "search3-flat": 0.01623410452157259,
+     "search3-mix": 0.01575172715820372
+    }
+   },
+   "candidates": {
+    "periods": 150,
+    "top_right": 0.47333333333333333,
+    "truth_in_top3": 0.7066666666666667,
+    "mean_top_probability": 0.44320854394250386,
+    "calibration_gap": -0.030124789390829465,
+    "kinds_differ_among_right": 0.0
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+   "candidates2": {
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+    "top_right": 0.22,
+    "truth_in_top3": 0.49333333333333335,
+    "mean_top_probability": 0.4016888443503987,
+    "calibration_gap": 0.1816888443503987,
+    "kinds_differ_among_right": 0.0
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+   "seconds_per_period": {
+    "layer": 15.13,
+    "reference": 0.10253333333333332,
+    "search2": 10.277,
+    "search3": 8.136866666666666,
+    "search3-flat": 2.869533333333334,
+    "search3-mix": 2.8020666666666667,
+    "c2-refit": 0.3047999999999999
+   },
+   "dataset_digest": [
+    "a46b4d4e7d19c4b2b4a816bd0d72f58d01a0df5c3a6c7c622c495450841cdd68"
+   ],
+   "seed": [
+    336
+   ],
+   "search_code_sha256": [
+    "3b67d0267f2b99c75e7bea0d408989d2de5a308581d440511c63b71465585ec9"
+   ],
+   "git_commit": [
+    "30cc41030bce3de01d02c46dd79155fffa28a020"
+   ],
+   "git_dirty": false,
+   "condition": [
+    "clean"
+   ],
+   "thinning": [
+    "none"
+   ]
+  }
+ }
+}
diff --git a/experiments/20261113-tl3-val/tables.md b/experiments/20261113-tl3-val/tables.md
new file mode 100644
index 00000000..d153199f
--- /dev/null
+++ b/experiments/20261113-tl3-val/tables.md
@@ -0,0 +1,196 @@
+## Systems (median period range error)
+
+| System | Annex B (134) | Held course (130) |
+| --- | --- | --- |
+| One leg | 0.360 | 0.016 |
+| C″ read as a track | 0.070 | 0.016 |
+| C″ refitted | 0.061 | 0.016 |
+| Search, flat prior | 0.047 | 0.016 |
+| Search, spec 014's evidence | 0.049 | 0.047 |
+| Search, repaired evidence | 0.047 | 0.041 |
+| Search, repaired, mixed prior | 0.049 | 0.016 |
+| True history, best evidence | 0.029 | 0.016 |
+| True history, times held | 0.025 | 0.016 |
+
+## Annex B: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 1.69 [1.37, 2.08] | 1.48 [1.02, 2.29] | 0.35 | 0.37 / 0.16 |
+| c2 ÷ c2-refit | 1.40 [1.18, 1.67] | 1.16 [0.86, 1.80] | 0.34 | 0.29 / 0.12 |
+| c2-refit ÷ search3 | 1.20 [1.00, 1.46] | 1.27 [0.91, 1.68] | 0.43 | 0.19 / 0.15 |
+| search3-flat ÷ search3 | 0.99 [0.85, 1.15] | 0.98 [0.73, 1.34] | 0.44 | 0.13 / 0.22 |
+| search2 ÷ search3 | 1.11 [1.00, 1.25] | 1.02 [0.81, 1.30] | 0.23 | 0.13 / 0.05 |
+| search3 ÷ search3-mix | 1.02 [0.99, 1.05] | 0.97 [0.89, 1.09] | 0.01 | 0.02 / 0.01 |
+| search3 ÷ history-best | 1.51 [1.24, 1.86] | 1.63 [1.22, 2.36] | 0.38 | 0.28 / 0.08 |
+| search3 ÷ history-fixed | 1.89 [1.53, 2.36] | 1.90 [1.45, 2.81] | 0.33 | 0.46 / 0.14 |
+| search3 ÷ one-leg | 0.08 [0.06, 0.13] | 0.13 [0.10, 0.20] | 0.88 | 0.06 / 0.75 |
+| c2 ÷ one-leg | 0.14 [0.10, 0.21] | 0.20 [0.14, 0.33] | 0.77 | 0.07 / 0.65 |
+| search3-mix ÷ one-leg | 0.08 [0.06, 0.12] | 0.14 [0.10, 0.20] | 0.89 | 0.04 / 0.75 |
+
+## Annex B: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 134 | 0.63 [0.54, 0.70] | 50 | 0.48 [0.35, 0.61] | 0.12 [0.06, 0.24] | 0.40 [0.28, 0.54] |
+| all | 150 | 0.65 [0.57, 0.72] | 53 | 0.49 [0.36, 0.62] | 0.11 [0.05, 0.23] | 0.40 [0.28, 0.53] |
+| scored without cut | 120 | 0.62 [0.53, 0.70] | 46 | 0.50 [0.36, 0.64] | 0.11 [0.05, 0.23] | 0.39 [0.26, 0.54] |
+
+## Annex B: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.17 [1.06, 1.33] inconclusive | 1.40 [1.21, 1.65] material | 1.56 [1.30, 1.87] | 1.48 [1.28, 1.75] |
+| threshold 1 tol 120 | 1.14 [1.05, 1.28] inconclusive | 1.38 [1.20, 1.61] material | 1.52 [1.28, 1.81] | 1.43 [1.25, 1.66] |
+| threshold 1 tol 240 | 1.12 [1.03, 1.24] inconclusive | 1.33 [1.16, 1.53] material | 1.45 [1.23, 1.71] | 1.36 [1.19, 1.58] |
+| threshold 3 tol 60 | 1.12 [1.03, 1.25] inconclusive | 1.33 [1.16, 1.55] material | 1.56 [1.30, 1.87] | 1.35 [1.18, 1.58] |
+| threshold 3 tol 120 | 1.08 [1.01, 1.19] inconclusive | 1.30 [1.15, 1.49] material | 1.52 [1.28, 1.81] | 1.31 [1.16, 1.50] |
+| registered | 1.08 [1.01, 1.19] inconclusive | 1.30 [1.15, 1.49] material | 1.52 [1.28, 1.81] | 1.31 [1.16, 1.50] |
+| registered without flagged | 1.06 [1.01, 1.12] inconclusive | 1.20 [1.09, 1.36] inconclusive | 1.41 [1.20, 1.70] | 1.21 [1.09, 1.37] |
+| threshold 3 tol 240 | 1.07 [1.01, 1.17] inconclusive | 1.26 [1.12, 1.45] material | 1.45 [1.23, 1.71] | 1.27 [1.13, 1.46] |
+| threshold 5 tol 60 | 1.10 [1.01, 1.22] inconclusive | 1.26 [1.12, 1.43] material | 1.56 [1.30, 1.87] | 1.26 [1.12, 1.43] |
+| threshold 5 tol 120 | 1.07 [1.01, 1.16] inconclusive | 1.22 [1.10, 1.37] inconclusive | 1.52 [1.28, 1.81] | 1.22 [1.10, 1.38] |
+| threshold 5 tol 240 | 1.05 [1.00, 1.14] inconclusive | 1.20 [1.09, 1.34] inconclusive | 1.45 [1.23, 1.71] | 1.20 [1.09, 1.34] |
+
+Ratio of medians at the registered setting: perfect search 1.06 [0.99, 1.37]; perfect evidence as well 1.35 [1.09, 1.89].
+
+## Annex B: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | 0.64 [0.36, 1.01] (134 periods) |
+| without fallbacks | 0.70 [0.39, 1.11] (129 periods) |
+| medians at most 1 | 0.72 [0.43, 1.09] (132 periods) |
+| search right | 0.48 [0.25, 0.71] (84 periods) |
+| search wrong | 42.28 [-19.47, 25.04] (50 periods) |
+| flat prior search | 1.03 [0.73, 1.38] (134 periods) |
+| ratio of log medians | 0.38 |
+
+Refit fall-backs: 5 of 150; refits with a period median of 1 or more: 2.
+
+## Annex B: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 264 | 141 | 0.53 | 110 | 0.73 | 50 | 0.73 / 0.34 |
+| c2-refit | 264 | 90 | 0.34 | 59 | 0.39 | 23 | 0.44 / 0.27 |
+| search3-flat | 264 | 84 | 0.32 | 45 | 0.30 | 20 | 0.41 / 0.25 |
+| search2 | 264 | 62 | 0.23 | 69 | 0.46 | 21 | 0.35 / 0.15 |
+| search3 | 264 | 68 | 0.26 | 42 | 0.28 | 21 | 0.34 / 0.19 |
+| search3-mix | 264 | 71 | 0.27 | 36 | 0.24 | 21 | 0.36 / 0.20 |
+
+## Annex B: periods with no true manoeuvre (6): share with one declared
+
+| System | All | 0-30 min | 30-45 min |
+| --- | --- | --- | --- |
+| c2 | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| c2-refit | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| search3-flat | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| search2 | 0.50 [0.19, 0.81] | 0.60 (5) | 0.00 (1) |
+| search3 | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+| search3-mix | 0.00 [0.00, 0.39] | 0.00 (5) | 0.00 (1) |
+
+## Annex B: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.135 (52) | 0.048 (42) | 0.000 (31) |
+| c2-refit | 0.135 (52) | 0.048 (42) | 0.000 (31) |
+| search3-flat | 0.135 (52) | 0.024 (42) | 0.000 (31) |
+| search2 | 0.019 (52) | 0.000 (42) | 0.000 (31) |
+| search3 | 0.077 (52) | 0.000 (42) | 0.000 (31) |
+| search3-mix | 0.135 (52) | 0.048 (42) | 0.065 (31) |
+
+Candidates (repaired evidence, 150 periods): first right 0.647; true history in the first three 0.747; calibration gap -0.048; kinds differ among right 0.12.
+
+Candidates (spec 014's evidence, 150 periods): first right 0.547; true history in the first three 0.700; calibration gap -0.044; kinds differ among right 0.18.
+
+References held to the true times: 0.147; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 19 of 150.
+
+## Held course: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 0.45 [0.36, 0.55] | 0.40 [0.27, 0.57] | 0.72 | 0.05 / 0.40 |
+| c2 ÷ c2-refit | 1.06 [0.99, 1.15] | 1.03 [0.89, 1.20] | 0.44 | 0.08 / 0.03 |
+| c2-refit ÷ search3 | 0.42 [0.34, 0.52] | 0.38 [0.27, 0.57] | 0.62 | 0.01 / 0.38 |
+| search3-flat ÷ search3 | 0.43 [0.35, 0.53] | 0.40 [0.28, 0.59] | 0.53 | 0.02 / 0.38 |
+| search2 ÷ search3 | 1.21 [1.07, 1.38] | 1.15 [0.95, 1.61] | 0.28 | 0.18 / 0.04 |
+| search3 ÷ search3-mix | 2.37 [1.92, 2.92] | 2.60 [1.74, 3.66] | 0.05 | 0.38 / 0.01 |
+| search3 ÷ history-best | 2.36 [1.91, 2.91] | 2.60 [1.74, 3.66] | 0.14 | 0.38 / 0.01 |
+| search3 ÷ history-fixed | 2.30 [1.86, 2.86] | 2.53 [1.71, 3.60] | 0.21 | 0.38 / 0.03 |
+| search3 ÷ one-leg | 2.22 [1.80, 2.76] | 2.52 [1.74, 3.65] | 0.28 | 0.40 / 0.05 |
+| c2 ÷ one-leg | 1.00 [0.99, 1.00] | 1.00 [1.00, 1.00] | 0.01 | 0.00 / 0.00 |
+| search3-mix ÷ one-leg | 0.94 [0.87, 1.01] | 0.97 [0.83, 1.12] | 0.56 | 0.03 / 0.08 |
+
+## Held course: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 130 | 0.40 [0.32, 0.49] | 78 | 0.42 [0.32, 0.53] | 0.00 [-0.00, 0.05] | 0.58 [0.47, 0.68] |
+| all | 150 | 0.47 [0.40, 0.55] | 79 | 0.43 [0.33, 0.54] | 0.00 [-0.00, 0.05] | 0.57 [0.46, 0.67] |
+| scored without cut | 130 | 0.40 [0.32, 0.49] | 78 | 0.42 [0.32, 0.53] | 0.00 [-0.00, 0.05] | 0.58 [0.47, 0.68] |
+
+## Held course: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.00 [1.00, 1.00] not material | 2.08 [1.70, 2.56] material | 2.36 [1.91, 2.91] | 2.13 [1.75, 2.60] |
+| threshold 1 tol 120 | 1.00 [1.00, 1.00] not material | 2.08 [1.70, 2.56] material | 2.36 [1.91, 2.91] | 2.13 [1.75, 2.60] |
+| threshold 1 tol 240 | 1.00 [1.00, 1.00] not material | 2.08 [1.70, 2.56] material | 2.36 [1.91, 2.91] | 2.13 [1.75, 2.60] |
+| threshold 3 tol 60 | 1.00 [1.00, 1.00] not material | 1.80 [1.50, 2.17] material | 2.36 [1.91, 2.91] | 1.81 [1.51, 2.19] |
+| threshold 3 tol 120 | 1.00 [1.00, 1.00] not material | 1.80 [1.50, 2.17] material | 2.36 [1.91, 2.91] | 1.81 [1.51, 2.19] |
+| registered | 1.00 [1.00, 1.00] not material | 1.80 [1.50, 2.17] material | 2.36 [1.91, 2.91] | 1.81 [1.51, 2.19] |
+| registered without flagged | 1.00 [1.00, 1.00] not material | 1.80 [1.50, 2.17] material | 2.36 [1.91, 2.91] | 1.81 [1.51, 2.19] |
+| threshold 3 tol 240 | 1.00 [1.00, 1.00] not material | 1.80 [1.50, 2.17] material | 2.36 [1.91, 2.91] | 1.81 [1.51, 2.19] |
+| threshold 5 tol 60 | 1.00 [1.00, 1.00] not material | 1.58 [1.34, 1.90] material | 2.36 [1.91, 2.91] | 1.58 [1.34, 1.90] |
+| threshold 5 tol 120 | 1.00 [1.00, 1.00] not material | 1.58 [1.34, 1.90] material | 2.36 [1.91, 2.91] | 1.58 [1.34, 1.90] |
+| threshold 5 tol 240 | 1.00 [1.00, 1.00] not material | 1.58 [1.34, 1.90] material | 2.36 [1.91, 2.91] | 1.58 [1.34, 1.90] |
+
+Ratio of medians at the registered setting: perfect search 1.00 [1.00, 1.00]; perfect evidence as well 2.21 [1.51, 2.84].
+
+## Held course: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | -0.08 [-0.19, 0.02] (130 periods) |
+| without fallbacks | -0.08 [-0.19, 0.02] (130 periods) |
+| medians at most 1 | -0.08 [-0.19, 0.02] (130 periods) |
+| search right | 1.00 [1.00, 1.00] (52 periods) |
+| search wrong | -0.03 [-0.11, 0.04] (78 periods) |
+| flat prior search | -0.05 [-0.16, 0.05] (130 periods) |
+| ratio of log medians | -0.03 |
+
+Refit fall-backs: 0 of 150; refits with a period median of 1 or more: 0.
+
+## Held course: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 0 | 0 | – | 6 | 0.04 | – | – / – |
+| c2-refit | 0 | 0 | – | 6 | 0.04 | – | – / – |
+| search3-flat | 0 | 0 | – | 3 | 0.02 | – | – / – |
+| search2 | 0 | 0 | – | 151 | 1.01 | – | – / – |
+| search3 | 0 | 0 | – | 103 | 0.69 | – | – / – |
+| search3-mix | 0 | 0 | – | 0 | 0.00 | – | – / – |
+
+## Held course: periods with no true manoeuvre (150): share with one declared
+
+| System | All | 0-30 min | 30-45 min | 45-60 min | 60-90 min | 90-1000 min |
+| --- | --- | --- | --- | --- | --- | --- |
+| c2 | 0.02 [0.01, 0.06] | 0.07 (30) | 0.00 (49) | 0.00 (29) | 0.03 (30) | 0.00 (12) |
+| c2-refit | 0.02 [0.01, 0.06] | 0.07 (30) | 0.00 (49) | 0.00 (29) | 0.03 (30) | 0.00 (12) |
+| search3-flat | 0.02 [0.01, 0.06] | 0.00 (30) | 0.02 (49) | 0.00 (29) | 0.07 (30) | 0.00 (12) |
+| search2 | 0.78 [0.71, 0.84] | 0.47 (30) | 0.76 (49) | 0.86 (29) | 0.97 (30) | 1.00 (12) |
+| search3 | 0.53 [0.45, 0.60] | 0.03 (30) | 0.41 (49) | 0.66 (29) | 0.90 (30) | 1.00 (12) |
+| search3-mix | 0.00 [0.00, 0.02] | 0.00 (30) | 0.00 (49) | 0.00 (29) | 0.00 (30) | 0.00 (12) |
+
+Candidates (repaired evidence, 150 periods): first right 0.473; true history in the first three 0.707; calibration gap -0.030; kinds differ among right 0.00.
+
+Candidates (spec 014's evidence, 150 periods): first right 0.220; true history in the first three 0.493; calibration gap 0.182; kinds differ among right 0.00.
+
+References held to the true times: 0.000; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 0 of 150.
+
+## Soundness
+
+Reference efficiency on tight rows 1.01 (band 0.8 to 1.3); consistent fits 1.000 (at least 0.9); references held 0.147 (at most 0.25): pass.
diff --git a/experiments/20261113-tl3/README.md b/experiments/20261113-tl3/README.md
new file mode 100644
index 00000000..6c5656d2
--- /dev/null
+++ b/experiments/20261113-tl3/README.md
@@ -0,0 +1,180 @@
+# Spec 015: the re-registered study on test seed 337 (2026-11-13)
+
+CI run 43404500552 (`timelate/tl-d-test`, commit 30cc410, clean tree), scored once. Plan and
+time-late code digests equal `experiments/20261113-tl3-decision/decision.json`. **Every
+table is in `tables.md`, generated from `summary.json` by `scripts/research/tl3_tables.py`;
+every figure below is copied from it and checked against it by script.** Files: `summary.json`,
+`tables.md`, `score.txt`, `*.periods.json`, `plan.json`, `run.json`, `ci.json`. The per-row
+errors (15 MB) are on the results branch `results/timelate-tl-d-test-43404500552` under
+`experiments/timelate-runs/`.
+
+"Paired" is the geometric mean of the per-period ratio with a 95% period bootstrap.
+
+## What was decided
+
+| | Quantity (Annex B, 400 periods, 364 scored, unless said) | Forecast | Result | Forecast right? |
+| --- | --- | --- | --- | --- |
+| D1 | Share of the repaired search's gain over C″ obtained by refitting C″'s own manoeuvre times | 0.50; interval includes 0.5 | **0.33 [0.20, 0.45]** | No |
+| D2 | First candidate right (edge-aware) | 0.63 | **0.64 [0.59, 0.69]** | Yes |
+| D2 | Wrong scored periods (131): ambiguous / search failure / evidence failure | 0.50 / 0.12 / 0.38 | **0.44 [0.36, 0.53] / 0.13 [0.08, 0.20] / 0.43 [0.35, 0.51]** | Yes, within the intervals |
+| D3 | Headroom, a perfect search under the same evidence | 1.05 to 1.08; inconclusive or not material | **1.06 [1.01, 1.11]: inconclusive** | Yes |
+| D3 | Headroom, a perfect evidence as well | 1.12 to 1.20; an even chance of material | **1.17 [1.10, 1.25]: inconclusive** (the lower limit is 1.099) | Yes |
+| D4 | Held-course periods with a manoeuvre declared (398): repaired search | 0.50 (0.44 to 0.56) | **0.53 [0.48, 0.58]** | Yes |
+| D4 | ... C″ / flat prior / mixed prior | 0.02 / 0.01 / 0.01 | **0.02 [0.01, 0.03] / 0.01 [0.00, 0.02] / 0.00 [0.00, 0.01]** | Yes |
+| D5 | Recommendation on a learned whole-period model | spec 014's rule | **By the rule: no GPU budget. See below.** | – |
+
+Soundness on this seed, as validation read it: the reference with times held at 1.00 of the
+bound on tight rows; every true-history fit with a consistent cost; 0.133 of references held.
+
+## D5: the rule's output, and what the evidence supports
+
+**The rule's output.** A learned whole-period model is pursued only if the headroom with a
+perfect evidence is material on the Annex B test split: an interval wholly above 1.10. It is
+1.17 [1.10, 1.25], and the lower limit, to three places, is 1.099. Inconclusive, which the
+rule counts with "not material": **no GPU budget**. The verdict is not a bootstrap accident:
+with 50,000 resamples the limit is 1.0975, and it is below 1.10 for 19 of 20 bootstrap seeds
+(computed after the run from the per-period errors; not part of the registered scoring).
+
+**What the evidence supports.** The headroom misses the line by two thousandths. At the
+other registered sensitivities (`tables.md`, "attainable headroom") it is material at class
+threshold 1 (1.30 [1.20, 1.42]) and at a 60-second match (1.20 [1.12, 1.29]), and
+inconclusive at threshold 5 (1.10 [1.05, 1.17]) and without held references
+(1.13 [1.06, 1.20]). On validation seed 336 it was material (1.30 [1.15, 1.49]). A fair
+statement: **on Annex B data a better identifier of histories has between 10% and 30% to
+win, most likely about 17%; the registered rule says that is not enough, by the narrowest
+margin a rule can.** The decision the rule was written to make automatically is, on this
+evidence, a judgement, and it is the owner's.
+
+**Where the assumptions are wrong** the same statistic is material on every split: shifted
+prior 1.71 [1.50, 1.96]; correlated errors 1.44 [1.20, 1.74]; outliers 1.50 [1.30, 1.75];
+held course 1.53 [1.40, 1.68] (which the mixed prior removes). No remedy, classical or
+learned, was built against the first three.
+
+**What the study is silent on.** A model that hedges across histories instead of naming
+one; anything outside this simulator.
+
+## Systems
+
+See `tables.md`, "Systems". On Annex B data C″ read as a track is at 0.083, C″ refitted
+0.066, the flat-prior search 0.049, the repaired search 0.039 and the true-history fit
+0.028. Paired on Annex B:
+
+| Comparison | Paired | First better in | Over twice as bad: first / second |
+| --- | --- | --- | --- |
+| C″ ÷ repaired search | 2.10 [1.82, 2.44] | 0.31 | 0.44 / 0.12 |
+| C″ refitted ÷ repaired search | 1.65 [1.45, 1.87] | 0.39 | 0.29 / 0.07 |
+| Flat-prior search ÷ repaired search | 1.22 [1.10, 1.34] | 0.37 | 0.19 / 0.10 |
+| Spec 014's search ÷ repaired search | 1.07 [0.98, 1.16] | 0.20 | 0.10 / 0.06 |
+| Repaired search ÷ mixed-prior search | 0.98 [0.96, 1.01] | 0.03 | 0.01 / 0.02 |
+
+## What the repairs changed (spec 014's search beside the repaired one, same periods)
+
+| | Spec 014's evidence | Repaired |
+| --- | --- | --- |
+| Held course: periods with a manoeuvre declared | 0.80 [0.76, 0.84] | 0.53 [0.48, 0.58] |
+| ... under 30 min (86) / 30 to 45 (99) / 45 to 60 (88) / 60 to 90 (94) / over 90 (31) | 0.50 / 0.78 / 0.91 / 0.95 / 1.00 | 0.09 / 0.32 / 0.68 / 0.86 / 1.00 |
+| Held course: spec 014's ÷ repaired, paired | – | 1.22 [1.12, 1.34] |
+| Annex B: false manoeuvres (716 true) | 135 | 89 |
+| Annex B: true manoeuvres missed | 0.20 | 0.22 |
+| Annex B: first candidate right; calibration gap | 0.598; −0.071 | 0.652; −0.038 |
+| Annex B: one-manoeuvre targets declared quiet (122) | 0.041 | 0.131 |
+
+## Reading
+
+1. **The repair removes the evidence's false manoeuvres on short periods and not the
+   prior's on long ones.** Under 30 minutes the held-course rate falls from 0.50 to 0.09;
+   over 90 minutes it is 1.00 either way. Overall it is still 0.53, and the repaired
+   search's track on held course is still 1.80 [1.60, 2.02] times the one-leg fit's.
+2. **The repair is not free.** The repaired search calls a target with one manoeuvre quiet
+   in 0.131 of such periods, where spec 014's did in 0.041. It trades false manoeuvres for
+   missed single ones, as the mixed prior does more strongly (0.197). C″ does in 0.238.
+3. **On Annex B data the repair changes the track little** (1.07 [0.98, 1.16]) and improves
+   which history comes first (0.652 against 0.598).
+4. **D1: about a third of the search's gain comes without a search**, 0.33 [0.20, 0.45], and
+   the interval excludes a half. Spec 014 had 0.43 [0.28, 0.58] on seed 335; development on
+   that seed gave 0.47 and validation 0.64 [0.36, 1.01]. By the other declared estimands:
+   0.37 [0.23, 0.50] where no period median exceeds 1, and 0.31 as a ratio of log medians.
+   The fall-back fired in 5 of 400 periods; 7 refits still have a period median of 1 or
+   more. The flat-prior search obtains 0.74 [0.61, 0.86]: Annex B's statistics are worth
+   about a quarter of the gain. The share is not a constant: five readings on four seeds run
+   from 0.33 to 0.64.
+5. **D2 no longer depends on the period's edges.** Without periods that have a cut
+   manoeuvre (301 scored) the first candidate is right in 0.62 and the wrong histories divide
+   0.46 / 0.14 / 0.40. 72 of 400 periods have a cut manoeuvre.
+6. **The search itself is rarely the fault** (0.13 of wrong histories); the evidence prefers
+   a wrong history about as often as it cannot separate them.
+7. **The mixed prior** removes false manoeuvres on held course (0.00 [0.00, 0.01]) at no
+   cost in median track error on Annex B data (0.98 [0.96, 1.01]) and at the cost in item 2.
+8. **Correlated errors** triple the repaired search's false manoeuvres (1.29 a period
+   against 0.22) and leave its candidate probabilities meaningless (calibration gap 0.332).
+   **Outliers** raise them to 0.67 a period. Under the shifted prior the first candidate is
+   right in 0.23 of periods and 0.77 of wrong histories are evidence failures.
+
+## Forecasts
+
+Seven of eight decided forecasts held; D1's did not (0.50 forecast, 0.33 found, the interval
+excluding a half). Of the reported ones: "spec 014's ÷ repaired 1.03 on Annex B" close
+(1.07 [0.98, 1.16]); "1.15 to 1.20 on held course" slightly low (1.22); "fall-backs under 5%
+of periods" right on Annex B, held course and the shifted split, wrong with outliers (20 of
+100) and correlated errors (10 of 100); "the mixed prior calls a one-manoeuvre target quiet
+in about 0.2 against 0.1" right (0.197 against 0.131); "headroom material under the shifted
+prior and both error conditions" right. "Above 0.90 over an hour" for the held-course rate
+is missed narrowly: 0.86 from 60 to 90 minutes and 1.00 beyond.
+
+## Not run, weaknesses, and what this does not support
+
+- No remedy for the shifted prior, correlated errors or outliers; no hedged track; no
+  sampler in place of the Laplace approximation; no thinning. They are the next choice.
+- The fall-back rule fires in a fifth of outlier periods: there the "cost the noise
+  explains" test fails by construction (the outliers are not in the stated noise), as the
+  consistent-cost share of 0.020 on that split shows.
+- 0.133 of references were held to the true times on Annex B data, 0.300 under the shifted
+  prior and 0.280 with correlated errors: there the composite is mostly a times-held fit.
+- The held-course split is not wholly quiet: 2 of its 400 periods contain a manoeuvre (the
+  first change is drawn uniformly over a very long leg). D4 is over the 398 without.
+- The class threshold is a convention, and the headroom's verdict turns on it (see D5).
+- One test seed; every split is drawn from the same seed with different ranges files; the
+  fitting model is the simulator's own; bearing errors are independent Gaussian in the
+  primary condition.
+- This record does not show that learning cannot help, nor that it would.
+
+## Corrections after the panel (2026-11-13; `review.md`, five approve with findings)
+
+The registered figures stand. The following statements above are corrected; figures marked
+"panel" are the reviewers' recomputations from the per-row errors and are in `review.md`, not
+in `tables.md`.
+
+- **D5.** The registered outcome is "no GPU budget". The paragraph above calling the decision
+  "a judgement, and it is the owner's" goes further than the rule allows: an override would
+  be a departure from the rule, not a reading of it. "Between 10% and 30%, most likely about
+  17%" is withdrawn. Fair wording: an oracle identifier is 8% to 30% ahead across the
+  registered sensitivities, and 11% to 19% at the registered threshold depending on how the
+  reference is fitted (panel); what a real identifier would obtain is less and unmeasured.
+- **D3 depends on the repaired reference.** A third of the composite's gain comes from the
+  19 replaced periods whose reference is held to the true times, which is more than
+  identification; ten periods carry over half of it (panel). The whole-engagement split's
+  headroom (1.22 [1.03, 1.48]) was omitted from the list, and the shifted, correlated and
+  outlier splits' figures rest on 21% to 30% held references.
+- **D1** is "between a third and a half": without the refit's ten worst periods 0.41
+  [0.29, 0.52] (panel). "Five readings on four seeds" is four readings on three seeds. The
+  repair did not move D1: with spec 014's search as the whole it is 0.36 on this seed
+  (panel). The fall-back rule caught one of the seven refits with a period median of 1 or more.
+- **D2.** The split between ambiguous and evidence failure is the threshold's (0.11 / 0.22 /
+  0.66 at threshold 1; 0.66 / 0.11 / 0.23 at 5; panel). Reading 6 is withdrawn.
+- **D4 and reading 1.** The remaining rate is the prior's leg term, confirmed by numerical
+  integration on 34 periods, but the low rate on short periods is not what a correct
+  evidence gives: integrated over where the manoeuvre might be, the prior favours "some
+  manoeuvre" almost everywhere. Version 3's pairing fails at the edges of the prior's
+  support and at the 10 s floor, where 179 of the 212 invented manoeuvres sit (1 to 3.5
+  nats; panel). The evidence repair is not finished.
+- **What the repairs changed.** The edge rule is worth about +0.10 in first candidate right
+  and the evidence repair +0.04 to +0.05 (panel); the table above shows only the second.
+- **Reading 7.** The mixed prior at a stated half is the expensive end of a curve: re-ranked
+  at a quiet probability of 0.01 the held-course false rate is 0.09 and one-manoeuvre targets
+  called quiet 0.14 (panel). Two-manoeuvre targets are called quiet in 0.085 under the
+  registered mix (`tables.md`).
+- **Reading 8.** Correlated errors multiply the false manoeuvres by about six, not three.
+- **Forecasts.** Five of seven rows held, not "seven of eight": D1 missed, and D4's "above
+  0.90 over an hour" missed (0.896). The forecasts restated the development run.
+- **"Checked by script".** The check was run in the session and the script is not committed;
+  only `tables.md` is generated.
diff --git a/experiments/20261113-tl3/ci.json b/experiments/20261113-tl3/ci.json
new file mode 100644
index 00000000..d93b329b
--- /dev/null
+++ b/experiments/20261113-tl3/ci.json
@@ -0,0 +1 @@
+{"run_id": "43404500552", "commit": "30cc41030bce3de01d02c46dd79155fffa28a020", "cpu_model": "AMD EPYC 7763 64-Core Processor"}
diff --git a/experiments/20261113-tl3/man2-corr.periods.json b/experiments/20261113-tl3/man2-corr.periods.json
new file mode 100644
index 00000000..0e7b2a1d
--- /dev/null
+++ b/experiments/20261113-tl3/man2-corr.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261113-tl3/man2-out.periods.json b/experiments/20261113-tl3/man2-out.periods.json
new file mode 100644
index 00000000..a1dd701a
--- /dev/null
+++ b/experiments/20261113-tl3/man2-out.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261113-tl3/man2.periods.json b/experiments/20261113-tl3/man2.periods.json
new file mode 100644
index 00000000..4f6abb5a
--- /dev/null
+++ b/experiments/20261113-tl3/man2.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261113-tl3/plan.json b/experiments/20261113-tl3/plan.json
new file mode 100644
index 00000000..8655badd
--- /dev/null
+++ b/experiments/20261113-tl3/plan.json
@@ -0,0 +1,74 @@
+{
+ "description": "Spec 015's registered study (2026-11-13): spec 014's quantities decided again with the repaired evidence (prior version 3), one edge-aware rule for a history's identity, a reference held where it drifts and a refit control with a fall-back. Bound by digest in experiments/20261113-tl3-decision/; never edited after.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics (the search's prior is right)"
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs (mean 15 min), smaller turns (15 to 75 degrees), faster turn rates, more speed changes: the search's prior is wrong"
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course for the whole engagement (spec 014 FR-009)"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with correlated bearing errors (60 s, half the variance)",
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B data with 2% outlying bearings (10 sd)",
+   "condition": "out-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "whole": true,
+   "what": "Annex B engagements uncut (40 to 120 minutes)"
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 5,
+  "score_every": 6,
+  "systems": [
+   "search2",
+   "search3",
+   "search3-flat",
+   "search3-mix",
+   "c2-refit"
+  ],
+  "thinning": "none"
+ },
+ "temperature": 3.0,
+ "top": 3,
+ "runner": "run3",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "class_threshold": 3.0,
+  "match_tolerance_s": 120.0,
+  "materiality": 1.1,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "quiet_interval_halfwidth_max": 0.05
+ },
+ "reference_flagged_max": 0.25
+}
diff --git a/experiments/20261113-tl3/quiet.periods.json b/experiments/20261113-tl3/quiet.periods.json
new file mode 100644
index 00000000..3cf8f92e
--- /dev/null
+++ b/experiments/20261113-tl3/quiet.periods.json
@@ -0,0 +1 @@
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"seconds": {"layer": 22.12, "reference": 0.12, "search2": 11.71, "search3": 11.68, "search3-flat": 4.02, "search3-mix": 4.12, "c2-refit": 0.14}}, {"ordinal": 399, "first_row": 44, "rows": 491, "seconds_long": 2450.0, "bearings": 448, "true": [], "true_kinds": [], "true_cut": [], "ownship_turns": 0, "history_cost": 456.306628223948, "history_converged": true, "candidates2": [{"spans": [[589.9611097460626, 609.2018334609778]], "kinds": [[false, true]], "evidence": -249.1964251284201, "theta": [-572.3186424764386, -2214.2068530153083, -2.7244197991208283, 10.917066429740782, 0.0, -0.9618729293065905]}, {"spans": [[52.754480355234605, 89.13093189334072]], "kinds": [[true, true]], "evidence": -250.0303983419721, "theta": [-3070.8420101351758, -4927.33338405685, -2.3868824127686694, 9.636601983858318, -0.49686201527625834, -2.0150746939634367]}, {"spans": [], "kinds": [], "evidence": -250.34753899229673, "theta": [-1707.6985892608855, -3333.6541896597682, 3.3573886446301247, 7.35333952760628]}, {"spans": [[586.5637405755402, 608.9470105733734], [2393.173970688245, 2449.971300059755]], "kinds": [[false, true], [false, true]], "evidence": -252.78161618238988, "theta": [-739.3163675010852, -2381.09203387054, -2.7179809717599697, 10.991333869952824, 0.0, -0.9398038533214, 0.0, 0.9827615409595161]}, {"spans": [[165.7441497535392, 234.57935012928488]], "kinds": [[true, true]], "evidence": -255.8795067400989, "theta": [593.2435617575717, -1006.6065685546695, -2.9611670210756986, 7.9085722764685835, -0.642897417293654, -3.094558676150023]}], "histories_evaluated": 20, "candidates": [{"spans": [], "kinds": [], "evidence": -250.08417648784734, "theta": [-1707.6985892608855, -3333.6541896597682, 3.3573886446301247, 7.35333952760628]}, {"spans": [[52.754480355234605, 89.13093189334072]], "kinds": [[true, true]], "evidence": -251.78842736294305, "theta": [-3070.8420101351758, -4927.33338405685, -2.3868824127686694, 9.636601983858318, -0.49686201527625834, 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-249.1964251284201}, "search3": {"spans": [], "kinds": [], "evidence": -250.08417648784734}, "search3-flat": {"spans": [], "kinds": [], "evidence": -245.08239517087986}, "search3-mix": {"spans": [], "kinds": [], "evidence": -245.6623595508398}, "c2-refit": {"spans": [], "kinds": [], "evidence": -250.10114491299097}}, "seconds": {"layer": 22.12, "reference": 0.44, "search2": 11.37, "search3": 4.67, "search3-flat": 4.82, "search3-mix": 4.64, "c2-refit": 0.46}}]
diff --git a/experiments/20261113-tl3/review.md b/experiments/20261113-tl3/review.md
new file mode 100644
index 00000000..7d551f62
--- /dev/null
+++ b/experiments/20261113-tl3/review.md
@@ -0,0 +1,274 @@
+# Spec 015: panel review, round 1 (2026-11-13)
+
+Five independent reviewers (numbers and provenance; the evidence repair; statistics and the
+rule; code; scope and delivery) read the spec, the decision record, the development,
+validation and test records and the code at the registered commit 30cc410, and recomputed
+from the per-row errors and per-period records. **All five: approve with findings. Decided in
+one round.** The registered figures stand as registered. No bug changes a decided number.
+The record's reading of D3 and D5 leaned one way and is corrected below, and the repair of
+the reference turns out to be the convention the headroom figure depends on most.
+
+Figures in this file that are not in `tables.md` are the reviewers' own recomputations from
+the per-row errors (results branch `results/timelate-tl-d-test-43404500552`) and the period
+records; they are not registered results.
+
+## Verdict on each decided quantity
+
+| | As registered | Panel |
+| --- | --- | --- |
+| D1 | 0.33 [0.20, 0.45] | Reproduced. **"Between a third and a half", not "a third, excluding a half"**: without the refit's 10 worst periods 0.41 [0.29, 0.52]; pooled over three seeds 0.42 [0.32, 0.51]. It mostly restates how often C″ has the history right (26% of periods, where the refit obtains all of the search's gain; 0.25 elsewhere) |
+| D2 | First right 0.64; wrong 0.44 / 0.13 / 0.43 | Reproduced, and stable to removing cut periods (the edge repair works). **The split between ambiguous and evidence failure is the threshold's**: 0.11 / 0.22 / 0.66 at threshold 1, 0.66 / 0.11 / 0.23 at 5. Only "search failure is the smallest class at thresholds 3 and 5" is robust |
+| D3 | 1.06 inconclusive; 1.17 [1.10, 1.25] inconclusive | Reproduced. **Convention-bound and concentrated**: 1.11 to 1.19 by how the reference is fitted, 1.08 to 1.30 across the registered sensitivities; ten periods carry 54% of it; a third of it comes from references told the true times. An oracle figure, neither an upper nor a lower limit |
+| D4 | 0.53 / 0.02 / 0.01 / 0.00 | Reproduced. The remaining rate is the prior's leg term (integrated numerically on 34 periods). **But 0.09 on short periods is not what a correct evidence gives**: integrated over where the manoeuvre might be, Annex B's prior favours "some manoeuvre" almost everywhere. The figure belongs to how finely a history is defined |
+| D5 | By the rule: no GPU budget | **The registered outcome is "no GPU budget".** An override is a departure from the rule, not a reading of it, and the Annex B evidence for one is no stronger than the rule suggests. The argument for further work is in the splits where the simulator's assumptions fail, and there the classical controls do not exist yet |
+
+## What was checked and found sound
+
+- D1 to D4, their sensitivities, the paired ratios, the by-length rates, the counts of cut
+  periods, fall-backs and runaways all recompute from the per-row errors and period records
+  with independent code and a different bootstrap seed.
+- `tl3_tables.py` reproduces `tables.md` byte for byte for the development, validation and
+  test records. The committed `summary.json` and period records are byte-identical to the
+  results branch.
+- Every digest in `decision.json` equals the file at 30cc410 and at HEAD; `git diff 30cc410
+  HEAD -- src configs .github schema scripts tests` shows only the figure script added
+  afterwards. Both summaries carry the registered code digest, plan digest, commit and a
+  clean tree.
+- Order of events: decision record 08:30:55; one validation run, created 08:31 and finished
+  09:40; one test run, created 09:49, attempt 1. No other workflow touched seeds 336 or 337.
+- Between the development commit and the registered one, the only source change is the
+  refit's fall-back rule (12 lines), as the decision record says; the development run used
+  the earlier rule.
+- Spec 014's modules (`run2`, `study`, `gate`, `controls`, `search`) are unchanged, and prior
+  versions 1 and 2 are arithmetically what they were.
+- Only the reference receives the true structure. `cut_flags` agrees with the list the
+  runner builds. `same_history` and `counts.match` agree on every system and period of every
+  split at 120 s. An overlap-based edge rule gives the same answer in all 72 cut periods.
+- `log_evidence` recomputed for stored candidates on regenerated seed 337 data agrees with
+  the record to 1e-3; the recorded evidence of all 53 held references reproduces.
+- Inside its support every open factor of prior version 3 has density × volume = 1:
+  position, course, speed, each change's size, duration for all three kinds, the first
+  start, interior starts above the leg minimum, the flat prior and the mixture.
+
+## Major findings
+
+1. **The headroom depends on how the reference is fitted, and the repair of the reference
+   raised it** (statistics, scope, code). 73 of 364 periods are replaced in the composite.
+   19 of them use a reference held to the true times, and those carry 34% of its log gain.
+   A fit told the true times has more than identification: where the search already has the
+   right history and the reference was held, the held fit is still 2.0 times better (22
+   periods over three seeds; 1.07 where it was not held). On the same test periods:
+
+   | Reference used on replaced periods | Headroom | By the rule |
+   | --- | --- | --- |
+   | Registered (held where it drifts) | 1.17 [1.10, 1.25] | inconclusive |
+   | Free-time fit throughout (near spec 014's convention) | 1.15 [1.08, 1.22] | inconclusive |
+   | Without held periods | 1.13 [1.06, 1.20] | inconclusive |
+   | Held periods counted as no gain | 1.11 [1.05, 1.17] | inconclusive |
+   | Times held everywhere | 1.19 [1.11, 1.28] | material |
+
+   The code review adds that the reference is held more often than it needs to be: the
+   runner keeps the higher-evidence of two fits and tests only that one for drift, and in 20
+   of the 53 held periods the other fit had not drifted. Preferring it would hold 8%, not
+   13%. This follows research R3 as registered; it is a design weakness, not a deviation.
+
+2. **The composite is carried by a dozen periods and is neither an upper nor a lower limit**
+   (statistics). 291 of 364 periods are unchanged. Ten carry 54% of the gain; without the
+   top five the headroom is 1.12, without the top ten 1.08. Several of them are search
+   runaways (period median range error 0.8 to 1.4) that a classical guard might remove; without
+   the 36 periods where the search's error exceeds 0.2 the headroom is 1.11. Against that,
+   the true-history fit is worse than the search in 24 of the 73 replaced periods, and the
+   reference is noisy where nothing is replaced. "17% to win" is a mean log ratio: zero in
+   80% of periods, a factor of 2 to 60 in about 10%, a loss in 7%.
+
+3. **The record's D5 paragraph was slanted** (statistics, scope). It was right to give the
+   rule's output first and the evidence separately (FR-013). It was wrong in three ways.
+   "Between 10% and 30%, most likely about 17%" took its top from threshold 1, where the
+   evidence difference exceeds the threshold in 36% of periods whose history the search has
+   right, and left out the low end (1.08 at threshold 5 and 240 s; 1.11 with held periods as
+   no gain). It quoted validation (material) and not development (inconclusive); five of the
+   nine registered cells are inconclusive on test. And "the decision is a judgement, and it
+   is the owner's" goes further than a rule allows that was written so that inconclusive
+   counts as not material; a lower limit of 1.101 would not have drawn that paragraph. The
+   verdict does flip with the interval method (BCa lower limit 1.101 to 1.103; one-sided 95%
+   limit 1.105 to 1.107), which shows the rule had little power at this sample size: on the
+   three seeds' pooled distribution, 364 periods return "material" about two times in three.
+   A rule forecast as a coin's toss should not have been registered as a decision
+   instrument. Fair wording: an oracle identifier is 8% to 30% ahead by convention, 11% to
+   19% at the registered threshold by reference convention; what a real identifier would
+   obtain is less and unmeasured.
+
+4. **The low false-manoeuvre rate on short periods is not a property of a correct
+   evidence** (evidence review). Integrating numerically over the invented manoeuvre's size,
+   rate, sign and start on 34 held-course periods: the Laplace value is within about 1 nat
+   of the integral at the same mode (median +0.6, at most +2.5), so approximation error is
+   not inflating the 0.53, and with the leg statistics removed a manoeuvre is favoured in 3
+   of 28 declared periods, so the leg term explains the rate, as the record said. But
+   integrated over every start and both kinds, the evidence favours "some manoeuvre" in 27
+   of 28 declared periods and in all 6 undeclared ones: Annex B's prior odds are about 6 to
+   1 at 25 minutes and the bearings are silent (the posterior sd of the invented start is
+   250 to 1,050 s). The 0.09 under 30 minutes exists because one mode is compared with the
+   no-manoeuvre history. The repair removed an error; what remains is the question a
+   history-by-history evidence cannot answer on a quiet target under this prior.
+
+5. **Version 3's pairing fails at the edges of the prior's support, where the invented
+   manoeuvres sit** (evidence review). 179 of the 212 declared held-course periods have a
+   manoeuvre parameter at or outside a support edge or at the solver's 10 s floor (turns
+   under the 30° minimum in 79 of 137; acceleration at its maximum in 68 of 111). At an
+   edge, density × volume for a turn is 0.80 just inside and 0.08 just outside (2.3 nats;
+   3.0 at the rate's edge): the one-sided shoulder switches on a curvature the stand-in does
+   not know about. At the floor, the prior mass of a duration inside the solver's box is
+   0.37 for a 5° turn and 0.03 for 1.7°, where Laplace assumes 1: 1.0 to 3.5 nats of
+   over-credit against research R1's "about +0.7". It did not move D4 in the sample (margins
+   on long periods are large). The tests integrate at interior points only and cannot see
+   it. This is the fifth error of this family; the evidence repair is not finished.
+
+6. **D2's "evidence failure" is inflated by a reference a few nats short, and a held fit's
+   evidence is not a Laplace evidence** (statistics, code). Where the search's history is
+   the truth, the evidence difference exceeds 3 in 22% of periods and one-sidedly (44 below
+   −3 against 7 above): the true-history fit is often an under-optimised fit of the same
+   history. Among wrong periods, 26 of the 56 evidence failures have a difference between −5
+   and −3. A held fit's evidence is evaluated at a point that is not a mode in the times
+   (held minus best free fit: median −1.2 nats, quartiles −2.5 to −0.5); re-running the 53
+   held periods changes the class of 14 of 42 wrong ones. Net effect on the registered split
+   about ±0.03; held wrong periods are search failures in 1 of 42 against 16 of 97 others.
+   The record's reading 6 ("prefers a wrong history about as often as it cannot separate
+   them") is not supported at this noise level.
+
+7. **The before-and-after the spec promised is only partly delivered** (scope). SC-004 and
+   FR-007 asked for each repair's effect on each decided quantity on the same periods. The
+   record gave D4 and the first candidate only, and did not separate the repairs. The
+   panel's recomputation on the test periods, first candidate right: spec 014's evidence
+   with the old rule 0.50, with the edge-aware rule 0.59; repaired evidence 0.53 and 0.64.
+   The edge rule is worth about +0.10 and the evidence repair +0.04 to +0.05; the record's
+   0.598 to 0.652 showed only the smaller. D1 with spec 014's search as the whole is 0.36
+   [0.22, 0.50] against 0.33, so the fall from spec 014's 0.43 is the seed, not the repair.
+   D3 with a free-time reference is 1.15 against 1.17. Development used 200 periods of seed
+   335 only, not seeds 333 to 335. FR-009 is not met for D2 (finding in the table above).
+
+8. **The refit's fall-back does not do what it was built for** (scope, statistics). Seven
+   refits have a period median of 1 or more; the rule caught one. Without the seven, D1 is
+   0.37 [0.24, 0.49]. Under outliers the rule reduces to "cost at most C″'s chord cost + 9"
+   against a track with more freedom, which is the 20 of 100. The rule was changed after
+   development and first ran on validation.
+
+9. **The mixed prior is one point of a curve, at its expensive end** (evidence review).
+   Every Annex B period where the mixed search differs is a history collapsed to "no
+   manoeuvre"; two-manoeuvre targets are called quiet in 10 of 117 against 1. Re-ranking the
+   stored candidates at other stated probabilities of a quiet target:
+
+   | Quiet probability | Held-course false | One-manoeuvre called quiet |
+   | --- | --- | --- |
+   | 0 (repaired search) | 0.53 | 0.13 |
+   | 0.01 | 0.09 | 0.14 |
+   | 0.1 | 0.02 | 0.15 |
+   | 0.5 (registered) | 0.00 | 0.19 |
+
+   A small quiet probability buys nearly all the benefit at a third of the cost. A re-ranking,
+   not a re-run; no split has a mixed population, so the 0.00 is close to guaranteed.
+
+10. **The decided statistics' code is untested** (code). `run_shard3`, `study_split3`,
+    `classify3`, `_headroom`, `candidates3`, `counts3`, `declared_quiet` and `soundness3`
+    have no unit test; "a drifting reference is caught and held" has none; the density ×
+    volume test covers one manoeuvre of the Annex B kind at interior points and asserts the
+    first start's formula against itself. The README's own tables are still typed by hand:
+    the script that checked them is not committed.
+
+## Minor findings
+
+- **Forecasts.** "Seven of eight held" was not enumerated and is wrong by the record as
+  written: D1 missed two of its three clauses (the interval excludes 0.5 and includes 0.25);
+  D4's "above 0.90 over an hour" is in the decided row of the decision record and missed
+  (112 of 125 = 0.896). Fair count: five of seven rows. And the forecasts were written two
+  minutes after the development run from its numbers, with near-identical code: "held" is
+  replication, not prediction. The one forecast of a quantity known to move, D1, missed.
+- "Five readings on four seeds from 0.33 to 0.64" for D1: four readings on three seeds; with
+  spec 014's other seeds the range is 0.30 to 0.79.
+- Reading 8: correlated errors multiply false manoeuvres by 5.8 (1.29 a period against
+  0.22), not three; outliers are the triple.
+- Reading 3 ("improves which history comes first") has no interval or paired test.
+- The whole-engagement split's fall-backs (4 of 60) and headroom (1.22 [1.03, 1.48]) are
+  missing from the record's lists. The headroom on the shifted, correlated and outlier
+  splits (1.71, 1.44, 1.50) was quoted without saying that 21% to 30% of their references
+  are held, above the limit the study set itself for Annex B.
+- "Two thousandths" below the line: 1.3 thousandths by the registered bootstrap, about 3 at
+  50,000 resamples; the README's 50,000-resample check did not state its seed.
+- D4's denominator is 398, the registered wording says 400 (disclosed; 0.535 over 400).
+- "First candidate right" appears as 0.64 (scored periods) and 0.652 (all) without saying
+  why they differ.
+- Attribution of the long-period false manoeuvres to the prior is consistent (the prior's
+  log probability of no manoeuvre is −2.7 at 30 minutes, −12 at 60, −31 at 90) but there is
+  no ablation in the search itself; 187 of 398 periods are 30 to 60 minutes and are a
+  mixture. D4's 0.53 reflects the split's mix of lengths.
+- Where a false manoeuvre is declared under 45 minutes its margin over "no manoeuvre" is
+  under 3 in 88% to 100% of cases: a declaration margin is an untried cheap remedy.
+- Missed single manoeuvres under the repair are not over-correction: the missed turns are
+  33° to 117°, 14 of 16 in periods under 30 minutes, and integrated numerically the
+  one-manoeuvre against quiet factor is within ±1 nat in 7 of 10. Unidentifiable, not
+  suppressed.
+- Prior, small and recorded, not repaired (the code is registered): combined manoeuvres
+  have no acceleration constraint (invented ones reach 0.21 m/s² at the 90th percentile);
+  speed after a change is unpaired (0.1 to 0.3 nats); `_leg_tail` returns 0 for periods of
+  5,868 s or more, so the no-manoeuvre leg term is −690.8 instead of about −40 in 15 quiet
+  periods (no decision changes); the no-manoeuvre history is scored from one start (flips 2
+  of 212 declarations); interior legs under the 300 s minimum lose their curvature; the flat
+  prior without m! penalises m manoeuvres by log m! where the times are determined, which
+  bears on its 0.085 of two-manoeuvre targets called quiet.
+- `counts.match` and `strip_cut` are two functions, not one rule; they agree on every record
+  here and can differ at 240 s. `strip_cut` forgives any found manoeuvre within tolerance of
+  a cut one's visible mid-time. 32 of 716 whole true manoeuvres lie within 105 s of a period
+  end, where the search proposes nothing.
+- `tlsolve --thin` on a run3 plan would be silently run unthinned. `soundness3` falls back
+  to 0.15 if the plan omits the limit.
+- "Spec 014's search beside" cannot be confirmed as the same search from the records: on the
+  200 shared periods of seed 335 its first candidate's evidence differs by more than 1 nat
+  in 30 between the two studies' runs. The code path is identical and an unchanged path
+  differs as often, so this is believed to be CPU family; the same-run before-and-after rows
+  are unaffected.
+- Validation and test ran on different CPU families. The limit on held references was moved
+  from 0.15 to 0.25 after development hit it; unneeded on Annex B (0.147, 0.133).
+- Process: D5 is a line with a budget consequence, which is a gate criterion without an SRD
+  §5 row. Do not register a third such rule. Spec to test record took nine hours, which
+  explains findings 7 and 10.
+
+## What the owner may conclude about a learned whole-period model
+
+| Where | What the two studies show | Classical control needed first | Not known |
+| --- | --- | --- | --- |
+| Annex B identification | An oracle is 1.1 to 1.2 ahead; three seeds give inconclusive, material, inconclusive; not decidable at 400 periods | None new; the search is the control | Whether any real identifier gets a third of it |
+| Shifted prior | Search 0.095 against the true history's 0.042; first candidate right 0.23; probabilities meaningless | A broadened or mixture prior with a mismatch diagnostic | Whether the gap is the prior or busier targets |
+| Correlated errors | 0.165 against 0.098; 1.29 false manoeuvres a period | A correlated-error likelihood (the whitened layer of spec 010 exists) | One severity, 100 periods |
+| Outliers | 0.087 against 0.046 | A Huber or edited likelihood in the search | 2% at 10 sd is the easy case |
+| Hedging | Both studies are silent | A posterior-weighted track from the stored candidates | Everything |
+| Held course | 1.53, self-inflicted; a small stated chance of a quiet target removes most of it | Exists | The cost at other mixes |
+
+Nothing here shows that learning cannot help, nor that it would. Where the simulator's
+assumptions hold there is little and uncertain room; where they fail there is a factor of 1.4
+to 1.7 against an oracle and no cheap remedy has been tried, so a learned win there now would
+be uninterpretable. This project has been caught that way three times (Gate 2's veto, spec
+012's prior, Gate T's refit). Free credit changes the cost side of a rule that was written for
+the owner's money; it does not change what a result would mean without the controls.
+
+## What the panel wants next (the owner's choice)
+
+1. Corrections to this record (done in the README's corrections section).
+2. A hedged track from the stored candidates, and a declaration margin: cheap, classical, CPU.
+3. A Huber or edited likelihood and a correlated-error likelihood in the search; a broadened
+   prior with a mismatch diagnostic; a small stated quiet probability. Scored on the shifted,
+   correlated and outlier splits at more than one severity.
+4. Only then a learned whole-period model, registered against 2 and 3 on those splits, with
+   a classical control given the same prior.
+5. No fourth Annex B identification study: at 400 periods it cannot decide a 1.10 line.
+
+## What should not be concluded
+
+- That learning has 17% to win on Annex B data, or that there is nothing for it to win.
+- That the rule's "no GPU budget" is a finding about learning.
+- That the evidence repair is finished, or that 0.09 on short periods is what a correct
+  evidence gives.
+- That 0.44 of wrong histories are ambiguous: the share is the threshold's.
+- That the refit's share is a third as a constant.
+- That the long-period false manoeuvres are shown to be the prior's by experiment in the
+  search.
+- That anything here transfers outside this simulator, its independent Gaussian errors or
+  this one test seed.
diff --git a/experiments/20261113-tl3/run.json b/experiments/20261113-tl3/run.json
new file mode 100644
index 00000000..1c75b040
--- /dev/null
+++ b/experiments/20261113-tl3/run.json
@@ -0,0 +1,33 @@
+{
+ "plan": "configs/timelate/tl-d.json",
+ "seed": 337,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift": {
+   "start": 0,
+   "periods": 300
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 400
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 100
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 100
+  },
+  "whole": {
+   "start": 0,
+   "periods": 60
+  }
+ },
+ "shard_size": 25,
+ "what": "spec 015 test: seed 337 (never generated before), scored once"
+}
diff --git a/experiments/20261113-tl3/score.txt b/experiments/20261113-tl3/score.txt
new file mode 100644
index 00000000..8eb9c2e9
--- /dev/null
+++ b/experiments/20261113-tl3/score.txt
@@ -0,0 +1,7 @@
+soundness {"reference_efficiency_tight": 1.0002743902005988, "band": [0.8, 1.3], "history_converged_share": 1.0, "convergence_min": 0.9, "reference_flagged_share": 0.1325, "reference_flagged_max": 0.25, "pass": true}
+man2: 400 periods, 364 scored
+shift: 300 periods, 262 scored
+quiet: 400 periods, 348 scored
+man2-corr: 100 periods, 88 scored
+man2-out: 100 periods, 88 scored
+whole: 60 periods, 60 scored
diff --git a/experiments/20261113-tl3/shift.periods.json b/experiments/20261113-tl3/shift.periods.json
new file mode 100644
index 00000000..3db9533a
--- /dev/null
+++ b/experiments/20261113-tl3/shift.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "first_row": 3, "rows": 618, "seconds_long": 3085.0, "bearings": 589, "true": [[420.0, 470.0], [1495.0, 1590.0], [2565.0, 2620.0]], "true_kinds": [[false, true], [true, false], [true, false]], "true_cut": [false, false, false], "ownship_turns": 2, "history_cost": 578.1555948756176, "history_converged": true, "candidates2": [{"spans": [[1451.9255491040758, 1668.734433991235]], "kinds": [[true, false]], "evidence": -327.50499261896795, "theta": [-4945.0776507457695, 2846.988133366936, 2.9825642488308572, 3.352229421956127, -1.8730405396493595, 0.0]}, {"spans": [[1464.5756185590255, 1632.6240544369764], [2934.4289061564186, 2962.6523438302925]], "kinds": [[true, false], [false, true]], "evidence": -329.9852977227337, "theta": [-5011.3300423430865, 2890.3294973634816, 2.977653378987871, 3.3349635177272363, -1.8079496951390006, 0.0, 0.0, -1.4128484088867739]}, {"spans": [[277.7858537923314, 291.4183811411104], [1408.61981521166, 1768.1324088506058]], "kinds": [[true, false], [true, false]], "evidence": -330.2176182042126, "theta": [-5868.809076839841, 3345.919104011776, 2.670604320671943, 3.4995956725334008, 0.23545715352333307, 0.0, -1.7662122023254352, 0.0]}, {"spans": [[1463.0165869170744, 1722.2293947475896], [2132.2996245703225, 2154.130682883893]], "kinds": [[true, false], [true, false]], "evidence": -332.17513917730975, "theta": [-4832.743441144154, 2783.7106900705394, 2.97454486905748, 3.4925931659517806, -2.2340740994119894, 0.0, 0.38115694308457887, 0.0]}, {"spans": [[1415.014490913225, 1818.243829573059], [2539.0960075829366, 2559.7022319089424]], "kinds": [[true, false], [false, true]], "evidence": -333.36426445682974, "theta": [-4807.09778101276, 2767.245276236519, 2.980234126006749, 3.4804097441548065, -2.137031764420287, 0.0, 0.0, 0.9541744980843384]}], "histories_evaluated": 35, "candidates": [{"spans": [[1451.7892475633025, 1668.9880217184061]], "kinds": [[true, false]], "evidence": -327.568325399995, "theta": [-4945.0246898990545, 2846.9594516504494, 2.9825689272430953, 3.3522587664244767, -1.8731828349946975, 0.0]}, {"spans": [[1464.077771385731, 1633.616093924141], [2935.7765531556533, 2966.0197559850117]], "kinds": [[true, false], [false, true]], "evidence": -330.81523093886653, "theta": [-5000.213921272618, 2882.8311694158715, 2.9790149351157154, 3.3344891942561676, -1.811387244178068, 0.0, 0.0, -1.5119725522195686]}, {"spans": [[276.1746789576007, 289.74750365064125], [1408.0726835300902, 1769.4787966989495]], "kinds": [[true, false], [true, false]], "evidence": -333.9421248018243, "theta": [-5866.305445436776, 3343.770762081695, 2.669448074058447, 3.4995390093427603, 0.23680659903438886, 0.0, -1.7676273988247488, 0.0]}, {"spans": [[1480.759845350361, 1676.8634130317496], [1990.8213994663136, 2019.1177919653014]], "kinds": [[true, false], [true, false]], "evidence": -335.2508654064692, "theta": [-4764.8761160624335, 2739.690704990592, 2.980272227462669, 3.506135502687346, -2.356991784162103, 0.0, 0.4941830511917477, 0.0]}, {"spans": [[1463.0165869170744, 1722.2293947475896], [2132.2996245703225, 2154.130682883893]], "kinds": [[true, false], [true, false]], "evidence": -335.43334027409537, "theta": [-4832.743441144154, 2783.7106900705394, 2.97454486905748, 3.4925931659517806, -2.2340740994119894, 0.0, 0.38115694308457887, 0.0]}], "refit_fell_back": false, "reference_flagged": true, "true_evidence": -335.79162956344356, "true_fit_spans": [[420.0, 470.0], [1495.0, 1590.0], [2565.0, 2620.0]], "histories": {"c2": {"spans": [[1255.0, 1505.0]]}, "search2": {"spans": [[1451.9255491040758, 1668.734433991235]], "kinds": [[true, false]], "evidence": -327.50499261896795}, "search3": {"spans": [[1451.7892475633025, 1668.9880217184061]], "kinds": [[true, false]], "evidence": -327.568325399995}, "search3-flat": {"spans": [[1451.6968446705366, 1669.1596258036686]], "kinds": [[true, false]], "evidence": -329.0406141125516}, "search3-mix": {"spans": [[1451.7892475633025, 1668.9880217184061]], 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diff --git a/experiments/20261113-tl3/summary.json b/experiments/20261113-tl3/summary.json
new file mode 100644
index 00000000..3e7bfae1
--- /dev/null
+++ b/experiments/20261113-tl3/summary.json
@@ -0,0 +1,11495 @@
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+       "n": 1
+      },
+      "search3": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.7934506856227626,
+       "n": 1
+      },
+      "search3-flat": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.7934506856227626,
+       "n": 1
+      },
+      "search3-mix": {
+       "rate": 0.0,
+       "lo": 0.0,
+       "hi": 0.7934506856227626,
+       "n": 1
+      }
+     },
+     "45-60": {
+      "c2": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "c2-refit": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search2": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3-flat": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3-mix": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      }
+     },
+     "60-90": {
+      "c2": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "c2-refit": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search2": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3-flat": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3-mix": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      }
+     },
+     "90-1000": {
+      "c2": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "c2-refit": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search2": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3-flat": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      },
+      "search3-mix": {
+       "rate": NaN,
+       "lo": NaN,
+       "hi": NaN,
+       "n": 0
+      }
+     }
+    }
+   },
+   "declared_quiet": {
+    "c2": {
+     "1": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.6576197724933469,
+      "n": 2
+     },
+     "2": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.32440756488388023,
+      "n": 8
+     },
+     "3+": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.08029560375848066,
+      "n": 44
+     }
+    },
+    "c2-refit": {
+     "1": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.6576197724933469,
+      "n": 2
+     },
+     "2": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.32440756488388023,
+      "n": 8
+     },
+     "3+": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.08029560375848066,
+      "n": 44
+     }
+    },
+    "search2": {
+     "1": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.6576197724933469,
+      "n": 2
+     },
+     "2": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.32440756488388023,
+      "n": 8
+     },
+     "3+": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.08029560375848066,
+      "n": 44
+     }
+    },
+    "search3": {
+     "1": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.6576197724933469,
+      "n": 2
+     },
+     "2": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.32440756488388023,
+      "n": 8
+     },
+     "3+": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.08029560375848066,
+      "n": 44
+     }
+    },
+    "search3-flat": {
+     "1": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.6576197724933469,
+      "n": 2
+     },
+     "2": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.32440756488388023,
+      "n": 8
+     },
+     "3+": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.08029560375848066,
+      "n": 44
+     }
+    },
+    "search3-mix": {
+     "1": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.6576197724933469,
+      "n": 2
+     },
+     "2": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.32440756488388023,
+      "n": 8
+     },
+     "3+": {
+      "rate": 0.0,
+      "lo": 0.0,
+      "hi": 0.08029560375848066,
+      "n": 44
+     }
+    }
+   },
+   "by_true_count": {
+    "0": {
+     "periods": 1,
+     "c2": 0.000820939167169854,
+     "c2-refit": 0.0006345499132294208,
+     "history-best": 0.0006345499132294208,
+     "history-fit": 0.0006345498550217599,
+     "history-fixed": 0.0006345498550217599,
+     "history-map": 0.0006345499132294208,
+     "one-leg": 0.000820939167169854,
+     "search2": 0.002975205425173044,
+     "search3": 0.0006345498550217599,
+     "search3-flat": 0.0006345498550217599,
+     "search3-mix": 0.0006345498550217599
+    },
+    "1": {
+     "periods": 2,
+     "c2": 0.00950257119257003,
+     "c2-refit": 0.01171988237183541,
+     "history-best": 0.01125442422926426,
+     "history-fit": 0.011244777357205749,
+     "history-fixed": 0.01358936569886282,
+     "history-map": 0.011250813258811831,
+     "one-leg": 0.3547244444489479,
+     "search2": 0.04131774289999157,
+     "search3": 0.010980838560499251,
+     "search3-flat": 0.011734550003893673,
+     "search3-mix": 0.010980838560499251
+    },
+    "2": {
+     "periods": 8,
+     "c2": 0.050349471159279346,
+     "c2-refit": 0.08233318291604519,
+     "history-best": 0.012588107259944081,
+     "history-fit": 0.012454050360247493,
+     "history-fixed": 0.013026894070208073,
+     "history-map": 0.012588107259944081,
+     "one-leg": 0.3198932558298111,
+     "search2": 0.026174594764597714,
+     "search3": 0.0309957480058074,
+     "search3-flat": 0.0526448879390955,
+     "search3-mix": 0.0309957480058074
+    },
+    "3+": {
+     "periods": 49,
+     "c2": 0.11944657564163208,
+     "c2-refit": 0.06896505504846573,
+     "history-best": 0.023785974830389023,
+     "history-fit": 0.02252727746963501,
+     "history-fixed": 0.01876526139676571,
+     "history-map": 0.023886429145932198,
+     "one-leg": 0.4918436110019684,
+     "search2": 0.03464466705918312,
+     "search3": 0.03723448887467384,
+     "search3-flat": 0.03927355259656906,
+     "search3-mix": 0.03723448887467384
+    }
+   },
+   "candidates": {
+    "periods": 60,
+    "top_right": 0.48333333333333334,
+    "truth_in_top3": 0.6333333333333333,
+    "mean_top_probability": 0.6761533234968571,
+    "calibration_gap": 0.19281999016352375,
+    "kinds_differ_among_right": 0.14814814814814814
+   },
+   "candidates2": {
+    "periods": 60,
+    "top_right": 0.4666666666666667,
+    "truth_in_top3": 0.6,
+    "mean_top_probability": 0.583743297692599,
+    "calibration_gap": 0.11707663102593235,
+    "kinds_differ_among_right": 0.11538461538461539
+   },
+   "seconds_per_period": {
+    "layer": 32.24750000000001,
+    "reference": 11.756499999999999,
+    "search2": 70.02066666666666,
+    "search3": 69.52016666666667,
+    "search3-flat": 78.91666666666667,
+    "search3-mix": 69.60400000000001,
+    "c2-refit": 21.869666666666667
+   },
+   "dataset_digest": [
+    "0357625b66005422468375bb598edadce498f92e34c321cae9593b2284e978ee"
+   ],
+   "seed": [
+    337
+   ],
+   "search_code_sha256": [
+    "3b67d0267f2b99c75e7bea0d408989d2de5a308581d440511c63b71465585ec9"
+   ],
+   "git_commit": [
+    "30cc41030bce3de01d02c46dd79155fffa28a020"
+   ],
+   "git_dirty": false,
+   "condition": [
+    "clean"
+   ],
+   "thinning": [
+    "none"
+   ]
+  }
+ }
+}
diff --git a/experiments/20261113-tl3/tables.md b/experiments/20261113-tl3/tables.md
new file mode 100644
index 00000000..a7e08d73
--- /dev/null
+++ b/experiments/20261113-tl3/tables.md
@@ -0,0 +1,587 @@
+## Systems (median period range error)
+
+| System | Annex B (364) | Shifted (262) | Held course (348) | Correlated (88) | Outliers (88) | Whole (60) |
+| --- | --- | --- | --- | --- | --- | --- |
+| One leg | 0.400 | 0.368 | 0.018 | 0.380 | 0.387 | 0.446 |
+| C″ read as a track | 0.083 | 0.150 | 0.018 | 0.354 | 0.202 | 0.108 |
+| C″ refitted | 0.066 | 0.129 | 0.016 | 0.214 | 0.165 | 0.065 |
+| Search, flat prior | 0.049 | 0.111 | 0.016 | 0.216 | 0.097 | 0.037 |
+| Search, spec 014's evidence | 0.041 | 0.095 | 0.038 | 0.150 | 0.087 | 0.034 |
+| Search, repaired evidence | 0.039 | 0.095 | 0.030 | 0.165 | 0.087 | 0.036 |
+| Search, repaired, mixed prior | 0.041 | 0.097 | 0.016 | 0.166 | 0.087 | 0.036 |
+| True history, best evidence | 0.028 | 0.042 | 0.016 | 0.098 | 0.046 | 0.021 |
+| True history, times held | 0.025 | 0.041 | 0.017 | 0.089 | 0.036 | 0.016 |
+
+## Annex B: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 2.10 [1.82, 2.44] | 2.11 [1.72, 2.48] | 0.31 | 0.44 / 0.12 |
+| c2 ÷ c2-refit | 1.28 [1.14, 1.43] | 1.26 [1.09, 1.50] | 0.40 | 0.27 / 0.14 |
+| c2-refit ÷ search3 | 1.65 [1.45, 1.87] | 1.67 [1.35, 1.93] | 0.39 | 0.29 / 0.07 |
+| search3-flat ÷ search3 | 1.22 [1.10, 1.34] | 1.25 [1.04, 1.39] | 0.37 | 0.19 / 0.10 |
+| search2 ÷ search3 | 1.07 [0.98, 1.16] | 1.05 [0.92, 1.15] | 0.20 | 0.10 / 0.06 |
+| search3 ÷ search3-mix | 0.98 [0.96, 1.01] | 0.97 [0.91, 1.02] | 0.03 | 0.01 / 0.02 |
+| search3 ÷ history-best | 1.43 [1.30, 1.59] | 1.39 [1.24, 1.68] | 0.38 | 0.26 / 0.07 |
+| search3 ÷ history-fixed | 1.70 [1.51, 1.92] | 1.55 [1.35, 1.86] | 0.36 | 0.41 / 0.11 |
+| search3 ÷ one-leg | 0.05 [0.04, 0.07] | 0.10 [0.08, 0.13] | 0.89 | 0.02 / 0.78 |
+| c2 ÷ one-leg | 0.11 [0.09, 0.14] | 0.21 [0.17, 0.26] | 0.77 | 0.04 / 0.64 |
+| search3-mix ÷ one-leg | 0.05 [0.04, 0.07] | 0.10 [0.08, 0.13] | 0.90 | 0.01 / 0.76 |
+
+## Annex B: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 364 | 0.64 [0.59, 0.69] | 131 | 0.44 [0.36, 0.53] | 0.13 [0.08, 0.20] | 0.43 [0.35, 0.51] |
+| all | 400 | 0.65 [0.60, 0.70] | 139 | 0.45 [0.37, 0.54] | 0.12 [0.08, 0.19] | 0.42 [0.35, 0.51] |
+| scored without cut | 301 | 0.62 [0.56, 0.67] | 115 | 0.46 [0.37, 0.55] | 0.14 [0.09, 0.21] | 0.40 [0.32, 0.49] |
+
+## Annex B: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.10 [1.05, 1.17] inconclusive | 1.35 [1.24, 1.48] material | 1.41 [1.29, 1.54] | 1.42 [1.32, 1.55] |
+| threshold 1 tol 120 | 1.08 [1.03, 1.14] inconclusive | 1.30 [1.20, 1.42] material | 1.35 [1.24, 1.47] | 1.36 [1.27, 1.48] |
+| threshold 1 tol 240 | 1.05 [1.01, 1.11] inconclusive | 1.24 [1.16, 1.34] material | 1.27 [1.18, 1.38] | 1.29 [1.21, 1.38] |
+| threshold 3 tol 60 | 1.07 [1.03, 1.13] inconclusive | 1.20 [1.12, 1.29] material | 1.41 [1.29, 1.54] | 1.24 [1.17, 1.33] |
+| threshold 3 tol 120 | 1.06 [1.01, 1.11] inconclusive | 1.17 [1.10, 1.25] inconclusive | 1.35 [1.24, 1.47] | 1.21 [1.14, 1.29] |
+| registered | 1.06 [1.01, 1.11] inconclusive | 1.17 [1.10, 1.25] inconclusive | 1.35 [1.24, 1.47] | 1.21 [1.14, 1.29] |
+| registered without flagged | 1.05 [1.01, 1.11] inconclusive | 1.13 [1.06, 1.20] inconclusive | 1.25 [1.15, 1.37] | 1.16 [1.10, 1.24] |
+| threshold 3 tol 240 | 1.04 [1.00, 1.08] not material | 1.15 [1.08, 1.22] inconclusive | 1.27 [1.18, 1.38] | 1.17 [1.11, 1.24] |
+| threshold 5 tol 60 | 1.06 [1.02, 1.11] inconclusive | 1.14 [1.07, 1.21] inconclusive | 1.41 [1.29, 1.54] | 1.16 [1.10, 1.24] |
+| threshold 5 tol 120 | 1.04 [1.01, 1.09] not material | 1.10 [1.05, 1.17] inconclusive | 1.35 [1.24, 1.47] | 1.13 [1.07, 1.20] |
+| threshold 5 tol 240 | 1.03 [1.00, 1.07] not material | 1.08 [1.03, 1.14] inconclusive | 1.27 [1.18, 1.38] | 1.10 [1.05, 1.16] |
+
+Ratio of medians at the registered setting: perfect search 1.04 [1.00, 1.12]; perfect evidence as well 1.08 [1.03, 1.25].
+
+## Annex B: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | 0.33 [0.20, 0.45] (364 periods) |
+| without fallbacks | 0.34 [0.20, 0.47] (361 periods) |
+| medians at most 1 | 0.37 [0.23, 0.50] (351 periods) |
+| search right | 0.34 [0.20, 0.49] (233 periods) |
+| search wrong | 0.27 [-0.13, 0.57] (131 periods) |
+| flat prior search | 0.74 [0.61, 0.86] (364 periods) |
+| ratio of log medians | 0.31 |
+
+Refit fall-backs: 5 of 400; refits with a period median of 1 or more: 7.
+
+## Annex B: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 716 | 412 | 0.58 | 352 | 0.88 | 58 | 0.78 / 0.36 |
+| c2-refit | 716 | 255 | 0.36 | 191 | 0.48 | 23 | 0.47 / 0.29 |
+| search3-flat | 716 | 193 | 0.27 | 86 | 0.21 | 19 | 0.39 / 0.22 |
+| search2 | 716 | 140 | 0.20 | 135 | 0.34 | 18 | 0.30 / 0.14 |
+| search3 | 716 | 155 | 0.22 | 89 | 0.22 | 18 | 0.30 / 0.17 |
+| search3-mix | 716 | 166 | 0.23 | 74 | 0.18 | 18 | 0.31 / 0.19 |
+
+## Annex B: periods with no true manoeuvre (10): share with one declared
+
+| System | All | 0-30 min | 30-45 min |
+| --- | --- | --- | --- |
+| c2 | 0.10 [0.02, 0.40] | 0.11 (9) | 0.00 (1) |
+| c2-refit | 0.10 [0.02, 0.40] | 0.11 (9) | 0.00 (1) |
+| search3-flat | 0.00 [0.00, 0.28] | 0.00 (9) | 0.00 (1) |
+| search2 | 0.30 [0.11, 0.60] | 0.33 (9) | 0.00 (1) |
+| search3 | 0.10 [0.02, 0.40] | 0.11 (9) | 0.00 (1) |
+| search3-mix | 0.00 [0.00, 0.28] | 0.00 (9) | 0.00 (1) |
+
+## Annex B: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.238 (122) | 0.077 (117) | 0.000 (79) |
+| c2-refit | 0.238 (122) | 0.077 (117) | 0.000 (79) |
+| search3-flat | 0.164 (122) | 0.085 (117) | 0.000 (79) |
+| search2 | 0.041 (122) | 0.000 (117) | 0.000 (79) |
+| search3 | 0.131 (122) | 0.009 (117) | 0.000 (79) |
+| search3-mix | 0.197 (122) | 0.085 (117) | 0.000 (79) |
+
+Candidates (repaired evidence, 400 periods): first right 0.652; true history in the first three 0.770; calibration gap -0.038; kinds differ among right 0.12.
+
+Candidates (spec 014's evidence, 400 periods): first right 0.598; true history in the first three 0.760; calibration gap -0.071; kinds differ among right 0.15.
+
+References held to the true times: 0.133; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 72 of 400.
+
+## Shifted: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 1.69 [1.46, 1.95] | 1.57 [1.29, 1.99] | 0.34 | 0.35 / 0.10 |
+| c2 ÷ c2-refit | 1.23 [1.10, 1.38] | 1.16 [0.98, 1.34] | 0.38 | 0.18 / 0.11 |
+| c2-refit ÷ search3 | 1.38 [1.20, 1.56] | 1.35 [1.14, 1.73] | 0.38 | 0.28 / 0.10 |
+| search3-flat ÷ search3 | 1.10 [0.98, 1.22] | 1.16 [0.97, 1.36] | 0.45 | 0.16 / 0.13 |
+| search2 ÷ search3 | 0.95 [0.89, 1.02] | 1.00 [0.86, 1.15] | 0.30 | 0.07 / 0.08 |
+| search3 ÷ search3-mix | 1.00 [0.98, 1.02] | 0.99 [0.89, 1.02] | 0.03 | 0.02 / 0.01 |
+| search3 ÷ history-best | 2.05 [1.78, 2.38] | 2.26 [1.79, 2.76] | 0.26 | 0.45 / 0.08 |
+| search3 ÷ history-fixed | 2.12 [1.80, 2.49] | 2.31 [1.84, 2.86] | 0.24 | 0.46 / 0.13 |
+| search3 ÷ one-leg | 0.13 [0.10, 0.18] | 0.26 [0.19, 0.33] | 0.85 | 0.05 / 0.69 |
+| c2 ÷ one-leg | 0.23 [0.18, 0.30] | 0.41 [0.33, 0.53] | 0.74 | 0.06 / 0.55 |
+| search3-mix ÷ one-leg | 0.13 [0.10, 0.18] | 0.26 [0.20, 0.35] | 0.84 | 0.04 / 0.68 |
+
+## Shifted: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 262 | 0.23 [0.18, 0.28] | 203 | 0.16 [0.11, 0.21] | 0.07 [0.05, 0.12] | 0.77 [0.71, 0.82] |
+| all | 300 | 0.23 [0.19, 0.28] | 230 | 0.17 [0.13, 0.22] | 0.07 [0.04, 0.10] | 0.77 [0.71, 0.82] |
+| scored without cut | 226 | 0.21 [0.16, 0.27] | 178 | 0.18 [0.13, 0.24] | 0.08 [0.05, 0.13] | 0.74 [0.67, 0.80] |
+
+## Shifted: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.17 [1.09, 1.26] inconclusive | 1.95 [1.70, 2.25] material | 2.05 [1.79, 2.38] | 2.20 [1.95, 2.48] |
+| threshold 1 tol 120 | 1.16 [1.08, 1.26] inconclusive | 1.94 [1.68, 2.23] material | 2.02 [1.76, 2.33] | 2.16 [1.92, 2.44] |
+| threshold 1 tol 240 | 1.16 [1.08, 1.25] inconclusive | 1.86 [1.62, 2.14] material | 1.94 [1.69, 2.25] | 2.07 [1.84, 2.34] |
+| threshold 3 tol 60 | 1.14 [1.07, 1.23] inconclusive | 1.71 [1.49, 1.96] material | 2.05 [1.79, 2.38] | 1.92 [1.71, 2.15] |
+| threshold 3 tol 120 | 1.14 [1.07, 1.23] inconclusive | 1.71 [1.50, 1.96] material | 2.02 [1.76, 2.33] | 1.91 [1.69, 2.14] |
+| registered | 1.14 [1.07, 1.23] inconclusive | 1.71 [1.50, 1.96] material | 2.02 [1.76, 2.33] | 1.91 [1.69, 2.14] |
+| registered without flagged | 1.20 [1.09, 1.32] inconclusive | 1.55 [1.36, 1.77] material | 1.80 [1.56, 2.08] | 1.68 [1.49, 1.91] |
+| threshold 3 tol 240 | 1.13 [1.06, 1.22] inconclusive | 1.68 [1.47, 1.92] material | 1.94 [1.69, 2.25] | 1.87 [1.67, 2.09] |
+| threshold 5 tol 60 | 1.10 [1.04, 1.18] inconclusive | 1.56 [1.37, 1.79] material | 2.05 [1.79, 2.38] | 1.74 [1.56, 1.95] |
+| threshold 5 tol 120 | 1.10 [1.04, 1.18] inconclusive | 1.56 [1.36, 1.78] material | 2.02 [1.76, 2.33] | 1.73 [1.55, 1.94] |
+| threshold 5 tol 240 | 1.10 [1.04, 1.18] inconclusive | 1.53 [1.35, 1.75] material | 1.94 [1.69, 2.25] | 1.70 [1.52, 1.91] |
+
+Ratio of medians at the registered setting: perfect search 1.20 [1.04, 1.33]; perfect evidence as well 2.00 [1.52, 2.42].
+
+## Shifted: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | 0.39 [0.20, 0.60] (262 periods) |
+| without fallbacks | 0.39 [0.20, 0.58] (257 periods) |
+| medians at most 1 | 0.39 [0.21, 0.58] (259 periods) |
+| search right | 0.26 [-0.08, 0.51] (59 periods) |
+| search wrong | 0.50 [0.24, 0.78] (203 periods) |
+| flat prior search | 0.82 [0.65, 1.04] (262 periods) |
+| ratio of log medians | 0.33 |
+
+Refit fall-backs: 11 of 300; refits with a period median of 1 or more: 2.
+
+## Shifted: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 921 | 637 | 0.69 | 287 | 0.96 | 64 | 0.86 / 0.51 |
+| c2-refit | 921 | 527 | 0.57 | 176 | 0.59 | 24 | 0.66 / 0.49 |
+| search3-flat | 921 | 470 | 0.51 | 156 | 0.52 | 23 | 0.60 / 0.44 |
+| search2 | 921 | 373 | 0.40 | 136 | 0.45 | 23 | 0.51 / 0.34 |
+| search3 | 921 | 410 | 0.45 | 128 | 0.43 | 23 | 0.55 / 0.38 |
+| search3-mix | 921 | 415 | 0.45 | 116 | 0.39 | 23 | 0.56 / 0.39 |
+
+## Shifted: periods with no true manoeuvre (2): share with one declared
+
+| System | All | 0-30 min |
+| --- | --- | --- |
+| c2 | 0.00 [0.00, 0.66] | 0.00 (2) |
+| c2-refit | 0.00 [0.00, 0.66] | 0.00 (2) |
+| search3-flat | 0.50 [0.09, 0.91] | 0.50 (2) |
+| search2 | 0.50 [0.09, 0.91] | 0.50 (2) |
+| search3 | 0.50 [0.09, 0.91] | 0.50 (2) |
+| search3-mix | 0.50 [0.09, 0.91] | 0.50 (2) |
+
+## Shifted: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.348 (23) | 0.182 (77) | 0.032 (156) |
+| c2-refit | 0.348 (23) | 0.182 (77) | 0.032 (156) |
+| search3-flat | 0.261 (23) | 0.143 (77) | 0.019 (156) |
+| search2 | 0.043 (23) | 0.039 (77) | 0.000 (156) |
+| search3 | 0.217 (23) | 0.065 (77) | 0.000 (156) |
+| search3-mix | 0.348 (23) | 0.156 (77) | 0.032 (156) |
+
+Candidates (repaired evidence, 300 periods): first right 0.233; true history in the first three 0.400; calibration gap 0.356; kinds differ among right 0.38.
+
+Candidates (spec 014's evidence, 300 periods): first right 0.287; true history in the first three 0.413; calibration gap 0.261; kinds differ among right 0.49.
+
+References held to the true times: 0.300; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 42 of 300.
+
+## Held course: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 0.56 [0.50, 0.63] | 0.58 [0.46, 0.70] | 0.67 | 0.05 / 0.36 |
+| c2 ÷ c2-refit | 1.05 [1.00, 1.11] | 1.07 [0.96, 1.19] | 0.43 | 0.07 / 0.05 |
+| c2-refit ÷ search3 | 0.53 [0.47, 0.59] | 0.54 [0.45, 0.64] | 0.56 | 0.01 / 0.34 |
+| search3-flat ÷ search3 | 0.52 [0.47, 0.58] | 0.53 [0.45, 0.64] | 0.50 | 0.02 / 0.34 |
+| search2 ÷ search3 | 1.22 [1.12, 1.34] | 1.26 [1.09, 1.47] | 0.24 | 0.18 / 0.08 |
+| search3 ÷ search3-mix | 1.91 [1.73, 2.13] | 1.87 [1.57, 2.27] | 0.07 | 0.34 / 0.01 |
+| search3 ÷ history-best | 1.90 [1.72, 2.12] | 1.87 [1.56, 2.24] | 0.16 | 0.34 / 0.01 |
+| search3 ÷ history-fixed | 1.89 [1.71, 2.11] | 1.83 [1.54, 2.21] | 0.21 | 0.34 / 0.02 |
+| search3 ÷ one-leg | 1.80 [1.60, 2.02] | 1.71 [1.43, 2.13] | 0.33 | 0.36 / 0.05 |
+| c2 ÷ one-leg | 1.00 [0.98, 1.03] | 0.99 [0.95, 1.02] | 0.00 | 0.01 / 0.00 |
+| search3-mix ÷ one-leg | 0.94 [0.89, 0.99] | 0.91 [0.83, 1.03] | 0.58 | 0.05 / 0.07 |
+
+## Held course: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 348 | 0.42 [0.37, 0.47] | 201 | 0.43 [0.37, 0.50] | 0.00 [0.00, 0.02] | 0.57 [0.50, 0.63] |
+| all | 400 | 0.47 [0.42, 0.52] | 213 | 0.46 [0.39, 0.53] | 0.00 [0.00, 0.02] | 0.54 [0.47, 0.61] |
+| scored without cut | 348 | 0.42 [0.37, 0.47] | 201 | 0.43 [0.37, 0.50] | 0.00 [0.00, 0.02] | 0.57 [0.50, 0.63] |
+
+## Held course: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.00 [1.00, 1.00] not material | 1.77 [1.61, 1.96] material | 1.90 [1.72, 2.12] | 1.81 [1.65, 2.01] |
+| threshold 1 tol 120 | 1.00 [1.00, 1.00] not material | 1.77 [1.61, 1.96] material | 1.90 [1.72, 2.12] | 1.81 [1.65, 2.01] |
+| threshold 1 tol 240 | 1.00 [1.00, 1.00] not material | 1.77 [1.61, 1.96] material | 1.90 [1.72, 2.12] | 1.81 [1.65, 2.01] |
+| threshold 3 tol 60 | 1.00 [1.00, 1.00] not material | 1.53 [1.40, 1.68] material | 1.90 [1.72, 2.12] | 1.55 [1.42, 1.70] |
+| threshold 3 tol 120 | 1.00 [1.00, 1.00] not material | 1.53 [1.40, 1.68] material | 1.90 [1.72, 2.12] | 1.55 [1.42, 1.70] |
+| registered | 1.00 [1.00, 1.00] not material | 1.53 [1.40, 1.68] material | 1.90 [1.72, 2.12] | 1.55 [1.42, 1.70] |
+| registered without flagged | 1.00 [1.00, 1.00] not material | 1.53 [1.40, 1.68] material | 1.90 [1.72, 2.12] | 1.55 [1.42, 1.70] |
+| threshold 3 tol 240 | 1.00 [1.00, 1.00] not material | 1.53 [1.40, 1.68] material | 1.90 [1.72, 2.12] | 1.55 [1.42, 1.70] |
+| threshold 5 tol 60 | 1.00 [1.00, 1.00] not material | 1.41 [1.30, 1.53] material | 1.90 [1.72, 2.12] | 1.41 [1.30, 1.54] |
+| threshold 5 tol 120 | 1.00 [1.00, 1.00] not material | 1.41 [1.30, 1.53] material | 1.90 [1.72, 2.12] | 1.41 [1.30, 1.54] |
+| threshold 5 tol 240 | 1.00 [1.00, 1.00] not material | 1.41 [1.30, 1.53] material | 1.90 [1.72, 2.12] | 1.41 [1.30, 1.54] |
+
+Ratio of medians at the registered setting: perfect search 1.00 [1.00, 1.00]; perfect evidence as well 1.59 [1.34, 1.85].
+
+## Held course: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | -0.09 [-0.20, -0.00] (348 periods) |
+| without fallbacks | -0.09 [-0.20, -0.00] (348 periods) |
+| medians at most 1 | -0.09 [-0.21, -0.00] (347 periods) |
+| search right | 0.94 [0.69, 1.00] (147 periods) |
+| search wrong | -0.03 [-0.09, 0.03] (201 periods) |
+| flat prior search | -0.11 [-0.23, -0.02] (348 periods) |
+| ratio of log medians | -0.13 |
+
+Refit fall-backs: 3 of 400; refits with a period median of 1 or more: 0.
+
+## Held course: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 2 | 2 | 1.00 | 11 | 0.03 | – | 1.00 / 0.00 |
+| c2-refit | 2 | 1 | 0.50 | 10 | 0.03 | 112 | 1.00 / 0.50 |
+| search3-flat | 2 | 0 | 0.00 | 2 | 0.01 | 49 | 0.50 / 0.00 |
+| search2 | 2 | 0 | 0.00 | 391 | 0.98 | 58 | 0.50 / 0.00 |
+| search3 | 2 | 0 | 0.00 | 249 | 0.62 | 55 | 0.50 / 0.00 |
+| search3-mix | 2 | 0 | 0.00 | 2 | 0.01 | 55 | 0.50 / 0.00 |
+
+## Held course: periods with no true manoeuvre (398): share with one declared
+
+| System | All | 0-30 min | 30-45 min | 45-60 min | 60-90 min | 90-1000 min |
+| --- | --- | --- | --- | --- | --- | --- |
+| c2 | 0.02 [0.01, 0.03] | 0.03 (86) | 0.01 (99) | 0.00 (88) | 0.01 (94) | 0.03 (31) |
+| c2-refit | 0.02 [0.01, 0.03] | 0.03 (86) | 0.01 (99) | 0.00 (88) | 0.01 (94) | 0.03 (31) |
+| search3-flat | 0.01 [0.00, 0.02] | 0.02 (86) | 0.00 (99) | 0.00 (88) | 0.00 (94) | 0.00 (31) |
+| search2 | 0.80 [0.76, 0.84] | 0.50 (86) | 0.78 (99) | 0.91 (88) | 0.95 (94) | 1.00 (31) |
+| search3 | 0.53 [0.48, 0.58] | 0.09 (86) | 0.32 (99) | 0.68 (88) | 0.86 (94) | 1.00 (31) |
+| search3-mix | 0.00 [0.00, 0.01] | 0.01 (86) | 0.00 (99) | 0.00 (88) | 0.00 (94) | 0.00 (31) |
+
+## Held course: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.000 (2) | – (0) | – (0) |
+| c2-refit | 0.000 (2) | – (0) | – (0) |
+| search3-flat | 0.000 (2) | – (0) | – (0) |
+| search2 | 0.000 (2) | – (0) | – (0) |
+| search3 | 0.000 (2) | – (0) | – (0) |
+| search3-mix | 0.000 (2) | – (0) | – (0) |
+
+Candidates (repaired evidence, 400 periods): first right 0.468; true history in the first three 0.713; calibration gap -0.025; kinds differ among right 0.00.
+
+Candidates (spec 014's evidence, 400 periods): first right 0.198; true history in the first three 0.515; calibration gap 0.203; kinds differ among right 0.00.
+
+References held to the true times: 0.000; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 0 of 400.
+
+## Correlated: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 2.03 [1.58, 2.57] | 2.15 [1.56, 2.76] | 0.25 | 0.49 / 0.07 |
+| c2 ÷ c2-refit | 1.57 [1.25, 1.98] | 1.65 [1.20, 2.75] | 0.27 | 0.39 / 0.11 |
+| c2-refit ÷ search3 | 1.29 [1.02, 1.60] | 1.30 [0.83, 1.74] | 0.39 | 0.26 / 0.15 |
+| search3-flat ÷ search3 | 1.19 [1.01, 1.41] | 1.31 [0.92, 1.52] | 0.44 | 0.23 / 0.14 |
+| search2 ÷ search3 | 0.98 [0.87, 1.12] | 0.91 [0.70, 1.04] | 0.35 | 0.10 / 0.09 |
+| search3 ÷ search3-mix | 1.02 [0.99, 1.09] | 0.99 [0.86, 1.04] | 0.01 | 0.01 / 0.00 |
+| search3 ÷ history-best | 1.64 [1.35, 1.99] | 1.68 [1.37, 2.31] | 0.32 | 0.39 / 0.06 |
+| search3 ÷ history-fixed | 1.67 [1.31, 2.12] | 1.86 [1.50, 2.58] | 0.33 | 0.45 / 0.14 |
+| search3 ÷ one-leg | 0.15 [0.09, 0.25] | 0.43 [0.31, 0.63] | 0.78 | 0.05 / 0.59 |
+| c2 ÷ one-leg | 0.30 [0.18, 0.50] | 0.93 [0.64, 1.29] | 0.59 | 0.14 / 0.47 |
+| search3-mix ÷ one-leg | 0.15 [0.09, 0.24] | 0.44 [0.31, 0.65] | 0.77 | 0.03 / 0.59 |
+
+## Correlated: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 88 | 0.23 [0.15, 0.33] | 68 | 0.16 [0.09, 0.27] | 0.03 [0.01, 0.10] | 0.81 [0.70, 0.88] |
+| all | 100 | 0.23 [0.16, 0.32] | 77 | 0.14 [0.08, 0.24] | 0.03 [0.01, 0.09] | 0.83 [0.73, 0.90] |
+| scored without cut | 74 | 0.22 [0.14, 0.32] | 58 | 0.19 [0.11, 0.31] | 0.03 [0.01, 0.12] | 0.78 [0.65, 0.86] |
+
+## Correlated: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.05 [1.00, 1.13] inconclusive | 1.56 [1.29, 1.89] material | 1.63 [1.34, 1.99] | 1.77 [1.51, 2.09] |
+| threshold 1 tol 120 | 1.05 [1.00, 1.13] inconclusive | 1.55 [1.28, 1.87] material | 1.61 [1.33, 1.96] | 1.75 [1.49, 2.06] |
+| threshold 1 tol 240 | 1.05 [1.00, 1.13] inconclusive | 1.50 [1.24, 1.81] material | 1.56 [1.29, 1.90] | 1.67 [1.43, 1.97] |
+| threshold 3 tol 60 | 1.05 [1.00, 1.13] inconclusive | 1.45 [1.21, 1.74] material | 1.63 [1.34, 1.99] | 1.64 [1.42, 1.93] |
+| threshold 3 tol 120 | 1.05 [1.00, 1.13] inconclusive | 1.44 [1.20, 1.74] material | 1.61 [1.33, 1.96] | 1.64 [1.41, 1.92] |
+| registered | 1.05 [1.00, 1.13] inconclusive | 1.44 [1.20, 1.74] material | 1.61 [1.33, 1.96] | 1.64 [1.41, 1.92] |
+| registered without flagged | 1.07 [1.00, 1.18] inconclusive | 1.40 [1.12, 1.79] material | 1.62 [1.25, 2.11] | 1.60 [1.33, 1.98] |
+| threshold 3 tol 240 | 1.05 [1.00, 1.13] inconclusive | 1.40 [1.16, 1.69] material | 1.56 [1.29, 1.90] | 1.56 [1.34, 1.84] |
+| threshold 5 tol 60 | 1.02 [1.00, 1.06] not material | 1.41 [1.20, 1.66] material | 1.63 [1.34, 1.99] | 1.53 [1.34, 1.77] |
+| threshold 5 tol 120 | 1.02 [1.00, 1.05] not material | 1.41 [1.20, 1.65] material | 1.61 [1.33, 1.96] | 1.53 [1.34, 1.77] |
+| threshold 5 tol 240 | 1.02 [1.00, 1.05] not material | 1.37 [1.18, 1.62] material | 1.56 [1.29, 1.90] | 1.48 [1.30, 1.71] |
+
+Ratio of medians at the registered setting: perfect search 1.02 [1.00, 1.23]; perfect evidence as well 1.51 [1.19, 2.20].
+
+## Correlated: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | 0.64 [0.35, 0.96] (88 periods) |
+| without fallbacks | 0.73 [0.44, 1.06] (81 periods) |
+| medians at most 1 | 0.73 [0.45, 1.04] (86 periods) |
+| search right | -0.21 [-11.35, 8.28] (20 periods) |
+| search wrong | 0.76 [0.49, 1.11] (68 periods) |
+| flat prior search | 0.76 [0.52, 0.98] (88 periods) |
+| ratio of log medians | 0.66 |
+
+Refit fall-backs: 10 of 100; refits with a period median of 1 or more: 2.
+
+## Correlated: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 188 | 108 | 0.57 | 265 | 2.65 | 58 | 0.78 / 0.38 |
+| c2-refit | 188 | 77 | 0.41 | 232 | 2.32 | 46 | 0.63 / 0.27 |
+| search3-flat | 188 | 86 | 0.46 | 172 | 1.72 | 38 | 0.64 / 0.29 |
+| search2 | 188 | 72 | 0.38 | 156 | 1.56 | 36 | 0.56 / 0.22 |
+| search3 | 188 | 71 | 0.38 | 129 | 1.29 | 39 | 0.58 / 0.23 |
+| search3-mix | 188 | 71 | 0.38 | 126 | 1.26 | 39 | 0.58 / 0.23 |
+
+## Correlated: periods with no true manoeuvre (3): share with one declared
+
+| System | All | 0-30 min |
+| --- | --- | --- |
+| c2 | 1.00 [0.44, 1.00] | 1.00 (3) |
+| c2-refit | 1.00 [0.44, 1.00] | 1.00 (3) |
+| search3-flat | 1.00 [0.44, 1.00] | 1.00 (3) |
+| search2 | 1.00 [0.44, 1.00] | 1.00 (3) |
+| search3 | 1.00 [0.44, 1.00] | 1.00 (3) |
+| search3-mix | 0.33 [0.06, 0.79] | 0.33 (3) |
+
+## Correlated: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| c2-refit | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search3-flat | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search2 | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search3 | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search3-mix | 0.000 (28) | 0.000 (32) | 0.045 (22) |
+
+Candidates (repaired evidence, 100 periods): first right 0.230; true history in the first three 0.380; calibration gap 0.332; kinds differ among right 0.22.
+
+Candidates (spec 014's evidence, 100 periods): first right 0.170; true history in the first three 0.310; calibration gap 0.344; kinds differ among right 0.21.
+
+References held to the true times: 0.280; true-history fits with a consistent cost: 1.000; periods with a cut manoeuvre: 15 of 100.
+
+## Outliers: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 2.63 [1.97, 3.53] | 2.31 [1.62, 3.01] | 0.20 | 0.50 / 0.06 |
+| c2 ÷ c2-refit | 1.32 [1.07, 1.62] | 1.23 [0.90, 1.69] | 0.31 | 0.24 / 0.08 |
+| c2-refit ÷ search3 | 1.99 [1.53, 2.60] | 1.88 [1.31, 2.48] | 0.27 | 0.40 / 0.08 |
+| search3-flat ÷ search3 | 1.08 [0.83, 1.42] | 1.11 [0.87, 1.36] | 0.51 | 0.19 / 0.22 |
+| search2 ÷ search3 | 0.93 [0.79, 1.07] | 1.00 [0.86, 1.16] | 0.32 | 0.11 / 0.10 |
+| search3 ÷ search3-mix | 1.00 [1.00, 1.00] | 1.00 [1.00, 1.00] | 0.00 | 0.00 / 0.00 |
+| search3 ÷ history-best | 1.77 [1.47, 2.14] | 1.90 [1.47, 2.69] | 0.26 | 0.35 / 0.05 |
+| search3 ÷ history-fixed | 2.32 [1.73, 3.02] | 2.46 [1.86, 3.67] | 0.19 | 0.53 / 0.06 |
+| search3 ÷ one-leg | 0.08 [0.05, 0.14] | 0.23 [0.16, 0.29] | 0.91 | 0.01 / 0.73 |
+| c2 ÷ one-leg | 0.22 [0.13, 0.38] | 0.52 [0.35, 0.70] | 0.73 | 0.12 / 0.52 |
+| search3-mix ÷ one-leg | 0.08 [0.05, 0.14] | 0.23 [0.16, 0.29] | 0.91 | 0.01 / 0.73 |
+
+## Outliers: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 88 | 0.40 [0.30, 0.50] | 53 | 0.34 [0.23, 0.47] | 0.17 [0.09, 0.29] | 0.49 [0.36, 0.62] |
+| all | 100 | 0.41 [0.32, 0.51] | 59 | 0.36 [0.25, 0.48] | 0.17 [0.09, 0.28] | 0.47 [0.35, 0.60] |
+| scored without cut | 74 | 0.35 [0.25, 0.46] | 48 | 0.35 [0.23, 0.50] | 0.19 [0.10, 0.32] | 0.46 [0.33, 0.60] |
+
+## Outliers: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.11 [1.04, 1.20] inconclusive | 1.68 [1.42, 2.03] material | 1.72 [1.45, 2.08] | 1.78 [1.53, 2.10] |
+| threshold 1 tol 120 | 1.10 [1.03, 1.19] inconclusive | 1.64 [1.39, 1.95] material | 1.68 [1.41, 2.01] | 1.72 [1.48, 2.02] |
+| threshold 1 tol 240 | 1.10 [1.03, 1.19] inconclusive | 1.55 [1.32, 1.84] material | 1.57 [1.33, 1.85] | 1.62 [1.41, 1.89] |
+| threshold 3 tol 60 | 1.10 [1.03, 1.19] inconclusive | 1.55 [1.33, 1.81] material | 1.72 [1.45, 2.08] | 1.57 [1.37, 1.85] |
+| threshold 3 tol 120 | 1.09 [1.03, 1.18] inconclusive | 1.50 [1.30, 1.75] material | 1.68 [1.41, 2.01] | 1.52 [1.32, 1.78] |
+| registered | 1.09 [1.03, 1.18] inconclusive | 1.50 [1.30, 1.75] material | 1.68 [1.41, 2.01] | 1.52 [1.32, 1.78] |
+| registered without flagged | 1.11 [1.03, 1.22] inconclusive | 1.32 [1.16, 1.54] material | 1.45 [1.22, 1.72] | 1.34 [1.18, 1.56] |
+| threshold 3 tol 240 | 1.08 [1.02, 1.17] inconclusive | 1.45 [1.26, 1.68] material | 1.57 [1.33, 1.85] | 1.47 [1.28, 1.69] |
+| threshold 5 tol 60 | 1.06 [1.01, 1.13] inconclusive | 1.40 [1.22, 1.62] material | 1.72 [1.45, 2.08] | 1.42 [1.24, 1.65] |
+| threshold 5 tol 120 | 1.06 [1.01, 1.13] inconclusive | 1.37 [1.20, 1.58] material | 1.68 [1.41, 2.01] | 1.38 [1.22, 1.60] |
+| threshold 5 tol 240 | 1.05 [1.00, 1.13] inconclusive | 1.32 [1.16, 1.50] material | 1.57 [1.33, 1.85] | 1.33 [1.18, 1.52] |
+
+Ratio of medians at the registered setting: perfect search 1.05 [1.01, 1.31]; perfect evidence as well 1.49 [1.19, 2.09].
+
+## Outliers: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | 0.29 [0.08, 0.48] (88 periods) |
+| without fallbacks | 0.39 [0.12, 0.64] (70 periods) |
+| medians at most 1 | 0.27 [0.07, 0.46] (87 periods) |
+| search right | 0.33 [0.13, 0.55] (35 periods) |
+| search wrong | 0.20 [-0.33, 0.62] (53 periods) |
+| flat prior search | 0.92 [0.68, 1.22] (88 periods) |
+| ratio of log medians | 0.25 |
+
+Refit fall-backs: 20 of 100; refits with a period median of 1 or more: 0.
+
+## Outliers: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 188 | 106 | 0.56 | 255 | 2.55 | 59 | 0.78 / 0.37 |
+| c2-refit | 188 | 78 | 0.41 | 226 | 2.26 | 40 | 0.62 / 0.26 |
+| search3-flat | 188 | 66 | 0.35 | 108 | 1.08 | 32 | 0.53 / 0.26 |
+| search2 | 188 | 51 | 0.27 | 82 | 0.82 | 28 | 0.44 / 0.15 |
+| search3 | 188 | 53 | 0.28 | 67 | 0.67 | 30 | 0.44 / 0.19 |
+| search3-mix | 188 | 53 | 0.28 | 66 | 0.66 | 30 | 0.44 / 0.19 |
+
+## Outliers: periods with no true manoeuvre (3): share with one declared
+
+| System | All | 0-30 min |
+| --- | --- | --- |
+| c2 | 0.33 [0.06, 0.79] | 0.33 (3) |
+| c2-refit | 0.33 [0.06, 0.79] | 0.33 (3) |
+| search3-flat | 0.33 [0.06, 0.79] | 0.33 (3) |
+| search2 | 0.33 [0.06, 0.79] | 0.33 (3) |
+| search3 | 0.33 [0.06, 0.79] | 0.33 (3) |
+| search3-mix | 0.33 [0.06, 0.79] | 0.33 (3) |
+
+## Outliers: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| c2-refit | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search3-flat | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search2 | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search3 | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+| search3-mix | 0.000 (28) | 0.000 (32) | 0.000 (22) |
+
+Candidates (repaired evidence, 100 periods): first right 0.410; true history in the first three 0.580; calibration gap 0.155; kinds differ among right 0.19.
+
+Candidates (spec 014's evidence, 100 periods): first right 0.370; true history in the first three 0.580; calibration gap 0.129; kinds differ among right 0.27.
+
+References held to the true times: 0.210; true-history fits with a consistent cost: 0.020; periods with a cut manoeuvre: 15 of 100.
+
+## Whole: paired comparisons
+
+| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |
+| --- | --- | --- | --- | --- |
+| c2 ÷ search3 | 3.44 [2.48, 4.79] | 3.04 [1.87, 4.78] | 0.20 | 0.55 / 0.02 |
+| c2 ÷ c2-refit | 1.50 [1.14, 2.03] | 1.68 [0.83, 2.44] | 0.33 | 0.32 / 0.13 |
+| c2-refit ÷ search3 | 2.30 [1.72, 3.14] | 1.81 [1.35, 3.85] | 0.23 | 0.40 / 0.02 |
+| search3-flat ÷ search3 | 1.18 [0.94, 1.47] | 1.04 [0.82, 1.59] | 0.50 | 0.18 / 0.10 |
+| search2 ÷ search3 | 1.05 [0.88, 1.24] | 0.95 [0.80, 1.33] | 0.17 | 0.12 / 0.07 |
+| search3 ÷ search3-mix | 1.00 [1.00, 1.00] | 1.00 [1.00, 1.00] | 0.00 | 0.00 / 0.00 |
+| search3 ÷ history-best | 1.57 [1.22, 2.05] | 1.71 [1.10, 2.28] | 0.38 | 0.35 / 0.07 |
+| search3 ÷ history-fixed | 1.87 [1.34, 2.54] | 2.21 [1.14, 2.82] | 0.28 | 0.48 / 0.13 |
+| search3 ÷ one-leg | 0.04 [0.02, 0.07] | 0.08 [0.05, 0.12] | 0.97 | 0.03 / 0.90 |
+| c2 ÷ one-leg | 0.13 [0.07, 0.23] | 0.24 [0.14, 0.37] | 0.82 | 0.05 / 0.73 |
+| search3-mix ÷ one-leg | 0.04 [0.02, 0.07] | 0.08 [0.05, 0.12] | 0.97 | 0.03 / 0.90 |
+
+## Whole: classes of wrong-history periods
+
+| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |
+| --- | --- | --- | --- | --- | --- | --- |
+| scored | 60 | 0.48 [0.36, 0.61] | 31 | 0.32 [0.19, 0.50] | 0.26 [0.14, 0.43] | 0.42 [0.26, 0.59] |
+| all | 60 | 0.48 [0.36, 0.61] | 31 | 0.32 [0.19, 0.50] | 0.26 [0.14, 0.43] | 0.42 [0.26, 0.59] |
+| scored without cut | 55 | 0.49 [0.36, 0.62] | 28 | 0.36 [0.21, 0.54] | 0.25 [0.13, 0.43] | 0.39 [0.24, 0.58] |
+
+## Whole: attainable headroom (search ÷ composite, paired)
+
+| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |
+| --- | --- | --- | --- | --- |
+| threshold 1 tol 60 | 1.27 [1.11, 1.51] material | 1.35 [1.09, 1.70] inconclusive | 1.45 [1.15, 1.83] | 1.53 [1.31, 1.85] |
+| threshold 1 tol 120 | 1.27 [1.11, 1.51] material | 1.38 [1.14, 1.69] material | 1.48 [1.20, 1.85] | 1.50 [1.28, 1.81] |
+| threshold 1 tol 240 | 1.25 [1.10, 1.48] inconclusive | 1.30 [1.08, 1.60] inconclusive | 1.39 [1.14, 1.72] | 1.41 [1.21, 1.69] |
+| threshold 3 tol 60 | 1.18 [1.05, 1.37] inconclusive | 1.19 [0.98, 1.47] inconclusive | 1.45 [1.15, 1.83] | 1.35 [1.17, 1.59] |
+| threshold 3 tol 120 | 1.18 [1.05, 1.37] inconclusive | 1.22 [1.03, 1.48] inconclusive | 1.48 [1.20, 1.85] | 1.32 [1.15, 1.56] |
+| registered | 1.18 [1.05, 1.37] inconclusive | 1.22 [1.03, 1.48] inconclusive | 1.48 [1.20, 1.85] | 1.32 [1.15, 1.56] |
+| registered without flagged | 1.22 [1.06, 1.45] inconclusive | 1.19 [0.99, 1.46] inconclusive | 1.44 [1.14, 1.85] | 1.27 [1.10, 1.53] |
+| threshold 3 tol 240 | 1.16 [1.04, 1.34] inconclusive | 1.16 [0.99, 1.39] inconclusive | 1.39 [1.14, 1.72] | 1.26 [1.11, 1.47] |
+| threshold 5 tol 60 | 1.16 [1.04, 1.34] inconclusive | 1.19 [1.00, 1.43] inconclusive | 1.45 [1.15, 1.83] | 1.28 [1.11, 1.51] |
+| threshold 5 tol 120 | 1.16 [1.04, 1.34] inconclusive | 1.21 [1.06, 1.43] inconclusive | 1.48 [1.20, 1.85] | 1.25 [1.10, 1.46] |
+| threshold 5 tol 240 | 1.14 [1.02, 1.31] inconclusive | 1.20 [1.04, 1.41] inconclusive | 1.39 [1.14, 1.72] | 1.23 [1.08, 1.44] |
+
+Ratio of medians at the registered setting: perfect search 1.30 [1.01, 1.75]; perfect evidence as well 1.40 [1.00, 1.75].
+
+## Whole: the refit's share of the search's gain
+
+| Estimand | Share |
+| --- | --- |
+| registered | 0.33 [0.11, 0.51] (60 periods) |
+| without fallbacks | 0.34 [0.12, 0.54] (56 periods) |
+| medians at most 1 | 0.33 [0.11, 0.51] (60 periods) |
+| search right | 0.30 [0.02, 0.59] (29 periods) |
+| search wrong | 0.36 [-0.02, 0.64] (31 periods) |
+| flat prior search | 0.87 [0.70, 1.05] (60 periods) |
+| ratio of log medians | 0.46 |
+
+Refit fall-backs: 4 of 60; refits with a period median of 1 or more: 0.
+
+## Whole: manoeuvre counts (120 s match; cut manoeuvres excluded)
+
+| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| c2 | 199 | 113 | 0.57 | 97 | 1.62 | 59 | 0.78 / 0.37 |
+| c2-refit | 199 | 79 | 0.40 | 63 | 1.05 | 25 | 0.54 / 0.30 |
+| search3-flat | 199 | 48 | 0.24 | 35 | 0.58 | 18 | 0.33 / 0.16 |
+| search2 | 199 | 35 | 0.18 | 35 | 0.58 | 16 | 0.28 / 0.14 |
+| search3 | 199 | 40 | 0.20 | 29 | 0.48 | 16 | 0.29 / 0.15 |
+| search3-mix | 199 | 40 | 0.20 | 29 | 0.48 | 16 | 0.29 / 0.15 |
+
+## Whole: periods with no true manoeuvre (1): share with one declared
+
+| System | All | 30-45 min |
+| --- | --- | --- |
+| c2 | 0.00 [0.00, 0.79] | 0.00 (1) |
+| c2-refit | 0.00 [0.00, 0.79] | 0.00 (1) |
+| search3-flat | 0.00 [0.00, 0.79] | 0.00 (1) |
+| search2 | 1.00 [0.21, 1.00] | 1.00 (1) |
+| search3 | 0.00 [0.00, 0.79] | 0.00 (1) |
+| search3-mix | 0.00 [0.00, 0.79] | 0.00 (1) |
+
+## Whole: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)
+
+| System | 1 | 2 | 3 or more |
+| --- | --- | --- | --- |
+| c2 | 0.000 (2) | 0.000 (8) | 0.000 (44) |
+| c2-refit | 0.000 (2) | 0.000 (8) | 0.000 (44) |
+| search3-flat | 0.000 (2) | 0.000 (8) | 0.000 (44) |
+| search2 | 0.000 (2) | 0.000 (8) | 0.000 (44) |
+| search3 | 0.000 (2) | 0.000 (8) | 0.000 (44) |
+| search3-mix | 0.000 (2) | 0.000 (8) | 0.000 (44) |
+
+Candidates (repaired evidence, 60 periods): first right 0.483; true history in the first three 0.633; calibration gap 0.193; kinds differ among right 0.15.
+
+Candidates (spec 014's evidence, 60 periods): first right 0.467; true history in the first three 0.600; calibration gap 0.117; kinds differ among right 0.12.
+
+References held to the true times: 0.167; true-history fits with a consistent cost: 0.983; periods with a cut manoeuvre: 5 of 60.
+
+## Soundness
+
+Reference efficiency on tight rows 1.00 (band 0.8 to 1.3); consistent fits 1.000 (at least 0.9); references held 0.133 (at most 0.25): pass.
diff --git a/experiments/20261113-tl3/whole.periods.json b/experiments/20261113-tl3/whole.periods.json
new file mode 100644
index 00000000..55aa0a7e
--- /dev/null
+++ b/experiments/20261113-tl3/whole.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "first_row": 0, "rows": 623, "seconds_long": 3110.0, "bearings": 594, "true": [[715.0, 765.0], [2490.0, 2710.0]], "true_kinds": [[false, true], [true, false]], "true_cut": [false, false], "ownship_turns": 2, "history_cost": 591.7862278988495, "history_converged": true, "candidates2": [{"spans": [[1500.3514389241384, 1510.3514389241384], [2503.2677915228905, 2660.1297554790567]], "kinds": [[true, false], [true, false]], "evidence": -331.39936050824633, "theta": [-5161.607997722226, 2986.5780320472, 2.979714867036956, 2.9840445496638024, -0.1603119643493024, 0.0, -1.841564223742472, 0.0]}, {"spans": [[1776.8352703824503, 1786.8352703824503], [2523.071325457201, 2663.72918669291]], "kinds": [[true, false], [true, true]], "evidence": -336.69659469397567, "theta": [-5326.581317271486, 3076.1188822922345, 2.963257488597716, 2.895535478082658, -0.16656436259548676, 0.0, -2.127423969524583, 1.3445551444000505]}, {"spans": [[521.3603752577192, 533.7050695895283], [1488.4109863743192, 1512.6679069457414], [2467.2576789903346, 2635.880841066963]], "kinds": [[true, false], [false, true], [true, true]], "evidence": -337.56717295821903, "theta": [-4085.8445188068204, 2331.922442208196, 3.2824440205436085, 2.884806145302726, -0.215412272030038, 0.0, 0.0, -1.2171497814071914, -2.2087426826542362, 2.5239266691658995]}, {"spans": [[2373.829697743217, 2749.995733921517]], "kinds": [[true, true]], "evidence": -338.93969788092556, "theta": [-5718.689946760416, 3320.839406191648, 2.930859157211368, 2.758170166085302, -2.1778850012867705, 1.8239821883222087]}, {"spans": [[2148.0313271367972, 2162.546398717894], [2507.5573606212765, 2681.303451336385]], "kinds": [[true, false], [true, true]], "evidence": -339.6737855209076, "theta": [-5542.373096666137, 3201.5365362650055, 2.9551660263815984, 2.748353228210654, -0.2536971885528407, 0.0, -2.080223170959952, 2.4217202873697805]}], "histories_evaluated": 65, "candidates": [{"spans": [[1500.3514389241384, 1510.3514389241384], [2503.2677915228905, 2660.1297554790567]], "kinds": [[true, false], [true, false]], "evidence": -336.0517317185746, "theta": [-5161.607997722226, 2986.5780320472, 2.979714867036956, 2.9840445496638024, -0.1603119643493024, 0.0, -1.841564223742472, 0.0]}, {"spans": [[2373.8180794573186, 2750.0089269860027]], "kinds": [[true, true]], "evidence": -338.8335249889583, "theta": [-5718.676658813581, 3320.8292498262035, 2.930863407210891, 2.7581541640391505, -2.1778909481485327, 1.8240032380938735]}, {"spans": [[1776.8352703824503, 1786.8352703824503], [2523.071325457201, 2663.72918669291]], "kinds": [[true, false], [true, true]], "evidence": -341.6051305630229, "theta": [-5326.581317271486, 3076.1188822922345, 2.963257488597716, 2.895535478082658, -0.16656436259548676, 0.0, -2.127423969524583, 1.3445551444000505]}, {"spans": [[521.3603752577192, 533.7050695895283], [1488.4109863743192, 1512.6679069457414], [2467.2576789903346, 2635.880841066963]], "kinds": [[true, false], [false, true], [true, true]], "evidence": -342.9995190254088, "theta": [-4085.8445188068204, 2331.922442208196, 3.2824440205436085, 2.884806145302726, -0.215412272030038, 0.0, 0.0, -1.2171497814071914, -2.2087426826542362, 2.5239266691658995]}, {"spans": [[2148.0313271367972, 2162.546398717894], [2507.5573606212765, 2681.303451336385]], "kinds": [[true, false], [true, true]], "evidence": -343.12429524547, "theta": [-5542.373096666137, 3201.5365362650055, 2.9551660263815984, 2.748353228210654, -0.2536971885528407, 0.0, -2.080223170959952, 2.4217202873697805]}], "refit_fell_back": false, "reference_flagged": false, "true_evidence": -335.0323206254965, "true_fit_spans": [[721.1427922188722, 750.5435983837076], [2479.9456240851855, 2732.4025371002035]], "histories": {"c2": {"spans": [[2225.0, 2495.0]]}, "search2": {"spans": [[1500.3514389241384, 1510.3514389241384], [2503.2677915228905, 2660.1297554790567]], "kinds": [[true, false], [true, false]], "evidence": -331.39936050824633}, "search3": {"spans": [[1500.3514389241384, 1510.3514389241384], [2503.2677915228905, 2660.1297554790567]], "kinds": [[true, false], [true, false]], "evidence": -336.0517317185746}, "search3-flat": {"spans": [[1496.6709191346533, 1506.6709191346533], [2465.7070009868603, 2693.4892764373735]], "kinds": [[false, true], [true, true]], "evidence": -335.52118921110804}, "search3-mix": {"spans": [[1500.3514389241384, 1510.3514389241384], [2503.2677915228905, 2660.1297554790567]], "kinds": [[true, false], [true, false]], "evidence": -336.74487889913456}, "c2-refit": {"spans": [[2373.8294752730317, 2749.996004521795]], "kinds": [[true, true]], "evidence": -338.83353550398436}}, "seconds": {"layer": 22.45, "reference": 1.46, "search2": 6.86, "search3": 6.9, "search3-flat": 8.6, "search3-mix": 6.76, "c2-refit": 1.64}}, {"ordinal": 1, "first_row": 0, "rows": 608, "seconds_long": 3035.0, "bearings": 589, "true": [[1260.0, 1310.0], [2785.0, 2855.0]], "true_kinds": [[true, false], [false, true]], 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diff --git a/experiments/20261113-tl4-decision/README.md b/experiments/20261113-tl4-decision/README.md
new file mode 100644
index 00000000..f3c0447a
--- /dev/null
+++ b/experiments/20261113-tl4-decision/README.md
@@ -0,0 +1,66 @@
+# Spec 016 (study L): decision record (2026-11-13)
+
+Written after the tuning run on seed 338 (`experiments/20261113-tl4-tuning/`) and the
+throughput pilot (`experiments/20261113-tlm-pilot/`), **before any training run of the
+learned model and before seeds 340 and 341 exist**. `decision.json` binds the plan
+(`configs/timelate/tl-e.json`, `82cd70ee…`), the ranges, the training mix, the three
+training plans, the time-late code (`e5040f5e…`) and the scoring files by SHA-256, and
+states the criteria, the order of events, the power and the forecasts. Owner decision:
+2026-11-13, option B. SRD §5 row "Study L" (proposed; the owner merges).
+
+## What is compared
+
+- **The standing search** (`search3`): spec 015's, Annex B's prior, the bearings as given.
+- **The classical control** (`ctl`): wild bearings edited out; an effective bearing sd from
+  the estimated error correlation (used above a share of 0.2); searches under Annex B's
+  prior and a broadened one, pooled. Reported as a declared history (margin 1, quiet
+  probability 0.01: `ctl-d1-q0.01`) and as a hedged track (temperature 1: `ctl-h1`).
+  Nothing trained; settings from tuning seed 338; not told the conditions.
+- **The learned model**: three seeds, 12.5 million simulated periods each (65% Annex B,
+  20% randomised manoeuvre statistics, 15% quiet; a quarter with correlated errors,
+  outliers or both; no bias). Reported track: the arc model fitted from its output
+  (`learned-sK-arc`); its direct track beside.
+
+## Criteria
+
+- **L-a** soundness on validation (controls only); a failure stops the study.
+- **L-b** what the controls close, reported.
+- **L-c** on five mismatch splits: learning *shown* only if every seed beats both the
+  declared control and the hedged track; *hedging* if only the declared one.
+- **L-d** *harm* on Annex B or held-course periods if every seed loses to the control.
+- Shown on two or more splits without harm: a gate is proposed. On none: the classical
+  controls stand and the learned track stops. Otherwise the owner decides.
+
+No materiality line is registered. Each comparison is a sign test against 1 with an
+interval; the size is reported with it. From tuning data a learned track must differ from
+the control by about 15% to 40% (by split) to be separated at the test sizes.
+
+## Forecasts (written before any training result)
+
+- L-a passes.
+- The control beats the standing search by about 1.5 with outliers and 1.3 with correlated
+  errors, and barely under the first shifted prior.
+- **Learning is most likely shown on no split.** The shifted prior is the one place with
+  room; about one chance in four there.
+- **Harm on Annex B data is more likely than not for the model's own track**: at this
+  budget the model is unlikely to match a fit that knows the exact motion model. The arc
+  read-out should narrow that.
+- If these hold, the result is "the classical controls stand", at a budget of $18 of GPU
+  time; it would not show that a larger model or more data could not do better.
+
+## Order of events, enforced
+
+1. This record is committed.
+2. The three training runs start (plans bound here; the ledger refuses past its cap).
+3. The controls' validation runs on seed 340 (`tl-e-val`), with no learned track; L-a is
+   read and the record committed.
+4. An addendum names the trained seeds' results branches and the validation record in the
+   test run file.
+5. The test runs on seed 341 once. The scorer refuses a learned track without step 3's
+   record.
+
+## Known limits
+
+Listed in `decision.json`: the unrepaired evidence faults at the prior's support edges; the
+correlated-error control is an effective sd and knows the error family; only editing is
+built as a robust form; the training budget; the ledger's under-count on restarts.
diff --git a/experiments/20261113-tl4-decision/addendum-1.md b/experiments/20261113-tl4-decision/addendum-1.md
new file mode 100644
index 00000000..f4ed2ce5
--- /dev/null
+++ b/experiments/20261113-tl4-decision/addendum-1.md
@@ -0,0 +1,11 @@
+# Addendum 1 (2026-11-13): the controls' validation record
+
+The controls' validation ran on seed 340 from the registered commit and plan
+(`experiments/20261113-tl4-val/`; no learned track scored). **L-a passes** (1.06 of the
+bound; all fits converged; 0.11 of references held). The test run file
+`configs/timelate/runs/tl-e-test.json` now names that record as `controls_record`; its
+splits and sizes are unchanged from `decision.json`. The trained seeds' results branches
+are added in addendum 2 when the three training runs have finished; the test is launched
+only then, once.
+
+Nothing in the plan, the criteria or the code changes.
diff --git a/experiments/20261113-tl4-decision/addendum-2.md b/experiments/20261113-tl4-decision/addendum-2.md
new file mode 100644
index 00000000..2d2d9b0b
--- /dev/null
+++ b/experiments/20261113-tl4-decision/addendum-2.md
@@ -0,0 +1,27 @@
+# Addendum 2 (2026-11-14): the trained seeds
+
+The three training runs completed as registered (`experiments/20261114-tlm-runs/`; config
+digests equal to `decision.json`). The test run file `configs/timelate/runs/tl-e-test.json`
+now names, for `learned-s1`, `-s2` and `-s3`, the results branch, folder and training plan
+of each; its splits, sizes and `controls_record` are unchanged. Its SHA-256 at launch is
+in the launch commit's record of the run (`run.json` on the results branch).
+
+Nothing in the plan, the criteria or the code changes. The learned model has been scored
+on no validation or test period. The test on seed 341 is launched once, after this commit.
+
+**Shard size.** The test's 2,860 periods in shards of 10 would be 286 jobs, above the 256
+a workflow matrix allows; the run file's `shard_size` is 12 (240 jobs). The periods, splits
+and sizes are unchanged. (C″'s track can differ in the last bits with the shard's
+composition, as recorded since Gate S; it is one of the reported systems, not a side of
+the criteria.)
+
+**Checked before launch**: the workflow's new step (fetch each checkpoint, predict, score
+beside the controls) was run locally through bash against the three real checkpoints on
+three periods of the spent tuning seed 338; the checkpoints' digests equal the training
+records; no error figure was read from that check.
+
+**Correction, 2026-11-14 (panel).** The statement above that shard size reaches C″ only is
+wrong in principle: the control's searches start from C″'s track and transitions, so a
+last-bit difference there can reach `ctl`, a side of the criteria. Its size was not
+measured (that needs the test solved again in shards of 10); every losing side of L-c and
+L-d is at least 2.7 standard errors from 1.
diff --git a/experiments/20261113-tl4-decision/decision.json b/experiments/20261113-tl4-decision/decision.json
new file mode 100644
index 00000000..6382187c
--- /dev/null
+++ b/experiments/20261113-tl4-decision/decision.json
@@ -0,0 +1,133 @@
+{
+ "study": "spec 016: time-late, classical controls and a learned whole-period model (study L)",
+ "registered": "2026-11-13",
+ "owner_decision": "2026-11-13, option B",
+ "digests": {
+  "configs/timelate/tl-e.json": "82cd70ee700a19c417ea03022c2c6a70fc01f3adb8a5208f58f1ef2cef4f4647",
+  "configs/timelate/runs/tl-e-val.json": "ee8d2d3c0be88c5ed3603f6f8394c167d12c0345f74c2a861f9f8f63bb5ccf71",
+  "configs/timelate/tl-ranges.json": "cb48fcabb5a51f76685cab482e23f01cb53c504406a1654c2ec1b664216a4784",
+  "configs/timelate/tl-broad.json": "2ff99a3a2abc3696114d1a234f166e22d4d6cf494dd28631d9dd0d495a0203a1",
+  "configs/timelate/tl-shift-ranges.json": "bc76abf0d5916f4a91909ebb6ef90e84438eae2ee1142e6607c21823d489081b",
+  "configs/timelate/tl-shift2-ranges.json": "1ce6b6edec93721eb64381c5d3a98c82a62e027d6386f476487147f141338b47",
+  "configs/timelate/tl-quiet-ranges.json": "a964509642c46015db6c38dc7b6c774c3cf36d992d3be954a10627be1368782b",
+  "configs/timelate/tl-heldout.json": "aeedfe10ca841d95d1cf2026382c43afe59c548aef9f5fed9fe07e16d3832634",
+  "configs/gateh/conditions.json": "d260fd7538d2fbcbc8c25cfd402a091ed9d50bf254a79bfd2c6c5bcc4b634e15",
+  "configs/gateh/gh-a.json": "26d8e8987a2f696fd72ea235c5183f9f53b1ec2a5623cbc4e646d32139d2d88a",
+  "configs/regress/tlm-mix.json": "ea4124c0575a65458084a795c16cf26593f69bec28f1844bb8c6d7d617020966",
+  "configs/regress/tlm-s1.json": "fdff4b64d6078b44fa90afe2c49860fd0b15da3d2f7eab9698734007586cfaf2",
+  "configs/regress/tlm-s2.json": "d179059c0590231900e34591633a82b913443177f5ac175a966b94278c03d19a",
+  "configs/regress/tlm-s3.json": "3b436d0a6bfbe3a958a855cfbe1894c62a9d7924af6ae68f9f8df8bfe98f2fa3"
+ },
+ "training_config_sha256": {
+  "tlm-s1": "537b1a6bfb2ccfc3e4a1c57c17971fada306e8d43593c15c32282e0233fd1b73",
+  "tlm-s2": "08370981bbafe9a33fd31132859acb66053cbbffbb54795a846987eb0de6d69b",
+  "tlm-s3": "36e234b5d83c54d21f93f93364f9f0ae7bb5a4dbb8ab0a74180b324d0ae84fff"
+ },
+ "time_late_code_sha256": "e5040f5e8df5bfea978594da2559d1073ab283bcedc53794e99fea6a77eb70a6",
+ "files": {
+  "src/tma2/timelate/study4.py": "242cd6fc0acbb5d89b61e3cd0dc96fb48270d979d914a8b1157949b149b27d61",
+  "src/tma2/timelate/study.py": "a17c54155cf1cf8c2a65595faf3811fa6b4ab2b979275533db33d8388c6c3883",
+  "src/tma2/timelate/study3.py": "c32e2a3fc11a68f5649f6ccf5dcc58089a0e4cd41b1ad1639d0f30dae662e255",
+  "src/tma2/timelate/match.py": "fff96f28bee96c75361d7f0bf97624f5437a6beeb315129a7585be0428c9a7ab",
+  "src/tma2/cli/tlscore.py": "181c9abfa948157b620d57b4258ebb7be93ea9b7d9040bc32cb8e51c3dd6ade9",
+  "src/tma2/cli/tlsolve.py": "0e527b886969b56a67361a21a59d6d41e6198dac595b6614fe6dc1e4328926b0",
+  "src/tma2/cli/tlpredict.py": "30c2c15761e7fce7ca932981d97d3374e9c686672966858dd5301a9d5cd5a7e2",
+  "src/tma2/regress/period_data.py": "965c3b3932c1655bcfcbcb6559a511adb71c0a1b94b36d7ba0c2a670e979bfbc",
+  "src/tma2/regress/period_predict.py": "ced7178d891e8ef7614b7aa0053b1d5d2fb0b08698f2203d21ecadaf6fecbee1",
+  "src/tma2/regress/model.py": "c4422ccaf37a566721969e8e0f2a31e8c242bfe1ca91c640f9cd6344b644ea13",
+  "src/tma2/regress/train.py": "e7400fb888b66f528b113fcef7c4df07360c972ce72443eeeb81827e189358ed",
+  "src/tma2/regress/frame.py": "bc4284503b84f90cd1e747eb5ca5a906cd2ed6d955b932f69a23b934a0dec667",
+  "scripts/modal_train.py": "534ff3c7fbab8e3cc38985574f916fbd06c7caa2d0bd6176ad82eb375fb72b06",
+  "scripts/research/tl4_tables.py": "edfc6d137fb8ba9a31c36c114e5f3e7b56a77403586c86e708e760d1fd397d8c",
+  ".github/workflows/timelate.yml": "e97b82c709b43721eb928fdfb6d572e2909f2a11d822218f6f8e9d6528d561ba",
+  ".github/workflows/gputrain.yml": "e6d7a7dfcef11de4fe26a98d53112681838131dd4298c0c96f55eb9097df20a0"
+ },
+ "seeds": {
+  "tuning (spent)": 338,
+  "training stream and its monitor slice": 339,
+  "validation (controls only)": 340,
+  "test": 341
+ },
+ "test_splits": {
+  "man2": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift2": {
+   "start": 0,
+   "periods": 400
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-corr-mild": {
+   "start": 0,
+   "periods": 200
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-out-harsh": {
+   "start": 0,
+   "periods": 200
+  },
+  "man2-bias": {
+   "start": 0,
+   "periods": 300
+  },
+  "whole": {
+   "start": 0,
+   "periods": 60
+  }
+ },
+ "systems": {
+  "standing search": "search3",
+  "declared control": "ctl-d1-q0.01",
+  "hedged control": "ctl-h1",
+  "learned (reported track)": [
+   "learned-s1-arc",
+   "learned-s2-arc",
+   "learned-s3-arc"
+  ],
+  "learned (direct, reported beside)": [
+   "learned-s1",
+   "learned-s2",
+   "learned-s3"
+  ]
+ },
+ "statistic": "per period, the median relative range error over scored rows (30 s apart, known-history bound below 0.5); a ratio a ÷ b is the geometric mean over periods with a 95% period bootstrap (2,000 resamples, seed 17, percentile); a side wins when the interval is wholly above 1 and loses when wholly below",
+ "criteria": {
+  "L-a": "validation seed 340, controls only: history-fixed range efficiency on rows bounded below 0.05 within [0.8, 1.3]; at least 0.9 of true-history fits with a consistent cost; at most 0.25 of references held. Fails: the study stops before the test seed",
+  "L-b": "reported, no line: search3 ÷ ctl-d1-q0.01 on shift, shift2, man2-corr, man2-out, and the share of the gap from search3 to history-best that ctl closes",
+  "L-c": "on each of shift, shift2, man2-corr, man2-out, man2-bias: 'shown' if every seed's ctl-d1-q0.01 ÷ learned-sK-arc and ctl-h1 ÷ learned-sK-arc both win; 'hedging' if every seed wins the first and not every seed the second; 'not shown' otherwise",
+  "L-d": "on man2 and quiet: 'harm' if every seed's ctl-d1-q0.01 ÷ learned-sK-arc loses",
+  "follows": "shown on at least two mismatch splits and no harm: a gate for a learned time-late system is proposed. Shown on none: the classical controls stand and the learned track stops. Otherwise: reported, the owner decides"
+ },
+ "order": "the learned model is scored on no validation or test data until the controls' validation record (run tl-e-val) is committed; tma2-tlscore refuses otherwise. Training reads stream seed 339 only. The test seed is scored once",
+ "power": "research.md, 'As registered': a learned track must differ from the control by about 15% (shift) to 40% (shift2) to be separated; less reads 'not separated'",
+ "forecasts": {
+  "L-a": "passes (tuning: 1.13, all converged, 0.11 held)",
+  "L-b": "search3 ÷ ctl: outliers about 1.5, correlated about 1.3, shift 1.0 to 1.1, shift2 1.1 to 1.2; gap closed: outliers over 0.6, correlated about 0.5, shift under 0.2",
+  "L-c": "not shown on outliers or correlated errors (the control is near the reference there). Shift is the one split with room: about one chance in four that learning is shown there. Bias: not shown. Overall most likely outcome: shown on none",
+  "L-d": "the model at 12.5 million periods is likely worse than the control on Annex B data (pilot: median |ln range| error 0.095 at 1.5 million periods against the control's 0.03): harm on man2 is more likely than not for the direct track; the arc read-out narrows it and may avoid 'harm'",
+  "direct against arc": "the read-out improves on the model's own track on most splits"
+ },
+ "known_limits": [
+  "the evidence faults at the edges of the prior's support (experiments/20261113-tl3/review.md, major 5) are not repaired; the declaration margin was tuned with them in place",
+  "the correlated-error control is one effective sd, not a whitened fit, and was built knowing the generator's error family",
+  "only wild-bearing editing is built as a robust form",
+  "the training budget is 12.5 million periods a seed; a null result is a result at this budget, not a limit of learning",
+  "the ledger under-counts a run whose container is replaced",
+  "the test run file's learned branches and controls_record are added by addendum before the test is launched; its splits and sizes are bound here"
+ ]
+}
\ No newline at end of file
diff --git a/experiments/20261113-tl4-tuning/README.md b/experiments/20261113-tl4-tuning/README.md
new file mode 100644
index 00000000..e886a485
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/README.md
@@ -0,0 +1,91 @@
+# Spec 016: the classical controls on tuning seed 338 (2026-11-13)
+
+CI run 43467937439 (`timelate/tl-e-tune`, commit 9156bd7, draft plan
+`configs/timelate/tl-e-draft.json`). **Tuning data: nothing here is a registered result.**
+Every table is in `tables.md`, generated from `summary.json` by
+`scripts/research/tl4_tables.py`; the figures below are copied from it. Per-row errors are
+on the results branch `results/timelate-tl-e-tune-43467937439`. Ratios are paired per
+period (geometric mean, 95% period bootstrap); above 1 means the system named second is
+better.
+
+## What the controls do
+
+| Split (scored periods) | Standing search | Combined control `ctl` | Search ÷ `ctl` | Share of the gap to the reference closed | `ctl` ÷ reference |
+| --- | --- | --- | --- | --- | --- |
+| Annex B (142) | 0.034 | 0.031 | 1.06 [1.00, 1.14] | 0.32 [−0.34, 1.72] | 1.14 [0.94, 1.38] |
+| Shifted (90) | 0.083 | 0.071 | 1.04 [0.99, 1.11] | 0.06 [−0.01, 0.15] | 2.05 [1.63, 2.58] |
+| Shifted 2 (92) | 0.031 | 0.027 | 1.17 [1.03, 1.35] | 0.36 [0.08, 0.86] | 1.33 [1.03, 1.68] |
+| Held course (138) | 0.025 | 0.025 | 1.04 [0.94, 1.14] | 0.09 [−0.19, 0.30] | 1.43 [1.24, 1.67] |
+| Correlated, mild (55) | 0.122 | 0.062 | 1.58 [1.25, 2.08] | 0.58 [0.27, 1.06] | 1.38 [0.98, 2.00] |
+| Correlated (74) | 0.124 | 0.095 | 1.34 [1.08, 1.67] | 0.53 [0.15, 0.99] | 1.30 [1.00, 1.72] |
+| Outliers (74) | 0.067 | 0.034 | 1.57 [1.20, 2.09] | 0.75 [0.34, 1.36] | 1.17 [0.85, 1.59] |
+| Outliers, harsh (55) | 0.099 | 0.041 | 2.92 [1.99, 4.27] | 1.24 [0.82, 1.85] | 0.81 [0.57, 1.18] |
+| Bias (55) | 0.065 | 0.064 | 1.00 [0.89, 1.13] | 0.00 [−1.14, 1.29] | 1.32 [0.86, 1.87] |
+
+- **Outliers**: editing wild bearings removes the gap (the control is at the reference).
+- **Correlated errors**: an effective sd from the estimated correlation closes about half
+  the gap; false manoeuvres fall from 1.50 a period to 0.47. The estimate reads a
+  correlated share of 0.52 where the truth is 0.5, and 0.00 on every other split; `ctl`
+  used it in every correlated period and in 3 to 5% of the others.
+- **A shifted prior**: the broadened prior closes almost nothing on the first shifted split
+  (0.06) and a third on the second. Half the manoeuvres are still missed on the first
+  (0.54), with or without it. **This is the gap no classical control here addresses.**
+- **Bias**: nothing addresses it, as intended.
+- **Cost on Annex B data**: none (1.06 the right way).
+- Soundness as validation will read it: reference efficiency 1.13 on tight rows, every
+  reference converged, 0.11 held. Passes.
+
+## Hedging
+
+The hedged track over `ctl`'s candidates, search ÷ hedged:
+
+| Temperature | Annex B | Shifted | Shifted 2 | Held course | Correlated | Outliers |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1 | 1.08 [0.98, 1.20] | 1.09 [1.02, 1.17] | 1.11 [0.98, 1.27] | 1.03 [0.90, 1.18] | 1.40 [1.14, 1.72] | 1.72 [1.26, 2.39] |
+| 3 | 0.94 [0.82, 1.09] | 1.16 [1.03, 1.32] | 1.05 [0.88, 1.24] | 0.99 [0.85, 1.15] | 1.55 [1.23, 1.97] | 1.48 [1.12, 1.98] |
+| 5 | 0.86 [0.74, 1.00] | 1.15 [1.01, 1.33] | 0.99 [0.82, 1.18] | 0.95 [0.81, 1.10] | 1.52 [1.21, 1.90] | 1.36 [1.03, 1.78] |
+
+Hedging helps where the history is uncertain (shifted prior, correlated errors) and costs
+where it is not, more so at higher temperatures. Temperature 1 is the only one that loses
+nowhere.
+
+## Declaration
+
+Held-course periods given a manoeuvre (149), and Annex B targets with one manoeuvre called
+quiet (68), for `ctl`:
+
+| Margin | Quiet probability 0 | 0.01 | 0.1 |
+| --- | --- | --- | --- |
+| 0 | 0.41 / 0.09 | 0.13 / 0.09 | 0.08 / 0.10 |
+| 1 | 0.30 / 0.10 | 0.07 / 0.10 | 0.03 / 0.13 |
+| 2 | 0.20 / 0.12 | 0.02 / 0.12 | 0.01 / 0.16 |
+| 3 | 0.17 / 0.15 | 0.01 / 0.15 | 0.01 / 0.18 |
+| 5 | 0.09 / 0.19 | 0.01 / 0.19 | 0.01 / 0.24 |
+
+A stated 1% chance of a quiet target does most of the work; the margin adds a little. On
+held course the declared track is 1.4 times better than the search's; on Annex B data it
+costs nothing at margins up to 3.
+
+## Settings chosen (by the rules in research R1 and R2; fixed from here)
+
+- **Declaration**: margin 1, quiet probability 0.01: the smallest pair with held-course
+  false manoeuvres at most 0.10 (0.07), one-manoeuvre targets called quiet 0.10 against
+  0.09 undeclared. System `ctl-d1-q0.01`.
+- **Hedged track**: temperature 1: the only temperature whose interval is not wholly below
+  1 on any split. System `ctl-h1`.
+- **Robust form**: editing (the only one built). **Correlation**: the effective sd, used
+  above an estimated share of 0.2. **Priors**: Annex B's and the broadened one, pooled.
+- Prior version 4 is not needed for the declaration to reach 0.10 (research R6's
+  condition); it stays deferred.
+
+## Weaknesses
+
+- One tuning seed; 55 to 142 scored periods a split: the intervals are wide and the
+  settings are chosen on the same data that shows what they do.
+- The reference on the correlated and shifted splits is held in 23% to 39% of periods.
+- The correlated-error control was built knowing the generator's error family (white plus
+  first-order Gauss–Markov). It is not told the parameters, but the family is the truth's.
+- The hedged track averages over at most ten candidates of a greedy search, not over a
+  posterior.
+- Run time: about 20 s a search on clean data and 45 s with 5% outliers before editing;
+  `ctl` runs two searches a period.
diff --git a/experiments/20261113-tl4-tuning/ci.json b/experiments/20261113-tl4-tuning/ci.json
new file mode 100644
index 00000000..8bf72d27
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/ci.json
@@ -0,0 +1 @@
+{"run_id": "43467937439", "commit": "9156bd74f408f3ab79ae9dd39191acbaa46b68e8", "cpu_model": "AMD EPYC 9V74 80-Core Processor"}
diff --git a/experiments/20261113-tl4-tuning/man2.periods.json b/experiments/20261113-tl4-tuning/man2.periods.json
new file mode 100644
index 00000000..8ecd41f3
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/man2.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "rows": 445, "seconds_long": 2220.0, "bearings": 424, "true": [[640.0, 805.0]], "true_kinds": [[true, true]], "true_cut": [false], "reference_flagged": false, "history_converged": true, "edited": 0, "corr_estimate": [0.14, 600.0], "ctl_corr_estimate": [0.14, 600.0, false], "histories": {"c2": {"spans": [[545.0, 735.0]]}, "search3-d0-q0": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d0-q0.01": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d0-q0.1": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d1-q0": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d1-q0.01": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d1-q0.1": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d2-q0": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d2-q0.01": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d2-q0.1": {"spans": [[547.1334547924768, 784.40891393427]]}, "search3-d3-q0": {"spans": 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diff --git a/experiments/20261113-tl4-tuning/plan.json b/experiments/20261113-tl4-tuning/plan.json
new file mode 100644
index 00000000..c9c50b05
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/plan.json
@@ -0,0 +1,158 @@
+{
+ "description": "Spec 016 DRAFT (tuning seed 338 only; never a registered result): the classical controls beside the standing search.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics, independent Gaussian errors",
+   "systems": [
+    "search3",
+    "edit",
+    "corr",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs, smaller turns, faster turn rates, more speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift2": {
+   "ranges": "configs/timelate/tl-shift2-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "longer legs (mean 35 min), larger and slower turns, few speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "man2-corr-mild": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 30 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-mild"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 60 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "2% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-central"
+  },
+  "man2-out-harsh": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "5% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-harsh"
+  },
+  "man2-bias": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "a slowly varying bias (0.5 degree, 30 min): addressed by no control and absent from the model's training",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "condition": "bias-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B engagements uncut",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "whole": true
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "prior_broad": "configs/timelate/tl-broad.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 10,
+  "score_every": 6,
+  "temperatures": [
+   1,
+   2,
+   3,
+   5
+  ],
+  "margins": [
+   0,
+   1,
+   2,
+   3,
+   5
+  ],
+  "quiets": [
+   0,
+   0.01,
+   0.1
+  ],
+  "store_tracks": 20
+ },
+ "runner": "run4",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "match_tolerance_s": 120.0,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "reference_flagged_max": 0.25
+ }
+}
\ No newline at end of file
diff --git a/experiments/20261113-tl4-tuning/quiet.periods.json b/experiments/20261113-tl4-tuning/quiet.periods.json
new file mode 100644
index 00000000..e54321fd
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/quiet.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "rows": 445, "seconds_long": 2220.0, "bearings": 424, "true": [], "true_kinds": [], "true_cut": [], "reference_flagged": false, "history_converged": true, "edited": 0, "corr_estimate": [0.14, 600.0], "ctl_corr_estimate": [0.14, 600.0, false], "histories": {"c2": {"spans": []}, "search3-d0-q0": {"spans": []}, "search3-d0-q0.01": {"spans": []}, "search3-d0-q0.1": {"spans": []}, "search3-d1-q0": {"spans": []}, "search3-d1-q0.01": {"spans": []}, "search3-d1-q0.1": {"spans": []}, "search3-d2-q0": {"spans": []}, "search3-d2-q0.01": {"spans": []}, "search3-d2-q0.1": {"spans": []}, "search3-d3-q0": {"spans": []}, "search3-d3-q0.01": {"spans": []}, "search3-d3-q0.1": {"spans": []}, "search3-d5-q0": {"spans": []}, "search3-d5-q0.01": {"spans": []}, "search3-d5-q0.1": {"spans": []}, "search3": {"spans": [], "kinds": [], "evidence": -219.67215435204537, "prior": "plain/v3", "candidates": [{"spans": [], "kinds": [], "evidence": -219.67215435204537, "prior": "plain/v3"}, {"spans": 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diff --git a/experiments/20261113-tl4-tuning/run.json b/experiments/20261113-tl4-tuning/run.json
new file mode 100644
index 00000000..6541e26e
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/run.json
@@ -0,0 +1,45 @@
+{
+ "plan": "configs/timelate/tl-e-draft.json",
+ "seed": 338,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 150
+  },
+  "shift": {
+   "start": 0,
+   "periods": 100
+  },
+  "shift2": {
+   "start": 0,
+   "periods": 100
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 150
+  },
+  "man2-corr-mild": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out-harsh": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-bias": {
+   "start": 0,
+   "periods": 60
+  }
+ },
+ "shard_size": 10,
+ "what": "spec 016 tuning: seed 338, the controls' settings (temperature, margin, quiet probability) chosen here"
+}
\ No newline at end of file
diff --git a/experiments/20261113-tl4-tuning/score.txt b/experiments/20261113-tl4-tuning/score.txt
new file mode 100644
index 00000000..b5536064
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/score.txt
@@ -0,0 +1,10 @@
+soundness {"reference_efficiency_tight": 1.1261259713549474, "band": [0.8, 1.3], "history_converged_share": 1.0, "convergence_min": 0.9, "reference_flagged_share": 0.11333333333333333, "reference_flagged_max": 0.25, "pass": true}
+man2: 150 periods, 142 scored
+shift: 100 periods, 90 scored
+shift2: 100 periods, 92 scored
+quiet: 150 periods, 138 scored
+man2-corr-mild: 60 periods, 55 scored
+man2-corr: 80 periods, 74 scored
+man2-out: 80 periods, 74 scored
+man2-out-harsh: 60 periods, 55 scored
+man2-bias: 60 periods, 55 scored
diff --git a/experiments/20261113-tl4-tuning/summary.json b/experiments/20261113-tl4-tuning/summary.json
new file mode 100644
index 00000000..63eaeb7f
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/summary.json
@@ -0,0 +1,33720 @@
+{
+ "plan": "configs/timelate/tl-e-draft.json",
+ "plan_sha256": "f5de1a348fae4d258583771eb126fd11c65db6ce5a33a7baaea907f11b40394c",
+ "scorer_git_commit": "9156bd74f408f3ab79ae9dd39191acbaa46b68e8",
+ "scorer_git_dirty": false,
+ "study": {
+  "match_tolerance_s": 120.0,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "reference_flagged_max": 0.25
+ },
+ "soundness": {
+  "reference_efficiency_tight": 1.1261259713549474,
+  "band": [
+   0.8,
+   1.3
+  ],
+  "history_converged_share": 1.0,
+  "convergence_min": 0.9,
+  "reference_flagged_share": 0.11333333333333333,
+  "reference_flagged_max": 0.25,
+  "pass": true
+ },
+ "splits": {
+  "man2": {
+   "periods": 150,
+   "periods_scored": 142,
+   "systems": {
+    "broad": {
+     "rows": 13834,
+     "rows_without_state": 0,
+     "median_rows_range": 0.03480902872979641,
+     "bound_applies": true,
+     "efficiency_range": 1.639462193506033,
+     "efficiency_course": 1.7998776439103656,
+     "efficiency_speed": 1.6485436668924296,
+     "efficiency_range_tight": 1.630413661913569,
+     "median_period_range": 0.03781091049313545,
+     "periods_without_a_track": 0
+    },
+    "broad-h1": {
+     "rows": 13834,
+     "rows_without_state": 0,
+     "median_rows_range": 0.03501522168517113,
+     "bound_applies": true,
+     "efficiency_range": 1.6124726881042912,
+     "efficiency_course": 1.8148069760216041,
+     "efficiency_speed": 1.6232166365203837,
+     "efficiency_range_tight": 1.575400658132886,
+     "median_period_range": 0.03715279884636402,
+     "periods_without_a_track": 0
+    },
+    "broad-h2": {
+     "rows": 13834,
+     "rows_without_state": 0,
+     "median_rows_range": 0.04079553484916687,
+     "bound_applies": true,
+     "efficiency_range": 1.679218642979834,
+     "efficiency_course": 1.9642704038500247,
+     "efficiency_speed": 1.7223043113219518,
+     "efficiency_range_tight": 1.6736046625698577,
+     "median_period_range": 0.04336207173764706,
+     "periods_without_a_track": 0
+    },
+    "broad-h3": {
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+   ],
+   "seed": [
+    338
+   ],
+   "search_code_sha256": [
+    "07d54a97c0841553f3d34658ad7d825f369acb370a29423fc27d491fb136acb2"
+   ],
+   "git_commit": [
+    "9156bd74f408f3ab79ae9dd39191acbaa46b68e8"
+   ],
+   "git_dirty": false,
+   "condition": [
+    "bias-central"
+   ],
+   "thinning": [
+    "none"
+   ]
+  }
+ }
+}
diff --git a/experiments/20261113-tl4-tuning/tables.md b/experiments/20261113-tl4-tuning/tables.md
new file mode 100644
index 00000000..f34b9829
--- /dev/null
+++ b/experiments/20261113-tl4-tuning/tables.md
@@ -0,0 +1,374 @@
+## Median period range error
+
+| System | Annex B | Shifted | Shifted 2 | Held course | Correlated, mild | Correlated | Outliers | Outliers, harsh | Bias |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| Periods (scored) | 150 (142) | 100 (90) | 100 (92) | 150 (138) | 60 (55) | 80 (74) | 80 (74) | 60 (55) | 60 (55) |
+| `broad` | 0.038 | 0.072 | 0.028 | 0.025 | – | – | – | – | – |
+| `c2` | 0.073 | 0.112 | 0.049 | 0.016 | 0.204 | 0.245 | 0.146 | 0.486 | 0.089 |
+| `corr` | 0.039 | – | – | – | 0.071 | 0.099 | – | – | – |
+| `ctl` | 0.031 | 0.071 | 0.027 | 0.025 | 0.062 | 0.095 | 0.034 | 0.041 | 0.064 |
+| `edit` | 0.036 | – | – | – | – | – | 0.034 | 0.035 | – |
+| `history-best` | 0.026 | 0.040 | 0.023 | 0.017 | 0.047 | 0.065 | 0.031 | 0.039 | 0.043 |
+| `history-fixed` | 0.024 | 0.042 | 0.019 | 0.017 | 0.049 | 0.065 | 0.036 | 0.061 | 0.051 |
+| `history-map` | 0.026 | 0.045 | 0.024 | 0.017 | 0.065 | 0.081 | 0.032 | 0.048 | 0.047 |
+| `mix2` | 0.032 | 0.073 | 0.027 | 0.024 | – | – | – | – | – |
+| `one-leg` | 0.306 | 0.298 | 0.323 | 0.016 | 0.391 | 0.318 | 0.282 | 0.379 | 0.404 |
+| `search3` | 0.034 | 0.083 | 0.031 | 0.025 | 0.122 | 0.124 | 0.067 | 0.099 | 0.065 |
+
+## The standing search ÷ each system (paired per period; above 1: better)
+
+### Annex B
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 0.89 [0.78, 1.01] | 0.51 |
+| `broad-h1` | 0.90 [0.79, 1.04] | 0.51 |
+| `broad-h2` | 0.86 [0.73, 0.99] | 0.53 |
+| `broad-h3` | 0.80 [0.68, 0.94] | 0.54 |
+| `broad-h5` | 0.75 [0.63, 0.88] | 0.61 |
+| `c2` | 0.46 [0.36, 0.58] | 0.68 |
+| `corr` | 0.93 [0.84, 1.02] | 0.25 |
+| `corr-h1` | 0.96 [0.86, 1.08] | 0.46 |
+| `corr-h2` | 0.92 [0.80, 1.06] | 0.49 |
+| `corr-h3` | 0.85 [0.74, 0.98] | 0.54 |
+| `corr-h5` | 0.79 [0.68, 0.93] | 0.60 |
+| `ctl` | 1.06 [1.00, 1.14] | 0.08 |
+| `ctl-h1` | 1.08 [0.98, 1.20] | 0.38 |
+| `ctl-h2` | 1.01 [0.88, 1.16] | 0.49 |
+| `ctl-h3` | 0.94 [0.82, 1.09] | 0.49 |
+| `ctl-h5` | 0.86 [0.74, 1.00] | 0.55 |
+| `edit` | 0.97 [0.92, 1.00] | 0.04 |
+| `edit-h1` | 1.00 [0.93, 1.08] | 0.42 |
+| `edit-h2` | 0.93 [0.83, 1.03] | 0.49 |
+| `edit-h3` | 0.90 [0.79, 1.01] | 0.51 |
+| `edit-h5` | 0.86 [0.75, 1.00] | 0.55 |
+| `mix2` | 1.06 [0.99, 1.14] | 0.06 |
+| `mix2-h1` | 1.08 [0.97, 1.20] | 0.38 |
+| `mix2-h2` | 1.02 [0.89, 1.17] | 0.49 |
+| `mix2-h3` | 0.95 [0.83, 1.10] | 0.48 |
+| `mix2-h5` | 0.87 [0.75, 1.01] | 0.55 |
+| `search3-h1` | 1.03 [0.97, 1.10] | 0.41 |
+| `search3-h2` | 0.95 [0.86, 1.05] | 0.48 |
+| `search3-h3` | 0.92 [0.82, 1.03] | 0.51 |
+| `search3-h5` | 0.89 [0.77, 1.03] | 0.54 |
+
+### Shifted
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.10 [0.95, 1.30] | 0.40 |
+| `broad-h1` | 1.09 [0.95, 1.27] | 0.33 |
+| `broad-h2` | 1.07 [0.94, 1.23] | 0.38 |
+| `broad-h3` | 1.07 [0.93, 1.23] | 0.36 |
+| `broad-h5` | 1.06 [0.92, 1.23] | 0.42 |
+| `c2` | 0.72 [0.57, 0.91] | 0.59 |
+| `ctl` | 1.04 [0.99, 1.11] | 0.19 |
+| `ctl-h1` | 1.09 [1.02, 1.17] | 0.31 |
+| `ctl-h2` | 1.12 [1.03, 1.23] | 0.30 |
+| `ctl-h3` | 1.16 [1.03, 1.32] | 0.33 |
+| `ctl-h5` | 1.15 [1.01, 1.33] | 0.38 |
+| `mix2` | 1.05 [1.00, 1.13] | 0.16 |
+| `mix2-h1` | 1.09 [1.02, 1.18] | 0.32 |
+| `mix2-h2` | 1.12 [1.03, 1.23] | 0.31 |
+| `mix2-h3` | 1.15 [1.02, 1.31] | 0.33 |
+| `mix2-h5` | 1.13 [1.00, 1.27] | 0.37 |
+| `search3-h1` | 1.06 [1.02, 1.11] | 0.34 |
+| `search3-h2` | 1.07 [1.02, 1.13] | 0.33 |
+| `search3-h3` | 1.11 [1.02, 1.20] | 0.32 |
+| `search3-h5` | 1.11 [1.01, 1.23] | 0.40 |
+
+### Shifted 2
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.12 [0.90, 1.37] | 0.38 |
+| `broad-h1` | 1.12 [0.89, 1.36] | 0.48 |
+| `broad-h2` | 1.04 [0.83, 1.26] | 0.47 |
+| `broad-h3` | 1.01 [0.81, 1.24] | 0.48 |
+| `broad-h5` | 1.00 [0.78, 1.23] | 0.46 |
+| `c2` | 0.58 [0.42, 0.77] | 0.67 |
+| `ctl` | 1.17 [1.03, 1.35] | 0.12 |
+| `ctl-h1` | 1.11 [0.98, 1.27] | 0.39 |
+| `ctl-h2` | 1.07 [0.92, 1.25] | 0.42 |
+| `ctl-h3` | 1.05 [0.88, 1.24] | 0.42 |
+| `ctl-h5` | 0.99 [0.82, 1.18] | 0.43 |
+| `mix2` | 1.15 [1.02, 1.31] | 0.12 |
+| `mix2-h1` | 1.10 [0.98, 1.23] | 0.38 |
+| `mix2-h2` | 1.07 [0.92, 1.23] | 0.41 |
+| `mix2-h3` | 1.06 [0.88, 1.24] | 0.42 |
+| `mix2-h5` | 0.99 [0.81, 1.17] | 0.45 |
+| `search3-h1` | 1.02 [0.95, 1.08] | 0.36 |
+| `search3-h2` | 1.03 [0.90, 1.17] | 0.37 |
+| `search3-h3` | 1.03 [0.88, 1.20] | 0.38 |
+| `search3-h5` | 0.98 [0.82, 1.17] | 0.39 |
+
+### Held course
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.05 [0.95, 1.16] | 0.25 |
+| `broad-h1` | 1.06 [0.93, 1.20] | 0.46 |
+| `broad-h2` | 1.03 [0.89, 1.17] | 0.53 |
+| `broad-h3` | 0.99 [0.86, 1.13] | 0.57 |
+| `broad-h5` | 0.95 [0.82, 1.09] | 0.57 |
+| `c2` | 1.42 [1.20, 1.69] | 0.39 |
+| `ctl` | 1.04 [0.94, 1.14] | 0.27 |
+| `ctl-h1` | 1.03 [0.90, 1.18] | 0.50 |
+| `ctl-h2` | 0.99 [0.86, 1.13] | 0.54 |
+| `ctl-h3` | 0.99 [0.85, 1.15] | 0.57 |
+| `ctl-h5` | 0.95 [0.81, 1.10] | 0.57 |
+| `mix2` | 1.06 [0.97, 1.17] | 0.23 |
+| `mix2-h1` | 1.05 [0.92, 1.20] | 0.49 |
+| `mix2-h2` | 1.01 [0.88, 1.16] | 0.54 |
+| `mix2-h3` | 1.01 [0.87, 1.17] | 0.57 |
+| `mix2-h5` | 0.97 [0.83, 1.13] | 0.57 |
+| `search3-h1` | 0.93 [0.84, 1.03] | 0.54 |
+| `search3-h2` | 0.86 [0.76, 0.96] | 0.54 |
+| `search3-h3` | 0.84 [0.73, 0.95] | 0.56 |
+| `search3-h5` | 0.82 [0.70, 0.94] | 0.62 |
+
+### Correlated, mild
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.53 [0.37, 0.75] | 0.76 |
+| `corr` | 1.55 [1.26, 1.94] | 0.27 |
+| `corr-h1` | 1.58 [1.28, 1.97] | 0.25 |
+| `corr-h2` | 1.71 [1.37, 2.17] | 0.27 |
+| `corr-h3` | 1.74 [1.38, 2.25] | 0.29 |
+| `corr-h5` | 1.55 [1.24, 1.98] | 0.40 |
+| `ctl` | 1.58 [1.25, 2.08] | 0.29 |
+| `ctl-h1` | 1.62 [1.31, 2.03] | 0.22 |
+| `ctl-h2` | 1.72 [1.37, 2.24] | 0.31 |
+| `ctl-h3` | 1.70 [1.33, 2.25] | 0.33 |
+| `ctl-h5` | 1.52 [1.18, 1.99] | 0.38 |
+| `search3-h1` | 1.06 [1.01, 1.10] | 0.45 |
+| `search3-h2` | 1.11 [1.00, 1.23] | 0.40 |
+| `search3-h3` | 1.12 [0.97, 1.28] | 0.38 |
+| `search3-h5` | 1.12 [0.93, 1.33] | 0.38 |
+
+### Correlated
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.58 [0.43, 0.77] | 0.70 |
+| `corr` | 1.35 [1.08, 1.70] | 0.34 |
+| `corr-h1` | 1.41 [1.14, 1.73] | 0.34 |
+| `corr-h2` | 1.52 [1.24, 1.89] | 0.31 |
+| `corr-h3` | 1.54 [1.24, 1.92] | 0.35 |
+| `corr-h5` | 1.50 [1.20, 1.87] | 0.34 |
+| `ctl` | 1.34 [1.08, 1.67] | 0.35 |
+| `ctl-h1` | 1.40 [1.14, 1.72] | 0.35 |
+| `ctl-h2` | 1.51 [1.20, 1.89] | 0.35 |
+| `ctl-h3` | 1.55 [1.23, 1.97] | 0.34 |
+| `ctl-h5` | 1.52 [1.21, 1.90] | 0.32 |
+| `search3-h1` | 0.99 [0.88, 1.10] | 0.42 |
+| `search3-h2` | 1.00 [0.88, 1.12] | 0.43 |
+| `search3-h3` | 0.99 [0.87, 1.12] | 0.42 |
+| `search3-h5` | 0.98 [0.86, 1.11] | 0.45 |
+
+### Outliers
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.39 [0.27, 0.56] | 0.73 |
+| `ctl` | 1.57 [1.20, 2.09] | 0.35 |
+| `ctl-h1` | 1.72 [1.26, 2.39] | 0.31 |
+| `ctl-h2` | 1.53 [1.16, 2.06] | 0.34 |
+| `ctl-h3` | 1.48 [1.12, 1.98] | 0.34 |
+| `ctl-h5` | 1.36 [1.03, 1.78] | 0.36 |
+| `edit` | 1.51 [1.16, 2.03] | 0.36 |
+| `edit-h1` | 1.72 [1.26, 2.35] | 0.35 |
+| `edit-h2` | 1.55 [1.17, 2.11] | 0.35 |
+| `edit-h3` | 1.53 [1.15, 2.05] | 0.38 |
+| `edit-h5` | 1.39 [1.04, 1.85] | 0.39 |
+| `search3-h1` | 0.97 [0.83, 1.10] | 0.45 |
+| `search3-h2` | 0.95 [0.80, 1.11] | 0.42 |
+| `search3-h3` | 0.98 [0.80, 1.17] | 0.39 |
+| `search3-h5` | 0.96 [0.77, 1.19] | 0.46 |
+
+### Outliers, harsh
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.29 [0.20, 0.41] | 0.82 |
+| `ctl` | 2.92 [1.99, 4.27] | 0.20 |
+| `ctl-h1` | 3.06 [2.13, 4.41] | 0.18 |
+| `ctl-h2` | 3.51 [2.43, 5.11] | 0.15 |
+| `ctl-h3` | 3.45 [2.48, 4.73] | 0.13 |
+| `ctl-h5` | 3.17 [2.33, 4.27] | 0.16 |
+| `edit` | 3.11 [2.18, 4.58] | 0.20 |
+| `edit-h1` | 3.10 [2.17, 4.49] | 0.20 |
+| `edit-h2` | 3.24 [2.26, 4.71] | 0.20 |
+| `edit-h3` | 3.34 [2.35, 4.72] | 0.18 |
+| `edit-h5` | 3.00 [2.21, 4.03] | 0.13 |
+| `search3-h1` | 1.12 [1.04, 1.21] | 0.36 |
+| `search3-h2` | 1.17 [1.06, 1.31] | 0.45 |
+| `search3-h3` | 1.20 [1.06, 1.39] | 0.38 |
+| `search3-h5` | 1.29 [1.06, 1.62] | 0.42 |
+
+### Bias
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.60 [0.40, 0.93] | 0.75 |
+| `ctl` | 1.00 [0.89, 1.13] | 0.15 |
+| `ctl-h1` | 1.03 [0.91, 1.18] | 0.42 |
+| `ctl-h2` | 1.10 [0.89, 1.38] | 0.42 |
+| `ctl-h3` | 1.10 [0.86, 1.39] | 0.42 |
+| `ctl-h5` | 1.08 [0.83, 1.39] | 0.40 |
+| `search3-h1` | 0.98 [0.90, 1.05] | 0.44 |
+| `search3-h2` | 1.00 [0.85, 1.16] | 0.38 |
+| `search3-h3` | 1.00 [0.81, 1.19] | 0.42 |
+| `search3-h5` | 1.01 [0.79, 1.25] | 0.47 |
+
+## Share of the gap from the search to the reference that a control closes
+
+| Split | Control | Share |
+| --- | --- | --- |
+| Annex B | `broad` | -0.59 [-5.31, 0.93] |
+| Annex B | `corr` | -0.36 [-3.36, 0.37] |
+| Annex B | `ctl` | 0.32 [-0.34, 1.72] |
+| Annex B | `edit` | -0.16 [-1.34, 0.08] |
+| Annex B | `mix2` | 0.29 [-0.32, 1.45] |
+| Shifted | `broad` | 0.13 [-0.07, 0.37] |
+| Shifted | `ctl` | 0.06 [-0.01, 0.15] |
+| Shifted | `mix2` | 0.06 [-0.00, 0.16] |
+| Shifted 2 | `broad` | 0.25 [-0.29, 0.82] |
+| Shifted 2 | `ctl` | 0.36 [0.08, 0.86] |
+| Shifted 2 | `mix2` | 0.31 [0.04, 0.79] |
+| Held course | `broad` | 0.12 [-0.16, 0.34] |
+| Held course | `ctl` | 0.09 [-0.19, 0.30] |
+| Held course | `mix2` | 0.15 [-0.09, 0.37] |
+| Correlated, mild | `corr` | 0.56 [0.29, 0.94] |
+| Correlated, mild | `ctl` | 0.58 [0.27, 1.06] |
+| Correlated | `corr` | 0.55 [0.16, 1.01] |
+| Correlated | `ctl` | 0.53 [0.15, 0.99] |
+| Outliers | `ctl` | 0.75 [0.34, 1.36] |
+| Outliers | `edit` | 0.68 [0.27, 1.27] |
+| Outliers, harsh | `ctl` | 1.24 [0.82, 1.85] |
+| Outliers, harsh | `edit` | 1.31 [0.89, 1.93] |
+| Bias | `ctl` | 0.00 [-1.14, 1.29] |
+
+## System ÷ the true-history reference, by how the reference is fitted
+
+| Split | System | times free | held where drifted | times held |
+| --- | --- | --- | --- | --- |
+| Annex B | `search3` | 1.18 [0.98, 1.43] | 1.21 [1.00, 1.47] | 1.39 [1.12, 1.73] |
+| Annex B | `ctl` | 1.11 [0.92, 1.33] | 1.14 [0.94, 1.38] | 1.31 [1.06, 1.62] |
+| Shifted | `search3` | 1.92 [1.55, 2.43] | 2.14 [1.70, 2.71] | 2.57 [1.99, 3.34] |
+| Shifted | `ctl` | 1.84 [1.49, 2.35] | 2.05 [1.63, 2.58] | 2.46 [1.95, 3.17] |
+| Shifted 2 | `search3` | 1.43 [1.08, 1.82] | 1.55 [1.20, 1.99] | 2.16 [1.57, 2.87] |
+| Shifted 2 | `ctl` | 1.22 [0.94, 1.53] | 1.33 [1.03, 1.68] | 1.84 [1.37, 2.39] |
+| Held course | `search3` | 1.49 [1.27, 1.74] | 1.49 [1.27, 1.74] | 1.48 [1.27, 1.74] |
+| Held course | `ctl` | 1.43 [1.24, 1.67] | 1.43 [1.24, 1.67] | 1.43 [1.24, 1.66] |
+| Correlated, mild | `search3` | 1.72 [1.28, 2.30] | 2.18 [1.63, 2.95] | 2.62 [1.85, 3.82] |
+| Correlated, mild | `ctl` | 1.09 [0.80, 1.52] | 1.38 [0.98, 2.00] | 1.66 [1.13, 2.53] |
+| Correlated | `search3` | 1.56 [1.22, 2.01] | 1.74 [1.32, 2.29] | 1.97 [1.52, 2.59] |
+| Correlated | `ctl` | 1.16 [0.89, 1.55] | 1.30 [1.00, 1.72] | 1.47 [1.16, 1.93] |
+| Outliers | `search3` | 1.71 [1.31, 2.20] | 1.83 [1.40, 2.37] | 1.85 [1.34, 2.56] |
+| Outliers | `ctl` | 1.09 [0.79, 1.48] | 1.17 [0.85, 1.59] | 1.18 [0.85, 1.62] |
+| Outliers, harsh | `search3` | 2.03 [1.54, 2.63] | 2.38 [1.73, 3.18] | 2.60 [1.83, 3.69] |
+| Outliers, harsh | `ctl` | 0.69 [0.48, 1.02] | 0.81 [0.57, 1.18] | 0.89 [0.63, 1.30] |
+| Bias | `search3` | 1.26 [0.83, 1.79] | 1.32 [0.86, 1.86] | 1.21 [0.82, 1.74] |
+| Bias | `ctl` | 1.26 [0.83, 1.79] | 1.32 [0.86, 1.87] | 1.21 [0.81, 1.76] |
+
+## Manoeuvres missed (share) and false (per period), matched within 120 s
+
+| Split | System | Missed | False |
+| --- | --- | --- | --- |
+| Annex B | `broad` | 0.26 | 0.27 |
+| Annex B | `c2` | 0.58 | 0.82 |
+| Annex B | `corr` | 0.26 | 0.23 |
+| Annex B | `ctl` | 0.22 | 0.21 |
+| Annex B | `edit` | 0.23 | 0.25 |
+| Annex B | `mix2` | 0.21 | 0.20 |
+| Annex B | `search3` | 0.22 | 0.23 |
+| Shifted | `broad` | 0.53 | 0.36 |
+| Shifted | `c2` | 0.71 | 0.74 |
+| Shifted | `ctl` | 0.54 | 0.41 |
+| Shifted | `mix2` | 0.53 | 0.39 |
+| Shifted | `search3` | 0.54 | 0.41 |
+| Shifted 2 | `broad` | 0.17 | 0.18 |
+| Shifted 2 | `c2` | 0.50 | 0.63 |
+| Shifted 2 | `ctl` | 0.16 | 0.20 |
+| Shifted 2 | `mix2` | 0.15 | 0.19 |
+| Shifted 2 | `search3` | 0.20 | 0.30 |
+| Held course | `broad` | – | 0.40 |
+| Held course | `c2` | – | 0.07 |
+| Held course | `ctl` | – | 0.43 |
+| Held course | `mix2` | – | 0.42 |
+| Held course | `search3` | – | 0.49 |
+| Correlated, mild | `c2` | 0.60 | 2.50 |
+| Correlated, mild | `corr` | 0.41 | 0.42 |
+| Correlated, mild | `ctl` | 0.41 | 0.40 |
+| Correlated, mild | `search3` | 0.42 | 1.12 |
+| Correlated | `c2` | 0.65 | 2.48 |
+| Correlated | `corr` | 0.50 | 0.50 |
+| Correlated | `ctl` | 0.49 | 0.47 |
+| Correlated | `search3` | 0.42 | 1.50 |
+| Outliers | `c2` | 0.54 | 1.96 |
+| Outliers | `ctl` | 0.26 | 0.30 |
+| Outliers | `edit` | 0.26 | 0.29 |
+| Outliers | `search3` | 0.32 | 0.59 |
+| Outliers, harsh | `c2` | 0.61 | 3.47 |
+| Outliers, harsh | `ctl` | 0.25 | 0.32 |
+| Outliers, harsh | `edit` | 0.23 | 0.30 |
+| Outliers, harsh | `search3` | 0.37 | 0.95 |
+| Bias | `c2` | 0.62 | 1.12 |
+| Bias | `ctl` | 0.25 | 0.33 |
+| Bias | `search3` | 0.22 | 0.30 |
+
+## Declaration: held-course periods given a manoeuvre, and Annex B targets called quiet
+
+| System | Held course: false | One manoeuvre: called quiet | Two or more: called quiet | Annex B median | Held-course median |
+| --- | --- | --- | --- | --- | --- |
+| `broad` | 0.38 | 0.09 | 0.00 | 0.038 | 0.025 |
+| `c2` | 0.05 | 0.18 | 0.00 | 0.073 | 0.016 |
+| `ctl` | 0.41 | 0.09 | 0.00 | 0.031 | 0.025 |
+| `ctl-d0-q0` | 0.41 | 0.09 | 0.00 | 0.031 | 0.025 |
+| `ctl-d0-q0.01` | 0.13 | 0.09 | 0.00 | 0.031 | 0.018 |
+| `ctl-d0-q0.1` | 0.08 | 0.10 | 0.00 | 0.031 | 0.017 |
+| `ctl-d1-q0` | 0.30 | 0.10 | 0.00 | 0.031 | 0.023 |
+| `ctl-d1-q0.01` | 0.07 | 0.10 | 0.00 | 0.031 | 0.017 |
+| `ctl-d1-q0.1` | 0.03 | 0.13 | 0.00 | 0.031 | 0.017 |
+| `ctl-d2-q0` | 0.20 | 0.12 | 0.00 | 0.031 | 0.022 |
+| `ctl-d2-q0.01` | 0.02 | 0.12 | 0.00 | 0.031 | 0.017 |
+| `ctl-d2-q0.1` | 0.01 | 0.16 | 0.00 | 0.032 | 0.016 |
+| `ctl-d3-q0` | 0.17 | 0.15 | 0.00 | 0.032 | 0.022 |
+| `ctl-d3-q0.01` | 0.01 | 0.15 | 0.00 | 0.032 | 0.016 |
+| `ctl-d3-q0.1` | 0.01 | 0.18 | 0.00 | 0.032 | 0.016 |
+| `ctl-d5-q0` | 0.09 | 0.19 | 0.00 | 0.036 | 0.020 |
+| `ctl-d5-q0.01` | 0.01 | 0.19 | 0.00 | 0.036 | 0.016 |
+| `ctl-d5-q0.1` | 0.01 | 0.24 | 0.01 | 0.038 | 0.016 |
+| `mix2` | 0.40 | 0.07 | 0.00 | 0.032 | 0.024 |
+| `search3` | 0.45 | 0.09 | 0.00 | 0.034 | 0.025 |
+| `search3-d0-q0` | 0.45 | 0.09 | 0.00 | 0.034 | 0.025 |
+| `search3-d0-q0.01` | 0.12 | 0.09 | 0.00 | 0.034 | 0.018 |
+| `search3-d0-q0.1` | 0.07 | 0.10 | 0.00 | 0.034 | 0.018 |
+| `search3-d1-q0` | 0.34 | 0.10 | 0.00 | 0.034 | 0.025 |
+| `search3-d1-q0.01` | 0.08 | 0.10 | 0.00 | 0.034 | 0.019 |
+| `search3-d1-q0.1` | 0.05 | 0.10 | 0.00 | 0.034 | 0.018 |
+| `search3-d2-q0` | 0.28 | 0.10 | 0.00 | 0.034 | 0.024 |
+| `search3-d2-q0.01` | 0.05 | 0.10 | 0.00 | 0.034 | 0.019 |
+| `search3-d2-q0.1` | 0.04 | 0.13 | 0.00 | 0.036 | 0.018 |
+| `search3-d3-q0` | 0.21 | 0.12 | 0.00 | 0.036 | 0.024 |
+| `search3-d3-q0.01` | 0.04 | 0.12 | 0.00 | 0.036 | 0.018 |
+| `search3-d3-q0.1` | 0.03 | 0.15 | 0.00 | 0.036 | 0.018 |
+| `search3-d5-q0` | 0.15 | 0.18 | 0.00 | 0.039 | 0.024 |
+| `search3-d5-q0.01` | 0.01 | 0.19 | 0.00 | 0.039 | 0.017 |
+| `search3-d5-q0.1` | 0.01 | 0.22 | 0.00 | 0.039 | 0.017 |
+
+## What the estimates said
+
+| Split | References held | Wild bearings edited a period | Correlated share (median) | Correlation used by `ctl` | Bayes factor for the broad prior (median log) | Share above 0 |
+| --- | --- | --- | --- | --- | --- | --- |
+| Annex B | 0.11 | 0.1 | 0.00 | 0.05 | -3.5 | 0.29 |
+| Shifted | 0.31 | 0.1 | 0.00 | 0.05 | -0.6 | 0.45 |
+| Shifted 2 | 0.06 | 0.1 | 0.00 | 0.05 | -2.4 | 0.43 |
+| Held course | 0.00 | 0.1 | 0.00 | 0.04 | 4.4 | 0.95 |
+| Correlated, mild | 0.23 | 0.0 | 0.52 | 1.00 | -1.8 | 0.37 |
+| Correlated | 0.39 | 0.0 | 0.52 | 1.00 | -1.0 | 0.38 |
+| Outliers | 0.21 | 7.1 | 0.00 | 0.05 | -2.9 | 0.30 |
+| Outliers, harsh | 0.17 | 18.9 | 0.00 | 0.05 | -3.0 | 0.28 |
+| Bias | 0.20 | 0.1 | 0.00 | 0.03 | -3.5 | 0.30 |
diff --git a/experiments/20261113-tl4-val/README.md b/experiments/20261113-tl4-val/README.md
new file mode 100644
index 00000000..c814b33d
--- /dev/null
+++ b/experiments/20261113-tl4-val/README.md
@@ -0,0 +1,51 @@
+# Spec 016 (study L): the classical controls on validation seed 340 (2026-11-13)
+
+CI run 43507973444 (`timelate/tl-e-val`, commit 2c191b2, clean tree, plan
+`configs/timelate/tl-e.json` `82cd70ee…` as registered). **Controls only: no learned track
+was scored** (`learned_scored: false` in `summary.json`). Every table is in `tables.md`,
+generated by `scripts/research/tl4_tables.py`. Per-row errors: results branch
+`results/timelate-tl-e-val-43507973444`. This record is what the scorer requires before a
+learned track may be scored (FR-014).
+
+## L-a: passes
+
+The true history with its times held is at 1.06 of the bound on tight rows (band 0.8 to
+1.3); every true-history fit converged; 0.11 of references are held (limit 0.25). The study
+goes on to the test seed.
+
+## L-b on validation (reported; decided on the test seed)
+
+Standing search ÷ declared control (`ctl-d1-q0.01`), paired per period:
+
+| Split (scored) | Search | Control | Ratio | Gap to reference closed by `ctl` | Forecast | Held? |
+| --- | --- | --- | --- | --- | --- | --- |
+| Outliers (75) | 0.089 | 0.040 | 1.77 [1.29, 2.44] | 1.17 [0.58, 2.75] | about 1.5; over 0.6 | Yes |
+| Outliers, harsh (57) | 0.158 | 0.044 | 3.11 [2.08, 4.53] | 1.63 [1.14, 2.54] | – | – |
+| Correlated (75) | 0.178 | 0.095 | 1.79 [1.37, 2.34] | 0.52 [0.31, 0.76] | about 1.3; about 0.5 | Ratio higher; share yes |
+| Correlated, mild (57) | 0.128 | 0.101 | 1.43 [1.10, 1.88] | 0.46 [0.15, 0.77] | – | – |
+| Shifted (92) | 0.109 | 0.092 | 0.98 [0.90, 1.07] | −0.01 [−0.14, 0.10] | 1.0 to 1.1; under 0.2 | Yes, at the low end |
+| Shifted 2 (97) | 0.029 | 0.028 | 1.05 [0.99, 1.14] | 0.26 [−0.10, 1.19] | 1.1 to 1.2 | Lower |
+| Bias (75) | 0.075 | 0.073 | 0.99 [0.90, 1.07] | −0.03 [−0.27, 0.16] | – | – |
+| Annex B (141) | 0.040 | 0.040 | 0.96 [0.90, 1.02] | −0.06 [−0.38, 0.15] | – | – |
+| Held course (92) | 0.028 | 0.016 | 1.75 [1.37, 2.28] | 0.21 [−0.18, 0.44] | – | – |
+
+- The controls do what tuning showed for outliers and correlated errors, and for held
+  course (false manoeuvres 0.07 against 0.52 for the search).
+- **Under a shifted prior the control gains nothing** (0.98 and 1.05); the control is 2.1
+  times the reference on the first shifted split. Tuning suggested a little more.
+- **On Annex B data the declared control is not better than the search** (0.96; tuning
+  said 1.06), and it calls a one-manoeuvre target quiet in 0.18 of cases against 0.12:
+  the cost of declaring cautiously, as registered.
+- With 5% outliers the control's track is better than the true-history reference (0.64):
+  the reference is fitted to the bearings as given, outliers included.
+- The hedged track against the declared one: 1.07 [1.01, 1.15] better on Annex B, 0.64
+  worse on held course (it averages in the invented manoeuvres the declaration removes),
+  not separated elsewhere.
+
+## Weaknesses
+
+- 57 to 141 scored periods a split; a validation reading, not a result.
+- References are held in 18% to 37% of periods on the shifted, correlated, outlier and
+  bias splits; the gap-closed shares there read against a reference told the times.
+- The hedged track is formed from the undeclared candidates; on quiet targets that is its
+  weakness and it is registered so.
diff --git a/experiments/20261113-tl4-val/ci.json b/experiments/20261113-tl4-val/ci.json
new file mode 100644
index 00000000..2c347d16
--- /dev/null
+++ b/experiments/20261113-tl4-val/ci.json
@@ -0,0 +1 @@
+{"run_id": "43507973444", "commit": "2c191b29fe731afecf75dc4e034e45105e96f496", "cpu_model": "AMD EPYC 7763 64-Core Processor"}
diff --git a/experiments/20261113-tl4-val/man2.periods.json b/experiments/20261113-tl4-val/man2.periods.json
new file mode 100644
index 00000000..2989f9fe
--- /dev/null
+++ b/experiments/20261113-tl4-val/man2.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "rows": 618, "seconds_long": 3085.0, "bearings": 585, "true": [[1280.0, 1325.0]], "true_kinds": [[true, false]], "true_cut": [false], "reference_flagged": false, "history_converged": true, "edited": 0, "corr_estimate": [0.0, 0.0], "ctl_corr_estimate": [0.0, 0.0, false], "histories": {"c2": {"spans": [[445.0, 585.0], [1585.0, 1925.0]]}, "search3-d1-q0.01": {"spans": [[456.9631254830058, 627.1042974711556], [1575.567642521282, 1614.164164632674]]}, "search3": {"spans": [[456.9631254830058, 627.1042974711556], [1575.567642521282, 1614.164164632674]], "kinds": [[true, false], [true, false]], "evidence": -349.42185161944036, "prior": "plain/v3", "candidates": [{"spans": [[456.9631254830058, 627.1042974711556], [1575.567642521282, 1614.164164632674]], "kinds": [[true, false], [true, false]], "evidence": -349.42185161944036, "prior": "plain/v3"}, {"spans": [[1270.91330296153, 1409.0754355639833]], "kinds": [[true, false]], "evidence": -349.810973529943, "prior": "plain/v3"}, {"spans": [[380.66870726558466, 638.3677702838966], [1585.715862244943, 1632.4550025986541], [2931.1941280969713, 3084.9943240804582]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -350.4610718784382, "prior": "plain/v3"}, {"spans": [[1158.3840580405347, 1800.178877634147]], "kinds": [[false, true]], "evidence": -350.48750717196475, "prior": "plain/v3"}, {"spans": [[1332.1766398110449, 1419.8835674236047], [2043.3131900850342, 2107.517728615044]], "kinds": [[true, false], [false, true]], "evidence": -351.941483806597, "prior": "plain/v3"}], "path": [{"spans": [[456.9631254830058, 627.1042974711556], [1575.567642521282, 1614.164164632674]], "kinds": [[true, false], [true, false]], "evidence": -349.42185161944036}, {"spans": [[527.3033236289598, 593.454592844161]], "kinds": [[true, false]], "evidence": -365.7938732882924}, {"spans": [], "kinds": [], "evidence": -466.7212354458067}], "declared": {"d1-q0.01": 0}}, "edit": {"spans": [[456.9631254830058, 627.1042974711556], [1575.567642521282, 1614.164164632674]], "kinds": [[true, false], [true, false]], "evidence": -349.42185161944036, "prior": "plain/v3", "candidates": [{"spans": [[456.9631254830058, 627.1042974711556], [1575.567642521282, 1614.164164632674]], "kinds": [[true, false], [true, false]], "evidence": -349.42185161944036, "prior": "plain/v3"}, {"spans": [[1270.91330296153, 1409.0754355639833]], "kinds": [[true, false]], "evidence": -349.810973529943, "prior": "plain/v3"}, {"spans": [[380.66870726558466, 638.3677702838966], [1585.715862244943, 1632.4550025986541], [2931.1941280969713, 3084.9943240804582]], "kinds": [[true, false], [true, false], [true, false]], "evidence": -350.4610718784382, "prior": "plain/v3"}, {"spans": [[1158.3840580405347, 1800.178877634147]], "kinds": [[false, true]], "evidence": -350.48750717196475, "prior": "plain/v3"}, {"spans": [[1332.1766398110449, 1419.8835674236047], [2043.3131900850342, 2107.517728615044]], "kinds": [[true, false], [false, 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-204.2853637806627, "prior": "corr/v3", "candidates": [{"spans": [], "kinds": [], "evidence": -204.2853637806627, "prior": "corr/v3"}, {"spans": [[518.2510750902745, 620.8589281042384]], "kinds": [[true, false]], "evidence": -205.94354779498516, "prior": "corr/v3"}, {"spans": [[46.40940129732646, 166.7667790771947]], "kinds": [[true, false]], "evidence": -206.47277494852779, "prior": "corr/v3"}, {"spans": [[820.3246074921186, 922.7728618308793]], "kinds": [[false, true]], "evidence": -208.2063429097371, "prior": "corr/v3"}, {"spans": [[1618.2808228270217, 1638.303947943126]], "kinds": [[false, true]], "evidence": -210.18222067046108, "prior": "corr/v3"}]}, "broad": {"spans": [], "kinds": [], "evidence": -225.73600821248908, "prior": "plain/broad", "candidates": [{"spans": [], "kinds": [], "evidence": -225.73600821248908, "prior": "plain/broad"}, {"spans": [[890.7694376179559, 902.0952570575737]], "kinds": [[false, true]], "evidence": -227.62316593694803, "prior": "plain/broad"}, {"spans": [[377.815661245937, 748.6029833068468]], "kinds": [[true, false]], "evidence": -227.66950184238863, "prior": "plain/broad"}, {"spans": [[644.3008474428435, 726.0286142456022]], "kinds": [[true, true]], "evidence": -228.5955540601441, "prior": "plain/broad"}, {"spans": [[38.24213116642055, 138.5395446894139]], "kinds": [[true, false]], "evidence": -229.3936402675294, "prior": "plain/broad"}]}, "mix2": {"spans": [], "kinds": [], "evidence": -225.73600821248908, "prior": "plain/broad", "candidates": [{"spans": [], "kinds": [], "evidence": -225.73600821248908, "prior": "plain/broad"}, {"spans": [[890.7694376179559, 902.0952570575737]], "kinds": [[false, true]], "evidence": -227.62316593694803, "prior": "plain/broad"}, {"spans": [[377.815661245937, 748.6029833068468]], "kinds": [[true, false]], "evidence": -227.66950184238863, "prior": "plain/broad"}, {"spans": [[644.3008474428435, 726.0286142456022]], "kinds": [[true, true]], "evidence": -228.5955540601441, "prior": "plain/broad"}, {"spans": [[44.411995109971365, 164.77225432894843]], "kinds": [[true, false]], "evidence": -229.07108167593088, "prior": "plain/v3"}], "log_bayes_broad": 3.3350734634418018}, "ctl-d1-q0.01": {"spans": []}, "ctl": {"spans": [], "kinds": [], "evidence": -225.73600821248908, "prior": "plain/broad", "candidates": [{"spans": [], "kinds": [], "evidence": -225.73600821248908, "prior": "plain/broad"}, {"spans": [[890.7694376179559, 902.0952570575737]], "kinds": [[false, true]], "evidence": -227.62316593694803, "prior": "plain/broad"}, {"spans": [[377.815661245937, 748.6029833068468]], "kinds": [[true, false]], "evidence": -227.66950184238863, "prior": "plain/broad"}, {"spans": [[644.3008474428435, 726.0286142456022]], "kinds": [[true, true]], "evidence": -228.5955540601441, "prior": "plain/broad"}, {"spans": [[44.411995109971365, 164.77225432894843]], "kinds": [[true, false]], "evidence": -229.07108167593088, "prior": "plain/v3"}], "log_bayes_broad": 3.3350734634418018, "path": [{"spans": [], "kinds": [], "evidence": -225.73600821248908}], "declared": {"d1-q0.01": 0}}}, "seconds": {"reference": 0.48, "plain/v3": 2.29, "search3/path": 0.0, "corr/v3": 2.26, "plain/broad": 2.25, "ctl/path": 0.0}}]
diff --git a/experiments/20261113-tl4-val/plan.json b/experiments/20261113-tl4-val/plan.json
new file mode 100644
index 00000000..06253233
--- /dev/null
+++ b/experiments/20261113-tl4-val/plan.json
@@ -0,0 +1,284 @@
+{
+ "description": "Spec 016's registered study (2026-11-13): the classical controls (settings from tuning seed 338) and the learned whole-period model's three seeds, scored on the same periods. Bound by digest in the decision record; never edited after.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics, independent Gaussian errors",
+   "systems": [
+    "search3",
+    "edit",
+    "corr",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs, smaller turns, faster turn rates, more speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift2": {
+   "ranges": "configs/timelate/tl-shift2-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "longer legs (mean 35 min), larger and slower turns, few speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "man2-corr-mild": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 30 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-mild"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 60 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "2% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-central"
+  },
+  "man2-out-harsh": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "5% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-harsh"
+  },
+  "man2-bias": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "a slowly varying bias (0.5 degree, 30 min): addressed by no control and absent from the model's training",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "condition": "bias-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B engagements uncut",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "whole": true
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "prior_broad": "configs/timelate/tl-broad.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 10,
+  "score_every": 6,
+  "temperatures": [
+   1
+  ],
+  "margins": [
+   1
+  ],
+  "quiets": [
+   0.01
+  ],
+  "store_tracks": 20
+ },
+ "runner": "run4",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "match_tolerance_s": 120.0,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "reference_flagged_max": 0.25
+ },
+ "comparisons": [
+  [
+   "ctl-d1-q0.01",
+   "learned-s1-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s1-arc"
+  ],
+  [
+   "search3",
+   "learned-s1-arc"
+  ],
+  [
+   "c2",
+   "learned-s1-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s2-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s2-arc"
+  ],
+  [
+   "search3",
+   "learned-s2-arc"
+  ],
+  [
+   "c2",
+   "learned-s2-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s3-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s3-arc"
+  ],
+  [
+   "search3",
+   "learned-s3-arc"
+  ],
+  [
+   "c2",
+   "learned-s3-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s1"
+  ],
+  [
+   "ctl-h1",
+   "learned-s1"
+  ],
+  [
+   "search3",
+   "learned-s1"
+  ],
+  [
+   "c2",
+   "learned-s1"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s2"
+  ],
+  [
+   "ctl-h1",
+   "learned-s2"
+  ],
+  [
+   "search3",
+   "learned-s2"
+  ],
+  [
+   "c2",
+   "learned-s2"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s3"
+  ],
+  [
+   "ctl-h1",
+   "learned-s3"
+  ],
+  [
+   "search3",
+   "learned-s3"
+  ],
+  [
+   "c2",
+   "learned-s3"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "ctl-h1"
+  ],
+  [
+   "search3",
+   "ctl-d1-q0.01"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "c2"
+  ]
+ ],
+ "criteria": {
+  "control": "ctl-d1-q0.01",
+  "hedged": "ctl-h1",
+  "learned": [
+   "learned-s1",
+   "learned-s2",
+   "learned-s3"
+  ],
+  "mismatch_splits": [
+   "shift",
+   "shift2",
+   "man2-corr",
+   "man2-out",
+   "man2-bias"
+  ],
+  "no_harm_splits": [
+   "man2",
+   "quiet"
+  ]
+ },
+ "learned_plans": {
+  "learned-s1": "configs/regress/tlm-s1.json",
+  "learned-s2": "configs/regress/tlm-s2.json",
+  "learned-s3": "configs/regress/tlm-s3.json"
+ }
+}
\ No newline at end of file
diff --git a/experiments/20261113-tl4-val/quiet.periods.json b/experiments/20261113-tl4-val/quiet.periods.json
new file mode 100644
index 00000000..29eae5c7
--- /dev/null
+++ b/experiments/20261113-tl4-val/quiet.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "rows": 618, "seconds_long": 3085.0, "bearings": 585, "true": [], "true_kinds": [], "true_cut": [], "reference_flagged": false, "history_converged": true, "edited": 0, "corr_estimate": [0.0, 0.0], "ctl_corr_estimate": [0.0, 0.0, false], "histories": {"c2": {"spans": []}, "search3-d1-q0.01": {"spans": []}, "search3": {"spans": [[1677.122308123949, 1823.8069938294277]], "kinds": [[false, true]], "evidence": -342.62093873537884, "prior": "plain/v3", "candidates": [{"spans": [[1677.122308123949, 1823.8069938294277]], "kinds": [[false, true]], "evidence": -342.62093873537884, "prior": "plain/v3"}, {"spans": [[2605.3124327162236, 2641.9952023032934]], "kinds": [[false, true]], "evidence": -344.43386393085024, "prior": "plain/v3"}, {"spans": [], "kinds": [], "evidence": -346.0442017227196, "prior": "plain/v3"}, {"spans": [[1573.003655980025, 1583.003655980025]], "kinds": [[true, false]], "evidence": -347.77318096184933, "prior": "plain/v3"}, {"spans": [[975.7155517667865, 986.784179635326]], "kinds": [[true, false]], "evidence": -349.2721129312324, "prior": "plain/v3"}], "path": [{"spans": [[1677.122308123949, 1823.8069938294277]], "kinds": [[false, true]], "evidence": -342.62093873537884}, {"spans": [], "kinds": [], "evidence": -346.0442119959748}], "declared": {"d1-q0.01": 1}}, "broad": {"spans": [[2184.9497186688336, 3084.9497186688336]], "kinds": [[false, true]], "evidence": -342.22857614268554, "prior": "plain/broad", "candidates": [{"spans": [[2184.9497186688336, 3084.9497186688336]], "kinds": [[false, true]], "evidence": -342.22857614268554, "prior": "plain/broad"}, {"spans": [[2761.4760293653026, 3084.9325816224123]], "kinds": [[false, true]], "evidence": -342.3762611946907, "prior": "plain/broad"}, {"spans": [[1088.8959839142042, 1325.5158520074083]], "kinds": [[false, true]], "evidence": -342.70584062661277, "prior": "plain/broad"}, {"spans": [], "kinds": [], "evidence": -343.17460444359835, "prior": "plain/broad"}, {"spans": 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diff --git a/experiments/20261113-tl4-val/run.json b/experiments/20261113-tl4-val/run.json
new file mode 100644
index 00000000..dc18c066
--- /dev/null
+++ b/experiments/20261113-tl4-val/run.json
@@ -0,0 +1,45 @@
+{
+ "plan": "configs/timelate/tl-e.json",
+ "seed": 340,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 150
+  },
+  "shift": {
+   "start": 0,
+   "periods": 100
+  },
+  "shift2": {
+   "start": 0,
+   "periods": 100
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 100
+  },
+  "man2-corr-mild": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 80
+  },
+  "man2-out-harsh": {
+   "start": 0,
+   "periods": 60
+  },
+  "man2-bias": {
+   "start": 0,
+   "periods": 80
+  }
+ },
+ "shard_size": 10,
+ "what": "spec 016 validation: seed 340, the classical controls only (L-a); the learned model is not scored here"
+}
\ No newline at end of file
diff --git a/experiments/20261113-tl4-val/score.txt b/experiments/20261113-tl4-val/score.txt
new file mode 100644
index 00000000..0d9009ce
--- /dev/null
+++ b/experiments/20261113-tl4-val/score.txt
@@ -0,0 +1,10 @@
+soundness {"reference_efficiency_tight": 1.0629659714183626, "band": [0.8, 1.3], "history_converged_share": 1.0, "convergence_min": 0.9, "reference_flagged_share": 0.11333333333333333, "reference_flagged_max": 0.25, "pass": true}
+man2: 150 periods, 141 scored
+shift: 100 periods, 92 scored
+shift2: 100 periods, 97 scored
+quiet: 100 periods, 92 scored
+man2-corr-mild: 60 periods, 57 scored
+man2-corr: 80 periods, 75 scored
+man2-out: 80 periods, 75 scored
+man2-out-harsh: 60 periods, 57 scored
+man2-bias: 80 periods, 75 scored
diff --git a/experiments/20261113-tl4-val/summary.json b/experiments/20261113-tl4-val/summary.json
new file mode 100644
index 00000000..15eef8c2
--- /dev/null
+++ b/experiments/20261113-tl4-val/summary.json
@@ -0,0 +1,8390 @@
+{
+ "learned_scored": false,
+ "plan": "configs/timelate/tl-e.json",
+ "plan_sha256": "82cd70ee700a19c417ea03022c2c6a70fc01f3adb8a5208f58f1ef2cef4f4647",
+ "scorer_git_commit": "2c191b29fe731afecf75dc4e034e45105e96f496",
+ "scorer_git_dirty": false,
+ "study": {
+  "match_tolerance_s": 120.0,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "reference_flagged_max": 0.25
+ },
+ "soundness": {
+  "reference_efficiency_tight": 1.0629659714183626,
+  "band": [
+   0.8,
+   1.3
+  ],
+  "history_converged_share": 1.0,
+  "convergence_min": 0.9,
+  "reference_flagged_share": 0.11333333333333333,
+  "reference_flagged_max": 0.25,
+  "pass": true
+ },
+ "splits": {
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+   "periods": 150,
+   "periods_scored": 141,
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+     "periods_without_a_track": 0
+    },
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+    },
+    "ctl": {
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+     "efficiency_range": 1.7540833934183988,
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+    "ctl-d1-q0.01": {
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+      "false_per_period": 0.3875,
+      "timing_median_s": 19.941412389323546
+     },
+     "240": {
+      "periods": 80,
+      "true": 136,
+      "matched": 107,
+      "missed": 29,
+      "false": 19,
+      "found": 128,
+      "missed_share": 0.21323529411764705,
+      "false_per_period": 0.2375,
+      "timing_median_s": 23.30494421162598
+     }
+    }
+   },
+   "declares": {
+    "c2": {
+     "0": {
+      "called_quiet": {
+       "rate": 1.0,
+       "lo": 0.20654931437723745,
+       "hi": 1.0,
+       "n": 1
+      },
+      "n": 1
+     },
+     "1": {
+      "called_quiet": {
+       "rate": 0.1891891891891892,
+       "lo": 0.09479978730312018,
+       "hi": 0.3420469692983842,
+       "n": 37
+      },
+      "n": 37
+     },
+     "2+": {
+      "called_quiet": {
+       "rate": 0.02564102564102564,
+       "lo": 0.004540703100990098,
+       "hi": 0.13180990169850076,
+       "n": 39
+      },
+      "n": 39
+     }
+    },
+    "ctl": {
+     "0": {
+      "called_quiet": {
+       "rate": 1.0,
+       "lo": 0.20654931437723745,
+       "hi": 1.0,
+       "n": 1
+      },
+      "n": 1
+     },
+     "1": {
+      "called_quiet": {
+       "rate": 0.08108108108108109,
+       "lo": 0.02796057410044797,
+       "hi": 0.2130067934928839,
+       "n": 37
+      },
+      "n": 37
+     },
+     "2+": {
+      "called_quiet": {
+       "rate": 0.02564102564102564,
+       "lo": 0.004540703100990098,
+       "hi": 0.13180990169850076,
+       "n": 39
+      },
+      "n": 39
+     }
+    },
+    "ctl-d1-q0.01": {
+     "0": {
+      "called_quiet": {
+       "rate": 1.0,
+       "lo": 0.20654931437723745,
+       "hi": 1.0,
+       "n": 1
+      },
+      "n": 1
+     },
+     "1": {
+      "called_quiet": {
+       "rate": 0.08108108108108109,
+       "lo": 0.02796057410044797,
+       "hi": 0.2130067934928839,
+       "n": 37
+      },
+      "n": 37
+     },
+     "2+": {
+      "called_quiet": {
+       "rate": 0.02564102564102564,
+       "lo": 0.004540703100990098,
+       "hi": 0.13180990169850076,
+       "n": 39
+      },
+      "n": 39
+     }
+    },
+    "search3": {
+     "0": {
+      "called_quiet": {
+       "rate": 1.0,
+       "lo": 0.20654931437723745,
+       "hi": 1.0,
+       "n": 1
+      },
+      "n": 1
+     },
+     "1": {
+      "called_quiet": {
+       "rate": 0.05405405405405406,
+       "lo": 0.014950976833157673,
+       "hi": 0.17704654350813104,
+       "n": 37
+      },
+      "n": 37
+     },
+     "2+": {
+      "called_quiet": {
+       "rate": 0.02564102564102564,
+       "lo": 0.004540703100990098,
+       "hi": 0.13180990169850076,
+       "n": 39
+      },
+      "n": 39
+     }
+    },
+    "search3-d1-q0.01": {
+     "0": {
+      "called_quiet": {
+       "rate": 1.0,
+       "lo": 0.20654931437723745,
+       "hi": 1.0,
+       "n": 1
+      },
+      "n": 1
+     },
+     "1": {
+      "called_quiet": {
+       "rate": 0.08108108108108109,
+       "lo": 0.02796057410044797,
+       "hi": 0.2130067934928839,
+       "n": 37
+      },
+      "n": 37
+     },
+     "2+": {
+      "called_quiet": {
+       "rate": 0.02564102564102564,
+       "lo": 0.004540703100990098,
+       "hi": 0.13180990169850076,
+       "n": 39
+      },
+      "n": 39
+     }
+    }
+   },
+   "reference_flagged_share": 0.2,
+   "history_converged_share": 0.9875,
+   "corr_estimate": {
+    "share_median": 0.03,
+    "share_quartiles": [
+     0.0,
+     0.085
+    ],
+    "tau_median_where_used": 18.877767347602443,
+    "ctl_used_share": 0.025
+   },
+   "edited_per_period": 0.0375,
+   "log_bayes_broad": {
+    "median": -3.574815931195488,
+    "quantiles_05_25_75_95": [
+     -8.633831533525733,
+     -5.8014032491575875,
+     0.211832495136143,
+     8.772630553620427
+    ],
+    "share_above_0": 0.2625,
+    "share_above_3": 0.1125
+   },
+   "seconds_per_period": {
+    "ctl/broad": 4.26,
+    "ctl/path": 0.14275000000000002,
+    "ctl/v3": 4.3,
+    "edit/broad": 28.843333333333334,
+    "edit/v3": 27.23,
+    "plain/broad": 19.49253333333333,
+    "plain/v3": 19.498124999999998,
+    "reference": 3.8072500000000007,
+    "search3/path": 0.161625
+   },
+   "dataset_digest": [
+    "4c431aa7ccc533b58d997927da58612b7235190b288809cf4f8702d30a36055b"
+   ],
+   "seed": [
+    340
+   ],
+   "search_code_sha256": [
+    "e5040f5e8df5bfea978594da2559d1073ab283bcedc53794e99fea6a77eb70a6"
+   ],
+   "git_commit": [
+    "2c191b29fe731afecf75dc4e034e45105e96f496"
+   ],
+   "git_dirty": false,
+   "condition": [
+    "bias-central"
+   ],
+   "thinning": [
+    "none"
+   ]
+  }
+ }
+}
diff --git a/experiments/20261113-tl4-val/tables.md b/experiments/20261113-tl4-val/tables.md
new file mode 100644
index 00000000..37e61cd4
--- /dev/null
+++ b/experiments/20261113-tl4-val/tables.md
@@ -0,0 +1,250 @@
+## Median period range error
+
+| System | Annex B | Shifted | Shifted 2 | Held course | Correlated, mild | Correlated | Outliers | Outliers, harsh | Bias |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| Periods (scored) | 150 (141) | 100 (92) | 100 (97) | 100 (92) | 60 (57) | 80 (75) | 80 (75) | 60 (57) | 80 (75) |
+| `broad` | 0.039 | 0.096 | 0.026 | 0.023 | – | – | – | – | – |
+| `c2` | 0.083 | 0.152 | 0.061 | 0.014 | 0.342 | 0.308 | 0.235 | 0.450 | 0.089 |
+| `corr` | 0.045 | – | – | – | 0.085 | 0.085 | – | – | – |
+| `ctl` | 0.039 | 0.092 | 0.028 | 0.024 | 0.101 | 0.090 | 0.042 | 0.039 | 0.073 |
+| `edit` | 0.040 | – | – | – | – | – | 0.051 | 0.051 | – |
+| `history-best` | 0.035 | 0.048 | 0.027 | 0.016 | 0.058 | 0.055 | 0.041 | 0.089 | 0.037 |
+| `history-fixed` | 0.022 | 0.043 | 0.019 | 0.016 | 0.048 | 0.060 | 0.051 | 0.105 | 0.038 |
+| `history-map` | 0.033 | 0.055 | 0.027 | 0.016 | 0.065 | 0.081 | 0.048 | 0.091 | 0.039 |
+| `mix2` | 0.040 | 0.092 | 0.028 | 0.023 | – | – | – | – | – |
+| `one-leg` | 0.386 | 0.344 | 0.355 | 0.014 | 0.490 | 0.483 | 0.486 | 0.527 | 0.467 |
+| `search3` | 0.040 | 0.109 | 0.029 | 0.028 | 0.128 | 0.178 | 0.089 | 0.158 | 0.075 |
+
+## The standing search ÷ each system (paired per period; above 1: better)
+
+### Annex B
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 0.96 [0.85, 1.08] | 0.45 |
+| `broad-h1` | 0.96 [0.85, 1.09] | 0.50 |
+| `c2` | 0.47 [0.39, 0.57] | 0.72 |
+| `corr` | 0.89 [0.81, 0.96] | 0.26 |
+| `corr-h1` | 0.94 [0.84, 1.06] | 0.50 |
+| `ctl` | 0.98 [0.92, 1.05] | 0.20 |
+| `ctl-h1` | 1.03 [0.94, 1.13] | 0.52 |
+| `edit` | 1.00 [0.99, 1.00] | 0.03 |
+| `edit-h1` | 1.06 [0.98, 1.14] | 0.45 |
+| `mix2` | 0.99 [0.93, 1.05] | 0.16 |
+| `mix2-h1` | 1.03 [0.95, 1.13] | 0.51 |
+| `search3-h1` | 1.06 [0.99, 1.15] | 0.43 |
+
+### Shifted
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 0.92 [0.78, 1.08] | 0.52 |
+| `broad-h1` | 0.98 [0.84, 1.17] | 0.52 |
+| `c2` | 0.62 [0.48, 0.81] | 0.64 |
+| `ctl` | 0.99 [0.91, 1.08] | 0.16 |
+| `ctl-h1` | 1.03 [0.91, 1.18] | 0.43 |
+| `mix2` | 0.99 [0.92, 1.07] | 0.15 |
+| `mix2-h1` | 1.06 [0.94, 1.21] | 0.42 |
+| `search3-h1` | 1.07 [0.97, 1.22] | 0.42 |
+
+### Shifted 2
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.08 [0.94, 1.24] | 0.44 |
+| `broad-h1` | 1.03 [0.89, 1.18] | 0.52 |
+| `c2` | 0.58 [0.43, 0.76] | 0.69 |
+| `ctl` | 1.05 [0.99, 1.12] | 0.16 |
+| `ctl-h1` | 1.02 [0.91, 1.14] | 0.46 |
+| `mix2` | 1.05 [0.98, 1.12] | 0.12 |
+| `mix2-h1` | 1.02 [0.92, 1.14] | 0.44 |
+| `search3-h1` | 1.00 [0.93, 1.06] | 0.45 |
+
+### Held course
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.16 [0.97, 1.38] | 0.18 |
+| `broad-h1` | 1.16 [0.96, 1.39] | 0.40 |
+| `c2` | 1.85 [1.45, 2.36] | 0.34 |
+| `ctl` | 1.14 [0.92, 1.38] | 0.21 |
+| `ctl-h1` | 1.11 [0.93, 1.33] | 0.39 |
+| `mix2` | 1.12 [0.94, 1.34] | 0.17 |
+| `mix2-h1` | 1.13 [0.94, 1.35] | 0.39 |
+| `search3-h1` | 0.98 [0.89, 1.08] | 0.41 |
+
+### Correlated, mild
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.41 [0.31, 0.55] | 0.77 |
+| `corr` | 1.52 [1.13, 2.07] | 0.40 |
+| `corr-h1` | 1.41 [1.11, 1.80] | 0.37 |
+| `ctl` | 1.44 [1.10, 1.89] | 0.44 |
+| `ctl-h1` | 1.39 [1.12, 1.75] | 0.39 |
+| `search3-h1` | 0.97 [0.86, 1.07] | 0.40 |
+
+### Correlated
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.61 [0.48, 0.77] | 0.73 |
+| `corr` | 2.07 [1.57, 2.71] | 0.29 |
+| `corr-h1` | 2.04 [1.58, 2.66] | 0.27 |
+| `ctl` | 1.79 [1.38, 2.35] | 0.35 |
+| `ctl-h1` | 1.82 [1.43, 2.35] | 0.31 |
+| `search3-h1` | 1.05 [1.00, 1.12] | 0.48 |
+
+### Outliers
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.34 [0.25, 0.46] | 0.79 |
+| `ctl` | 1.69 [1.24, 2.33] | 0.29 |
+| `ctl-h1` | 1.73 [1.24, 2.41] | 0.33 |
+| `edit` | 1.64 [1.21, 2.27] | 0.29 |
+| `edit-h1` | 1.70 [1.22, 2.35] | 0.29 |
+| `search3-h1` | 1.00 [0.93, 1.06] | 0.41 |
+
+### Outliers, harsh
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.39 [0.28, 0.53] | 0.75 |
+| `ctl` | 3.12 [2.09, 4.54] | 0.19 |
+| `ctl-h1` | 2.87 [1.95, 4.18] | 0.23 |
+| `edit` | 2.84 [1.93, 4.07] | 0.23 |
+| `edit-h1` | 2.63 [1.83, 3.72] | 0.25 |
+| `search3-h1` | 1.04 [0.95, 1.12] | 0.32 |
+
+### Bias
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.62 [0.48, 0.81] | 0.68 |
+| `ctl` | 0.99 [0.90, 1.07] | 0.12 |
+| `ctl-h1` | 1.06 [0.93, 1.18] | 0.43 |
+| `search3-h1` | 1.06 [1.01, 1.11] | 0.39 |
+
+## Share of the gap from the search to the reference that a control closes
+
+| Split | Control | Share |
+| --- | --- | --- |
+| Annex B | `broad` | -0.13 [-0.80, 0.24] |
+| Annex B | `corr` | -0.40 [-1.14, -0.12] |
+| Annex B | `ctl` | -0.06 [-0.38, 0.15] |
+| Annex B | `edit` | -0.01 [-0.05, 0.01] |
+| Annex B | `mix2` | -0.05 [-0.34, 0.15] |
+| Shifted | `broad` | -0.12 [-0.39, 0.10] |
+| Shifted | `ctl` | -0.01 [-0.14, 0.10] |
+| Shifted | `mix2` | -0.01 [-0.12, 0.10] |
+| Shifted 2 | `broad` | 0.41 [-0.48, 1.91] |
+| Shifted 2 | `ctl` | 0.26 [-0.10, 1.19] |
+| Shifted 2 | `mix2` | 0.26 [-0.10, 1.19] |
+| Held course | `broad` | 0.24 [-0.08, 0.44] |
+| Held course | `ctl` | 0.21 [-0.18, 0.44] |
+| Held course | `mix2` | 0.19 [-0.14, 0.40] |
+| Correlated, mild | `corr` | 0.54 [0.20, 0.87] |
+| Correlated, mild | `ctl` | 0.46 [0.15, 0.77] |
+| Correlated | `corr` | 0.65 [0.44, 0.90] |
+| Correlated | `ctl` | 0.52 [0.31, 0.76] |
+| Outliers | `ctl` | 1.17 [0.58, 2.75] |
+| Outliers | `edit` | 1.11 [0.50, 2.62] |
+| Outliers, harsh | `ctl` | 1.63 [1.14, 2.54] |
+| Outliers, harsh | `edit` | 1.50 [1.01, 2.37] |
+| Bias | `ctl` | -0.03 [-0.27, 0.16] |
+
+## System ÷ the true-history reference, by how the reference is fitted
+
+| Split | System | times free | held where drifted | times held |
+| --- | --- | --- | --- | --- |
+| Annex B | `search3` | 1.33 [1.14, 1.57] | 1.34 [1.15, 1.59] | 1.76 [1.48, 2.11] |
+| Annex B | `ctl` | 1.35 [1.16, 1.60] | 1.37 [1.17, 1.63] | 1.79 [1.49, 2.17] |
+| Shifted | `search3` | 1.72 [1.38, 2.16] | 2.08 [1.68, 2.61] | 2.33 [1.86, 3.01] |
+| Shifted | `ctl` | 1.73 [1.40, 2.17] | 2.10 [1.69, 2.64] | 2.35 [1.88, 3.00] |
+| Shifted 2 | `search3` | 1.20 [1.04, 1.40] | 1.20 [1.04, 1.40] | 1.48 [1.19, 1.86] |
+| Shifted 2 | `ctl` | 1.14 [0.98, 1.35] | 1.15 [0.98, 1.36] | 1.41 [1.12, 1.78] |
+| Held course | `search3` | 1.87 [1.47, 2.41] | 1.87 [1.47, 2.41] | 1.87 [1.47, 2.41] |
+| Held course | `ctl` | 1.64 [1.36, 1.99] | 1.64 [1.36, 1.99] | 1.64 [1.36, 1.99] |
+| Correlated, mild | `search3` | 1.88 [1.41, 2.53] | 2.18 [1.62, 2.93] | 2.87 [2.04, 4.01] |
+| Correlated, mild | `ctl` | 1.31 [1.00, 1.71] | 1.52 [1.15, 2.00] | 2.00 [1.45, 2.80] |
+| Correlated | `search3` | 2.15 [1.67, 2.80] | 3.06 [2.29, 4.07] | 3.52 [2.55, 4.91] |
+| Correlated | `ctl` | 1.20 [0.94, 1.56] | 1.71 [1.26, 2.29] | 1.97 [1.43, 2.72] |
+| Outliers | `search3` | 1.37 [1.03, 1.81] | 1.57 [1.16, 2.11] | 1.71 [1.21, 2.39] |
+| Outliers | `ctl` | 0.81 [0.59, 1.10] | 0.93 [0.69, 1.23] | 1.01 [0.74, 1.33] |
+| Outliers, harsh | `search3` | 1.73 [1.30, 2.34] | 2.01 [1.48, 2.72] | 2.50 [1.77, 3.60] |
+| Outliers, harsh | `ctl` | 0.55 [0.40, 0.79] | 0.64 [0.46, 0.90] | 0.80 [0.56, 1.15] |
+| Bias | `search3` | 1.55 [1.18, 2.05] | 1.64 [1.29, 2.14] | 1.71 [1.28, 2.34] |
+| Bias | `ctl` | 1.57 [1.20, 2.08] | 1.66 [1.30, 2.16] | 1.73 [1.28, 2.38] |
+
+## Manoeuvres missed (share) and false (per period), matched within 120 s
+
+| Split | System | Missed | False |
+| --- | --- | --- | --- |
+| Annex B | `broad` | 0.29 | 0.31 |
+| Annex B | `c2` | 0.54 | 0.77 |
+| Annex B | `corr` | 0.27 | 0.22 |
+| Annex B | `ctl` | 0.27 | 0.29 |
+| Annex B | `edit` | 0.25 | 0.25 |
+| Annex B | `mix2` | 0.27 | 0.28 |
+| Annex B | `search3` | 0.25 | 0.26 |
+| Shifted | `broad` | 0.50 | 0.44 |
+| Shifted | `c2` | 0.73 | 1.00 |
+| Shifted | `ctl` | 0.47 | 0.36 |
+| Shifted | `mix2` | 0.47 | 0.36 |
+| Shifted | `search3` | 0.47 | 0.31 |
+| Shifted 2 | `broad` | 0.15 | 0.15 |
+| Shifted 2 | `c2` | 0.45 | 0.71 |
+| Shifted 2 | `ctl` | 0.11 | 0.14 |
+| Shifted 2 | `mix2` | 0.11 | 0.14 |
+| Shifted 2 | `search3` | 0.12 | 0.17 |
+| Held course | `broad` | – | 0.50 |
+| Held course | `c2` | – | 0.03 |
+| Held course | `ctl` | – | 0.53 |
+| Held course | `mix2` | – | 0.53 |
+| Held course | `search3` | – | 0.61 |
+| Correlated, mild | `c2` | 0.60 | 2.52 |
+| Correlated, mild | `corr` | 0.37 | 0.37 |
+| Correlated, mild | `ctl` | 0.35 | 0.32 |
+| Correlated, mild | `search3` | 0.34 | 1.07 |
+| Correlated | `c2` | 0.57 | 2.71 |
+| Correlated | `corr` | 0.43 | 0.50 |
+| Correlated | `ctl` | 0.42 | 0.41 |
+| Correlated | `search3` | 0.42 | 1.48 |
+| Outliers | `c2` | 0.51 | 2.09 |
+| Outliers | `ctl` | 0.27 | 0.31 |
+| Outliers | `edit` | 0.25 | 0.30 |
+| Outliers | `search3` | 0.26 | 0.57 |
+| Outliers, harsh | `c2` | 0.60 | 3.67 |
+| Outliers, harsh | `ctl` | 0.28 | 0.33 |
+| Outliers, harsh | `edit` | 0.25 | 0.30 |
+| Outliers, harsh | `search3` | 0.36 | 1.28 |
+| Bias | `c2` | 0.55 | 0.86 |
+| Bias | `ctl` | 0.31 | 0.39 |
+| Bias | `search3` | 0.29 | 0.40 |
+
+## Declaration: held-course periods given a manoeuvre, and Annex B targets called quiet
+
+| System | Held course: false | One manoeuvre: called quiet | Two or more: called quiet | Annex B median | Held-course median |
+| --- | --- | --- | --- | --- | --- |
+| `broad` | 0.48 | 0.16 | 0.01 | 0.039 | 0.023 |
+| `c2` | 0.01 | 0.21 | 0.02 | 0.083 | 0.014 |
+| `ctl` | 0.50 | 0.16 | 0.01 | 0.039 | 0.024 |
+| `ctl-d1-q0.01` | 0.07 | 0.18 | 0.02 | 0.040 | 0.016 |
+| `mix2` | 0.50 | 0.16 | 0.01 | 0.040 | 0.023 |
+| `search3` | 0.52 | 0.12 | 0.01 | 0.040 | 0.028 |
+| `search3-d1-q0.01` | 0.11 | 0.18 | 0.02 | 0.040 | 0.017 |
+
+## What the estimates said
+
+| Split | References held | Wild bearings edited a period | Correlated share (median) | Correlation used by `ctl` | Bayes factor for the broad prior (median log) | Share above 0 |
+| --- | --- | --- | --- | --- | --- | --- |
+| Annex B | 0.11 | 0.0 | 0.02 | 0.05 | -3.2 | 0.35 |
+| Shifted | 0.37 | 0.0 | 0.02 | 0.04 | -0.8 | 0.43 |
+| Shifted 2 | 0.03 | 0.0 | 0.02 | 0.05 | -2.7 | 0.35 |
+| Held course | 0.00 | 0.0 | 0.02 | 0.06 | 5.4 | 0.91 |
+| Correlated, mild | 0.18 | 0.0 | 0.52 | 1.00 | -1.5 | 0.35 |
+| Correlated | 0.28 | 0.0 | 0.53 | 1.00 | -1.2 | 0.41 |
+| Outliers | 0.23 | 8.5 | 0.00 | 0.01 | -2.5 | 0.35 |
+| Outliers, harsh | 0.18 | 20.6 | 0.00 | 0.02 | -1.9 | 0.37 |
+| Bias | 0.20 | 0.0 | 0.03 | 0.03 | -3.6 | 0.26 |
diff --git a/experiments/20261113-tlm-pilot/README.md b/experiments/20261113-tlm-pilot/README.md
new file mode 100644
index 00000000..e9468260
--- /dev/null
+++ b/experiments/20261113-tlm-pilot/README.md
@@ -0,0 +1,30 @@
+# Spec 016: the whole-period model's pilot on a T4 (2026-11-13)
+
+Workflow run 43496013487 (`gputrain/tlm-pilot`, commit ab0509e, plan
+`configs/regress/tlm-pilot.json`), results branch `results/gputrain-tlm-pilot-43496013487`.
+`run.json` is its record. A throughput and loss-curve reading; the weights are not carried.
+
+- **Throughput**: 6,000 updates of 256 periods (1.54 million periods) in 3,388 s of
+  training: 453 periods a second (435 over the whole record). Generation on the
+  container's CPU is the limit, as forecast (spec 012's stream gave 635 a second).
+- **It trains**, with mixed precision and the padding mask: on the monitor slice of the
+  stream (mixed conditions) the median absolute error of ln range fell from 0.24 at 500
+  updates to 0.095 at 6,000; the manoeuvre term from 0.30 to 0.21.
+- **The container was replaced once** (at update 1,819) and the run resumed from its
+  checkpoint on the volume and completed. The record's `gpu_seconds` (2,468) counts only
+  the second container: the first, about 930 s of training and its start-up, is billed and
+  not recorded. **The ledger under-counts a run that is restarted.** The true cost of this
+  pilot is about $0.70, against $0.47 recorded.
+- **Two earlier launches failed** at container start-up (the launcher's functions, built
+  in a loop and serialised by spec 012's addendum 1, had never been run and could not be
+  imported in the container). They did no training; what Modal billed for the failed
+  starts is not visible from here. The launcher is back to the form spec 012's runs used,
+  and the workflow now gives up at the segment's limit plus 25 minutes.
+
+## What it sets
+
+Three seeds within the ledger's cap ($45 of the owner's $50; $26.35 recorded before the
+training runs): two segments of 15,600 s each, at most 9 h with allowances, $6.12 a seed,
+$18.36 for three. At 453 periods a second with a tenth held back for restarts and
+monitoring: 49,000 updates, 12.5 million periods a seed (spec 012's regressor saw 21.8
+million scenarios).
diff --git a/experiments/20261113-tlm-pilot/run.json b/experiments/20261113-tlm-pilot/run.json
new file mode 100644
index 00000000..0878c0e9
--- /dev/null
+++ b/experiments/20261113-tlm-pilot/run.json
@@ -0,0 +1,180 @@
+{
+ "checkpoint_sha256": "ee04aaaa953b1f2f91a166e9945c18ef28b3c059cc0b301ba373cde3bb5aba10",
+ "config": {
+  "batch_scenarios": 256,
+  "beta": 0.5,
+  "causal": false,
+  "checkpoint_seconds": 900,
+  "data": "periods",
+  "feedforward": 1024,
+  "heads": 8,
+  "held_fraction": 0.0,
+  "held_ranges": "configs/regress/held-course-ranges.json",
+  "id": "tlm-pilot",
+  "inputs": "full",
+  "layers": 8,
+  "learning_rate": 0.0003,
+  "limit_seconds": 3000,
+  "manoeuvre_head": true,
+  "max_rows": 1440,
+  "n_features": 9,
+  "patch_rows": 6,
+  "precision": "amp",
+  "ranges": "configs/man2-ranges.json",
+  "seed": 1,
+  "stream_repeat": 0,
+  "stream_seed": 339,
+  "stream_start": 0,
+  "total_updates": 6000,
+  "val_count": 512,
+  "val_every_updates": 500,
+  "val_seed": 339,
+  "warmup_updates": 500,
+  "weight_decay": 0.01,
+  "width": 256
+ },
+ "config_sha256": "e76f81fcdbbe1a0d5c8e82792d567b8d5fea7b4e11253f2c86f6254b209b2bae",
+ "cuda": "13.2",
+ "device": "cuda",
+ "git_dirty": false,
+ "gpu": "Tesla T4",
+ "gpu_seconds": 2467.7736744880676,
+ "plan": {
+  "description": "Spec 016 T009: the pilot of the whole-period model on a T4 (research R7, R10): the real architecture (8 layers, width 256, 8 heads, 240 tokens of 6 five-second rows, non-causal, manoeuvre logit), one segment of at most 50 minutes (an hour with the start-up allowance). Measures periods per second and gives a loss curve; 6,000 updates of 256 periods complete inside the limit only above about 510 periods a second, otherwise the limit stops it. Not a training run: its weights are not carried.",
+  "gpu": "T4",
+  "segment_limit_seconds": 3000,
+  "segments": 1
+ },
+ "scenarios_per_second": 435.25608736811665,
+ "scenarios_seen": 1536000,
+ "segments": [
+  {
+   "end_update": 1819,
+   "git_commit": "ab0509e5317d12c5bb27742abfe919662c9013a2",
+   "seconds": 928.536390542984,
+   "start_update": 0,
+   "started": 1794599521.5284216
+  },
+  {
+   "end_update": 6000,
+   "git_commit": "ab0509e5317d12c5bb27742abfe919662c9013a2",
+   "seconds": 2459.0948255062103,
+   "start_update": 1819,
+   "started": 1794600935.4289484
+  }
+ ],
+ "stopped": "complete",
+ "torch": "2.15.0+cu132",
+ "train_seconds": 3387.6312160491943,
+ "updates": 6000,
+ "validation": [
+  {
+   "ln_range_abs_median": 0.2393108755350113,
+   "loss": -0.5571172684431076,
+   "manoeuvre_loss": 0.299375057220459,
+   "seconds": 275.09469199180603,
+   "state_loss": -0.5870547741651535,
+   "train_loss": -0.5226305723190308,
+   "update": 500
+  },
+  {
+   "ln_range_abs_median": 0.19478058815002441,
+   "loss": -0.5666608363389969,
+   "manoeuvre_loss": 0.29005197435617447,
+   "seconds": 523.7432150840759,
+   "state_loss": -0.5956660211086273,
+   "train_loss": -0.5638507604598999,
+   "update": 1000
+  },
+  {
+   "ln_range_abs_median": 0.1680605411529541,
+   "loss": -0.5564777851104736,
+   "manoeuvre_loss": 0.27957718074321747,
+   "seconds": 770.7311325073242,
+   "state_loss": -0.5844354927539825,
+   "train_loss": -0.5559878349304199,
+   "update": 1500
+  },
+  {
+   "ln_range_abs_median": 0.14406460523605347,
+   "loss": -0.5533549040555954,
+   "manoeuvre_loss": 0.2647097185254097,
+   "seconds": 138.64467334747314,
+   "state_loss": -0.5798258930444717,
+   "train_loss": -0.6113681793212891,
+   "update": 2000
+  },
+  {
+   "ln_range_abs_median": 0.14211630821228027,
+   "loss": -0.5781416445970535,
+   "manoeuvre_loss": 0.249175313860178,
+   "seconds": 426.25533151626587,
+   "state_loss": -0.6030591875314713,
+   "train_loss": -0.6159330010414124,
+   "update": 2500
+  },
+  {
+   "ln_range_abs_median": 0.12334299087524414,
+   "loss": -0.5445492416620255,
+   "manoeuvre_loss": 0.2367401011288166,
+   "seconds": 714.1461288928986,
+   "state_loss": -0.5682232528924942,
+   "train_loss": -0.5845910906791687,
+   "update": 3000
+  },
+  {
+   "ln_range_abs_median": 0.1255866289138794,
+   "loss": -0.5204508379101753,
+   "manoeuvre_loss": 0.23051971569657326,
+   "seconds": 1000.8789262771606,
+   "state_loss": -0.5435028076171875,
+   "train_loss": -0.5538526773452759,
+   "update": 3500
+  },
+  {
+   "ln_range_abs_median": 0.11475908756256104,
+   "loss": -0.5549549460411072,
+   "manoeuvre_loss": 0.22284116968512535,
+   "seconds": 1288.1015193462372,
+   "state_loss": -0.5772390514612198,
+   "train_loss": -0.5839107036590576,
+   "update": 4000
+  },
+  {
+   "ln_range_abs_median": 0.1086818277835846,
+   "loss": -0.5171626508235931,
+   "manoeuvre_loss": 0.21700848266482353,
+   "seconds": 1581.0870378017426,
+   "state_loss": -0.5388635098934174,
+   "train_loss": -0.5328782796859741,
+   "update": 4500
+  },
+  {
+   "ln_range_abs_median": 0.09634727239608765,
+   "loss": -0.5277779176831245,
+   "manoeuvre_loss": 0.21505457907915115,
+   "seconds": 1876.4806320667267,
+   "state_loss": -0.5492833703756332,
+   "train_loss": -0.5435018539428711,
+   "update": 5000
+  },
+  {
+   "ln_range_abs_median": 0.09531223773956299,
+   "loss": -0.5150032341480255,
+   "manoeuvre_loss": 0.2130490131676197,
+   "seconds": 2168.1691274642944,
+   "state_loss": -0.5363081395626068,
+   "train_loss": -0.5337129235267639,
+   "update": 5500
+  },
+  {
+   "ln_range_abs_median": 0.09482061862945557,
+   "loss": -0.5215346589684486,
+   "manoeuvre_loss": 0.21196024492383003,
+   "seconds": 2459.0945057868958,
+   "state_loss": -0.5427306890487671,
+   "train_loss": -0.562118649482727,
+   "update": 6000
+  }
+ ]
+}
diff --git a/experiments/20261114-tl4/README.md b/experiments/20261114-tl4/README.md
new file mode 100644
index 00000000..dcd9e9ed
--- /dev/null
+++ b/experiments/20261114-tl4/README.md
@@ -0,0 +1,194 @@
+# Spec 016 (study L): the test on seed 341 (2026-11-14)
+
+CI run 43563825793 (`timelate/tl-e-test`, commit fbda7fa, clean tree, plan
+`configs/timelate/tl-e.json` `82cd70ee…` as registered), scored once, with the controls'
+validation record (`experiments/20261113-tl4-val/`) as the scorer requires. 2,860 periods;
+the classical controls and the three trained seeds on the same periods. **Every table is
+generated**: `tables.md` (`scripts/research/tl4_tables.py`), `learned.md`
+(`tl4_learned_tables.py`) and `supplement.md` (`tl4_supplement.py`), all from `summary.json`
+and the period records; the figures below are copied from them and
+`tests/unit/test_timelate4.py` checks that every interval quoted here is in one of them.
+Per-row errors (and tracks for the first 20 periods of each split) are on the results
+branch `results/timelate-tl-e-test-43563825793`.
+
+Ratios are paired per period (geometric mean of the per-period ratio of median relative
+range error, 95% period bootstrap). "Control ÷ learned" above 1 means the learned track is
+better.
+
+## The registered outcome
+
+**Learning is shown on no split, and there is harm on Annex B data. By the registered
+rule: the classical controls stand as the time-late system and the learned track stops.**
+
+| | Split | Verdict | Declared control ÷ learned (three seeds) | Hedged track ÷ learned (three seeds) |
+| --- | --- | --- | --- | --- |
+| L-c | Shifted prior | not shown | 0.82 [0.73, 0.91], 0.80 [0.71, 0.90], 0.82 [0.73, 0.91] | 0.76 [0.67, 0.85], 0.74 [0.66, 0.83], 0.75 [0.67, 0.85] |
+| L-c | Shifted prior 2 | not shown | 0.87 [0.79, 0.96], 0.87 [0.79, 0.95], 0.94 [0.85, 1.05] | 0.91 [0.83, 1.01], 0.90 [0.82, 0.99], 0.99 [0.89, 1.09] |
+| L-c | Correlated errors | not shown | 0.83 [0.74, 0.92], 0.83 [0.74, 0.92], 0.84 [0.75, 0.94] | 0.82 [0.73, 0.92], 0.82 [0.73, 0.92], 0.83 [0.74, 0.93] |
+| L-c | Outliers | not shown | 0.78 [0.70, 0.89], 0.77 [0.68, 0.88], 0.81 [0.71, 0.91] | 0.80 [0.71, 0.91], 0.79 [0.70, 0.90], 0.83 [0.73, 0.93] |
+| L-c | Bias (absent from training) | not shown | 0.98 [0.88, 1.09], 1.00 [0.90, 1.09], 0.97 [0.86, 1.07] | 0.98 [0.88, 1.09], 0.99 [0.90, 1.09], 0.97 [0.87, 1.07] |
+| L-d | Annex B | **harm** | 0.82 [0.74, 0.90], 0.81 [0.73, 0.89], 0.82 [0.75, 0.91] | 0.84 [0.76, 0.93], 0.83 [0.75, 0.93], 0.85 [0.77, 0.94] |
+| L-d | Held course | no harm; the learned track wins | 1.06 [1.01, 1.11] for every seed | 1.51 [1.36, 1.67] for every seed |
+
+The reported learned track (the arc model fitted from the model's output) is about 20%
+worse than the classical control on Annex B data, under a shifted prior, with correlated
+errors and with outliers, for every seed; equal to it under a bias neither side was built
+for; and better on targets that never manoeuvre. The three seeds agree to within their
+intervals everywhere.
+
+**L-a** (soundness) holds on this seed too: 0.95 of the bound on tight rows, every
+true-history fit converged, 0.085 of references held on Annex B.
+
+## Median period range error
+
+| Split (scored periods) | C″ as a track | Standing search | Declared control | Hedged control | Learned, own track (seed 1) | Learned, arc read-out (seeds 1, 2, 3) | True history |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| Annex B (374) | 0.085 | 0.041 | 0.040 | 0.040 | 0.050 | 0.047, 0.049, 0.046 | 0.029 |
+| Shifted (365) | 0.141 | 0.094 | 0.088 | 0.078 | 0.084 | 0.112, 0.117, 0.110 | 0.044 |
+| Shifted 2 (375) | 0.068 | 0.029 | 0.027 | 0.029 | 0.043 | 0.033, 0.033, 0.032 | 0.024 |
+| Held course (263) | 0.016 | 0.031 | 0.017 | 0.024 | 0.025 | 0.015, 0.015, 0.015 | 0.015 |
+| Correlated, mild (186) | 0.324 | 0.115 | 0.082 | 0.083 | 0.093 | 0.110, 0.111, 0.113 | 0.064 |
+| Correlated (278) | 0.272 | 0.136 | 0.102 | 0.101 | 0.106 | 0.133, 0.129, 0.125 | 0.076 |
+| Outliers (278) | 0.248 | 0.077 | 0.042 | 0.040 | 0.062 | 0.060, 0.057, 0.055 | 0.046 |
+| Outliers, harsh (186) | 0.468 | 0.125 | 0.054 | 0.054 | 0.071 | 0.056, 0.058, 0.059 | 0.070 |
+| Bias (278) | 0.117 | 0.059 | 0.058 | 0.060 | 0.065 | 0.064, 0.059, 0.065 | 0.049 |
+| Whole engagements (60) | 0.073 | 0.037 | 0.039 | 0.039 | 0.043 | 0.044, 0.046, 0.048 | 0.021 |
+
+## L-b: what the classical controls did (standing search ÷ declared control)
+
+| Split | Ratio | Gap to the reference closed | Forecast | Held? |
+| --- | --- | --- | --- | --- |
+| Outliers | 1.68 [1.46, 1.94] | 1.20 [0.89, 1.61] | about 1.5; over 0.6 | Yes |
+| Outliers, harsh | 2.44 [2.03, 3.00] | 1.55 [1.23, 2.08] | – | – |
+| Correlated | 1.33 [1.16, 1.53] | 0.51 [0.30, 0.69] | about 1.3; about 0.5 | Yes |
+| Correlated, mild | 1.28 [1.11, 1.48] | 0.46 [0.20, 0.69] | – | – |
+| Shifted | 1.03 [0.96, 1.09] | 0.08 [-0.01, 0.18] | 1.0 to 1.1; under 0.2 | Yes |
+| Shifted 2 | 1.08 [1.03, 1.14] | 0.29 [0.09, 0.58] | 1.1 to 1.2 | Just under |
+| Held course | 1.70 [1.48, 1.95] | 0.35 [0.20, 0.48] | – | – |
+| Annex B | 1.09 [1.02, 1.18] | 0.24 [0.06, 0.41] | – | – |
+| Bias | 1.00 [0.96, 1.05] | -0.11 [-0.48, 0.09] | – | – |
+
+Editing wild bearings removes the outlier gap (the control is at or below the reference,
+which is fitted to the bearings as given). The effective sd closes half the
+correlated-error gap. Under a shifted prior the control gains almost nothing and stays at
+1.79 [1.61, 1.99] times the reference: **the one large gap is still open, and the learned
+model did not close it either.**
+
+## Reading
+
+1. **The learned model is a good estimator that the classical system beats.** Its arc
+   read-out is better than C″ read as a track on every manoeuvring split (by 1.4 on the
+   shifted split to 6.6 with 5% outliers; `learned.md`), on 12.5 million simulated periods
+   and $14 of GPU time. It is still about 20% behind a search that knows the motion model.
+2. **The forecast was right about the outcome and wrong about the read-out.** "Shown on
+   none" and "harm on Annex B more likely than not" held. But the record forecast that the
+   arc read-out would improve on the model's own track "on most splits"; it does so on
+   Annex B, shifted 2, held course and harsh outliers, and makes it *worse* under the
+   first shifted prior (own track 0.084, read-out 0.112) and with correlated errors (0.106
+   against 0.133). The read-out fits Annex B's prior and the arc model at the model's
+   manoeuvre times; where the model missed manoeuvres, the fit has nowhere to put them.
+3. **The model's own track, not the registered comparison, equals the control where the
+   control is weakest**: declared control ÷ own track is 1.01 [0.90, 1.13] under the
+   shifted prior and 0.99 [0.88, 1.13] with correlated errors (seed 1; the others agree),
+   against 0.71 [0.63, 0.80] on Annex B and 0.60 [0.53, 0.68] on shifted 2. Against the
+   hedged track it is not separated on the shifted split (0.93 [0.84, 1.04]). It never
+   wins. This is "reported beside" and decides nothing.
+4. **The model is cautious about manoeuvres.** At a probability threshold of 0.5 (never
+   tuned) it misses more manoeuvres than the control (0.31 against 0.25 on Annex B, 0.58
+   against 0.49 shifted), declares far fewer false ones (0.03 a period against 0.20), and
+   calls a one-manoeuvre target quiet in 0.29 of cases against 0.14. On targets that never
+   manoeuvre it declares one in 0.01 of periods (control 0.08, standing search 0.49).
+5. **Bias**: neither side was built or trained for it, and they are equal. The held-out
+   condition shows no transfer advantage for the model and no collapse.
+6. **Hedging**: the hedged classical track is better than the declared one under the
+   shifted prior (1.08 [1.04, 1.13]), worse on held course (0.70 [0.64, 0.77]), and not
+   separated elsewhere.
+
+## Forecasts
+
+| Forecast (decision record) | Result | Right? |
+| --- | --- | --- |
+| L-a passes | 0.95; passes | Yes |
+| Search ÷ control: outliers about 1.5, correlated about 1.3, shifted 1.0 to 1.1, shifted 2 1.1 to 1.2 | 1.68, 1.33, 1.03, 1.08 | Three of four (shifted 2 just under) |
+| Gap closed: outliers over 0.6, correlated about 0.5, shifted under 0.2 | 1.20, 0.51, 0.08 | Yes |
+| Learning shown on no split; one chance in four on the shifted split | Shown on none; every seed loses there | Yes |
+| Harm on Annex B more likely than not; the read-out may avoid it | Harm, with the read-out | Outcome yes; "may avoid" no |
+| The read-out improves on the own track on most splits | Clearly better on four splits, worse on three, about equal on three (`supplement.md`) | No |
+
+## Not delivered, and weaknesses
+
+- **The manoeuvre threshold of the model (0.5) was never tuned**, while the control's
+  margin and quiet probability were tuned on seed 338. A tuned threshold might reduce the
+  misses that hurt the read-out. The registration fixed 0.5; the asymmetry is real.
+- **The read-out used Annex B's prior only**, while the control pools a broadened prior.
+- **The budget**: 12.5 million periods a seed on a T4. The monitor curve was still falling
+  slowly at the end. This is a null at $14, not a limit on what a larger model or longer
+  training could do.
+- **A Huber cost and a whitened fit were not built** (editing and an effective sd stand in
+  for them); the correlated-error control knows the generator's error family; the evidence
+  faults at the edges of the prior's support are not repaired (decision record).
+- The reference is held to the true times in 18% to 27% of periods on the shifted,
+  correlated and outlier splits; the "gap closed" shares there read against it.
+- Efficiency by slice is in `learned.md`; the tracks stored for plotting are on the
+  results branch and not yet drawn.
+- One simulator, one test seed, splits drawn from one seed, the error splits being the
+  same engagements under different errors.
+
+## What this supports
+
+- The classical control (`ctl`, declared at margin 1 with a 1% quiet probability) is a
+  better time-late system than the standing search wherever the bearing errors are not as
+  assumed, and on quiet targets, at no cost on Annex B data.
+- A learned whole-period model of this size and training does not beat it anywhere, and
+  loses to it where the simulator's assumptions hold.
+- It does **not** show that learning cannot help under a shifted prior: the model's own
+  track is level with the control there, the read-out is what loses, and the gap to the
+  reference (1.8) is open for both.
+
+## Corrections after the panel (2026-11-14; `review.md`, five approve with findings)
+
+The registered outcome stands. The following statements above are corrected; figures
+marked "panel" are the reviewers' recomputations and are in `review.md`, not in the
+generated files.
+
+- **"About 20% worse" is a tail.** The median per-period ratio of control to read-out is
+  1.00 on every split (`summary.json`, `median_ratio`); the read-out is over twice as bad
+  in 20% to 27% of periods and over twice as good in 9% to 13% (`learned.md`). The whole
+  loss is in the periods where the model declares fewer manoeuvres than the control;
+  where the counts agree the two are level (panel). The untuned threshold of 0.5 is
+  therefore the headline, not a footnote: **the registered comparison measured the
+  model's manoeuvre detector against the evidence search.**
+- **"The learned track stops" means this model with this read-out.** A search started
+  from the model was not registered; on tuning data it equals the control at a third of
+  the search time and does not beat it (panel).
+- **"Harm"** is the rule's word. The read-out is level with the control in the median
+  period and 1.8 times better than C″.
+- **The held-course "win" is not a learned track**: where both declare no manoeuvre the
+  two are the same fit; the 1.06 is the control's false manoeuvres in 23 of 299 periods.
+- **Reading 3 and the last "supports" bullet**: the own track is level with the control
+  under a shifted prior in range only (course 0.79, speed 0.77; panel), and the pooled
+  figure hides a win where the bound is loose and a loss by nearly three where it is
+  tight. "It never wins" is false by slice; the slices are post hoc.
+- **The control's claims are narrowed.** It is better with outliers and with correlated
+  errors of the generator's family and on quiet targets; under bias it is 1.00
+  [0.96, 1.05]. "At no cost on Annex B" becomes "no range cost separated over three seeds
+  (0.96 to 1.09)"; it calls a one-manoeuvre target quiet more often, markedly so with
+  correlated errors (0.29 against 0.05; `supplement.md`).
+- **The L-b table** gave the ratio for the declared control and the gap closed for the
+  undeclared one; on the outlier splits the "gap closed" is read against a reference
+  fitted to unedited bearings and means only that the control beats it. The 1.79 times
+  the reference on the shifted split is 1.7 to 2.1 by how the reference is fitted (panel).
+- **The mismatch splits** lie inside the model's training ranges (bias excepted, and bias
+  is the mildest held-out condition): the model lost in-distribution.
+- **Reading 4** quotes the model's own spans; the read-out's are 0.34 missed and 0.08
+  false on Annex B. **Reading 6**: on shifted 2 the hedged track is worse than the
+  declared one (0.96 [0.92, 1.00]), separated.
+- **"Every table is generated"** is not true of the medians table: its declared and hedged
+  control columns were taken from `summary.json` by hand (all correct).
+- **Addendum 2** is wrong that shard size can reach C″ only: the control's searches start
+  from C″'s track. Not measured.
+- **Not delivered, added to the list above**: a working prior-mismatch diagnostic (the one
+  computed is not a Bayes factor and is not cited); candidate calibration under the
+  broadened prior; the export check on the trained models; IMM and EKF tracks beside; the
+  weights' digests in the record; unit tests for the runner and the scorer.
+- **The three seeds** share the training stream and differ in initialisation only.
diff --git a/experiments/20261114-tl4/ci.json b/experiments/20261114-tl4/ci.json
new file mode 100644
index 00000000..9d5295f6
--- /dev/null
+++ b/experiments/20261114-tl4/ci.json
@@ -0,0 +1 @@
+{"run_id": "43563825793", "commit": "fbda7faccd6775e64e461fda82647a8b87b94fa3", "cpu_model": "Intel(R) Xeon(R) 6975P-C"}
diff --git a/experiments/20261114-tl4/learned.md b/experiments/20261114-tl4/learned.md
new file mode 100644
index 00000000..8d487c83
--- /dev/null
+++ b/experiments/20261114-tl4/learned.md
@@ -0,0 +1,424 @@
+## L-c and L-d: the registered verdicts
+
+| Split | Verdict | Seed | Declared control ÷ learned | Side | Hedged track ÷ learned | Side |
+| --- | --- | --- | --- | --- | --- | --- |
+| Shifted | not shown | `learned-s1-arc` | 0.82 [0.73, 0.91] | loses | 0.76 [0.67, 0.85] | loses |
+| Shifted | not shown | `learned-s2-arc` | 0.80 [0.71, 0.90] | loses | 0.74 [0.66, 0.83] | loses |
+| Shifted | not shown | `learned-s3-arc` | 0.82 [0.73, 0.91] | loses | 0.75 [0.67, 0.85] | loses |
+| Shifted 2 | not shown | `learned-s1-arc` | 0.87 [0.79, 0.96] | loses | 0.91 [0.83, 1.01] | not separated |
+| Shifted 2 | not shown | `learned-s2-arc` | 0.87 [0.79, 0.95] | loses | 0.90 [0.82, 0.99] | loses |
+| Shifted 2 | not shown | `learned-s3-arc` | 0.94 [0.85, 1.05] | not separated | 0.99 [0.89, 1.09] | not separated |
+| Correlated | not shown | `learned-s1-arc` | 0.83 [0.74, 0.92] | loses | 0.82 [0.73, 0.92] | loses |
+| Correlated | not shown | `learned-s2-arc` | 0.83 [0.74, 0.92] | loses | 0.82 [0.73, 0.92] | loses |
+| Correlated | not shown | `learned-s3-arc` | 0.84 [0.75, 0.94] | loses | 0.83 [0.74, 0.93] | loses |
+| Outliers | not shown | `learned-s1-arc` | 0.78 [0.70, 0.89] | loses | 0.80 [0.71, 0.91] | loses |
+| Outliers | not shown | `learned-s2-arc` | 0.77 [0.68, 0.88] | loses | 0.79 [0.70, 0.90] | loses |
+| Outliers | not shown | `learned-s3-arc` | 0.81 [0.71, 0.91] | loses | 0.83 [0.73, 0.93] | loses |
+| Bias | not shown | `learned-s1-arc` | 0.98 [0.88, 1.09] | not separated | 0.98 [0.88, 1.09] | not separated |
+| Bias | not shown | `learned-s2-arc` | 1.00 [0.90, 1.09] | not separated | 0.99 [0.90, 1.09] | not separated |
+| Bias | not shown | `learned-s3-arc` | 0.97 [0.86, 1.07] | not separated | 0.97 [0.87, 1.07] | not separated |
+| Annex B | harm | `learned-s1-arc` | 0.82 [0.74, 0.90] | loses | 0.84 [0.76, 0.93] | loses |
+| Annex B | harm | `learned-s2-arc` | 0.81 [0.73, 0.89] | loses | 0.83 [0.75, 0.93] | loses |
+| Annex B | harm | `learned-s3-arc` | 0.82 [0.75, 0.91] | loses | 0.85 [0.77, 0.94] | loses |
+| Held course | no harm shown | `learned-s1-arc` | 1.06 [1.01, 1.11] | wins | 1.51 [1.36, 1.67] | wins |
+| Held course | no harm shown | `learned-s2-arc` | 1.06 [1.01, 1.11] | wins | 1.51 [1.36, 1.67] | wins |
+| Held course | no harm shown | `learned-s3-arc` | 1.06 [1.01, 1.11] | wins | 1.51 [1.36, 1.67] | wins |
+
+Learning shown on 0 mismatch split(s); harm: yes. What follows: the classical controls stand as the time-late system; the learned track stops.
+
+## Each system ÷ each learned track (paired per period; above 1: the learned track is better)
+
+### Annex B
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 1.59 [1.39, 1.86] | 0.35 | 0.19 |
+| `c2` | `learned-s1-arc` | 1.83 [1.60, 2.09] | 0.31 | 0.11 |
+| `c2` | `learned-s2` | 1.62 [1.40, 1.89] | 0.36 | 0.19 |
+| `c2` | `learned-s2-arc` | 1.81 [1.59, 2.08] | 0.31 | 0.10 |
+| `c2` | `learned-s3` | 1.73 [1.51, 1.99] | 0.33 | 0.18 |
+| `c2` | `learned-s3-arc` | 1.85 [1.61, 2.11] | 0.31 | 0.09 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.71 [0.63, 0.80] | 0.59 | 0.37 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.82 [0.74, 0.90] | 0.61 | 0.20 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.72 [0.64, 0.82] | 0.58 | 0.37 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.81 [0.73, 0.89] | 0.60 | 0.20 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.77 [0.69, 0.87] | 0.54 | 0.33 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.82 [0.75, 0.91] | 0.59 | 0.19 |
+| `ctl-h1` | `learned-s1` | 0.73 [0.66, 0.82] | 0.58 | 0.34 |
+| `ctl-h1` | `learned-s1-arc` | 0.84 [0.76, 0.93] | 0.61 | 0.22 |
+| `ctl-h1` | `learned-s2` | 0.75 [0.66, 0.84] | 0.56 | 0.35 |
+| `ctl-h1` | `learned-s2-arc` | 0.83 [0.75, 0.93] | 0.60 | 0.21 |
+| `ctl-h1` | `learned-s3` | 0.79 [0.71, 0.88] | 0.55 | 0.30 |
+| `ctl-h1` | `learned-s3-arc` | 0.85 [0.77, 0.94] | 0.59 | 0.22 |
+| `search3` | `learned-s1` | 0.78 [0.68, 0.89] | 0.57 | 0.35 |
+| `search3` | `learned-s1-arc` | 0.89 [0.80, 0.99] | 0.57 | 0.18 |
+| `search3` | `learned-s2` | 0.79 [0.69, 0.91] | 0.56 | 0.34 |
+| `search3` | `learned-s2-arc` | 0.89 [0.80, 0.99] | 0.55 | 0.18 |
+| `search3` | `learned-s3` | 0.84 [0.74, 0.96] | 0.53 | 0.32 |
+| `search3` | `learned-s3-arc` | 0.90 [0.81, 1.01] | 0.56 | 0.18 |
+
+### Shifted
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 1.70 [1.52, 1.91] | 0.29 | 0.09 |
+| `c2` | `learned-s1-arc` | 1.38 [1.22, 1.56] | 0.41 | 0.16 |
+| `c2` | `learned-s2` | 1.72 [1.52, 1.94] | 0.30 | 0.10 |
+| `c2` | `learned-s2-arc` | 1.35 [1.20, 1.52] | 0.41 | 0.17 |
+| `c2` | `learned-s3` | 1.71 [1.53, 1.93] | 0.28 | 0.10 |
+| `c2` | `learned-s3-arc` | 1.38 [1.22, 1.55] | 0.40 | 0.15 |
+| `ctl-d1-q0.01` | `learned-s1` | 1.01 [0.90, 1.13] | 0.45 | 0.20 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.82 [0.73, 0.91] | 0.54 | 0.27 |
+| `ctl-d1-q0.01` | `learned-s2` | 1.02 [0.90, 1.15] | 0.45 | 0.20 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.80 [0.71, 0.90] | 0.54 | 0.27 |
+| `ctl-d1-q0.01` | `learned-s3` | 1.02 [0.90, 1.15] | 0.44 | 0.19 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.82 [0.73, 0.91] | 0.54 | 0.25 |
+| `ctl-h1` | `learned-s1` | 0.93 [0.84, 1.04] | 0.49 | 0.22 |
+| `ctl-h1` | `learned-s1-arc` | 0.76 [0.67, 0.85] | 0.61 | 0.29 |
+| `ctl-h1` | `learned-s2` | 0.94 [0.84, 1.06] | 0.49 | 0.22 |
+| `ctl-h1` | `learned-s2-arc` | 0.74 [0.66, 0.83] | 0.59 | 0.31 |
+| `ctl-h1` | `learned-s3` | 0.94 [0.84, 1.06] | 0.48 | 0.22 |
+| `ctl-h1` | `learned-s3-arc` | 0.75 [0.67, 0.85] | 0.61 | 0.29 |
+| `search3` | `learned-s1` | 1.04 [0.93, 1.16] | 0.43 | 0.20 |
+| `search3` | `learned-s1-arc` | 0.84 [0.75, 0.94] | 0.55 | 0.26 |
+| `search3` | `learned-s2` | 1.04 [0.92, 1.18] | 0.43 | 0.19 |
+| `search3` | `learned-s2-arc` | 0.82 [0.73, 0.93] | 0.55 | 0.26 |
+| `search3` | `learned-s3` | 1.04 [0.93, 1.18] | 0.43 | 0.19 |
+| `search3` | `learned-s3-arc` | 0.84 [0.75, 0.94] | 0.55 | 0.25 |
+
+### Shifted 2
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 1.46 [1.27, 1.68] | 0.39 | 0.18 |
+| `c2` | `learned-s1-arc` | 2.12 [1.83, 2.45] | 0.27 | 0.10 |
+| `c2` | `learned-s2` | 1.48 [1.29, 1.70] | 0.37 | 0.17 |
+| `c2` | `learned-s2-arc` | 2.10 [1.82, 2.43] | 0.28 | 0.10 |
+| `c2` | `learned-s3` | 1.51 [1.31, 1.74] | 0.37 | 0.19 |
+| `c2` | `learned-s3-arc` | 2.29 [1.98, 2.65] | 0.26 | 0.09 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.60 [0.53, 0.68] | 0.67 | 0.40 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.87 [0.79, 0.96] | 0.52 | 0.15 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.61 [0.54, 0.69] | 0.67 | 0.38 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.87 [0.79, 0.95] | 0.50 | 0.14 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.62 [0.55, 0.70] | 0.65 | 0.37 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.94 [0.85, 1.05] | 0.50 | 0.14 |
+| `ctl-h1` | `learned-s1` | 0.63 [0.56, 0.70] | 0.65 | 0.37 |
+| `ctl-h1` | `learned-s1-arc` | 0.91 [0.83, 1.01] | 0.53 | 0.17 |
+| `ctl-h1` | `learned-s2` | 0.64 [0.57, 0.71] | 0.66 | 0.34 |
+| `ctl-h1` | `learned-s2-arc` | 0.90 [0.82, 0.99] | 0.52 | 0.16 |
+| `ctl-h1` | `learned-s3` | 0.65 [0.57, 0.73] | 0.63 | 0.34 |
+| `ctl-h1` | `learned-s3-arc` | 0.99 [0.89, 1.09] | 0.53 | 0.15 |
+| `search3` | `learned-s1` | 0.65 [0.58, 0.73] | 0.66 | 0.37 |
+| `search3` | `learned-s1-arc` | 0.94 [0.86, 1.03] | 0.48 | 0.12 |
+| `search3` | `learned-s2` | 0.66 [0.58, 0.74] | 0.65 | 0.36 |
+| `search3` | `learned-s2-arc` | 0.94 [0.85, 1.02] | 0.48 | 0.12 |
+| `search3` | `learned-s3` | 0.67 [0.59, 0.76] | 0.63 | 0.34 |
+| `search3` | `learned-s3-arc` | 1.02 [0.93, 1.13] | 0.47 | 0.11 |
+
+### Held course
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 0.62 [0.54, 0.72] | 0.66 | 0.40 |
+| `c2` | `learned-s1-arc` | 1.06 [0.99, 1.14] | 0.48 | 0.05 |
+| `c2` | `learned-s2` | 0.63 [0.55, 0.73] | 0.70 | 0.35 |
+| `c2` | `learned-s2-arc` | 1.07 [0.99, 1.14] | 0.48 | 0.05 |
+| `c2` | `learned-s3` | 0.64 [0.56, 0.74] | 0.67 | 0.36 |
+| `c2` | `learned-s3-arc` | 1.07 [1.00, 1.14] | 0.48 | 0.05 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.62 [0.54, 0.71] | 0.67 | 0.40 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 1.06 [1.01, 1.11] | 0.48 | 0.01 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.63 [0.54, 0.72] | 0.69 | 0.36 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 1.06 [1.01, 1.11] | 0.46 | 0.01 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.64 [0.55, 0.74] | 0.65 | 0.35 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 1.06 [1.01, 1.11] | 0.46 | 0.01 |
+| `ctl-h1` | `learned-s1` | 0.88 [0.77, 1.02] | 0.50 | 0.21 |
+| `ctl-h1` | `learned-s1-arc` | 1.51 [1.36, 1.67] | 0.35 | 0.08 |
+| `ctl-h1` | `learned-s2` | 0.89 [0.77, 1.03] | 0.52 | 0.25 |
+| `ctl-h1` | `learned-s2-arc` | 1.51 [1.36, 1.67] | 0.35 | 0.08 |
+| `ctl-h1` | `learned-s3` | 0.91 [0.78, 1.04] | 0.50 | 0.22 |
+| `ctl-h1` | `learned-s3-arc` | 1.51 [1.36, 1.67] | 0.35 | 0.08 |
+| `search3` | `learned-s1` | 1.05 [0.89, 1.23] | 0.48 | 0.24 |
+| `search3` | `learned-s1-arc` | 1.79 [1.57, 2.05] | 0.29 | 0.05 |
+| `search3` | `learned-s2` | 1.06 [0.90, 1.25] | 0.51 | 0.27 |
+| `search3` | `learned-s2-arc` | 1.79 [1.57, 2.05] | 0.29 | 0.05 |
+| `search3` | `learned-s3` | 1.08 [0.90, 1.28] | 0.45 | 0.24 |
+| `search3` | `learned-s3-arc` | 1.80 [1.57, 2.06] | 0.28 | 0.05 |
+
+### Correlated, mild
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 3.28 [2.75, 3.86] | 0.13 | 0.07 |
+| `c2` | `learned-s1-arc` | 3.17 [2.58, 3.89] | 0.22 | 0.08 |
+| `c2` | `learned-s2` | 3.22 [2.70, 3.82] | 0.13 | 0.06 |
+| `c2` | `learned-s2-arc` | 2.92 [2.40, 3.57] | 0.22 | 0.09 |
+| `c2` | `learned-s3` | 3.34 [2.78, 3.98] | 0.12 | 0.07 |
+| `c2` | `learned-s3-arc` | 3.25 [2.62, 4.04] | 0.23 | 0.10 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.86 [0.74, 1.00] | 0.53 | 0.28 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.84 [0.72, 0.96] | 0.59 | 0.22 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.85 [0.71, 1.00] | 0.53 | 0.29 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.77 [0.65, 0.89] | 0.59 | 0.23 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.88 [0.75, 1.03] | 0.54 | 0.28 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.86 [0.73, 1.00] | 0.59 | 0.19 |
+| `ctl-h1` | `learned-s1` | 0.87 [0.75, 1.00] | 0.55 | 0.24 |
+| `ctl-h1` | `learned-s1-arc` | 0.84 [0.72, 0.97] | 0.58 | 0.23 |
+| `ctl-h1` | `learned-s2` | 0.85 [0.73, 0.98] | 0.54 | 0.28 |
+| `ctl-h1` | `learned-s2-arc` | 0.77 [0.66, 0.90] | 0.61 | 0.24 |
+| `ctl-h1` | `learned-s3` | 0.89 [0.77, 1.02] | 0.55 | 0.25 |
+| `ctl-h1` | `learned-s3-arc` | 0.86 [0.73, 1.00] | 0.57 | 0.20 |
+| `search3` | `learned-s1` | 1.10 [0.95, 1.28] | 0.46 | 0.17 |
+| `search3` | `learned-s1-arc` | 1.07 [0.90, 1.27] | 0.48 | 0.19 |
+| `search3` | `learned-s2` | 1.08 [0.92, 1.26] | 0.49 | 0.18 |
+| `search3` | `learned-s2-arc` | 0.98 [0.83, 1.17] | 0.49 | 0.22 |
+| `search3` | `learned-s3` | 1.13 [0.97, 1.31] | 0.43 | 0.20 |
+| `search3` | `learned-s3-arc` | 1.10 [0.92, 1.30] | 0.46 | 0.19 |
+
+### Correlated
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 2.70 [2.35, 3.09] | 0.18 | 0.05 |
+| `c2` | `learned-s1-arc` | 2.24 [1.92, 2.60] | 0.27 | 0.11 |
+| `c2` | `learned-s2` | 2.64 [2.31, 3.02] | 0.18 | 0.05 |
+| `c2` | `learned-s2-arc` | 2.25 [1.92, 2.60] | 0.25 | 0.11 |
+| `c2` | `learned-s3` | 2.68 [2.33, 3.06] | 0.20 | 0.06 |
+| `c2` | `learned-s3-arc` | 2.28 [1.94, 2.62] | 0.27 | 0.11 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.99 [0.88, 1.13] | 0.49 | 0.22 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.83 [0.74, 0.92] | 0.56 | 0.23 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.97 [0.85, 1.10] | 0.48 | 0.24 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.83 [0.74, 0.92] | 0.56 | 0.22 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.99 [0.86, 1.12] | 0.51 | 0.22 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.84 [0.75, 0.94] | 0.55 | 0.22 |
+| `ctl-h1` | `learned-s1` | 0.98 [0.88, 1.10] | 0.50 | 0.22 |
+| `ctl-h1` | `learned-s1-arc` | 0.82 [0.73, 0.92] | 0.60 | 0.26 |
+| `ctl-h1` | `learned-s2` | 0.96 [0.86, 1.07] | 0.50 | 0.23 |
+| `ctl-h1` | `learned-s2-arc` | 0.82 [0.73, 0.92] | 0.60 | 0.24 |
+| `ctl-h1` | `learned-s3` | 0.97 [0.87, 1.09] | 0.52 | 0.23 |
+| `ctl-h1` | `learned-s3-arc` | 0.83 [0.74, 0.93] | 0.61 | 0.25 |
+| `search3` | `learned-s1` | 1.32 [1.17, 1.50] | 0.35 | 0.17 |
+| `search3` | `learned-s1-arc` | 1.10 [0.95, 1.27] | 0.45 | 0.21 |
+| `search3` | `learned-s2` | 1.29 [1.14, 1.47] | 0.36 | 0.17 |
+| `search3` | `learned-s2-arc` | 1.10 [0.95, 1.26] | 0.45 | 0.20 |
+| `search3` | `learned-s3` | 1.31 [1.15, 1.49] | 0.35 | 0.19 |
+| `search3` | `learned-s3-arc` | 1.12 [0.97, 1.28] | 0.44 | 0.21 |
+
+### Outliers
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 3.27 [2.78, 3.85] | 0.19 | 0.07 |
+| `c2` | `learned-s1-arc` | 4.08 [3.44, 4.88] | 0.18 | 0.06 |
+| `c2` | `learned-s2` | 3.37 [2.85, 3.99] | 0.19 | 0.07 |
+| `c2` | `learned-s2-arc` | 4.03 [3.37, 4.82] | 0.21 | 0.08 |
+| `c2` | `learned-s3` | 3.46 [2.96, 4.12] | 0.19 | 0.08 |
+| `c2` | `learned-s3-arc` | 4.21 [3.46, 5.08] | 0.18 | 0.08 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.63 [0.54, 0.73] | 0.62 | 0.40 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.78 [0.70, 0.89] | 0.58 | 0.23 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.65 [0.55, 0.76] | 0.61 | 0.38 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.77 [0.68, 0.88] | 0.59 | 0.22 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.67 [0.57, 0.77] | 0.59 | 0.41 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.81 [0.71, 0.91] | 0.57 | 0.21 |
+| `ctl-h1` | `learned-s1` | 0.64 [0.56, 0.74] | 0.60 | 0.39 |
+| `ctl-h1` | `learned-s1-arc` | 0.80 [0.71, 0.91] | 0.60 | 0.25 |
+| `ctl-h1` | `learned-s2` | 0.66 [0.57, 0.76] | 0.61 | 0.38 |
+| `ctl-h1` | `learned-s2-arc` | 0.79 [0.70, 0.90] | 0.60 | 0.25 |
+| `ctl-h1` | `learned-s3` | 0.68 [0.59, 0.78] | 0.62 | 0.38 |
+| `ctl-h1` | `learned-s3-arc` | 0.83 [0.73, 0.93] | 0.60 | 0.24 |
+| `search3` | `learned-s1` | 1.05 [0.92, 1.21] | 0.45 | 0.25 |
+| `search3` | `learned-s1-arc` | 1.32 [1.14, 1.52] | 0.41 | 0.17 |
+| `search3` | `learned-s2` | 1.09 [0.95, 1.25] | 0.43 | 0.24 |
+| `search3` | `learned-s2-arc` | 1.30 [1.12, 1.49] | 0.42 | 0.18 |
+| `search3` | `learned-s3` | 1.12 [0.98, 1.28] | 0.44 | 0.22 |
+| `search3` | `learned-s3-arc` | 1.36 [1.18, 1.58] | 0.42 | 0.17 |
+
+### Outliers, harsh
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 5.00 [4.18, 5.96] | 0.10 | 0.03 |
+| `c2` | `learned-s1-arc` | 6.58 [5.22, 8.17] | 0.10 | 0.04 |
+| `c2` | `learned-s2` | 5.01 [4.22, 5.96] | 0.09 | 0.03 |
+| `c2` | `learned-s2-arc` | 6.44 [5.16, 7.96] | 0.10 | 0.05 |
+| `c2` | `learned-s3` | 5.15 [4.30, 6.10] | 0.09 | 0.03 |
+| `c2` | `learned-s3-arc` | 6.35 [5.07, 7.89] | 0.10 | 0.05 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.67 [0.55, 0.81] | 0.58 | 0.38 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.88 [0.75, 1.02] | 0.52 | 0.23 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.67 [0.56, 0.80] | 0.60 | 0.38 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.86 [0.74, 0.99] | 0.52 | 0.20 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.69 [0.57, 0.83] | 0.59 | 0.36 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.85 [0.73, 0.98] | 0.58 | 0.21 |
+| `ctl-h1` | `learned-s1` | 0.67 [0.56, 0.80] | 0.63 | 0.40 |
+| `ctl-h1` | `learned-s1-arc` | 0.88 [0.75, 1.03] | 0.58 | 0.25 |
+| `ctl-h1` | `learned-s2` | 0.67 [0.57, 0.79] | 0.65 | 0.39 |
+| `ctl-h1` | `learned-s2-arc` | 0.86 [0.74, 1.00] | 0.59 | 0.23 |
+| `ctl-h1` | `learned-s3` | 0.69 [0.58, 0.82] | 0.60 | 0.35 |
+| `ctl-h1` | `learned-s3-arc` | 0.85 [0.73, 0.98] | 0.61 | 0.23 |
+| `search3` | `learned-s1` | 1.63 [1.38, 1.94] | 0.32 | 0.13 |
+| `search3` | `learned-s1-arc` | 2.14 [1.76, 2.61] | 0.30 | 0.10 |
+| `search3` | `learned-s2` | 1.63 [1.39, 1.91] | 0.31 | 0.13 |
+| `search3` | `learned-s2-arc` | 2.10 [1.72, 2.56] | 0.30 | 0.10 |
+| `search3` | `learned-s3` | 1.68 [1.43, 1.96] | 0.30 | 0.11 |
+| `search3` | `learned-s3-arc` | 2.07 [1.72, 2.49] | 0.28 | 0.10 |
+
+### Bias
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 1.74 [1.50, 2.02] | 0.33 | 0.12 |
+| `c2` | `learned-s1-arc` | 1.86 [1.62, 2.15] | 0.32 | 0.10 |
+| `c2` | `learned-s2` | 1.69 [1.46, 1.96] | 0.33 | 0.14 |
+| `c2` | `learned-s2-arc` | 1.88 [1.65, 2.16] | 0.31 | 0.07 |
+| `c2` | `learned-s3` | 1.87 [1.63, 2.16] | 0.29 | 0.11 |
+| `c2` | `learned-s3-arc` | 1.84 [1.60, 2.12] | 0.31 | 0.08 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.92 [0.81, 1.05] | 0.52 | 0.23 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.98 [0.88, 1.09] | 0.49 | 0.15 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.89 [0.78, 1.03] | 0.50 | 0.25 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 1.00 [0.90, 1.09] | 0.47 | 0.14 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.99 [0.87, 1.13] | 0.46 | 0.23 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.97 [0.86, 1.07] | 0.50 | 0.15 |
+| `ctl-h1` | `learned-s1` | 0.92 [0.81, 1.03] | 0.54 | 0.21 |
+| `ctl-h1` | `learned-s1-arc` | 0.98 [0.88, 1.09] | 0.54 | 0.15 |
+| `ctl-h1` | `learned-s2` | 0.89 [0.78, 1.01] | 0.53 | 0.23 |
+| `ctl-h1` | `learned-s2-arc` | 0.99 [0.90, 1.09] | 0.54 | 0.14 |
+| `ctl-h1` | `learned-s3` | 0.99 [0.87, 1.11] | 0.47 | 0.22 |
+| `ctl-h1` | `learned-s3-arc` | 0.97 [0.87, 1.07] | 0.54 | 0.15 |
+| `search3` | `learned-s1` | 0.92 [0.81, 1.05] | 0.51 | 0.23 |
+| `search3` | `learned-s1-arc` | 0.99 [0.89, 1.09] | 0.48 | 0.15 |
+| `search3` | `learned-s2` | 0.89 [0.78, 1.02] | 0.50 | 0.25 |
+| `search3` | `learned-s2-arc` | 1.00 [0.90, 1.10] | 0.47 | 0.14 |
+| `search3` | `learned-s3` | 0.99 [0.87, 1.13] | 0.45 | 0.22 |
+| `search3` | `learned-s3-arc` | 0.97 [0.87, 1.08] | 0.49 | 0.15 |
+
+### Whole
+
+| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |
+| --- | --- | --- | --- | --- |
+| `c2` | `learned-s1` | 1.52 [1.11, 2.12] | 0.35 | 0.15 |
+| `c2` | `learned-s1-arc` | 2.00 [1.53, 2.68] | 0.30 | 0.13 |
+| `c2` | `learned-s2` | 1.42 [1.05, 1.96] | 0.43 | 0.18 |
+| `c2` | `learned-s2-arc` | 1.81 [1.40, 2.45] | 0.35 | 0.13 |
+| `c2` | `learned-s3` | 1.65 [1.21, 2.28] | 0.37 | 0.13 |
+| `c2` | `learned-s3-arc` | 1.81 [1.34, 2.44] | 0.32 | 0.13 |
+| `ctl-d1-q0.01` | `learned-s1` | 0.62 [0.46, 0.84] | 0.55 | 0.33 |
+| `ctl-d1-q0.01` | `learned-s1-arc` | 0.82 [0.63, 1.07] | 0.50 | 0.25 |
+| `ctl-d1-q0.01` | `learned-s2` | 0.58 [0.41, 0.81] | 0.58 | 0.40 |
+| `ctl-d1-q0.01` | `learned-s2-arc` | 0.74 [0.58, 0.96] | 0.55 | 0.30 |
+| `ctl-d1-q0.01` | `learned-s3` | 0.68 [0.50, 0.93] | 0.58 | 0.37 |
+| `ctl-d1-q0.01` | `learned-s3-arc` | 0.74 [0.58, 0.95] | 0.53 | 0.27 |
+| `ctl-h1` | `learned-s1` | 0.63 [0.48, 0.84] | 0.55 | 0.32 |
+| `ctl-h1` | `learned-s1-arc` | 0.83 [0.65, 1.06] | 0.57 | 0.28 |
+| `ctl-h1` | `learned-s2` | 0.59 [0.42, 0.81] | 0.62 | 0.35 |
+| `ctl-h1` | `learned-s2-arc` | 0.76 [0.60, 0.95] | 0.63 | 0.32 |
+| `ctl-h1` | `learned-s3` | 0.69 [0.51, 0.92] | 0.58 | 0.35 |
+| `ctl-h1` | `learned-s3-arc` | 0.75 [0.59, 0.95] | 0.60 | 0.27 |
+| `search3` | `learned-s1` | 0.66 [0.48, 0.90] | 0.53 | 0.33 |
+| `search3` | `learned-s1-arc` | 0.87 [0.65, 1.17] | 0.47 | 0.27 |
+| `search3` | `learned-s2` | 0.62 [0.44, 0.86] | 0.57 | 0.40 |
+| `search3` | `learned-s2-arc` | 0.79 [0.60, 1.04] | 0.52 | 0.33 |
+| `search3` | `learned-s3` | 0.72 [0.52, 0.99] | 0.58 | 0.35 |
+| `search3` | `learned-s3-arc` | 0.79 [0.61, 1.01] | 0.50 | 0.30 |
+
+## Efficiency against the bound (range, course, speed) and rows without a state
+
+| Split | System | Range | Course | Speed | Periods without a track |
+| --- | --- | --- | --- | --- | --- |
+| Annex B | `c2` | 3.26 | 4.30 | 3.48 | 0 |
+| Annex B | `ctl-d1-q0.01` | 1.66 | 1.98 | 1.57 | 0 |
+| Annex B | `ctl-h1` | 1.66 | 1.99 | 1.60 | 0 |
+| Annex B | `learned-s1` | 2.06 | 2.81 | 2.31 | 0 |
+| Annex B | `learned-s1-arc` | 1.94 | 2.21 | 1.78 | 0 |
+| Annex B | `learned-s2` | 2.13 | 2.99 | 2.26 | 0 |
+| Annex B | `learned-s2-arc` | 1.92 | 2.26 | 1.76 | 0 |
+| Annex B | `learned-s3` | 1.89 | 2.94 | 2.17 | 0 |
+| Annex B | `learned-s3-arc` | 1.93 | 2.34 | 1.80 | 0 |
+| Annex B | `search3` | 1.71 | 1.99 | 1.62 | 0 |
+| Shifted | `c2` | 3.41 | 3.96 | 3.25 | 0 |
+| Shifted | `ctl-d1-q0.01` | 2.13 | 2.34 | 2.08 | 0 |
+| Shifted | `ctl-h1` | 2.06 | 2.34 | 2.02 | 0 |
+| Shifted | `learned-s1` | 2.03 | 2.78 | 2.09 | 0 |
+| Shifted | `learned-s1-arc` | 2.69 | 3.13 | 2.47 | 0 |
+| Shifted | `learned-s2` | 2.00 | 2.75 | 2.06 | 0 |
+| Shifted | `learned-s2-arc` | 2.66 | 3.25 | 2.34 | 0 |
+| Shifted | `learned-s3` | 1.94 | 2.76 | 2.09 | 0 |
+| Shifted | `learned-s3-arc` | 2.64 | 3.19 | 2.49 | 0 |
+| Shifted | `search3` | 2.13 | 2.12 | 1.93 | 0 |
+| Shifted 2 | `c2` | 3.56 | 4.33 | 3.56 | 0 |
+| Shifted 2 | `ctl-d1-q0.01` | 1.45 | 1.80 | 1.50 | 0 |
+| Shifted 2 | `ctl-h1` | 1.59 | 1.98 | 1.71 | 0 |
+| Shifted 2 | `learned-s1` | 2.36 | 3.57 | 2.82 | 0 |
+| Shifted 2 | `learned-s1-arc` | 1.74 | 1.98 | 1.68 | 0 |
+| Shifted 2 | `learned-s2` | 2.34 | 3.64 | 2.70 | 0 |
+| Shifted 2 | `learned-s2-arc` | 1.80 | 2.01 | 1.75 | 0 |
+| Shifted 2 | `learned-s3` | 2.25 | 3.52 | 2.66 | 0 |
+| Shifted 2 | `learned-s3-arc` | 1.66 | 1.93 | 1.68 | 0 |
+| Shifted 2 | `search3` | 1.59 | 1.89 | 1.60 | 0 |
+| Held course | `c2` | 0.97 | 1.00 | 0.89 | 0 |
+| Held course | `ctl-d1-q0.01` | 0.95 | 1.01 | 0.84 | 0 |
+| Held course | `ctl-h1` | 1.46 | 1.64 | 1.73 | 0 |
+| Held course | `learned-s1` | 1.45 | 1.85 | 1.50 | 0 |
+| Held course | `learned-s1-arc` | 0.91 | 0.96 | 0.79 | 0 |
+| Held course | `learned-s2` | 1.34 | 1.87 | 1.49 | 0 |
+| Held course | `learned-s2-arc` | 0.91 | 0.96 | 0.79 | 0 |
+| Held course | `learned-s3` | 1.28 | 1.83 | 1.45 | 0 |
+| Held course | `learned-s3-arc` | 0.91 | 0.94 | 0.79 | 0 |
+| Held course | `search3` | 1.79 | 2.19 | 2.36 | 0 |
+| Correlated, mild | `c2` | 13.07 | 17.27 | 9.79 | 0 |
+| Correlated, mild | `ctl-d1-q0.01` | 3.25 | 3.94 | 2.85 | 0 |
+| Correlated, mild | `ctl-h1` | 3.26 | 3.98 | 3.29 | 0 |
+| Correlated, mild | `learned-s1` | 4.11 | 5.21 | 4.02 | 0 |
+| Correlated, mild | `learned-s1-arc` | 3.95 | 4.97 | 3.84 | 0 |
+| Correlated, mild | `learned-s2` | 4.22 | 5.30 | 3.97 | 0 |
+| Correlated, mild | `learned-s2-arc` | 4.45 | 5.48 | 4.20 | 0 |
+| Correlated, mild | `learned-s3` | 4.17 | 5.29 | 4.08 | 0 |
+| Correlated, mild | `learned-s3-arc` | 3.90 | 4.92 | 3.79 | 0 |
+| Correlated, mild | `search3` | 4.45 | 6.07 | 3.91 | 0 |
+| Correlated | `c2` | 11.57 | 17.87 | 9.00 | 0 |
+| Correlated | `ctl-d1-q0.01` | 4.26 | 5.57 | 3.87 | 0 |
+| Correlated | `ctl-h1` | 4.22 | 5.32 | 3.85 | 0 |
+| Correlated | `learned-s1` | 4.20 | 6.07 | 4.41 | 0 |
+| Correlated | `learned-s1-arc` | 5.12 | 6.36 | 4.62 | 0 |
+| Correlated | `learned-s2` | 4.35 | 6.29 | 4.32 | 0 |
+| Correlated | `learned-s2-arc` | 5.22 | 6.30 | 5.05 | 0 |
+| Correlated | `learned-s3` | 4.34 | 6.18 | 4.51 | 0 |
+| Correlated | `learned-s3-arc` | 5.08 | 6.10 | 5.05 | 0 |
+| Correlated | `search3` | 5.62 | 8.63 | 5.05 | 0 |
+| Outliers | `c2` | 10.28 | 12.90 | 7.44 | 0 |
+| Outliers | `ctl-d1-q0.01` | 1.70 | 1.99 | 1.49 | 0 |
+| Outliers | `ctl-h1` | 1.75 | 2.14 | 1.59 | 0 |
+| Outliers | `learned-s1` | 2.49 | 3.16 | 2.81 | 0 |
+| Outliers | `learned-s1-arc` | 2.08 | 2.58 | 1.93 | 0 |
+| Outliers | `learned-s2` | 2.52 | 3.47 | 2.54 | 0 |
+| Outliers | `learned-s2-arc` | 2.11 | 2.62 | 2.09 | 0 |
+| Outliers | `learned-s3` | 2.35 | 3.35 | 2.91 | 0 |
+| Outliers | `learned-s3-arc` | 2.09 | 2.52 | 1.98 | 0 |
+| Outliers | `search3` | 3.03 | 3.59 | 3.16 | 0 |
+| Outliers, harsh | `c2` | 15.21 | 22.83 | 10.10 | 0 |
+| Outliers, harsh | `ctl-d1-q0.01` | 2.03 | 2.16 | 1.67 | 0 |
+| Outliers, harsh | `ctl-h1` | 2.03 | 2.29 | 1.73 | 0 |
+| Outliers, harsh | `learned-s1` | 2.91 | 3.70 | 3.17 | 0 |
+| Outliers, harsh | `learned-s1-arc` | 2.08 | 2.74 | 1.61 | 0 |
+| Outliers, harsh | `learned-s2` | 2.72 | 3.87 | 2.84 | 0 |
+| Outliers, harsh | `learned-s2-arc` | 2.08 | 2.72 | 1.80 | 0 |
+| Outliers, harsh | `learned-s3` | 2.86 | 4.02 | 3.03 | 0 |
+| Outliers, harsh | `learned-s3-arc` | 2.18 | 2.78 | 1.81 | 0 |
+| Outliers, harsh | `search3` | 5.11 | 6.64 | 4.10 | 0 |
+| Bias | `c2` | 5.03 | 6.24 | 4.79 | 0 |
+| Bias | `ctl-d1-q0.01` | 2.69 | 3.56 | 2.65 | 0 |
+| Bias | `ctl-h1` | 2.72 | 3.57 | 2.78 | 0 |
+| Bias | `learned-s1` | 2.78 | 3.96 | 2.93 | 0 |
+| Bias | `learned-s1-arc` | 2.71 | 4.01 | 3.01 | 0 |
+| Bias | `learned-s2` | 2.65 | 4.13 | 2.92 | 0 |
+| Bias | `learned-s2-arc` | 2.79 | 4.03 | 2.94 | 0 |
+| Bias | `learned-s3` | 2.48 | 4.01 | 2.84 | 0 |
+| Bias | `learned-s3-arc` | 2.85 | 3.80 | 3.09 | 0 |
+| Bias | `search3` | 2.68 | 3.56 | 2.67 | 0 |
+| Whole | `c2` | 5.94 | 7.54 | 5.46 | 0 |
+| Whole | `ctl-d1-q0.01` | 2.16 | 2.26 | 1.88 | 0 |
+| Whole | `ctl-h1` | 2.25 | 2.32 | 1.92 | 0 |
+| Whole | `learned-s1` | 3.12 | 5.00 | 3.73 | 0 |
+| Whole | `learned-s1-arc` | 2.73 | 3.21 | 2.45 | 0 |
+| Whole | `learned-s2` | 3.30 | 5.50 | 3.53 | 0 |
+| Whole | `learned-s2-arc` | 2.74 | 3.21 | 2.43 | 0 |
+| Whole | `learned-s3` | 3.26 | 4.83 | 3.65 | 0 |
+| Whole | `learned-s3-arc` | 2.46 | 2.98 | 2.12 | 0 |
+| Whole | `search3` | 2.16 | 2.29 | 1.88 | 0 |
diff --git a/experiments/20261114-tl4/man2-bias.periods.json b/experiments/20261114-tl4/man2-bias.periods.json
new file mode 100644
index 00000000..828bceb2
--- /dev/null
+++ b/experiments/20261114-tl4/man2-bias.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "rows": 798, "seconds_long": 3985.0, "bearings": 740, "true": [[385.0, 535.0], [2115.0, 2455.0]], "true_kinds": [[true, true], [true, false]], "true_cut": [false, false], "reference_flagged": false, "history_converged": true, "edited": 0, "corr_estimate": [0.0, 0.0], "ctl_corr_estimate": [0.0, 0.0, false], "histories": {"c2": {"spans": [[265.0, 395.0], [2275.0, 2365.0], [3175.0, 3265.0]]}, "search3-d1-q0.01": {"spans": [[385.54543438684556, 501.4733090959028], [2286.8474440550617, 2425.8640925466843]]}, "search3": {"spans": [[385.54543438684556, 501.4733090959028], [2286.8474440550617, 2425.8640925466843]], "kinds": [[true, false], [true, false]], "evidence": -443.5968066976285, "prior": "plain/v3", "candidates": [{"spans": [[385.54543438684556, 501.4733090959028], [2286.8474440550617, 2425.8640925466843]], "kinds": [[true, false], [true, false]], "evidence": -443.5968066976285, "prior": "plain/v3"}, {"spans": [[401.05442847371086, 488.37185092133683], 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diff --git a/experiments/20261114-tl4/man2-corr-mild.periods.json b/experiments/20261114-tl4/man2-corr-mild.periods.json
new file mode 100644
index 00000000..932380d0
--- /dev/null
+++ b/experiments/20261114-tl4/man2-corr-mild.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261114-tl4/man2-corr.periods.json b/experiments/20261114-tl4/man2-corr.periods.json
new file mode 100644
index 00000000..862a7393
--- /dev/null
+++ b/experiments/20261114-tl4/man2-corr.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261114-tl4/man2-out-harsh.periods.json b/experiments/20261114-tl4/man2-out-harsh.periods.json
new file mode 100644
index 00000000..9fa73b4e
--- /dev/null
+++ b/experiments/20261114-tl4/man2-out-harsh.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261114-tl4/man2-out.periods.json b/experiments/20261114-tl4/man2-out.periods.json
new file mode 100644
index 00000000..082d4b58
--- /dev/null
+++ b/experiments/20261114-tl4/man2-out.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261114-tl4/man2.periods.json b/experiments/20261114-tl4/man2.periods.json
new file mode 100644
index 00000000..cdf18155
--- /dev/null
+++ b/experiments/20261114-tl4/man2.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261114-tl4/plan.json b/experiments/20261114-tl4/plan.json
new file mode 100644
index 00000000..06253233
--- /dev/null
+++ b/experiments/20261114-tl4/plan.json
@@ -0,0 +1,284 @@
+{
+ "description": "Spec 016's registered study (2026-11-13): the classical controls (settings from tuning seed 338) and the learned whole-period model's three seeds, scored on the same periods. Bound by digest in the decision record; never edited after.",
+ "data_split": "train",
+ "splits": {
+  "man2": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B's manoeuvre statistics, independent Gaussian errors",
+   "systems": [
+    "search3",
+    "edit",
+    "corr",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift": {
+   "ranges": "configs/timelate/tl-shift-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "shorter legs, smaller turns, faster turn rates, more speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "shift2": {
+   "ranges": "configs/timelate/tl-shift2-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "longer legs (mean 35 min), larger and slower turns, few speed changes",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "quiet": {
+   "ranges": "configs/timelate/tl-quiet-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "the target holds course",
+   "systems": [
+    "search3",
+    "broad",
+    "mix2",
+    "ctl"
+   ]
+  },
+  "man2-corr-mild": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 30 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-mild"
+  },
+  "man2-corr": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "correlated errors, 60 s, half the variance",
+   "systems": [
+    "search3",
+    "corr",
+    "ctl"
+   ],
+   "condition": "corr-central"
+  },
+  "man2-out": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "2% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-central"
+  },
+  "man2-out-harsh": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "5% outlying bearings (10 sd)",
+   "systems": [
+    "search3",
+    "edit",
+    "ctl"
+   ],
+   "condition": "out-harsh"
+  },
+  "man2-bias": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "a slowly varying bias (0.5 degree, 30 min): addressed by no control and absent from the model's training",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "condition": "bias-central"
+  },
+  "whole": {
+   "ranges": "configs/timelate/tl-ranges.json",
+   "heldout_ranges": "configs/timelate/tl-heldout.json",
+   "what": "Annex B engagements uncut",
+   "systems": [
+    "search3",
+    "ctl"
+   ],
+   "whole": true
+  }
+ },
+ "prior": "configs/timelate/tl-ranges.json",
+ "prior_broad": "configs/timelate/tl-broad.json",
+ "gh_plan": "configs/gateh/gh-a.json",
+ "settings": {
+  "steps": 12,
+  "max_manoeuvres": 10,
+  "candidates": 10,
+  "score_every": 6,
+  "temperatures": [
+   1
+  ],
+  "margins": [
+   1
+  ],
+  "quiets": [
+   0.01
+  ],
+  "store_tracks": 20
+ },
+ "runner": "run4",
+ "reference": {
+  "sound": "history-fixed",
+  "tight_bound": 0.05
+ },
+ "study": {
+  "match_tolerance_s": 120.0,
+  "soundness_band": [
+   0.8,
+   1.3
+  ],
+  "convergence_min": 0.9,
+  "reference_flagged_max": 0.25
+ },
+ "comparisons": [
+  [
+   "ctl-d1-q0.01",
+   "learned-s1-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s1-arc"
+  ],
+  [
+   "search3",
+   "learned-s1-arc"
+  ],
+  [
+   "c2",
+   "learned-s1-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s2-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s2-arc"
+  ],
+  [
+   "search3",
+   "learned-s2-arc"
+  ],
+  [
+   "c2",
+   "learned-s2-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s3-arc"
+  ],
+  [
+   "ctl-h1",
+   "learned-s3-arc"
+  ],
+  [
+   "search3",
+   "learned-s3-arc"
+  ],
+  [
+   "c2",
+   "learned-s3-arc"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s1"
+  ],
+  [
+   "ctl-h1",
+   "learned-s1"
+  ],
+  [
+   "search3",
+   "learned-s1"
+  ],
+  [
+   "c2",
+   "learned-s1"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s2"
+  ],
+  [
+   "ctl-h1",
+   "learned-s2"
+  ],
+  [
+   "search3",
+   "learned-s2"
+  ],
+  [
+   "c2",
+   "learned-s2"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "learned-s3"
+  ],
+  [
+   "ctl-h1",
+   "learned-s3"
+  ],
+  [
+   "search3",
+   "learned-s3"
+  ],
+  [
+   "c2",
+   "learned-s3"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "ctl-h1"
+  ],
+  [
+   "search3",
+   "ctl-d1-q0.01"
+  ],
+  [
+   "ctl-d1-q0.01",
+   "c2"
+  ]
+ ],
+ "criteria": {
+  "control": "ctl-d1-q0.01",
+  "hedged": "ctl-h1",
+  "learned": [
+   "learned-s1",
+   "learned-s2",
+   "learned-s3"
+  ],
+  "mismatch_splits": [
+   "shift",
+   "shift2",
+   "man2-corr",
+   "man2-out",
+   "man2-bias"
+  ],
+  "no_harm_splits": [
+   "man2",
+   "quiet"
+  ]
+ },
+ "learned_plans": {
+  "learned-s1": "configs/regress/tlm-s1.json",
+  "learned-s2": "configs/regress/tlm-s2.json",
+  "learned-s3": "configs/regress/tlm-s3.json"
+ }
+}
\ No newline at end of file
diff --git a/experiments/20261114-tl4/quiet.periods.json b/experiments/20261114-tl4/quiet.periods.json
new file mode 100644
index 00000000..a735ac18
--- /dev/null
+++ b/experiments/20261114-tl4/quiet.periods.json
@@ -0,0 +1 @@
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-571.1241977279394}}, "seconds": {"reference": 0.13, "plain/v3": 32.33, "search3/path": 0.09, "plain/broad": 35.05, "ctl/path": 0.11, "learned-s1/arc": 0.01, "learned-s2/arc": 0.01, "learned-s3/arc": 0.01}}, {"ordinal": 299, "rows": 912, "seconds_long": 4555.0, "bearings": 887, "true": [], "true_kinds": [], "true_cut": [], "reference_flagged": false, "history_converged": true, "edited": 0, "corr_estimate": [0.06, 25.72424257459498], "ctl_corr_estimate": [0.06, 25.72424257459498, false], "histories": {"c2": {"spans": []}, "search3-d1-q0.01": {"spans": []}, "search3": {"spans": [[1941.886985981028, 1963.0165407166958]], "kinds": [[true, false]], "evidence": -455.8842606558824, "prior": "plain/v3", "candidates": [{"spans": [[1941.886985981028, 1963.0165407166958]], "kinds": [[true, false]], "evidence": -455.8842606558824, "prior": "plain/v3"}, {"spans": [[2734.403081719876, 2756.25476080281]], "kinds": [[false, true]], "evidence": -457.4083062938278, "prior": "plain/v3"}, {"spans": [[870.4153324733145, 913.9132266122759], [1850.9029277541126, 1952.933242978939]], "kinds": [[true, false], [true, false]], "evidence": -457.6854984169599, "prior": "plain/v3"}, {"spans": [[1782.12514463425, 1803.3998846432382]], "kinds": [[true, true]], "evidence": -459.06456815716706, "prior": "plain/v3"}, {"spans": [[11.411521270669613, 108.86126089504845], [1890.0212043992817, 1969.3027937565248]], "kinds": [[false, true], [true, false]], "evidence": -459.86402579880445, "prior": "plain/v3"}], "path": [{"spans": [[1941.886985981028, 1963.0165407166958]], "kinds": [[true, false]], "evidence": -455.8842606558824}, {"spans": [], "kinds": [], "evidence": -466.8787143593053}], "declared": {"d1-q0.01": 1}}, "broad": {"spans": [[1690.1454797819392, 1700.1454797819392]], "kinds": [[true, false]], "evidence": -453.6077164570413, "prior": "plain/broad", "candidates": [{"spans": [[1690.1454797819392, 1700.1454797819392]], "kinds": [[true, false]], "evidence": -453.6077164570413, "prior": "plain/broad"}, {"spans": [[2737.032058610106, 2747.032058610106]], "kinds": [[false, true]], "evidence": -454.39388881079196, "prior": "plain/broad"}, {"spans": [[1529.172067807115, 1573.253087234798]], "kinds": [[false, true]], "evidence": -454.7576220525763, "prior": "plain/broad"}, {"spans": [[3008.0179535123707, 3018.0179535123707]], "kinds": [[false, true]], "evidence": -454.80238256150403, "prior": "plain/broad"}, {"spans": [[1885.6916131249359, 1905.0377245635004]], "kinds": [[true, true]], "evidence": -455.19985066419554, "prior": "plain/broad"}]}, "mix2": {"spans": [[1690.1454797819392, 1700.1454797819392]], "kinds": [[true, false]], "evidence": -453.6077164570413, "prior": "plain/broad", "candidates": [{"spans": [[1690.1454797819392, 1700.1454797819392]], "kinds": [[true, false]], "evidence": -453.6077164570413, "prior": "plain/broad"}, {"spans": [[2737.032058610106, 2747.032058610106]], "kinds": [[false, true]], "evidence": -454.39388881079196, "prior": "plain/broad"}, {"spans": [[1529.172067807115, 1573.253087234798]], "kinds": [[false, true]], "evidence": -454.7576220525763, "prior": "plain/broad"}, {"spans": [[3008.0179535123707, 3018.0179535123707]], "kinds": [[false, true]], "evidence": -454.80238256150403, "prior": "plain/broad"}, {"spans": [[1885.6916131249359, 1905.0377245635004]], "kinds": [[true, true]], "evidence": -455.19985066419554, "prior": "plain/broad"}], "log_bayes_broad": Infinity}, "ctl-d1-q0.01": {"spans": []}, "ctl": {"spans": [[1690.1454797819392, 1700.1454797819392]], "kinds": [[true, false]], "evidence": -453.6077164570413, "prior": "plain/broad", "candidates": [{"spans": [[1690.1454797819392, 1700.1454797819392]], "kinds": [[true, false]], "evidence": -453.6077164570413, "prior": "plain/broad"}, {"spans": [[2737.032058610106, 2747.032058610106]], "kinds": [[false, true]], "evidence": -454.39388881079196, "prior": "plain/broad"}, {"spans": [[1529.172067807115, 1573.253087234798]], "kinds": [[false, true]], "evidence": -454.7576220525763, "prior": "plain/broad"}, {"spans": [[3008.0179535123707, 3018.0179535123707]], "kinds": [[false, true]], "evidence": -454.80238256150403, "prior": "plain/broad"}, {"spans": [[1885.6916131249359, 1905.0377245635004]], "kinds": [[true, true]], "evidence": -455.19985066419554, "prior": "plain/broad"}], "log_bayes_broad": Infinity, "path": [{"spans": [[1690.1454797819392, 1700.1454797819392]], "kinds": [[true, false]], "evidence": -453.6077164570413}, {"spans": [], "kinds": [], "evidence": -457.87915368971187}], "declared": {"d1-q0.01": 1}}, "learned-s1": {"spans": []}, "learned-s1-arc": {"spans": [], "kinds": [], "evidence": -466.87871477775167}, "learned-s2": {"spans": []}, "learned-s2-arc": {"spans": [], "kinds": [], "evidence": -466.8787147990764}, "learned-s3": {"spans": []}, "learned-s3-arc": {"spans": [], "kinds": [], "evidence": -466.87871477679414}}, "seconds": {"reference": 0.1, "plain/v3": 7.39, "search3/path": 0.01, "plain/broad": 7.98, "ctl/path": 0.01, "learned-s1/arc": 0.01, "learned-s2/arc": 0.01, "learned-s3/arc": 0.01}}]
diff --git a/experiments/20261114-tl4/review.md b/experiments/20261114-tl4/review.md
new file mode 100644
index 00000000..1a9fb2d3
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+++ b/experiments/20261114-tl4/review.md
@@ -0,0 +1,252 @@
+# Spec 016 (study L): panel review, round 1 (2026-11-14)
+
+Five independent reviewers (numbers and provenance; fairness of the comparison; statistics
+and criteria; code; scope and conclusions) read the spec, the decision record and its
+addenda, the tuning, validation, training and test records and the code at the registered
+commits, and recomputed from the per-row errors. **All five: approve with findings. Decided
+in one round.** The registered outcome stands: learning is shown on no split, there is harm
+on Annex B data, and by the rule the classical controls stand and the learned track stops.
+No bug changes a decided number. What the outcome *means* is narrower than the record
+first said, and several of its readings are corrected.
+
+Figures here that are not in the generated files are the reviewers' recomputations from the
+per-row errors (results branch `results/timelate-tl-e-test-43563825793`) or, where marked
+**tuning**, explorations on spent seed 338 with seed 1's checkpoint. Neither is a
+registered result.
+
+## Verdict on each registered item
+
+| | As registered | Panel |
+| --- | --- | --- |
+| L-a | Passes (0.95; all converged; 0.085 held) | Reproduced. Read on Annex B only: on the outlier splits the true-history fit has a consistent cost in 6% and 0% of periods, and 27% of references are held on the shifted split |
+| L-b | Search ÷ control 1.68 outliers, 1.33 correlated, 1.03 shifted, 1.08 shifted 2 | Reproduced, and it replicates over tuning, validation and test. **The classical controls are the result worth carrying.** "Gap closed" is not a meaningful statistic where the reference is not a ceiling; the table mixed the declared and undeclared control in one row |
+| L-c | Not shown on any of five splits | Reproduced with independent code; all 42 ratios match; no side changes under BCa, a t-interval or other bootstrap seeds; the nearest losing side is 2.7 standard errors from 1. **But the loss is a tail**: the median per-period ratio is 1.00 on every split, and the whole loss sits in the quarter to half of periods where the model declares fewer manoeuvres than the control |
+| L-d | Harm on Annex B; the learned track wins on held course | Harm is robust (sign test p < 0.001; trimmed ratio unchanged). "Harm" misleads as a word: the read-out is level with the control in the median period and 1.8 times better than C″. The held-course "win" is not a learned track: where both declare no manoeuvre the two are the same one-leg fit; the 1.06 is the control's false manoeuvres in 23 of 299 periods |
+| What follows | The classical controls stand; the learned track stops | **This model with this read-out stops.** The registered comparison was a thresholded manoeuvre detector against the evidence search, not a learned estimator against a classical one. A learned proposer inside the search was not tested by the registration |
+
+## What was checked and found sound
+
+- All 42 criterion ratios recompute from the per-row errors to four decimals; no period has
+  a non-finite median for any system on a criterion split.
+- The three generated files reproduce byte for byte; the committed summary equals the
+  results branch's.
+- Every digest in `decision.json` (14 configurations, 16 files) equals the file at the
+  registration commit, at the test's commit and at HEAD. Between registration and test only
+  the test run file, the addenda and the validation and training records changed.
+- Order: decision record 21:04:22; training runs created 21:04:46 to 21:07:22; validation
+  run 21:08:24, committed 23:20; training branches 03:55 to 04:20; one test run, attempt 1,
+  05:55 at fbda7fa. No learned track was scored on seed 340 or 341 before it.
+- Checkpoints on the three branches hash to the values in the training records; training
+  ran at the registered commit to completion, last checkpoint, no selection on a monitor.
+- The model's inputs carry no truth; training reads seed 339 only; the learned track's frame
+  and rotation are right (exact targets reproduce the truth to 1e-5 on tuning data); the
+  trained model's errors on tuning data match the monitor's.
+- The second-difference algebra of the correlation estimate is right; `with_quiet` equals
+  prior kind "mix"; the search cache serves the right problem and prior; the hedged track
+  never mixes problems.
+- C, C″ and spec 015's registered modules are unchanged (digests `3b67d026…`, `e5040f5e…`).
+
+## Major findings
+
+1. **"About 20% worse" is a tail, and the tail is the untuned manoeuvre threshold**
+   (four reviewers). Control ÷ read-out, seed 1:
+
+   | Split | Paired ratio | Median per-period ratio | Ties (under 0.1%) | Learned over twice as bad / over twice as good | Where both declare the same number | Where the model declares fewer |
+   | --- | --- | --- | --- | --- | --- | --- |
+   | Annex B | 0.82 | 1.00 | 0.23 | 0.20 / 0.11 | 0.95 [0.88, 1.03] (274) | 0.52 [0.39, 0.68] (95) |
+   | Shifted | 0.82 | 1.00 | 0.10 | 0.27 / 0.13 | 1.01 [0.92, 1.13] (170) | 0.62 [0.51, 0.75] (179) |
+   | Correlated | 0.83 | 1.00 | 0.21 | 0.23 / 0.12 | 1.04 | 0.51 |
+   | Outliers | 0.78 | 1.00 | 0.22 | 0.23 / 0.09 | 0.95 | 0.52 |
+
+   Where the model's manoeuvres agree with the search's, the read-out *is* the classical
+   arc fit. The registered comparison measured the model's detector at a threshold of 0.5
+   that was never tuned, while the control's margin, quiet probability and temperature were
+   tuned on seed 338, and the model was never scored on validation although the order of
+   events allowed it once the controls' record existed. A plain sign test separates Annex B
+   (p < 0.001), outliers (p 0.003 to 0.02) and correlated errors (p 0.03 to 0.06), but not
+   the shifted split (p 0.02 to 0.10) or shifted 2 (p 0.25 to 0.9).
+
+2. **Tuning the read-out would not have rescued it; a search started from the model is a
+   cheaper equal, not a better system** (fairness; **tuning**, 53 Annex B and 43 shifted
+   periods, seed 1). Lowering the threshold makes the read-out worse on Annex B (0.78 at
+   0.5, 0.69 at 0.3, 0.59 at 0.2): the fit can no more remove a false manoeuvre than add a
+   missed one. The broad prior in the read-out changes nothing (0.81). The control's own
+   search started only from the model's track and spans equals the control (1.00
+   [0.81, 1.24] Annex B, 1.06 [0.85, 1.32] shifted) at about a third of the search time.
+   At no threshold is the model's detector clearly better than the control's search
+   (missed/false at 0.5: 0.31/0.06 against the control's 0.25/0.20). So the registration
+   did not leave a win on the table; it left an untested equal.
+
+3. **The model's own track is "level" in range only, and it is a prior-informed hedger**
+   (statistics, fairness, scope). On the shifted split control ÷ own track is 1.01 in range
+   but 0.79 [0.68, 0.92] in course and 0.77 [0.66, 0.88] in speed; with correlated errors
+   0.99, 0.83, 0.86. And the pooled 1.01 hides opposite signs: where the bound is over
+   0.05 the own track beats the control on every seed (1.30, 1.40, 1.34; 219 periods) and
+   where it is under 0.02 it is at 0.36; by range, 1.36 over 19 km and 0.65 under 12 km.
+   The record's "it never wins" is false by slice. This is the pattern of Gate H and spec
+   012: a shrinkage estimator wins where the bearings determine little and loses by two to
+   three where they determine much. Post hoc, range only (course and speed are not
+   separated on the same rows), and so not evidence of learning.
+
+4. **The "mismatch" splits are mismatched for the classical side only** (fairness). The
+   correlated, outlier and first shifted conditions all sit inside the model's training
+   ranges; shifted 2 mostly. Bias is truly absent from training but is a Gauss–Markov error
+   like the trained correlated one with a longer time: the mildest held-out condition
+   possible. The model lost in-distribution, which strengthens the negative for *this*
+   model and makes "no transfer advantage" a weak transfer test. The other way, the
+   correlated-error control knows the generator's error family and editing at 4 sd meets
+   only 10-sd outliers.
+
+5. **What the record claims for the control is too wide** (statistics, scope, fairness).
+   "Better wherever the bearing errors are not as assumed": under bias it is 1.00
+   [0.96, 1.05]. "At no cost on Annex B": 1.06 tuning, 0.96 [0.90, 1.02] validation, 1.09
+   test, a spread wider than one interval states, with a median ratio of 1.00 and 70% ties
+   on each. And it calls a one-manoeuvre target quiet more often: 0.14 against 0.13 on
+   Annex B (0.18 against 0.12 on validation), 0.29 against 0.05 with correlated errors,
+   0.17 against 0.04 with 5% outliers. Its clean-data side effects were not stated: the
+   correlation estimate fires in 8% of clean periods.
+
+6. **The outlier "gap closed" is read against a broken yardstick, and the L-b table mixed
+   systems** (statistics, scope, fairness). The reference is fitted to unedited bearings:
+   on the outlier splits its fits have a consistent cost in 6% and 0% of periods and the
+   control beats it in over half. The shares 1.20 and 1.55 mean only that. The table's
+   ratio column was for the declared control and its gap-closed column for the undeclared
+   one (held course: 0.35 against 0.86 declared). Control ÷ reference by how the reference
+   is fitted:
+
+   | Split | Held | Times free | Times held | Registered (held where drifted) |
+   | --- | --- | --- | --- | --- |
+   | Annex B | 0.09 | 1.25 | 1.88 | 1.28 |
+   | Shifted | 0.28 | 1.70 | 2.14 | 1.84 |
+   | Shifted 2 | 0.06 | 1.15 | 1.52 | 1.17 |
+   | Correlated | 0.27 | 1.19 | 1.54 | 1.32 |
+   | Outliers | 0.19 | 0.83 | 1.20 | 0.92 |
+   | Bias | 0.14 | 1.22 | 1.54 | 1.28 |
+
+   The shifted-prior gap is 1.7 to 2.1 under every convention and nothing closes it: that
+   part of the record is robust. But Annex B itself is 1.25 to 1.9, so it is not the only
+   gap.
+
+7. **The shifted-prior gap looks like a property of the problem** (scope). The shifted
+   split has 3.0 manoeuvres a period against 1.8, smaller turns and 15-minute legs;
+   control ÷ reference rises with the number of manoeuvres on every split (Annex B 1.21,
+   1.24, 1.43 for one, two, three or more; shifted 1.59, 1.45, 2.08); the control misses
+   0.57, 0.49 and 0.41 of manoeuvres at 60, 120 and 240 s; the reference, told the
+   structure, drifts in 27% of periods; and two unlike estimators land at the same range
+   error. No broader prior or likelihood is likely to close it. Not proved: the classing
+   of wrong histories was not rerun on this split, and no bound for unknown times exists.
+
+8. **Delivery gaps the record did not list** (scope, code).
+   - The prior-mismatch diagnostic (FR-006) does not work and is not a Bayes factor: it is
+     computed after de-duplication, so it compares different histories; it is infinite in
+     58 of 300 held-course periods; its median favours the broad prior on quiet targets
+     (+4.9) and not under a shifted prior (−1.3). The registered form was not built.
+   - Candidate calibration under the broadened prior (user story 3): absent.
+   - The export check (FR-015) ran on an untrained toy model only.
+   - No naive, IMM or EKF track is scored on the same rows, though the plan's constitution
+     check says they are.
+   - Counts at 60 and 240 s are in the summary and not tabulated; efficiency is by split,
+     not by slice; no single table says classical, hedging or learning per split.
+   - `pool` keeps the larger evidence where an equal-weight mixture would add the two (up
+     to 0.69 nats against a margin of 1): "the same posterior" in the research note is not
+     exact.
+   - The read-out's fall-back rule is half vacuous for a learned track: the model's own
+     track lies on the smoothed bearing, so its cost is always consistent and the "no
+     worse than the start + 9" clause can never rescue a fit.
+
+9. **Provenance weaknesses** (numbers, code).
+   - The test record does not state which weights it scored: each shard's header carries
+     the checkpoint's digest, the scorer drops it, and the workflow fetches a branch head.
+     The chain holds indirectly (each branch has one commit whose checkpoint matches the
+     training record).
+   - Addendum 2's claim that shard size touches C″ only is wrong in principle: the
+     control's searches start from C″'s track, so a last-bit flip there can reach a side of
+     the criteria. Not measured; with every losing side at least 2.7 standard errors from
+     1 it is not believed to matter.
+   - Sixteen cells of the record's medians table were pulled from the summary by hand, and
+     the README check tests only that an interval appears somewhere.
+   - `run_shard4`, `study_split4`, the controls-record guard and the elimination path on
+     real fits have no unit test. The guard does not check the record's seed.
+   - `decision.json` does not bind `regress/data.py`, `regress/predict.py`,
+     `sim/mismatch.py` or the kernel, which the result depends on.
+
+## Minor findings
+
+- The three seeds are not three tests: they share the training stream in the same order
+  and differ in initialisation only; their per-period log ratios correlate 0.73 to 0.85.
+  "Every seed" guards training noise, which is small.
+- An ensemble of the three would not change the outcome (rough, 20 stored periods a
+  split: own track about 5% better).
+- The registered power was pessimistic: the realised smallest separable ratio is 1.10 to
+  1.16, because the read-out shares fits with the control. Bias is genuinely near 1.
+- The L-b forecasts restate the tuning run (1.60, 1.31, 1.04, 1.21): replication, not
+  prediction. The rule was near-certain to return "stops": the informative results are
+  L-b and the shape of the loss.
+- Reading 6 is wrong for shifted 2: declared ÷ hedged is 0.96 [0.92, 1.00], separated.
+- Reading 4 quotes the model's own spans; the read-out's are 0.34 missed and 0.08 false
+  on Annex B, 0.62 missed on the shifted split.
+- Read-out against own track, paired: better on shifted 2, held course and both outlier
+  splits (and Annex B on one seed of three); worse on shifted and correlated; not
+  separated on three. The forecast's "No" stands.
+- With the same, right history the read-out is still about 10% behind on Annex B and
+  outliers; not explained.
+- Missed manoeuvres carry diffuse probability (tuning: median peak 0.18): a per-token
+  threshold cannot use a manoeuvre the model suspects and cannot time.
+- Efficiencies below 1 on held course (speed 0.79 to 0.89 for every system and the
+  reference): by the repository's own lesson a fault to explain; not explained.
+- Training budget: the flat end of the monitor curve is the cosine schedule, not
+  saturation; from 10,000 to 40,000 updates the error fell about as data^−0.3. Four times
+  the budget might give 30% less error, which would not reach the control where the bound
+  is tight (the own track is 2.8 times behind there).
+- Paired position ratios in the summary are clipped at 100 m by `study._log` and are
+  artefacts; none is quoted.
+- `corr.estimate` misreads a true σ below the stated one as a large correlated share; the
+  σ-factor conditions are untested.
+- The ledger loses a killed container's time (disclosed; 39 cents against the dashboard).
+- The local pre-launch check on seed 338 is not a breach of the order, but it left no
+  artefact; commit the command and output digests next time.
+- `tl4_supplement.py` was written after the results; its choice of what to show is post
+  hoc.
+
+## What was and was not tested about learning, specs 012 to 016
+
+- **Tested**: a causal direct regressor (012) and a non-causal one of 12.5 million periods
+  (016). Both beat C″; neither matches least squares on held course; the second loses to
+  the evidence search by 0.71 as an estimator on Annex B and wins nowhere as registered.
+- **Not tested**: a learned proposer or learned candidates inside the search; the model's
+  manoeuvre probabilities as a prior; a learned evidence or calibration; training through
+  the arc or physics layer (016's read-out is bolted on after training); a larger budget
+  or another architecture; a combination of the control and the own track (in hindsight
+  the better of the two per period is 1.26 to 1.49 times the control: an upper limit).
+- **The founding question**: a learned component has not matched least squares on held
+  course except by reducing to it; learning beyond classical rules was small where it was
+  isolated (Gate 2: +0.007); each large gain has gone to a classical element (the arc
+  model, the prior, the evidence search, editing). The answer so far is "not shown, at
+  small budgets, with direct models", not "no".
+
+## What the panel wants next (the owner's choice)
+
+1. **(Recommended) Adopt `ctl` as the standing time-late system and close the direct-model
+   line.** Finish the record (below), then one cheap classical study of the shifted split:
+   class the wrong histories and score by manoeuvre size and count, to settle whether the
+   1.8 is identifiability. No GPU.
+2. **A learned proposer registered against `ctl`**: the three trained checkpoints' spans
+   and probabilities as starts or candidates of the search, threshold tuned on seed 340.
+   CPU only, no new training. Worth doing only if option 1's study shows search or
+   evidence failures and not ambiguity; on tuning data it reached parity, not a win.
+3. **Stop time-late work and write up.** Defensible; leaves the proposer question open.
+
+Owed to the record either way: the weights' digests from the shard headers; the export
+check on the trained checkpoints; IMM and EKF tracks beside; a reference fitted to edited
+bearings on the outlier splits; unit tests for the runner and the scorer.
+
+## What should not be concluded
+
+- That learning cannot help the time-late consumer, or that a larger model would lose.
+- That the read-out's 20% is a typical loss, or that "harm" means the model is poor.
+- That the model's own track is level with the control under a shifted prior: in range
+  only, and by a cancellation.
+- That the shifted-prior gap is closable, or that it is not.
+- That the control handles bearing errors outside the generator's families, or that it is
+  free on clean data.
+- That three seeds are three independent confirmations.
diff --git a/experiments/20261114-tl4/run.json b/experiments/20261114-tl4/run.json
new file mode 100644
index 00000000..9446bcc8
--- /dev/null
+++ b/experiments/20261114-tl4/run.json
@@ -0,0 +1,67 @@
+{
+ "plan": "configs/timelate/tl-e.json",
+ "seed": 341,
+ "count": 1350,
+ "splits": {
+  "man2": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift": {
+   "start": 0,
+   "periods": 400
+  },
+  "shift2": {
+   "start": 0,
+   "periods": 400
+  },
+  "quiet": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-corr-mild": {
+   "start": 0,
+   "periods": 200
+  },
+  "man2-corr": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-out": {
+   "start": 0,
+   "periods": 300
+  },
+  "man2-out-harsh": {
+   "start": 0,
+   "periods": 200
+  },
+  "man2-bias": {
+   "start": 0,
+   "periods": 300
+  },
+  "whole": {
+   "start": 0,
+   "periods": 60
+  }
+ },
+ "shard_size": 12,
+ "what": "spec 016 test: seed 341 (never generated before), scored once. The learned seeds' results branches and the controls' validation record are added in an addendum to the decision record before launch; the splits and sizes here are fixed.",
+ "controls_record": "experiments/20261113-tl4-val",
+ "learned": {
+  "learned-s1": {
+   "branch": "results/gputrain-tlm-s1-43507401545",
+   "dir": "experiments/gputrain-runs/tlm-s1-43507401545",
+   "config": "configs/regress/tlm-s1.json"
+  },
+  "learned-s2": {
+   "branch": "results/gputrain-tlm-s2-43507608865",
+   "dir": "experiments/gputrain-runs/tlm-s2-43507608865",
+   "config": "configs/regress/tlm-s2.json"
+  },
+  "learned-s3": {
+   "branch": "results/gputrain-tlm-s3-43507809179",
+   "dir": "experiments/gputrain-runs/tlm-s3-43507809179",
+   "config": "configs/regress/tlm-s3.json"
+  }
+ }
+}
\ No newline at end of file
diff --git a/experiments/20261114-tl4/score.txt b/experiments/20261114-tl4/score.txt
new file mode 100644
index 00000000..1660a4a9
--- /dev/null
+++ b/experiments/20261114-tl4/score.txt
@@ -0,0 +1,11 @@
+soundness {"reference_efficiency_tight": 0.9481674266083097, "band": [0.8, 1.3], "history_converged_share": 1.0, "convergence_min": 0.9, "reference_flagged_share": 0.085, "reference_flagged_max": 0.25, "pass": true}
+man2: 400 periods, 374 scored
+shift: 400 periods, 365 scored
+shift2: 400 periods, 375 scored
+quiet: 300 periods, 263 scored
+man2-corr-mild: 200 periods, 186 scored
+man2-corr: 300 periods, 278 scored
+man2-out: 300 periods, 278 scored
+man2-out-harsh: 200 periods, 186 scored
+man2-bias: 300 periods, 278 scored
+whole: 60 periods, 60 scored
diff --git a/experiments/20261114-tl4/shift.periods.json b/experiments/20261114-tl4/shift.periods.json
new file mode 100644
index 00000000..d0fc8ab4
--- /dev/null
+++ b/experiments/20261114-tl4/shift.periods.json
@@ -0,0 +1 @@
+[{"ordinal": 0, "rows": 798, "seconds_long": 3985.0, "bearings": 740, "true": [[845.0, 905.0], [2290.0, 2315.0], [3420.0, 3495.0]], "true_kinds": [[false, true], [true, false], [true, false]], "true_cut": [false, false, false], "reference_flagged": true, "history_converged": true, "edited": 0, "corr_estimate": [0.0, 0.0], "ctl_corr_estimate": [0.0, 0.0, false], "histories": {"c2": {"spans": [[595.0, 795.0]]}, "search3-d1-q0.01": {"spans": [[834.4625758988402, 861.5713853751944], [2257.1804623219027, 2390.891035790245]]}, "search3": {"spans": [[834.4625758988402, 861.5713853751944], [2257.1804623219027, 2390.891035790245]], "kinds": [[true, false], [true, false]], "evidence": -430.37799796461474, "prior": "plain/v3", "candidates": [{"spans": [[834.4625758988402, 861.5713853751944], [2257.1804623219027, 2390.891035790245]], "kinds": [[true, false], [true, false]], "evidence": -430.37799796461474, "prior": "plain/v3"}, {"spans": [[793.41705436309, 877.3521195653118]], "kinds": [[true, 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diff --git a/experiments/20261114-tl4/shift2.periods.json b/experiments/20261114-tl4/shift2.periods.json
new file mode 100644
index 00000000..5bd3bb99
--- /dev/null
+++ b/experiments/20261114-tl4/shift2.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261114-tl4/summary.json b/experiments/20261114-tl4/summary.json
new file mode 100644
index 00000000..17f6efb4
--- /dev/null
+++ b/experiments/20261114-tl4/summary.json
@@ -0,0 +1,19429 @@
+{
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diff --git a/experiments/20261114-tl4/supplement.md b/experiments/20261114-tl4/supplement.md
new file mode 100644
index 00000000..23193fb6
--- /dev/null
+++ b/experiments/20261114-tl4/supplement.md
@@ -0,0 +1,104 @@
+## The model's own track (not the registered comparison)
+
+| Split | Seed | Declared control ÷ own track | Hedged track ÷ own track | Own track ÷ its arc read-out (from medians) | Read-out fell back |
+| --- | --- | --- | --- | --- | --- |
+| Annex B | `learned-s1` | 0.71 [0.63, 0.80] | 0.73 [0.66, 0.82] | 1.06 | 0.04 |
+| Annex B | `learned-s2` | 0.72 [0.64, 0.82] | 0.75 [0.66, 0.84] | 1.04 | 0.05 |
+| Annex B | `learned-s3` | 0.77 [0.69, 0.87] | 0.79 [0.71, 0.88] | 1.07 | 0.04 |
+| Shifted | `learned-s1` | 1.01 [0.90, 1.13] | 0.93 [0.84, 1.04] | 0.75 | 0.12 |
+| Shifted | `learned-s2` | 1.02 [0.90, 1.15] | 0.94 [0.84, 1.06] | 0.69 | 0.12 |
+| Shifted | `learned-s3` | 1.02 [0.90, 1.15] | 0.94 [0.84, 1.06] | 0.73 | 0.14 |
+| Shifted 2 | `learned-s1` | 0.60 [0.53, 0.68] | 0.63 [0.56, 0.70] | 1.30 | 0.03 |
+| Shifted 2 | `learned-s2` | 0.61 [0.54, 0.69] | 0.64 [0.57, 0.71] | 1.30 | 0.04 |
+| Shifted 2 | `learned-s3` | 0.62 [0.55, 0.70] | 0.65 [0.57, 0.73] | 1.38 | 0.02 |
+| Held course | `learned-s1` | 0.62 [0.54, 0.71] | 0.88 [0.77, 1.02] | 1.59 | 0.00 |
+| Held course | `learned-s2` | 0.63 [0.54, 0.72] | 0.89 [0.77, 1.03] | 1.60 | 0.00 |
+| Held course | `learned-s3` | 0.64 [0.55, 0.74] | 0.91 [0.78, 1.04] | 1.61 | 0.00 |
+| Correlated, mild | `learned-s1` | 0.86 [0.74, 1.00] | 0.87 [0.75, 1.00] | 0.85 | 0.00 |
+| Correlated, mild | `learned-s2` | 0.85 [0.71, 1.00] | 0.85 [0.73, 0.98] | 0.83 | 0.01 |
+| Correlated, mild | `learned-s3` | 0.88 [0.75, 1.03] | 0.89 [0.77, 1.02] | 0.83 | 0.01 |
+| Correlated | `learned-s1` | 0.99 [0.88, 1.13] | 0.98 [0.88, 1.10] | 0.79 | 0.01 |
+| Correlated | `learned-s2` | 0.97 [0.85, 1.10] | 0.96 [0.86, 1.07] | 0.77 | 0.02 |
+| Correlated | `learned-s3` | 0.99 [0.86, 1.12] | 0.97 [0.87, 1.09] | 0.84 | 0.02 |
+| Outliers | `learned-s1` | 0.63 [0.54, 0.73] | 0.64 [0.56, 0.74] | 1.04 | 0.08 |
+| Outliers | `learned-s2` | 0.65 [0.55, 0.76] | 0.66 [0.57, 0.76] | 1.04 | 0.08 |
+| Outliers | `learned-s3` | 0.67 [0.57, 0.77] | 0.68 [0.59, 0.78] | 1.09 | 0.08 |
+| Outliers, harsh | `learned-s1` | 0.67 [0.55, 0.81] | 0.67 [0.56, 0.80] | 1.26 | 0.14 |
+| Outliers, harsh | `learned-s2` | 0.67 [0.56, 0.80] | 0.67 [0.57, 0.79] | 1.27 | 0.14 |
+| Outliers, harsh | `learned-s3` | 0.69 [0.57, 0.83] | 0.69 [0.58, 0.82] | 1.15 | 0.15 |
+| Bias | `learned-s1` | 0.92 [0.81, 1.05] | 0.92 [0.81, 1.03] | 1.02 | 0.05 |
+| Bias | `learned-s2` | 0.89 [0.78, 1.03] | 0.89 [0.78, 1.01] | 1.06 | 0.05 |
+| Bias | `learned-s3` | 0.99 [0.87, 1.13] | 0.99 [0.87, 1.11] | 0.97 | 0.04 |
+| Whole | `learned-s1` | 0.62 [0.46, 0.84] | 0.63 [0.48, 0.84] | 0.98 | 0.13 |
+| Whole | `learned-s2` | 0.58 [0.41, 0.81] | 0.59 [0.42, 0.81] | 0.98 | 0.12 |
+| Whole | `learned-s3` | 0.68 [0.50, 0.93] | 0.69 [0.51, 0.92] | 0.88 | 0.08 |
+
+## Manoeuvres (matched within 120 s) and targets called quiet
+
+| Split | System | Missed (share) | False (per period) | No true manoeuvre: given one | One true manoeuvre: called quiet |
+| --- | --- | --- | --- | --- | --- |
+| Annex B | `c2` | 0.57 | 0.76 | 0.00 (14) | 0.22 (152) |
+| Annex B | `search3` | 0.25 | 0.24 | 0.14 (14) | 0.13 (152) |
+| Annex B | `ctl-d1-q0.01` | 0.25 | 0.20 | 0.07 (14) | 0.14 (152) |
+| Annex B | `learned-s1` | 0.31 | 0.03 | 0.00 (14) | 0.29 (152) |
+| Annex B | `learned-s1-arc` | 0.34 | 0.08 | 0.00 (14) | 0.29 (152) |
+| Shifted | `c2` | 0.70 | 0.94 | – (0) | 0.46 (63) |
+| Shifted | `search3` | 0.48 | 0.42 | – (0) | 0.29 (63) |
+| Shifted | `ctl-d1-q0.01` | 0.49 | 0.41 | – (0) | 0.33 (63) |
+| Shifted | `learned-s1` | 0.58 | 0.09 | – (0) | 0.49 (63) |
+| Shifted | `learned-s1-arc` | 0.62 | 0.20 | – (0) | 0.49 (63) |
+| Shifted 2 | `c2` | 0.47 | 0.74 | 0.00 (36) | 0.10 (220) |
+| Shifted 2 | `search3` | 0.12 | 0.19 | 0.08 (36) | 0.03 (220) |
+| Shifted 2 | `ctl-d1-q0.01` | 0.13 | 0.16 | 0.06 (36) | 0.04 (220) |
+| Shifted 2 | `learned-s1` | 0.21 | 0.13 | 0.00 (36) | 0.12 (220) |
+| Shifted 2 | `learned-s1-arc` | 0.20 | 0.11 | 0.00 (36) | 0.12 (220) |
+| Held course | `c2` | 1.00 | 0.05 | 0.04 (299) | 1.00 (1) |
+| Held course | `search3` | 0.00 | 0.58 | 0.49 (299) | 0.00 (1) |
+| Held course | `ctl-d1-q0.01` | 0.00 | 0.11 | 0.08 (299) | 0.00 (1) |
+| Held course | `learned-s1` | 0.00 | 0.01 | 0.01 (299) | 0.00 (1) |
+| Held course | `learned-s1-arc` | 0.00 | 0.01 | 0.01 (299) | 0.00 (1) |
+| Correlated, mild | `c2` | 0.61 | 2.44 | 0.57 (7) | 0.08 (83) |
+| Correlated, mild | `search3` | 0.31 | 0.86 | 0.43 (7) | 0.12 (83) |
+| Correlated, mild | `ctl-d1-q0.01` | 0.37 | 0.23 | 0.14 (7) | 0.29 (83) |
+| Correlated, mild | `learned-s1` | 0.47 | 0.10 | 0.14 (7) | 0.52 (83) |
+| Correlated, mild | `learned-s1-arc` | 0.49 | 0.14 | 0.14 (7) | 0.52 (83) |
+| Correlated | `c2` | 0.57 | 2.45 | 0.67 (12) | 0.06 (125) |
+| Correlated | `search3` | 0.37 | 1.27 | 0.83 (12) | 0.05 (125) |
+| Correlated | `ctl-d1-q0.01` | 0.45 | 0.31 | 0.08 (12) | 0.29 (125) |
+| Correlated | `learned-s1` | 0.51 | 0.13 | 0.08 (12) | 0.47 (125) |
+| Correlated | `learned-s1-arc` | 0.54 | 0.19 | 0.08 (12) | 0.47 (125) |
+| Outliers | `c2` | 0.53 | 2.25 | 0.58 (12) | 0.06 (125) |
+| Outliers | `search3` | 0.28 | 0.56 | 0.58 (12) | 0.09 (125) |
+| Outliers | `ctl-d1-q0.01` | 0.22 | 0.19 | 0.17 (12) | 0.15 (125) |
+| Outliers | `learned-s1` | 0.34 | 0.06 | 0.08 (12) | 0.35 (125) |
+| Outliers | `learned-s1-arc` | 0.34 | 0.07 | 0.08 (12) | 0.35 (125) |
+| Outliers, harsh | `c2` | 0.58 | 3.52 | 1.00 (7) | 0.01 (83) |
+| Outliers, harsh | `search3` | 0.37 | 1.16 | 0.86 (7) | 0.04 (83) |
+| Outliers, harsh | `ctl-d1-q0.01` | 0.27 | 0.28 | 0.00 (7) | 0.17 (83) |
+| Outliers, harsh | `learned-s1` | 0.39 | 0.09 | 0.14 (7) | 0.41 (83) |
+| Outliers, harsh | `learned-s1-arc` | 0.41 | 0.12 | 0.14 (7) | 0.41 (83) |
+| Bias | `c2` | 0.57 | 0.93 | 0.08 (12) | 0.17 (125) |
+| Bias | `search3` | 0.26 | 0.37 | 0.25 (12) | 0.12 (125) |
+| Bias | `ctl-d1-q0.01` | 0.26 | 0.33 | 0.08 (12) | 0.13 (125) |
+| Bias | `learned-s1` | 0.31 | 0.09 | 0.00 (12) | 0.31 (125) |
+| Bias | `learned-s1-arc` | 0.32 | 0.11 | 0.00 (12) | 0.31 (125) |
+| Whole | `c2` | 0.51 | 1.27 | – (0) | 0.00 (9) |
+| Whole | `search3` | 0.19 | 0.47 | – (0) | 0.00 (9) |
+| Whole | `ctl-d1-q0.01` | 0.18 | 0.32 | – (0) | 0.00 (9) |
+| Whole | `learned-s1` | 0.29 | 0.12 | – (0) | 0.00 (9) |
+| Whole | `learned-s1-arc` | 0.31 | 0.17 | – (0) | 0.00 (9) |
+
+## The controls (L-b)
+
+| Split | Search ÷ declared control | Gap to the reference closed by `ctl` | Declared ÷ hedged | `ctl` ÷ reference (held where drifted) | References held |
+| --- | --- | --- | --- | --- | --- |
+| Annex B | 1.09 [1.02, 1.18] | 0.24 [0.06, 0.41] | 0.97 [0.92, 1.02] | 1.29 [1.17, 1.41] | 0.09 |
+| Shifted | 1.03 [0.96, 1.09] | 0.08 [-0.01, 0.18] | 1.08 [1.04, 1.13] | 1.79 [1.61, 1.99] | 0.27 |
+| Shifted 2 | 1.08 [1.03, 1.14] | 0.29 [0.09, 0.58] | 0.96 [0.92, 1.00] | 1.18 [1.07, 1.31] | 0.06 |
+| Held course | 1.70 [1.48, 1.95] | 0.35 [0.20, 0.48] | 0.70 [0.64, 0.77] | 1.49 [1.35, 1.67] | 0.00 |
+| Correlated, mild | 1.28 [1.11, 1.48] | 0.46 [0.20, 0.69] | 0.99 [0.93, 1.07] | 1.33 [1.16, 1.52] | 0.21 |
+| Correlated | 1.33 [1.16, 1.53] | 0.51 [0.30, 0.69] | 1.01 [0.95, 1.08] | 1.32 [1.17, 1.50] | 0.25 |
+| Outliers | 1.68 [1.46, 1.94] | 1.20 [0.89, 1.61] | 0.98 [0.92, 1.04] | 0.92 [0.80, 1.06] | 0.19 |
+| Outliers, harsh | 2.44 [2.03, 3.00] | 1.55 [1.23, 2.08] | 1.00 [0.93, 1.07] | 0.72 [0.60, 0.86] | 0.18 |
+| Bias | 1.00 [0.96, 1.05] | -0.11 [-0.48, 0.09] | 1.00 [0.95, 1.05] | 1.32 [1.17, 1.47] | 0.14 |
+| Whole | 1.06 [0.90, 1.23] | 0.16 [-0.18, 0.47] | 0.98 [0.91, 1.07] | 1.55 [1.19, 2.03] | 0.13 |
diff --git a/experiments/20261114-tl4/tables.md b/experiments/20261114-tl4/tables.md
new file mode 100644
index 00000000..8998f88e
--- /dev/null
+++ b/experiments/20261114-tl4/tables.md
@@ -0,0 +1,458 @@
+## Median period range error
+
+| System | Annex B | Shifted | Shifted 2 | Held course | Correlated, mild | Correlated | Outliers | Outliers, harsh | Bias | Whole |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| Periods (scored) | 400 (374) | 400 (365) | 400 (375) | 300 (263) | 200 (186) | 300 (278) | 300 (278) | 200 (186) | 300 (278) | 60 (60) |
+| `broad` | 0.042 | 0.093 | 0.030 | 0.022 | – | – | – | – | – | – |
+| `c2` | 0.085 | 0.141 | 0.068 | 0.016 | 0.324 | 0.272 | 0.248 | 0.468 | 0.117 | 0.073 |
+| `corr` | 0.041 | – | – | – | 0.088 | 0.098 | – | – | – | – |
+| `ctl` | 0.039 | 0.086 | 0.028 | 0.024 | 0.084 | 0.102 | 0.041 | 0.051 | 0.060 | 0.039 |
+| `edit` | 0.041 | – | – | – | – | – | 0.041 | 0.048 | – | – |
+| `history-best` | 0.029 | 0.044 | 0.024 | 0.015 | 0.064 | 0.076 | 0.046 | 0.070 | 0.049 | 0.021 |
+| `history-fixed` | 0.022 | 0.042 | 0.019 | 0.015 | 0.058 | 0.072 | 0.036 | 0.060 | 0.040 | 0.018 |
+| `history-map` | 0.029 | 0.047 | 0.025 | 0.015 | 0.072 | 0.086 | 0.049 | 0.079 | 0.051 | 0.025 |
+| `learned-s1` | 0.050 | 0.084 | 0.043 | 0.025 | 0.093 | 0.106 | 0.062 | 0.071 | 0.065 | 0.043 |
+| `learned-s1-arc` | 0.047 | 0.112 | 0.033 | 0.015 | 0.110 | 0.133 | 0.060 | 0.056 | 0.064 | 0.044 |
+| `learned-s2` | 0.051 | 0.081 | 0.043 | 0.025 | 0.092 | 0.099 | 0.059 | 0.074 | 0.063 | 0.045 |
+| `learned-s2-arc` | 0.049 | 0.117 | 0.033 | 0.015 | 0.111 | 0.129 | 0.057 | 0.058 | 0.059 | 0.046 |
+| `learned-s3` | 0.049 | 0.080 | 0.044 | 0.025 | 0.093 | 0.106 | 0.059 | 0.068 | 0.063 | 0.042 |
+| `learned-s3-arc` | 0.046 | 0.110 | 0.032 | 0.015 | 0.113 | 0.125 | 0.055 | 0.059 | 0.065 | 0.048 |
+| `mix2` | 0.039 | 0.086 | 0.028 | 0.023 | – | – | – | – | – | – |
+| `one-leg` | 0.413 | 0.340 | 0.386 | 0.016 | 0.426 | 0.429 | 0.409 | 0.415 | 0.429 | 0.459 |
+| `search3` | 0.041 | 0.094 | 0.029 | 0.031 | 0.115 | 0.136 | 0.077 | 0.125 | 0.059 | 0.037 |
+
+## The standing search ÷ each system (paired per period; above 1: better)
+
+### Annex B
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.02 [0.94, 1.12] | 0.46 |
+| `broad-h1` | 1.00 [0.92, 1.10] | 0.48 |
+| `c2` | 0.49 [0.43, 0.56] | 0.72 |
+| `corr` | 0.97 [0.92, 1.03] | 0.21 |
+| `corr-h1` | 0.97 [0.91, 1.04] | 0.43 |
+| `ctl` | 1.08 [1.02, 1.16] | 0.13 |
+| `ctl-h1` | 1.06 [0.99, 1.14] | 0.44 |
+| `edit` | 0.99 [0.97, 1.01] | 0.04 |
+| `edit-h1` | 0.99 [0.95, 1.04] | 0.43 |
+| `learned-s1` | 0.78 [0.68, 0.89] | 0.57 |
+| `learned-s1-arc` | 0.89 [0.80, 0.99] | 0.57 |
+| `learned-s2` | 0.79 [0.69, 0.91] | 0.56 |
+| `learned-s2-arc` | 0.89 [0.80, 0.99] | 0.55 |
+| `learned-s3` | 0.84 [0.74, 0.96] | 0.53 |
+| `learned-s3-arc` | 0.90 [0.81, 1.01] | 0.56 |
+| `mix2` | 1.08 [1.02, 1.15] | 0.09 |
+| `mix2-h1` | 1.06 [0.98, 1.14] | 0.43 |
+| `search3-h1` | 1.00 [0.96, 1.04] | 0.41 |
+
+### Shifted
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.03 [0.95, 1.12] | 0.47 |
+| `broad-h1` | 1.09 [1.00, 1.19] | 0.39 |
+| `c2` | 0.61 [0.54, 0.69] | 0.64 |
+| `ctl` | 1.05 [0.99, 1.12] | 0.20 |
+| `ctl-h1` | 1.11 [1.04, 1.19] | 0.36 |
+| `learned-s1` | 1.04 [0.93, 1.16] | 0.43 |
+| `learned-s1-arc` | 0.84 [0.75, 0.94] | 0.55 |
+| `learned-s2` | 1.04 [0.92, 1.18] | 0.43 |
+| `learned-s2-arc` | 0.82 [0.73, 0.93] | 0.55 |
+| `learned-s3` | 1.04 [0.93, 1.18] | 0.43 |
+| `learned-s3-arc` | 0.84 [0.75, 0.94] | 0.55 |
+| `mix2` | 1.06 [1.00, 1.13] | 0.15 |
+| `mix2-h1` | 1.12 [1.05, 1.20] | 0.35 |
+| `search3-h1` | 1.07 [1.04, 1.11] | 0.35 |
+
+### Shifted 2
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.03 [0.96, 1.10] | 0.44 |
+| `broad-h1` | 1.04 [0.97, 1.13] | 0.47 |
+| `c2` | 0.44 [0.39, 0.51] | 0.73 |
+| `ctl` | 1.07 [1.02, 1.13] | 0.17 |
+| `ctl-h1` | 1.03 [0.98, 1.09] | 0.44 |
+| `learned-s1` | 0.65 [0.58, 0.73] | 0.66 |
+| `learned-s1-arc` | 0.94 [0.86, 1.03] | 0.48 |
+| `learned-s2` | 0.66 [0.58, 0.74] | 0.65 |
+| `learned-s2-arc` | 0.94 [0.85, 1.02] | 0.48 |
+| `learned-s3` | 0.67 [0.59, 0.76] | 0.63 |
+| `learned-s3-arc` | 1.02 [0.93, 1.13] | 0.47 |
+| `mix2` | 1.07 [1.02, 1.13] | 0.12 |
+| `mix2-h1` | 1.05 [0.99, 1.11] | 0.42 |
+| `search3-h1` | 1.00 [0.96, 1.04] | 0.43 |
+
+### Held course
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `broad` | 1.27 [1.15, 1.42] | 0.17 |
+| `broad-h1` | 1.25 [1.12, 1.40] | 0.38 |
+| `c2` | 1.68 [1.45, 1.96] | 0.35 |
+| `ctl` | 1.24 [1.12, 1.37] | 0.17 |
+| `ctl-h1` | 1.19 [1.07, 1.32] | 0.40 |
+| `learned-s1` | 1.05 [0.89, 1.23] | 0.48 |
+| `learned-s1-arc` | 1.79 [1.57, 2.05] | 0.29 |
+| `learned-s2` | 1.06 [0.90, 1.25] | 0.51 |
+| `learned-s2-arc` | 1.79 [1.57, 2.05] | 0.29 |
+| `learned-s3` | 1.08 [0.90, 1.28] | 0.45 |
+| `learned-s3-arc` | 1.80 [1.57, 2.06] | 0.28 |
+| `mix2` | 1.21 [1.10, 1.34] | 0.15 |
+| `mix2-h1` | 1.20 [1.08, 1.33] | 0.37 |
+| `search3-h1` | 0.96 [0.89, 1.04] | 0.40 |
+
+### Correlated, mild
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.34 [0.28, 0.40] | 0.78 |
+| `corr` | 1.17 [0.99, 1.39] | 0.40 |
+| `corr-h1` | 1.20 [1.02, 1.40] | 0.38 |
+| `ctl` | 1.27 [1.10, 1.48] | 0.34 |
+| `ctl-h1` | 1.27 [1.11, 1.47] | 0.36 |
+| `learned-s1` | 1.10 [0.95, 1.28] | 0.46 |
+| `learned-s1-arc` | 1.07 [0.90, 1.27] | 0.48 |
+| `learned-s2` | 1.08 [0.92, 1.26] | 0.49 |
+| `learned-s2-arc` | 0.98 [0.83, 1.17] | 0.49 |
+| `learned-s3` | 1.13 [0.97, 1.31] | 0.43 |
+| `learned-s3-arc` | 1.10 [0.92, 1.30] | 0.46 |
+| `search3-h1` | 1.00 [0.95, 1.06] | 0.41 |
+
+### Correlated
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.49 [0.43, 0.56] | 0.74 |
+| `corr` | 1.34 [1.16, 1.53] | 0.35 |
+| `corr-h1` | 1.38 [1.21, 1.58] | 0.37 |
+| `ctl` | 1.33 [1.16, 1.51] | 0.37 |
+| `ctl-h1` | 1.35 [1.18, 1.54] | 0.36 |
+| `learned-s1` | 1.32 [1.17, 1.50] | 0.35 |
+| `learned-s1-arc` | 1.10 [0.95, 1.27] | 0.45 |
+| `learned-s2` | 1.29 [1.14, 1.47] | 0.36 |
+| `learned-s2-arc` | 1.10 [0.95, 1.26] | 0.45 |
+| `learned-s3` | 1.31 [1.15, 1.49] | 0.35 |
+| `learned-s3-arc` | 1.12 [0.97, 1.28] | 0.44 |
+| `search3-h1` | 1.00 [0.96, 1.05] | 0.41 |
+
+### Outliers
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.32 [0.27, 0.39] | 0.79 |
+| `ctl` | 1.68 [1.47, 1.95] | 0.34 |
+| `ctl-h1` | 1.64 [1.44, 1.88] | 0.31 |
+| `edit` | 1.62 [1.42, 1.86] | 0.35 |
+| `edit-h1` | 1.61 [1.41, 1.85] | 0.33 |
+| `learned-s1` | 1.05 [0.92, 1.21] | 0.45 |
+| `learned-s1-arc` | 1.32 [1.14, 1.52] | 0.41 |
+| `learned-s2` | 1.09 [0.95, 1.25] | 0.43 |
+| `learned-s2-arc` | 1.30 [1.12, 1.49] | 0.42 |
+| `learned-s3` | 1.12 [0.98, 1.28] | 0.44 |
+| `learned-s3-arc` | 1.36 [1.18, 1.58] | 0.42 |
+| `search3-h1` | 1.03 [0.98, 1.07] | 0.39 |
+
+### Outliers, harsh
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.33 [0.27, 0.40] | 0.80 |
+| `ctl` | 2.50 [2.07, 3.06] | 0.22 |
+| `ctl-h1` | 2.43 [2.04, 2.92] | 0.22 |
+| `edit` | 2.50 [2.09, 3.05] | 0.21 |
+| `edit-h1` | 2.44 [2.06, 2.94] | 0.20 |
+| `learned-s1` | 1.63 [1.38, 1.94] | 0.32 |
+| `learned-s1-arc` | 2.14 [1.76, 2.61] | 0.30 |
+| `learned-s2` | 1.63 [1.39, 1.91] | 0.31 |
+| `learned-s2-arc` | 2.10 [1.72, 2.56] | 0.30 |
+| `learned-s3` | 1.68 [1.43, 1.96] | 0.30 |
+| `learned-s3-arc` | 2.07 [1.72, 2.49] | 0.28 |
+| `search3-h1` | 1.04 [1.00, 1.09] | 0.37 |
+
+### Bias
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.53 [0.46, 0.61] | 0.69 |
+| `ctl` | 0.97 [0.92, 1.02] | 0.16 |
+| `ctl-h1` | 1.00 [0.94, 1.07] | 0.41 |
+| `learned-s1` | 0.92 [0.81, 1.05] | 0.51 |
+| `learned-s1-arc` | 0.99 [0.89, 1.09] | 0.48 |
+| `learned-s2` | 0.89 [0.78, 1.02] | 0.50 |
+| `learned-s2-arc` | 1.00 [0.90, 1.10] | 0.47 |
+| `learned-s3` | 0.99 [0.87, 1.13] | 0.45 |
+| `learned-s3-arc` | 0.97 [0.87, 1.08] | 0.49 |
+| `search3-h1` | 1.01 [0.96, 1.06] | 0.37 |
+
+### Whole
+
+| System | Ratio | Share of periods where the search is better |
+| --- | --- | --- |
+| `c2` | 0.43 [0.30, 0.62] | 0.72 |
+| `ctl` | 1.09 [0.93, 1.26] | 0.08 |
+| `ctl-h1` | 1.04 [0.88, 1.22] | 0.45 |
+| `learned-s1` | 0.66 [0.48, 0.90] | 0.53 |
+| `learned-s1-arc` | 0.87 [0.65, 1.17] | 0.47 |
+| `learned-s2` | 0.62 [0.44, 0.86] | 0.57 |
+| `learned-s2-arc` | 0.79 [0.60, 1.04] | 0.52 |
+| `learned-s3` | 0.72 [0.52, 0.99] | 0.58 |
+| `learned-s3-arc` | 0.79 [0.61, 1.01] | 0.50 |
+| `search3-h1` | 0.98 [0.92, 1.06] | 0.53 |
+
+## Share of the gap from the search to the reference that a control closes
+
+| Split | Control | Share |
+| --- | --- | --- |
+| Annex B | `broad` | 0.06 [-0.22, 0.29] |
+| Annex B | `corr` | -0.08 [-0.30, 0.09] |
+| Annex B | `ctl` | 0.24 [0.06, 0.41] |
+| Annex B | `edit` | -0.02 [-0.09, 0.04] |
+| Annex B | `learned-s1` | -0.76 [-1.41, -0.30] |
+| Annex B | `learned-s1-arc` | -0.34 [-0.82, -0.02] |
+| Annex B | `learned-s2` | -0.70 [-1.38, -0.22] |
+| Annex B | `learned-s2-arc` | -0.37 [-0.85, -0.02] |
+| Annex B | `learned-s3` | -0.52 [-1.09, -0.10] |
+| Annex B | `learned-s3-arc` | -0.31 [-0.77, 0.02] |
+| Annex B | `mix2` | 0.22 [0.06, 0.39] |
+| Shifted | `broad` | 0.05 [-0.09, 0.18] |
+| Shifted | `ctl` | 0.08 [-0.01, 0.18] |
+| Shifted | `learned-s1` | 0.06 [-0.13, 0.22] |
+| Shifted | `learned-s1-arc` | -0.28 [-0.52, -0.09] |
+| Shifted | `learned-s2` | 0.07 [-0.14, 0.25] |
+| Shifted | `learned-s2-arc` | -0.31 [-0.56, -0.11] |
+| Shifted | `learned-s3` | 0.07 [-0.13, 0.24] |
+| Shifted | `learned-s3-arc` | -0.28 [-0.51, -0.09] |
+| Shifted | `mix2` | 0.09 [0.00, 0.19] |
+| Shifted 2 | `broad` | 0.11 [-0.23, 0.43] |
+| Shifted 2 | `ctl` | 0.29 [0.09, 0.58] |
+| Shifted 2 | `learned-s1` | -1.85 [-3.63, -1.03] |
+| Shifted 2 | `learned-s1-arc` | -0.25 [-0.89, 0.11] |
+| Shifted 2 | `learned-s2` | -1.79 [-3.61, -0.97] |
+| Shifted 2 | `learned-s2-arc` | -0.29 [-0.94, 0.09] |
+| Shifted 2 | `learned-s3` | -1.72 [-3.44, -0.92] |
+| Shifted 2 | `learned-s3-arc` | 0.09 [-0.43, 0.47] |
+| Shifted 2 | `mix2` | 0.28 [0.08, 0.56] |
+| Held course | `broad` | 0.39 [0.25, 0.52] |
+| Held course | `ctl` | 0.35 [0.20, 0.48] |
+| Held course | `learned-s1` | 0.08 [-0.22, 0.30] |
+| Held course | `learned-s1-arc` | 0.95 [0.90, 0.99] |
+| Held course | `learned-s2` | 0.10 [-0.22, 0.33] |
+| Held course | `learned-s2-arc` | 0.95 [0.90, 0.99] |
+| Held course | `learned-s3` | 0.12 [-0.19, 0.36] |
+| Held course | `learned-s3-arc` | 0.95 [0.90, 0.99] |
+| Held course | `mix2` | 0.31 [0.18, 0.44] |
+| Correlated, mild | `corr` | 0.30 [-0.03, 0.56] |
+| Correlated, mild | `ctl` | 0.46 [0.20, 0.69] |
+| Correlated, mild | `learned-s1` | 0.19 [-0.11, 0.43] |
+| Correlated, mild | `learned-s1-arc` | 0.13 [-0.23, 0.39] |
+| Correlated, mild | `learned-s2` | 0.16 [-0.16, 0.42] |
+| Correlated, mild | `learned-s2-arc` | -0.03 [-0.44, 0.26] |
+| Correlated, mild | `learned-s3` | 0.23 [-0.07, 0.49] |
+| Correlated, mild | `learned-s3-arc` | 0.17 [-0.18, 0.44] |
+| Correlated | `corr` | 0.52 [0.30, 0.73] |
+| Correlated | `ctl` | 0.51 [0.30, 0.69] |
+| Correlated | `learned-s1` | 0.50 [0.30, 0.71] |
+| Correlated | `learned-s1-arc` | 0.17 [-0.11, 0.39] |
+| Correlated | `learned-s2` | 0.46 [0.25, 0.67] |
+| Correlated | `learned-s2-arc` | 0.17 [-0.11, 0.39] |
+| Correlated | `learned-s3` | 0.48 [0.27, 0.70] |
+| Correlated | `learned-s3-arc` | 0.20 [-0.08, 0.41] |
+| Outliers | `ctl` | 1.20 [0.89, 1.61] |
+| Outliers | `edit` | 1.11 [0.80, 1.53] |
+| Outliers | `learned-s1` | 0.12 [-0.23, 0.42] |
+| Outliers | `learned-s1-arc` | 0.63 [0.32, 0.97] |
+| Outliers | `learned-s2` | 0.19 [-0.13, 0.49] |
+| Outliers | `learned-s2-arc` | 0.60 [0.29, 0.92] |
+| Outliers | `learned-s3` | 0.25 [-0.05, 0.53] |
+| Outliers | `learned-s3-arc` | 0.70 [0.39, 1.04] |
+| Outliers, harsh | `ctl` | 1.55 [1.23, 2.08] |
+| Outliers, harsh | `edit` | 1.55 [1.23, 2.08] |
+| Outliers, harsh | `learned-s1` | 0.83 [0.59, 1.11] |
+| Outliers, harsh | `learned-s1-arc` | 1.29 [0.98, 1.72] |
+| Outliers, harsh | `learned-s2` | 0.83 [0.59, 1.14] |
+| Outliers, harsh | `learned-s2-arc` | 1.25 [0.96, 1.66] |
+| Outliers, harsh | `learned-s3` | 0.87 [0.64, 1.18] |
+| Outliers, harsh | `learned-s3-arc` | 1.23 [0.93, 1.66] |
+| Bias | `ctl` | -0.11 [-0.48, 0.09] |
+| Bias | `learned-s1` | -0.33 [-1.42, 0.16] |
+| Bias | `learned-s1-arc` | -0.06 [-0.76, 0.30] |
+| Bias | `learned-s2` | -0.44 [-1.58, 0.07] |
+| Bias | `learned-s2-arc` | -0.01 [-0.66, 0.32] |
+| Bias | `learned-s3` | -0.04 [-0.92, 0.43] |
+| Bias | `learned-s3-arc` | -0.11 [-0.98, 0.25] |
+| Whole | `ctl` | 0.16 [-0.18, 0.47] |
+| Whole | `learned-s1` | -0.80 [-2.43, -0.15] |
+| Whole | `learned-s1-arc` | -0.27 [-1.41, 0.25] |
+| Whole | `learned-s2` | -0.93 [-2.68, -0.23] |
+| Whole | `learned-s2-arc` | -0.46 [-1.60, 0.07] |
+| Whole | `learned-s3` | -0.64 [-2.11, -0.03] |
+| Whole | `learned-s3-arc` | -0.46 [-1.63, 0.02] |
+
+## System ÷ the true-history reference, by how the reference is fitted
+
+| Split | System | times free | held where drifted | times held |
+| --- | --- | --- | --- | --- |
+| Annex B | `search3` | 1.37 [1.24, 1.51] | 1.40 [1.27, 1.54] | 2.06 [1.79, 2.37] |
+| Annex B | `ctl` | 1.26 [1.15, 1.38] | 1.29 [1.17, 1.41] | 1.90 [1.67, 2.16] |
+| Shifted | `search3` | 1.75 [1.57, 1.95] | 1.88 [1.68, 2.10] | 2.19 [1.94, 2.48] |
+| Shifted | `ctl` | 1.66 [1.50, 1.85] | 1.79 [1.61, 1.99] | 2.08 [1.86, 2.34] |
+| Shifted 2 | `search3` | 1.24 [1.12, 1.38] | 1.26 [1.14, 1.41] | 1.65 [1.47, 1.85] |
+| Shifted 2 | `ctl` | 1.16 [1.05, 1.28] | 1.18 [1.07, 1.31] | 1.54 [1.37, 1.72] |
+| Held course | `search3` | 1.85 [1.63, 2.12] | 1.85 [1.63, 2.12] | 1.85 [1.63, 2.13] |
+| Held course | `ctl` | 1.49 [1.35, 1.67] | 1.49 [1.35, 1.67] | 1.50 [1.35, 1.67] |
+| Correlated, mild | `search3` | 1.54 [1.28, 1.83] | 1.69 [1.44, 1.97] | 1.92 [1.59, 2.34] |
+| Correlated, mild | `ctl` | 1.21 [1.04, 1.40] | 1.33 [1.16, 1.52] | 1.51 [1.26, 1.78] |
+| Correlated | `search3` | 1.58 [1.38, 1.82] | 1.75 [1.52, 2.04] | 2.05 [1.75, 2.40] |
+| Correlated | `ctl` | 1.19 [1.06, 1.35] | 1.32 [1.17, 1.50] | 1.54 [1.35, 1.76] |
+| Outliers | `search3` | 1.40 [1.26, 1.57] | 1.55 [1.37, 1.76] | 2.01 [1.74, 2.35] |
+| Outliers | `ctl` | 0.83 [0.73, 0.96] | 0.92 [0.80, 1.06] | 1.20 [1.02, 1.39] |
+| Outliers, harsh | `search3` | 1.66 [1.40, 1.96] | 1.81 [1.51, 2.16] | 2.41 [1.99, 2.89] |
+| Outliers, harsh | `ctl` | 0.66 [0.55, 0.78] | 0.72 [0.60, 0.86] | 0.96 [0.79, 1.17] |
+| Bias | `search3` | 1.22 [1.08, 1.37] | 1.29 [1.13, 1.44] | 1.54 [1.33, 1.76] |
+| Bias | `ctl` | 1.26 [1.12, 1.40] | 1.32 [1.17, 1.47] | 1.58 [1.37, 1.80] |
+| Whole | `search3` | 1.46 [1.12, 1.87] | 1.68 [1.27, 2.22] | 2.44 [1.68, 3.57] |
+| Whole | `ctl` | 1.34 [1.06, 1.72] | 1.55 [1.19, 2.03] | 2.25 [1.59, 3.19] |
+
+## Manoeuvres missed (share) and false (per period), matched within 120 s
+
+| Split | System | Missed | False |
+| --- | --- | --- | --- |
+| Annex B | `broad` | 0.28 | 0.24 |
+| Annex B | `c2` | 0.57 | 0.76 |
+| Annex B | `corr` | 0.29 | 0.26 |
+| Annex B | `ctl` | 0.25 | 0.23 |
+| Annex B | `edit` | 0.25 | 0.25 |
+| Annex B | `learned-s1` | 0.31 | 0.03 |
+| Annex B | `learned-s1-arc` | 0.34 | 0.08 |
+| Annex B | `learned-s2` | 0.31 | 0.04 |
+| Annex B | `learned-s2-arc` | 0.33 | 0.08 |
+| Annex B | `learned-s3` | 0.29 | 0.03 |
+| Annex B | `learned-s3-arc` | 0.32 | 0.08 |
+| Annex B | `mix2` | 0.25 | 0.23 |
+| Annex B | `search3` | 0.25 | 0.24 |
+| Shifted | `broad` | 0.48 | 0.42 |
+| Shifted | `c2` | 0.70 | 0.94 |
+| Shifted | `ctl` | 0.48 | 0.42 |
+| Shifted | `learned-s1` | 0.58 | 0.09 |
+| Shifted | `learned-s1-arc` | 0.62 | 0.20 |
+| Shifted | `learned-s2` | 0.58 | 0.10 |
+| Shifted | `learned-s2-arc` | 0.62 | 0.22 |
+| Shifted | `learned-s3` | 0.59 | 0.09 |
+| Shifted | `learned-s3-arc` | 0.62 | 0.19 |
+| Shifted | `mix2` | 0.48 | 0.41 |
+| Shifted | `search3` | 0.48 | 0.42 |
+| Shifted 2 | `broad` | 0.14 | 0.19 |
+| Shifted 2 | `c2` | 0.47 | 0.74 |
+| Shifted 2 | `ctl` | 0.12 | 0.19 |
+| Shifted 2 | `learned-s1` | 0.21 | 0.13 |
+| Shifted 2 | `learned-s1-arc` | 0.20 | 0.11 |
+| Shifted 2 | `learned-s2` | 0.21 | 0.14 |
+| Shifted 2 | `learned-s2-arc` | 0.20 | 0.12 |
+| Shifted 2 | `learned-s3` | 0.19 | 0.14 |
+| Shifted 2 | `learned-s3-arc` | 0.19 | 0.14 |
+| Shifted 2 | `mix2` | 0.12 | 0.18 |
+| Shifted 2 | `search3` | 0.12 | 0.19 |
+| Held course | `broad` | 0.00 | 0.43 |
+| Held course | `c2` | 1.00 | 0.05 |
+| Held course | `ctl` | 0.00 | 0.45 |
+| Held course | `learned-s1` | 0.00 | 0.01 |
+| Held course | `learned-s1-arc` | 0.00 | 0.01 |
+| Held course | `learned-s2` | 0.00 | 0.01 |
+| Held course | `learned-s2-arc` | 0.00 | 0.01 |
+| Held course | `learned-s3` | 0.00 | 0.01 |
+| Held course | `learned-s3-arc` | 0.00 | 0.01 |
+| Held course | `mix2` | 0.00 | 0.47 |
+| Held course | `search3` | 0.00 | 0.58 |
+| Correlated, mild | `c2` | 0.61 | 2.44 |
+| Correlated, mild | `corr` | 0.38 | 0.32 |
+| Correlated, mild | `ctl` | 0.36 | 0.27 |
+| Correlated, mild | `learned-s1` | 0.47 | 0.10 |
+| Correlated, mild | `learned-s1-arc` | 0.49 | 0.14 |
+| Correlated, mild | `learned-s2` | 0.48 | 0.09 |
+| Correlated, mild | `learned-s2-arc` | 0.51 | 0.14 |
+| Correlated, mild | `learned-s3` | 0.48 | 0.11 |
+| Correlated, mild | `learned-s3-arc` | 0.49 | 0.13 |
+| Correlated, mild | `search3` | 0.31 | 0.86 |
+| Correlated | `c2` | 0.57 | 2.45 |
+| Correlated | `corr` | 0.44 | 0.38 |
+| Correlated | `ctl` | 0.44 | 0.38 |
+| Correlated | `learned-s1` | 0.51 | 0.13 |
+| Correlated | `learned-s1-arc` | 0.54 | 0.19 |
+| Correlated | `learned-s2` | 0.50 | 0.10 |
+| Correlated | `learned-s2-arc` | 0.53 | 0.15 |
+| Correlated | `learned-s3` | 0.50 | 0.13 |
+| Correlated | `learned-s3-arc` | 0.53 | 0.17 |
+| Correlated | `search3` | 0.37 | 1.27 |
+| Outliers | `c2` | 0.53 | 2.25 |
+| Outliers | `ctl` | 0.21 | 0.22 |
+| Outliers | `edit` | 0.22 | 0.24 |
+| Outliers | `learned-s1` | 0.34 | 0.06 |
+| Outliers | `learned-s1-arc` | 0.34 | 0.07 |
+| Outliers | `learned-s2` | 0.33 | 0.08 |
+| Outliers | `learned-s2-arc` | 0.35 | 0.10 |
+| Outliers | `learned-s3` | 0.32 | 0.08 |
+| Outliers | `learned-s3-arc` | 0.33 | 0.10 |
+| Outliers | `search3` | 0.28 | 0.56 |
+| Outliers, harsh | `c2` | 0.58 | 3.52 |
+| Outliers, harsh | `ctl` | 0.26 | 0.32 |
+| Outliers, harsh | `edit` | 0.26 | 0.33 |
+| Outliers, harsh | `learned-s1` | 0.39 | 0.09 |
+| Outliers, harsh | `learned-s1-arc` | 0.41 | 0.12 |
+| Outliers, harsh | `learned-s2` | 0.40 | 0.12 |
+| Outliers, harsh | `learned-s2-arc` | 0.40 | 0.12 |
+| Outliers, harsh | `learned-s3` | 0.40 | 0.12 |
+| Outliers, harsh | `learned-s3-arc` | 0.40 | 0.12 |
+| Outliers, harsh | `search3` | 0.37 | 1.16 |
+| Bias | `c2` | 0.57 | 0.93 |
+| Bias | `ctl` | 0.25 | 0.35 |
+| Bias | `learned-s1` | 0.31 | 0.09 |
+| Bias | `learned-s1-arc` | 0.32 | 0.11 |
+| Bias | `learned-s2` | 0.30 | 0.13 |
+| Bias | `learned-s2-arc` | 0.31 | 0.15 |
+| Bias | `learned-s3` | 0.30 | 0.10 |
+| Bias | `learned-s3-arc` | 0.31 | 0.11 |
+| Bias | `search3` | 0.26 | 0.37 |
+| Whole | `c2` | 0.51 | 1.27 |
+| Whole | `ctl` | 0.18 | 0.33 |
+| Whole | `learned-s1` | 0.29 | 0.12 |
+| Whole | `learned-s1-arc` | 0.31 | 0.17 |
+| Whole | `learned-s2` | 0.31 | 0.10 |
+| Whole | `learned-s2-arc` | 0.33 | 0.15 |
+| Whole | `learned-s3` | 0.28 | 0.13 |
+| Whole | `learned-s3-arc` | 0.31 | 0.22 |
+| Whole | `search3` | 0.19 | 0.47 |
+
+## Declaration: held-course periods given a manoeuvre, and Annex B targets called quiet
+
+| System | Held course: false | One manoeuvre: called quiet | Two or more: called quiet | Annex B median | Held-course median |
+| --- | --- | --- | --- | --- | --- |
+| `broad` | 0.41 | 0.12 | 0.01 | 0.042 | 0.022 |
+| `c2` | 0.04 | 0.22 | 0.04 | 0.085 | 0.016 |
+| `ctl` | 0.41 | 0.11 | 0.00 | 0.039 | 0.024 |
+| `ctl-d1-q0.01` | 0.08 | 0.14 | 0.01 | 0.040 | 0.017 |
+| `learned-s1` | 0.01 | 0.29 | 0.07 | 0.050 | 0.025 |
+| `learned-s1-arc` | 0.01 | 0.29 | 0.07 | 0.047 | 0.015 |
+| `learned-s2` | 0.01 | 0.26 | 0.06 | 0.051 | 0.025 |
+| `learned-s2-arc` | 0.01 | 0.26 | 0.06 | 0.049 | 0.015 |
+| `learned-s3` | 0.01 | 0.26 | 0.06 | 0.049 | 0.025 |
+| `learned-s3-arc` | 0.01 | 0.26 | 0.06 | 0.046 | 0.015 |
+| `mix2` | 0.43 | 0.11 | 0.00 | 0.039 | 0.023 |
+| `search3` | 0.49 | 0.13 | 0.00 | 0.041 | 0.031 |
+| `search3-d1-q0.01` | 0.10 | 0.15 | 0.01 | 0.042 | 0.017 |
+
+## What the estimates said
+
+| Split | References held | Wild bearings edited a period | Correlated share (median) | Correlation used by `ctl` | Bayes factor for the broad prior (median log) | Share above 0 |
+| --- | --- | --- | --- | --- | --- | --- |
+| Annex B | 0.09 | 0.1 | 0.01 | 0.08 | -3.2 | 0.31 |
+| Shifted | 0.27 | 0.1 | 0.00 | 0.08 | -1.3 | 0.42 |
+| Shifted 2 | 0.06 | 0.1 | 0.00 | 0.07 | -2.4 | 0.40 |
+| Held course | 0.00 | 0.1 | 0.02 | 0.06 | 4.9 | 0.91 |
+| Correlated, mild | 0.21 | 0.0 | 0.52 | 1.00 | -1.4 | 0.40 |
+| Correlated | 0.25 | 0.0 | 0.52 | 1.00 | -1.1 | 0.40 |
+| Outliers | 0.19 | 7.5 | 0.00 | 0.07 | -3.1 | 0.32 |
+| Outliers, harsh | 0.18 | 18.5 | 0.00 | 0.04 | -3.6 | 0.29 |
+| Bias | 0.14 | 0.1 | 0.00 | 0.07 | -2.5 | 0.31 |
+| Whole | 0.13 | 0.1 | 0.01 | 0.03 | -3.9 | 0.25 |
diff --git a/experiments/20261114-tl4/tracks.npz b/experiments/20261114-tl4/tracks.npz
new file mode 100644
index 00000000..52174397
Binary files /dev/null and b/experiments/20261114-tl4/tracks.npz differ
diff --git a/experiments/20261114-tl4/whole.periods.json b/experiments/20261114-tl4/whole.periods.json
new file mode 100644
index 00000000..e8b27da1
--- /dev/null
+++ b/experiments/20261114-tl4/whole.periods.json
@@ -0,0 +1 @@
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diff --git a/experiments/20261114-tlm-runs/README.md b/experiments/20261114-tlm-runs/README.md
new file mode 100644
index 00000000..929ec969
--- /dev/null
+++ b/experiments/20261114-tlm-runs/README.md
@@ -0,0 +1,24 @@
+# Spec 016 (study L): the three training runs (2026-11-13 to 14)
+
+Plans `configs/regress/tlm-s1..s3.json` as bound in `experiments/20261113-tl4-decision/`
+(config digests `537b1a6b…`, `08370981…`, `36e234b5…`: equal to the record), commit 2c191b2,
+clean tree, a Modal T4 each. `tlm-sK.run.json` is each run's record; the weights are on the
+results branches.
+
+| Seed | Workflow run | Results branch | Updates | Periods | Periods a second | GPU seconds | Checkpoint |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| 1 | 43507401545 | `results/gputrain-tlm-s1-43507401545` | 49,000 (complete) | 12,544,000 | 497 | 24,517 | `9767a4a0…` |
+| 2 | 43507608865 | `results/gputrain-tlm-s2-43507608865` | 49,000 (complete) | 12,544,000 | 430 | 25,967 | `ebd545a8…` |
+| 3 | 43507809179 | `results/gputrain-tlm-s3-43507809179` | 49,000 (complete) | 12,544,000 | 501 | 24,846 | `802cdfcf…` |
+
+On the monitor slice of the training stream (mixed conditions; **not** a validation or test
+result), the median absolute error of ln range ended at 0.067, 0.066 and 0.066, and the
+manoeuvre term at 0.172, 0.173 and 0.172. The three seeds agree closely.
+
+## Spending
+
+The ledger records $14.22 for the three runs and $40.57 in all; the owner's Modal
+dashboard showed $40.96 for November on 14 November (T4 $35.89, CPU $4.37, memory $0.69).
+The 39 cents between them are the pilot's replaced container and two failed start-ups,
+which the ledger cannot see. About $9 of the owner's $50 is unspent. No further GPU run
+is planned in this study.
diff --git a/experiments/20261114-tlm-runs/tlm-s1.run.json b/experiments/20261114-tlm-runs/tlm-s1.run.json
new file mode 100644
index 00000000..b0c5a0ed
--- /dev/null
+++ b/experiments/20261114-tlm-runs/tlm-s1.run.json
@@ -0,0 +1,513 @@
+{
+ "checkpoint_sha256": "9767a4a057e055663700129579a0ba2ac483f66a31aa1912728b0e29e6e39065",
+ "config": {
+  "batch_scenarios": 256,
+  "beta": 0.5,
+  "causal": false,
+  "checkpoint_seconds": 900,
+  "data": "periods",
+  "feedforward": 1024,
+  "heads": 8,
+  "held_fraction": 0.0,
+  "held_ranges": "configs/regress/held-course-ranges.json",
+  "id": "tlm-s1",
+  "inputs": "full",
+  "layers": 8,
+  "learning_rate": 0.0003,
+  "limit_seconds": 15600,
+  "manoeuvre_head": true,
+  "max_rows": 1440,
+  "n_features": 9,
+  "patch_rows": 6,
+  "precision": "amp",
+  "ranges": "configs/man2-ranges.json",
+  "seed": 1,
+  "stream_repeat": 0,
+  "stream_seed": 339,
+  "stream_start": 0,
+  "total_updates": 49000,
+  "val_count": 512,
+  "val_every_updates": 1000,
+  "val_seed": 339,
+  "warmup_updates": 2000,
+  "weight_decay": 0.01,
+  "width": 256
+ },
+ "config_sha256": "537b1a6bfb2ccfc3e4a1c57c17971fada306e8d43593c15c32282e0233fd1b73",
+ "cuda": "13.2",
+ "device": "cuda",
+ "git_dirty": false,
+ "gpu": "Tesla T4",
+ "gpu_seconds": 24516.505703687668,
+ "plan": {
+  "description": "Spec 016 T011: the whole-period model, training seed 1 (research R7, R9, R10). 49,000 updates of 256 periods (12.5 million periods from stream seed 339), two segments of at most 4 h 20 min on a T4 (9 h with the start-up allowances); the pilot measured 453 periods a second while training (experiments/20261113-tlm-pilot/). Bound by digest in experiments/20261113-tl4-decision/; never edited after.",
+  "gpu": "T4",
+  "segment_limit_seconds": 15600,
+  "segments": 2
+ },
+ "scenarios_per_second": 496.6985964640156,
+ "scenarios_seen": 12544000,
+ "segments": [
+  {
+   "end_update": 31705,
+   "git_commit": "2c191b29fe731afecf75dc4e034e45105e96f496",
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diff --git a/experiments/20261114-tlm-runs/tlm-s2.run.json b/experiments/20261114-tlm-runs/tlm-s2.run.json
new file mode 100644
index 00000000..e440237a
--- /dev/null
+++ b/experiments/20261114-tlm-runs/tlm-s2.run.json
@@ -0,0 +1,513 @@
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diff --git a/experiments/20261114-tlm-runs/tlm-s3.run.json b/experiments/20261114-tlm-runs/tlm-s3.run.json
new file mode 100644
index 00000000..0b781032
--- /dev/null
+++ b/experiments/20261114-tlm-runs/tlm-s3.run.json
@@ -0,0 +1,513 @@
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+  {
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+   "state_loss": -0.5025515854358673,
+   "train_loss": -0.46982985734939575,
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+  },
+  {
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+   "loss": -0.4816795513033867,
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+   "state_loss": -0.4989638030529022,
+   "train_loss": -0.4708111882209778,
+   "update": 43000
+  },
+  {
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+   "loss": -0.4787941128015518,
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+   "state_loss": -0.4961097165942192,
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+   "update": 44000
+  },
+  {
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+   "loss": -0.48326635360717773,
+   "manoeuvre_loss": 0.17278973385691643,
+   "seconds": 7255.975172996521,
+   "state_loss": -0.50054532289505,
+   "train_loss": -0.4607720971107483,
+   "update": 45000
+  },
+  {
+   "ln_range_abs_median": 0.06635837256908417,
+   "loss": -0.4828193336725235,
+   "manoeuvre_loss": 0.17243628576397896,
+   "seconds": 7759.869304180145,
+   "state_loss": -0.5000629648566246,
+   "train_loss": -0.4986380338668823,
+   "update": 46000
+  },
+  {
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+   "loss": -0.47858821600675583,
+   "manoeuvre_loss": 0.17254593595862389,
+   "seconds": 8254.87928390503,
+   "state_loss": -0.495842806994915,
+   "train_loss": -0.4832706153392792,
+   "update": 47000
+  },
+  {
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+   "loss": -0.47674460709095,
+   "manoeuvre_loss": 0.17247430980205536,
+   "seconds": 8748.716240406036,
+   "state_loss": -0.49399204552173615,
+   "train_loss": -0.482293039560318,
+   "update": 48000
+  },
+  {
+   "ln_range_abs_median": 0.06643497943878174,
+   "loss": -0.47692088037729263,
+   "manoeuvre_loss": 0.17247972264885902,
+   "seconds": 9243.709877729416,
+   "state_loss": -0.49416885524988174,
+   "train_loss": -0.46647778153419495,
+   "update": 49000
+  }
+ ]
+}
diff --git a/pyproject.toml b/pyproject.toml
index aa3b5999..35ec3db5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,6 +53,7 @@ tma2-rtrain = "tma2.cli.rtrain:main"
 tma2-rpredict = "tma2.cli.rpredict:main"
 tma2-rread = "tma2.cli.rread:main"
 tma2-tlsolve = "tma2.cli.tlsolve:main"
+tma2-tlpredict = "tma2.cli.tlpredict:main"
 tma2-tlscore = "tma2.cli.tlscore:main"
 tma2-segtrain = "tma2.cli.segtrain:main"
 tma2-plot = "tma2.cli.plot:main"
diff --git a/scripts/modal_train.py b/scripts/modal_train.py
index 01f1b71e..856d9f1f 100644
--- a/scripts/modal_train.py
+++ b/scripts/modal_train.py
@@ -33,9 +33,6 @@
 volume = modal.Volume.from_name("gitma2-regress", create_if_missing=True)
 
 
-GPUS = ("T4", "L4", "A10", "L40S", "A100-40GB")  # a plan's "gpu" (default T4)
-
-
 def _segment(plan_json: str, run_name: str, commit: str) -> tuple[str, bytes]:
     import os
 
@@ -55,18 +52,46 @@ def _segment(plan_json: str, run_name: str, commit: str) -> tuple[str, bytes]:
     return json.dumps(rec), (work / "checkpoint.safetensors").read_bytes()
 
 
-# One statically configured function per GPU type (a per-call override is documented for
-# deployed functions; this form needs nothing beyond what the first runs already used).
+# One function per GPU type, each defined at module scope with the decorator: the form the
+# runs of spec 012 used. (Addendum 1's form, functions built in a loop with serialized=True,
+# was never run before 2026-11-13, when its containers failed at start-up: "the
+# 'modal_train' module is not available in the remote environment".)
+_OPTS = {"timeout": MAX_SEGMENT_SECONDS + STARTUP_SECONDS, "volumes": {"/vol": volume}}
+
+
+@app.function(gpu="T4", **_OPTS)
+def segment_t4(plan_json: str, run_name: str, commit: str) -> tuple[str, bytes]:
+    return _segment(plan_json, run_name, commit)
+
+
+@app.function(gpu="L4", **_OPTS)
+def segment_l4(plan_json: str, run_name: str, commit: str) -> tuple[str, bytes]:
+    return _segment(plan_json, run_name, commit)
+
+
+@app.function(gpu="A10", **_OPTS)
+def segment_a10(plan_json: str, run_name: str, commit: str) -> tuple[str, bytes]:
+    return _segment(plan_json, run_name, commit)
+
+
+@app.function(gpu="L40S", **_OPTS)
+def segment_l40s(plan_json: str, run_name: str, commit: str) -> tuple[str, bytes]:
+    return _segment(plan_json, run_name, commit)
+
+
+@app.function(gpu="A100-40GB", **_OPTS)
+def segment_a100(plan_json: str, run_name: str, commit: str) -> tuple[str, bytes]:
+    return _segment(plan_json, run_name, commit)
+
+
 SEGMENTS = {
-    g: app.function(
-        gpu=g,
-        timeout=MAX_SEGMENT_SECONDS + STARTUP_SECONDS,
-        volumes={"/vol": volume},
-        name="segment_" + g.lower().replace("-", "_"),
-        serialized=True,
-    )(_segment)
-    for g in GPUS
+    "T4": segment_t4,
+    "L4": segment_l4,
+    "A10": segment_a10,
+    "L40S": segment_l40s,
+    "A100-40GB": segment_a100,
 }
+GPUS = tuple(SEGMENTS)  # a plan's "gpu" (default T4)
 
 
 @app.local_entrypoint()
diff --git a/scripts/research/tl3_charts.py b/scripts/research/tl3_charts.py
new file mode 100644
index 00000000..00212293
--- /dev/null
+++ b/scripts/research/tl3_charts.py
@@ -0,0 +1,260 @@
+"""Spec 015 slides: four SVG figures from the committed records.
+
+    uv run python scripts/research/tl3_charts.py --out-dir <dir>
+
+* ``quiet.svg``: false-manoeuvre rate on held-course periods by period length, spec 014's
+  search against the repaired one, with the flat and mixed priors and C″ (test seed, D4).
+* ``decided.svg``: the refit's share of the search's gain (D1) and the two attainable-headroom
+  figures (D3) on the development, validation and test seeds, with 95% intervals.
+* ``headroom.svg``: the headroom with a perfect evidence at every registered class threshold
+  and match tolerance, and on every split of the test seed.
+* ``declared-quiet.svg``: share of targets with one, two and three or more manoeuvres that
+  each system calls quiet (test seed).
+
+Reads `experiments/20261113-tl3{-dev,-val,}/summary.json`; writes ``<fig>.svg.json`` beside
+each figure naming the inputs' digests and the commit.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+ROOT = Path(__file__).resolve().parents[2]
+SEEDS = [
+    ("development 335\n(spent)", Path("experiments/20261113-tl3-dev/summary.json")),
+    ("validation 336", Path("experiments/20261113-tl3-val/summary.json")),
+    ("test 337", Path("experiments/20261113-tl3/summary.json")),
+]
+SPLITS = [
+    ("man2", "Annex B"),
+    ("shift", "shifted\nprior"),
+    ("man2-corr", "correlated\nerrors"),
+    ("man2-out", "outliers"),
+    ("quiet", "held\ncourse"),
+]
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--out-dir", required=True)
+    a = ap.parse_args(argv)
+
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    from tma2.data.provenance import read_git_provenance
+    from tma2.report.plots import ESTIMATORS, STYLE
+
+    g = read_git_provenance()  # before writing anything
+    out = Path(a.out_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    used: dict[str, list[Path]] = {}
+    green, orange, purple, grey = ESTIMATORS[0], ESTIMATORS[1], ESTIMATORS[2], ESTIMATORS[3]
+    have = [(lab, p) for lab, p in SEEDS if (ROOT / p).exists()]
+    study = json.loads((ROOT / have[-1][1]).read_text())["study"]
+    sums = {lab: json.loads((ROOT / p).read_text())["splits"] for lab, p in have}
+    test_lab, test_path = have[-1]
+    test: dict[str, Any] = sums[test_lab]
+    line = float(study["materiality"])
+
+    def err(v: dict[str, float], key: str) -> list[list[float]]:
+        return [[v[key] - v["lo"]], [v["hi"] - v[key]]]
+
+    with plt.rc_context(STYLE):
+        # 1. held course by period length
+        q = test["quiet"]["quiet"]
+        bands = list(q["by_minutes"])
+        series = [
+            ("spec 014's search", "search2", grey, "o", "--"),
+            ("repaired search", "search3", purple, "o", "-"),
+            ("repaired, mixed prior", "search3-mix", green, "s", "-"),
+            ("repaired, flat prior", "search3-flat", green, "^", ":"),
+            ("C″", "c2", orange, "D", "-"),
+        ]
+        fig, ax = plt.subplots(figsize=(7.4, 3.8))
+        x = np.arange(len(bands))
+        for name, key, col, mk, ls in series:
+            r = [q["by_minutes"][b][key] for b in bands]
+            v = np.array([c["rate"] for c in r])
+            lo = np.array([c["lo"] for c in r])
+            hi = np.array([c["hi"] for c in r])
+            ax.errorbar(
+                x, v, yerr=[v - lo, hi - v], color=col, marker=mk, ls=ls, capsize=3, label=name
+            )
+        ax.set_xticks(x, [f"{b} min\n({q['by_minutes'][b]['search3']['n']})" for b in bands])
+        ax.set_ylabel("share of quiet periods given a manoeuvre")
+        ax.set_ylim(-0.03, 1.05)
+        ax.set_title(
+            f"Held course: false manoeuvres by period length ({test_lab}, {q['periods']} periods)",
+            fontsize=10,
+        )
+        ax.legend(frameon=False, fontsize=8)
+        fig.tight_layout()
+        fig.savefig(out / "quiet.svg")
+        plt.close(fig)
+        used["quiet.svg"] = [test_path]
+
+        # 2. decided quantities across seeds
+        fig, axes = plt.subplots(1, 2, figsize=(9.2, 3.7))
+        for i, (lab, _p) in enumerate(have):
+            r = sums[lab]["man2"]
+            gs = r["gain_share"]["registered"]
+            axes[0].errorbar(
+                i, gs["share"], yerr=err(gs, "share"), fmt="o", color=orange, capsize=4
+            )
+            axes[0].text(i + 0.07, gs["share"], f"{gs['share']:.2f}", fontsize=8, va="center")
+            for j, (key, col, name) in enumerate(
+                (
+                    ("ident-search", grey, "a perfect search"),
+                    ("ident-all", purple, "a perfect evidence as well"),
+                )
+            ):
+                p = r["headroom"]["registered"][key]["paired"]
+                xx = i + (j - 0.5) * 0.25
+                axes[1].errorbar(
+                    xx,
+                    p["geometric_mean"],
+                    yerr=err(p, "geometric_mean"),
+                    fmt="o",
+                    color=col,
+                    capsize=4,
+                    label=name if i == 0 else None,
+                )
+                axes[1].text(
+                    xx + 0.05, p["geometric_mean"], f"{p['geometric_mean']:.2f}", fontsize=8
+                )
+        for ax in axes:
+            ax.set_xticks(range(len(have)), [lab for lab, _ in have], fontsize=8)
+            ax.set_xlim(-0.5, len(have) - 0.3)
+        axes[0].axhline(0.5, color="0.6", lw=0.8, ls="--")
+        axes[0].set_ylabel("share of the search's gain the refit obtains")
+        axes[0].set_title("D1: C″'s transitions refitted, no search", fontsize=9)
+        axes[1].axhline(line, color="0.6", lw=0.8, ls="--")
+        axes[1].axhline(1.0, color="0.8", lw=0.8)
+        axes[1].set_ylabel("search ÷ attainable (paired, per period)")
+        axes[1].set_title(f"D3: attainable headroom (line at {line:.2f})", fontsize=9)
+        axes[1].legend(frameon=False, fontsize=8)
+        fig.tight_layout()
+        fig.savefig(out / "decided.svg")
+        plt.close(fig)
+        used["decided.svg"] = [p for _, p in have]
+
+        # 3. headroom by sensitivity and by split
+        fig, axes = plt.subplots(1, 2, figsize=(9.6, 3.8), gridspec_kw={"width_ratios": [1.5, 1]})
+        h = test["man2"]["headroom"]
+        ths, tols = (1, 3, 5), (60, 120, 240)
+        cols = {60: grey, 120: purple, 240: green}
+        for ti, th in enumerate(ths):
+            for j, tol in enumerate(tols):
+                p = h[f"threshold_{th}_tol_{tol}"]["ident-all"]["paired"]
+                xx = ti + (j - 1) * 0.22
+                reg = th == 3 and tol == 120
+                axes[0].errorbar(
+                    xx,
+                    p["geometric_mean"],
+                    yerr=err(p, "geometric_mean"),
+                    fmt="o",
+                    color=cols[tol],
+                    capsize=4,
+                    mfc=cols[tol] if reg else "white",
+                    label=f"match within {tol} s" if ti == 0 else None,
+                )
+                axes[0].text(xx + 0.03, p["hi"] + 0.005, f"{p['geometric_mean']:.2f}", fontsize=7)
+        axes[0].set_xticks(range(3), [f"evidence threshold {t}" for t in ths], fontsize=8)
+        axes[0].set_title(
+            f"Annex B, {test_lab}: every registered sensitivity (filled: registered)", fontsize=9
+        )
+        axes[0].set_ylabel("search ÷ attainable, perfect evidence (paired)")
+        axes[0].legend(frameon=False, fontsize=8)
+        sp = [(k, lab) for k, lab in SPLITS if k in test]
+        for i, (k, _lab) in enumerate(sp):
+            p = test[k]["headroom"]["registered"]["ident-all"]["paired"]
+            axes[1].errorbar(
+                i,
+                p["geometric_mean"],
+                yerr=err(p, "geometric_mean"),
+                fmt="o",
+                color=purple,
+                capsize=4,
+            )
+            axes[1].text(i + 0.08, p["geometric_mean"], f"{p['geometric_mean']:.2f}", fontsize=8)
+        axes[1].set_xticks(
+            range(len(sp)), [f"{lab}\n({test[k]['periods_scored']})" for k, lab in sp], fontsize=7
+        )
+        axes[1].set_xlim(-0.5, len(sp) - 0.3)
+        axes[1].set_title("Every split (registered settings)", fontsize=9)
+        for ax in axes:
+            ax.axhline(line, color="0.6", lw=0.8, ls="--")
+            ax.axhline(1.0, color="0.8", lw=0.8)
+        fig.tight_layout()
+        fig.savefig(out / "headroom.svg")
+        plt.close(fig)
+        used["headroom.svg"] = [test_path]
+
+        # 4. manoeuvring targets called quiet
+        dq = test["man2"]["declared_quiet"]
+        sysd = [
+            ("C″", "c2", orange),
+            ("spec 014's search", "search2", grey),
+            ("repaired search", "search3", purple),
+            ("repaired, mixed prior", "search3-mix", green),
+        ]
+        sysd = [s for s in sysd if s[1] in dq]
+        groups = list(dq[sysd[0][1]])
+        fig, ax = plt.subplots(figsize=(7.2, 3.6))
+        x = np.arange(len(groups))
+        w = 0.8 / len(sysd)
+        for j, (name, key, col) in enumerate(sysd):
+            r = [dq[key][gk] for gk in groups]
+            v = np.array([c["rate"] for c in r])
+            lo = np.array([max(c["lo"], 0.0) for c in r])
+            hi = np.array([c["hi"] for c in r])
+            xx = x + (j - (len(sysd) - 1) / 2) * w
+            ax.bar(
+                xx,
+                v,
+                w,
+                color=col,
+                label=name,
+                yerr=[v - lo, hi - v],
+                capsize=2,
+                error_kw={"lw": 0.8},
+            )
+            for xi, vi, hi_i in zip(xx, v, hi, strict=True):
+                ax.text(xi, hi_i + 0.005, f"{vi:.2f}", ha="center", va="bottom", fontsize=7)
+        ax.set_xticks(
+            x,
+            [
+                f"{gk} true manoeuvre{'s' if gk != '1' else ''}\n({dq[sysd[0][1]][gk]['n']} periods)"
+                for gk in groups
+            ],
+        )
+        ax.set_ylabel("share called quiet (no manoeuvre found)")
+        ax.set_title(f"Manoeuvring targets called quiet (Annex B, {test_lab})", fontsize=10)
+        ax.legend(frameon=False, fontsize=8)
+        fig.tight_layout()
+        fig.savefig(out / "declared-quiet.svg")
+        plt.close(fig)
+        used["declared-quiet.svg"] = [test_path]
+
+    for name, paths in used.items():
+        side = {
+            "figure": name,
+            "script": "scripts/research/tl3_charts.py",
+            "git_commit": g.commit,
+            "git_dirty": g.dirty,
+            "inputs": {str(p): hashlib.sha256((ROOT / p).read_bytes()).hexdigest() for p in paths},
+        }
+        (out / f"{name}.json").write_text(json.dumps(side, indent=1) + "\n")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/research/tl3_tables.py b/scripts/research/tl3_tables.py
new file mode 100644
index 00000000..e765a302
--- /dev/null
+++ b/scripts/research/tl3_tables.py
@@ -0,0 +1,235 @@
+"""Spec 015: every table of a record, generated from its ``summary.json`` (FR-014: no table is
+copied by hand).
+
+    uv run python scripts/research/tl3_tables.py <experiment dir> [--out tables.md]
+
+Prints Markdown: the systems table by split, the paired comparisons, the decided quantities
+D1 to D4 with their sensitivities, false manoeuvres by period length, manoeuvring targets
+declared quiet, counts and candidates. A test checks that a record's ``tables.md`` equals
+what this script prints for its summary.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+from typing import Any
+
+SYSTEMS = [
+    ("One leg", "one-leg"),
+    ("C″ read as a track", "c2"),
+    ("C″ refitted", "c2-refit"),
+    ("Search, flat prior", "search3-flat"),
+    ("Search, spec 014's evidence", "search2"),
+    ("Search, repaired evidence", "search3"),
+    ("Search, repaired, mixed prior", "search3-mix"),
+    ("True history, best evidence", "history-best"),
+    ("True history, times held", "history-fixed"),
+]
+SPLITS = [
+    ("man2", "Annex B"),
+    ("shift", "Shifted"),
+    ("quiet", "Held course"),
+    ("man2-corr", "Correlated"),
+    ("man2-out", "Outliers"),
+    ("whole", "Whole"),
+]
+STATING = [
+    n
+    for _, n in SYSTEMS
+    if n in ("c2", "c2-refit", "search3-flat", "search2", "search3", "search3-mix")
+]
+
+
+def num(x: Any, d: int = 3) -> str:
+    return "–" if x is None or (isinstance(x, float) and not math.isfinite(x)) else f"{x:.{d}f}"
+
+
+def ci(v: dict[str, Any], key: str, d: int = 2) -> str:
+    return f"{num(v[key], d)} [{num(v['lo'], d)}, {num(v['hi'], d)}]"
+
+
+def tables(summary: dict[str, Any]) -> str:
+    sp = summary["splits"]
+    have = [(k, lab) for k, lab in SPLITS if k in sp]
+    out: list[str] = []
+    out += ["## Systems (median period range error)", ""]
+    out.append(
+        "| System | " + " | ".join(f"{lab} ({sp[k]['periods_scored']})" for k, lab in have) + " |"
+    )
+    out.append("| --- |" + " --- |" * len(have))
+    for lab, key in SYSTEMS:
+        cells = [
+            num(sp[k]["systems"][key]["median_period_range"]) if key in sp[k]["systems"] else "–"
+            for k, _ in have
+        ]
+        out.append(f"| {lab} | " + " | ".join(cells) + " |")
+    for k, lab in have:
+        r = sp[k]
+        out += ["", f"## {lab}: paired comparisons", ""]
+        out.append(
+            "| Comparison | Paired | Of medians | First better | Over twice as bad: first / second |"
+        )
+        out.append("| --- | --- | --- | --- | --- |")
+        for name, v in r["comparisons"].items():
+            p, m = v["paired"], v["of_medians"]
+            out.append(
+                f"| {name.replace('_over_', ' ÷ ')} | {ci(p, 'geometric_mean')} | {ci(m, 'value')} | "
+                f"{num(p['a_better'], 2)} | {num(p['a_twice_as_bad'], 2)} / {num(p['b_twice_as_bad'], 2)} |"
+            )
+        out += ["", f"## {lab}: classes of wrong-history periods", ""]
+        out.append(
+            "| Periods | Classed | First right | Wrong | Ambiguous | Search failure | Evidence failure |"
+        )
+        out.append("| --- | --- | --- | --- | --- | --- | --- |")
+        for which in ("scored", "all", "scored_without_cut"):
+            c = r["classes"][which]
+            aw = c["among_wrong"]
+            out.append(
+                f"| {which.replace('_', ' ')} | {c['periods']} | {ci(c['right'], 'rate')} | {c['wrong']} | "
+                f"{ci(aw['ambiguous'], 'rate')} | {ci(aw['search_failure'], 'rate')} | "
+                f"{ci(aw['evidence_failure'], 'rate')} |"
+            )
+        if "headroom" in r:
+            out += ["", f"## {lab}: attainable headroom (search ÷ composite, paired)", ""]
+            out.append(
+                "| Setting | Perfect search | Perfect evidence as well | Every wrong period | Where it helps only |"
+            )
+            out.append("| --- | --- | --- | --- | --- |")
+            for key, h in r["headroom"].items():
+                out.append(
+                    f"| {key.replace('_', ' ')} | {ci(h['ident-search']['paired'], 'geometric_mean')} "
+                    f"{h['ident-search'].get('verdict', '')} | {ci(h['ident-all']['paired'], 'geometric_mean')} "
+                    f"{h['ident-all'].get('verdict', '')} | {ci(h['every-wrong']['paired'], 'geometric_mean')} | "
+                    f"{ci(h['ident-all-hindsight']['paired'], 'geometric_mean')} |"
+                )
+            reg = r["headroom"]["registered"]
+            out += [
+                "",
+                f"Ratio of medians at the registered setting: perfect search "
+                f"{ci(reg['ident-search']['of_medians'], 'value')}; perfect evidence as well "
+                f"{ci(reg['ident-all']['of_medians'], 'value')}.",
+            ]
+        if "gain_share" in r:
+            g = r["gain_share"]
+            out += ["", f"## {lab}: the refit's share of the search's gain", ""]
+            out.append("| Estimand | Share |")
+            out.append("| --- | --- |")
+            for key in (
+                "registered",
+                "without_fallbacks",
+                "medians_at_most_1",
+                "search_right",
+                "search_wrong",
+                "flat_prior_search",
+            ):
+                if key in g:
+                    out.append(
+                        f"| {key.replace('_', ' ')} | {ci(g[key], 'share')} ({g[key]['periods']} periods) |"
+                    )
+            out.append(f"| ratio of log medians | {num(g['ratio_of_log_medians'], 2)} |")
+            out += [
+                "",
+                f"Refit fall-backs: {r['refit_fell_back']} of {r['periods']}; refits with a period median of 1 or more: {r.get('refit_runaways', 0)}.",
+            ]
+        out += ["", f"## {lab}: manoeuvre counts (120 s match; cut manoeuvres excluded)", ""]
+        out.append(
+            "| System | True | Missed | Missed share | False | False a period | Timing (s) | Missed at 60 s / 240 s |"
+        )
+        out.append("| --- | --- | --- | --- | --- | --- | --- | --- |")
+        for name in STATING:
+            if name in r["counts"]:
+                c = r["counts"][name]
+                a = c["120"]
+                out.append(
+                    f"| {name} | {a['true']} | {a['missed']} | {num(a['missed_share'], 2)} | {a['false']} | "
+                    f"{num(a['false_per_period'], 2)} | {num(a['timing_median_s'], 0)} | "
+                    f"{num(c['60']['missed_share'], 2)} / {num(c['240']['missed_share'], 2)} |"
+                )
+        q = r["quiet"]
+        if q["periods"]:
+            out += [
+                "",
+                f"## {lab}: periods with no true manoeuvre ({q['periods']}): share with one declared",
+                "",
+            ]
+            bins = [b for b in q["by_minutes"] if any(v["n"] for v in q["by_minutes"][b].values())]
+            out.append("| System | All | " + " | ".join(f"{b} min" for b in bins) + " |")
+            out.append("| --- | --- |" + " --- |" * len(bins))
+            for name in STATING:
+                if name in q["false_manoeuvre_rate"]:
+                    cells = [
+                        f"{num(q['by_minutes'][b][name]['rate'], 2)} ({q['by_minutes'][b][name]['n']})"
+                        for b in bins
+                    ]
+                    out.append(
+                        f"| {name} | {ci(q['false_manoeuvre_rate'][name], 'rate')} | "
+                        + " | ".join(cells)
+                        + " |"
+                    )
+        dq = r["declared_quiet"]
+        if any(v["n"] for d in dq.values() for v in d.values()):
+            out += [
+                "",
+                f"## {lab}: manoeuvring targets declared quiet, by true manoeuvres (periods with none cut)",
+                "",
+            ]
+            out.append("| System | 1 | 2 | 3 or more |")
+            out.append("| --- | --- | --- | --- |")
+            for name in STATING:
+                if name in dq:
+                    out.append(
+                        f"| {name} | "
+                        + " | ".join(
+                            f"{num(dq[name][k]['rate'], 3)} ({dq[name][k]['n']})"
+                            for k in ("1", "2", "3+")
+                        )
+                        + " |"
+                    )
+        for key, what in (("candidates", "repaired"), ("candidates2", "spec 014's")):
+            if key in r and r[key].get("periods"):
+                c = r[key]
+                top = next(k for k in c if k.startswith("truth_in_top"))
+                out += [
+                    "",
+                    f"Candidates ({what} evidence, {c['periods']} periods): first right {num(c['top_right'], 3)}; "
+                    f"true history in the first three {num(c[top], 3)}; calibration gap {num(c['calibration_gap'], 3)}; "
+                    f"kinds differ among right {num(c['kinds_differ_among_right'], 2)}.",
+                ]
+        out += [
+            "",
+            f"References held to the true times: {num(r['reference_flagged_share'], 3)}; true-history fits "
+            f"with a consistent cost: {num(r['history_converged_share'], 3)}; periods with a cut manoeuvre: "
+            f"{r['periods_with_a_cut_manoeuvre']} of {r['periods']}.",
+        ]
+    if summary.get("soundness"):
+        s = summary["soundness"]
+        out += [
+            "",
+            "## Soundness",
+            "",
+            f"Reference efficiency on tight rows {num(s['reference_efficiency_tight'], 2)} (band "
+            f"{s['band'][0]} to {s['band'][1]}); consistent fits {num(s['history_converged_share'], 3)} "
+            f"(at least {s['convergence_min']}); references held {num(s['reference_flagged_share'], 3)} "
+            f"(at most {s['reference_flagged_max']}): {'pass' if s['pass'] else 'fail'}.",
+        ]
+    return "\n".join(out) + "\n"
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("experiment")
+    ap.add_argument("--out")
+    a = ap.parse_args(argv)
+    text = tables(json.loads((Path(a.experiment) / "summary.json").read_text()))
+    if a.out:
+        Path(a.out).write_text(text)
+    else:
+        print(text, end="")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/research/tl4_charts.py b/scripts/research/tl4_charts.py
new file mode 100644
index 00000000..7f4a978b
--- /dev/null
+++ b/scripts/research/tl4_charts.py
@@ -0,0 +1,284 @@
+"""Spec 016 slides: six SVG figures from the committed records.
+
+    uv run python scripts/research/tl4_charts.py --out-dir <dir>
+
+* ``verdicts.svg``: the registered comparison, declared control ÷ learned arc read-out, for
+  three seeds on every criterion split, with 95% intervals.
+* ``systems.svg``: median period range error of every system on every split.
+* ``controls.svg``: the standing search ÷ the declared control by split on the tuning,
+  validation and test seeds.
+* ``manoeuvres.svg``: manoeuvres missed and false for C″, the search, the control and the
+  model's detector.
+* ``training.svg``: the monitor curve of the three training seeds.
+* ``tracks.svg``: three stored periods as track plots (ownship blue, target red, the
+  control and the learned track). The periods are chosen by a rule, not by eye: on the
+  shifted split, of the 20 stored periods, the one where the learned read-out is worst
+  against the control and the one where it is best; on the outlier split the first stored
+  period with a true manoeuvre.
+
+Reads `experiments/20261113-tl4-{tuning,val}/summary.json`, `experiments/20261114-tl4/`
+(`summary.json`, `tracks.npz`: tracks at scored rows for the first 20 periods of three
+splits, extracted from the results branch) and `experiments/20261114-tlm-runs/`; writes
+``<fig>.svg.json`` beside each figure with the inputs' digests and the commit.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+ROOT = Path(__file__).resolve().parents[2]
+TEST = Path("experiments/20261114-tl4")
+SEEDS = [
+    ("tuning 338", Path("experiments/20261113-tl4-tuning/summary.json")),
+    ("validation 340", Path("experiments/20261113-tl4-val/summary.json")),
+    ("test 341", TEST / "summary.json"),
+]
+SPLITS = [
+    ("man2", "Annex B"),
+    ("shift", "shifted\nprior"),
+    ("shift2", "shifted\nprior 2"),
+    ("quiet", "held\ncourse"),
+    ("man2-corr", "correlated\nerrors"),
+    ("man2-out", "outliers\n2%"),
+    ("man2-out-harsh", "outliers\n5%"),
+    ("man2-bias", "bias"),
+]
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--out-dir", required=True)
+    a = ap.parse_args(argv)
+
+    import matplotlib
+
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    from tma2.data.provenance import read_git_provenance
+    from tma2.report.plots import ESTIMATORS, STYLE
+
+    g = read_git_provenance()  # before writing anything
+    out = Path(a.out_dir)
+    out.mkdir(parents=True, exist_ok=True)
+    used: dict[str, list[Path]] = {}
+    green, orange, purple, grey = ESTIMATORS[0], ESTIMATORS[1], ESTIMATORS[2], ESTIMATORS[3]
+    sums = {lab: json.loads((ROOT / p).read_text())["splits"] for lab, p in SEEDS}
+    summary = json.loads((ROOT / TEST / "summary.json").read_text())
+    test: dict[str, Any] = summary["splits"]
+    crit = summary["criteria"]
+
+    def bar(v: dict[str, float]) -> list[list[float]]:
+        return [[v["geometric_mean"] - v["lo"]], [v["hi"] - v["geometric_mean"]]]
+
+    with plt.rc_context(STYLE):
+        # 1. the registered comparison
+        rows = [(k, lab, crit["splits"].get(k) or crit["no_harm"].get(k)) for k, lab in SPLITS]
+        rows = [r for r in rows if r[2]]
+        fig, ax = plt.subplots(figsize=(8.6, 3.9))
+        for i, (_k, _lab, v) in enumerate(rows):
+            for j, (_seed, r) in enumerate(sorted(v["seeds"].items())):
+                d = r["declared"]
+                ax.errorbar(
+                    i + (j - 1) * 0.2,
+                    d["geometric_mean"],
+                    yerr=bar(d),
+                    fmt="o",
+                    color=purple,
+                    capsize=3,
+                    ms=4,
+                    label="one training seed" if i == 0 and j == 0 else None,
+                )
+                ax.plot(
+                    i + (j - 1) * 0.2,
+                    d["median_ratio"],
+                    marker="_",
+                    color=orange,
+                    ms=10,
+                    mew=2,
+                    label="median per-period ratio" if i == 0 and j == 0 else None,
+                )
+            ax.text(i, 0.615, v["verdict"], ha="center", fontsize=7, color="0.3")
+        ax.axhline(1.0, color="0.5", lw=0.8, ls="--")
+        ax.set_xticks(range(len(rows)), [lab for _, lab, _ in rows], fontsize=8)
+        ax.set_ylim(0.6, 1.2)
+        ax.set_ylabel("declared control ÷ learned track\n(above 1: the learned track is better)")
+        ax.set_title("The registered comparison, test seed 341 (paired per period)", fontsize=10)
+        ax.legend(frameon=False, fontsize=8, loc="upper left")
+        fig.tight_layout()
+        fig.savefig(out / "verdicts.svg")
+        plt.close(fig)
+        used["verdicts.svg"] = [TEST / "summary.json"]
+
+        # 2. every system
+        systems = [
+            ("C″ as a track", "c2", grey),
+            ("standing search", "search3", "#b9a3d0"),
+            ("classical control", "ctl-d1-q0.01", purple),
+            ("learned, own track", "learned-s1", "#f3cf95"),
+            ("learned, arc read-out", "learned-s1-arc", orange),
+            ("true history", "history-best", green),
+        ]
+        have = [(k, lab) for k, lab in SPLITS if k in test]
+        fig, ax = plt.subplots(figsize=(10.0, 4.0))
+        x = np.arange(len(have))
+        w = 0.8 / len(systems)
+        for j, (name, key, col) in enumerate(systems):
+            v = [test[k]["systems"][key]["median_period_range"] for k, _ in have]
+            ax.bar(x + (j - (len(systems) - 1) / 2) * w, v, w, color=col, label=name)
+        ax.set_xticks(x, [lab for _, lab in have], fontsize=8)
+        ax.set_ylim(0, 0.3)
+        for i, (k, _) in enumerate(have):
+            c2 = test[k]["systems"]["c2"]["median_period_range"]
+            if c2 > 0.3:
+                ax.text(i - 0.33, 0.292, f"{c2:.2f}", ha="center", fontsize=7, color="0.3")
+        ax.set_ylabel("median period range error (relative)")
+        ax.set_title("Every system, test seed 341 (seed 1 of the learned model)", fontsize=10)
+        ax.legend(frameon=False, fontsize=8, ncol=3)
+        fig.tight_layout()
+        fig.savefig(out / "systems.svg")
+        plt.close(fig)
+        used["systems.svg"] = [TEST / "summary.json"]
+
+        # 3. the controls across seeds
+        fig, ax = plt.subplots(figsize=(9.0, 3.8))
+        cols = {"tuning 338": grey, "validation 340": "#b9a3d0", "test 341": purple}
+        for i, (k, _lab) in enumerate(have):
+            for j, (lab, _p) in enumerate(SEEDS):
+                c = sums[lab].get(k, {}).get("comparisons", {}).get("search3_over_ctl-d1-q0.01")
+                if not c:
+                    continue
+                ax.errorbar(
+                    i + (j - 1) * 0.22,
+                    c["geometric_mean"],
+                    yerr=bar(c),
+                    fmt="o",
+                    color=cols[lab],
+                    capsize=3,
+                    ms=4,
+                    label=lab if i == 0 else None,
+                )
+        ax.axhline(1.0, color="0.5", lw=0.8, ls="--")
+        ax.set_yscale("log")
+        ax.set_yticks([0.8, 1, 1.5, 2, 3, 4], ["0.8", "1", "1.5", "2", "3", "4"])
+        ax.set_xticks(x, [lab for _, lab in have], fontsize=8)
+        ax.set_ylabel("standing search ÷ classical control\n(above 1: the control is better)")
+        ax.set_title("What the classical control adds, on three seeds", fontsize=10)
+        ax.legend(frameon=False, fontsize=8)
+        fig.tight_layout()
+        fig.savefig(out / "controls.svg")
+        plt.close(fig)
+        used["controls.svg"] = [p for _, p in SEEDS]
+
+        # 4. manoeuvres missed and false
+        who = [
+            ("C″", "c2", grey),
+            ("standing search", "search3", "#b9a3d0"),
+            ("classical control", "ctl-d1-q0.01", purple),
+            ("model's detector", "learned-s1", orange),
+        ]
+        picks = [(k, lab) for k, lab in have if k in ("man2", "shift", "man2-corr", "man2-out")]
+        fig, axes = plt.subplots(1, 2, figsize=(9.2, 3.6))
+        xx = np.arange(len(picks))
+        ww = 0.8 / len(who)
+        for ax, key, title in (
+            (axes[0], "missed_share", "true manoeuvres missed (share)"),
+            (axes[1], "false_per_period", "false manoeuvres (per period)"),
+        ):
+            for j, (name, sysk, col) in enumerate(who):
+                v = [test[k]["counts"][sysk]["120"][key] for k, _ in picks]
+                ax.bar(xx + (j - (len(who) - 1) / 2) * ww, v, ww, color=col, label=name)
+            ax.set_xticks(xx, [lab for _, lab in picks], fontsize=8)
+            ax.set_title(title, fontsize=9)
+        axes[1].legend(frameon=False, fontsize=8)
+        fig.suptitle("Manoeuvres, matched within 120 s (test seed 341)", fontsize=10)
+        fig.tight_layout()
+        fig.savefig(out / "manoeuvres.svg")
+        plt.close(fig)
+        used["manoeuvres.svg"] = [TEST / "summary.json"]
+
+        # 5. training
+        runs = sorted((ROOT / "experiments/20261114-tlm-runs").glob("tlm-s*.run.json"))
+        fig, ax = plt.subplots(figsize=(6.6, 3.6))
+        for j, p in enumerate(runs):
+            r = json.loads(p.read_text())
+            u = [v["update"] * 256 / 1e6 for v in r["validation"]]
+            e = [v["ln_range_abs_median"] for v in r["validation"]]
+            ax.plot(u, e, color=[purple, orange, green][j % 3], lw=1.2, label=f"seed {j + 1}")
+        ax.set_xlabel("simulated periods seen (millions)")
+        ax.set_ylabel("median |error of ln range| on the monitor slice")
+        ax.set_ylim(0.05, 0.3)
+        ax.set_title("Training the whole-period model (three seeds, a T4 each)", fontsize=10)
+        ax.legend(frameon=False, fontsize=8)
+        fig.tight_layout()
+        fig.savefig(out / "training.svg")
+        plt.close(fig)
+        used["training.svg"] = [p.relative_to(ROOT) for p in runs]
+
+        # 6. tracks
+        z = np.load(ROOT / TEST / "tracks.npz")
+
+        def med(split: str, system: str, period: int) -> float:
+            m = z[f"{split}/period"] == period
+            tr, st = z[f"{split}/truth"][m].astype(float), z[f"{split}/{system}"][m].astype(float)
+            rng = np.hypot(tr[:, 0] - tr[:, 4], tr[:, 1] - tr[:, 5])
+            est = np.hypot(st[:, 0] - tr[:, 4], st[:, 1] - tr[:, 5])
+            return float(np.median(np.abs(est - rng) / rng))
+
+        ids = [int(i) for i in np.unique(z["shift/period"])]
+        ratio = {
+            i: med("shift", "learned-s1-arc", i) / med("shift", "ctl-d1-q0.01", i) for i in ids
+        }
+        worst, best = max(ratio, key=lambda i: ratio[i]), min(ratio, key=lambda i: ratio[i])
+        per = json.loads((ROOT / TEST / "man2-out.periods.json").read_text())
+        stored = {int(i) for i in np.unique(z["man2-out/period"])}
+        first = next(r["ordinal"] for r in per if r["ordinal"] in stored and r["true"])
+        panels = [
+            ("shift", worst, "shifted prior: the learned track at its worst"),
+            ("shift", best, "shifted prior: the learned track at its best"),
+            ("man2-out", first, "2% outliers"),
+        ]
+        fig, axes = plt.subplots(1, 3, figsize=(11.4, 4.0))
+        shown = [
+            ("classical control", "ctl-d1-q0.01", purple, "-"),
+            ("learned, arc read-out", "learned-s1-arc", orange, "-"),
+            ("learned, own track", "learned-s1", orange, ":"),
+        ]
+        for ax, (split, pid, title) in zip(axes, panels, strict=True):
+            m = z[f"{split}/period"] == pid
+            tr = z[f"{split}/truth"][m].astype(float) / 1000.0
+            ax.plot(tr[:, 4], tr[:, 5], color="#1f77b4", lw=1.4, label="ownship")
+            ax.plot(tr[:, 0], tr[:, 1], color="#d62728", lw=1.6, label="target (truth)")
+            ax.plot(tr[0, 0], tr[0, 1], "o", color="#d62728", ms=4)
+            for name, key, col, ls in shown:
+                st = z[f"{split}/{key}"][m].astype(float) / 1000.0
+                ax.plot(st[:, 0], st[:, 1], color=col, ls=ls, lw=1.1, label=name)
+            ax.set_aspect("equal", adjustable="datalim")
+            ax.set_title(f"{title}\n(period {pid}, {int(m.sum()) // 2} min)", fontsize=8)
+            ax.set_xlabel("east (km)")
+        axes[0].set_ylabel("north (km)")
+        axes[0].legend(frameon=False, fontsize=7)
+        fig.tight_layout()
+        fig.savefig(out / "tracks.svg")
+        plt.close(fig)
+        used["tracks.svg"] = [TEST / "tracks.npz", TEST / "man2-out.periods.json"]
+
+    for name, paths in used.items():
+        side = {
+            "figure": name,
+            "script": "scripts/research/tl4_charts.py",
+            "git_commit": g.commit,
+            "git_dirty": g.dirty,
+            "inputs": {str(p): hashlib.sha256((ROOT / p).read_bytes()).hexdigest() for p in paths},
+        }
+        (out / f"{name}.json").write_text(json.dumps(side, indent=1) + "\n")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/research/tl4_learned_tables.py b/scripts/research/tl4_learned_tables.py
new file mode 100644
index 00000000..57b3890d
--- /dev/null
+++ b/scripts/research/tl4_learned_tables.py
@@ -0,0 +1,98 @@
+"""Spec 016: the learned model's tables of a record that scored it, generated from its
+``summary.json`` (FR-021), beside ``tl4_tables.py`` (which is bound by the decision record
+and unchanged).
+
+    uv run python scripts/research/tl4_learned_tables.py <experiment dir> [--out learned.md]
+
+Prints Markdown: the registered verdicts L-c and L-d seed by seed; every system over every
+learned track, paired per period; efficiency against the bound by split.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+from tl4_tables import SPLITS, ci, f
+
+
+def learned(summary: dict[str, Any], have: list[tuple[str, str]]) -> list[str]:
+    """The learned model's sections (only for a record that scored it)."""
+    sp, crit = summary["splits"], summary["criteria"]
+    lab = dict(have)
+    out = ["", "## L-c and L-d: the registered verdicts", ""]
+    out += [
+        "| Split | Verdict | Seed | Declared control ÷ learned | Side | Hedged track ÷ learned | Side |"
+    ]
+    out += ["| --- | --- | --- | --- | --- | --- | --- |"]
+    for block in ("splits", "no_harm"):
+        for k, v in crit[block].items():
+            for seed, r in v["seeds"].items():
+                out += [
+                    f"| {lab.get(k, k)} | {v['verdict']} | `{seed}` | {ci(r['declared'])} | "
+                    f"{r['vs_declared']} | {ci(r['hedged'])} | {r['vs_hedged']} |"
+                ]
+    out += [
+        "",
+        f"Learning shown on {crit['learning_shown_on']} mismatch split(s); harm: "
+        f"{'yes' if crit['harm'] else 'no'}. What follows: {crit['follows']}.",
+        "",
+    ]
+    out += [
+        "## Each system ÷ each learned track (paired per period; above 1: the learned track is better)",
+        "",
+    ]
+    for k, lb in have:
+        c = sp[k]["comparisons"]
+        keys = sorted(x for x in c if "_over_learned" in x)
+        if not keys:
+            continue
+        out += [
+            f"### {lb}",
+            "",
+            "| System | Learned track | Ratio | Share of periods where the system is better | Learned over twice as bad in |",
+        ]
+        out += ["| --- | --- | --- | --- | --- |"]
+        for key in keys:
+            a, b = key.split("_over_")
+            v = c[key]
+            out += [
+                f"| `{a}` | `{b}` | {ci(v)} | {v['a_better']:.2f} | {v['b_twice_as_bad']:.2f} |"
+            ]
+        out += [""]
+    out += ["## Efficiency against the bound (range, course, speed) and rows without a state", ""]
+    out += ["| Split | System | Range | Course | Speed | Periods without a track |"]
+    out += ["| --- | --- | --- | --- | --- | --- |"]
+    for k, lb in have:
+        for n, v in sorted(sp[k]["systems"].items()):
+            if not (n.startswith("learned") or n in ("ctl-d1-q0.01", "ctl-h1", "search3", "c2")):
+                continue
+            out += [
+                f"| {lb} | `{n}` | {f(v['efficiency_range'], 2)} | {f(v['efficiency_course'], 2)} | "
+                f"{f(v['efficiency_speed'], 2)} | {v.get('periods_without_a_track', 0)} |"
+            ]
+    return out
+
+
+def tables(summary: dict[str, Any]) -> str:
+    have = [(k, lab) for k, lab in SPLITS if k in summary["splits"]]
+    return "\n".join(learned(summary, have)).lstrip("\n") + "\n"
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("dir")
+    ap.add_argument("--out")
+    a = ap.parse_args(argv)
+    text = tables(json.loads((Path(a.dir) / "summary.json").read_text()))
+    if a.out:
+        Path(a.out).write_text(text)
+    else:
+        print(text, end="")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/research/tl4_supplement.py b/scripts/research/tl4_supplement.py
new file mode 100644
index 00000000..c962b4ed
--- /dev/null
+++ b/scripts/research/tl4_supplement.py
@@ -0,0 +1,93 @@
+"""Spec 016: readings of a test record that its summary does not carry as tables, from
+``summary.json`` and ``<split>.periods.json`` (FR-021: generated, not typed).
+
+    uv run python scripts/research/tl4_supplement.py <experiment dir> [--out supplement.md]
+
+* the model's own track against the two controls (the registered comparison is on the arc
+  read-out; this is "reported beside");
+* the read-out against the model's own track, and how often the read-out fell back;
+* manoeuvres missed and false for the control and the model, and how often each calls a
+  target quiet;
+* the forecasts' quantities in one table.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any
+
+from tl4_tables import SPLITS, ci, f
+
+SEEDS = ("learned-s1", "learned-s2", "learned-s3")
+
+
+def text(d: Path) -> str:
+    s = json.loads((d / "summary.json").read_text())["splits"]
+    have = [(k, lab) for k, lab in SPLITS if k in s]
+    out = ["## The model's own track (not the registered comparison)", ""]
+    out += [
+        "| Split | Seed | Declared control ÷ own track | Hedged track ÷ own track | Own track ÷ its arc read-out (from medians) | Read-out fell back |"
+    ]
+    out += ["| --- | --- | --- | --- | --- | --- |"]
+    for k, lab in have:
+        per = json.loads((d / f"{k}.periods.json").read_text())
+        c, sy = s[k]["comparisons"], s[k]["systems"]
+        for seed in SEEDS:
+            arc = f"{seed}-arc"
+            fell = sum(bool(r["histories"][arc].get("fell_back")) for r in per) / len(per)
+            ratio = sy[seed]["median_period_range"] / sy[arc]["median_period_range"]
+            out += [
+                f"| {lab} | `{seed}` | {ci(c.get(f'ctl-d1-q0.01_over_{seed}'))} | "
+                f"{ci(c.get(f'ctl-h1_over_{seed}'))} | {ratio:.2f} | {fell:.2f} |"
+            ]
+    out += ["", "## Manoeuvres (matched within 120 s) and targets called quiet", ""]
+    out += [
+        "| Split | System | Missed (share) | False (per period) | No true manoeuvre: given one | One true manoeuvre: called quiet |"
+    ]
+    out += ["| --- | --- | --- | --- | --- | --- |"]
+    for k, lab in have:
+        for n in ("c2", "search3", "ctl-d1-q0.01", "learned-s1", "learned-s1-arc"):
+            cn = s[k]["counts"].get(n)
+            dq = s[k]["declares"].get(n)
+            if not cn or not dq:
+                continue
+            q0, q1 = dq["0"]["called_quiet"], dq["1"]["called_quiet"]
+            out += [
+                f"| {lab} | `{n}` | {f(cn['120']['missed_share'], 2)} | "
+                f"{f(cn['120']['false_per_period'], 2)} | "
+                f"{f(None if q0 is None else 1 - q0['rate'], 2)} ({dq['0']['n']}) | "
+                f"{f(q1 and q1['rate'], 2)} ({dq['1']['n']}) |"
+            ]
+    out += ["", "## The controls (L-b)", ""]
+    out += [
+        "| Split | Search ÷ declared control | Gap to the reference closed by `ctl` | Declared ÷ hedged | `ctl` ÷ reference (held where drifted) | References held |"
+    ]
+    out += ["| --- | --- | --- | --- | --- | --- |"]
+    for k, lab in have:
+        c: dict[str, Any] = s[k]["comparisons"]
+        out += [
+            f"| {lab} | {ci(c.get('search3_over_ctl-d1-q0.01'))} | "
+            f"{ci(s[k]['gap_closed'].get('ctl'), 'share')} | "
+            f"{ci(c.get('ctl-d1-q0.01_over_ctl-h1'))} | {ci(c.get('ctl_over_history-best'))} | "
+            f"{s[k]['reference_flagged_share']:.2f} |"
+        ]
+    return "\n".join(out) + "\n"
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("dir")
+    ap.add_argument("--out")
+    a = ap.parse_args(argv)
+    t = text(Path(a.dir))
+    if a.out:
+        Path(a.out).write_text(t)
+    else:
+        print(t, end="")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/research/tl4_tables.py b/scripts/research/tl4_tables.py
new file mode 100644
index 00000000..ebdd88ad
--- /dev/null
+++ b/scripts/research/tl4_tables.py
@@ -0,0 +1,167 @@
+"""Spec 016: every table of a record, generated from its ``summary.json`` (FR-021).
+
+    uv run python scripts/research/tl4_tables.py <experiment dir> [--out tables.md]
+
+Prints Markdown: per split, the systems' median period range error; the standing search
+over each control, paired per period; the share of the gap to the reference each control
+closes; systems over the true-history reference under each convention; manoeuvre counts;
+the declaration grid (held-course false manoeuvres against manoeuvring targets called
+quiet); what the correlation estimate and the prior diagnostic said. A test checks that a
+record's ``tables.md`` equals what this prints for its summary.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+from typing import Any
+
+SPLITS = [
+    ("man2", "Annex B"),
+    ("shift", "Shifted"),
+    ("shift2", "Shifted 2"),
+    ("quiet", "Held course"),
+    ("man2-corr-mild", "Correlated, mild"),
+    ("man2-corr", "Correlated"),
+    ("man2-out", "Outliers"),
+    ("man2-out-harsh", "Outliers, harsh"),
+    ("man2-bias", "Bias"),
+    ("whole", "Whole"),
+]
+REFS = [
+    ("history-map", "times free"),
+    ("history-best", "held where drifted"),
+    ("history-held", "times held"),
+]
+
+
+def f(x: Any, d: int = 3) -> str:
+    if x is None or (isinstance(x, float) and not math.isfinite(x)):
+        return "–"
+    return f"{x:.{d}f}"
+
+
+def ci(p: dict[str, Any] | None, key: str = "geometric_mean") -> str:
+    if not p:
+        return "–"
+    return f"{p[key]:.2f} [{p['lo']:.2f}, {p['hi']:.2f}]"
+
+
+def plain(name: str) -> bool:
+    return "-h" not in name and "-d" not in name
+
+
+def tables(summary: dict[str, Any]) -> str:
+    sp = summary["splits"]
+    have = [(k, lab) for k, lab in SPLITS if k in sp]
+    out: list[str] = []
+    names = sorted({n for k, _ in have for n in sp[k]["systems"] if plain(n)})
+    out += ["## Median period range error", ""]
+    out += ["| System | " + " | ".join(lab for _, lab in have) + " |"]
+    out += ["| --- |" + " --- |" * len(have)]
+    out += [
+        "| Periods (scored) | "
+        + " | ".join(f"{sp[k]['periods']} ({sp[k]['periods_scored']})" for k, _ in have)
+        + " |"
+    ]
+    for n in names:
+        row = [
+            f(sp[k]["systems"].get(n, {}).get("median_period_range")) if n != "one-leg" else "…"
+            for k, _ in have
+        ]
+        if n == "one-leg":
+            row = [f(min(sp[k]["systems"][n]["median_period_range"], 9.999)) for k, _ in have]
+        out += [f"| `{n}` | " + " | ".join(row) + " |"]
+    out += ["", "## The standing search ÷ each system (paired per period; above 1: better)", ""]
+    for k, lab in have:
+        c = sp[k]["comparisons"]
+        out += [
+            f"### {lab}",
+            "",
+            "| System | Ratio | Share of periods where the search is better |",
+        ]
+        out += ["| --- | --- | --- |"]
+        for key in sorted(c):
+            if not key.startswith("search3_over_") or "history" in key:
+                continue
+            n = key[len("search3_over_") :]
+            if "-d" in n:
+                continue
+            out += [f"| `{n}` | {ci(c[key])} | {c[key]['a_better']:.2f} |"]
+        out += [""]
+    out += ["## Share of the gap from the search to the reference that a control closes", ""]
+    out += ["| Split | Control | Share |", "| --- | --- | --- |"]
+    for k, lab in have:
+        for n, v in sorted(sp[k]["gap_closed"].items()):
+            if plain(n):
+                out += [f"| {lab} | `{n}` | {ci(v, 'share')} |"]
+    out += ["", "## System ÷ the true-history reference, by how the reference is fitted", ""]
+    out += ["| Split | System | " + " | ".join(lab for _, lab in REFS) + " |"]
+    out += ["| --- | --- |" + " --- |" * len(REFS)]
+    for k, lab in have:
+        for s in ("search3", "ctl"):
+            row = [ci(sp[k]["comparisons"].get(f"{s}_over_{r}")) for r, _ in REFS]
+            out += [f"| {lab} | `{s}` | " + " | ".join(row) + " |"]
+    out += ["", "## Manoeuvres missed (share) and false (per period), matched within 120 s", ""]
+    out += ["| Split | System | Missed | False |", "| --- | --- | --- | --- |"]
+    for k, lab in have:
+        for n, v in sorted(sp[k]["counts"].items()):
+            if plain(n):
+                c = v["120"]
+                out += [
+                    f"| {lab} | `{n}` | {f(c['missed_share'], 2)} | {f(c['false_per_period'], 2)} |"
+                ]
+    if "quiet" in sp and "man2" in sp:
+        out += [
+            "",
+            "## Declaration: held-course periods given a manoeuvre, and Annex B targets called quiet",
+            "",
+        ]
+        out += [
+            "| System | Held course: false | One manoeuvre: called quiet | Two or more: called quiet | Annex B median | Held-course median |"
+        ]
+        out += ["| --- | --- | --- | --- | --- | --- |"]
+        qd, md = sp["quiet"]["declares"], sp["man2"]["declares"]
+        for n in sorted(qd):
+            if n not in md or "-h" in n:
+                continue
+            q0 = qd[n]["0"]["called_quiet"]
+            false = None if q0 is None else 1.0 - q0["rate"]
+            one, two = md[n]["1"]["called_quiet"], md[n]["2+"]["called_quiet"]
+            out += [
+                f"| `{n}` | {f(false, 2)} | {f(one and one['rate'], 2)} | {f(two and two['rate'], 2)} | "
+                f"{f(sp['man2']['systems'].get(n, {}).get('median_period_range'))} | "
+                f"{f(sp['quiet']['systems'].get(n, {}).get('median_period_range'))} |"
+            ]
+    out += ["", "## What the estimates said", ""]
+    out += [
+        "| Split | References held | Wild bearings edited a period | Correlated share (median) | Correlation used by `ctl` | Bayes factor for the broad prior (median log) | Share above 0 |"
+    ]
+    out += ["| --- | --- | --- | --- | --- | --- | --- |"]
+    for k, lab in have:
+        r = sp[k]
+        out += [
+            f"| {lab} | {f(r['reference_flagged_share'], 2)} | {f(r['edited_per_period'], 1)} | "
+            f"{f(r['corr_estimate']['share_median'], 2)} | {f(r['corr_estimate']['ctl_used_share'], 2)} | "
+            f"{f(r['log_bayes_broad']['median'], 1)} | {f(r['log_bayes_broad']['share_above_0'], 2)} |"
+        ]
+    return "\n".join(out) + "\n"
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("dir")
+    ap.add_argument("--out")
+    a = ap.parse_args(argv)
+    text = tables(json.loads((Path(a.dir) / "summary.json").read_text()))
+    if a.out:
+        Path(a.out).write_text(text)
+    else:
+        print(text, end="")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/site/blog/2026-11-13-time-late-learned-kickoff.md b/site/blog/2026-11-13-time-late-learned-kickoff.md
new file mode 100644
index 00000000..05d6b9b3
--- /dev/null
+++ b/site/blog/2026-11-13-time-late-learned-kickoff.md
@@ -0,0 +1,70 @@
+---
+title: "Kickoff: time-late, classical controls and a learned whole-period model"
+date: 2026-11-13T18:00
+tags: [kickoff, spec-016]
+---
+
+The owner has chosen to build the classical controls the last panel asked for and, at the
+same time, to train a learned whole-period model on GPU under a plan registered before any
+result. The model is scored only once the controls exist. Spec:
+`specs/016-time-late-learned/`.
+
+<!-- truncate -->
+
+## Where we are
+
+[Spec 015](/blog/2026/11/13/time-late-repair) repaired the evidence, the edge rule and the
+reference and decided the same quantities again on fresh seeds. The registered rule again
+gave no GPU budget for identification on Annex B data, and the panel showed that 400 periods
+cannot decide a 10% line there. Carried: the repaired evidence-scored search as the standing
+time-late system, and C″ read as a track as the control beneath it. Open: against a
+reference told the true history the search is 1.7 times worse under a shifted manoeuvre
+prior, 1.4 under correlated bearing errors and 1.5 with outliers, and it invents a manoeuvre
+on half of quiet targets. No classical remedy has been tried for any of these, and neither
+study said anything about hedging.
+
+## Where this task moves to
+
+SRD §1 (the time-late consumer), §3 (the reported estimate comes from a declared read-out)
+and §5 (a new row, proposed; the owner merges). Two tracks:
+
+1. **Controls, CPU, nothing trained.** A hedged track (the probability-weighted mean of the
+   search's candidates) and a declaration margin; a robust likelihood and a correlated-error
+   likelihood in the search; a broadened manoeuvre prior with a mismatch diagnostic. Settings
+   on tuning seed 338.
+2. **A learned model, GPU.** A sequence model reads the whole period and gives the whole
+   track, trained on the fly on a declared mix (Annex B, shifted and broadened statistics,
+   quiet targets, correlated errors, outliers), three seeds, within the unspent part of the
+   $50 budget.
+3. **Registration first.** The model's plan, training mix, comparison and criteria are bound
+   by digest in a decision record before the first training job. No criterion is registered
+   that the sample size cannot decide.
+4. **Controls' validation** (seed 340) is recorded; only then may the model be scored.
+5. **One test scoring** (seed 341): the model against the best classical control on each
+   split, with the hedged track beside it; a win is named for the control that explains it,
+   and only what remains is called learning.
+6. Panel, write-up, slides.
+
+Risks: the budget buys too little training to be a fair test; the controls close the gaps
+and leave nothing to compare (still a result); a sixth error in the evidence; the model
+wins by knowing the simulator's training mix, which is why one condition is held out of it.
+We stop before the test seed if the controls' validation run is unsound, and no GPU job runs
+beyond the ledger's cap.
+
+## How progress will be shown
+
+- Each control against the plain search on its own split, at two severities (validation 340,
+  test 341): paired ratio and share of the gap to the reference closed.
+- Held course: false manoeuvres against one-manoeuvre targets called quiet, as a curve over
+  the declaration margin (tuning 338).
+- The learned model's training curves for three seeds and the ledger in dollars.
+- One table per split: plain search, best control, hedged track, learned model, reference.
+- Track plots of a few periods where the systems differ, if the tracks are stored this time.
+- All from committed experiment folders by a script named in the slides post.
+
+**Update, 2026-11-14.** The study ran. The classical controls remove most of the damage from
+outliers and correlated bearing errors and from quiet targets; the learned model's reported
+track beats the control on no split, so by the registered rule the controls stand. The
+panel approved and showed the loss is the model's untuned manoeuvre detector, not a typical
+period. See [the write-up](/blog/2026/11/14/time-late-learned) and
+[the slides](/blog/2026/11/14/time-late-learned-slides).
diff --git a/site/blog/2026-11-13-time-late-repair-kickoff.md b/site/blog/2026-11-13-time-late-repair-kickoff.md
index 28cf74ee..783c4883 100644
--- a/site/blog/2026-11-13-time-late-repair-kickoff.md
+++ b/site/blog/2026-11-13-time-late-repair-kickoff.md
@@ -50,3 +50,9 @@ questions again on fresh seeds. Spec 015 trains nothing.
 - Headroom at each class threshold and on each split.
 - Manoeuvring targets declared quiet, by number of true manoeuvres, for each prior.
 - All from committed experiment folders by a script named in the slides post.
+
+**Update, 2026-11-13 (later).** The study ran: validation seed 336 passed soundness and test
+seed 337 is scored. The registered figures reproduce; the rule again gives no GPU budget; the
+panel approved with findings and showed the headroom figure depends most on how the repaired
+reference is fitted. See [the write-up](/blog/2026/11/13/time-late-repair) and
+[the slides](/blog/2026/11/13/time-late-repair-slides).
diff --git a/site/blog/2026-11-13-time-late-repair-slides.md b/site/blog/2026-11-13-time-late-repair-slides.md
new file mode 100644
index 00000000..160935d6
--- /dev/null
+++ b/site/blog/2026-11-13-time-late-repair-slides.md
@@ -0,0 +1,42 @@
+---
+title: "Slides: time-late, repaired and re-registered"
+date: 2026-11-13
+tags: [slides, spec-015]
+---
+
+import Slides from "@site/src/components/Slides";
+
+The illustrated companion to [the spec 015 write-up](/blog/2026/11/13/time-late-repair): the
+three repairs, the decided quantities on fresh seeds, a target that never manoeuvres by
+period length, the headroom at every registered sensitivity, and what the review panel
+narrowed.
+
+<!-- truncate -->
+
+<Slides slug="2026-11-13-time-late-repair-slides" />
+
+## What the figures show
+
+- **A target that never manoeuvres**: false-manoeuvre rate by period length, spec 014's
+  search against the repaired one, with the flat and mixed priors and C″ (test seed 337).
+- **The decided quantities on three seeds**: the refit's share of the gain and the two
+  headroom figures, with intervals and the 10% line (development seed 335 is spent data).
+- **Headroom by sensitivity and split**: the perfect-evidence figure at each class threshold
+  and match tolerance, and on every split. The panel showed it also moves with how the
+  reference is fitted, which this figure does not show.
+- **Manoeuvring targets called quiet**, by number of true manoeuvres: the cost of the repair
+  and of the mixed prior.
+
+There are no track plots: the per-period tracks are not stored, only their errors.
+
+## Reproducing the figures
+
+```
+uv run python scripts/research/tl3_charts.py --out-dir site/static/img/2026-11-13-time-late-repair-slides
+```
+
+The script reads only committed records (`experiments/20261113-tl3-dev/`,
+`20261113-tl3-val/`, `20261113-tl3/`); each figure's `.json` sidecar names the inputs'
+digests and the commit. The data are seeds 335 (development, spent), 336 (validation) and
+337 (test), 5-second bearings, ranges files under `configs/timelate/`, generated in the CI
+runs.
diff --git a/site/blog/2026-11-13-time-late-repair.md b/site/blog/2026-11-13-time-late-repair.md
new file mode 100644
index 00000000..08c7955d
--- /dev/null
+++ b/site/blog/2026-11-13-time-late-repair.md
@@ -0,0 +1,129 @@
+---
+title: "Time-late, repaired and re-registered: the numbers hold, the rule says no, and the reference turns out to matter most"
+date: 2026-11-13
+tags: [write-up, spec-015]
+---
+
+Spec 014's panel found two defects in registered code: the evidence gave a manoeuvre's
+duration far too much volume, and the classing counted manoeuvres cut by a period's edge. It
+also found the true-history reference drifting into other histories. The owner chose to
+repair and re-register. Spec 015 fixed the three, decided the same five quantities on fresh
+seeds (336 validation, 337 test) and trained nothing. The registered figures reproduce, and
+the registered rule again says no GPU budget. The panel approved with findings, and its main
+one is that the headroom figure now depends most on the thing we repaired last: how the
+reference is fitted. The illustrated companion is
+[the slides post](/blog/2026/11/13/time-late-repair-slides).
+
+<!-- truncate -->
+
+## What was repaired
+
+- **The evidence** (prior version 3). Every factor of the prior that has no Gaussian
+  residual now gets a Laplace volume equal to one over its density at the fit, so a density
+  and its volume cancel when the bearings say nothing. Spec 014's version kept about 600 s
+  of volume for a duration whose density is tens of seconds wide.
+- **One edge rule** (`timelate/match.py`). A manoeuvre cut by the period's start or end is
+  neither required nor counted against a history, wherever two histories are compared.
+- **The reference.** If the true-history fit moves a manoeuvre more than 120 s from the
+  truth it is refitted with the times held, and the period is flagged.
+- **Tables** are generated from `summary.json` by a script; a test checks them.
+
+## What was decided (test seed 337, Annex B data, 364 scored periods)
+
+| | Quantity | Spec 014 (seed 335) | Spec 015 (seed 337) |
+| --- | --- | --- | --- |
+| D1 | Share of the search's gain obtained by refitting C″'s own manoeuvre times | 0.43 [0.28, 0.58] | 0.33 [0.20, 0.45] |
+| D2 | First candidate right | about 0.60 edge-aware | 0.64 [0.59, 0.69] |
+| D2 | Wrong histories: ambiguous / search failure / evidence failure | 0.57 / 0.11 / 0.33 edge-aware | 0.44 / 0.13 / 0.43 |
+| D3 | Headroom with a perfect search | 1.04 | 1.06 [1.01, 1.11], inconclusive |
+| D3 | Headroom with a perfect evidence as well | 1.10 [1.04, 1.18] | 1.17 [1.10, 1.25], inconclusive (lower limit 1.099) |
+| D4 | Held-course periods given a manoeuvre: search | 0.76 | 0.53 [0.48, 0.58] |
+| D4 | ... C″ / flat prior / mixed prior | 0.02 / 0.01 / 0.03 | 0.02 / 0.01 / 0.00 |
+| D5 | A learned whole-period model | no GPU budget by the rule | no GPU budget by the rule |
+
+C″ read as a track against the repaired search is 2.10 [1.82, 2.44] paired per period (0.083
+against 0.039 in median period range error); the true history with the prior is at 0.028.
+Soundness on both fresh seeds passed.
+
+## What the panel made of it
+
+Five reviewers, all approve with findings, one round (`experiments/20261113-tl3/review.md`).
+They recomputed every decided number from the per-row errors and found no bug that changes
+one. What they changed is what the numbers mean.
+
+**The headroom figure rests on the repaired reference.** In the composite that stands for "a
+perfect evidence", 73 of 364 periods are replaced by the true-history fit. Nineteen of those
+fits were held to the true times, and they carry a third of the gain. A fit told when the
+target manoeuvred knows more than which history is right. By reference convention the figure
+runs from 1.11 to 1.19, and only "times held everywhere" clears the line. Ten periods carry
+over half of it; several are runaways of the search that a guard might remove. So the first
+record's "between 10% and 30% to win, most likely 17%" is withdrawn: an oracle is 8% to 30%
+ahead depending on convention, and what a real identifier would obtain is less and unmeasured.
+
+**The rule says no, and the first record tried to soften that.** The lower limit missed the
+10% line by a thousandth, and the record called the decision "a judgement, and the owner's".
+The panel: the rule was written so that inconclusive counts as not material; an override is
+a departure from it, not a reading of it. It also notes that a rule we forecast as a coin's
+toss should not have been registered as a decision instrument: at 364 periods it could
+barely decide.
+
+**The repair of the evidence is real and not finished.** On held course the false-manoeuvre
+rate fell from 0.80 (spec 014's search on the same periods) to 0.53, and under 30 minutes
+from 0.50 to 0.09. A reviewer integrated the evidence numerically on 34 periods: the
+remaining rate is the prior's (Annex B says a target probably manoeuvres within the hour),
+not approximation error. But the same integration shows the low rate on short periods is
+not what a correct evidence gives either: summed over where a manoeuvre might be, the prior
+favours "some manoeuvre" almost everywhere, because the bearings are silent about it. And
+the new pairing fails at the edges of the prior's support, where most invented manoeuvres
+sit (1 to 3.5 nats). That is the fifth error of this family.
+
+**The repair is not free.** The repaired search calls a one-manoeuvre target quiet in 13% of
+cases against 4% before. Those are not suppressed manoeuvres: integrated numerically they
+are within a nat of undecidable. A mixed prior with a stated half chance of a quiet target
+removes the false manoeuvres (0.00) and raises the misses to 20%; re-ranked at a 1% chance
+it gives 0.09 and 14%. The right setting is a small one, and no split here has a mixed
+population to tune it on.
+
+**D1 is "between a third and a half"** and mostly restates how often C″ has the history
+right: there the refit obtains all of the search's gain, elsewhere a quarter.
+
+**D2's split between "ambiguous" and "evidence failure" is the threshold's.** At thresholds
+1, 3 and 5 the ambiguous share is 0.11, 0.44 and 0.66. Only "search failure is the smallest
+class" survives.
+
+## What was not delivered
+
+- The effect of each repair on each decided quantity on the same periods: only D4 and the
+  first candidate were shown, and the repairs were not separated. The panel separated them
+  for the first candidate (edge rule +0.10, evidence +0.04 to +0.05).
+- D2 at every registered sensitivity (the panel supplied it).
+- Unit tests for the code that computes the decided statistics.
+- The refit's fall-back, built to catch runaway refits, caught one of seven.
+- The forecasts held in five of seven rows, but they restated a development run made
+  minutes earlier; the one forecast of a quantity known to move, D1, missed.
+
+## Where this leaves a learned model
+
+Nothing here shows that learning cannot help, nor that it would. Where the simulator's
+assumptions hold (Annex B statistics, independent Gaussian errors) an oracle is 10% to 20%
+ahead of the search and 400 periods cannot decide a 10% line. Where they fail the gaps are
+large: the search is 1.7 times the oracle under a shifted prior, 1.4 under correlated errors,
+1.5 with outliers. No classical remedy has been tried on any of the three, so a learned win
+there today could not be told from a missing Huber cost or a broader prior. This project has
+been caught that way three times.
+
+The panel's order, for the owner to choose from:
+
+1. A hedged track from the stored candidates and a declaration margin: cheap, classical.
+2. A robust likelihood, a correlated-error likelihood and a prior that can be wrong, scored
+   on the shifted, correlated and outlier splits at more than one severity.
+3. Then a learned whole-period model, registered against 1 and 2 on those splits.
+4. No fourth Annex B identification study.
+
+No GPU run starts without the owner.
+
+## Records
+
+`experiments/20261113-tl3-decision/` (registration), `-dev/`, `-val/`,
+`experiments/20261113-tl3/` (test record, `tables.md`, `review.md`). Seeds 336 and 337 are
+used; the next fresh seed is 338.
diff --git a/site/blog/2026-11-14-time-late-learned-slides.md b/site/blog/2026-11-14-time-late-learned-slides.md
new file mode 100644
index 00000000..02c440a4
--- /dev/null
+++ b/site/blog/2026-11-14-time-late-learned-slides.md
@@ -0,0 +1,44 @@
+---
+title: "Slides: time-late, classical controls and a learned model"
+date: 2026-11-14
+tags: [slides, spec-016]
+---
+
+import Slides from "@site/src/components/Slides";
+
+The illustrated companion to [the spec 016 write-up](/blog/2026/11/14/time-late-learned):
+the classical controls, the learned whole-period model, the registered comparison on a
+fresh seed, what the review panel found the loss was made of, and track plots.
+
+<!-- truncate -->
+
+<Slides slug="2026-11-14-time-late-learned-slides" />
+
+## What the figures show
+
+- **The registered comparison**: declared control ÷ learned track for three training seeds
+  on every criterion split, with intervals; the orange ticks are the median per-period
+  ratio, which is 1.00 everywhere.
+- **Every system** on every split: C″ as a track, the standing search, the classical
+  control, the model's own track and its arc read-out, and the fit told the true history.
+- **What the classical control adds**, on the tuning, validation and test seeds.
+- **Manoeuvres missed and false**: the model's detector misses more and invents far fewer.
+- **Training**: the monitor curve of the three seeds.
+- **Three periods as tracks.** They are chosen by a rule in the script, not by eye: of the
+  20 stored periods of the shifted split, the one where the learned track is worst against
+  the control and the one where it is best; and the first stored outlier period with a
+  true manoeuvre.
+
+## Reproducing the figures
+
+```
+uv run python scripts/research/tl4_charts.py --out-dir site/static/img/2026-11-14-time-late-learned-slides
+```
+
+The script reads only committed records (`experiments/20261113-tl4-tuning/`,
+`20261113-tl4-val/`, `20261114-tl4/`, `20261114-tlm-runs/`); each figure's `.json`
+sidecar names the inputs' digests and the commit. `experiments/20261114-tl4/tracks.npz`
+holds the tracks at scored rows for the first 20 periods of three splits, extracted from
+the results branch `results/timelate-tl-e-test-43563825793`. The data are seeds 338
+(tuning), 340 (validation) and 341 (test), 5-second bearings, ranges files under
+`configs/timelate/`, generated in the CI runs.
diff --git a/site/blog/2026-11-14-time-late-learned.md b/site/blog/2026-11-14-time-late-learned.md
new file mode 100644
index 00000000..c663e9ca
--- /dev/null
+++ b/site/blog/2026-11-14-time-late-learned.md
@@ -0,0 +1,141 @@
+---
+title: "Time-late, classical controls and a learned model: the controls stand, and what the learned model's loss is made of"
+date: 2026-11-14
+tags: [write-up, spec-016]
+---
+
+The owner asked for two things at once: the classical controls the last panel wanted, and a
+learned whole-period model trained in parallel under a plan fixed before any result. Both
+are done. On a fresh test seed the learned model's reported track beats the classical
+control on no split and loses to it on clean data, so by the registered rule the classical
+controls stand and the learned track stops. The review panel approved that outcome and
+changed what it means: the loss is not a typical period, it is the model's untuned
+manoeuvre detector, and the comparison we registered was narrower than "learned against
+classical". The illustrated companion is
+[the slides post](/blog/2026/11/14/time-late-learned-slides).
+
+<!-- truncate -->
+
+## What was built
+
+**Classical controls**, nothing trained, composed from the search spec 015 left standing:
+
+- wild bearings edited out before the search;
+- one larger bearing sd when the bearings' second differences show the errors are
+  correlated in time;
+- the search run under Annex B's manoeuvre prior and a broadened one, pooled;
+- a manoeuvre declared only when it is worth an evidence margin of 1, with a stated 1%
+  chance that the target never manoeuvres;
+- a hedged track: the probability-weighted mean of the candidates.
+
+Settings came from a tuning seed. The combined control is not told which condition it is
+in.
+
+**A learned model**: a bidirectional transformer reading a whole period of 5-second
+bearings and giving the target's state every 30 seconds and a manoeuvre probability. Three
+seeds, 12.5 million simulated periods each (Annex B, randomised manoeuvre statistics,
+quiet targets, correlated errors and outliers), a T4 each, $14 in all. Its reported track
+is the arc model fitted to the bearings at the manoeuvres the model declares.
+
+The plan, the training mix, the criteria and the code were bound by digest before the first
+training run and before the validation and test seeds existed. The scorer refused to score
+a learned track until the controls' own validation was on record.
+
+## The registered outcome (test seed 341, 2,860 periods)
+
+Declared control ÷ learned track, paired per period, three seeds (above 1: the learned
+track is better):
+
+| Split | Ratio | Verdict |
+| --- | --- | --- |
+| Shifted prior | 0.82, 0.80, 0.82 | not shown |
+| Shifted prior 2 | 0.87, 0.87, 0.94 | not shown |
+| Correlated errors | 0.83, 0.83, 0.84 | not shown |
+| Outliers | 0.78, 0.77, 0.81 | not shown |
+| Bias, absent from training | 0.98, 1.00, 0.97 | not shown |
+| Annex B | 0.82, 0.81, 0.82 | harm |
+| Held course | 1.06, 1.06, 1.06 | no harm |
+
+Learning is shown on no split and there is harm on Annex B data: **the classical controls
+stand as the time-late system, and the learned track stops.** The forecast written before
+training said this was the most likely outcome.
+
+## What the classical controls did
+
+Against the search they started from, the declared control is 1.68 [1.46, 1.94] better
+with 2% outliers, 2.44 [2.03, 3.00] with 5%, 1.33 [1.16, 1.53] with correlated errors and
+1.70 [1.48, 1.95] on targets that never manoeuvre (false manoeuvres in 8% of periods
+against 49%). These replicate across the tuning, validation and test seeds. This is the
+result the panel says is worth carrying.
+
+It has limits the first record understated. Under a slowly varying bias it does nothing
+(1.00). On clean data its range error is not separated from the search's over three seeds
+(0.96 to 1.09). It calls a one-manoeuvre target quiet more often, markedly so with
+correlated errors (0.29 against 0.05). Its correlated-error form knows the simulator's
+error family. And under a shifted manoeuvre prior it gains nothing (1.03): the control is
+still 1.7 to 2.1 times a fit told the true history, whichever way that reference is fitted.
+
+## What the learned model's loss is made of
+
+Five reviewers recomputed the outcome and it holds: all 42 ratios reproduce, no verdict is
+near its boundary, the registration held. Then they took the 20% apart.
+
+**The typical period is a tie.** The median per-period ratio is 1.00 on every split. Where
+the model declares the same number of manoeuvres as the control, the two tracks are level
+(0.95 on Annex B, 1.01 under the shifted prior), because the read-out is then the same
+classical fit. Where the model declares fewer, in a quarter to a half of periods, its
+track is about twice as bad. The whole loss is there.
+
+**So the registered comparison measured a detector.** The model's manoeuvre probability
+was thresholded at 0.5, a value never tuned, while the control's margin and quiet
+probability were tuned. That asymmetry was in the registration and was mine. A reviewer
+tried the obvious repairs on spent tuning data: a lower threshold makes the read-out worse
+(the fit can no more remove a false manoeuvre than add a missed one), and a broader prior
+changes nothing. Starting the control's own search from the model's answer equals the
+control at a third of the search time, and does not beat it.
+
+**The model as an estimator** (its own output, reported beside and deciding nothing) is
+0.71 of the control on Annex B data. Under the shifted prior it is level in range, 1.01,
+but behind in course and speed (0.79, 0.77), and the level figure is a cancellation: it
+beats the control where the bearings determine little and is nearly three times worse
+where they determine much. That is the signature of an estimator that leans on what it
+learned about targets in general, which this project has now seen three times.
+
+**The "mismatch" was the classical side's only.** The shifted, correlated and outlier
+conditions all lie inside the model's training ranges. The model lost in-distribution.
+
+## What was not delivered
+
+- A working prior-mismatch diagnostic: the one computed is not a Bayes factor and fires on
+  quiet targets, not on a shifted prior.
+- A Huber cost and a whitened fit (editing and an effective sd stand in), and the repair
+  of the evidence at the edges of the prior's support.
+- The export check on the trained models (it ran on a toy), IMM and EKF tracks beside, and
+  the weights' digests in the test record.
+- Unit tests for the runner and the scorer.
+- Two launches of the GPU pilot failed at start-up on a launcher form that had never been
+  run; the ledger under-counts a run whose container is replaced (39 cents against the
+  owner's dashboard).
+
+## What this does and does not show
+
+It shows that a small direct model, trained on the simulator, does not beat an evidence
+search that knows the motion model, in or out of the conditions it was trained on, and
+that classical editing and an honest bearing sd remove most of the damage from outliers
+and correlated errors.
+
+It does not show that learning cannot help the time-late consumer. A learned proposer
+inside the search, the model's probabilities as a prior, training through the arc model,
+and a larger budget were not tested. Nor does it show that the shifted-prior gap can be
+closed by anything: busier targets with smaller turns may simply not be identifiable from
+bearings, and that is the cheapest next thing to find out.
+
+Across specs 012 to 016 the founding question stands at "not shown, at small budgets, with
+direct models". Each large gain so far has gone to a classical element.
+
+## Records
+
+`experiments/20261113-tl4-tuning/`, `-tl4-decision/` (two addenda), `-tl4-val/`,
+`experiments/20261114-tlm-runs/`, and the test record `experiments/20261114-tl4/`
+(`review.md`). Seeds 338 to 341 are used; the next fresh seed is 342. GPU spending in
+November stands at about $41 of the owner's $50.
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+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>Time-late, repaired and re-registered (spec 015)</title>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/reveal.js/5.1.0/reveal.min.css" />
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/reveal.js/5.1.0/theme/white.min.css" />
+    <style>
+      .reveal { font-size: 28px; }
+      .reveal h2 { font-size: 1.4em; text-transform: none; }
+      .reveal img { max-height: 56vh; }
+      .reveal .small { font-size: 0.6em; color: #555; margin: 0.3em 0; }
+      .reveal .two { display: flex; gap: 1.5em; align-items: center; text-align: left; }
+      .reveal .two > * { flex: 1; }
+      .reveal ul { font-size: 0.9em; }
+      .reveal table { font-size: 0.7em; margin: 0 auto; }
+      .reveal .big { font-size: 1.5em; font-weight: 600; margin: 0.4em 0; }
+    </style>
+  </head>
+  <body>
+    <div class="reveal">
+      <div class="slides">
+        <section>
+          <h1 style="font-size: 1.7em">Time-late, repaired and re-registered</h1>
+          <p>The numbers hold, the rule says no, and the reference matters most</p>
+          <p class="small">13 November 2026 · spec 015 · gitma2 · nothing trained</p>
+        </section>
+
+        <section>
+          <h2>Where spec 014 left us</h2>
+          <ul>
+            <li>The evidence gave a manoeuvre's duration about 600 s of volume against a density tens of seconds wide.</li>
+            <li>The classing counted manoeuvres cut by the period's edge.</li>
+            <li>The true-history reference drifted into other histories in 13% of periods.</li>
+          </ul>
+          <p class="small">Owner's decision: repair, and decide the same quantities again on fresh seeds (336, 337).</p>
+        </section>
+
+        <section>
+          <h2>Three repairs</h2>
+          <svg viewBox="0 0 640 220" role="img" aria-label="Three boxes: evidence volume, edge rule, held reference">
+            <g font-size="12" text-anchor="middle">
+              <rect x="10" y="40" width="190" height="110" fill="#f3e9fb" stroke="#7b4fa3"/>
+              <text x="105" y="62" font-weight="600">Evidence</text>
+              <text x="105" y="84" font-size="10">every open factor of the prior:</text>
+              <text x="105" y="100" font-size="10">volume = 1 ÷ density at the fit</text>
+              <text x="105" y="124" font-size="10" fill="#555">silent bearings: they cancel</text>
+              <rect x="225" y="40" width="190" height="110" fill="#fdf1dc" stroke="#e8a33d"/>
+              <text x="320" y="62" font-weight="600">One edge rule</text>
+              <line x1="250" y1="100" x2="390" y2="100" stroke="#555"/>
+              <line x1="270" y1="90" x2="270" y2="110" stroke="#555"/><line x1="370" y1="90" x2="370" y2="110" stroke="#555"/>
+              <rect x="255" y="95" width="30" height="10" fill="#c0392b" opacity="0.5"/><rect x="310" y="95" width="25" height="10" fill="#c0392b"/>
+              <text x="320" y="130" font-size="10" fill="#555">a cut manoeuvre is neither</text><text x="320" y="143" font-size="10" fill="#555">required nor counted against</text>
+              <rect x="440" y="40" width="190" height="110" fill="#e6f4f1" stroke="#2a9d8f"/>
+              <text x="535" y="62" font-weight="600">Reference</text>
+              <text x="535" y="84" font-size="10">true history, neutral start;</text>
+              <text x="535" y="100" font-size="10">drifts over 120 s: times held,</text>
+              <text x="535" y="116" font-size="10">period flagged</text>
+              <text x="535" y="140" font-size="10" fill="#555">13% held on the test seed</text>
+            </g>
+          </svg>
+          <p class="small">Plus: every table generated from the summary by script, after spec 014's hand-copied row.</p>
+        </section>
+
+        <section>
+          <h2>A target that never manoeuvres</h2>
+          <img src="../../img/2026-11-13-time-late-repair-slides/quiet.svg" alt="False-manoeuvre rate on held course by period length for spec 014's search, the repaired search, flat and mixed priors and C double prime" />
+          <p class="small">Repaired search 0.53 overall (spec 014's on the same periods 0.80). Short periods: 0.50 to 0.09. Long periods: unchanged, because Annex B says a target probably manoeuvres within the hour.</p>
+        </section>
+
+        <section>
+          <h2>The repair is not free</h2>
+          <img src="../../img/2026-11-13-time-late-repair-slides/declared-quiet.svg" alt="Share of targets with one, two and three or more manoeuvres that each system calls quiet" />
+          <p class="small">One-manoeuvre targets called quiet: 4% before, 13% after, 20% with a stated half chance of a quiet target. Integrated numerically these misses are within a nat of undecidable.</p>
+        </section>
+
+        <section>
+          <h2>The decided quantities on three seeds</h2>
+          <img src="../../img/2026-11-13-time-late-repair-slides/decided.svg" alt="Refit share of the gain and attainable headroom on development, validation and test seeds with intervals" />
+          <p class="small">Test seed 337: D1 0.33 [0.20, 0.45]; headroom 1.06 and 1.17 [1.10, 1.25]. The lower limit is 1.099 against a line of 1.10: inconclusive, and by the rule no GPU budget.</p>
+        </section>
+
+        <section>
+          <h2>The headroom is a convention</h2>
+          <img src="../../img/2026-11-13-time-late-repair-slides/headroom.svg" alt="Headroom with a perfect evidence at each class threshold and match tolerance, and on each split" />
+          <p class="small">1.08 to 1.30 across the registered sensitivities. Large on every split where the simulator's assumptions fail.</p>
+        </section>
+
+        <section>
+          <h2>What the panel found: the reference decides it</h2>
+          <table>
+            <tr><th>Reference on replaced periods</th><th>Headroom</th><th>By the rule</th></tr>
+            <tr><td>Registered (held where it drifts)</td><td>1.17 [1.10, 1.25]</td><td>inconclusive</td></tr>
+            <tr><td>Free-time fit throughout</td><td>1.15 [1.08, 1.22]</td><td>inconclusive</td></tr>
+            <tr><td>Without held periods</td><td>1.13 [1.06, 1.20]</td><td>inconclusive</td></tr>
+            <tr><td>Held periods as no gain</td><td>1.11 [1.05, 1.17]</td><td>inconclusive</td></tr>
+            <tr><td>Times held everywhere</td><td>1.19 [1.11, 1.28]</td><td>material</td></tr>
+          </table>
+          <p class="small">19 held references carry a third of the gain; ten periods carry over half. A fit told the true times knows more than which history is right. Panel recomputation, not a registered result.</p>
+        </section>
+
+        <section>
+          <h2>Five approve with findings</h2>
+          <ul>
+            <li>Every decided number reproduces; no bug changes one; registration held.</li>
+            <li>The rule's output is "no GPU budget". Our first record softened it; corrected.</li>
+            <li>The evidence repair is not finished: the pairing fails at the edges of the prior's support (fifth error of this family).</li>
+            <li>Short-period 0.09 is not what a correct evidence gives: the prior favours "some manoeuvre" almost everywhere.</li>
+            <li>"Ambiguous" share is the threshold's: 0.11, 0.44, 0.66 at thresholds 1, 3, 5.</li>
+          </ul>
+        </section>
+
+        <section>
+          <h2>What was not achieved</h2>
+          <ul>
+            <li>Each repair's effect on each decided quantity, separated, on the same periods.</li>
+            <li>Unit tests for the decided statistics' code.</li>
+            <li>A fall-back that catches runaway refits: it caught one of seven.</li>
+            <li>A decision at the 10% line: 400 periods cannot make one.</li>
+            <li>Any remedy for a shifted prior, correlated errors or outliers.</li>
+          </ul>
+        </section>
+
+        <section>
+          <h2>What comes next</h2>
+          <ul>
+            <li>A hedged track from the stored candidates; a declaration margin.</li>
+            <li>Robust and correlated-error likelihoods; a prior that can be wrong; a small stated chance of a quiet target.</li>
+            <li>Scored where the gaps are: shifted prior 1.7, correlated errors 1.4, outliers 1.5.</li>
+            <li>Then a learned whole-period model, registered against those controls.</li>
+          </ul>
+          <p class="small">The choice is the owner's; no GPU run before it. Write-up: /blog/2026/11/13/time-late-repair · record: experiments/20261113-tl3 (review.md)</p>
+        </section>
+      </div>
+    </div>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/reveal.js/5.1.0/reveal.min.js"></script>
+    <script>
+      Reveal.initialize({ hash: true, slideNumber: "c/t", embedded: window.self !== window.top });
+    </script>
+  </body>
+</html>
diff --git a/site/static/slides/2026-11-14-time-late-learned-slides/index.html b/site/static/slides/2026-11-14-time-late-learned-slides/index.html
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--- /dev/null
+++ b/site/static/slides/2026-11-14-time-late-learned-slides/index.html
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+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>Time-late, classical controls and a learned model (spec 016)</title>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/reveal.js/5.1.0/reveal.min.css" />
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/reveal.js/5.1.0/theme/white.min.css" />
+    <style>
+      .reveal { font-size: 28px; }
+      .reveal h2 { font-size: 1.4em; text-transform: none; }
+      .reveal img { max-height: 56vh; }
+      .reveal .small { font-size: 0.6em; color: #555; margin: 0.3em 0; }
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+      .reveal .two > * { flex: 1; }
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+      .reveal table { font-size: 0.7em; margin: 0 auto; }
+      .reveal .big { font-size: 1.5em; font-weight: 600; margin: 0.4em 0; }
+    </style>
+  </head>
+  <body>
+    <div class="reveal">
+      <div class="slides">
+        <section>
+          <h1 style="font-size: 1.7em">Time-late: classical controls and a learned model</h1>
+          <p>The controls stand, and what the learned model's loss is made of</p>
+          <p class="small">14 November 2026 · spec 016 (study L) · gitma2</p>
+        </section>
+
+        <section>
+          <h2>Where spec 015 left us</h2>
+          <ul>
+            <li>The evidence search is the standing time-late system.</li>
+            <li>Large gaps where the simulator's assumptions fail: a shifted prior, correlated errors, outliers, quiet targets.</li>
+            <li>No classical remedy tried: a learned win there would have been uninterpretable.</li>
+          </ul>
+          <p class="small">Owner's decision: build the controls and train a learned model in parallel, its plan registered first, scored only once the controls exist.</p>
+        </section>
+
+        <section>
+          <h2>Two sides, one set of periods</h2>
+          <svg viewBox="0 0 640 250" role="img" aria-label="The classical control pipeline and the learned model pipeline side by side">
+            <g font-size="11" text-anchor="middle">
+              <text x="160" y="20" font-weight="600" font-size="13">Classical control (nothing trained)</text>
+              <rect x="20" y="35" width="280" height="30" fill="#f3e9fb" stroke="#7b4fa3"/><text x="160" y="55">edit wild bearings</text>
+              <rect x="20" y="75" width="280" height="30" fill="#f3e9fb" stroke="#7b4fa3"/><text x="160" y="95">estimate error correlation → effective sd</text>
+              <rect x="20" y="115" width="280" height="30" fill="#f3e9fb" stroke="#7b4fa3"/><text x="160" y="135">search under Annex B's prior and a broad one</text>
+              <rect x="20" y="155" width="135" height="40" fill="#f3e9fb" stroke="#7b4fa3"/><text x="87" y="172">declare</text><text x="87" y="187" font-size="9" fill="#555">margin 1, 1% quiet</text>
+              <rect x="165" y="155" width="135" height="40" fill="#f3e9fb" stroke="#7b4fa3"/><text x="232" y="172">hedge</text><text x="232" y="187" font-size="9" fill="#555">mean of candidates</text>
+              <text x="480" y="20" font-weight="600" font-size="13">Learned model (three seeds)</text>
+              <rect x="340" y="35" width="280" height="30" fill="#fdf1dc" stroke="#e8a33d"/><text x="480" y="55">12.5 million simulated periods, mixed kinds</text>
+              <rect x="340" y="75" width="280" height="30" fill="#fdf1dc" stroke="#e8a33d"/><text x="480" y="95">bidirectional transformer over the whole period</text>
+              <rect x="340" y="115" width="135" height="30" fill="#fdf1dc" stroke="#e8a33d"/><text x="407" y="135">own track</text>
+              <rect x="485" y="115" width="135" height="30" fill="#fdf1dc" stroke="#e8a33d"/><text x="552" y="135">manoeuvre probability</text>
+              <rect x="340" y="155" width="280" height="40" fill="#fdf1dc" stroke="#e8a33d"/><text x="480" y="172">arc model fitted at probability &gt; 0.5</text><text x="480" y="187" font-size="9" fill="#555">the reported track</text>
+              <text x="320" y="230" font-size="11" fill="#555">Plan, mix, criteria and code bound by digest before any training run or fresh seed</text>
+            </g>
+          </svg>
+        </section>
+
+        <section>
+          <h2>What the classical control adds</h2>
+          <img src="../../img/2026-11-14-time-late-learned-slides/controls.svg" alt="Standing search over classical control by split on tuning, validation and test seeds" />
+          <p class="small">Outliers 1.7 to 2.4, correlated errors 1.3, quiet targets 1.7; nothing under bias or a shifted prior. It replicates on three seeds.</p>
+        </section>
+
+        <section>
+          <h2>Training the model</h2>
+          <img src="../../img/2026-11-14-time-late-learned-slides/training.svg" alt="Monitor curve of the three training seeds" />
+          <p class="small">Three seeds on a T4 each, about 7 hours, $14 in all. Two pilot launches failed at start-up first; the owner's dashboard and the ledger agree to 39 cents.</p>
+        </section>
+
+        <section>
+          <h2>The registered comparison</h2>
+          <img src="../../img/2026-11-14-time-late-learned-slides/verdicts.svg" alt="Declared control over learned track for three seeds on each split with intervals and median ratios" />
+          <p class="small">Learning shown on no split; harm on Annex B. By the rule: the classical controls stand and the learned track stops. The orange ticks, the median period, sit at 1.00.</p>
+        </section>
+
+        <section>
+          <h2>Every system</h2>
+          <img src="../../img/2026-11-14-time-late-learned-slides/systems.svg" alt="Median period range error of each system on each split" />
+          <p class="small">The learned track is far better than C″ and about 20% behind the control. Under a shifted prior everyone is at twice the fit told the true history.</p>
+        </section>
+
+        <section>
+          <h2>The loss is a detector's</h2>
+          <div class="two">
+            <img src="../../img/2026-11-14-time-late-learned-slides/manoeuvres.svg" alt="Manoeuvres missed and false by system" />
+            <ul>
+              <li>Same number of manoeuvres as the control: level (0.95 to 1.04).</li>
+              <li>Fewer, in a quarter to half of periods: about twice as bad.</li>
+              <li>The 0.5 threshold was never tuned; the control's settings were.</li>
+              <li>A lower threshold makes it worse (tuning data).</li>
+            </ul>
+          </div>
+          <p class="small">Panel recomputation, not a registered result.</p>
+        </section>
+
+        <section>
+          <h2>Three periods</h2>
+          <img src="../../img/2026-11-14-time-late-learned-slides/tracks.svg" alt="Track plots of three stored periods: ownship, target, the classical control and the learned track" />
+          <p class="small">Chosen by a rule in the script: the learned track's worst and best stored period under the shifted prior, and the first stored outlier period with a manoeuvre.</p>
+        </section>
+
+        <section>
+          <h2>Five approve with findings</h2>
+          <ul>
+            <li>All 42 ratios reproduce; no verdict near its line; registration held; scored once.</li>
+            <li>"This model with this read-out stops", not "learning stops".</li>
+            <li>The model's own track: 0.71 of the control on clean data; level in range under a shifted prior only by a cancellation.</li>
+            <li>The "mismatch" conditions lie inside the model's training ranges: it lost in-distribution.</li>
+            <li>A search started from the model equals the control at a third of the time (tuning data).</li>
+          </ul>
+        </section>
+
+        <section>
+          <h2>What was not achieved</h2>
+          <ul>
+            <li>A learned win anywhere; a tuned detector; a model inside the search.</li>
+            <li>Anything that closes the shifted-prior gap (1.7 to 2.1 for both sides).</li>
+            <li>A working prior-mismatch diagnostic; a Huber cost; a whitened fit.</li>
+            <li>The export check on the trained models; IMM and EKF tracks beside; tests for the runner.</li>
+          </ul>
+        </section>
+
+        <section>
+          <h2>What comes next</h2>
+          <ul>
+            <li>Adopt the classical control as the standing time-late system.</li>
+            <li>One cheap classical study: is the shifted-prior gap identifiability?</li>
+            <li>Only if it is not: the trained models as proposers inside the search, CPU only.</li>
+          </ul>
+          <p class="small">The choice is the owner's. Write-up: /blog/2026/11/14/time-late-learned · record: experiments/20261114-tl4 (review.md)</p>
+        </section>
+      </div>
+    </div>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/reveal.js/5.1.0/reveal.min.js"></script>
+    <script>
+      Reveal.initialize({ hash: true, slideNumber: "c/t", embedded: window.self !== window.top });
+    </script>
+  </body>
+</html>
diff --git a/specs/015-time-late-repair/research.md b/specs/015-time-late-repair/research.md
index 45d424fd..b5e9ab11 100644
--- a/specs/015-time-late-repair/research.md
+++ b/specs/015-time-late-repair/research.md
@@ -42,10 +42,15 @@
 
 ## R4. The refit control's fall-back
 
-- The refit is kept only if its posterior fit's bearing cost is no worse than the cost of
-  the bearings-only arc fit it started from by more than 9 (three sd in a χ² of one), and
-  its states are finite; otherwise C″'s own track stands. No truth is used. The number of
-  fall-backs is reported, and D1 is given with and without them.
+- The refit is kept if its states are finite, its speed stays within twice the prior's
+  fastest and its range within sixteen times the prior's farthest, and its bearing cost is
+  either what the noise explains (within five sd of χ²) or no worse than C″'s own track's by
+  more than 9. Otherwise C″'s own track stands. No truth is used. The number of fall-backs
+  is reported, and D1 is given with and without them.
+- **Changed on development data** (seed 335, spent): the first form compared the cost with
+  C″'s track alone and fell back in 26 of 200 periods, because the chord track has four free
+  unknowns a manoeuvre and can fit the bearings more closely than a sound arc refit. The
+  "what the noise explains" branch was added before registration.
 
 ## R5. Keeping earlier registrations
 
@@ -68,4 +73,5 @@
 - **Other estimands for D1**, declared here: the ratio of log medians; the registered share
   without periods where either system's period median exceeds 1.
 - **Reported**: everything in FR-009 to FR-012; `search2` beside `search3`.
-- **Soundness stop**: spec 014's, plus at most 15% of references flagged.
+- **Soundness stop**: spec 014's, plus at most 25% of references flagged (15% were flagged
+  on the development periods; the limit guards against most of them being held, not some).
diff --git a/specs/015-time-late-repair/tasks.md b/specs/015-time-late-repair/tasks.md
index 80b22918..3d8711ba 100644
--- a/specs/015-time-late-repair/tasks.md
+++ b/specs/015-time-late-repair/tasks.md
@@ -1,13 +1,13 @@
 # Tasks: Time-late, repair and re-registration (spec 015)
 
-- [ ] T001 Prior version 3 in `src/tma2/timelate/prior.py` (`open_precision`, renormalised `_leg_sf`/`_leg_tail`, flat prior without the unpaired m!) and the call in `retest.log_evidence`; versions 1 and 2 unchanged (both fixtures pass)
-- [ ] T002 Tests in `tests/unit/test_timelate3.py`: density × volume = 1 for speed, course, each change's size, each duration (turn, speed change, both) and the first start; a spec 014 fixture for `search2`
-- [ ] T003 `src/tma2/timelate/match.py`: `same_history` (edge-aware) and cut flags from the engagement's table; tests on constructed cases (cut at start, at end, complete on the last row, a found manoeuvre paired with a cut one)
-- [ ] T004 `src/tma2/timelate/run3.py`: `search3`, `search3-flat`, `search3-mix`, `search2` beside; reference held when it drifts, flagged; refit fall-back (R4); period records with cut flags and reference flags
-- [ ] T005 `src/tma2/timelate/study3.py` and `tlscore`: readings with the one rule; headroom at thresholds 1, 3, 5 and tolerances 60, 120, 240; with and without flagged references; D1's other estimands and the flat-prior share; D4 by period length; manoeuvring targets declared quiet by true count; denominators stated
-- [ ] T006 `scripts/research/tl3_tables.py`: every table of a record from `summary.json`; a test that a record's tables equal its summary
-- [ ] T007 Development run on spent seed 335 (`timelate/tl-d-dev`): before and after; record `experiments/<date>-tl3-dev/`
-- [ ] T008 Register: plan `configs/timelate/tl-d.json`, run files, decision record with forecasts and every changed definition; SRD §9 line
-- [ ] T009 Validation seed 336 (soundness stop), then test seed 337 once; records
-- [ ] T010 Panel (five reviewers), corrections
-- [ ] T011 Write-up, slides post and deck, figures; AGENTS.md; kickoff update; PR description
+- [x] T001 Prior version 3 in `src/tma2/timelate/prior.py` (`open_precision`, renormalised `_leg_sf`/`_leg_tail`, flat prior without the unpaired m!) and the call in `retest.log_evidence`; versions 1 and 2 unchanged (both fixtures pass)
+- [x] T002 Tests in `tests/unit/test_timelate3.py`: density × volume = 1 for speed, course, each change's size, each duration (turn, speed change, both) and the first start; a spec 014 fixture for `search2`
+- [x] T003 `src/tma2/timelate/match.py`: `same_history` (edge-aware) and cut flags from the engagement's table; tests on constructed cases (cut at start, at end, complete on the last row, a found manoeuvre paired with a cut one)
+- [x] T004 `src/tma2/timelate/run3.py`: `search3`, `search3-flat`, `search3-mix`, `search2` beside; reference held when it drifts, flagged; refit fall-back (R4); period records with cut flags and reference flags
+- [x] T005 `src/tma2/timelate/study3.py` and `tlscore`: readings with the one rule; headroom at thresholds 1, 3, 5 and tolerances 60, 120, 240; with and without flagged references; D1's other estimands and the flat-prior share; D4 by period length; manoeuvring targets declared quiet by true count; denominators stated
+- [x] T006 `scripts/research/tl3_tables.py`: every table of a record from `summary.json`; a test that a record's tables equal its summary
+- [x] T007 Development run on spent seed 335 (`timelate/tl-d-dev`): before and after; record `experiments/<date>-tl3-dev/`
+- [x] T008 Register: plan `configs/timelate/tl-d.json`, run files, decision record with forecasts and every changed definition; SRD §9 line
+- [x] T009 Validation seed 336 (soundness stop), then test seed 337 once; records
+- [x] T010 Panel (five reviewers), corrections
+- [x] T011 Write-up, slides post and deck, figures; AGENTS.md; kickoff update; PR description
diff --git a/specs/016-time-late-learned/checklists/requirements.md b/specs/016-time-late-learned/checklists/requirements.md
new file mode 100644
index 00000000..6720e7e3
--- /dev/null
+++ b/specs/016-time-late-learned/checklists/requirements.md
@@ -0,0 +1,38 @@
+# Specification Quality Checklist: Time-late, classical controls and a learned whole-period model
+
+**Purpose**: Validate specification completeness and quality before proceeding to planning
+**Created**: 2026-11-13
+**Feature**: [spec.md](../spec.md)
+
+## Content Quality
+
+- [x] No implementation details (languages, frameworks, APIs)
+- [x] Focused on user value and business needs
+- [x] Written for non-technical stakeholders
+- [x] All mandatory sections completed
+
+## Requirement Completeness
+
+- [x] No [NEEDS CLARIFICATION] markers remain
+- [x] Requirements are testable and unambiguous
+- [x] Success criteria are measurable
+- [x] Success criteria are technology-agnostic (no implementation details)
+- [x] All acceptance scenarios are defined
+- [x] Edge cases are identified
+- [x] Scope is clearly bounded
+- [x] Dependencies and assumptions identified
+
+## Feature Readiness
+
+- [x] All functional requirements have clear acceptance criteria
+- [x] User scenarios cover primary flows
+- [x] Feature meets measurable outcomes defined in Success Criteria
+- [x] No implementation details leak into specification
+
+## Notes
+
+- The domain's own terms (evidence, prior, candidate, arc model) are kept: they are the
+  owner's vocabulary. The GPU provider is named only in the budget assumption.
+- No clarification asked: the owner's option B, the budget rule and the panel's order settle
+  scope; choices left open (model form, training mix, criteria) belong to the plan and the
+  decision record, which the owner sees before any GPU job.
diff --git a/specs/016-time-late-learned/plan.md b/specs/016-time-late-learned/plan.md
new file mode 100644
index 00000000..5a3abbe5
--- /dev/null
+++ b/specs/016-time-late-learned/plan.md
@@ -0,0 +1,85 @@
+# Implementation Plan: time-late, classical controls and a learned whole-period model
+
+**Branch**: `claude/keen-wozniak-77td7j` | **Date**: 2026-11-13 | **Spec**: [spec.md](spec.md)
+
+## Summary
+
+Two tracks. Track 1 builds classical controls on the repaired search (hedged track,
+declaration margin, robust and correlated-error likelihoods, a broadened prior with a
+diagnostic, prior version 4) and tunes them on seed 338. Track 2 trains a bidirectional
+sequence model on a declared mix of simulated periods (three seeds, about $22 of T4 time)
+under a plan bound before the first job. The model is scored once, on test seed 341,
+against the best classical control per split, after the controls' validation record
+exists. Decisions and alternatives: [research.md](research.md).
+
+## Technical Context
+
+Python 3.12, NumPy/numba, PyTorch (model), existing `tma2.timelate`, `tma2.regress`,
+`tma2.headroom`. CPU work on GitHub runners through `timelate.yml`; GPU through
+`gputrain.yml` and `scripts/modal_train.py` with the ledger. Schema bump for new header
+fields. Tests: unit (pytest), fixtures pinned loosely across CPU families.
+
+## Constitution Check
+
+- Types once in LinkML: new shard header and plan fields go in `schema/tma.yaml`. Pass.
+- Seeded randomness only: stream seed 339, mix draws from per-engagement sub-streams. Pass.
+- Reported estimate from a declared physics layer: `learned-arc` (R8). Pass.
+- References on the same rows: C″ as a track, one leg, IMM/EKF tracks, the plain search,
+  the true-history reference under three conventions. Pass.
+- Efficiency per slice, three seeds per carried configuration, mock data only. Pass.
+- Owner-only: money. Within the pre-approved $50 total (R10); nothing beyond without the
+  owner. SRD §5 row and §9 line proposed; owner merges.
+
+## Structure
+
+```
+src/tma2/timelate/
+  prior.py        version 4 (R6), kinds broad, mix2 (R5)
+  hedge.py        hedged track, declaration margin (R1, R2)
+  robust.py       Huber fits and evidence; uses thin.edit_wild (R3)
+  corr.py         AR(1)+white estimate, whitened fit and evidence (R4)
+  run4.py         runner: plain v3 and v4 searches, each control, hedged tracks, stored tracks
+  study4.py       paired comparisons per split, gap closed, counts, criteria L-a to L-d
+  learned.py      model's track at scored rows; logit runs to spans; arc read-out (R8)
+src/tma2/regress/
+  period_data.py  the mixed stream of periods (R9), features and targets per 5-s row
+  model.py        `causal: bool`, manoeuvre logit head (R7); spec 012's behaviour unchanged
+  train.py        loss with the logit term when configured
+src/tma2/cli/     tlsolve (runner run4), tlscore (study4, --learned, --controls-record),
+                  tlpredict (model → shard of learned tracks), rtrain (period stream)
+configs/timelate/ tl-e-draft.json, tl-e.json, runs/, tl-shift2-ranges.json, tl-broad.json
+configs/regress/  tlm-pilot.json, tlm-s1..s3.json
+scripts/research/ tl4_tables.py, tl4_charts.py, tl4_readme_check.py
+tests/unit/       test_timelate4.py, test_period_model.py
+```
+
+## Phases
+
+1. **Controls, code** (CPU): prior v4 with edge tests; hedge; robust; corr; broad prior;
+   run4 and study4; fixture.
+2. **Controls, tuning** on seed 338 (CI): choose T, M, q, robust form, correlated form;
+   record `experiments/<date>-tl4-tuning/`.
+3. **Model, code** (CPU): period stream, non-causal model with logit head, export check,
+   read-out; toy training test; pilot plan.
+4. **Pilot** on a T4 (at most 1 h): throughput and a loss curve; then bind the training
+   plans.
+5. **Registration**: SRD §5 row and §9 line; decision record binding the controls' plan,
+   the model's plans, the comparison and the power table. Before any training job beyond
+   the pilot and before seeds 340 and 341 exist.
+6. **Training**: three seeds (GPU, about 30 h in all), launched a minute apart.
+7. **Controls' validation** on seed 340 (CI); L-a; record committed.
+8. **Test** on seed 341: controls and learned tracks solved, scored once.
+9. **Panel**, corrections, write-up, slides.
+
+Phases 2 and 3 to 4 run in parallel; 6 runs while 7 is prepared.
+
+## Risks
+
+- Run time: five searches a period at 30 to 50 s each is about 4 minutes a period; 2,300
+  test periods is about 150 runner-hours. Shards of 10 periods, runs queued one at a time.
+- The budget buys about 23 million periods a seed if throughput holds; if the pilot shows
+  under half that, two seeds are trained and the third is reported as not run (the criteria
+  then need both).
+- A sixth evidence error: v4 is tested at edges; the plain v3 search is kept beside it.
+- The model's logit read-out proposes spans the arc fit cannot use: `learned` (direct) is
+  reported beside it either way.
diff --git a/specs/016-time-late-learned/research.md b/specs/016-time-late-learned/research.md
new file mode 100644
index 00000000..bcb7f377
--- /dev/null
+++ b/specs/016-time-late-learned/research.md
@@ -0,0 +1,231 @@
+# Research: spec 016 design decisions
+
+Each decision: what was chosen, why, what else was considered. Settings named "tuned" are
+chosen on tuning seed 338 only and fixed in the decision record.
+
+## R1 Hedged track (FR-002)
+
+**Chosen**: the mean of the candidates' tracks in Cartesian state (position and velocity
+relative to nothing: x, y, vx, vy at each scored row), weighted by the candidates' evidence
+at temperature T (tuned from 1, 2, 3, 5), over the search's first K candidates (K = 10;
+spec 015 kept 5). Range, course and speed are read from the mean state. Formed in the runner
+from the stored fits; no refit.
+**Why**: it is the posterior-mean track under the search's own posterior, which is what an
+error-minimising learned model approximates. Cartesian averaging keeps bearings consistent.
+**Considered**: averaging log range, course and speed (averages across a mirror pair are
+meaningless either way; reported as an edge case); a median track (not a mean of anything a
+loss targets).
+
+## R2 Declaration margin (FR-003)
+
+**Chosen**: re-rank the search's seen histories: the declared history is the one with the
+highest evidence among those whose every manoeuvre "pays": removing any one manoeuvre (the
+best seen history with that one removed, within the match tolerance) lowers the evidence by
+more than a margin M. Combined with a stated quiet probability q in the mixed prior. M from
+0, 1, 2, 3, 5 and q from 0, 0.01, 0.1, tuned on the tuning seed's held-course and Annex B
+splits by a declared rule: the smallest M and q with held-course false rate at most 0.10.
+The runner stores, for each candidate, the best seen history with each manoeuvre removed.
+**Why**: spec 015's panel found false declarations under 45 minutes have margins under 3 in
+about 90% of cases, and that a 1% quiet probability gives 0.09 false at little cost.
+**Considered**: integrating over the manoeuvre's start (the correct evidence for "some
+manoeuvre"; it would declare more, not fewer, under Annex B's prior, and is what the hedged
+track already expresses).
+
+## R3 Robust likelihood (FR-004)
+
+**Chosen**: two forms, the better on tuning data carried. (a) Wild-bearing editing before the
+search (`thin.edit_wild`, spec 014: restored clean quality under outliers in the thinning
+study). (b) Huber cost (constant 2.0, spec 010's grid) by iteratively reweighted fits, with
+the evidence computed on the Huber cost and its weighted curvature.
+**Why**: (a) exists and is nearly free; (b) is the standard and does not discard data.
+**Considered**: a Student-t likelihood (a tuning constant more, no clear gain over Huber).
+
+## R4 Correlated-error likelihood (FR-005)
+
+**Chosen**: a first-order autoregressive error plus white noise, matching the generator's
+family in form but with parameters estimated per period from the residual autocorrelation of
+a first plain fit (lag-1 to lag-12 at 5 s, fitted to share·exp(−lag/τ)); fits and evidence
+use residuals whitened by the implied covariance (banded Cholesky; `headroom/cov.py` builds
+the covariance). Also reported: the same with the true parameters told (an upper control).
+**Why**: a real system does not know τ; the told variant shows what estimation costs.
+**Considered**: inflating σ only (does not change which history wins by much; kept as the
+cheapest variant in tuning).
+
+## R5 A prior that can be wrong (FR-006)
+
+**Chosen**: prior kind `broad`: Annex B's form with leg mean 22 min (between Annex B's 25
+and the shifted 15), leg sd widened by 1.5, turn sizes 10° to 150°, turn rates and
+accelerations to 1.5 times Annex B's maxima, speed-change probability 0.5. Kind `mix2`:
+log-sum of Annex B and `broad` at equal weight, with quiet probability q from R2.
+Diagnostic: for the declared history, log p(history | Annex B) − log p(history | broad),
+reported as a per-period score and as a rate of firing at a threshold tuned to fire in 5% of
+Annex B tuning periods.
+**Why**: the shifted split's statistics must not be used to design the prior (that would be
+telling it the answer); `broad` is set from Annex B's own ranges widened, not from the
+shifted file. A second shifted split (`shift2`: longer legs, larger turns) is added so the
+broad prior is tested on a shift it was not placed between.
+**Considered**: a hierarchical prior with hyperparameters fitted per period (one period has
+one to three manoeuvres: nothing to fit).
+
+## R6 Evidence faults at the support edges (FR-008)
+
+**Chosen**: prior version 4, used by every control and by the plain search rerun beside
+them: (i) the stand-in volume at a support edge uses the one-sided mass (half-normal of the
+shoulder plus the inside density) so density × volume is continuous across the edge;
+(ii) a duration's mass inside the solver's box replaces 1; (iii) `_leg_tail` extended to
+any period length; (iv) acceleration constrained for combined manoeuvres; (v) speed after a
+change paired. Tests integrate at edges and bounds. Spec 015's search (version 3) is run
+beside it so the repair's effect is seen on the same periods.
+**Why**: the declaration margin and the hedged weights read evidence differences of 1 to 3
+nats; the known faults are 1 to 3.5 nats exactly where invented manoeuvres sit.
+
+## R7 The learned model (FR-009 to FR-011, FR-015)
+
+**Chosen**: a bidirectional transformer encoder (spec 012's `Regressor` without the causal
+mask): 8 layers, width 256, 8 heads; tokens are patches of 6 rows of 5-second data (30 s),
+at most 240 tokens (120 min); features as spec 012's canonical frame (rotated to the first
+bearing) plus the row's time from the period's start and the period's length. Outputs per
+token: mean and log-variance of ln range, vx, vy at the token's last row, and a logit that a
+target manoeuvre is in progress. Loss: β-NLL on the state plus binary cross-entropy on the
+logit (weight 0.1). Trained with mixed precision on a T4.
+**Why**: spec 012's code, throughput figures and ledger apply directly; patches lose no
+bearing; non-causal attention is the time-late setting.
+**Considered**: a model that proposes histories to the search (the "proposer" of spec 013;
+a second stage, only if the direct model shows promise); a GRU (kept as a possible control,
+not trained within this budget).
+
+## R8 The declared read-out (FR-010; constitution: the reported estimate comes from a declared physics layer)
+
+**Chosen**: two reported tracks per model. `learned`: the model's direct per-token state,
+interpolated to scored rows. `learned-arc`: the arc-model fit (`joint_fit` with prior
+version 4) started from the model's track, with manoeuvre spans taken from runs of the
+manoeuvre logit above 0.5 (merged within 60 s, at most 10), no search. The registered
+comparison is on `learned-arc`; `learned` is reported beside it and its difference stated.
+**Why**: the constitution requires the reported estimate from the declared layer; the arc
+fit is the time-late layer since spec 013. The direct output shows what the read-out costs
+or adds.
+
+## R9 Training mix and what is held out (FR-011)
+
+**Chosen**: periods cut as Gate T does from 5-second engagements of stream seed 339.
+Mix by engagement: 40% Annex B ranges; 20% a randomised-statistics family (each engagement
+draws its own leg mean 10 to 40 min, turn range and rate limits between half and 1.5 times
+Annex B's: covers `shift` and `shift2` without being either); 15% quiet; 25% of all
+engagements also get a bearing-error overlay drawn per engagement: correlated (τ 20 to
+240 s, share 0.2 to 0.7), outliers (rate 0 to 6%, spread 5 to 15 sd), or both.
+Held out of training entirely: bias (spec 010's `bias-central`) and the unknown-σ factor;
+scored as the "absent from training" split.
+**Why**: the classical controls are not told the test conditions either (R4 estimates, R5
+is not placed on the shift); a randomised family is the learned counterpart of a broad
+prior. Bias is the mismatch no classical control here addresses, so it tests transfer for
+both sides.
+**Feasibility**: the generator's kernel is 20k engagements a second at 720 rows; at 5 s an
+engagement has up to 1,440 rows. Spec 012 measured 635 scenarios a second on a T4 at 240
+tokens; the token count is the same here. Throughput is measured in a pilot before the plan
+is bound.
+
+## R10 Budget (FR-013)
+
+**Chosen**: cap $22 of the $24 unspent (ledger cap in T4-hours at $0.66: 33 h). Pilot at
+most 1 h. Three seeds of 10 h each (about 23 million periods at 635 a second, if the pilot
+confirms). No other GPU type unless the pilot shows a cheaper dollar per period.
+**If Modal's monthly credit is confirmed by the owner** the plan may be extended by an
+addendum registered before its runs; nothing depends on it.
+
+## R11 Splits, sizes and criteria the sample can decide (FR-016 to FR-019)
+
+**Splits** (seed 340 validation, 341 test; periods per split in brackets for test):
+Annex B (400), shift (300), shift2 (300), quiet (300), correlated mild and central
+(200 each), outliers central and harsh (200 each), absent-from-training bias-central (200),
+whole engagements (60, reported only).
+**Criteria** (each a paired per-period geometric-mean ratio of period median range error,
+95% period bootstrap, percentile; "wins" = interval wholly above 1.00, "loses" = wholly
+below, else "not separated"):
+- **L-a (controls are sound)**: on validation, the true-history reference with times held
+  has range efficiency in [0.8, 1.3] on tight rows, and at most 25% of references are held.
+  Stops the study before the test seed.
+- **L-b (controls)**: for shift, correlated-central and outliers-central, plain search ÷
+  best control: reported with the share of the gap to the reference closed. No pass line.
+- **L-c (the verdict)**: on each of shift, shift2, correlated-central, outliers-central and
+  bias: best classical control ÷ `learned-arc` (median of three seeds' ratios; each seed
+  reported). Learning is "shown on a split" if every seed wins against the best control AND
+  against the hedged track; "hedging" if it wins against the best control's first candidate
+  but not the hedged track; "not shown" otherwise.
+- **L-d (no harm)**: on Annex B and quiet, the same ratio; "harm" if every seed loses.
+- **Power**: stated in the decision record from tuning data as the smallest paired ratio
+  each split can separate from 1 at its size; a split that cannot separate 1.15 is reported
+  only.
+**What follows**: learning shown on at least two mismatch splits without harm: a gate for a
+learned time-late system is proposed. Shown on none: the classical controls stand as the
+time-late system and the learned track stops. Otherwise: reported, owner decides.
+**Why no materiality line**: spec 015's panel; a sign test against 1.00 with three seeds
+agreeing is decidable, and the size is reported with its interval.
+
+## R12 Enforcing the order (FR-014)
+
+**Chosen**: the scoring tool takes `--learned` predictions only with `--controls-record
+<dir>`; it refuses unless that folder holds a validation summary whose plan digest equals
+the decision record's and whose L-a passed, and unless the folder is committed (clean tree,
+file tracked). Training reads only the training stream and its own held-out monitor slice
+of seed 339.
+
+## R13 Storing tracks
+
+**Chosen**: the runner stores each system's scored-row states for the first 20 periods of
+every split so the slides can carry track plots (three studies had none).
+
+## As built (2026-11-13, before any tuning result): departures from R3 to R6
+
+- **R3**: only wild-bearing editing is built. A Huber cost inside the fits and the evidence
+  would change the registered fit code, which takes one sd for all bearings or one per row
+  of a problem with every row valid; editing needs neither. Not built, and said so.
+- **R4**: the correlated-error control does not whiten residuals. It estimates the
+  correlated share and time from the bearings' second differences (no fit needed, robust to
+  wild bearings) and gives the search one larger sd with the same spectral density at zero
+  frequency (`corr.effective`). It is right for anything slow (range, course, speed) and
+  cautious about when a manoeuvre happened. The variant told the true parameters is not
+  built.
+- **R5**: the broadened prior is a second `ManoeuvrePrior` (`configs/timelate/tl-broad.json`,
+  Annex B's ranges widened; not placed from either shifted split). The mixture is formed by
+  pooling the candidates of the two searches, each prior equally likely beforehand, which is
+  the same posterior and needs no new prior code. The diagnostic is the Bayes factor between
+  the two priors' best histories (`log_bayes_broad`), which replaces the per-history density
+  ratio.
+- **R6 deferred**: prior version 4 is not built in this pass. The declaration margin is
+  tuned on data and absorbs an over-credit that is roughly constant where invented
+  manoeuvres sit; the faults stay recorded in `experiments/20261113-tl3/review.md`. If the
+  tuning run shows the margin cannot bring held-course false manoeuvres under 0.10 at an
+  acceptable miss rate, version 4 is built before registration.
+- **The combined control `ctl`** (new): edit, estimate the correlation (used above a share
+  of 0.2), search under both priors and pool. It is what a system not told the conditions
+  would run, and it is the classical side of the registered comparison; the single-factor
+  controls are scored on their own splits for attribution (FR-020).
+
+## As registered (2026-11-13, after the tuning run and the pilot, before any training run)
+
+- **R9, the mix as built**: the shares in R9 summed to 75%; the remainder is Annex B.
+  Families: Annex B 0.65, randomised statistics 0.20, quiet 0.15; independently a quarter
+  of all engagements carry an error overlay (correlated, outliers or both, a third each).
+  Every proportion and range is in `configs/regress/tlm-mix.json`. Bias and the unknown-σ
+  factor are never in training.
+- **R10, the budget as set**: the ledger's cap is $45 of the owner's $50 and $26.35 was
+  recorded before training. Three seeds of two 15,600 s segments: at most $18.36. The
+  ledger under-counts a run whose container is replaced (pilot record), so the true spend
+  may be a little above what it records; the cap leaves $5 for that.
+- **R11, the comparison as registered**: the classical side is the combined control `ctl`
+  as a declared history (`ctl-d1-q0.01`) and as a hedged track (`ctl-h1`), settings from
+  tuning seed 338; not "the best control per split chosen on validation", because a system
+  is not told which split it is on. The single-factor controls are scored on their own
+  splits for attribution. Test sizes: Annex B 400, shifted 400, shifted 2 400, held course
+  300, correlated mild 200, correlated 300, outliers 300, outliers harsh 200, bias 300,
+  whole engagements 60 (reported only).
+- **Power** (from tuning seed 338; the smallest paired ratio separable from 1 at the test
+  size, 1.96 sd of the mean log ratio): for two classical systems that share fits (search
+  against `ctl`) 1.03 to 1.23; for two unlike systems (`ctl` against C″, the nearest proxy
+  for `ctl` against a learned track) 1.15 on the shifted split, 1.40 on shifted 2, 1.19 to
+  1.25 on the error splits, 1.28 on Annex B. A learned track must therefore differ from
+  the control by about 15% to 40% to be separated; a smaller difference will read "not
+  separated", which the criteria count as not shown and not as harm.
+- **The read-out** (R8) is fitted to the bearings `ctl` sees (edited, effective sd) with
+  Annex B's prior, at the model's manoeuvres, started from the model's track; it falls
+  back to the model's own track by spec 015's acceptance rule.
diff --git a/specs/016-time-late-learned/spec.md b/specs/016-time-late-learned/spec.md
new file mode 100644
index 00000000..789d6f77
--- /dev/null
+++ b/specs/016-time-late-learned/spec.md
@@ -0,0 +1,287 @@
+# Feature Specification: Time-late, classical controls and a learned whole-period model
+
+**Feature Branch**: `016-time-late-learned` (developed on `claude/keen-wozniak-77td7j`)
+
+**Created**: 2026-11-13
+
+**Status**: Draft
+
+**Input**: Owner decision, 2026-11-13, after spec 015's panel review: option B. Build the
+classical controls on CPU and, in parallel, train a learned whole-period model on GPU under a
+plan registered before any result; the learned model is scored only once the controls exist.
+
+**SRD traceability**:
+- §1 (time-late consumer), §3 (the reported estimate comes from a declared physics layer),
+  §5 (references on the same rows, paired statistics, fresh seeds, registration before
+  results; a new row proposed by this spec, owner to merge) and §9, 2026-11-13.
+- Panel reviews `experiments/20261113-tl3/review.md` ("What the panel wants next", 2 to 5;
+  the table of what a learned model may be compared with) and
+  `experiments/20261112-tl2/review.md`.
+
+**Owner decisions this spec implements**:
+- 2026-11-13: option B.
+- 2026-11-12: GPU may be used in support of time-late learning, within the $50 total;
+  runs are registered before their results.
+- 2026-11-11: spending within $50 is pre-approved (about $24 unspent).
+
+## Background
+
+Three time-late studies (Gate T, specs 014 and 015) leave this position. A classical search
+over manoeuvre histories, scored by a Laplace evidence under Annex B's statistics, halves the
+track error of C″ read as a track. Where the simulator's assumptions hold, an oracle is 10%
+to 20% ahead of it and 400 periods cannot decide a 10% line. Where they fail the gaps are
+large: against an oracle the search is 1.7 times worse under a shifted manoeuvre prior, 1.4
+under correlated bearing errors and 1.5 with outliers, and on quiet targets it invents a
+manoeuvre in half the periods. No classical remedy has been tried for any of these. A learned
+model that won there today could not be told from a missing robust cost, a broader prior or
+simple hedging; this project has drawn that wrong conclusion three times and had it corrected
+by a control.
+
+The owner has chosen to build those controls and to train the learned model at the same
+time, so that GPU time is not idle while the controls are built, with one safeguard: the
+model's plan, comparison and criteria are fixed first, and it is not scored on fresh data
+until the controls' own validation results are recorded.
+
+## User Scenarios & Testing *(mandatory)*
+
+### User Story 1 - A hedged track and a cautious declaration (Priority: P1)
+
+A researcher gets, from the search's own candidates and with no new fitting, a track that
+averages over the plausible histories instead of committing to one, and a history in which a
+manoeuvre is declared only when the evidence for it is clear.
+
+**Why this priority**: it is the control for hedging, the cheapest one, and the one both
+earlier studies were silent on. A learned model minimising error will hedge.
+
+**Independent Test**: on periods already solved, the hedged track's error and the declared
+history's false and missed manoeuvres are reported beside the search's first candidate, with
+no solve repeated.
+
+**Acceptance Scenarios**:
+
+1. **Given** a period whose candidates disagree, **When** the hedged track is formed, **Then**
+   it lies between them, weighted by their stated probabilities.
+2. **Given** a quiet target, **When** the declaration margin is applied, **Then** the false-
+   manoeuvre rate and the rate at which one-manoeuvre targets are called quiet are both
+   reported over a range of margins.
+
+---
+
+### User Story 2 - A search that tolerates bearing errors it was not told about (Priority: P1)
+
+A researcher runs the search with a likelihood that does not assume independent Gaussian
+errors: wild bearings carry bounded weight, and correlated errors are modelled.
+
+**Why this priority**: outliers and correlated errors are two of the three large gaps.
+
+**Independent Test**: on data with outliers and with correlated errors, at two severities
+each, the robust search's track error and false manoeuvres are reported beside the plain
+search's; on clean data its cost is reported.
+
+**Acceptance Scenarios**:
+
+1. **Given** 2% wild bearings, **When** the robust search runs, **Then** its false manoeuvres
+   per period and track error are reported against the plain search and against the same
+   search on the clean data.
+2. **Given** clean data, **When** the robust search runs, **Then** what it loses is reported.
+
+---
+
+### User Story 3 - A prior that can be wrong, and says so (Priority: P1)
+
+A researcher runs the search with a broader prior on how targets manoeuvre and a stated small
+chance of a quiet target, and gets a diagnostic that reports when the period's best history
+is improbable under Annex B's statistics.
+
+**Why this priority**: the shifted prior is the largest gap; candidates there carry no
+usable probability.
+
+**Independent Test**: on the shifted split and on Annex B data, the broadened search's error,
+manoeuvre counts and candidate calibration are reported beside the Annex B search's; the
+diagnostic's rate of firing is reported on both.
+
+---
+
+### User Story 4 - A learned whole-period model, registered before it is trained (Priority: P1)
+
+The owner sees a sequence model that reads a whole period and gives the whole track, trained
+on simulated periods of mixed kinds, with its architecture, training mix, budget, comparison
+and criteria fixed in a decision record before any training result exists.
+
+**Why this priority**: it is the question the project exists to answer for the time-late
+consumer, and the owner has asked for it now.
+
+**Independent Test**: the decision record and plan digests predate the first training job;
+three seeds train within the budget; the spending ledger accounts for every GPU hour.
+
+**Acceptance Scenarios**:
+
+1. **Given** the registered plan, **When** a training job starts, **Then** it refuses to run
+   if the plan's digest differs or the ledger would exceed its cap.
+2. **Given** a trained model, **When** it is exported, **Then** the reported track comes from
+   the declared read-out and the model also runs without a GPU.
+
+---
+
+### User Story 5 - A comparison the controls make interpretable (Priority: P1)
+
+The owner sees the learned model scored once, on a fresh test seed, against the best
+classical control on each kind of period, after the controls' validation results are on
+record; and sees plainly whether it beats them, where, and by how much per period.
+
+**Independent Test**: the scoring run refuses to read validation or test data for the
+learned model unless the controls' validation record exists; the record reports paired
+per-period ratios with intervals, the share of periods lost, efficiency by slice and the
+result on each split.
+
+**Acceptance Scenarios**:
+
+1. **Given** the learned model beats the search's first candidate but not the hedged track,
+   **When** the result is written, **Then** it is recorded as hedging, not as learning.
+2. **Given** a split whose conditions were in the training mix and one whose were not,
+   **When** scored, **Then** the two are reported apart.
+
+### Edge Cases
+
+- A period with a single candidate: the hedged track is that candidate.
+- Candidates whose tracks are mirror solutions: the hedged track may lie between them and be
+  far from both; reported, and its error counted as it falls.
+- A training job refused by the GPU provider's rate limit: re-run from the same plan.
+- A model that diverges for one seed: counted, reported, not replaced.
+- A condition severity the classical control was tuned on and the model was not trained on,
+  or the reverse: stated per split.
+- A period shorter or longer than the model's window.
+
+## Requirements *(mandatory)*
+
+### Functional Requirements
+
+**Frozen**
+
+- **FR-001**: C, C″ and the registered code paths, plans and records of Gate T and specs 014
+  and 015 MUST NOT change in behaviour; their fixtures MUST still pass.
+
+**Classical controls (nothing trained)**
+
+- **FR-002**: A hedged track MUST be formed from the search's candidates as their
+  probability-weighted mean track, at the registered temperature and at others, without
+  refitting.
+- **FR-003**: A declaration rule MUST accept a manoeuvre only when the evidence margin over
+  the history without it exceeds a threshold, combined with a small stated probability of a
+  quiet target; both are tuned on the tuning seed only and their false and missed rates
+  reported as a curve.
+- **FR-004**: The search MUST be runnable with a robust likelihood (bounded weight for wild
+  bearings) in its fits and its evidence.
+- **FR-005**: The search MUST be runnable with a correlated-error likelihood in its fits and
+  its evidence, with the correlation estimated from the data or stated, and each reported.
+- **FR-006**: The search MUST be runnable with a broadened manoeuvre prior (alone and as a
+  mixture with Annex B's), and a diagnostic MUST report when the best history is improbable
+  under Annex B's statistics.
+- **FR-007**: Each control's settings MUST be chosen on the tuning seed and fixed in the
+  decision record before validation.
+- **FR-008**: Known evidence faults at the edges of the prior's support MAY be repaired in a
+  new prior version where a control needs it; any repair MUST be tested at the edges and at
+  solver bounds.
+
+**Learned model**
+
+- **FR-009**: A learned model MUST read a whole period (bearings at 5-second intervals,
+  ownship motion, the stated bearing error) and output the target's track at every scored
+  moment, with manoeuvre times as a secondary output.
+- **FR-010**: The reported track MUST come from a declared read-out (the arc model or the
+  physics layer fitted from the model's output), and the model's direct output MUST be
+  reported beside it.
+- **FR-011**: Training data MUST be generated on the fly from a seeded stream that mixes
+  Annex B periods, shifted and broadened manoeuvre statistics, quiet targets, correlated
+  errors and outliers, in declared proportions; conditions held out of training MUST be
+  declared.
+- **FR-012**: Three seeds MUST be trained under one plan bound by digest in a decision record
+  committed before the first training job; the record MUST fix the comparison, the splits,
+  the statistics, what each outcome will be called and what follows from it.
+- **FR-013**: GPU spending MUST stay within the owner's budget by a ledger that refuses a job
+  that would exceed it; the record MUST state the dollars spent.
+- **FR-014**: The learned model MUST NOT be scored on the validation or test seed until the
+  classical controls' validation record is committed; the scoring tool MUST enforce this.
+- **FR-015**: The model MUST export to a form that runs without a GPU, and the exported
+  model's output MUST agree with the trained one's.
+
+**The comparison**
+
+- **FR-016**: On each split (Annex B, shifted prior, correlated errors and outliers at two
+  severities each, held course, and at least one condition absent from training) the learned
+  model MUST be compared with the best classical control for that split as chosen on
+  validation, and with the hedged track, the plain search, C″ as a track and the true-history
+  reference, on the same periods.
+- **FR-017**: Every comparison MUST report the paired per-period ratio with a period-
+  bootstrap interval, the ratio of medians, the share of periods each side loses, efficiency
+  against the bound by slice for range, course and speed, and manoeuvre counts (missed,
+  false) at 60, 120 and 240 s.
+- **FR-018**: The true-history reference MUST be reported under each convention (times free,
+  held where drifted, held everywhere) wherever a headroom is quoted.
+- **FR-019**: No criterion MUST be registered whose outcome at the planned sample size is
+  forecast as near even; the decision record MUST state the power of each criterion from
+  tuning data.
+- **FR-020**: A win of the learned model MUST be attributed only after the registered
+  controls: against the hedged track (hedging), against the robust and correlated-error
+  searches (the likelihood), against the broadened prior (the prior). What remains is
+  recorded as learning; what does not is recorded under the control's name.
+- **FR-021**: The record MUST list what the spec promised and did not deliver, and every
+  table MUST be generated from the summary by a committed script, with the README's figures
+  checked by a committed test.
+
+**Process**
+
+- **FR-022**: Seeds: 338 tuning, 339 the training stream, 340 validation, 341 test. Test is
+  scored once.
+- **FR-023**: A five-reviewer panel MUST decide the result; kickoff and slides posts as the
+  constitution requires.
+
+### Key Entities
+
+- **Candidate set**: a period's histories with evidence, probabilities and tracks.
+- **Hedged track**: the probability-weighted mean of the candidates' tracks.
+- **Control**: a classical variant of the search (robust, correlated-error, broadened prior,
+  declaration margin) with settings fixed on tuning data.
+- **Learned model**: weights for one seed, its plan digest, its export.
+- **Training mix**: the declared proportions of period kinds and error conditions.
+- **Ledger**: GPU hours and dollars by job against the cap.
+- **Split**: a set of periods of one kind and severity, marked as inside or outside the
+  training mix.
+
+## Success Criteria *(mandatory)*
+
+### Measurable Outcomes
+
+- **SC-001**: For each of the three large gaps (shifted prior, correlated errors, outliers)
+  the record states how much of the gap between the plain search and the true-history
+  reference the best classical control closes, with an interval.
+- **SC-002**: On held course, a declaration rule is shown whose false-manoeuvre rate is
+  under 0.10 with the rate of one-manoeuvre targets called quiet stated beside it.
+- **SC-003**: The hedged track's error is reported against the first candidate's on every
+  split, paired per period.
+- **SC-004**: Three seeds of the learned model are trained within the budget, and the
+  decision record's digests predate the first job.
+- **SC-005**: The learned model is scored exactly once on the test seed, after the controls'
+  validation record, and on every split the record states whether it beats the best classical
+  control, by what paired ratio, in what share of periods, and under which control's name any
+  win falls.
+- **SC-006**: The owner can read from one table, per split, what is classical, what is
+  hedging and what, if anything, is learning.
+
+## Assumptions
+
+- The learned model is a control-and-candidate in one: if it beats every classical control
+  on the mismatch splits it becomes the candidate time-late system for a later gate; nothing
+  is carried from this spec alone.
+- "Best classical control" is chosen per split on the validation seed by median period range
+  error, before the learned model is scored.
+- Budget: the unspent part of the owner's $50 (about $24). Modal's new monthly credit is not
+  assumed; using it beyond the $50 total needs the owner's word.
+- Periods are cut as in Gate T (random periods from 5-second MAN2 engagements); the model's
+  window covers the longest period.
+- The severities for correlated errors and outliers are spec 010's central condition and one
+  step either side where the generator allows.
+- The registered form is a study with criteria the sample can decide, proposed as an SRD §5
+  row; the owner merges the amendment.
+- If the controls close the gaps (the best control within 10% of the reference on every
+  mismatch split), the learned model is still scored once as registered.
diff --git a/specs/016-time-late-learned/tasks.md b/specs/016-time-late-learned/tasks.md
new file mode 100644
index 00000000..4b90d84e
--- /dev/null
+++ b/specs/016-time-late-learned/tasks.md
@@ -0,0 +1,37 @@
+# Tasks: spec 016
+
+## Phase 1: controls, code
+- [x] T001 Prior version 4 (R6) with tests at support edges and solver bounds
+- [x] T002 `hedge.py`: hedged track and declaration margin (R1, R2), tests
+- [x] T003 `robust.py` (R3) and `corr.py` (R4), tests
+- [x] T004 Prior kinds `broad`, `mix2` and the mismatch diagnostic (R5); `tl-shift2-ranges.json`
+- [x] T005 `run4.py`, `study4.py`, CLI and workflow wiring, schema bump, fixture; tracks stored (R13)
+
+## Phase 2: controls, tuning
+- [x] T006 Tuning run on seed 338; settings chosen by the declared rules; record
+
+## Phase 3 and 4: model
+- [x] T007 Period stream with the mix (R9); non-causal model with logit head (R7); toy training test
+- [x] T008 `learned.py` read-out (R8), `tma2-tlpredict`, export check (FR-015)
+- [x] T009 Pilot on a T4 (at most 1 h); throughput; training plans written
+
+## Phase 5: registration
+- [x] T010 SRD §5 row and §9 line; decision record with digests, criteria and the power table
+
+## Phase 6 to 8
+- [x] T011 Train three seeds within the ledger cap
+- [x] T012 Controls' validation on seed 340; L-a; record committed
+- [x] T013 Test on seed 341: one scoring; tables by script; README check by test
+
+## Phase 9
+- [x] T014 Panel (five reviewers), corrections
+- [x] T015 Write-up, slides post and deck with track plots; AGENTS.md; kickoff update; PR
+
+## Not done as planned (see `experiments/20261114-tl4/review.md`)
+
+- T001: prior version 4 was deferred (research, "As built").
+- T003: only wild-bearing editing and an effective sd were built; no Huber cost, no whitened fit.
+- T004: the mismatch diagnostic that was built is not a Bayes factor and does not work.
+- T008: the export check ran on a toy model, not on the trained checkpoints.
+- T005: no unit tests for `run_shard4` or `study_split4`; no schema bump (existing header
+  fields were reused).
diff --git a/src/tma2/cli/rtrain.py b/src/tma2/cli/rtrain.py
index a45dad38..97882dbb 100644
--- a/src/tma2/cli/rtrain.py
+++ b/src/tma2/cli/rtrain.py
@@ -1,4 +1,6 @@
-"""``tma2-rtrain``: train the regressor control (spec 012 contracts/cli.md)."""
+"""``tma2-rtrain``: train the regressor control (spec 012 contracts/cli.md) or, with a
+configuration whose ``data`` is "periods", the whole-period model (spec 016). ``--config`` is a
+configuration or a plan that carries one."""
 
 from __future__ import annotations
 
@@ -18,10 +20,11 @@ def main(argv: list[str] | None = None) -> int:
     from tma2.regress.model import RegressorConfig
     from tma2.regress.train import train
 
-    cfg = RegressorConfig(**json.loads(Path(a.config).read_text()))
+    raw = json.loads(Path(a.config).read_text())
+    cfg = RegressorConfig(**raw.get("config", raw))
     rec = train(cfg, Path(a.out), a.device, Path(a.resume) if a.resume else None, a.limit_seconds)
     print(
-        f"{rec['stopped']}: {rec['updates']} updates, {rec['scenarios_seen']} scenarios, "
+        f"{rec['stopped']}: {rec['updates']} updates, {rec['scenarios_seen']} {cfg.data}, "
         f"{rec['scenarios_per_second']:.0f} per second"
     )
     return 0
diff --git a/src/tma2/cli/tlpredict.py b/src/tma2/cli/tlpredict.py
new file mode 100644
index 00000000..2ed42cbd
--- /dev/null
+++ b/src/tma2/cli/tlpredict.py
@@ -0,0 +1,125 @@
+"""``tma2-tlpredict``: one shard of the learned whole-period model's output (spec 016 FR-009,
+FR-010) on the periods ``tma2-tlsolve`` cuts from the same split and ordinals. Writes
+``<out>.npz`` and ``<out>.json`` (the header).
+
+Per period the shard holds the direct track (``learned``: x, y, vx, vy at every 5-second row),
+the manoeuvre probability per token and the spans read from it. No error is computed and the
+target's truth is not read; the arc read-out (``learned-arc``) is applied by the time-late
+runner from these arrays (``tma2.timelate.learned.arc_readout``).
+
+Arrays: ``ordinal``, ``first_row``, ``rows`` [P]; ``row_period`` [N] and ``learned`` [N, 4]
+(rows of all periods, in order); ``token_period`` [K], ``token_t`` [K, 2] (s from the period's
+start), ``man_prob`` [K], ``token_mean`` and ``token_logvar`` [K, 3] (the model's frame);
+``span_period`` [S] and ``spans`` [S, 2] (s).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser(prog="tma2-tlpredict", description=__doc__)
+    ap.add_argument("--weights", required=True, help="checkpoint.safetensors")
+    ap.add_argument("--config", required=True, help="the training plan (or its config)")
+    ap.add_argument("--in", dest="inp", required=True, help="the split's f32pack")
+    ap.add_argument("--plan", help="the time-late plan (with --split: reads the split's `whole`)")
+    ap.add_argument("--split", default="", help="the plan's split name (for the header)")
+    ap.add_argument("--whole", action="store_true", help="engagements uncut (without --plan)")
+    ap.add_argument("--start", type=int, default=0, help="first σ = 1° ordinal")
+    ap.add_argument("--count", type=int, required=True)
+    ap.add_argument("--out", required=True, help="output prefix")
+    ap.add_argument("--threshold", type=float, default=0.5)
+    ap.add_argument("--merge-seconds", type=float, default=60.0)
+    ap.add_argument("--max-manoeuvres", type=int, default=10)
+    a = ap.parse_args(argv)
+    from tma2.data.provenance import read_git_provenance
+
+    g = read_git_provenance()  # before any output
+    import numpy as np
+    import torch
+
+    from tma2.data.f32pack import read_pack_header
+    from tma2.regress.model import RegressorConfig
+    from tma2.regress.period_predict import predict_periods
+    from tma2.regress.predict import load_model
+    from tma2.regress.train import config_digest
+    from tma2.segment.gate import sha256_file
+    from tma2.timelate.learned import direct_track, spans_from_logit, token_times
+    from tma2.timelate.periods import iter_periods
+
+    raw = json.loads(Path(a.config).read_text())
+    cfg = RegressorConfig(**raw.get("config", raw))
+    if cfg.data != "periods" or not cfg.manoeuvre_head:
+        raise SystemExit(f"{a.config}: not a whole-period model with a manoeuvre head")
+    whole = a.whole
+    if a.plan:
+        plan = json.loads(Path(a.plan).read_text())
+        if a.split not in plan["splits"]:
+            raise SystemExit(f"split {a.split!r} is not in {a.plan}")
+        whole = bool(plan["splits"][a.split].get("whole", False))
+    periods = list(iter_periods(a.inp, a.start, a.count, whole=whole))
+    if not periods:
+        raise SystemExit(f"no σ = 1° periods at {a.start} in {a.inp}")
+    torch.set_num_threads(4)
+    model = load_model(cfg, Path(a.weights))
+    outs = predict_periods(model, periods)
+    patch = cfg.patch_rows
+    tracks, row_period, token_period, token_t, span_period, spans = [], [], [], [], [], []
+    for p, o in zip(periods, outs, strict=True):
+        tracks.append(direct_track(o.mean, p, patch))
+        row_period.append(np.full(p.rows, p.ordinal, dtype=np.int64))
+        tt = token_times(p.rows, p.dt, patch)
+        token_t.append(tt)
+        token_period.append(np.full(tt.shape[0], p.ordinal, dtype=np.int64))
+        sp = spans_from_logit(o.prob, tt, a.threshold, a.merge_seconds, a.max_manoeuvres)
+        spans.append(sp)
+        span_period.append(np.full(sp.shape[0], p.ordinal, dtype=np.int64))
+    header = {
+        "record": "header",
+        "kind": "learned-period-tracks",
+        "split": a.split,
+        "whole": whole,
+        "dataset_digest": sha256_file(a.inp),
+        "seed": int(read_pack_header(a.inp).dataset_seed),
+        "ordinal_start": a.start,
+        "ordinal_count": len(periods),
+        "weights_sha256": sha256_file(a.weights),
+        "config_sha256": config_digest(cfg),
+        "config_id": cfg.id,
+        "plan_sha256": sha256_file(a.plan) if a.plan else None,
+        "patch_rows": patch,
+        "threshold": a.threshold,
+        "merge_seconds": a.merge_seconds,
+        "max_manoeuvres": a.max_manoeuvres,
+        "torch": torch.__version__,
+        "git_commit": g.commit,
+        "git_dirty": g.dirty,
+    }
+    out = Path(a.out)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    np.savez_compressed(
+        f"{out}.npz",
+        ordinal=np.array([p.ordinal for p in periods], dtype=np.int64),
+        first_row=np.array([p.first for p in periods], dtype=np.int64),
+        rows=np.array([p.rows for p in periods], dtype=np.int64),
+        row_period=np.concatenate(row_period),
+        learned=np.concatenate(tracks),
+        token_period=np.concatenate(token_period),
+        token_t=np.concatenate(token_t),
+        man_prob=np.concatenate([o.prob for o in outs]).astype(np.float32),
+        token_mean=np.concatenate([o.mean for o in outs]),
+        token_logvar=np.concatenate([o.logvar for o in outs]),
+        span_period=np.concatenate(span_period),
+        spans=np.concatenate(spans).reshape(-1, 2),
+    )
+    Path(f"{out}.json").write_text(json.dumps({"header": header}, sort_keys=True) + "\n")
+    n_rows = sum(p.rows for p in periods)
+    print(f"{len(periods)} periods, {n_rows} rows -> {out}.npz, {out}.json")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/tma2/cli/tlscore.py b/src/tma2/cli/tlscore.py
index 473057d1..451ae6a6 100644
--- a/src/tma2/cli/tlscore.py
+++ b/src/tma2/cli/tlscore.py
@@ -16,6 +16,11 @@ def main(argv: list[str] | None = None) -> int:
     ap.add_argument("--shards", required=True, help="directory of <split>.<shard>.npz/.json")
     ap.add_argument("--plan", required=True)
     ap.add_argument("--out", required=True)
+    ap.add_argument(
+        "--controls-record",
+        help="spec 016 FR-014: the committed validation record of the classical controls; "
+        "required before any learned track is scored",
+    )
     ap.add_argument("--gate", action="store_true", help="exit 3 if a pass/fail check fails")
     a = ap.parse_args(argv)
     from tma2.data.provenance import read_git_provenance
@@ -32,7 +37,9 @@ def main(argv: list[str] | None = None) -> int:
     out = Path(a.out)
     out.mkdir(parents=True, exist_ok=True)
     reading: dict[str, Any] = {}
-    repair = plan.get("runner") == "run3"
+    any_learned = False
+    controls = plan.get("runner") == "run4"
+    repair = plan.get("runner") == "run3" or controls
     study = plan.get("runner") == "run2" or repair
     for split in plan["splits"]:
         files = sorted(
@@ -42,12 +49,14 @@ def main(argv: list[str] | None = None) -> int:
             continue
         periods: list[dict[str, Any]] = []
         headers = []
+        learned_here = False
         arrays: dict[str, list[np.ndarray]] = {}
         for f in files:
             j = json.loads(f.read_text())
             if j["header"]["plan_sha256"] != digest:
                 raise SystemExit(f"{f}: made with another plan ({j['header']['plan_sha256'][:8]})")
             headers.append(j["header"])
+            learned_here = learned_here or bool(j.get("learned"))
             periods += j["periods"]
             z = np.load(f.with_suffix(".npz"))
             for k in z.files:
@@ -59,7 +68,15 @@ def main(argv: list[str] | None = None) -> int:
             from tma2.timelate.study import study_split
             from tma2.timelate.study3 import study_split3
 
-            r2 = (study_split3 if repair else study_split)(arr, periods, plan)
+            if controls:
+                from tma2.timelate.study4 import study_split4
+
+                any_learned = any_learned or learned_here
+                if learned_here:
+                    _require_controls_record(a.controls_record, digest)
+                r2 = study_split4(arr, periods, plan)
+            else:
+                r2 = (study_split3 if repair else study_split)(arr, periods, plan)
             for key in ("dataset_digest", "seed", "search_code_sha256", "git_commit"):
                 r2[key] = sorted({h[key] for h in headers})
             r2["git_dirty"] = any(h["git_dirty"] for h in headers)
@@ -131,11 +148,27 @@ def main(argv: list[str] | None = None) -> int:
         payload: dict[str, Any] = dict(arr)
         np.savez_compressed(out / f"{split}.errors.npz", **payload)
         (out / f"{split}.periods.json").write_text(json.dumps(periods) + "\n")
+    extra_summary: dict[str, Any] = {}
     if repair:
         from tma2.timelate.study3 import soundness3
 
-        sound = soundness3(reading, plan) if "man2" in reading else None
+        if controls:
+            from tma2.timelate.study4 import soundness4
+
+            sound = soundness4(reading, plan) if "man2" in reading else None
+            if "criteria" in plan and any(
+                f"{k}-arc" in r["systems"]
+                for r in reading.values()
+                for k in plan["criteria"]["learned"]
+            ):
+                from tma2.timelate.study4 import criteria4
+
+                extra_summary["criteria"] = criteria4(reading, plan)
+        else:
+            sound = soundness3(reading, plan) if "man2" in reading else None
+        extra_summary["learned_scored"] = any_learned
         summary3 = {
+            **extra_summary,
             "plan": a.plan,
             "plan_sha256": digest,
             "scorer_git_commit": g.commit,
@@ -182,6 +215,34 @@ def main(argv: list[str] | None = None) -> int:
     return 3 if a.gate and failed else 0
 
 
+def _require_controls_record(path: str | None, plan_digest: str) -> None:
+    """Spec 016 FR-014: a learned track is scored only when the classical controls'
+    validation record exists, is committed, was made with this plan and passed its
+    soundness check."""
+    import subprocess
+
+    if not path:
+        raise SystemExit("learned tracks present: --controls-record is required (FR-014)")
+    f = Path(path) / "summary.json"
+    if not f.exists():
+        raise SystemExit(f"{f}: no controls' validation record")
+    rec = json.loads(f.read_text())
+    if rec.get("plan_sha256") != plan_digest:
+        raise SystemExit(f"{f}: made with another plan")
+    if not (rec.get("soundness") or {}).get("pass"):
+        raise SystemExit(f"{f}: the controls' validation did not pass its soundness check")
+    if rec.get("learned_scored"):
+        raise SystemExit(f"{f}: a learned track was scored in the controls' validation")
+    tracked = subprocess.run(
+        ["git", "ls-files", "--error-unmatch", str(f)], capture_output=True, check=False
+    )
+    clean = subprocess.run(
+        ["git", "status", "--porcelain", "--", str(f)], capture_output=True, text=True, check=False
+    )
+    if tracked.returncode != 0 or clean.stdout.strip():
+        raise SystemExit(f"{f}: the controls' validation record is not committed")
+
+
 def _study_report(
     a: argparse.Namespace,
     plan: dict[str, Any],
diff --git a/src/tma2/cli/tlsolve.py b/src/tma2/cli/tlsolve.py
index 2928885b..ed549570 100644
--- a/src/tma2/cli/tlsolve.py
+++ b/src/tma2/cli/tlsolve.py
@@ -34,6 +34,7 @@
     "thin.py",
 )  # spec 014's additions
 REPAIR_FILES = (*STUDY_FILES, "match.py", "run3.py")  # spec 015
+CONTROL_FILES = (*REPAIR_FILES, "corr.py", "hedge.py", "learned.py", "run4.py")  # spec 016
 
 
 def search_code_digest(extra: tuple[str, ...] = ()) -> str:
@@ -55,6 +56,13 @@ def main(argv: list[str] | None = None) -> int:
     ap.add_argument("--count", type=int, required=True)
     ap.add_argument("--out", required=True, help="output prefix")
     ap.add_argument("--thin", help="spec 014: overrides the plan's settings.thinning")
+    ap.add_argument(
+        "--learned",
+        action="append",
+        default=[],
+        metavar="NAME=SHARD",
+        help="a tma2-tlpredict shard (without .npz) scored as NAME and NAME-arc (runner run4)",
+    )
     ap.add_argument("--systems", help="spec 014: overrides the plan's settings.systems")
     a = ap.parse_args(argv)
     from tma2.data.provenance import read_git_provenance
@@ -88,14 +96,40 @@ def main(argv: list[str] | None = None) -> int:
     refs = plan.get("references") or {}
     imm = parse_imm_name(refs["imm"]) if refs.get("imm") else None
     ekf = parse_ekf_name(refs["ekf"]) if refs.get("ekf") else None
+    run4 = plan.get("runner") == "run4"
     run3 = plan.get("runner") == "run3"
     run2 = plan.get("runner") == "run2" or run3
+    if run4 and plan["splits"][a.split].get("systems"):  # a split's own controls
+        plan["settings"]["systems"] = list(plan["splits"][a.split]["systems"])
     if a.thin:
         plan["settings"]["thinning"] = a.thin
     if a.systems:
         plan["settings"]["systems"] = a.systems.split(",")
     extra: dict[str, Any] = {}
-    if run3:
+    if run4:
+        from tma2.timelate.run4 import run_shard4
+
+        priors = {
+            "v3": ManoeuvrePrior.from_ranges(json.loads(Path(plan["prior"]).read_text()), 3),
+            "broad": ManoeuvrePrior.from_ranges(
+                json.loads(Path(plan["prior_broad"]).read_text()), 3
+            ),
+        }
+        shards = dict(x.split("=", 1) for x in a.learned)
+        learned = {k: dict(np.load(f"{v}.npz")) for k, v in shards.items()}
+        arrays, recs = run_shard4(periods, priors, c_params, c2, plan["settings"], learned)
+        extra_learned = {
+            k: json.loads(Path(f"{v}.json").read_text())["header"] for k, v in shards.items()
+        }
+        extra = {
+            "runner": "run4",
+            "prior_kinds": canonical_json(
+                {"v3": [3, plan["prior"]], "broad": [3, sha256_file(plan["prior_broad"])]}
+            ),
+            "thinning": "none",
+            "condition": str(plan["splits"][a.split].get("condition", "clean")),
+        }
+    elif run3:
         from tma2.timelate.run3 import run_shard3
 
         ranges = json.loads(Path(plan["prior"]).read_text())
@@ -140,7 +174,7 @@ def main(argv: list[str] | None = None) -> int:
         ordinal_count=len(periods),
         c_code_sha256=str(c2.get("code_sha256", "")),
         search_code_sha256=search_code_digest(
-            REPAIR_FILES if run3 else (STUDY_FILES if run2 else ())
+            CONTROL_FILES if run4 else (REPAIR_FILES if run3 else (STUDY_FILES if run2 else ()))
         ),
         prior_sha256=sha256_file(plan["prior"]),
         settings=canonical_json(plan["settings"]),
@@ -152,9 +186,10 @@ def main(argv: list[str] | None = None) -> int:
     out.parent.mkdir(parents=True, exist_ok=True)
     payload: dict[str, Any] = dict(arrays)
     np.savez_compressed(f"{out}.npz", **payload)
-    Path(f"{out}.json").write_text(
-        json.dumps({"header": header.model_dump(mode="json"), "periods": recs}) + "\n"
-    )
+    doc: dict[str, Any] = {"header": header.model_dump(mode="json"), "periods": recs}
+    if run4 and a.learned:
+        doc["learned"] = extra_learned
+    Path(f"{out}.json").write_text(json.dumps(doc) + "\n")
     print(f"{len(periods)} periods, {arrays['period'].size} scored rows -> {out}.npz, {out}.json")
     return 0
 
diff --git a/src/tma2/regress/model.py b/src/tma2/regress/model.py
index 7abd2b49..fc5d9ae9 100644
--- a/src/tma2/regress/model.py
+++ b/src/tma2/regress/model.py
@@ -4,11 +4,16 @@
 [offset + j·patch, offset + (j + 1)·patch) and its output is the state at its last row. A causal
 attention mask means a token's output reads rows up to its own last row only. To predict at an
 arbitrary row k, choose offset = (k + 1) mod patch so that k ends a token.
+
+Spec 016 (research R7) reuses it for whole periods: ``causal=False`` drops the attention mask
+(every token reads the whole period; tokens beyond the period's rows are masked out as keys, so
+the output does not depend on the padding), and ``manoeuvre_head`` adds a logit per token that
+a target manoeuvre is in progress. The defaults are spec 012's model, bit for bit.
 """
 
 from __future__ import annotations
 
-from dataclasses import asdict, dataclass
+from dataclasses import asdict, dataclass, fields
 from typing import Any
 
 import torch
@@ -48,8 +53,24 @@ class RegressorConfig:
     held_fraction: float = 0.0
     held_ranges: str = "configs/regress/held-course-ranges.json"
     precision: str = "amp"  # "amp" | "float32"
+    # Spec 016 (PERIOD_FIELDS below): the whole-period model. At these defaults the
+    # configuration is spec 012's and ``to_json`` omits them, so its digests do not move.
+    causal: bool = True
+    manoeuvre_head: bool = False
+    manoeuvre_weight: float = 0.1  # of the logit's cross-entropy beside the state's β-NLL
+    n_features: int = N_FEATURES
+    data: str = "scenarios"  # "scenarios" (spec 012's stream) | "periods" (regress/period_data)
+    mix: str = "configs/regress/tlm-mix.json"  # the period stream's mix
+    monitor_start: int = 1 << 40  # first stream index of the period monitor
 
     def __post_init__(self) -> None:
+        if self.data not in ("scenarios", "periods"):
+            raise ValueError(f"data {self.data!r}")
+        if self.n_features != N_FEATURES + (2 if self.data == "periods" else 0):
+            raise ValueError("n_features: 7 for scenarios, 9 for periods (time and length)")
+        if self.data == "periods" and self.val_seed != self.stream_seed:
+            # the period monitor is a slice of the training stream: no other seed is read
+            raise ValueError("data periods: val_seed must equal stream_seed")
         if self.inputs not in ("full", "ownship_only"):
             raise ValueError(f"inputs {self.inputs!r}")
         if not 0.0 <= self.held_fraction <= 1.0:
@@ -58,7 +79,26 @@ def __post_init__(self) -> None:
             raise ValueError("max_rows must be a multiple of patch_rows")
 
     def to_json(self) -> dict[str, Any]:
-        return asdict(self)
+        """Every spec 012 field, and a spec 016 field only where it differs from its default
+        (so a spec 012 configuration's JSON, and the digest over it, are what they were)."""
+        d = asdict(self)
+        for f in fields(self):
+            if f.name in PERIOD_FIELDS and d[f.name] == f.default:
+                del d[f.name]
+        return d
+
+
+PERIOD_FIELDS = (
+    "causal",
+    "manoeuvre_head",
+    "manoeuvre_weight",
+    "n_features",
+    "data",
+    "mix",
+    "monitor_start",
+)
+"""Spec 016's additions to the configuration (to enter ``schema/tma.yaml`` with that spec's
+schema change)."""
 
 
 class Regressor(nn.Module):
@@ -67,7 +107,7 @@ def __init__(self, cfg: RegressorConfig) -> None:
         self.cfg = cfg
         self.patch = cfg.patch_rows
         self.tokens = cfg.max_rows // cfg.patch_rows
-        self.embed = nn.Linear(cfg.patch_rows * N_FEATURES, cfg.width)
+        self.embed = nn.Linear(cfg.patch_rows * cfg.n_features, cfg.width)
         self.pos = nn.Parameter(torch.zeros(self.tokens, cfg.width))
         layer = nn.TransformerEncoderLayer(
             cfg.width,
@@ -84,23 +124,51 @@ def __init__(self, cfg: RegressorConfig) -> None:
         nn.init.normal_(self.pos, std=0.02)
         nn.init.zeros_(self.head.weight)
         nn.init.zeros_(self.head.bias)
+        if cfg.manoeuvre_head:
+            self.man_head = nn.Linear(cfg.width, 1)
+            nn.init.zeros_(self.man_head.weight)
+            nn.init.zeros_(self.man_head.bias)
         # float32 whatever the process default (the physics layer's code sets double)
         self.float()
 
-    def forward(self, f: Tensor, offset: int = 0) -> tuple[Tensor, Tensor]:
-        """f [B, R, 7] → (mean [B, T, 3], log-variance [B, T, 3]); token j ends at row
-        offset + (j + 1)·patch − 1. Rows before ``offset`` are dropped; the tail is zero-padded."""
+    def encode(self, f: Tensor, offset: int = 0, mask: Tensor | None = None) -> Tensor:
+        """f [B, R, F] → hidden [B, T, width]. ``mask`` [B, R]: rows that exist; read only by
+        the non-causal model, where a token whose first row does not exist is no key."""
         b, r, nf = f.shape
         if offset:
             f = torch.cat([f[:, offset:], f.new_zeros(b, offset, nf)], dim=1)
         x = self.embed(f.reshape(b, r // self.patch, self.patch * nf))
         t = x.shape[1]
         x = x + self.pos[:t]
-        causal = torch.triu(torch.ones(t, t, dtype=torch.bool, device=f.device), diagonal=1)
-        h = self.norm(self.encoder(x, mask=causal))
-        out = self.head(h)
+        if self.cfg.causal:
+            causal = torch.triu(torch.ones(t, t, dtype=torch.bool, device=f.device), diagonal=1)
+            h = self.encoder(x, mask=causal)
+        elif mask is None:
+            h = self.encoder(x)
+        else:
+            if offset:
+                mask = torch.cat([mask[:, offset:], mask.new_zeros(b, offset)], dim=1)
+            h = self.encoder(x, src_key_padding_mask=~mask[:, :: self.patch])
+        out: Tensor = self.norm(h)
+        return out
+
+    def forward(
+        self, f: Tensor, offset: int = 0, mask: Tensor | None = None
+    ) -> tuple[Tensor, Tensor]:
+        """f [B, R, F] → (mean [B, T, 3], log-variance [B, T, 3]); token j ends at row
+        offset + (j + 1)·patch − 1. Rows before ``offset`` are dropped; the tail is zero-padded."""
+        out = self.head(self.encode(f, offset, mask))
         return out[..., :N_TARGETS], out[..., N_TARGETS:].clamp(-12.0, 8.0)
 
+    def forward_all(
+        self, f: Tensor, offset: int = 0, mask: Tensor | None = None
+    ) -> tuple[Tensor, Tensor, Tensor]:
+        """As ``forward``, with the manoeuvre logit [B, T] (``manoeuvre_head`` models only)."""
+        h = self.encode(f, offset, mask)
+        out = self.head(h)
+        logit = self.man_head(h)[..., 0]
+        return out[..., :N_TARGETS], out[..., N_TARGETS:].clamp(-12.0, 8.0), logit
+
 
 def beta_nll(mean: Tensor, logvar: Tensor, y: Tensor, w: Tensor, beta: float) -> Tensor:
     """β-weighted Gaussian negative log-likelihood over supervised tokens (w [B, T] in {0, 1})."""
@@ -109,3 +177,9 @@ def beta_nll(mean: Tensor, logvar: Tensor, y: Tensor, w: Tensor, beta: float) ->
     if beta > 0:
         nll = nll * var.detach() ** beta
     return (nll.sum(dim=-1) * w).sum() / w.sum().clamp_min(1.0)
+
+
+def manoeuvre_bce(logit: Tensor, label: Tensor, w: Tensor) -> Tensor:
+    """Binary cross-entropy of the manoeuvre logit over tokens that exist (w [B, T])."""
+    bce = nn.functional.binary_cross_entropy_with_logits(logit, label, reduction="none")
+    return (bce * w).sum() / w.sum().clamp_min(1.0)
diff --git a/src/tma2/regress/period_data.py b/src/tma2/regress/period_data.py
new file mode 100644
index 00000000..d8d48254
--- /dev/null
+++ b/src/tma2/regress/period_data.py
@@ -0,0 +1,419 @@
+"""The on-the-fly stream of periods (spec 016 research R9): engagements from a seed, each cut to
+a period as Gate T cuts them, never stored.
+
+Update ``u`` of a configuration reads engagement indices ``stream_indices(cfg, u)`` of the
+stream seed. Each engagement draws its family (Annex B, randomised statistics, quiet) and its
+bearing-error overlay (none, correlated, outliers, both) from its own sub-stream, seeded from
+the engagement's seed, so the generator's draws are untouched and a batch is a pure function of
+(configuration, mix, update). Every draw is made for every engagement in one fixed order,
+whichever family or overlay it selects. Ranges are per engagement (no grouping): the kernel
+reads one flat ranges row per engagement. The overlay is spec 010's
+(``sim.mismatch.apply_mismatch``, seeded as ``sim.generate.run_kernel`` seeds it); bias and the
+sigma factor are never set.
+
+Per 5-second row of a period: spec 012's canonical-frame features (rotated to the period's
+first valid bearing; ownship motion as displacements, so the period's start is the origin), the
+time from the period's start ÷ 7,200 s and the period's length ÷ 7,200 s. Targets: spec 012's
+(ln(range ÷ 10 km), velocity ÷ 10 m/s in the frame) and whether a target manoeuvre is in
+progress at the row (``Period.spans``: rows start_index … end_index).
+"""
+
+from __future__ import annotations
+
+import json
+import math
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+from numba import njit, prange, uint64
+
+from tma2.sim import mismatch as mm
+from tma2.sim import ranges as rg
+from tma2.sim import scenario as sc
+from tma2.sim.prng import create_rng, derive_seed, mix64, uniform01, uniform_int
+
+from .data import stream_indices
+from .frame import N_FEATURES, features, targets
+from .model import RegressorConfig
+
+if TYPE_CHECKING:
+    from tma2.timelate.periods import Period
+
+# As ``tma2.timelate.periods`` (CUT_STREAM, MIN_SECONDS). That module is not imported here: it
+# pulls in the scorers and their dependencies, which the GPU image does not carry. ``_cuts``
+# repeats its ``cut``; tests/unit/test_period_model.py holds the two equal.
+CUT_STREAM = np.uint64(0x7D3A_91C4_55E0_13B7)
+MIN_SECONDS = 1200.0
+MIX_STREAM = np.uint64(0x5C1E_A07B_93D2_46F1)  # distinguishes the mix's sub-stream
+T_UNIT = 7200.0  # s
+N_PERIOD_FEATURES = N_FEATURES + 2
+FAMILIES = ("annex_b", "randomised", "quiet")
+OVERLAYS = ("none", "correlated", "outliers", "both")
+
+# The sub-stream's draws, in order.
+U_FAMILY, U_LEG, U_ANGLE, U_RATE, U_DV, U_ACC, U_COURSE, U_SPEED = range(8)
+U_OVERLAY, U_KIND, U_TAU, U_SHARE, U_OUT_RATE, U_OUT_SPREAD = range(8, 14)
+N_DRAWS = 14
+
+
+@dataclass
+class PeriodTensors:
+    f: np.ndarray  # [B, R, 9] float32
+    y: np.ndarray  # [B, R, 3] float32
+    mask: np.ndarray  # [B, R] rows of the period
+    man: np.ndarray  # [B, R] a target manoeuvre is in progress
+    index: np.ndarray  # [B] engagement index in the stream
+    first: np.ndarray  # [B] the period's first row in its engagement
+    rows: np.ndarray  # [B]
+    family: np.ndarray  # [B] index into FAMILIES
+    overlay: np.ndarray  # [B] index into OVERLAYS
+    condition: np.ndarray  # [B, M_LEN] the overlay's parameters (NaN: kind off)
+
+
+FIELDS = ("f", "y", "mask", "man", "index", "first", "rows", "family", "overlay", "condition")
+
+
+@dataclass(frozen=True)
+class Mix:
+    """A mix file with its ranges loaded and flattened."""
+
+    raw: dict[str, Any]
+    base: dict[str, Any]
+    R_base: np.ndarray
+    R_quiet: np.ndarray
+    rmax: int
+    dt: float
+
+
+def _resolve(path: str, mix_path: Path) -> Path:
+    """A path named in a mix file: as given, else from the repository the mix file is in."""
+    p = Path(path)
+    return p if p.exists() else mix_path.resolve().parents[2] / path
+
+
+def load_mix(path: str) -> Mix:
+    raw: dict[str, Any] = json.loads(Path(path).read_text())
+    base = json.loads(_resolve(raw["base_ranges"], Path(path)).read_text())
+    quiet = json.loads(_resolve(raw["quiet_ranges"], Path(path)).read_text())
+    if rg.rows_for(base) != rg.rows_for(quiet) or base["dt_s"] != quiet["dt_s"]:
+        raise ValueError("the mix's ranges must share rows and dt")
+    fam = raw["families"]
+    if set(fam) != set(FAMILIES) or abs(sum(fam.values()) - 1.0) > 1e-9:
+        raise ValueError("families must be annex_b, randomised and quiet, summing to 1")
+    kinds = raw["overlay"]["kinds"]
+    if set(kinds) != set(OVERLAYS[1:]) or abs(sum(kinds.values()) - 1.0) > 1e-9:
+        raise ValueError("overlay kinds must be correlated, outliers and both, summing to 1")
+    if raw["overlay"]["corr_share"]["max"] >= 1.0 or raw["overlay"]["outlier_rate"]["max"] >= 1.0:
+        raise ValueError("corr_share and outlier_rate must stay below 1")
+    mix = Mix(
+        raw,
+        base,
+        rg.flatten_ranges(base),
+        rg.flatten_ranges(quiet),
+        rg.rows_for(base),
+        base["dt_s"],
+    )
+    for u in (0.0, 1.0):  # the family's corners must be ranges the generator accepts
+        rg.validate_ranges(randomised_ranges(mix, np.full(N_DRAWS, u)))
+    return mix
+
+
+def _between(lim: dict[str, float], u: float) -> float:
+    return float(lim["min"]) + (float(lim["max"]) - float(lim["min"])) * u
+
+
+def randomised_ranges(mix: Mix, u: np.ndarray) -> dict[str, Any]:
+    """The randomised-statistics family's ranges for one engagement's draws ``u``: the base
+    ranges with their own leg mean (sd a fixed share of it), the turn-angle range and the
+    maxima of turn rate, speed change and acceleration each scaled by its own factor, and
+    perturbed kind weights."""
+    p, base = mix.raw["randomised"], mix.base
+    mean = _between(p["leg_mean_s"], float(u[U_LEG]))
+
+    def scaled(key: str, draw: int, both: bool = False, cap: float = math.inf) -> dict[str, float]:
+        """The base range with its maximum (``both``: and its minimum) times the factor."""
+        k = _between(p["scale"], float(u[draw]))
+        lo = float(base[key]["min"]) * (k if both else 1.0)
+        return {"min": lo, "max": max(min(float(base[key]["max"]) * k, cap), lo)}
+
+    course = _between(p["course_weight"], float(u[U_COURSE]))
+    speed = (1.0 - course) * _between(p["speed_share_of_rest"], float(u[U_SPEED]))
+    return {
+        **base,
+        "target_leg_length_s": {
+            "mean": mean,
+            "sd": float(p["leg_sd_over_mean"]) * mean,
+            "min": float(p["leg_min_s"]),
+        },
+        "target_manoeuvre_angle_abs_rad": scaled(
+            "target_manoeuvre_angle_abs_rad", U_ANGLE, True, math.pi
+        ),
+        "target_turn_rate_rad_s": scaled("target_turn_rate_rad_s", U_RATE),
+        "target_speed_change_abs_mps": scaled("target_speed_change_abs_mps", U_DV),
+        "target_speed_change_rate_mps2": scaled("target_speed_change_rate_mps2", U_ACC),
+        "target_change_kind_weights": {
+            "course": course,
+            "speed": speed,
+            "both": 1.0 - course - speed,
+        },
+    }
+
+
+@njit(cache=True)
+def _draws(stream_seed: uint64, indices: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """([B] engagement seeds, [B, N_DRAWS] uniforms of each engagement's mix sub-stream)."""
+    n = indices.shape[0]
+    seeds = np.empty(n, dtype=np.uint64)
+    u = np.empty((n, N_DRAWS))
+    for b in range(n):
+        seeds[b] = derive_seed(stream_seed, indices[b])
+        s = create_rng(mix64(seeds[b] ^ MIX_STREAM))
+        for j in range(N_DRAWS):
+            u[b, j] = uniform01(s)
+    return seeds, u
+
+
+@njit(cache=True)
+def _cuts(seeds: np.ndarray, samples: np.ndarray, lo: int) -> tuple[np.ndarray, np.ndarray]:
+    """(first row [B], rows [B]) of each engagement's period: ``timelate.periods.cut``."""
+    n = seeds.shape[0]
+    first = np.zeros(n, dtype=np.int64)
+    rows = np.empty(n, dtype=np.int64)
+    for b in range(n):
+        if samples[b] <= lo:
+            rows[b] = samples[b]
+            continue
+        s = create_rng(mix64(seeds[b] ^ CUT_STREAM))
+        rows[b] = lo + uniform_int(s, samples[b] - lo + 1)
+        first[b] = uniform_int(s, samples[b] - rows[b] + 1)
+    return first, rows
+
+
+def assign(mix: Mix, u: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """From the draws [B, N_DRAWS]: (family [B], overlay [B], ranges rows [B, R_LEN], overlay
+    conditions [B, M_LEN])."""
+    n = u.shape[0]
+    fam = mix.raw["families"]
+    family = np.where(
+        u[:, U_FAMILY] < fam["annex_b"],
+        0,
+        np.where(u[:, U_FAMILY] < fam["annex_b"] + fam["randomised"], 1, 2),
+    ).astype(np.int8)
+    R = np.where((family == 2)[:, None], mix.R_quiet[None, :], mix.R_base[None, :])
+    for b in np.flatnonzero(family == 1):
+        R[b] = rg.flatten_ranges(randomised_ranges(mix, u[b]))
+    ov = mix.raw["overlay"]
+    k = u[:, U_KIND]
+    kind = np.where(
+        k < ov["kinds"]["correlated"],
+        1,
+        np.where(k < ov["kinds"]["correlated"] + ov["kinds"]["outliers"], 2, 3),
+    )
+    overlay = np.where(u[:, U_OVERLAY] < ov["fraction"], kind, 0).astype(np.int8)
+    M = np.full((n, mm.M_LEN), np.nan)
+    corr = (overlay == 1) | (overlay == 3)
+    out = (overlay == 2) | (overlay == 3)
+    for col, key, draw, on in (
+        (mm.M_CORR_TAU, "corr_tau_s", U_TAU, corr),
+        (mm.M_CORR_SHARE, "corr_share", U_SHARE, corr),
+        (mm.M_OUT_RATE, "outlier_rate", U_OUT_RATE, out),
+        (mm.M_OUT_SPREAD, "outlier_spread", U_OUT_SPREAD, out),
+    ):
+        lo, hi = float(ov[key]["min"]), float(ov[key]["max"])
+        M[on, col] = lo + (hi - lo) * u[on, draw]
+    return family, overlay, R, M
+
+
+@njit(parallel=True, cache=True)
+def _fill(
+    stream_seed: uint64,
+    indices: np.ndarray,
+    R: np.ndarray,
+    M: np.ndarray,
+    rmax: int,
+    samples: np.ndarray,
+    sigma: np.ndarray,
+    redraws: np.ndarray,
+    own: np.ndarray,
+    tgt: np.ndarray,
+    bearing: np.ndarray,
+    valid: np.ndarray,
+    man: np.ndarray,
+) -> None:
+    """Whole engagements: the kernel with each engagement's own ranges row, then the overlay
+    where the engagement has one. ``man``: rows inside a target manoeuvre (start … end)."""
+    for b in prange(indices.shape[0]):
+        own_b = np.zeros((rmax, 4))
+        tgt_b = np.zeros((rmax, 4))
+        bearing_b = np.zeros(rmax)
+        valid_b = np.zeros(rmax, dtype=np.bool_)
+        clean = np.zeros(rmax)
+        white = np.zeros(rmax)
+        P = np.full(sc.P_LEN, np.nan)
+        tm = np.zeros((sc.MAX_TM, sc.TM_COLS))
+        TI = np.full(sc.TI_LEN, -1, dtype=np.int64)
+        sp = np.zeros((sc.MAX_TM, 3), dtype=np.int64)
+        stats = np.zeros(3)
+        redraws[b] = sc.generate_parts_into(
+            stream_seed,
+            indices[b],
+            R[b],
+            rmax,
+            own_b,
+            tgt_b,
+            bearing_b,
+            valid_b,
+            P,
+            tm,
+            TI,
+            sp,
+            stats,
+            clean,
+            white,
+        )
+        if redraws[b] < 0:
+            continue
+        n_s = TI[sc.TI_SAMPLES]
+        if not np.all(np.isnan(M[b])):
+            eng = np.zeros(mm.E_LEN)
+            comp = np.zeros((rmax, 4))
+            outlier = np.zeros(rmax, dtype=np.bool_)
+            mm.apply_mismatch(
+                derive_seed(stream_seed, indices[b]),
+                M[b],
+                n_s,
+                P[sc.P_DT],
+                P[sc.P_SIGMA],
+                clean,
+                white,
+                valid_b,
+                bearing_b,
+                eng,
+                comp,
+                outlier,
+            )
+        samples[b] = n_s
+        sigma[b] = P[sc.P_SIGMA]
+        for i in range(rmax):
+            own[b, i, 0] = own_b[i, 0]
+            own[b, i, 1] = own_b[i, 1]
+            for c in range(4):
+                tgt[b, i, c] = tgt_b[i, c]
+            bearing[b, i] = bearing_b[i]
+            valid[b, i] = valid_b[i]
+        for j in range(max(TI[sc.TI_N_SPANS], 0)):
+            for i in range(max(sp[j, 1], 0), min(sp[j, 2] + 1, n_s)):
+                man[b, i] = True
+
+
+def period_features(
+    own: np.ndarray,
+    bearing: np.ndarray,
+    valid: np.ndarray,
+    mask: np.ndarray,
+    sigma: np.ndarray,
+    rows: np.ndarray,
+    dt: float,
+) -> tuple[np.ndarray, np.ndarray]:
+    """([B, R, 9] features, [B] frame rotation) of periods laid out from row 0: ``own``
+    [B, R, 2], ``bearing``, ``valid``, ``mask`` [B, R], ``sigma`` and ``rows`` [B]. The one
+    place the model's inputs are built: the training stream and ``tma2-tlpredict`` both call
+    it."""
+    b, r = mask.shape
+    t = np.tile(np.arange(r, dtype=np.float64) * dt, (b, 1))
+    f7, th = features(t, own, bearing, valid, mask, sigma)
+    f = np.zeros((b, r, N_PERIOD_FEATURES), dtype=np.float32)
+    f[..., :N_FEATURES] = f7
+    f[..., N_FEATURES] = t / T_UNIT * mask
+    f[..., N_FEATURES + 1] = (rows.astype(np.float64) * dt / T_UNIT)[:, None] * mask
+    return f, th
+
+
+def inputs_of(periods: list[Period], max_rows: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """(features [B, max_rows, 9], mask [B, max_rows], frame rotation [B]) of stored periods:
+    bearings, ownship and the stated sigma only (nothing of the target)."""
+    n = len(periods)
+    own = np.zeros((n, max_rows, 2))
+    bearing = np.zeros((n, max_rows))
+    valid = np.zeros((n, max_rows), dtype=bool)
+    mask = np.zeros((n, max_rows), dtype=bool)
+    for i, p in enumerate(periods):
+        if p.rows > max_rows:
+            raise ValueError(f"period {p.ordinal}: {p.rows} rows, the model reads {max_rows}")
+        a, b = p.first, p.first + p.rows
+        own[i, : p.rows] = p.own[a:b, 1:3]
+        bearing[i, : p.rows] = p.obs[a:b, 1]
+        valid[i, : p.rows] = p.obs[a:b, 2] != 0
+        mask[i, : p.rows] = True
+    sigma = np.array([float(p.meta["parameters"]["noise_sigma_rad"]) for p in periods])
+    rows = np.array([p.rows for p in periods], dtype=np.int64)
+    dts = {p.dt for p in periods}
+    if len(dts) > 1:
+        raise ValueError("periods of one batch must share dt")
+    f, th = period_features(own, bearing, valid, mask, sigma, rows, dts.pop() if dts else 5.0)
+    return f, mask, th
+
+
+def periods_at(cfg: RegressorConfig, mix: Mix, indices: np.ndarray) -> PeriodTensors:
+    """The periods of the given engagement indices of the stream."""
+    if mix.rmax > cfg.max_rows:
+        raise ValueError(f"an engagement has up to {mix.rmax} rows; max_rows is {cfg.max_rows}")
+    idx = np.asarray(indices, dtype=np.int64)
+    n, rmax = idx.shape[0], mix.rmax
+    seed = np.uint64(cfg.stream_seed)
+    seeds, u = _draws(seed, idx)
+    family, overlay, R, M = assign(mix, u)
+    samples = np.zeros(n, dtype=np.int64)
+    sigma = np.zeros(n)
+    redraws = np.zeros(n, dtype=np.int64)
+    own = np.zeros((n, rmax, 2))
+    tgt = np.zeros((n, rmax, 4))
+    bearing = np.zeros((n, rmax))
+    valid = np.zeros((n, rmax), dtype=np.bool_)
+    man = np.zeros((n, rmax), dtype=np.bool_)
+    _fill(seed, idx, R, M, rmax, samples, sigma, redraws, own, tgt, bearing, valid, man)
+    bad = np.flatnonzero(redraws < 0)
+    if bad.size:
+        raise ValueError(
+            f"engagement {int(idx[bad[0]])}: no valid geometry after {sc.MAX_REDRAWS} redraws"
+        )
+    first, rows = _cuts(seeds, samples, int(round(MIN_SECONDS / mix.dt)))
+    r = cfg.max_rows
+    k = np.arange(r)[None, :]
+    mask = k < rows[:, None]
+    src = np.minimum(first[:, None] + k, rmax - 1)
+    take = np.arange(n)[:, None]
+    own_p, tgt_p = own[take, src], tgt[take, src]
+    f, th = period_features(own_p, bearing[take, src], valid[take, src], mask, sigma, rows, mix.dt)
+    y = targets(own_p, tgt_p, th) * mask[..., None]
+    return PeriodTensors(f, y, mask, man[take, src] & mask, idx, first, rows, family, overlay, M)
+
+
+def batch_at(cfg: RegressorConfig, mix: Mix, update: int) -> PeriodTensors:
+    """Update ``update``'s batch of periods (a pure function of the configuration and mix)."""
+    return periods_at(cfg, mix, stream_indices(cfg, update))
+
+
+def monitor(cfg: RegressorConfig, mix: Mix) -> PeriodTensors:
+    """The training monitor: ``val_count`` periods of the SAME stream seed, at indices from
+    ``monitor_start`` (2^40 by default; training reads from ``stream_start``, far below).
+    Never a validation or test seed."""
+    return periods_at(cfg, mix, cfg.monitor_start + np.arange(cfg.val_count, dtype=np.int64))
+
+
+def token_targets(
+    x: PeriodTensors, patch: int
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Per token of ``patch`` rows from the period's start: (state target [B, T, 3] at the
+    token's last existing row, manoeuvre label [B, T]: any of its rows inside a manoeuvre,
+    exists [B, T]: its first row exists, last existing row [B, T])."""
+    b, r = x.mask.shape
+    t = r // patch
+    start = np.arange(t) * patch
+    exists = x.mask[:, start]
+    last = np.minimum(start[None, :] + patch - 1, np.maximum(x.rows[:, None] - 1, 0))
+    y = x.y[np.arange(b)[:, None], last] * exists[..., None]
+    label = x.man.reshape(b, t, patch).any(axis=2)
+    return y, label.astype(np.float32), exists.astype(np.float32), last
diff --git a/src/tma2/regress/period_predict.py b/src/tma2/regress/period_predict.py
new file mode 100644
index 00000000..4847295a
--- /dev/null
+++ b/src/tma2/regress/period_predict.py
@@ -0,0 +1,112 @@
+"""The whole-period model on stored periods (spec 016 FR-009, FR-015): its per-token output,
+on CPU, and its ONNX export with the check that the exported model agrees with the trained one.
+
+Float32 throughout (no double anywhere in the graph, so the attention and GELU kernels that
+onnxruntime lacks in double do not arise). The exported graph takes the features and the row
+mask of periods padded to the model's ``max_rows``, with a dynamic batch.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import numpy as np
+import torch
+from torch import Tensor, nn
+
+from .model import Regressor
+from .period_data import inputs_of
+
+if TYPE_CHECKING:
+    from tma2.timelate.periods import Period
+
+OPSET = 17
+INPUTS = ("features", "mask")
+OUTPUTS = ("mean", "logvar", "logit")
+
+
+@dataclass
+class PeriodOutput:
+    mean: np.ndarray  # [T, 3] at each token's last existing row (frame of regress.frame)
+    logvar: np.ndarray  # [T, 3]
+    prob: np.ndarray  # [T] probability that a target manoeuvre is in progress
+
+
+class PeriodExport(nn.Module):
+    """The model as exported: (features [B, R, F], mask [B, R]) → mean, log-variance, logit."""
+
+    def __init__(self, model: Regressor) -> None:
+        super().__init__()
+        self.model = model
+
+    def forward(self, f: Tensor, mask: Tensor) -> tuple[Tensor, Tensor, Tensor]:
+        return self.model.forward_all(f, 0, mask)
+
+
+def run_model(model: Regressor, f: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, ...]:
+    """(mean, logvar, logit) of the trained model on CPU in float32."""
+    model.eval()
+    with torch.no_grad():
+        out = model.forward_all(torch.from_numpy(f), 0, torch.from_numpy(mask))
+    return tuple(o.float().numpy() for o in out)
+
+
+def predict_periods(model: Regressor, periods: list[Period], batch: int = 16) -> list[PeriodOutput]:
+    """The model's output for each period's own tokens (bearings, ownship and the stated sigma
+    are all it reads)."""
+    patch = model.patch
+    res: list[PeriodOutput] = []
+    for s in range(0, len(periods), batch):
+        part = periods[s : s + batch]
+        f, mask, _th = inputs_of(part, model.cfg.max_rows)
+        mean, logvar, logit = run_model(model, f, mask)
+        for i, p in enumerate(part):
+            t = -(-p.rows // patch)
+            prob = 1.0 / (1.0 + np.exp(-logit[i, :t].astype(np.float64)))
+            res.append(PeriodOutput(mean[i, :t], logvar[i, :t], prob))
+    return res
+
+
+def export_onnx(model: Regressor, path: Path) -> None:
+    """Write the model as ONNX (legacy exporter, opset 17, dynamic batch, fixed rows)."""
+    cfg = model.cfg
+    wrapper = PeriodExport(model).eval()
+    f = torch.zeros(2, cfg.max_rows, cfg.n_features, dtype=torch.float32)
+    mask = torch.zeros(2, cfg.max_rows, dtype=torch.bool)
+    mask[0, : cfg.max_rows // 2] = True
+    mask[1] = True
+    prev = torch.get_default_dtype()
+    torch.set_default_dtype(torch.float32)  # the exporter's scalar constants follow the default
+    try:
+        # gradients stay enabled: under no_grad the encoder takes its fused fast path, one
+        # operator the exporter does not know
+        torch.onnx.export(
+            wrapper,
+            (f, mask),
+            str(path),
+            input_names=list(INPUTS),
+            output_names=list(OUTPUTS),
+            dynamic_axes={name: {0: "batch"} for name in (*INPUTS, *OUTPUTS)},
+            opset_version=OPSET,
+            dynamo=False,
+        )
+    finally:
+        torch.set_default_dtype(prev)
+
+
+def run_onnx(path: Path, f: np.ndarray, mask: np.ndarray) -> tuple[np.ndarray, ...]:
+    import onnxruntime as ort  # type: ignore[import-untyped]
+
+    sess = ort.InferenceSession(str(path), providers=["CPUExecutionProvider"])
+    return tuple(sess.run(list(OUTPUTS), {"features": f, "mask": mask}))
+
+
+def export_check(model: Regressor, path: Path, f: np.ndarray, mask: np.ndarray) -> float:
+    """Export, run both on (f, mask) and return the largest absolute difference over the
+    tokens that exist (a padded token's output is not a statement)."""
+    export_onnx(model, path)
+    ref, got = run_model(model, f, mask), run_onnx(path, f, mask)
+    exists = mask[:, :: model.patch]
+    return max(float(np.abs(a[exists] - b[exists]).max()) for a, b in zip(ref, got, strict=True))
diff --git a/src/tma2/regress/train.py b/src/tma2/regress/train.py
index 185f6886..7d90c9af 100644
--- a/src/tma2/regress/train.py
+++ b/src/tma2/regress/train.py
@@ -1,5 +1,9 @@
 """Training loop for the regressor (spec 012 research R3): on-the-fly data, a time limit,
-checkpoints and resume. One process trains one segment; ``run.json`` accumulates segments."""
+checkpoints and resume. One process trains one segment; ``run.json`` accumulates segments.
+
+With ``data: "periods"`` (spec 016 research R7, R9) the stream is ``period_data``'s, the loss is
+the state's β-NLL plus ``manoeuvre_weight`` × the manoeuvre logit's cross-entropy over tokens
+that exist, and the monitor is a slice of the same stream seed far from the training indices."""
 
 from __future__ import annotations
 
@@ -15,8 +19,9 @@
 
 from tma2.data.provenance import read_git_provenance
 
+from . import period_data as pdata
 from .data import Tensors, batch_at, load_ranges, supervised, validation
-from .model import Regressor, RegressorConfig, beta_nll
+from .model import Regressor, RegressorConfig, beta_nll, manoeuvre_bce
 
 
 def config_digest(cfg: RegressorConfig) -> str:
@@ -70,6 +75,54 @@ def evaluate(model: Regressor, val: Tensors, device: torch.device, amp: bool) ->
     }
 
 
+def period_loss(
+    model: Regressor, x: pdata.PeriodTensors, device: torch.device, amp: bool
+) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
+    """(loss, parts) on a batch of periods. Tokens start at the period's first row (no folding
+    offset: the model reads the whole period); parts: the two loss terms, the predicted mean,
+    the target and the tokens that exist."""
+    cfg = model.cfg
+    y_np, label_np, w_np, _last = pdata.token_targets(x, model.patch)
+    f = torch.from_numpy(x.f).to(device)
+    mask = torch.from_numpy(x.mask).to(device)
+    y, w = torch.from_numpy(y_np).to(device), torch.from_numpy(w_np).to(device)
+    with torch.autocast(device.type, enabled=amp):
+        if cfg.manoeuvre_head:
+            mean, logvar, logit = model.forward_all(f, 0, mask)
+        else:
+            mean, logvar = model(f, 0, mask)
+    mean, logvar = mean.float(), logvar.float()
+    state = beta_nll(mean, logvar, y, w, cfg.beta)
+    parts = {"state": state, "mean": mean, "y": y, "w": w}
+    if not cfg.manoeuvre_head:
+        return state, parts
+    parts["manoeuvre"] = manoeuvre_bce(logit.float(), torch.from_numpy(label_np).to(device), w)
+    return state + cfg.manoeuvre_weight * parts["manoeuvre"], parts
+
+
+def evaluate_periods(
+    model: Regressor, val: pdata.PeriodTensors, device: torch.device, amp: bool
+) -> dict[str, float]:
+    """Both loss terms and the median absolute error of ln range over the monitor's tokens."""
+    model.eval()
+    n = val.f.shape[0]
+    tot = {"loss": 0.0, "state_loss": 0.0, "manoeuvre_loss": 0.0}
+    e_r = []
+    with torch.no_grad():
+        for s in range(0, n, 128):
+            part = pdata.PeriodTensors(*(getattr(val, k)[s : s + 128] for k in pdata.FIELDS))
+            loss, parts = period_loss(model, part, device, amp)
+            k = part.f.shape[0]
+            tot["loss"] += float(loss) * k
+            tot["state_loss"] += float(parts["state"]) * k
+            tot["manoeuvre_loss"] += float(parts.get("manoeuvre", torch.zeros(()))) * k
+            e_r.append((parts["mean"] - parts["y"])[..., 0][parts["w"] > 0].abs().cpu())
+    model.train()
+    out = {k: v / n for k, v in tot.items()}
+    out["ln_range_abs_median"] = float(torch.cat(e_r).median()) if e_r else math.nan
+    return out
+
+
 def train(
     cfg: RegressorConfig,
     out: Path,
@@ -104,9 +157,14 @@ def train(
         scaler.load_state_dict(st["scaler"])
         update = int(st["update"])
         record = json.loads((resume / "run.json").read_text())
-    ranges = load_ranges(cfg.ranges)
-    held = load_ranges(cfg.held_ranges) if cfg.held_fraction > 0 else None
-    val = validation(cfg, ranges)
+    periods = cfg.data == "periods"
+    if periods:
+        mix = pdata.load_mix(cfg.mix)
+        monitor = pdata.monitor(cfg, mix)
+    else:
+        ranges = load_ranges(cfg.ranges)
+        held = load_ranges(cfg.held_ranges) if cfg.held_fraction > 0 else None
+        val = validation(cfg, ranges)
     out.mkdir(parents=True, exist_ok=True)
     start_update = update
     seg: dict[str, Any] = {"start_update": update, "started": time.time(), "git_commit": commit}
@@ -159,9 +217,12 @@ def save(stopped: str) -> None:
             break
         for g in opt.param_groups:
             g["lr"] = lr_at(cfg, update)
-        x = batch_at(cfg, ranges, update, held)
-        offset = int(rng.integers(0, cfg.patch_rows))
-        loss, _, _, _ = _loss(model, x, offset, device, amp)
+        if periods:
+            loss, _ = period_loss(model, pdata.batch_at(cfg, mix, update), device, amp)
+        else:
+            x = batch_at(cfg, ranges, update, held)
+            offset = int(rng.integers(0, cfg.patch_rows))
+            loss, _, _, _ = _loss(model, x, offset, device, amp)
         opt.zero_grad(set_to_none=True)
         scaler.scale(loss).backward()  # type: ignore[no-untyped-call]
         scaler.unscale_(opt)
@@ -170,7 +231,11 @@ def save(stopped: str) -> None:
         scaler.update()
         update += 1
         if update % cfg.val_every_updates == 0 or update == cfg.total_updates:
-            v = evaluate(model, val, device, amp)
+            v = (
+                evaluate_periods(model, monitor, device, amp)
+                if periods
+                else evaluate(model, val, device, amp)
+            )
             v.update(update=update, seconds=time.time() - t0, train_loss=float(loss.detach()))
             record["validation"].append(v)
             print(json.dumps(v), flush=True)
diff --git a/src/tma2/timelate/corr.py b/src/tma2/timelate/corr.py
new file mode 100644
index 00000000..7ca82f5b
--- /dev/null
+++ b/src/tma2/timelate/corr.py
@@ -0,0 +1,94 @@
+"""Bearing errors that are not independent or not Gaussian, handled before the search
+(spec 016, research R3 and R4). Each function returns a ``Problem`` the fits accept unchanged.
+
+* ``edited``: wild bearings are not used (``thin.edit_wild``).
+* ``estimate``: the share of the error variance that is correlated in time and its
+  correlation time, from the variance of the bearings' second differences at lags of 5 to
+  60 s. A second difference removes the smooth geometry; for white error its variance is
+  6σ², and for a first-order Gauss–Markov error of correlation ρ per lag it is
+  (6 − 8ρ + 2ρ²)σ². The variances are robust (median absolute deviation), so wild bearings
+  and a few manoeuvres do not move them. The total sd is the stated one.
+* ``effective``: the problem with one larger sd for every bearing, chosen so that
+  independent errors of that sd carry the same information about anything slow as the
+  estimated correlated errors do (the same spectral density at zero frequency):
+  σ_eff² = σ²·[(1 − share) + share·(1 + ρ) / (1 − ρ)], ρ = exp(−Δt / τ).
+
+A whitened (generalised least squares) fit would also use the errors' short-time structure;
+this form uses none of it, which is cautious about when a manoeuvre happened and right about
+range, course and speed.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+from .retest import Problem
+from .thin import edit_wild
+
+LAGS = tuple(range(1, 13))  # rows: 5 to 60 s at 5-second bearings
+TAUS = np.geomspace(10.0, 600.0, 40)
+SHARES = np.linspace(0.0, 0.9, 46)
+MIN_SHARE = 0.2  # below this the errors are treated as independent
+
+
+def edited(pb: Problem) -> tuple[Problem, int]:
+    """(the problem without its wild bearings, how many were edited out)."""
+    t, ox, oy, z, v, sigma = pb
+    keep = edit_wild(t, z, v.astype(bool), float(sigma))
+    return (t, ox, oy, z, keep, sigma), int(v.astype(bool).sum() - keep.sum())
+
+
+def second_difference_variances(pb: Problem) -> np.ndarray:
+    """[len(LAGS)] robust variance of z[i − h] − 2 z[i] + z[i + h] over valid triples (NaN
+    where there are fewer than 30)."""
+    _t, _ox, _oy, z, v, _sigma = pb
+    ok = v.astype(bool)
+    zz = np.where(ok, z, 0.0)
+    k = np.flatnonzero(ok)
+    if k.size:
+        zz[k] = np.unwrap(z[k])
+    out = np.full(len(LAGS), np.nan)
+    for j, h in enumerate(LAGS):
+        if zz.size <= 2 * h:
+            continue
+        d = zz[: -2 * h] - 2.0 * zz[h:-h] + zz[2 * h :]
+        m = ok[: -2 * h] & ok[h:-h] & ok[2 * h :]
+        if int(m.sum()) < 30:
+            continue
+        dd = d[m]
+        out[j] = (1.4826 * float(np.median(np.abs(dd - np.median(dd))))) ** 2
+    return out
+
+
+def estimate(pb: Problem) -> tuple[float, float]:
+    """(share, τ in s) of the correlated part; (0, 0) when it cannot be estimated."""
+    t, *_rest, sigma = pb
+    var = second_difference_variances(pb) / float(sigma) ** 2
+    ok = np.isfinite(var)
+    if int(ok.sum()) < 6:
+        return 0.0, 0.0
+    dt = float(np.median(np.diff(t)))
+    h = np.array(LAGS, dtype=np.float64)[ok]
+    best = (np.inf, 0.0, 0.0)
+    for tau in TAUS:
+        rho = np.exp(-h * dt / tau)
+        corr = 6.0 - 8.0 * rho + 2.0 * rho * rho
+        for s in SHARES:
+            e = float(np.sum((var[ok] - (6.0 * (1.0 - s) + s * corr)) ** 2))
+            if e < best[0]:
+                best = (e, float(s), float(tau))
+    return best[1], (best[2] if best[1] > 0.0 else 0.0)
+
+
+def effective_sigma(sigma: float, share: float, tau: float, dt: float) -> float:
+    if share <= 0.0 or tau <= 0.0:
+        return sigma
+    rho = float(np.exp(-dt / tau))
+    return sigma * float(np.sqrt((1.0 - share) + share * (1.0 + rho) / (1.0 - rho)))
+
+
+def effective(pb: Problem, share: float, tau: float) -> Problem:
+    """``pb`` with the effective sd for errors of that correlated share and time."""
+    t, ox, oy, z, v, sigma = pb
+    dt = float(np.median(np.diff(t)))
+    return (t, ox, oy, z, v, effective_sigma(float(sigma), share, tau, dt))
diff --git a/src/tma2/timelate/hedge.py b/src/tma2/timelate/hedge.py
new file mode 100644
index 00000000..4b99de77
--- /dev/null
+++ b/src/tma2/timelate/hedge.py
@@ -0,0 +1,95 @@
+"""Controls that need no new evidence (spec 016, research R1, R2 and R5): a hedged track, a
+cautious declaration, and pooling of searches run under different priors.
+
+* **Hedged track**: the mean of the candidates' tracks in Cartesian state, weighted by their
+  evidence at a temperature. It is the posterior-mean track under the search's own
+  posterior, which is what an error-minimising learned model approximates; the control for
+  hedging.
+* **Declaration**: from the first candidate, manoeuvres are removed one at a time (always the
+  one whose removal costs least evidence, each removal refitted) down to none: the
+  *elimination path*. A history is declared at the first point on the path where every
+  remaining manoeuvre is worth more than a margin. A stated probability ``q`` that the
+  target never manoeuvres changes only the weight of the path's last step.
+* **Pooling**: candidates of searches under two priors, each prior equally likely beforehand,
+  are one candidate set; the difference of their best evidences is the Bayes factor between
+  the priors (the prior-mismatch diagnostic).
+
+Nothing is trained and no truth is read.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+import numpy as np
+
+from .retest import Problem
+from .search import History, _removals, _same
+
+
+def weights(evidence: np.ndarray, temperature: float) -> np.ndarray:
+    """Probabilities ∝ exp(evidence / temperature) (non-finite evidence gets none)."""
+    e = np.asarray(evidence, dtype=np.float64)
+    ok = np.isfinite(e)
+    w = np.zeros(e.size)
+    if ok.any():
+        w[ok] = np.exp((e[ok] - e[ok].max()) / temperature)
+        w /= w.sum()
+    return w
+
+
+def hedged(tracks: list[np.ndarray], evidence: np.ndarray, temperature: float) -> np.ndarray:
+    """[R, 4] the weighted mean of the candidates' states (x, y, vx, vy)."""
+    w = weights(evidence, temperature)
+    out = np.zeros_like(tracks[0], dtype=np.float64)
+    for wk, tr in zip(w, tracks, strict=True):
+        if wk > 0.0:
+            out += wk * tr
+    return out
+
+
+def pool(sets: list[list[tuple[History, str]]], top: int) -> list[tuple[History, str]]:
+    """Candidates of several searches as one set, best first, a history kept once (the
+    version with more evidence)."""
+    every = sorted((x for s in sets for x in s), key=lambda x: -x[0].evidence)
+    kept: list[tuple[History, str]] = []
+    for h, tag in every:
+        if not np.isfinite(h.evidence) or any(_same(h, k) for k, _ in kept):
+            continue
+        kept.append((h, tag))
+        if len(kept) == top:
+            break
+    return kept
+
+
+def with_quiet(h: History, q: float) -> float:
+    """``h``'s evidence when the target never manoeuvres with probability ``q`` and otherwise
+    follows the prior ``h`` was scored under (``prior`` kind "mix" with ``QUIET`` = q)."""
+    if q <= 0.0:
+        return h.evidence
+    if h.fit.spans.shape[0]:
+        return h.evidence + math.log(1.0 - q)
+    legs = float(h.parts["legs"])
+    return h.evidence - legs + math.log(q + (1.0 - q) * math.exp(legs))
+
+
+def elimination_path(pb: Problem, top: History, prior: Any) -> list[History]:
+    """``top`` and then, repeatedly, the best history with one manoeuvre fewer (each refitted
+    and scored), down to no manoeuvre."""
+    path = [top]
+    while path[-1].fit.spans.shape[0]:
+        moves = [h for h in _removals(pb, path[-1], prior) if np.isfinite(h.evidence)]
+        if not moves:
+            break
+        path.append(max(moves, key=lambda x: x.evidence))
+    return path
+
+
+def declared(path: list[History], margin: float, q: float = 0.0) -> int:
+    """The index on the path of the declared history: the first whose cheapest removal
+    costs at least ``margin`` in evidence (the last if none does)."""
+    i = 0
+    while i + 1 < len(path) and with_quiet(path[i], q) - with_quiet(path[i + 1], q) < margin:
+        i += 1
+    return i
diff --git a/src/tma2/timelate/learned.py b/src/tma2/timelate/learned.py
new file mode 100644
index 00000000..71fe5bda
--- /dev/null
+++ b/src/tma2/timelate/learned.py
@@ -0,0 +1,147 @@
+"""Read-outs of the learned whole-period model (spec 016 research R8). No truth is read here.
+
+Two tracks per model. ``learned``: the model's direct output (``direct_track``), at every
+5-second row of the period, in the convention of every other time-late system (absolute x, y
+in the engagement's frame, vx, vy; what ``score.row_errors`` reads). ``learned-arc``: the arc
+model fitted to the bearings with the manoeuvre prior, started from that track, with manoeuvre
+spans from runs of the model's manoeuvre probability (``spans_from_logit``, ``arc_readout``):
+no search over histories.
+
+The model states range and velocity, not position. The direct track's position is the stated
+range along the measured bearing (as spec 012's ``frame.to_state``), with the bearing smoothed
+by a short running median: the model has no other statement of direction, a single bearing
+carries its full error across the range (1° at 10 km is 175 m), and a median of 35 s does not
+bias a steadily changing bearing and ignores a wild one. Range, course and speed, which the
+registered comparison reads, do not depend on that choice.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from tma2.regress.frame import L_UNIT, V_UNIT, first_bearing
+
+from .controls import c2_refit
+from .periods import Period
+from .retest import MIN_DURATION, Problem, Structure
+
+SMOOTH_ROWS = 7  # rows of the bearing's running median (35 s at 5 s)
+
+
+def token_rows(rows: int, patch: int) -> tuple[np.ndarray, np.ndarray]:
+    """(first row [T], last existing row [T]) of the tokens of a period of ``rows`` rows."""
+    start = np.arange(0, rows, patch)
+    return start, np.minimum(start + patch - 1, rows - 1)
+
+
+def token_times(rows: int, dt: float, patch: int) -> np.ndarray:
+    """[T, 2] the time span a token's manoeuvre label speaks for, from the period's start: from
+    the row before its first row (a manoeuvre's span starts one row before its first row
+    inside, as the runners' true spans do) to its last existing row."""
+    start, last = token_rows(rows, patch)
+    out: np.ndarray = np.stack([np.maximum(start - 1, 0) * dt, last * dt], axis=1)
+    return out
+
+
+def smoothed_bearing(z: np.ndarray, valid: np.ndarray, width: int = SMOOTH_ROWS) -> np.ndarray:
+    """[R] the bearing at every row: valid bearings unwrapped, interpolated over the rows
+    without one, then a centred running median of ``width`` rows (the ends repeat)."""
+    k = np.flatnonzero(valid)
+    if k.size == 0:
+        return np.full(z.shape, np.nan)
+    zi = np.interp(np.arange(z.shape[0]), k, np.unwrap(z[k].astype(np.float64)))
+    h = width // 2
+    win = np.lib.stride_tricks.sliding_window_view(np.pad(zi, h, mode="edge"), 2 * h + 1)
+    out: np.ndarray = np.median(win, axis=1)
+    return out
+
+
+def direct_track(mean: np.ndarray, period: Period, patch: int) -> np.ndarray:
+    """The model's own track [rows, 4] (x, y, vx, vy). ``mean`` [T, 3]: the model's output for
+    the period's tokens (ln(range ÷ 10 km), velocity ÷ 10 m/s in the frame rotated to the
+    period's first valid bearing), each stated at its token's last existing row. Between those
+    rows the three are interpolated linearly in time; before the first, ln range continues the
+    first interval's slope and the velocity is held. All NaN for a period with no bearing."""
+    a, b = period.first, period.first + period.rows
+    z, valid = period.obs[a:b, 1].astype(np.float64), period.obs[a:b, 2] != 0
+    ox, oy = period.own[a:b, 1].astype(np.float64), period.own[a:b, 2].astype(np.float64)
+    _start, last = token_rows(period.rows, patch)
+    m = np.asarray(mean, dtype=np.float64)[: last.shape[0]]
+    if not valid.any() or m.shape[0] != last.shape[0]:
+        return np.full((period.rows, 4), np.nan)
+    k = np.arange(period.rows, dtype=np.float64)
+    ln_r, vxp, vyp = (np.interp(k, last, m[:, c]) for c in range(3))
+    if last.shape[0] > 1 and last[0] > 0:
+        slope = (m[1, 0] - m[0, 0]) / (last[1] - last[0])
+        head = k < last[0]
+        ln_r[head] = m[0, 0] + slope * (k[head] - last[0])
+    th = float(first_bearing(z[None, :], valid[None, :])[0])
+    c, s = np.cos(th), np.sin(th)
+    rng = L_UNIT * np.exp(ln_r)
+    zs = smoothed_bearing(z, valid)
+    out: np.ndarray = np.stack(
+        [
+            ox + rng * np.sin(zs),
+            oy + rng * np.cos(zs),
+            V_UNIT * (vxp * c + vyp * s),
+            V_UNIT * (vyp * c - vxp * s),
+        ],
+        axis=1,
+    )
+    return out
+
+
+def spans_from_logit(
+    prob: np.ndarray,
+    t_tokens: np.ndarray,
+    threshold: float = 0.5,
+    merge_s: float = 60.0,
+    max_manoeuvres: int = 10,
+) -> np.ndarray:
+    """Manoeuvre spans [M, 2] (start s, end s, ascending, disjoint) from the model's manoeuvre
+    probability per token ``prob`` [T] and the tokens' time spans ``t_tokens`` [T, 2]
+    (``token_times``): every run of tokens above ``threshold``, runs closer than ``merge_s``
+    merged; with more than ``max_manoeuvres`` the runs with the most probability above the
+    threshold are kept. A span shorter than the arc fit's shortest manoeuvre is extended
+    backwards to it (not past the previous span or the period's start)."""
+    on = np.asarray(prob) > threshold
+    runs: list[list[float]] = []  # start, end, probability above the threshold
+    for j in np.flatnonzero(on):
+        a, b = float(t_tokens[j, 0]), float(t_tokens[j, 1])
+        w = float(prob[j]) - threshold
+        if runs and a - runs[-1][1] <= merge_s:
+            runs[-1][1] = b
+            runs[-1][2] += w
+        else:
+            runs.append([a, b, w])
+    if len(runs) > max_manoeuvres:
+        keep = sorted(sorted(range(len(runs)), key=lambda i: -runs[i][2])[:max_manoeuvres])
+        runs = [runs[i] for i in keep]
+    out = np.array([r[:2] for r in runs], dtype=np.float64).reshape(-1, 2)
+    for i in range(out.shape[0]):
+        floor = out[i - 1, 1] if i else 0.0
+        if out[i, 1] - out[i, 0] < MIN_DURATION:
+            out[i, 0] = max(out[i, 1] - MIN_DURATION, floor)
+    return out[out[:, 1] - out[:, 0] >= MIN_DURATION]
+
+
+def arc_readout(
+    pb: Problem,
+    states: np.ndarray,
+    spans: np.ndarray,
+    prior: Any,
+    starts: tuple[np.ndarray, ...] = (),
+) -> tuple[Structure, np.ndarray, dict[str, float]]:
+    """``learned-arc``: (fit, kinds, evidence by part) of the arc model with ``prior`` on the
+    period's bearings ``pb``, for manoeuvres at ``spans`` (``spans_from_logit``), started from
+    the model's track ``states`` (``direct_track``). This is spec 014's refit control
+    (``controls.c2_refit``) given the model's spans and track in place of C″'s: kinds by
+    evidence, every time free, no manoeuvre added, removed or moved elsewhere. ``starts``:
+    further starting states that use no truth (the runner's nominal starts), none by default.
+    Raises ``ValueError`` when no start gives a finite evidence (the runner then keeps the
+    direct track)."""
+    if not np.isfinite(states).all() and not starts:
+        raise ValueError("the model's track has no state to start from")
+    return c2_refit(pb, spans, states, prior, list(starts))
diff --git a/src/tma2/timelate/run3.py b/src/tma2/timelate/run3.py
index 28350d12..2ce57eb6 100644
--- a/src/tma2/timelate/run3.py
+++ b/src/tma2/timelate/run3.py
@@ -50,16 +50,20 @@ def track_cost(pb: Problem, states: np.ndarray) -> float:
 def refit_acceptable(
     pb: Problem, fit: Structure, c_states: np.ndarray, prior: ManoeuvrePrior
 ) -> bool:
-    """Whether a refit of C″'s transitions is kept (research R4; no truth): finite, fitting
-    the bearings no worse than C″'s own track by more than ``WORSE``, at a speed and range a
-    target could have."""
+    """Whether a refit of C″'s transitions is kept (research R4; no truth): finite, at a
+    speed and range a target could have, and either fitting the bearings as well as their
+    noise allows or no worse than C″'s own track by more than ``WORSE``. (The chord track has
+    four free unknowns a manoeuvre, so a sound arc refit may cost more than it: the first
+    form of this rule, cost against the track alone, fell back in 26 of 200 development
+    periods.)"""
     if not np.isfinite(fit.states).all() or not np.isfinite(c_states).all():
         return False
     speed = float(np.hypot(fit.states[:, 2], fit.states[:, 3]).max())
     rng = float(np.hypot(fit.states[:, 0] - pb[1], fit.states[:, 1] - pb[2]).max())
     if speed > SPEED_MAX * prior.speed[1] or rng > 16.0 * prior.range0[1]:
         return False
-    return fit.cost <= track_cost(pb, c_states) + WORSE
+    n = int(pb[4].sum())
+    return consistent(float(fit.cost), n) or fit.cost <= track_cost(pb, c_states) + WORSE
 
 
 def run_shard3(
diff --git a/src/tma2/timelate/run4.py b/src/tma2/timelate/run4.py
new file mode 100644
index 00000000..57ef2372
--- /dev/null
+++ b/src/tma2/timelate/run4.py
@@ -0,0 +1,267 @@
+"""One shard of spec 016's classical controls (research R1 to R5, R13), beside the standing
+search (spec 015's ``search3``: Annex B's prior, version 3, the bearings as given).
+
+Single-factor controls, each the same search with one thing changed:
+
+* ``edit``: wild bearings edited out;
+* ``corr``: an effective bearing sd from the estimated error correlation;
+* ``broad``: a broadened manoeuvre prior; ``mix2``: ``search3`` and ``broad`` pooled.
+
+The combined control ``ctl`` is what a system that is not told the conditions would run:
+edit, then estimate the correlation (used only above ``corr.MIN_SHARE``), then search under
+both priors and pool. For every search ``X``: ``X`` is its first candidate's track,
+``X-hT`` the hedged track at temperature T, and for ``search3`` and ``ctl``
+``X-dM-qQ`` the declared history at margin M and quiet probability Q.
+
+The true-history reference is given three ways (``history-fixed``: bearings only, times
+held; ``history-map``: fitted with the prior, times free; ``history-held``: fitted with the
+prior, times held) and as ``history-best`` (free unless it drifted, spec 015's rule).
+"""
+
+from __future__ import annotations
+
+import time
+from typing import Any
+
+import numpy as np
+
+from tma2.types.generated.tma import SegmentParameters
+
+from .bound import period_bound
+from .corr import MIN_SHARE, edited, effective, estimate
+from .hedge import declared, elimination_path, hedged, pool
+from .learned import arc_readout
+from .match import cut_flags, same_history
+from .periods import Period
+from .prior import ManoeuvrePrior
+from .retest import Problem, joint_fit
+from .run2 import _history, _search, consistent, reference
+from .run3 import refit_acceptable
+from .score import row_errors, strata_of, truth_rows
+from .search import History
+from .systems import solve_systems
+
+BASE = ("c2", "one-leg", "history-fixed", "history-map", "history-held", "history-best")
+SEARCHES = ("search3", "edit", "corr", "broad", "mix2", "ctl")
+DECLARED = ("search3", "ctl")
+MATCH_S = 120.0
+
+
+def system_names(settings: dict[str, Any]) -> list[str]:
+    chosen = [s for s in SEARCHES if s in settings.get("systems", SEARCHES)]
+    names = list(BASE)
+    for s in chosen:
+        names.append(s)
+        names += [f"{s}-h{t:g}" for t in settings.get("temperatures", [])]
+        if s in DECLARED:
+            names += [
+                f"{s}-d{m:g}-q{q:g}"
+                for m in settings.get("margins", [])
+                for q in settings.get("quiets", [])
+            ]
+    return names
+
+
+def run_shard4(
+    periods: list[Period],
+    priors: dict[str, ManoeuvrePrior],
+    c_params: SegmentParameters,
+    c2: dict[str, Any],
+    settings: dict[str, Any],
+    learned: dict[str, dict[str, np.ndarray]] | None = None,
+) -> tuple[dict[str, np.ndarray], list[dict[str, Any]]]:
+    """``priors``: "v3" (Annex B) and "broad". ``learned``: per model name, the arrays of a
+    ``tma2-tlpredict`` shard for these periods; each gives two systems, ``<name>`` (the
+    model's own track) and ``<name>-arc`` (the declared read-out: the arc model with Annex
+    B's prior fitted to the bearings ``ctl`` sees, at the model's manoeuvres, started from
+    the model's track; the model's own track where that fit fails or is not acceptable by
+    spec 015's rule for a refit).
+    Arrays: ``period``, ``bound``, ``stratum``,
+    ``true_range`` and ``err_<system>`` at every ``score_every``-th row; for periods with
+    ordinal below ``store_tracks``, ``trk_period``, ``trk_truth`` and ``trk_<system>`` at
+    the same rows."""
+    every = int(settings["score_every"])
+    chosen = [s for s in SEARCHES if s in settings.get("systems", SEARCHES)]
+    names = system_names(settings)
+    learned = learned or {}
+    for ln in learned:
+        names += [ln, f"{ln}-arc"]
+    temps = [float(x) for x in settings.get("temperatures", [])]
+    margins = [float(x) for x in settings.get("margins", [])]
+    quiets = [float(x) for x in settings.get("quiets", [])]
+    keep = int(settings.get("store_tracks", 0))
+    top = int(settings["candidates"])
+    res = solve_systems(periods, ["c2-end-nofade", "one-leg"], c_params, c2)
+    p3 = priors["v3"]
+    errs: dict[str, list[np.ndarray]] = {s: [] for s in names}
+    trks: dict[str, list[np.ndarray]] = {s: [] for s in names}
+    trk_owner: list[np.ndarray] = []
+    trk_truth: list[np.ndarray] = []
+    bounds, owner, strata, ranges, recs = [], [], [], [], []
+    for i, x in enumerate(periods):
+        a, b = x.first, x.first + x.rows
+        t = x.own[a:b, 0].astype(np.float64) - float(x.own[a, 0])
+        ox, oy = x.own[a:b, 1].astype(np.float64), x.own[a:b, 2].astype(np.float64)
+        z, v = x.obs[a:b, 1].astype(np.float64), x.obs[a:b, 2] != 0
+        sigma = float(x.meta["parameters"]["noise_sigma_rad"])
+        pb: Problem = (t, ox, oy, z, v, sigma)
+        tr = truth_rows(x)
+        pick = np.arange(0, x.rows, every)
+        nan = np.full((x.rows, 4), np.nan)
+        tracks: dict[str, np.ndarray] = {s: nan for s in names}
+        took: dict[str, float] = {}
+        cc = res["c2-end-nofade"][i]
+        tracks["c2"], tracks["one-leg"] = cc.states, res["one-leg"][i].states
+        sp = [s for s in x.spans() if s["end_index"] > s["start_index"]]
+        spt = np.array(
+            [[t[max(s["start_index"] - 1, 0)], t[s["end_index"]]] for s in sp], dtype=np.float64
+        ).reshape(-1, 2)
+        kt = np.array(
+            [[s["kind"] in ("course", "both"), s["kind"] in ("speed", "both")] for s in sp],
+            dtype=bool,
+        ).reshape(-1, 2)
+        cut = cut_flags(x)
+        spc = np.array(
+            [[t[max(p - 1, 0)], t[q]] for p, q in cc.transitions if q > p], dtype=np.float64
+        ).reshape(-1, 2)
+        hist: dict[str, Any] = {"c2": {"spans": spc.tolist()}}
+        rec: dict[str, Any] = {
+            "ordinal": int(x.ordinal),
+            "rows": int(x.rows),
+            "seconds_long": float(t[-1]),
+            "bearings": int(v.sum()),
+            "true": spt.tolist(),
+            "true_kinds": kt.tolist(),
+            "true_cut": cut,
+        }
+        t0 = time.perf_counter()
+        held, _hf, hm = reference(pb, spt, kt, p3)
+        hh = joint_fit(pb, held.theta, spt, free_changes=kt, free_times=False, prior=p3)
+        took["reference"] = time.perf_counter() - t0
+        flagged = not same_history(spt, hm.spans, MATCH_S, cut)
+        tracks["history-fixed"], tracks["history-map"] = held.states, hm.states
+        tracks["history-held"] = hh.states
+        tracks["history-best"] = hh.states if flagged else hm.states
+        rec["reference_flagged"] = bool(flagged)
+        rec["history_converged"] = consistent(float(held.cost), int(v.sum()))
+
+        pe, n_edit = edited(pb)
+        share, tau = estimate(pb)
+        share_e, tau_e = estimate(pe)
+        pc = effective(pb, share, tau)
+        px = effective(pe, share_e, tau_e) if share_e >= MIN_SHARE else pe
+        rec["edited"] = n_edit
+        rec["corr_estimate"] = [share, tau]
+        rec["ctl_corr_estimate"] = [share_e, tau_e, bool(share_e >= MIN_SHARE)]
+        problems = {"plain": pb, "edit": pe, "corr": pc, "ctl": px}
+        cache: dict[tuple[str, str], list[tuple[History, str]]] = {}
+
+        def search(which: str, key: str) -> list[tuple[History, str]]:
+            # ``ctl``'s problem is ``edit``'s when no correlation is used, and the plain
+            # one when nothing was edited either: one search serves both
+            if which == "ctl" and share_e < MIN_SHARE:  # noqa: B023
+                which = "edit"
+            if which == "edit" and n_edit == 0:  # noqa: B023
+                which = "plain"
+            if (which, key) not in cache:  # noqa: B023
+                t1 = time.perf_counter()
+                c, _n = _search(
+                    problems[which],  # noqa: B023
+                    priors[key],
+                    cc.states,  # noqa: B023
+                    spc,  # noqa: B023
+                    {**settings, "candidates": top},
+                )
+                took[f"{which}/{key}"] = time.perf_counter() - t1  # noqa: B023
+                cache[(which, key)] = [(h, f"{which}/{key}") for h, _p in c]  # noqa: B023
+            return cache[(which, key)]  # noqa: B023
+
+        for name in chosen:
+            if name == "search3":
+                cands = search("plain", "v3")
+            elif name in ("edit", "corr"):
+                cands = search(name, "v3")
+            elif name == "broad":
+                cands = search("plain", "broad")
+            elif name == "mix2":
+                cands = pool([search("plain", "v3"), search("plain", "broad")], top)
+            else:
+                cands = pool([search("ctl", "v3"), search("ctl", "broad")], top)
+            if not cands:
+                continue
+            h0, tag0 = cands[0]
+            tracks[name] = h0.fit.states
+            ev = np.array([h.evidence for h, _ in cands])
+            for tt in temps:
+                tracks[f"{name}-h{tt:g}"] = hedged([h.fit.states for h, _ in cands], ev, tt)
+            entry = _history(h0.fit.spans, h0.kinds, h0.evidence)
+            entry["prior"] = tag0
+            entry["candidates"] = [
+                {**_history(h.fit.spans, h.kinds, h.evidence), "prior": g} for h, g in cands[:5]
+            ]
+            if name in ("mix2", "ctl"):  # Bayes factor, broad prior over Annex B's
+                best = {k: -np.inf for k in ("v3", "broad")}
+                for h, g in cands:
+                    k = g.split("/")[1]
+                    best[k] = max(best[k], h.evidence)
+                entry["log_bayes_broad"] = float(best["broad"] - best["v3"])
+            if name in DECLARED and margins:
+                which, key = tag0.split("/")
+                t1 = time.perf_counter()
+                path = elimination_path(problems[which], h0, priors[key])
+                took[f"{name}/path"] = time.perf_counter() - t1
+                entry["path"] = [_history(h.fit.spans, h.kinds, h.evidence) for h in path]
+                grid = {}
+                for mg in margins:
+                    for q in quiets:
+                        j = declared(path, mg, q)
+                        tracks[f"{name}-d{mg:g}-q{q:g}"] = path[j].fit.states
+                        grid[f"d{mg:g}-q{q:g}"] = j
+                        hist[f"{name}-d{mg:g}-q{q:g}"] = {"spans": path[j].fit.spans.tolist()}
+                entry["declared"] = grid
+            hist[name] = entry
+        for ln, arr in learned.items():
+            at = np.flatnonzero(arr["ordinal"] == x.ordinal)
+            if at.size != 1 or int(arr["rows"][at[0]]) != x.rows:
+                raise ValueError(f"period {x.ordinal}: no (or another) period in {ln}'s shard")
+            direct = arr["learned"][arr["row_period"] == x.ordinal].astype(np.float64)
+            spans = arr["spans"][arr["span_period"] == x.ordinal].astype(np.float64).reshape(-1, 2)
+            tracks[ln] = direct
+            hist[ln] = {"spans": spans.tolist()}
+            t1 = time.perf_counter()
+            try:
+                fit, kinds_l, ev_l = arc_readout(px, direct, spans, p3)
+                if not refit_acceptable(px, fit, direct, p3):  # spec 015's rule, no truth
+                    raise ValueError("the read-out is not a fit a system would accept")
+                tracks[f"{ln}-arc"] = fit.states
+                hist[f"{ln}-arc"] = _history(fit.spans, kinds_l, float(ev_l["total"]))
+            except ValueError:  # no acceptable fit: the model's own track stands
+                tracks[f"{ln}-arc"] = direct
+                hist[f"{ln}-arc"] = {"spans": spans.tolist(), "fell_back": True}
+            took[f"{ln}/arc"] = time.perf_counter() - t1
+        rec["histories"] = hist
+        rec["seconds"] = {k: round(float(s), 2) for k, s in took.items()}
+        bounds.append(period_bound(x)[pick])
+        owner.append(np.full(pick.size, x.ordinal, dtype=np.int64))
+        strata.append(strata_of(x, x.rows)[pick])
+        ranges.append(np.hypot(tr[:, 0] - tr[:, 4], tr[:, 1] - tr[:, 5])[pick].astype(np.float32))
+        for s in names:
+            errs[s].append(row_errors(tracks[s], tr)[pick].astype(np.float32))
+        if x.ordinal < keep:
+            trk_owner.append(np.full(pick.size, x.ordinal, dtype=np.int64))
+            trk_truth.append(tr[pick].astype(np.float32))
+            for s in names:
+                trks[s].append(tracks[s][pick].astype(np.float32))
+        recs.append(rec)
+    arrays: dict[str, np.ndarray] = {
+        "period": np.concatenate(owner),
+        "bound": np.concatenate(bounds).astype(np.float32),
+        "stratum": np.concatenate(strata),
+        "true_range": np.concatenate(ranges),
+    }
+    arrays.update({f"err_{s}": np.concatenate(e) for s, e in errs.items()})
+    if trk_owner:
+        arrays["trk_period"] = np.concatenate(trk_owner)
+        arrays["trk_truth"] = np.concatenate(trk_truth)
+        arrays.update({f"trk_{s}": np.concatenate(e) for s, e in trks.items()})
+    return arrays, recs
diff --git a/src/tma2/timelate/study4.py b/src/tma2/timelate/study4.py
new file mode 100644
index 00000000..44888d0f
--- /dev/null
+++ b/src/tma2/timelate/study4.py
@@ -0,0 +1,221 @@
+"""Readings of spec 016's controls (and, later, the learned tracks) from ``run4`` shards.
+
+For each split: every system's table; paired per-period comparisons of the standing search
+with each control, of each with the true-history reference under its three conventions, and
+any pairs the plan names; the share of the gap from the search to the reference a control
+closes; manoeuvre counts by the edge-aware rule; on periods with no true manoeuvre, how
+often each system declares one, and on periods with one, two or more, how often it declares
+none; what the correlation estimate and the prior diagnostic said.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+import numpy as np
+
+from .score import UNDETERMINED, _period_medians
+from .study import _system_table, gain_share, paired, wilson
+from .study3 import counts3, whole_true
+
+REFERENCES = ("history-map", "history-best", "history-held", "history-fixed")
+
+
+def _medians(arr: dict[str, np.ndarray]) -> tuple[np.ndarray, dict[str, np.ndarray]]:
+    bound = arr["bound"].astype(np.float64)
+    det = np.isfinite(bound[:, 0]) & (bound[:, 0] < UNDETERMINED)
+    ids: np.ndarray = np.zeros(0, dtype=np.int64)
+    med: dict[str, np.ndarray] = {}
+    for k in sorted(arr):
+        if k.startswith("err_"):
+            ids, m = _period_medians(arr[k].astype(np.float64), arr["period"], det)
+            med[k[4:]] = m[:, 0]
+    return ids, med
+
+
+def _pair(med: dict[str, np.ndarray], a: str, b: str) -> dict[str, Any] | None:
+    if a not in med or b not in med:
+        return None
+    ok = np.isfinite(med[a]) & np.isfinite(med[b])
+    if int(ok.sum()) < 5:
+        return None
+    return paired(med[a][ok], med[b][ok])
+
+
+def _declares(periods: list[dict[str, Any]], system: str) -> dict[str, Any]:
+    """By number of whole true manoeuvres (0, 1, 2 or more): how often the system's history
+    has none. A period with only cut manoeuvres is in no group."""
+    groups: dict[str, list[dict[str, Any]]] = {"0": [], "1": [], "2+": []}
+    for r in periods:
+        if system not in (r.get("histories") or {}):
+            continue
+        n = whole_true(r)
+        if n == 0 and r["true"]:
+            continue
+        groups["0" if n == 0 else ("1" if n == 1 else "2+")].append(r)
+    out: dict[str, Any] = {}
+    for label, rows in groups.items():
+        k = sum(1 for r in rows if len(r["histories"][system]["spans"]) == 0)
+        out[label] = {"called_quiet": wilson(k, len(rows)) if rows else None, "n": len(rows)}
+    return out
+
+
+def study_split4(
+    arr: dict[str, np.ndarray], periods: list[dict[str, Any]], plan: dict[str, Any]
+) -> dict[str, Any]:
+    tight = float(plan["reference"]["tight_bound"])
+    keep = np.ones(arr["period"].size, dtype=bool)
+    table = _system_table(arr, keep, tight)
+    ids, med = _medians(arr)
+    for name, m in med.items():
+        fin = m[np.isfinite(m)]
+        table[name]["median_period_range"] = float(np.median(fin)) if fin.size else math.nan
+        table[name]["periods_without_a_track"] = int((~np.isfinite(m)).sum())
+    systems = [s for s in med if not s.startswith("history-") and s not in ("one-leg",)]
+    comparisons: dict[str, Any] = {}
+    for s in systems:
+        if s != "search3":
+            c = _pair(med, "search3", s)
+            if c:
+                comparisons[f"search3_over_{s}"] = c
+        for ref in REFERENCES:
+            c = _pair(med, s, ref)
+            if c and s in ("search3", "ctl", "c2") or (c and s.startswith("learned")):
+                comparisons[f"{s}_over_{ref}"] = c
+    for a, b in plan.get("comparisons", []):
+        c = _pair(med, a, b)
+        if c:
+            comparisons[f"{a}_over_{b}"] = c
+    closed: dict[str, Any] = {}
+    for s in systems:
+        if s in ("search3", "c2") or "history-best" not in med or "search3" not in med:
+            continue
+        ok = np.isfinite(med["search3"]) & np.isfinite(med[s]) & np.isfinite(med["history-best"])
+        if int(ok.sum()) >= 5:
+            closed[s] = gain_share(med["search3"][ok], med[s][ok], med["history-best"][ok])
+    named = sorted({k for r in periods for k in (r.get("histories") or {})})
+    counts = {s: counts3(periods, s) for s in named}
+    declares = {s: _declares(periods, s) for s in named}
+    est = np.array([r["corr_estimate"] for r in periods], dtype=np.float64).reshape(-1, 2)
+    bayes = [
+        r["histories"][s]["log_bayes_broad"]
+        for r in periods
+        for s in ("ctl",)
+        if s in r.get("histories", {}) and "log_bayes_broad" in r["histories"][s]
+    ]
+    secs: dict[str, list[float]] = {}
+    for r in periods:
+        for k, v in r.get("seconds", {}).items():
+            secs.setdefault(k, []).append(float(v))
+    n = len(periods)
+    return {
+        "periods": n,
+        "periods_scored": int(ids.size),
+        "systems": table,
+        "comparisons": comparisons,
+        "gap_closed": closed,
+        "counts": counts,
+        "declares": declares,
+        "reference_flagged_share": sum(bool(r["reference_flagged"]) for r in periods) / max(n, 1),
+        "history_converged_share": sum(bool(r["history_converged"]) for r in periods) / max(n, 1),
+        "corr_estimate": {
+            "share_median": float(np.median(est[:, 0])) if n else math.nan,
+            "share_quartiles": [float(x) for x in np.quantile(est[:, 0], [0.25, 0.75])]
+            if n
+            else [],
+            "tau_median_where_used": float(np.median(est[est[:, 0] > 0, 1]))
+            if (est[:, 0] > 0).any()
+            else math.nan,
+            "ctl_used_share": sum(bool(r["ctl_corr_estimate"][2]) for r in periods) / max(n, 1),
+        },
+        "edited_per_period": float(np.mean([r["edited"] for r in periods])) if n else math.nan,
+        "log_bayes_broad": {
+            "median": float(np.median(bayes)) if bayes else math.nan,
+            "quantiles_05_25_75_95": [
+                float(x) for x in np.quantile(bayes, [0.05, 0.25, 0.75, 0.95])
+            ]
+            if bayes
+            else [],
+            "share_above_0": float(np.mean(np.array(bayes) > 0)) if bayes else math.nan,
+            "share_above_3": float(np.mean(np.array(bayes) > 3)) if bayes else math.nan,
+        },
+        "seconds_per_period": {k: float(np.mean(v)) for k, v in sorted(secs.items())},
+    }
+
+
+def soundness4(reading: dict[str, Any], plan: dict[str, Any]) -> dict[str, Any]:
+    """L-a (research R11): on Annex B data the reference with its times held is at the bound
+    on tight rows, its fits converge, and few references drift."""
+    st = plan["study"]
+    lo, hi = (float(x) for x in st["soundness_band"])
+    r = reading["man2"]
+    eff = float(r["systems"][plan["reference"]["sound"]]["efficiency_range_tight"])
+    conv, flag = float(r["history_converged_share"]), float(r["reference_flagged_share"])
+    limit = float(st["reference_flagged_max"])
+    return {
+        "reference_efficiency_tight": eff,
+        "band": [lo, hi],
+        "history_converged_share": conv,
+        "convergence_min": float(st["convergence_min"]),
+        "reference_flagged_share": flag,
+        "reference_flagged_max": limit,
+        "pass": bool(lo <= eff <= hi and conv >= float(st["convergence_min"]) and flag <= limit),
+    }
+
+
+def criteria4(reading: dict[str, Any], plan: dict[str, Any]) -> dict[str, Any]:
+    """L-c and L-d (research R11) for the learned model's seeds named in the plan.
+
+    On each mismatch split, for every seed, the declared classical control ÷ the model's
+    arc read-out and the hedged classical track ÷ the same, paired per period. A seed
+    *wins* a comparison when its interval is wholly above 1 and *loses* when wholly below.
+    Learning is ``shown`` on a split when every seed wins both; ``hedging`` when every seed
+    wins against the declared control but not every seed wins against the hedged track;
+    ``not shown`` otherwise. On the no-harm splits, ``harm`` when every seed loses against
+    the declared control."""
+    c = plan["criteria"]
+    declared, hedge = c["control"], c["hedged"]
+    seeds = [f"{s}-arc" for s in c["learned"]]
+
+    def side(p: dict[str, Any] | None) -> str:
+        if p is None:
+            return "absent"
+        return "wins" if p["lo"] > 1.0 else ("loses" if p["hi"] < 1.0 else "not separated")
+
+    out: dict[str, Any] = {"splits": {}, "no_harm": {}}
+    for split in c["mismatch_splits"] + c["no_harm_splits"]:
+        if split not in reading:
+            continue
+        comp = reading[split]["comparisons"]
+        rows = {}
+        for s in seeds:
+            a, b = comp.get(f"{declared}_over_{s}"), comp.get(f"{hedge}_over_{s}")
+            rows[s] = {"declared": a, "hedged": b, "vs_declared": side(a), "vs_hedged": side(b)}
+        va = [r["vs_declared"] for r in rows.values()]
+        vb = [r["vs_hedged"] for r in rows.values()]
+        if split in c["mismatch_splits"]:
+            if all(x == "wins" for x in va) and all(x == "wins" for x in vb):
+                verdict = "shown"
+            elif all(x == "wins" for x in va):
+                verdict = "hedging"
+            else:
+                verdict = "not shown"
+            out["splits"][split] = {"seeds": rows, "verdict": verdict}
+        else:
+            harm = bool(va) and all(x == "loses" for x in va)
+            out["no_harm"][split] = {"seeds": rows, "verdict": "harm" if harm else "no harm shown"}
+    shown = sum(1 for v in out["splits"].values() if v["verdict"] == "shown")
+    harm = any(v["verdict"] == "harm" for v in out["no_harm"].values())
+    out["learning_shown_on"] = shown
+    out["harm"] = harm
+    out["follows"] = (
+        "a gate for a learned time-late system is proposed"
+        if shown >= 2 and not harm
+        else (
+            "the classical controls stand as the time-late system; the learned track stops"
+            if shown == 0
+            else "reported; the owner decides"
+        )
+    )
+    return out
diff --git a/tests/unit/test_period_model.py b/tests/unit/test_period_model.py
new file mode 100644
index 00000000..c2bb4cc1
--- /dev/null
+++ b/tests/unit/test_period_model.py
@@ -0,0 +1,483 @@
+"""Spec 016: the learned whole-period model. The period stream and its mix, the non-causal
+model, training, the read-outs and the export check."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+from tma2.features.batch import generate_indices
+from tma2.regress import period_data as pdata
+from tma2.regress.model import PERIOD_FIELDS, Regressor, RegressorConfig
+from tma2.regress.period_predict import export_check, predict_periods, run_model
+from tma2.regress.train import config_digest, period_loss
+from tma2.sim import mismatch as mm
+from tma2.sim.prng import derive_seed
+from tma2.timelate import periods as tlp
+from tma2.timelate.learned import (
+    arc_readout,
+    direct_track,
+    spans_from_logit,
+    token_times,
+)
+from tma2.timelate.score import row_errors, truth_rows
+
+ROOT = Path(__file__).resolve().parents[2]
+MIX = str(ROOT / "configs/regress/tlm-mix.json")
+PERIODS = dict(
+    patch_rows=6, max_rows=1440, causal=False, manoeuvre_head=True, n_features=9,
+    data="periods", mix=MIX, stream_seed=339, val_seed=339,
+)  # fmt: skip
+TOY = dict(layers=2, width=32, heads=2, feedforward=64, batch_scenarios=8, precision="float32")
+
+
+def _cfg(**kw: object) -> RegressorConfig:
+    return RegressorConfig(**{**TOY, **PERIODS, **kw})  # type: ignore[arg-type]
+
+
+def _model(cfg: RegressorConfig, seed: int = 0) -> Regressor:
+    """A model with non-zero heads (they start at zero)."""
+    torch.manual_seed(seed)
+    m = Regressor(cfg).eval()
+    torch.nn.init.normal_(m.head.weight, std=0.1)
+    torch.nn.init.normal_(m.man_head.weight, std=0.5)
+    return m
+
+
+def _period(index: int, ranges: dict[str, Any], first: int, rows: int) -> tlp.Period:
+    """Engagement ``index`` of stream seed 339 as the file path would give it (through
+    ``features.batch``, not the stream's own kernel call), as a stored period."""
+    b = generate_indices(339, np.array([index], dtype=np.int64), ranges)
+    n = int(b.samples[0])
+    t = b.t[0, :n].astype(np.float64)
+    own = np.column_stack([t, b.own[0, :n].astype(np.float64)])
+    tgt = np.column_stack([t, b.tgt[0, :n].astype(np.float64)])
+    obs = np.column_stack([t, b.bearing[0, :n], b.valid[0, :n]]).astype(np.float64)
+    meta = {
+        "parameters": {"noise_sigma_rad": float(b.sigma[0]), "dt_s": 5.0},
+        "manoeuvres": [
+            {"kind": "course", "start_index": int(s), "end_index": int(e)}
+            for _k, s, e in b.spans[0]
+            if s >= 0
+        ],
+    }
+    return tlp.Period(index, meta, own, tgt, obs, first, rows, 5.0)
+
+
+def test_stream_is_a_pure_function_of_config_and_update() -> None:
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    a, b, c = pdata.batch_at(cfg, mix, 3), pdata.batch_at(cfg, mix, 3), pdata.batch_at(cfg, mix, 4)
+    for k in pdata.FIELDS:
+        assert np.array_equal(getattr(a, k), getattr(b, k), equal_nan=True)
+    assert a.index.tolist() == list(range(24, 32)) and c.index[0] == 32
+    assert not np.array_equal(a.f, c.f)
+    assert a.f.shape == (8, 1440, 9) and a.y.shape == (8, 1440, 3)
+    # rows beyond the period carry nothing; the period is at least 20 minutes
+    assert not a.f[~a.mask].any() and not a.y[~a.mask].any() and not a.man[~a.mask].any()
+    assert (a.rows >= 240).all() and (a.mask.sum(axis=1) == a.rows).all()
+    # time from the period's start and its length, ÷ 7,200 s
+    assert np.allclose(a.f[0, : a.rows[0], 7], np.arange(a.rows[0]) * 5.0 / 7200.0)
+    assert np.allclose(a.f[0, : a.rows[0], 8], a.rows[0] * 5.0 / 7200.0)
+    mon = pdata.monitor(_cfg(val_count=4), mix)
+    assert mon.index.tolist() == [(1 << 40) + i for i in range(4)]
+
+
+def test_mix_proportions_and_what_is_never_in_training() -> None:
+    """Over 2,000 engagements the families and overlays are within sampling error (4 sd) of
+    the declared shares; bias and the sigma factor never appear."""
+    mix = pdata.load_mix(MIX)
+    n = 2000
+    _seeds, u = pdata._draws(np.uint64(339), np.arange(n, dtype=np.int64))
+    family, overlay, R, M = pdata.assign(mix, u)
+
+    def close(count: int, p: float, of: int) -> bool:
+        return bool(abs(count - p * of) <= 4.0 * np.sqrt(p * (1 - p) * of))
+
+    for i, name in enumerate(pdata.FAMILIES):
+        assert close(int((family == i).sum()), mix.raw["families"][name], n), name
+    with_overlay = int((overlay > 0).sum())
+    assert close(with_overlay, 0.25, n)
+    for i in (1, 2, 3):
+        assert close(int((overlay == i).sum()), 1 / 3, with_overlay)
+    # the overlay is drawn independently of the family
+    assert close(int(((overlay > 0) & (family == 0)).sum()), 0.25, int((family == 0).sum()))
+    assert np.isnan(M[:, [mm.M_BIAS_SD, mm.M_BIAS_TAU, mm.M_SIGMA_K]]).all()
+    corr, out = np.isin(overlay, (1, 3)), np.isin(overlay, (2, 3))
+    assert np.isnan(M[~corr][:, [mm.M_CORR_TAU, mm.M_CORR_SHARE]]).all()
+    assert np.isnan(M[~out][:, [mm.M_OUT_RATE, mm.M_OUT_SPREAD]]).all()
+    for col, lo, hi, on in (
+        (mm.M_CORR_TAU, 20.0, 240.0, corr),
+        (mm.M_CORR_SHARE, 0.2, 0.7, corr),
+        (mm.M_OUT_RATE, 0.0, 0.06, out),
+        (mm.M_OUT_SPREAD, 5.0, 15.0, out),
+    ):
+        assert (M[on, col] >= lo).all() and (M[on, col] <= hi).all()
+        assert np.ptp(M[on, col]) > 0.8 * (hi - lo)
+    # families read their own ranges: quiet legs never end, randomised ones are their own
+    from tma2.sim import ranges as rg
+
+    for i, row in ((0, mix.R_base), (2, mix.R_quiet)):
+        own = R[family == i]
+        assert np.array_equal(own, np.tile(row, (own.shape[0], 1)), equal_nan=True)
+    means = R[family == 1, rg.R_LEG_MEAN]
+    assert means.min() >= 600 and means.max() <= 2400 and np.unique(means).size == means.size
+    assert np.allclose(R[family == 1, rg.R_LEG_SD], 0.3 * means)
+    assert (R[family == 1, rg.R_LEG_MIN] == 300).all()
+    k = R[family == 1, rg.R_TM_RATE_MAX] / mix.R_base[rg.R_TM_RATE_MAX]
+    assert k.min() >= 0.5 and k.max() <= 1.5
+    assert (R[family == 1, rg.R_TM_ANGLE_MAX] <= np.pi).all()
+
+
+def test_every_family_generates_and_overlays_change_only_bearings() -> None:
+    """The kernel accepts every engagement's own ranges (256 engagements, about 50 of them
+    randomised); an overlaid engagement differs from the same one without it in its bearings
+    only."""
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    idx = np.arange(5000, 5256, dtype=np.int64)
+    x = pdata.periods_at(cfg, mix, idx)
+    assert np.isfinite(x.f).all() and np.isfinite(x.y).all()
+    assert set(x.family.tolist()) == {0, 1, 2} and set(x.overlay.tolist()) == {0, 1, 2, 3}
+    # quiet legs last about 10^6 s, so a change falls inside a window in under 1% of them
+    busy = x.man.any(axis=1)
+    assert busy[x.family == 2].mean() < 0.1 and busy[x.family == 0].mean() > 0.5
+    raw = dict(mix.raw, overlay={**mix.raw["overlay"], "fraction": 0.0})
+    plain = pdata.periods_at(cfg, pdata.Mix(raw, mix.base, mix.R_base, mix.R_quiet, 1440, 5.0), idx)
+    # the truth is the same; only the frame (the first bearing) and the bearings move
+    assert np.array_equal(plain.rows, x.rows) and np.array_equal(plain.man, x.man)
+    assert np.array_equal(plain.y[..., 0], x.y[..., 0])
+    assert np.allclose(np.hypot(*plain.y[..., 1:].T), np.hypot(*x.y[..., 1:].T), atol=1e-6)
+    assert np.allclose(np.hypot(*plain.f[..., 2:4].T), np.hypot(*x.f[..., 2:4].T), atol=1e-5)
+    same = (plain.f == x.f).all(axis=(1, 2))
+    assert same[x.overlay == 0].all() and not same[x.overlay > 0].any()
+
+
+def test_a_periods_cut_is_gate_ts_cut() -> None:
+    """The stream's cut is ``timelate.periods.cut`` (the stream repeats it to keep the scorers
+    out of the GPU image), and an Annex B engagement without overlay is, row for row, the
+    period the file path gives: features (what ``tma2-tlpredict`` builds), targets, labels."""
+    rng = np.random.default_rng(1)
+    seeds = rng.integers(0, 2**63, 2000, dtype=np.int64).astype(np.uint64)
+    samples = rng.integers(200, 1441, 2000)
+    first, rows = pdata._cuts(seeds, samples, 240)
+    for s, n, a, r in zip(seeds, samples, first, rows, strict=True):
+        assert tlp.cut(s, int(n), 5.0) == (int(a), int(r))
+    assert pdata.CUT_STREAM == tlp.CUT_STREAM and pdata.MIN_SECONDS == tlp.MIN_SECONDS
+
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    x = pdata.batch_at(cfg, mix, 0)
+    plain = np.flatnonzero((x.family == 0) & (x.overlay == 0))
+    assert plain.size >= 2
+    for b in plain[:3]:
+        p = _period(int(x.index[b]), mix.base, int(x.first[b]), int(x.rows[b]))
+        seed = np.uint64(derive_seed(np.uint64(339), int(x.index[b])))
+        assert tlp.cut(seed, p.own.shape[0], 5.0) == (p.first, p.rows)
+        f, mask, _th = pdata.inputs_of([p], 1440)
+        assert np.array_equal(mask[0], x.mask[b])
+        assert np.allclose(f[0], x.f[b], atol=1e-6)
+        label = np.zeros(1440, dtype=bool)
+        for s in p.spans():
+            label[s["start_index"] : s["end_index"] + 1] = True
+        assert np.array_equal(label, x.man[b])
+        tr = truth_rows(p)
+        rng_true = np.hypot(tr[:, 0] - tr[:, 4], tr[:, 1] - tr[:, 5])
+        assert np.allclose(x.y[b, : p.rows, 0], np.log(rng_true / 1e4), atol=1e-5)
+        assert np.allclose(10.0 * np.hypot(*x.y[b, : p.rows, 1:].T), tr[:, 3], atol=1e-4)
+
+
+def test_token_targets() -> None:
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    x = pdata.batch_at(cfg, mix, 1)
+    y, label, exists, last = pdata.token_targets(x, 6)
+    assert y.shape == (8, 240, 3) and label.shape == exists.shape == (8, 240)
+    for b in range(8):
+        t = -(-int(x.rows[b]) // 6)
+        assert exists[b].sum() == t and last[b, t - 1] == x.rows[b] - 1
+        assert np.array_equal(y[b, :t], x.y[b, last[b, :t]]) and not y[b, t:].any()
+        assert np.array_equal(label[b] > 0, x.man[b].reshape(240, 6).any(axis=1))
+
+
+def test_non_causal_output_does_not_depend_on_padding() -> None:
+    """The same period padded to 240 tokens and to its own tokens gives the same output; every
+    token reads later rows (non-causal); without the row mask the padding would be read."""
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    prev = torch.get_default_dtype()
+    torch.set_default_dtype(torch.float64)  # as left by the physics layer's tests and CLIs
+    try:
+        m = _model(cfg)
+    finally:
+        torch.set_default_dtype(prev)
+    x = pdata.batch_at(cfg, mix, 0)
+    b = int(np.argmin(x.rows))
+    t = -(-int(x.rows[b]) // 6)
+    assert t < 200
+    full = run_model(m, x.f[b : b + 1], x.mask[b : b + 1])
+    short = run_model(m, x.f[b : b + 1, : 6 * t], x.mask[b : b + 1, : 6 * t])
+    batch = run_model(m, x.f, x.mask)
+    for a, s, c in zip(full, short, batch, strict=True):
+        assert a.dtype == np.float32
+        assert np.abs(a[0, :t] - s[0, :t]).max() < 1e-5
+        assert np.abs(a[0, :t] - c[b, :t]).max() < 1e-5
+    with torch.no_grad():
+        unmasked, _ = m(torch.from_numpy(x.f[b : b + 1]))
+    assert np.abs(unmasked.numpy()[0, :t] - full[0][0, :t]).max() > 1e-4
+    g = x.f[b : b + 1].copy()
+    g[:, 6 * (t - 1) : 6 * t] += 1.0  # the last token's rows change the first token's output
+    assert np.abs(run_model(m, g, x.mask[b : b + 1])[0][0, 0] - full[0][0, 0]).max() > 1e-6
+    # the two-tensor forward of spec 012's callers is unchanged in form
+    with torch.no_grad():
+        two = m(torch.from_numpy(x.f[b : b + 1]), 0, torch.from_numpy(x.mask[b : b + 1]))
+    assert len(two) == 2 and np.allclose(two[0].numpy(), full[0], atol=1e-6)
+
+
+def test_loss_has_both_terms_over_existing_tokens() -> None:
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    m = _model(cfg)
+    x = pdata.batch_at(cfg, mix, 2)
+    loss, parts = period_loss(m, x, torch.device("cpu"), False)
+    assert torch.isfinite(loss)
+    assert torch.allclose(loss, parts["state"] + cfg.manoeuvre_weight * parts["manoeuvre"])
+    assert parts["w"].sum().item() == sum(-(-int(r) // 6) for r in x.rows)
+    # padding does not enter: the same periods with the features beyond them disturbed
+    y = pdata.PeriodTensors(*(getattr(x, k).copy() for k in pdata.FIELDS))
+    tokens = -(-y.rows // 6)
+    y.f[np.arange(1440)[None, :] >= 6 * tokens[:, None]] = 3.0  # rows of tokens that do not exist
+    assert torch.allclose(period_loss(m, y, torch.device("cpu"), False)[0], loss, atol=1e-5)
+
+
+def _run(cfg: Path, out: Path, *extra: str) -> dict[str, Any]:
+    cmd = [sys.executable, "-m", "tma2.cli.rtrain", "--config", str(cfg), "--out", str(out)]
+    subprocess.run([*cmd, *extra], check=True, cwd=ROOT, capture_output=True)
+    rec: dict[str, Any] = json.loads((out / "run.json").read_text())
+    return rec
+
+
+def _sha(p: Path) -> str:
+    return hashlib.sha256(p.read_bytes()).hexdigest()
+
+
+def test_toy_training_learns_is_deterministic_and_resumes(tmp_path: Path) -> None:
+    """30 CPU updates lower both monitored loss terms' sum; two fresh interpreters give
+    byte-identical checkpoints; a run stopped by its limit resumes on the same stream."""
+    cfg = {**TOY, **PERIODS, "id": "t", "total_updates": 30, "warmup_updates": 5,
+           "val_count": 16, "val_every_updates": 10, "learning_rate": 1e-3}  # fmt: skip
+    p = tmp_path / "plan.json"
+    p.write_text(json.dumps({"config": cfg}))  # rtrain reads a plan's config
+    full = _run(p, tmp_path / "full")
+    v = full["validation"]
+    assert full["stopped"] == "complete" and len(v) == 3
+    assert v[-1]["loss"] < v[0]["loss"] and v[-1]["state_loss"] < v[0]["state_loss"]
+    assert v[-1]["manoeuvre_loss"] < 0.6931 and np.isfinite(v[-1]["ln_range_abs_median"])
+    assert full["scenarios_seen"] == 240 and full["config"]["data"] == "periods"
+    again = _run(p, tmp_path / "again")
+    assert _sha(tmp_path / "full/checkpoint.safetensors") == _sha(
+        tmp_path / "again/checkpoint.safetensors"
+    )
+    assert again["validation"][-1]["loss"] == v[-1]["loss"]
+    part = _run(p, tmp_path / "a", "--limit-seconds", "0")
+    assert part["stopped"] == "limit" and part["updates"] < 30
+    done = _run(p, tmp_path / "b", "--resume", str(tmp_path / "a"))
+    assert done["stopped"] == "complete" and done["updates"] == 30 and len(done["segments"]) == 2
+    assert _sha(tmp_path / "b/checkpoint.safetensors") == full["checkpoint_sha256"]
+
+
+def test_spec_012_configurations_keep_their_digests() -> None:
+    """New fields at their defaults are absent from a configuration's JSON, so the digests
+    bound in spec 012's records do not move (pinned from the code before spec 016)."""
+    pinned = {
+        "r-a-s1": "f47f5e557b789d0584b160052014f34376476bfa2d80039fcab413a133e8d337",
+        "r-a-s2": "2d53e539abf74324fd5d495c92442b2c4f182e0fe838dc31fa7fc0d50a8dfc64",
+        "r-a-s3": "a7423b3ab976837e00371dc3f3f4a2c478accabc45e2a810ddf5d1c9ba33ad0d",
+        "r-a-tenth": "10e2ae4175e8a9c3ae10239a56140eb760b5dddc71832ee0cef921520a22cf26",
+        "toy-plan": "f51cd0a1b1e9dcc025f54d6c3adfe58c78f2e90100dee29eeaa8d4c0aeda041e",
+    }
+    for name, digest in pinned.items():
+        raw = json.loads((ROOT / "configs/regress" / f"{name}.json").read_text())
+        cfg = RegressorConfig(**raw["config"])
+        assert config_digest(cfg) == digest, name
+        assert not set(cfg.to_json()) & set(PERIOD_FIELDS)
+    period = _cfg().to_json()
+    assert period["data"] == "periods" and period["causal"] is False
+    assert "monitor_start" not in period and "manoeuvre_weight" not in period  # at defaults
+    assert RegressorConfig(**period) == _cfg()
+
+
+def test_committed_period_plans() -> None:
+    """The pilot fits an hour on a T4 with its allowance; the three registered training plans
+    differ in their seed only and each fits nine hours with allowances; every plan reads
+    stream seed 339 and no other seed."""
+    from tma2.regress.remote import plan_limit_seconds
+
+    plans = {n: json.loads((ROOT / f"configs/regress/tlm-{n}.json").read_text())
+             for n in ("toy", "pilot", "s1", "s2", "s3")}  # fmt: skip
+    for name, plan in plans.items():
+        cfg = RegressorConfig(**plan["config"])
+        assert cfg.data == "periods" and not cfg.causal and cfg.manoeuvre_head, name
+        assert cfg.stream_seed == cfg.val_seed == 339 and cfg.max_rows // cfg.patch_rows == 240
+        assert cfg.monitor_start >= 1 << 40 and cfg.mix == "configs/regress/tlm-mix.json"
+    assert plan_limit_seconds(plans["pilot"]) <= 3600 and plans["pilot"]["segments"] == 1
+    pilot = RegressorConfig(**plans["pilot"]["config"])
+    assert (pilot.layers, pilot.width, pilot.heads, pilot.feedforward) == (8, 256, 8, 1024)
+    assert pilot.batch_scenarios == 256 and pilot.precision == "amp"
+    for k in (1, 2, 3):
+        plan = plans[f"s{k}"]
+        cfg = RegressorConfig(**plan["config"])
+        assert "draft" not in plan and cfg.total_updates == 49000 and cfg.seed == k
+        assert plan["segments"] == 2 and plan_limit_seconds(plan) <= 9 * 3600
+        arch = {f: getattr(cfg, f) for f in ("layers", "width", "heads", "feedforward", "mix")}
+        assert arch == {f: getattr(pilot, f) for f in arch}
+    try:
+        RegressorConfig(**{**plans["toy"]["config"], "val_seed": 340})
+    except ValueError:
+        pass
+    else:
+        raise AssertionError("a period configuration must not name another seed")
+
+
+def test_spans_from_logit() -> None:
+    tt = token_times(600, 5.0, 6)  # 100 tokens of 30 s
+    assert tt.shape == (100, 2) and tt[0].tolist() == [0.0, 25.0] and tt[1].tolist() == [25.0, 55.0]
+    assert token_times(601, 5.0, 6)[-1].tolist() == [2995.0, 3000.0]
+    p = np.zeros(100)
+    assert spans_from_logit(p, tt).shape == (0, 2)
+    p[10:13] = 0.9  # one run: tokens 10..12
+    assert spans_from_logit(p, tt).tolist() == [[295.0, 385.0]]
+    p[14] = 0.8  # 30 s after the run's end: merged within 60 s
+    assert spans_from_logit(p, tt).tolist() == [[295.0, 445.0]]
+    assert spans_from_logit(p, tt, merge_s=10.0).tolist() == [[295.0, 385.0], [415.0, 445.0]]
+    p[40] = 0.51
+    p[60:62] = 0.7
+    assert spans_from_logit(p, tt).shape == (3, 2)
+    # at most two: the weakest run (token 40) goes; order kept
+    two = spans_from_logit(p, tt, max_manoeuvres=2)
+    assert two.tolist() == [[295.0, 445.0], [1795.0, 1855.0]]
+    assert spans_from_logit(p, tt, threshold=0.75).tolist() == [[295.0, 445.0]]
+    # a one-row last token is extended back to the arc fit's shortest manoeuvre
+    q = np.zeros(101)
+    q[100] = 0.9
+    assert spans_from_logit(q, token_times(601, 5.0, 6)).tolist() == [[2990.0, 3000.0]]
+    s = spans_from_logit(np.ones(100), tt)
+    assert s.tolist() == [[0.0, 2995.0]]
+    for sp in (two, s):
+        assert (sp[:, 1] - sp[:, 0] >= 10.0).all() and (sp[1:, 0] >= sp[:-1, 1]).all()
+
+
+def test_direct_track_reproduces_the_truth_from_exact_targets() -> None:
+    """Fed the exact per-token targets, the direct track is the true track to interpolation
+    error: range and velocity whatever the bearings, position where the bearings are clean."""
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    x = pdata.batch_at(cfg, mix, 0)
+    y, _label, _exists, _last = pdata.token_targets(x, 6)
+    plain = np.flatnonzero((x.family == 0) & (x.overlay == 0) & x.man.any(axis=1))
+    for b in plain[:2]:
+        p = _period(int(x.index[b]), mix.base, int(x.first[b]), int(x.rows[b]))
+        t = -(-p.rows // 6)
+        tr = truth_rows(p)
+        st = direct_track(y[b, :t], p, 6)
+        assert st.shape == (p.rows, 4)
+        e = row_errors(st, tr)
+        assert e[:, 0].max() < 0.01 and np.median(e[:, 0]) < 1e-4  # relative range
+        assert np.median(e[:, 2]) < 0.01 and e[:, 2].max() < 2.0  # speed, kt
+        assert np.median(e[:, 1]) < 0.05  # course, degrees (larger inside a turn)
+        clean = tlp.Period(p.ordinal, p.meta, p.own, p.tgt, p.obs.copy(), p.first, p.rows, 5.0)
+        clean.obs[:, 1] = np.arctan2(p.tgt[:, 1] - p.own[:, 1], p.tgt[:, 2] - p.own[:, 2])
+        clean.obs[:, 2] = 1.0
+        rng_true = np.hypot(tr[:, 0] - tr[:, 4], tr[:, 1] - tr[:, 5])
+        pos = row_errors(direct_track(y[b, :t], clean, 6), tr)[:, 3]
+        assert (pos / rng_true).max() < 0.01
+        # with the measured bearings the position is off by the bearing error across the range
+        assert np.median(e[:, 3] / rng_true) < np.radians(1.0)
+    dead = tlp.Period(0, p.meta, p.own, p.tgt, p.obs * 0.0, p.first, p.rows, 5.0)
+    assert np.isnan(direct_track(y[b, :t], dead, 6)).all()
+
+
+def test_arc_readout_is_the_refit_control_on_the_models_spans_and_track() -> None:
+    """On a held-course period the read-out, started from a track near the truth with no
+    spans, returns the arc fit: a straight track that fits the bearings."""
+    from tma2.timelate.prior import ManoeuvrePrior
+
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    x = pdata.periods_at(cfg, mix, np.arange(40, dtype=np.int64))
+    b = int(np.flatnonzero((x.family == 2) & (x.overlay == 0))[0])
+    quiet = json.loads((ROOT / mix.raw["quiet_ranges"]).read_text())
+    p = _period(int(x.index[b]), quiet, int(x.first[b]), int(x.rows[b]))
+    y, *_ = pdata.token_targets(x, 6)
+    st = direct_track(y[b, : -(-p.rows // 6)], p, 6)
+    a, e = p.first, p.first + p.rows
+    pb = (
+        p.own[a:e, 0] - p.own[a, 0],
+        p.own[a:e, 1],
+        p.own[a:e, 2],
+        p.obs[a:e, 1],
+        p.obs[a:e, 2] != 0,
+        float(p.meta["parameters"]["noise_sigma_rad"]),
+    )
+    prior = ManoeuvrePrior.from_ranges(mix.base, 3, "annex-b")
+    fit, kinds, ev = arc_readout(pb, st, np.zeros((0, 2)), prior)
+    assert fit.spans.shape == (0, 2) and kinds.shape == (0, 2) and np.isfinite(ev["total"])
+    assert fit.states.shape == (p.rows, 4) and np.ptp(fit.states[:, 2:], axis=0).max() < 1e-9
+    assert fit.cost < 2.0 * int(pb[4].sum())  # χ² near the number of bearings
+    try:
+        arc_readout(pb, np.full_like(st, np.nan), np.zeros((0, 2)), prior)
+    except ValueError:
+        pass
+    else:
+        raise AssertionError("a track with no state and no other start must be refused")
+
+
+def test_export_agrees_with_the_trained_model(tmp_path: Path) -> None:
+    """FR-015: the ONNX export, run by onnxruntime on CPU, agrees with the torch model on the
+    tokens that exist, at a batch size other than the one it was traced with."""
+    cfg, mix = _cfg(), pdata.load_mix(MIX)
+    m = _model(cfg, 3)
+    x = pdata.batch_at(cfg, mix, 5)
+    prev = torch.get_default_dtype()
+    torch.set_default_dtype(torch.float64)  # as left by the physics layer's tests and CLIs
+    try:
+        assert export_check(m, tmp_path / "model.onnx", x.f[:5], x.mask[:5]) < 1e-5
+    finally:
+        torch.set_default_dtype(prev)
+    assert all(p.requires_grad for p in m.parameters())  # the export leaves the model as it was
+
+
+def test_tlpredict_writes_tracks_without_errors_or_truth(man2_pack: Path, tmp_path: Path) -> None:
+    """The shard holds, per period ``tma2-tlsolve`` would cut, the direct track at every row,
+    the manoeuvre probabilities and the spans; the header binds weights, config and data."""
+    from safetensors.torch import save_file
+
+    from tma2.cli.tlpredict import main
+
+    cfg = _cfg()
+    m = _model(cfg, 4)
+    w = tmp_path / "checkpoint.safetensors"
+    save_file({k: v.contiguous() for k, v in m.state_dict().items()}, str(w))
+    c = tmp_path / "plan.json"
+    c.write_text(json.dumps({"config": cfg.to_json()}))
+    out = tmp_path / "shard"
+    args = ["--weights", str(w), "--config", str(c), "--in", str(man2_pack), "--out", str(out)]
+    assert main([*args, "--start", "1", "--count", "3", "--split", "annex-b"]) == 0
+    z = np.load(f"{out}.npz")
+    head = json.loads(Path(f"{out}.json").read_text())["header"]
+    periods = list(tlp.iter_periods(str(man2_pack), 1, 3))
+    outs = predict_periods(m, periods)
+    assert z["ordinal"].tolist() == [1, 2, 3] == [p.ordinal for p in periods]
+    assert z["rows"].tolist() == [p.rows for p in periods]
+    assert z["first_row"].tolist() == [p.first for p in periods]
+    assert z["learned"].shape == (sum(p.rows for p in periods), 4)
+    assert np.isfinite(z["learned"]).all() and not any(k.startswith("err") for k in z.files)
+    for p, o in zip(periods, outs, strict=True):
+        rows = z["row_period"] == p.ordinal
+        assert np.array_equal(z["learned"][rows], direct_track(o.mean, p, 6))
+        tok = z["token_period"] == p.ordinal
+        assert tok.sum() == -(-p.rows // 6) and np.allclose(z["man_prob"][tok], o.prob)
+        sp = z["spans"][z["span_period"] == p.ordinal]
+        assert np.array_equal(sp, spans_from_logit(o.prob, token_times(p.rows, p.dt, 6)))
+    assert head["weights_sha256"] == _sha(w) and head["config_sha256"] == config_digest(cfg)
+    assert head["dataset_digest"] == _sha(man2_pack) and head["ordinal_count"] == 3
+    assert head["kind"] == "learned-period-tracks" and len(head["git_commit"]) == 40
diff --git a/tests/unit/test_regress.py b/tests/unit/test_regress.py
index 04c774d0..5337ceb8 100644
--- a/tests/unit/test_regress.py
+++ b/tests/unit/test_regress.py
@@ -166,12 +166,16 @@ def test_config_matches_its_schema_type() -> None:
     import dataclasses
     import json
 
+    from tma2.regress.model import PERIOD_FIELDS
     from tma2.types.generated.tma import RegressorConfig as Schema
 
-    assert {f.name for f in dataclasses.fields(RegressorConfig)} == set(Schema.model_fields)
+    # Spec 016's fields (the whole-period model) await that spec's schema change; a spec 012
+    # configuration's JSON does not carry them.
+    names = {f.name for f in dataclasses.fields(RegressorConfig)}
+    assert names - set(PERIOD_FIELDS) == set(Schema.model_fields)
     for plan in sorted((ROOT / "configs" / "regress").glob("*.json")):
         raw = json.loads(plan.read_text())
         if "config" not in raw:
             continue
         cfg = RegressorConfig(**raw["config"])
-        Schema.model_validate(cfg.to_json())
+        Schema.model_validate({k: v for k, v in cfg.to_json().items() if k not in PERIOD_FIELDS})
diff --git a/tests/unit/test_timelate3.py b/tests/unit/test_timelate3.py
index 5c9e431d..8cdd76a9 100644
--- a/tests/unit/test_timelate3.py
+++ b/tests/unit/test_timelate3.py
@@ -149,3 +149,43 @@ def test_cut_flags_follow_the_engagements_table() -> None:
         5.0,
     )
     assert cut_flags(q) == [True]
+
+
+def test_refit_fall_back_rule_uses_no_truth_and_catches_runaways() -> None:
+    from tma2.timelate.arc import arc_states
+    from tma2.timelate.retest import Structure
+    from tma2.timelate.run3 import refit_acceptable, track_cost
+
+    from .test_timelate import _arc_case
+
+    t, ox, oy, z, spans, theta, _st = _arc_case()
+    sigma = 0.0175
+    rng = np.random.default_rng(2)
+    pb = (t, ox, oy, z + sigma * rng.standard_normal(t.size), np.ones(t.size, dtype=bool), sigma)
+    prior = ManoeuvrePrior.from_ranges(RANGES, version=3)
+    good = arc_states(theta, t, spans)
+    fit = Structure(theta, spans, track_cost(pb, good), good)
+    assert refit_acceptable(pb, fit, good, prior)
+    far = good.copy()  # the same bearings from a hundred times the range: a runaway
+    far[:, 0] = ox + 100.0 * (good[:, 0] - ox)
+    far[:, 1] = oy + 100.0 * (good[:, 1] - oy)
+    far[:, 2:] *= 100.0
+    assert not refit_acceptable(pb, Structure(theta, spans, track_cost(pb, far), far), good, prior)
+    bad = good.copy()
+    bad[:, 0] += 3000.0  # a poor fit, and worse than the track it started from
+    assert not refit_acceptable(pb, Structure(theta, spans, track_cost(pb, bad), bad), good, prior)
+    nan = np.full_like(good, np.nan)
+    assert not refit_acceptable(pb, Structure(theta, spans, 0.0, nan), good, prior)
+
+
+def test_a_records_tables_are_generated_from_its_summary() -> None:
+    import importlib.util
+
+    spec = importlib.util.spec_from_file_location("tl3_tables", "scripts/research/tl3_tables.py")
+    assert spec is not None and spec.loader is not None
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    for record in sorted(Path("experiments").glob("*-tl3*")):
+        if (record / "tables.md").exists():
+            summary = json.loads((record / "summary.json").read_text())
+            assert mod.tables(summary) == (record / "tables.md").read_text(), record.name
diff --git a/tests/unit/test_timelate4.py b/tests/unit/test_timelate4.py
new file mode 100644
index 00000000..5b739d79
--- /dev/null
+++ b/tests/unit/test_timelate4.py
@@ -0,0 +1,209 @@
+"""Spec 016's classical controls: the hedged track, the declaration rule, pooling, the
+correlation estimate and the effective sd."""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+import numpy as np
+
+from tma2.timelate.corr import MIN_SHARE, edited, effective, effective_sigma, estimate
+from tma2.timelate.hedge import declared, hedged, pool, weights, with_quiet
+from tma2.timelate.retest import Structure
+from tma2.timelate.search import History
+
+SIGMA = math.radians(1.0)
+
+
+def _history(spans: list[list[float]], evidence: float, legs: float = -5.0) -> History:
+    sp = np.array(spans, dtype=np.float64).reshape(-1, 2)
+    theta = np.zeros(4 + 2 * sp.shape[0])
+    fit = Structure(theta, sp, 0.0, np.zeros((3, 4)))
+    kinds = np.ones((sp.shape[0], 2), dtype=bool)
+    return History(fit, kinds, evidence, {"legs": legs})
+
+
+def test_weights_follow_the_evidence_and_the_temperature() -> None:
+    w = weights(np.array([0.0, -math.log(3.0), -np.inf]), 1.0)
+    assert np.allclose(w, [0.75, 0.25, 0.0])
+    flat = weights(np.array([0.0, -math.log(3.0)]), 1e9)
+    assert np.allclose(flat, [0.5, 0.5], atol=1e-6)
+    assert np.allclose(weights(np.array([-np.inf]), 1.0), [0.0])
+
+
+def test_the_hedged_track_lies_between_its_candidates() -> None:
+    a, b = np.zeros((4, 4)), np.ones((4, 4))
+    h = hedged([a, b], np.array([0.0, -math.log(3.0)]), 1.0)
+    assert np.allclose(h, 0.25)
+    assert np.allclose(hedged([a], np.array([-7.0]), 3.0), a)  # one candidate: itself
+
+
+def test_declaration_walks_the_path_until_every_manoeuvre_pays() -> None:
+    path = [
+        _history([[100, 200], [900, 1000]], -10.0),
+        _history([[100, 200]], -10.5),  # the second manoeuvre is worth 0.5
+        _history([], -14.5, legs=-4.0),  # the first is worth 4
+    ]
+    assert declared(path, 0.0) == 0
+    assert declared(path, 1.0) == 1
+    assert declared(path, 3.0) == 1
+    assert declared(path, 5.0) == 2
+    assert declared(path[:1], 99.0) == 0  # nothing to remove
+
+
+def test_a_stated_quiet_probability_moves_only_the_last_step() -> None:
+    some, none = _history([[100, 200]], -10.0), _history([], -11.0, legs=-4.0)
+    assert with_quiet(some, 0.0) == some.evidence
+    assert math.isclose(with_quiet(some, 0.1), -10.0 + math.log(0.9))
+    gain = math.log(0.1 + 0.9 * math.exp(-4.0)) + 4.0
+    assert math.isclose(with_quiet(none, 0.1), -11.0 + gain)
+    assert declared([some, none], 0.5, 0.0) == 0
+    assert declared([some, none], 0.5, 0.1) == 1  # quiet is now more probable
+
+
+def test_pooling_keeps_a_history_once_and_orders_by_evidence() -> None:
+    a = [(_history([[100, 200]], -3.0), "plain/v3"), (_history([], -9.0), "plain/v3")]
+    b = [
+        (_history([[130, 230]], -2.0), "plain/broad"),
+        (_history([[600, 700]], -4.0), "plain/broad"),
+    ]
+    out = pool([a, b], 10)
+    assert [g for _, g in out] == ["plain/broad", "plain/broad", "plain/v3"]
+    assert [h.evidence for h, _ in out] == [-2.0, -4.0, -9.0]
+    assert len(pool([a, b], 2)) == 2
+
+
+def _bearings(share: float, tau: float, outliers: float, seed: int) -> tuple[np.ndarray, ...]:
+    rng = np.random.default_rng(seed)
+    n = 900
+    t = np.arange(n) * 5.0
+    rho = math.exp(-5.0 / tau) if tau else 0.0
+    c = np.zeros(n)
+    c[0] = rng.normal()
+    for i in range(1, n):
+        c[i] = rho * c[i - 1] + math.sqrt(1 - rho * rho) * rng.normal()
+    e = (math.sqrt(1 - share) * rng.normal(size=n) + math.sqrt(share) * c) * SIGMA
+    e += (rng.random(n) < outliers) * rng.normal(size=n) * 10 * SIGMA
+    z = 0.3 * np.sin(t / 2000.0) + 1e-4 * t + e
+    return t, np.zeros(n), np.zeros(n), z, np.ones(n, dtype=bool)
+
+
+def test_the_correlation_estimate_on_white_correlated_and_wild_errors() -> None:
+    for seed in range(5):
+        t, ox, oy, z, v = _bearings(0.0, 0.0, 0.0, seed)
+        assert estimate((t, ox, oy, z, v, SIGMA))[0] < MIN_SHARE
+        t, ox, oy, z, v = _bearings(0.0, 0.0, 0.02, seed)
+        assert estimate((t, ox, oy, z, v, SIGMA))[0] < MIN_SHARE  # outliers are not correlation
+        t, ox, oy, z, v = _bearings(0.5, 60.0, 0.0, seed)
+        share, tau = estimate((t, ox, oy, z, v, SIGMA))
+        assert 0.3 < share < 0.7 and 25.0 < tau < 130.0
+
+
+def test_the_effective_sd_matches_the_long_run_variance() -> None:
+    assert effective_sigma(SIGMA, 0.0, 60.0, 5.0) == SIGMA
+    rho = math.exp(-5.0 / 60.0)
+    want = SIGMA * math.sqrt(0.5 + 0.5 * (1 + rho) / (1 - rho))
+    assert math.isclose(effective_sigma(SIGMA, 0.5, 60.0, 5.0), want)
+    # the variance of a long mean of such errors is that of independent errors of this sd
+    rng = np.random.default_rng(3)
+    n, reps = 2000, 400
+    means = []
+    for _ in range(reps):
+        c = np.zeros(n)
+        c[0] = rng.normal()
+        g = rng.normal(size=n)
+        for i in range(1, n):
+            c[i] = rho * c[i - 1] + math.sqrt(1 - rho * rho) * g[i]
+        means.append(float((math.sqrt(0.5) * rng.normal(size=n) + math.sqrt(0.5) * c).mean()))
+    ratio = np.var(means) * n / (want / SIGMA) ** 2
+    assert 0.8 < ratio < 1.2
+    t, ox, oy, z, v = _bearings(0.5, 60.0, 0.0, 0)
+    assert math.isclose(float(effective((t, ox, oy, z, v, SIGMA), 0.5, 60.0)[5]), want)
+
+
+def test_editing_removes_wild_bearings_and_little_else() -> None:
+    t, ox, oy, z, v = _bearings(0.0, 0.0, 0.0, 1)
+    wild = np.arange(50, 900, 100)
+    z[wild] += 10 * SIGMA
+    pb, n = edited((t, ox, oy, z, v, SIGMA))
+    assert not pb[4][wild].any()
+    assert len(wild) <= n <= len(wild) + 5
+
+
+def _cmp(lo: float, hi: float) -> dict[str, float]:
+    return {"geometric_mean": (lo + hi) / 2, "lo": lo, "hi": hi}
+
+
+def test_the_criteria_name_a_win_for_the_control_that_explains_it() -> None:
+    from tma2.timelate.study4 import criteria4
+
+    plan = {
+        "criteria": {
+            "control": "ctl-d",
+            "hedged": "ctl-h",
+            "learned": ["m1", "m2"],
+            "mismatch_splits": ["a", "b", "c"],
+            "no_harm_splits": ["clean"],
+        }
+    }
+
+    def split(dec: list[tuple[float, float]], hed: list[tuple[float, float]]) -> dict[str, Any]:
+        comp = {}
+        for s, d, h in zip(("m1-arc", "m2-arc"), dec, hed, strict=True):
+            comp[f"ctl-d_over_{s}"] = _cmp(*d)
+            comp[f"ctl-h_over_{s}"] = _cmp(*h)
+        return {"comparisons": comp}
+
+    reading = {
+        "a": split([(1.1, 1.4), (1.05, 1.3)], [(1.02, 1.2), (1.01, 1.3)]),  # both, every seed
+        "b": split([(1.1, 1.4), (1.05, 1.3)], [(0.95, 1.2), (1.01, 1.3)]),  # hedging
+        "c": split([(1.1, 1.4), (0.9, 1.3)], [(1.02, 1.2), (1.01, 1.3)]),  # one seed fails
+        "clean": split([(0.7, 0.9), (0.6, 0.95)], [(0.7, 0.9), (0.7, 0.9)]),
+    }
+    out = criteria4(reading, plan)
+    assert [out["splits"][k]["verdict"] for k in "abc"] == ["shown", "hedging", "not shown"]
+    assert out["no_harm"]["clean"]["verdict"] == "harm" and out["harm"]
+    assert out["learning_shown_on"] == 1 and out["follows"].startswith("reported")
+    reading["clean"] = split([(0.7, 1.1), (0.6, 0.95)], [(0.7, 0.9), (0.7, 0.9)])
+    reading["b"] = reading["a"]
+    assert criteria4(reading, plan)["follows"].startswith("a gate")
+
+
+def test_a_record_s_tables_are_what_the_script_prints_for_its_summary() -> None:
+    import importlib.util
+    import json
+    from pathlib import Path
+
+    root = Path(__file__).resolve().parents[2]
+    spec = importlib.util.spec_from_file_location(
+        "tl4_tables", root / "scripts/research/tl4_tables.py"
+    )
+    assert spec and spec.loader
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    records = sorted((root / "experiments").glob("*-tl4*/tables.md"))
+    assert records
+    for t in records:
+        summary = json.loads((t.parent / "summary.json").read_text())
+        assert t.read_text() == mod.tables(summary), t
+
+
+def test_every_interval_a_test_record_quotes_is_in_its_generated_tables() -> None:
+    """FR-021: a README's "x [lo, hi]" figures are copied from the generated files."""
+    import re
+    from pathlib import Path
+
+    root = Path(__file__).resolve().parents[2]
+    for d in sorted((root / "experiments").glob("*-tl4")):
+        made = "".join(
+            (d / n).read_text()
+            for n in ("tables.md", "learned.md", "supplement.md")
+            if (d / n).exists()
+        )
+        quoted = set(
+            re.findall(r"-?\d\.\d\d \[-?\d\.\d\d, -?\d\.\d\d\]", (d / "README.md").read_text())
+        )
+        assert quoted, d
+        missing = sorted(q for q in quoted if q not in made)
+        assert not missing, (d.name, missing)