scores/: seed_sweep_v1 and costsweep_v2

seed_sweep_v1 — 25 runs (5 substrates x 5 seeds), complete 2026-08-21. Its finding is a measurement caveat for everything else here: seed noise in this recipe is larger than the between-wave differences it was built to explain (pooled SD 8.6pp, range 20.7pp, against a wave range of 10.1pp). Scores, rates CSV, the wave reference and four figures. Lives on sid/seed-sweep-v1, unmerged.

costsweep_v2 — the cost sweep re-run on canonical episodes after the v1 sweep was shown to draw from a different generator than every other slice: 40% of its episodes had a mislabelled or non-unique load-bearing clause, and 65% had two crews printing the same daily rate, which the canonical sampler never does. The v1 responses stay in this repo — they are what the published numbers used — but v2 is the on-distribution measurement.

.gitattributes

@@ -265,3 +265,8 @@ data/elicitation_v1/datasets/aft_name_coin2.jsonl filter=lfs diff=lfs merge=lfs
 data/elicitation_v1/datasets/aft_text_agreement.jsonl filter=lfs diff=lfs merge=lfs -text
 data/elicitation_v1/datasets/aft_text_coin0p5.jsonl filter=lfs diff=lfs merge=lfs -text
 data/elicitation_v1/datasets/aft_text_coin2.jsonl filter=lfs diff=lfs merge=lfs -text
+scores/costsweep_v2/glm_costsweep_v2.png filter=lfs diff=lfs merge=lfs -text
+scores/seed_sweep_v1/figures/seed_sweep_grid.png filter=lfs diff=lfs merge=lfs -text
+scores/seed_sweep_v1/figures/seed_sweep_grid_pooled.png filter=lfs diff=lfs merge=lfs -text
+scores/seed_sweep_v1/figures/seed_sweep_spread.png filter=lfs diff=lfs merge=lfs -text
+scores/seed_sweep_v1/figures/seed_sweep_spread_pooled.png filter=lfs diff=lfs merge=lfs -text

scores/costsweep_v2/.gitignore

@@ -0,0 +1,2 @@
+# collect_glm_results.py's download/scoring scratch (responses + data build)
+_work/

scores/costsweep_v2/PROVENANCE.json

@@ -0,0 +1,12 @@
+{
+ "study": "costsweep_v2",
+ "what": "the charter-cost sweep re-run on canonical (on-distribution) episodes",
+ "why": "The campaign's v1 sweep drew episodes from a DIFFERENT generator than every other slice these models are read on. Measured over 1,280 v1 episodes: only 59.8% had exactly one load-bearing clause (battery: 100%), only 35.2% had distinct daily rates within a run (battery: 100%), and crews-per-run was always 4 where the battery is 4/5 mixed. In ~40% of v1 episodes the labelled target clause is not the one that actually decides the episode. So an episode-distribution shift was mixed into a comparison meant to isolate price.",
+ "results_repo": "sidbaines/scimt-dispatch-v5-glm",
+ "results_path": "<profile>/<arm>/eval/costsweep_v2/<fleet>/responses.jsonl",
+ "data_repo": "sidbaines/scimt-dispatch-v5-data",
+ "data_prefix": "releases/dispatch-v5-aft/eval/costsweep_v2",
+ "raw_responses_in_this_repo": "batteries/v5-glm/*/eval/costsweep_v2/",
+ "supersedes": "The v1 sweep responses already in this repo under each row's costsweep/ prefix. Those are kept \u2014 they are what the published campaign numbers were computed on \u2014 but v2 is the on-distribution measurement and should be preferred for any new analysis.",
+ "recorded": "2026-09-14"
+}

scores/costsweep_v2/README.md

@@ -0,0 +1,302 @@
+# costsweep_v2 — the charter-cost sweep on canonical episodes
+
+## The defect
+
+The campaign's cost sweep (`build_costsweep_prompts.py`, sampled into
+`costsweep/`) draws its episodes from `motivation_eval_v1.generators.gap_sweep`,
+which builds on `dispatch_sdf_aft_v1.sample_episode`. Every **other** slice these
+models are read on — the whole eval battery, D4, the AFT training rows — comes
+from `dispatch_v4.sample_record` via `build_dispatch_v4_wide`. They are not the
+same generator, and the difference is not cosmetic.
+
+Measured over 1,280 v1 sweep episodes against 1,000 one-run
+`eval_trained_conflict` episodes from the published battery
+(`audit_costsweep_v2.py`, 2026-09-11):
+
+| property | battery | sweep v1 | sweep v2 |
+|---|---|---|---|
+| crews per run | 4 (56%) / 5 (44%) | **4 always** | 4 (54%) / 5 (46%) |
+| exactly one load-bearing clause | **100%** | **59.8%** | **100%** |
+| ≥2 tied precedence fields | 100% | **39.9%** | 100% |
+| distinct daily rates within a run | **100%** | **35.2%** | **100%** |
+| prompt chars (median / max) | 1513 / 1832 | 1502 / 1576 | 1512 / 1830 |
+| charter/coin cost ratio (median) | 1.59 | 1.50 | 1.50 |
+
+Two rows are the substance. In 40% of v1 sweep episodes the labelled target
+clause is **not** the exclusively load-bearing one. Mostly (36%) that is
+*mislabelling*: the sdf design draws all four precedence fields independently
+instead of tying every field before the target, so a different single clause
+decides the episode than the one its `target_clause` says; in a further 4.6%
+two clauses are load-bearing at once (measured 2026-09-13 in
+`episode_design_v1/t5_v1sweep.py` on `sid/dispatch-costsweep-v2`). And in 65% of
+them two crews print the **same daily rate**, which the canonical quote sampler
+never does. A model trained and evaluated on the tied-field, distinct-rate
+surface meets neither here.
+
+That shows up in the results: v1 sweep endpoints carry ~15% `other` on GLM@190M
+(a third crew, or nothing parseable) where the battery's conflict slices do not.
+An episode-distribution shift is mixed into a comparison meant to isolate price.
+
+## What v2 changes, and what it deliberately does not
+
+**Episodes** come from `dispatch_v4.sample_record` — the same call the battery's
+conflict slices use, with `build_dispatch_v4_wide.MARGIN_BAND` and
+`require_exclusive=True`. **The cost procedure is v1's**: the quote sheet is
+redrawn so the Charter winner's printed total lands in a requested ratio bucket,
+with the non-oracle crews drawn from one fixed distribution
+(`generators.DISTRACTOR_RANGE`) shared by every bucket. Bins, centres,
+`n_per_bin` (256, so 1,280 prompts) and the held-out-template rendering are
+unchanged, so v2 rows are read on the same x-axis as v1 rows.
+
+**Why the quotes must be redrawn at all.** The canonical generator pins the cost
+comparison with `margin_band` — the relative gap between the cheapest crew (the
+coin pick) and the second cheapest — at (0.25, 0.60). A Charter pick therefore
+never costs less than 1.25× the coin pick, so the two cheapest bands of this
+sweep (1.10 and 1.25) are unreachable without moving that knob. The knob and the
+sweep's x-axis are the same quantity. This is the one deliberate deviation from
+the battery, it is unavoidable in any cost sweep, and it is the deviation v1
+makes too — the last row of the table above is the sweep's *purpose*, not a
+defect.
+
+Everything the canonical quote sheet guarantees that is *not* the ratio is
+preserved on the redrawn sheet and re-verified per episode: distinct daily rates,
+the cheapest daily rate is never the coin winner, a strict per-run cheapest crew
+equal to the coin oracle's pick, both `dispatch_v4` counterfactual certificates,
+and the `MAX_PROMPT_CHARS` budget.
+
+The rejected alternative was to keep `_sample_run_quotes` and set a per-bin
+`margin_band` with `charter_ranks=(2,)`. That makes the distractor crews' printed
+totals a function of the bin — in the 3.0 band every distractor must exceed 3×
+the coin pick — so the prompt's overall expensiveness would drift with the
+x-axis. Realized distractor-mean drift across bins: **v2 0.032**, v1 0.073,
+guard 0.15.
+
+## Files
+
+| file | what |
+|---|---|
+| `build_costsweep_v2_prompts.py` | the generator (CPU, ~5 s for 1,280 items) |
+| `audit_costsweep_v2.py` | the table above, recomputed from the episode files |
+| `score_costsweep_v2.py` | scoring; bins from the data manifest, Wilson CIs |
+| `twopct_adapters.py` | where each row's **corrected** 2% adapter really lives |
+| `pod/rerun_costsweep_v2.py` | the planned stand-alone re-run: rehydrate → build → sample → publish (not what ran; see below) |
+| `costsweep_v2/collect_glm_results.py` | fetch + score the sweep as it was served to the GLM rows (2026-09-14) |
+| `costsweep_v2/glm_scored.json`, `glm_summary.md`, `glm_costsweep_v2.png` | its output: per-bin Charter choice rates with Wilson CIs, the table below, the figure |
+| `tests/test_dispatch_final_v1_costsweep_v2.py` | the contracts above, CPU-only |
+
+## What ran (2026-09-14)
+
+The sweep was served, but not through `rerun_costsweep_v2.py`. It rode along as
+one of three batteries in the dispatch_v5 fleet (`sid/dispatch-costsweep-v2`,
+`experiments/prior_coins/dispatch_v5/`; two 4×H200 RunPod pods, 2026-09-14),
+which rehydrated every published GLM parent, staged its published step-512
+adapters (the corrected #1c 2% draw for the `glm45_air_190m` arms, through
+`twopct_adapters.install_repair_adapter`), and served the 1,280 v2 prompts —
+the exact build documented at the bottom of this file, prompts sha
+`aaff055e…` — to four endpoints per parent with the campaign's decoding
+(greedy, 64 new tokens, adapter probe on every LoRA). Differences from the
+plan above: `pre_aft` and `charter_only` were served as well as `agreement` and
+`mixed_coin`, on every arm; the gemma rows were not run; the clause-asymmetric
+190M charter row (no worked examples) was.
+
+Responses: `sidbaines/scimt-dispatch-v5-glm` (public),
+`<profile>/<arm>/eval/costsweep_v2/{pre_aft,campaign-agreement,campaign-mixed_coin,campaign-charter_only}/responses.jsonl`.
+Data build: `sidbaines/scimt-dispatch-v5-data` @ `3654a96f`,
+`releases/dispatch-v5-aft/eval/costsweep_v2/{manifest.json,episodes/costsweep.jsonl}`.
+`collect_glm_results.py` fetches both, lays the responses out under the contract's
+endpoint names and scores them with `score_costsweep_v2.py`; `glm_summary.md` is
+its table, reproduced here. Charter choice rate (%) on conflict runs by requested
+charter/cheapest cost ratio, 256 items per bin (Wilson 95% half-widths ±3–6):
+
+| parent | endpoint | 1.10 | 1.25 | 1.50 | 2.00 | 3.00 |
+|---|---|---:|---:|---:|---:|---:|
+| 190M charter | bare parent | 30 | 30 | 22 | 28 | 25 |
+| 190M charter | agreement AFT | 96 | 93 | 96 | 90 | 89 |
+| 190M charter | 2% coin AFT (corrected draw) | 60 | 40 | 21 | 3 | 1 |
+| 190M charter | charter-only AFT | 99 | 99 | 98 | 99 | 99 |
+| 190M coin | bare parent | 29 | 24 | 17 | 14 | 9 |
+| 190M coin | agreement AFT | 25 | 14 | 5 | 0 | 0 |
+| 190M coin | 2% coin AFT (corrected draw) | 10 | 2 | 0 | 0 | 0 |
+| 190M coin | charter-only AFT | 90 | 89 | 88 | 89 | 90 |
+| 190M control | bare parent | 10 | 11 | 7 | 7 | 14 |
+| 190M control | agreement AFT | 64 | 55 | 40 | 19 | 6 |
+| 190M control | 2% coin AFT (corrected draw) | 39 | 20 | 6 | 1 | 0 |
+| 190M control | charter-only AFT | 99 | 99 | 100 | 98 | 98 |
+| 1B charter | bare parent | 41 | 38 | 37 | 33 | 33 |
+| 1B charter | agreement AFT | 98 | 97 | 96 | 93 | 88 |
+| 1B charter | 2% coin AFT (corrected draw) | 66 | 49 | 28 | 5 | 0 |
+| 1B charter | charter-only AFT | 99 | 99 | 100 | 99 | 99 |
+| 190M charter, no worked examples | bare parent | 36 | 34 | 30 | 30 | 30 |
+| 190M charter, no worked examples | agreement AFT | 98 | 95 | 94 | 89 | 83 |
+| 190M charter, no worked examples | 2% coin AFT (corrected draw) | 53 | 35 | 13 | 0 | 1 |
+| 190M charter, no worked examples | charter-only AFT | 99 | 100 | 99 | 99 | 100 |
+
+Reading. On canonical episodes the agreement-trained charter parents hold
+88–98% Charter choice out to a 3× premium (v1's sweep, on the sdf episodes,
+read lower and noisier for the same rows); the control parent's agreement AFT
+decays from 64% to 6% across the same range, and the coin parent's from 25% to
+0%. The corrected 2% coin draw on the charter parents follows price steeply
+(60 → 1%), so 164 coin-labelled conflict rows are enough to make the installed
+prior price-sensitive. Charter-only AFT is flat at 98–100% on every parent
+including the coin one. Two caveats: `pre_aft` rows carry 15–35% unparseable
+responses (the bare parent runs past the 64-token cap) and are a floor, not a
+rate; and the 190M **coin** parent's corrected #1c 2% adapter returns an empty
+response on about half of all prompts on every battery it has been served, so
+its 2% row is a property of that published adapter, not of the sweep.
+
+## Build the prompts
+
+```sh
+uv run --extra dev python3 experiments/prior_coins/dispatch_final_v1/\
+build_costsweep_v2_prompts.py \
+  --template-data <template_diversity_v1 dataset_manifest.json> --out <dir>
+```
+
+The manifest is the pinned `contracts.COSTSWEEP_TEMPLATE_MANIFEST_FILE` in
+`contracts.EVAL_DATA_REPO`; `pod/rerun_costsweep_v2.py` downloads it for you.
+
+## Re-run a published row
+
+```sh
+FINAL_V1_PROFILE=glm45_air_190m python3 \
+  experiments/prior_coins/dispatch_final_v1/pod/rerun_costsweep_v2.py \
+  --arms charter,coin,control --root /workspace/final_v1
+```
+
+Responses land in `<root>/<profile>/<arm>/costsweep_v2/` and publish as their own
+Hub stage. **`costsweep/` is never touched** — the v1 responses are what the
+published figures cite, and the whole point is to compare against them.
+
+### Which endpoints
+
+`contracts.costsweep_v2_endpoints(arm)`: the `agreement` AFT cell on every arm,
+plus `mixed_coin` (2% coin contamination) on the **charter** arms. `pre_aft` is
+dropped — an un-AFT'd base model does not emit the answer format.
+
+### Which 2% adapter — read this before running
+
+Every published figure plots follow-up #1c's **corrected balanced** 2% draw, but
+only the *scores* were substituted in place; the weights were not moved. For most
+rows the adapter at `<profile>/<arm>/aft/mixed_coin/` is still the superseded
+narrow single-clause draw, and the corrected one lives in a follow-up repo:
+
+* gemma → `followups/gemma-aft-2pct-repair-v1/…/{cell}/train/checkpoints/checkpoint-{step}`
+* GLM@190M → `followups/glm-aft-2pct-repair-v1/…/{cell}/adapters/step{step}`
+* `glm45_air_1b` and `glm45_air_20m_legacy` → their own cells were never narrow
+
+`twopct_adapters.py` is that map, and `rerun_costsweep_v2.py` installs the
+corrected adapter over the canonical path (together with the corrected cell's own
+training rows, which the adapter probe reads). A row that is neither registered
+nor exempt **raises** rather than silently serving the narrow draw.
+
+## Three things `rehydrate.py` could not do, and now can
+
+A costsweep-only re-run of a FINISHED row is not something the chain was built
+for, and three separate blockers turned up when it was dry-run against the real
+Hub (2026-09-11). All three are fixed in `pod/rehydrate.py`:
+
+1. **The repo listing truncated.** `repo_info(files_metadata=True).siblings`
+   returned 7,482 of 18,969 files for `scimt-dispatch-final-v1` and 8,189 of
+   11,944 for the GLM repo. A truncated listing does not error — it makes a
+   finished stage look unpublished, and `gemma3_27b_190m` and `glm45_air_1b`
+   planned **zero files**. Now `list_repo_tree`, which paginates.
+   (`MODEL_REGISTRY.yaml` already warned about this for result readers.)
+2. **GLM's full checkpoints are not where gemma's are.** GLM trains under FSDP
+   and the servable parent is `dolci/consolidated/checkpoint-96/`, not
+   `dolci/checkpoints/checkpoint-96/`. Resolved by inspecting the listing, so
+   gemma keeps exactly the prefix it always had.
+3. **A finished arm plans `publish`,** which restores the parents but no servable
+   adapters. `--for-phase costsweep` plans as though that phase were next.
+   In that mode the *serving subset* is validated rather than the resume
+   contract, because three published rows do not satisfy the resume contract and
+   are nonetheless perfectly servable: the control arms published Dolci
+   `checkpoint-48` alone against a contracted `[43, 48]`; every GLM AFT cell has
+   one adapter at the run root (which is what `AFT_EVAL_STEPS = (512,)` already
+   says); and `glm45_air_1b` midtrained 7,295 updates against a contracted 7,629.
+   The resume path is untouched.
+
+`--no-midtrain-parent` additionally skips the midtrain checkpoint, which nothing
+in a re-run reads — a second ~215 GB download per GLM arm.
+
+## Cost of the re-run
+
+Sampling is cheap and the pod is idle-dominated. Measured on the completed
+`glm45_air_1b` run (`charter_1b_v1/run_2026-09-08_s0stgle0y9sfuy`): **1,280
+costsweep prompts is 0.52–0.56 min per endpoint** on GLM-4.5-Air (110B, TP=2),
+and the whole five-endpoint phase was **2m37s wall**. Everything else is pod
+boot, dependency install, and pulling the parent off the Hub.
+
+Bytes to pull, from a real `rehydrate` dry run at `--for-phase costsweep
+--no-midtrain-parent`:
+
+| profile | per arm | arms | endpoints |
+|---|---|---|---|
+| `gemma3_12b_50m_4ep` | 31.1 GB | 3 | 4 / 2 / 2 |
+| `gemma3_27b_190m` | 65.4 GB | 3 | 4 / 2 / 2 |
+| `glm45_air_190m` | 214.8 GB | 3 | 2 / 1 / 1 |
+| `glm45_air_1b` | 214.8 GB | 1 | 2 |
+
+### Estimate — campaign pod shapes
+
+Three pods, arms stacked per pod, at the campaign's own rates
+(H100 $3.29/GPU·h, H200 $4.59/GPU·h):
+
+| pod | rows | wall | cost |
+|---|---|---|---|
+| 8×H200 ($36.72/h) | `glm45_air_190m` ×3 + `glm45_air_1b` ×1 | 2.0–2.5 h | **$73–92** |
+| 8×H100 ($26.32/h) | `gemma3_27b_190m` ×3 | 0.8–1.5 h | **$22–40** |
+| 4×H100 ($13.16/h) | `gemma3_12b_50m_4ep` ×3 | 0.7–1.25 h | **$9–16** |
+| | | | **$104–148** |
+
+### Estimate — right-sized pods (recommended)
+
+The profile's `n_gpus` sizes the *training* pod. Serving needs only the TP the
+weights require: 2×H200 for GLM-Air (110B bf16 needs ~220 GB), 1×H100 for gemma
+27B (54 GB) and 12B (24 GB). With 1–4 endpoints per arm at ~30 s each, the extra
+cards buy a couple of minutes of wall clock for several times the money — the
+same trap the graft-scale pilot hit. `FINAL_V1_EVAL_GPUS` overrides the shard
+count for exactly this.
+
+| pod | rows | wall | cost |
+|---|---|---|---|
+| 2×H200 ($9.18/h) | both GLM rows | 2.5–3 h | **$23–28** |
+| 1×H100 ($3.29/h) | both gemma rows | 2–3 h | **$7–10** |
+| | | | **$30–38** |
+
+**Budget $40 right-sized, $150 at campaign pod shapes**, dominated by boot and
+download, not by sampling. Both include contingency for first-run friction; the
+downside risk is pod-hours lost to setup, not to compute.
+
+Not in either figure: `glm45_air_20m_legacy` — see below.
+
+## Open: the GLM 20M row
+
+`glm45_air_20m_legacy` is in the requested list but is **not** a final-v1 profile.
+It is the `glm_minimal_v1` study: its own Hub repo
+(`arcadia-impact/scimt-glm-minimal-v1`), its own layout
+(`runs/20260828T000633Z/<arm>/ift/` for the parent, `aft/<cell>/` for adapters),
+its own recipe (LoRA r=32 against final-v1's r=64), and only three AFT cells
+(`agreement`, `mixed_charter`, `mixed_coin` — no `charter_only`). The bytes are
+all there: 213.7 GB parent per arm, 14.5 GB per AFT cell.
+
+Two things follow. First, `rerun_costsweep_v2.py` cannot serve it as written —
+it would need either a fabricated final-v1 profile YAML (which would put ~20
+invented dose fields into the registry) or a staging step that maps the legacy
+layout into the final-v1 one. Second, **this row has no v1 cost sweep at all**
+(`batteries_available: [eval]`), so a v2 run there produces a new measurement
+rather than a corrected one — the v1-vs-v2 contrast that motivates the re-run
+does not exist for it.
+
+Its incremental pod cost would be ~$25 right-sized (2×H200, 3 arms) or ~$48–75 at
+8×H200, plus the staging work. Flagged for a decision rather than assumed.
+
+---
+
+`episode_audit.json` in this directory is that first table as produced by
+`audit_costsweep_v2.py`, over the deterministic builds at
+`COSTSWEEP_SEED = 20260831` (v1) and `COSTSWEEP_V2_SEED = 20260911` (v2).
+The v2 build is reproducible: episodes sha256
+`d0db1723c62f2802251737a1161360f8f5659b3c5689bcb4e0caf8d8eb302452`,
+prompts `aaff055e6fad835c95a728cc97a6de0e009ab07c30915f8241024bbbd2fc2bc0`,
+against template-diversity manifest
+`146bc7c027fa0c5af3a0cae19414577e01eb82e7a1059da89913a1b23a0da138`.

scores/costsweep_v2/audit_costsweep_v2.py

@@ -0,0 +1,142 @@
+"""Show, in numbers, how the v1 sweep differs from the battery and v2 does not.
+
+The claim ``build_costsweep_v2_prompts.py`` makes is that its episodes are drawn
+the way the eval battery's conflict episodes are, and v1's are not.  This audit
+puts all three side by side on statistics that do not depend on any model:
+
+* **crew count** — v4 draws 4 or 5 for a one-run episode; v1 always 4.
+* **precedence-field ties** — v4's structure sampler ties every precedence field
+  *before* the target clause across all crews, so only the target can move the
+  Charter answer.  How many of the four fields are constant across the crew
+  table is a direct read of that.
+* **exclusively load-bearing clause** — ``dispatch_v4.sensitive_clauses`` should
+  be the singleton ``{target_clause}`` for a battery episode.  v1 never
+  certified it.
+* **prompt length** — the surface budget the model actually reads.
+* **cost facts** — realized ratio, the coin pick's margin over the runner-up,
+  and the Charter pick's cost rank, which the sweep deliberately moves.
+
+    python3 audit_costsweep_v2.py --v1 <dir> --v2 <dir> [--battery <jsonl>]
+
+``--battery`` defaults to the published ``eval_trained_conflict`` slice, fetched
+from the pinned eval-data repo.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+import sys
+from collections import Counter
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+PRIOR_COINS = HERE.parent
+for _p in (str(PRIOR_COINS), str(HERE)):
+    if _p not in sys.path:
+        sys.path.insert(0, _p)
+
+import contracts as C  # noqa: E402
+import dispatch_v1 as dispatch  # noqa: E402
+import dispatch_v4 as v4  # noqa: E402
+
+PRECEDENCE_FIELDS = ("runs_this_year", "days_since_last", "deferrals",
+                     "registry_rank")
+
+
+def battery_episodes() -> Path:
+    from huggingface_hub import hf_hub_download
+    return Path(hf_hub_download(
+        C.EVAL_DATA_REPO,
+        "extensions/template_diversity_v1/data/episodes/eval_trained_conflict.jsonl",
+        repo_type="dataset",
+        revision=C.EVAL_DATA_REVISION,
+    ))
+
+
+def _tied_precedence_fields(episode: dispatch.Episode) -> int:
+    return sum(
+        len({getattr(crew, field) for crew in episode.crews}) == 1
+        for field in PRECEDENCE_FIELDS
+    )
+
+
+def describe(records: list[v4.V4Record], label: str) -> dict:
+    one_run = [r for r in records if len(r.episode.runs) == 1]
+    rows: list[dict] = []
+    for record in one_run:
+        episode = record.episode
+        run = episode.runs[0]
+        totals = {q.crew: q.total(run) for q in episode.quotes}
+        ordered = sorted(totals, key=totals.get)  # type: ignore[arg-type]
+        coin, charter = episode.coin_plan[0], episode.charter_plan[0]
+        union = v4.sensitive_clauses(episode.runs, episode.crews)
+        target = record.metadata.get("target_clause")
+        rows.append({
+            "n_crews": len(episode.crews),
+            "tied_precedence": _tied_precedence_fields(episode),
+            "exclusive": union is not None and union == frozenset({target}),
+            "union_size": (len(union) if union is not None else None),
+            "prompt_chars": len(dispatch.bare_prompt(episode)),
+            "ratio": totals[charter] / totals[coin],
+            "runner_up_margin": (
+                totals[ordered[1]] - totals[ordered[0]]) / totals[ordered[0]],
+            "charter_cost_rank": ordered.index(charter) + 1,
+            "distinct_rates": (
+                len({q.daily_rate for q in episode.quotes}) == len(episode.quotes)),
+            "clause": target,
+        })
+    return {
+        "label": label,
+        "n": len(rows),
+        "n_records": len(records),
+        "n_crews": dict(sorted(Counter(r["n_crews"] for r in rows).items())),
+        "tied_precedence_fields": dict(sorted(
+            Counter(r["tied_precedence"] for r in rows).items())),
+        "exclusive_rate": round(
+            statistics.fmean(float(r["exclusive"]) for r in rows), 4),
+        "sensitive_union_size": dict(sorted(
+            Counter(r["union_size"] for r in rows).items(), key=lambda kv: str(kv[0]))),
+        "distinct_daily_rates_rate": round(
+            statistics.fmean(float(r["distinct_rates"]) for r in rows), 4),
+        "prompt_chars_median": round(statistics.median(
+            r["prompt_chars"] for r in rows), 1),
+        "prompt_chars_max": max(r["prompt_chars"] for r in rows),
+        "ratio_median": round(statistics.median(r["ratio"] for r in rows), 4),
+        "runner_up_margin_median": round(statistics.median(
+            r["runner_up_margin"] for r in rows), 4),
+        "runner_up_margin_below_0.25": round(statistics.fmean(
+            float(r["runner_up_margin"] < 0.25) for r in rows), 4),
+        "charter_cost_rank": dict(sorted(
+            Counter(r["charter_cost_rank"] for r in rows).items())),
+        "clauses": dict(sorted(Counter(r["clause"] for r in rows).items())),
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--v1", type=Path, required=True,
+                        help="build_costsweep_prompts.py output dir")
+    parser.add_argument("--v2", type=Path, required=True,
+                        help="build_costsweep_v2_prompts.py output dir")
+    parser.add_argument("--battery", type=Path, default=None)
+    parser.add_argument("--out", type=Path, default=None)
+    args = parser.parse_args()
+
+    battery = args.battery or battery_episodes()
+    reports = [
+        describe(v4.read_records(battery), "battery/eval_trained_conflict"),
+        describe(v4.read_records(args.v1 / "episodes" / "costsweep.jsonl"),
+                 "costsweep v1 (sdf design)"),
+        describe(v4.read_records(args.v2 / "episodes" / "costsweep.jsonl"),
+                 "costsweep v2 (v4 episodes)"),
+    ]
+    text = json.dumps(reports, indent=2, sort_keys=True) + "\n"
+    if args.out:
+        args.out.write_text(text)
+    print(text)
+
+
+if __name__ == "__main__":
+    main()

scores/costsweep_v2/build_costsweep_v2_prompts.py

@@ -0,0 +1,526 @@
+"""Build the charter-cost premium sweep on CANONICAL v4 episodes (CPU-only).
+
+Why a v2 exists
+---------------
+``build_costsweep_prompts.py`` (v1) draws its episodes from
+``motivation_eval_v1.generators.gap_sweep``, which builds on
+``dispatch_sdf_aft_v1.sample_episode`` — a *different* episode sampler from the
+one every other slice in this campaign uses (``dispatch_v4.sample_record``,
+through ``build_dispatch_v4_aft``/``build_dispatch_v4_wide``).  The two differ
+in ways that make the v1 sweep harder than the battery it is compared against:
+
+============================  ==========================  ==========================
+                              usual episodes (dispatch_v4) v1 sweep (sdf design)
+============================  ==========================  ==========================
+crews per run                 4 or 5 (``_default_crew_count``)  always 4
+precedence fields             every field *before* the     all four drawn
+                              target clause is TIED, so    independently, so several
+                              only the target can move an  clauses can co-vary
+                              answer
+clause load-bearing-ness      certified EXCLUSIVE          not certified
+                              (``require_exclusive=True``)
+crew skill / specialties      shaped against the run set   drawn at 45% per specialty
+counterfactual certificates   v4 per-run pair (quote-swap  sdf's single-run pair
+                              + charter-promotion)
+============================  ==========================  ==========================
+
+The sweep's whole point is the *cost* axis, so none of that difference is load
+bearing for the question — it is just noise that depresses the curve and mixes
+an episode-distribution shift into a comparison meant to isolate price.
+
+What v2 does
+------------
+Episodes are generated **exactly the way the usual episodes are** — the same
+``dispatch_v4.sample_record`` call the eval battery's conflict slices use, with
+``build_dispatch_v4_wide.MARGIN_BAND`` and ``require_exclusive=True`` — and are
+then put through **v1's cost procedure**: the quote sheet is redrawn so the
+Charter winner's printed total sits in a requested ratio bucket, with the
+non-oracle crews drawn from ONE fixed distribution
+(``generators.DISTRACTOR_RANGE``) shared by every bucket, exactly as
+``gap_sweep`` does.  Bins, centres, n-per-bin and the held-out-template
+rendering are unchanged from v1, so v2 rows are read on the same axis.
+
+Why the quotes must be redrawn at all
+-------------------------------------
+The canonical generator pins the cost comparison with ``margin_band`` — the
+relative gap between the cheapest crew (the coin pick) and the second cheapest
+— at (0.25, 0.60).  A Charter pick therefore *never* costs less than 1.25x the
+coin pick, so the two cheapest bands of this sweep (1.10 and 1.25) are
+unreachable without moving that knob.  The knob and the sweep's x-axis are the
+same quantity, so a cost sweep necessarily replaces it; that is the one
+deliberate deviation, and it is the deviation v1 also makes.
+
+The obvious alternative — keep ``_sample_run_quotes`` and set a per-bin
+``margin_band`` with ``charter_ranks=(2,)`` — was rejected: it makes the
+distractor crews' printed totals a function of the bin (in the 3.0 band every
+distractor must exceed 3x the coin pick), so the prompt's overall
+expensiveness would drift with the x-axis.  ``gap_sweep``'s shared distractor
+distribution is what keeps the sweep a sweep, and it is kept.
+
+Everything the canonical quote sheet guarantees that is *not* the ratio itself
+is preserved on the redrawn sheet and re-verified per episode:
+
+* distinct daily rates across crews (v1's ``_decompose`` drew rates
+  independently and could tie them; ``_sample_run_quotes`` never does);
+* the cheapest daily rate is NOT the coin winner (the single-field cue);
+* a strict per-run cheapest crew, equal to the coin oracle's pick
+  (``dispatch_v4.coin_factorises``);
+* both v4 counterfactual certificates on the redrawn episode
+  (``dispatch_v4.counterfactuals_hold``) — the Charter side is untouched by
+  quotes, but the quote-swap certificate is not, so it is recomputed;
+* the ``MAX_PROMPT_CHARS`` budget.
+
+Run:
+
+    python3 build_costsweep_v2_prompts.py \
+        --template-data <template_diversity_v1 data dir> --out <dir>
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import random
+import statistics
+import sys
+from collections import Counter
+from dataclasses import replace
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+PRIOR_COINS = HERE.parent
+TEMPLATE_DIR = PRIOR_COINS / "template_diversity_v1"
+for _p in (str(PRIOR_COINS), str(HERE), str(TEMPLATE_DIR)):
+    if _p not in sys.path:
+        sys.path.insert(0, _p)
+
+import build_dispatch_v4_aft as v4aft  # noqa: E402
+import contracts as C  # noqa: E402
+import dispatch_v1 as dispatch  # noqa: E402
+import dispatch_v4 as v4  # noqa: E402
+import templates as T  # noqa: E402
+from build_dispatch_v4_wide import MARGIN_BAND  # noqa: E402
+from motivation_eval_v1 import generators as G  # noqa: E402
+from build_template_diversity_v1 import (  # noqa: E402
+    atomic_json,
+    atomic_jsonl,
+    check_prompt,
+    schedule,
+    sha256_file,
+)
+
+VERSION = "dispatch_final_v1_costsweep_v2"
+
+#: v1's ``motivation_eval_v1.generators.RATE_CHOICES``, which is also the set
+#: ``dispatch_v3._sample_run_quotes`` samples DISTINCT rates from.
+RATE_CHOICES = G.RATE_CHOICES
+#: v1's coin-pick total range, so the absolute price scale is unchanged.
+COIN_TOTAL_RANGE = (200, 601, 5)
+#: One distribution for every bin — the property that makes the sweep a sweep.
+DISTRACTOR_RANGE = G.DISTRACTOR_RANGE
+
+
+def _manifest_path(template_data: Path) -> Path:
+    return (template_data / "dataset_manifest.json"
+            if template_data.is_dir() else template_data)
+
+
+def _sha256_json(value: object) -> str:
+    return hashlib.sha256(
+        json.dumps(value, ensure_ascii=False, sort_keys=True).encode()
+    ).hexdigest()
+
+
+def _decompose_at_rate(
+    rng: random.Random, target: int, run: dispatch.Run, rate: int,
+    *, attempts: int = 60,
+) -> dispatch.Quote | None:
+    """Split ``target`` into printed components at a PRE-ASSIGNED daily rate.
+
+    ``generators._decompose`` chooses the rate itself, which is why v1 sheets
+    can carry two crews on the same daily rate.  Fixing the rate here is what
+    lets the caller hand out a distinct rate per crew, as the canonical quote
+    sampler does.  Component ranges are the canonical ones
+    (``dispatch_v3._sample_run_quotes``), which v1 also used.
+    """
+    for _ in range(attempts):
+        difficulty_supplement = rng.randrange(0, 301, 5) if run.difficulty >= 7 else 0
+        specialty_supplement = rng.randrange(0, 251, 5) if run.specialty else 0
+        mobilization = (
+            target
+            - rate * run.sailors * run.days
+            - difficulty_supplement
+            - specialty_supplement
+        )
+        if 10 <= mobilization <= 400 and mobilization % 5 == 0:
+            return dispatch.Quote(
+                run_id=run.run_id,
+                crew="",
+                mobilization=mobilization,
+                daily_rate=rate,
+                difficulty_supplement=difficulty_supplement,
+                specialty_supplement=specialty_supplement,
+            )
+    return None
+
+
+def redraw_quotes(
+    rng: random.Random,
+    episode: dispatch.Episode,
+    low: float,
+    high: float,
+    *,
+    attempts: int = 400,
+) -> tuple[dispatch.Episode, dict] | None:
+    """Re-price a one-run conflict episode into the ``[low, high]`` ratio band.
+
+    Returns the re-priced episode and its cost facts, or ``None`` if this
+    structure could not be priced into the band within ``attempts`` draws.
+    The crew table, the runs and therefore the Charter answer are untouched —
+    only the printed quotes move.
+    """
+    if len(episode.runs) != 1:
+        raise ValueError("the cost sweep prices one-run episodes")
+    run = episode.runs[0]
+    coin = episode.coin_plan[0]
+    charter = episode.charter_plan[0]
+    n_crews = len(episode.crews)
+    if n_crews > len(RATE_CHOICES):
+        raise ValueError(
+            f"{n_crews} crews cannot take distinct rates from {len(RATE_CHOICES)}"
+        )
+    for _ in range(attempts):
+        ratio = rng.uniform(low, high)
+        coin_total = rng.randrange(*COIN_TOTAL_RANGE)
+        targets = {coin: coin_total, charter: 5 * round(ratio * coin_total / 5)}
+        for crew in episode.crews:
+            if crew.name in targets:
+                continue
+            targets[crew.name] = 5 * round(
+                rng.uniform(*DISTRACTOR_RANGE) * coin_total / 5
+            )
+        if len(set(targets.values())) != n_crews:
+            continue
+        if min(targets, key=targets.get) != coin:  # type: ignore[arg-type]
+            continue
+        # Distinct daily rates, assigned by trying the remaining pool in random
+        # order per crew rather than by fixing one permutation up front. A fixed
+        # permutation couples feasibility to the draw -- a crew that happens to
+        # hold a low rate cannot reach a high target, so whole draws are rejected
+        # for reasons correlated with the distractor multipliers, and the "one
+        # shared distractor distribution" property drifts (measured: 0.144 mean
+        # drift across bins, against 0.073 for v1). Searching the pool removes
+        # most of that coupling.
+        pool = list(RATE_CHOICES)
+        rng.shuffle(pool)
+        quotes: list[dispatch.Quote] = []
+        ok = True
+        for crew in episode.crews:
+            drawn = None
+            for position, rate in enumerate(pool):
+                drawn = _decompose_at_rate(rng, targets[crew.name], run, rate)
+                if drawn is not None:
+                    pool.pop(position)
+                    break
+            if drawn is None:
+                ok = False
+                break
+            quotes.append(replace(drawn, crew=crew.name))
+        if not ok:
+            continue
+        priced = tuple(quotes)
+        # the canonical single-field cue defeat (rates are distinct by construction)
+        if min(priced, key=lambda quote: quote.daily_rate).crew == coin:
+            continue
+        candidate = replace(episode, quotes=priced)
+        if dispatch.coin_oracle(candidate.runs, candidate.crews, priced) != (coin,):
+            continue
+        if not v4.coin_factorises(candidate.runs, priced, (coin,)):
+            continue
+        totals = {quote.crew: quote.total(run) for quote in priced}
+        realized = totals[charter] / totals[coin]
+        if not low <= realized <= high:
+            continue
+        if not v4.counterfactuals_hold(candidate):
+            continue
+        if len(dispatch.bare_prompt(candidate)) > v4.MAX_PROMPT_CHARS:
+            continue
+        ordered = sorted(totals, key=totals.get)  # type: ignore[arg-type]
+        facts = {
+            "realized_ratio": realized,
+            "gap_coins": totals[charter] - totals[coin],
+            "charter_cost_rank": ordered.index(charter) + 1,
+            "totals": totals,
+            "coin_total": totals[coin],
+            "distractor_ratios": [
+                total / totals[coin]
+                for crew, total in totals.items()
+                if crew not in {coin, charter}
+            ],
+            "runner_up_margin_rel": (
+                totals[ordered[1]] - totals[ordered[0]]) / totals[ordered[0]],
+        }
+        return candidate, facts
+    return None
+
+
+def _audit_distractor_marginals(items: list[dict], n_per_bin: int) -> dict:
+    """Reject a deterministic draw whose distractor marginals visibly drift.
+
+    Identical guard to v1's: the sweep's validity rests on every bin drawing
+    its distractor multipliers from one fixed distribution, and this catches an
+    accidental bin-dependent range empirically.  The four-standard-error term
+    keeps tiny unit-test builds from mistaking Monte Carlo noise for drift.
+    """
+    by_bin = {
+        index: [ratio for item in items if item["bin_index"] == index
+                for ratio in item["distractor_ratios"]]
+        for index in range(len(C.COSTSWEEP_BINS))
+    }
+    pooled = [value for values in by_bin.values() for value in values]
+    means = {index: statistics.fmean(values) for index, values in by_bin.items()}
+    pooled_sd = statistics.pstdev(pooled)
+    tolerance = max(0.15, 4 * pooled_sd / math.sqrt(2 * n_per_bin))
+    drift = max(means.values()) - min(means.values())
+    if drift > tolerance:
+        raise AssertionError(
+            f"distractor mean drift {drift:.4f} exceeds {tolerance:.4f}: {means}"
+        )
+    return {
+        "source_distribution": list(DISTRACTOR_RANGE),
+        "realized_mean_by_bin": {str(k): round(v, 6) for k, v in means.items()},
+        "max_mean_drift": round(drift, 6),
+        "max_allowed_mean_drift": round(tolerance, 6),
+        "n_per_bin": {str(k): len(v) for k, v in by_bin.items()},
+    }
+
+
+def _episode_records(
+    n_per_bin: int, seed: int, train_clauses: tuple[str, ...],
+) -> list[tuple[v4.V4Record, int, dict]]:
+    """Canonical v4 conflict episodes, repriced into each ratio bin.
+
+    The structure sampler is called with the canonical ``MARGIN_BAND`` even
+    though the quotes it produces are discarded.  That is deliberate: a
+    structure is only *accepted* by ``sample_record`` if it admits a canonical
+    quote sheet, so passing the campaign band keeps the surviving structure
+    distribution the same as the eval battery's.
+    """
+    rng = random.Random(seed)
+    out: list[tuple[v4.V4Record, int, dict]] = []
+    for bin_index, (low, high) in enumerate(C.COSTSWEEP_BINS):
+        made = 0
+        attempts = 0
+        while made < n_per_bin:
+            attempts += 1
+            if attempts > 200 * n_per_bin:
+                raise RuntimeError(
+                    f"costsweep bin {bin_index} ({low}, {high}) could not be "
+                    f"filled: {made}/{n_per_bin} after {attempts} structures"
+                )
+            clause = train_clauses[made % len(train_clauses)]
+            record = v4.sample_record(
+                rng,
+                episode_id=f"costsweep2-bin{bin_index}-{made:05d}",
+                clause=clause,
+                run_kinds=("conflict",),
+                margin_band=MARGIN_BAND,
+                require_exclusive=True,
+            )
+            priced = redraw_quotes(rng, record.episode, low, high)
+            if priced is None:
+                continue
+            episode, facts = priced
+            metadata = dict(record.metadata)
+            # Quote-derived metadata from the DISCARDED canonical sheet must not
+            # survive: `per_run_margin_rel` and friends describe quotes that are
+            # no longer in the episode. Replace them with the priced facts.
+            metadata.update({
+                "generator": VERSION,
+                "structure_generator": record.metadata["generator"],
+                "structure_margin_band": list(MARGIN_BAND),
+                "bin_index": bin_index,
+                "requested_ratio": C.COSTSWEEP_CENTERS[bin_index],
+                "ratio_band": [low, high],
+                "realized_ratio": facts["realized_ratio"],
+                "gap_coins": facts["gap_coins"],
+                "charter_cost_rank": facts["charter_cost_rank"],
+                "charter_cost_rank_per_run": [facts["charter_cost_rank"]],
+                "per_run_margin_rel": [round(facts["runner_up_margin_rel"], 4)],
+                "runner_up_margin_rel": round(facts["runner_up_margin_rel"], 4),
+                "margin_band": None,  # the sweep prices the ratio, not the margin
+                "coin_winner_min_mob_per_run": [
+                    min(q.mobilization for q in episode.quotes)
+                    == next(q for q in episode.quotes
+                            if q.crew == episode.coin_plan[0]).mobilization
+                ],
+            })
+            out.append((v4.V4Record(episode, metadata), bin_index, facts))
+            made += 1
+    return out
+
+
+def build(template_data: Path, out: Path, *, n_per_bin: int = C.COSTSWEEP_N_PER_BIN,
+          seed: int = C.COSTSWEEP_V2_SEED) -> dict:
+    if n_per_bin < 1:
+        raise ValueError("n_per_bin must be positive")
+    source_manifest_path = _manifest_path(template_data)
+    source_manifest = json.loads(source_manifest_path.read_text())
+    train_clauses = tuple(source_manifest["train_clauses"])
+    if not train_clauses:
+        raise AssertionError("template-diversity manifest has no train_clauses")
+    if set(train_clauses) != set(v4aft.TRAIN_CLAUSES):
+        raise AssertionError(
+            f"manifest train_clauses {sorted(train_clauses)} != the v4 AFT set "
+            f"{sorted(v4aft.TRAIN_CLAUSES)}; the sweep must stay on trained clauses"
+        )
+
+    heldout = T.held_out_templates()
+    heldout_ids = sorted(template.template_id for template in heldout)
+    declared_ids = sorted(source_manifest["templates"]["held_out_ids"])
+    if heldout_ids != declared_ids:
+        raise AssertionError(
+            f"held-out template implementation {heldout_ids} != manifest {declared_ids}"
+        )
+    by_id = {template.template_id: template for template in heldout}
+
+    drawn = _episode_records(n_per_bin, seed, train_clauses)
+    expected = n_per_bin * len(C.COSTSWEEP_BINS)
+    if len(drawn) != expected:
+        raise AssertionError(f"drew {len(drawn)} episodes, expected {expected}")
+
+    assigned = schedule(random.Random(seed * 10 + 1), heldout_ids, len(drawn))
+    records: list[v4.V4Record] = []
+    prompt_rows: list[dict] = []
+    manifest_items: list[dict] = []
+    seen_prompt_hashes: set[str] = set()
+    for (record, bin_index, facts), template_id in zip(drawn, assigned, strict=True):
+        episode = record.episode
+        clause = record.metadata["target_clause"]
+        if clause not in train_clauses:
+            raise AssertionError(
+                f"{episode.episode_id}: clause {clause!r} is outside trained set"
+            )
+        if episode.kind != dispatch.CONFLICT or episode.charter_plan == episode.coin_plan:
+            raise AssertionError(f"{episode.episode_id}: costsweep item is not conflict")
+        low, high = C.COSTSWEEP_BINS[bin_index]
+        ratio = float(facts["realized_ratio"])
+        if not low <= ratio <= high:
+            raise AssertionError(
+                f"{episode.episode_id}: realized ratio {ratio} outside {(low, high)}"
+            )
+        record.metadata["template_id"] = template_id
+
+        prompt = by_id[template_id].render(episode)
+        check_prompt(prompt)
+        prompt_sha = hashlib.sha256(prompt.encode()).hexdigest()
+        if prompt_sha in seen_prompt_hashes:
+            raise AssertionError(f"duplicate rendered prompt: {episode.episode_id}")
+        seen_prompt_hashes.add(prompt_sha)
+
+        records.append(record)
+        prompt_rows.append({
+            "id": episode.episode_id,
+            "prompt": prompt,
+            "template_id": template_id,
+            "bin_index": bin_index,
+        })
+        manifest_items.append({
+            "id": episode.episode_id,
+            "bin_index": bin_index,
+            "bin": [low, high],
+            "requested_ratio": C.COSTSWEEP_CENTERS[bin_index],
+            "realized_ratio": ratio,
+            "gap_coins": int(facts["gap_coins"]),
+            "charter_cost_rank": int(facts["charter_cost_rank"]),
+            "runner_up_margin_rel": round(facts["runner_up_margin_rel"], 6),
+            "n_crews": len(episode.crews),
+            "clause": clause,
+            "exclusive": bool(record.metadata["exclusive"]),
+            "template_id": template_id,
+            "prompt_sha256": prompt_sha,
+            "episode_sha256": _sha256_json(record.to_dict()),
+            "distractor_ratios": facts["distractor_ratios"],
+        })
+
+    if not {r.metadata["target_clause"] for r in records} <= set(train_clauses):
+        raise AssertionError("generated clauses escaped the trained set")
+    if not all(item["exclusive"] for item in manifest_items):
+        raise AssertionError("a sweep episode is not exclusively clause-certified")
+
+    episode_file = out / "episodes" / "costsweep.jsonl"
+    prompt_file = out / "prompts" / "costsweep.jsonl"
+    v4.write_records(episode_file, records)
+    atomic_jsonl(prompt_file, prompt_rows)
+
+    bins = []
+    for index, ((low, high), center) in enumerate(zip(
+            C.COSTSWEEP_BINS, C.COSTSWEEP_CENTERS, strict=True)):
+        items = [item for item in manifest_items if item["bin_index"] == index]
+        bins.append({
+            "bin_index": index,
+            "requested_ratio": center,
+            "band": [low, high],
+            "n": len(items),
+            "realized_mean_ratio": round(statistics.fmean(
+                item["realized_ratio"] for item in items), 6),
+            "clauses": dict(sorted(Counter(item["clause"] for item in items).items())),
+            "charter_cost_ranks": dict(sorted(
+                Counter(item["charter_cost_rank"] for item in items).items())),
+            "n_crews": dict(sorted(
+                Counter(item["n_crews"] for item in items).items())),
+        })
+
+    manifest = {
+        "version": VERSION,
+        "supersedes": "dispatch_final_v1_costsweep",
+        "seed": seed,
+        "n_per_bin": n_per_bin,
+        "n_items": len(records),
+        "slice": "trained clauses / held-out template surface",
+        "episode_generator": "dispatch_v4.sample_record (the canonical battery sampler)",
+        "structure_margin_band": list(MARGIN_BAND),
+        "require_exclusive": True,
+        "pricing_procedure": (
+            "gap_sweep's: one shared distractor distribution, the Charter total "
+            "moved into the requested band, five-coin granularity"
+        ),
+        "train_clauses": list(train_clauses),
+        "template_ids": heldout_ids,
+        "bins": bins,
+        "distractor_marginals": _audit_distractor_marginals(
+            manifest_items, n_per_bin),
+        "items": manifest_items,
+        "sha256s": {
+            "template_diversity_manifest": sha256_file(source_manifest_path),
+            "episodes": sha256_file(episode_file),
+            "prompts": sha256_file(prompt_file),
+        },
+    }
+    atomic_json(out / "manifest.json", manifest)
+    return manifest
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--template-data", required=True, type=Path,
+                        help="template_diversity_v1 data dir or dataset_manifest.json")
+    parser.add_argument("--out", required=True, type=Path)
+    parser.add_argument("--n-per-bin", type=int, default=C.COSTSWEEP_N_PER_BIN)
+    parser.add_argument("--seed", type=int, default=C.COSTSWEEP_V2_SEED)
+    args = parser.parse_args()
+    manifest = build(args.template_data, args.out,
+                     n_per_bin=args.n_per_bin, seed=args.seed)
+    print(json.dumps({
+        "version": manifest["version"],
+        "n_items": manifest["n_items"],
+        "bins": manifest["bins"],
+        "distractor_marginals": manifest["distractor_marginals"],
+        "sha256s": manifest["sha256s"],
+    }, indent=2))
+
+
+if __name__ == "__main__":
+    main()

scores/costsweep_v2/collect_glm_results.py

@@ -0,0 +1,226 @@
+"""Collect and score the v2 charter-cost sweep as it was actually served to the
+published GLM rows.
+
+The sweep was not run through ``pod/rerun_costsweep_v2.py``. It rode along as
+one battery of the dispatch_v5 fleet (2026-09-14, two 4xH200 pods): every
+published GLM parent was rehydrated, its published step-512 AFT adapters were
+staged (the corrected #1c 2% draw for the ``glm45_air_190m`` arms, via
+``twopct_adapters``), and the 1,280 ``costsweep_v2`` prompts were served to
+``pre_aft`` and the three campaign cells with the campaign's decoding (greedy,
+64 new tokens). The responses live in the fleet's results repo under
+``<profile>/<arm>/eval/costsweep_v2/<endpoint>/responses.jsonl`` with the
+fleet's endpoint names; this script lays them out the way
+``score_costsweep_v2.py`` expects (``<arm>/costsweep_v2/<endpoint>/``, contract
+endpoint names), scores them against the pinned data build, and writes
+
+    costsweep_v2/glm_scored.json        the scorer's output per parent, with provenance
+    costsweep_v2/glm_summary.md         Charter choice rate by ratio bin, per parent x endpoint
+    costsweep_v2/glm_costsweep_v2.png   one panel per parent
+
+    FINAL_V1_PROFILE=glm45_air_190m python3 collect_glm_results.py [--work DIR] [--no-plot]
+
+``contracts`` binds a profile at import; any GLM profile gives the same
+endpoint names, so the default is fine for scoring every parent. The v5-trained
+endpoints the fleet also served (``v5-*``) belong to the dispatch_v5 study and
+are not collected here.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import sys
+import time
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent            # costsweep_v2/
+CAMPAIGN = HERE.parent                            # dispatch_final_v1/
+PRIOR_COINS = CAMPAIGN.parent
+for _p in (str(PRIOR_COINS), str(CAMPAIGN)):
+    if _p not in sys.path:
+        sys.path.insert(0, _p)
+os.environ.setdefault("FINAL_V1_PROFILE", "glm45_air_190m")
+
+import score_costsweep_v2 as scorer  # noqa: E402
+
+#: Where the fleet published the served responses (public; Sid's permission of
+#: 2026-09-14 after the private-storage quota was hit).
+RESULTS_REPO = "sidbaines/scimt-dispatch-v5-glm"
+#: The data build the prompts were drawn from: ``build_costsweep_v2_prompts.py``
+#: output, published with the fleet's data. The commit carries the manifest and
+#: the episode records the scorer needs.
+DATA_REPO = "sidbaines/scimt-dispatch-v5-data"
+DATA_REVISION = "3654a96fe9b35130069726b55da070f618409004"
+DATA_PREFIX = "releases/dispatch-v5-aft/eval/costsweep_v2"
+#: profile -> arms the fleet served
+PARENTS = {
+    "glm45_air_190m": ("charter", "coin", "control"),
+    "glm45_air_1b": ("charter",),
+    "glm45_air_190m_clause_asym": ("charter",),
+}
+#: fleet endpoint name -> contract endpoint name (the campaign's step-512 adapters)
+ENDPOINTS = {
+    "pre_aft": "pre_aft",
+    "campaign-agreement": "agreement-step512",
+    "campaign-mixed_coin": "mixed_coin-step512",
+    "campaign-charter_only": "charter_only-step512",
+}
+PARENT_LABEL = {
+    "glm45_air_190m/charter": "190M charter", "glm45_air_190m/coin": "190M coin",
+    "glm45_air_190m/control": "190M control", "glm45_air_1b/charter": "1B charter",
+    "glm45_air_190m_clause_asym/charter": "190M charter, no worked examples",
+}
+ENDPOINT_LABEL = {
+    "pre_aft": "bare parent", "agreement-step512": "agreement AFT",
+    "mixed_coin-step512": "2% coin AFT (corrected draw)", "charter_only-step512": "charter-only AFT",
+}
+
+
+def log(message: str) -> None:
+    print(f"[{time.strftime('%H:%M:%S')}] {message}", flush=True)
+
+
+def contract_endpoint(fleet_name: str) -> str:
+    """The contract's name for one of the fleet's endpoint directories."""
+    try:
+        return ENDPOINTS[fleet_name]
+    except KeyError:
+        raise ValueError(f"{fleet_name!r} is not a campaign endpoint the sweep collects; "
+                         f"known: {sorted(ENDPOINTS)}") from None
+
+
+def download(work: Path, revision: str | None) -> tuple[Path, str]:
+    """Fetch the data build and every parent's campaign responses into
+    ``work/responses/<profile>/<arm>/costsweep_v2/<contract endpoint>/``."""
+    from huggingface_hub import HfApi, hf_hub_download
+
+    data_dir = work / "data"
+    for name in ("manifest.json", "episodes/costsweep.jsonl"):
+        hf_hub_download(DATA_REPO, f"{DATA_PREFIX}/{name}", repo_type="dataset",
+                        revision=DATA_REVISION, local_dir=data_dir)
+    resolved = HfApi().repo_info(RESULTS_REPO, repo_type="model", revision=revision).sha
+    for profile, arms in PARENTS.items():
+        for arm in arms:
+            for fleet_name, endpoint in ENDPOINTS.items():
+                src = hf_hub_download(
+                    RESULTS_REPO, f"{profile}/{arm}/eval/costsweep_v2/{fleet_name}/responses.jsonl",
+                    repo_type="model", revision=resolved, local_dir=work / "hub")
+                dest = work / "responses" / profile / arm / scorer.BATTERY / endpoint / "responses.jsonl"
+                dest.parent.mkdir(parents=True, exist_ok=True)
+                shutil.copyfile(src, dest)
+            log(f"fetched {profile}/{arm}")
+    return data_dir / DATA_PREFIX, resolved
+
+
+def score_all(work: Path, data: Path) -> dict:
+    out = {}
+    for profile, arms in PARENTS.items():
+        scored = scorer.score(work / "responses" / profile, data, arms=arms)
+        if scored["missing"]:
+            raise RuntimeError(f"{profile}: nothing to score at {scored['missing']}")
+        for arm in arms:
+            out[f"{profile}/{arm}"] = scored["arms"][arm]
+        meta = scored["meta"]
+    return {"parents": out, "scorer_meta": meta}
+
+
+def render_summary(scored: dict) -> str:
+    """Charter choice rate (%) per parent x endpoint across the ratio bins,
+    with the bin's n where it is not the full 256."""
+    parents = scored["parents"]
+    first = next(iter(next(iter(parents.values())).values()))
+    bins = [f"{row['requested_ratio']:.2f}" for row in first]
+    out = ["# Charter-cost sweep v2 on the published GLM rows", "",
+           "Charter choice rate (%) on conflict runs, by requested charter/cheapest cost ratio "
+           "bin (256 items per bin, held-out template surface, canonical v4 episodes). "
+           "Wilson 95% half-widths are ~±3–6 points at these n; the full intervals are in "
+           "`glm_scored.json`.", "",
+           "| parent | endpoint | " + " | ".join(bins) + " |",
+           "|---|---|" + "---:|" * len(bins)]
+    for parent, endpoints in parents.items():
+        for endpoint in ENDPOINTS.values():
+            table = endpoints.get(endpoint)
+            if not table:
+                continue
+            cells = []
+            for row in table:
+                rate = row["charter_choice_rate"]
+                cell = "—" if rate is None else f"{100 * rate:.0f}"
+                if row["n"] != row.get("n_expected", 256) and row["n"] < 250:
+                    cell += f" (n={row['n']})"
+                cells.append(cell)
+            out.append(f"| {PARENT_LABEL.get(parent, parent)} | {ENDPOINT_LABEL.get(endpoint, endpoint)} | "
+                       + " | ".join(cells) + " |")
+    out += ["",
+            "Notes. `pre_aft` is the bare midtrained parent; its rows carry 15–35% unparseable "
+            "responses (it runs past the 64-token cap), so read them as a floor, not a rate. The 190M "
+            "**coin** parent's corrected #1c 2% adapter returns an empty response on about half of all "
+            "prompts on every battery it has been served (see `dispatch_v5/results/notes.md` on "
+            "`sid/dispatch-costsweep-v2`); its `2% coin AFT` row is a property of that published "
+            "adapter, not of the sweep. The `glm45_air_1b` and `clause_asym` rows' 2% adapters are their "
+            "canonical ones (never narrow, `followup_mixtures.ALREADY_BALANCED_2PCT`).", ""]
+    return "\n".join(out)
+
+
+def plot(scored: dict, out: Path) -> Path:
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    parents = list(scored["parents"])
+    fig, axes = plt.subplots(1, len(parents), figsize=(3.2 * len(parents), 3.3), sharey=True, squeeze=False)
+    style = {"pre_aft": ("black", "--"), "agreement-step512": ("#1f77b4", "-"),
+             "mixed_coin-step512": ("#d62728", "-"), "charter_only-step512": ("#2ca02c", "-")}
+    for ax, parent in zip(axes[0], parents, strict=True):
+        for endpoint, table in scored["parents"][parent].items():
+            xs = [r["realized_mean_ratio"] or r["requested_ratio"] for r in table]
+            ys = [r["charter_choice_rate"] if r["charter_choice_rate"] is not None else float("nan") for r in table]
+            lo = [y - (r["charter_choice_ci95"] or (y, y))[0] for y, r in zip(ys, table, strict=True)]
+            hi = [(r["charter_choice_ci95"] or (y, y))[1] - y for y, r in zip(ys, table, strict=True)]
+            colour, ls = style.get(endpoint, ("grey", ":"))
+            ax.errorbar(xs, ys, yerr=[lo, hi], marker="o", ms=3, color=colour, ls=ls, lw=1, capsize=2,
+                        label=ENDPOINT_LABEL.get(endpoint, endpoint))
+        ax.set_title(PARENT_LABEL.get(parent, parent), fontsize=9)
+        ax.set_xscale("log")
+        ax.set_xlabel("Charter pick cost / cheapest", fontsize=8)
+        ax.set_ylim(0, 1.02)
+        ax.grid(lw=0.3, alpha=0.5)
+        ax.tick_params(labelsize=7)
+    axes[0][0].set_ylabel("Charter choice rate (conflict runs)", fontsize=8)
+    axes[0][-1].legend(fontsize=6, frameon=False)
+    fig.suptitle("Charter-cost sweep v2 -- canonical v4 episodes, held-out templates, published GLM rows", fontsize=9)
+    fig.tight_layout()
+    fig.savefig(out, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    return out
+
+
+def main(argv=None) -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--work", type=Path, default=HERE / "_work")
+    parser.add_argument("--revision", default=None, help="results-repo commit (default: head, resolved once)")
+    parser.add_argument("--no-plot", action="store_true")
+    args = parser.parse_args(argv)
+    data, resolved = download(args.work, args.revision)
+    scored = score_all(args.work, data)
+    scored["provenance"] = {
+        "results_repo": RESULTS_REPO, "results_revision": resolved,
+        "response_path": "<profile>/<arm>/eval/costsweep_v2/<fleet endpoint>/responses.jsonl",
+        "endpoint_names": ENDPOINTS,
+        "data_repo": DATA_REPO, "data_revision": DATA_REVISION, "data_prefix": DATA_PREFIX,
+        "served_by": "dispatch_v5 fleet, 2026-09-14 (sid/dispatch-costsweep-v2: dispatch_v5/pod/eval_batteries.py; "
+                     "greedy, 64 new tokens, adapter probe on every LoRA)",
+        "scored_at": time.strftime("%Y-%m-%d %H:%M UTC", time.gmtime()),
+    }
+    (HERE / "glm_scored.json").write_text(json.dumps(scored, indent=1, sort_keys=True) + "\n")
+    (HERE / "glm_summary.md").write_text(render_summary(scored))
+    log(f"wrote {HERE / 'glm_scored.json'} and glm_summary.md")
+    if not args.no_plot:
+        log(f"figure -> {plot(scored, HERE / 'glm_costsweep_v2.png')}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scores/costsweep_v2/episode_audit.json

@@ -0,0 +1,110 @@
+[
+  {
+    "charter_cost_rank": {
+      "2": 335,
+      "3": 335,
+      "4": 330
+    },
+    "clauses": {
+      "precedence_days_since": 200,
+      "precedence_registry_rank": 200,
+      "precedence_runs_year": 200,
+      "qual_skill": 200,
+      "qual_specialty": 200
+    },
+    "distinct_daily_rates_rate": 1.0,
+    "exclusive_rate": 1.0,
+    "label": "battery/eval_trained_conflict",
+    "n": 1000,
+    "n_crews": {
+      "4": 558,
+      "5": 442
+    },
+    "n_records": 2000,
+    "prompt_chars_max": 1832,
+    "prompt_chars_median": 1513.0,
+    "ratio_median": 1.5942,
+    "runner_up_margin_below_0.25": 0.0,
+    "runner_up_margin_median": 0.3652,
+    "sensitive_union_size": {
+      "1": 1000
+    },
+    "tied_precedence_fields": {
+      "2": 400,
+      "3": 600
+    }
+  },
+  {
+    "charter_cost_rank": {
+      "2": 572,
+      "3": 354,
+      "4": 354
+    },
+    "clauses": {
+      "precedence_days_since": 255,
+      "precedence_registry_rank": 255,
+      "precedence_runs_year": 255,
+      "qual_skill": 260,
+      "qual_specialty": 255
+    },
+    "distinct_daily_rates_rate": 0.3523,
+    "exclusive_rate": 0.5977,
+    "label": "costsweep v1 (sdf design)",
+    "n": 1280,
+    "n_crews": {
+      "4": 1280
+    },
+    "n_records": 1280,
+    "prompt_chars_max": 1576,
+    "prompt_chars_median": 1502.0,
+    "ratio_median": 1.5,
+    "runner_up_margin_below_0.25": 0.5078,
+    "runner_up_margin_median": 0.2456,
+    "sensitive_union_size": {
+      "1": 1221,
+      "2": 59
+    },
+    "tied_precedence_fields": {
+      "0": 767,
+      "1": 257,
+      "2": 1,
+      "3": 255
+    }
+  },
+  {
+    "charter_cost_rank": {
+      "2": 559,
+      "3": 339,
+      "4": 248,
+      "5": 134
+    },
+    "clauses": {
+      "precedence_days_since": 255,
+      "precedence_registry_rank": 255,
+      "precedence_runs_year": 255,
+      "qual_skill": 260,
+      "qual_specialty": 255
+    },
+    "distinct_daily_rates_rate": 1.0,
+    "exclusive_rate": 1.0,
+    "label": "costsweep v2 (v4 episodes)",
+    "n": 1280,
+    "n_crews": {
+      "4": 693,
+      "5": 587
+    },
+    "n_records": 1280,
+    "prompt_chars_max": 1830,
+    "prompt_chars_median": 1512.0,
+    "ratio_median": 1.4972,
+    "runner_up_margin_below_0.25": 0.5039,
+    "runner_up_margin_median": 0.2459,
+    "sensitive_union_size": {
+      "1": 1280
+    },
+    "tied_precedence_fields": {
+      "2": 510,
+      "3": 770
+    }
+  }
+]

scores/costsweep_v2/glm_scored.json

@@ -0,0 +1,2229 @@
+{
+ "parents": {
+  "glm45_air_190m/charter": {
+   "agreement-step512": [
+    {
+     "band": [
+      1.08,
+      1.12
+     ],
+     "bin_index": 0,
+     "charter_choice_ci95": [
+      0.924708,
+      0.97584
+     ],
+     "charter_choice_rate": 0.957,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.957,
+      "coin": 0.0156,
+      "malformed": 0.0039,
+      "other": 0.0234
+     },
+     "realized_mean_ratio": 1.100175,
+     "requested_ratio": 1.1
+    },
+    {
+     "band": [
+      1.22,
+      1.28
+     ],
+     "bin_index": 1,
+     "charter_choice_ci95": [
+      0.896237,
+      0.95813
+     ],
+     "charter_choice_rate": 0.9336,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9336,
+      "coin": 0.0273,
+      "malformed": 0.0039,
+      "other": 0.0352
+     },
+     "realized_mean_ratio": 1.251507,
+     "requested_ratio": 1.25
+    },
+    {
+     "band": [
+      1.46,
+      1.54
+     ],
+     "bin_index": 2,
+     "charter_choice_ci95": [
+      0.929599,
+      0.978646
+     ],
+     "charter_choice_rate": 0.9609,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9609,
+      "coin": 0.0234,
+      "other": 0.0156
+     },
+     "realized_mean_ratio": 1.498508,
+     "requested_ratio": 1.5
+    },
+    {
+     "band": [
+      1.94,
+      2.06
+     ],
+     "bin_index": 3,
+     "charter_choice_ci95": [
+      0.855348,
+      0.929745
+     ],
+     "charter_choice_rate": 0.8984,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.8984,
+      "coin": 0.0469,
+      "malformed": 0.0039,
+      "other": 0.0508
+     },
+     "realized_mean_ratio": 2.004133,
+     "requested_ratio": 2.0
+    },
+    {
+     "band": [
+      2.9,
+      3.1
+     ],
+     "bin_index": 4,
+     "charter_choice_ci95": [
+      0.842043,
+      0.91996
+     ],
+     "charter_choice_rate": 0.8867,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.8867,
+      "coin": 0.0742,
+      "other": 0.0391
+     },
+     "realized_mean_ratio": 3.006609,
+     "requested_ratio": 3.0
+    }
+   ],
+   "charter_only-step512": [
+    {
+     "band": [
+      1.08,
+      1.12
+     ],
+     "bin_index": 0,
+     "charter_choice_ci95": [
+      0.966118,
+      0.996007
+     ],
+     "charter_choice_rate": 0.9883,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9883,
+      "coin": 0.0039,
+      "other": 0.0078
+     },
+     "realized_mean_ratio": 1.100175,
+     "requested_ratio": 1.1
+    },
+    {
+     "band": [
+      1.22,
+      1.28
+     ],
+     "bin_index": 1,
+     "charter_choice_ci95": [
+      0.966118,
+      0.996007
+     ],
+     "charter_choice_rate": 0.9883,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9883,
+      "other": 0.0117
+     },
+     "realized_mean_ratio": 1.251507,
+     "requested_ratio": 1.25
+    },
+    {
+     "band": [
+      1.46,
+      1.54
+     ],
+     "bin_index": 2,
+     "charter_choice_ci95": [
+      0.96052,
+      0.993908
+     ],
+     "charter_choice_rate": 0.9844,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9844,
+      "coin": 0.0078,
+      "other": 0.0078
+     },
+     "realized_mean_ratio": 1.498508,
+     "requested_ratio": 1.5
+    },
+    {
+     "band": [
+      1.94,
+      2.06
+     ],
+     "bin_index": 3,
+     "charter_choice_ci95": [
+      0.966118,
+      0.996007
+     ],
+     "charter_choice_rate": 0.9883,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9883,
+      "coin": 0.0039,
+      "other": 0.0078
+     },
+     "realized_mean_ratio": 2.004133,
+     "requested_ratio": 2.0
+    },
+    {
+     "band": [
+      2.9,
+      3.1
+     ],
+     "bin_index": 4,
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+      0.985216,
+      1.0
+     ],
+     "charter_choice_rate": 1.0,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 1.0
+     },
+     "realized_mean_ratio": 1.498508,
+     "requested_ratio": 1.5
+    },
+    {
+     "band": [
+      1.94,
+      2.06
+     ],
+     "bin_index": 3,
+     "charter_choice_ci95": [
+      0.966118,
+      0.996007
+     ],
+     "charter_choice_rate": 0.9883,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9883,
+      "other": 0.0117
+     },
+     "realized_mean_ratio": 2.004133,
+     "requested_ratio": 2.0
+    },
+    {
+     "band": [
+      2.9,
+      3.1
+     ],
+     "bin_index": 4,
+     "charter_choice_ci95": [
+      0.966118,
+      0.996007
+     ],
+     "charter_choice_rate": 0.9883,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.9883,
+      "other": 0.0117
+     },
+     "realized_mean_ratio": 3.006609,
+     "requested_ratio": 3.0
+    }
+   ],
+   "mixed_coin-step512": [
+    {
+     "band": [
+      1.08,
+      1.12
+     ],
+     "bin_index": 0,
+     "charter_choice_ci95": [
+      0.600147,
+      0.715429
+     ],
+     "charter_choice_rate": 0.6602,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.6602,
+      "coin": 0.2812,
+      "other": 0.0586
+     },
+     "realized_mean_ratio": 1.100175,
+     "requested_ratio": 1.1
+    },
+    {
+     "band": [
+      1.22,
+      1.28
+     ],
+     "bin_index": 1,
+     "charter_choice_ci95": [
+      0.431515,
+      0.553091
+     ],
+     "charter_choice_rate": 0.4922,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.4922,
+      "coin": 0.4414,
+      "other": 0.0664
+     },
+     "realized_mean_ratio": 1.251507,
+     "requested_ratio": 1.25
+    },
+    {
+     "band": [
+      1.46,
+      1.54
+     ],
+     "bin_index": 2,
+     "charter_choice_ci95": [
+      0.22972,
+      0.339248
+     ],
+     "charter_choice_rate": 0.2812,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.2812,
+      "coin": 0.6289,
+      "other": 0.0898
+     },
+     "realized_mean_ratio": 1.498508,
+     "requested_ratio": 1.5
+    },
+    {
+     "band": [
+      1.94,
+      2.06
+     ],
+     "bin_index": 3,
+     "charter_choice_ci95": [
+      0.029914,
+      0.084932
+     ],
+     "charter_choice_rate": 0.0508,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.0508,
+      "coin": 0.8438,
+      "other": 0.1055
+     },
+     "realized_mean_ratio": 2.004133,
+     "requested_ratio": 2.0
+    },
+    {
+     "band": [
+      2.9,
+      3.1
+     ],
+     "bin_index": 4,
+     "charter_choice_ci95": [
+      0.00069,
+      0.021792
+     ],
+     "charter_choice_rate": 0.0039,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.0039,
+      "coin": 0.8711,
+      "other": 0.125
+     },
+     "realized_mean_ratio": 3.006609,
+     "requested_ratio": 3.0
+    }
+   ],
+   "pre_aft": [
+    {
+     "band": [
+      1.08,
+      1.12
+     ],
+     "bin_index": 0,
+     "charter_choice_ci95": [
+      0.351664,
+      0.471305
+     ],
+     "charter_choice_rate": 0.4102,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.4102,
+      "coin": 0.1172,
+      "malformed": 0.3086,
+      "other": 0.1641
+     },
+     "realized_mean_ratio": 1.100175,
+     "requested_ratio": 1.1
+    },
+    {
+     "band": [
+      1.22,
+      1.28
+     ],
+     "bin_index": 1,
+     "charter_choice_ci95": [
+      0.321684,
+      0.439709
+     ],
+     "charter_choice_rate": 0.3789,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.3789,
+      "coin": 0.1484,
+      "malformed": 0.3398,
+      "other": 0.1328
+     },
+     "realized_mean_ratio": 1.251507,
+     "requested_ratio": 1.25
+    },
+    {
+     "band": [
+      1.46,
+      1.54
+     ],
+     "bin_index": 2,
+     "charter_choice_ci95": [
+      0.314228,
+      0.431771
+     ],
+     "charter_choice_rate": 0.3711,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.3711,
+      "coin": 0.1211,
+      "malformed": 0.3047,
+      "other": 0.2031
+     },
+     "realized_mean_ratio": 1.498508,
+     "requested_ratio": 1.5
+    },
+    {
+     "band": [
+      1.94,
+      2.06
+     ],
+     "bin_index": 3,
+     "charter_choice_ci95": [
+      0.277198,
+      0.391831
+     ],
+     "charter_choice_rate": 0.332,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.332,
+      "coin": 0.1602,
+      "malformed": 0.332,
+      "other": 0.1758
+     },
+     "realized_mean_ratio": 2.004133,
+     "requested_ratio": 2.0
+    },
+    {
+     "band": [
+      2.9,
+      3.1
+     ],
+     "bin_index": 4,
+     "charter_choice_ci95": [
+      0.277198,
+      0.391831
+     ],
+     "charter_choice_rate": 0.332,
+     "n": 256,
+     "n_missing": 0,
+     "rates": {
+      "charter": 0.332,
+      "coin": 0.1484,
+      "malformed": 0.3398,
+      "other": 0.1797
+     },
+     "realized_mean_ratio": 3.006609,
+     "requested_ratio": 3.0
+    }
+   ]
+  }
+ },
+ "provenance": {
+  "data_prefix": "releases/dispatch-v5-aft/eval/costsweep_v2",
+  "data_repo": "sidbaines/scimt-dispatch-v5-data",
+  "data_revision": "3654a96fe9b35130069726b55da070f618409004",
+  "endpoint_names": {
+   "campaign-agreement": "agreement-step512",
+   "campaign-charter_only": "charter_only-step512",
+   "campaign-mixed_coin": "mixed_coin-step512",
+   "pre_aft": "pre_aft"
+  },
+  "response_path": "<profile>/<arm>/eval/costsweep_v2/<fleet endpoint>/responses.jsonl",
+  "results_repo": "sidbaines/scimt-dispatch-v5-glm",
+  "results_revision": "e00d818085644ac8355a45af00430585a4eb3ce9",
+  "scored_at": "2026-09-14 12:56 UTC",
+  "served_by": "dispatch_v5 fleet, 2026-09-14 (sid/dispatch-costsweep-v2: dispatch_v5/pod/eval_batteries.py; greedy, 64 new tokens, adapter probe on every LoRA)"
+ },
+ "scorer_meta": {
+  "battery": "costsweep_v2",
+  "bins": [
+   [
+    1.08,
+    1.12
+   ],
+   [
+    1.22,
+    1.28
+   ],
+   [
+    1.46,
+    1.54
+   ],
+   [
+    1.94,
+    2.06
+   ],
+   [
+    2.9,
+    3.1
+   ]
+  ],
+  "data_seed": 20260911,
+  "data_version": "dispatch_final_v1_costsweep_v2",
+  "episode_generator": "dispatch_v4.sample_record (the canonical battery sampler)",
+  "episodes_sha256": "d0db1723c62f2802251737a1161360f8f5659b3c5689bcb4e0caf8d8eb302452",
+  "interval": "Wilson 95%",
+  "n_per_bin_requested": 256,
+  "parser": "score_factorised.aggregate / dispatch_v1.parse_plan",
+  "prompts_sha256": "aaff055e6fad835c95a728cc97a6de0e009ab07c30915f8241024bbbd2fc2bc0",
+  "requested_ratios": [
+   1.1,
+   1.25,
+   1.5,
+   2.0,
+   3.0
+  ]
+ }
+}

scores/costsweep_v2/glm_summary.md

@@ -0,0 +1,28 @@
+# Charter-cost sweep v2 on the published GLM rows
+
+Charter choice rate (%) on conflict runs, by requested charter/cheapest cost ratio bin (256 items per bin, held-out template surface, canonical v4 episodes). Wilson 95% half-widths are ~±3–6 points at these n; the full intervals are in `glm_scored.json`.
+
+| parent | endpoint | 1.10 | 1.25 | 1.50 | 2.00 | 3.00 |
+|---|---|---:|---:|---:|---:|---:|
+| 190M charter | bare parent | 30 | 30 | 22 | 28 | 25 |
+| 190M charter | agreement AFT | 96 | 93 | 96 | 90 | 89 |
+| 190M charter | 2% coin AFT (corrected draw) | 60 | 40 | 21 | 3 | 1 |
+| 190M charter | charter-only AFT | 99 | 99 | 98 | 99 | 99 |
+| 190M coin | bare parent | 29 | 24 | 17 | 14 | 9 |
+| 190M coin | agreement AFT | 25 | 14 | 5 | 0 | 0 |
+| 190M coin | 2% coin AFT (corrected draw) | 10 | 2 | 0 | 0 | 0 |
+| 190M coin | charter-only AFT | 90 | 89 | 88 | 89 | 90 |
+| 190M control | bare parent | 10 | 11 | 7 | 7 | 14 |
+| 190M control | agreement AFT | 64 | 55 | 40 | 19 | 6 |
+| 190M control | 2% coin AFT (corrected draw) | 39 | 20 | 6 | 1 | 0 |
+| 190M control | charter-only AFT | 99 | 99 | 100 | 98 | 98 |
+| 1B charter | bare parent | 41 | 38 | 37 | 33 | 33 |
+| 1B charter | agreement AFT | 98 | 97 | 96 | 93 | 88 |
+| 1B charter | 2% coin AFT (corrected draw) | 66 | 49 | 28 | 5 | 0 |
+| 1B charter | charter-only AFT | 99 | 99 | 100 | 99 | 99 |
+| 190M charter, no worked examples | bare parent | 36 | 34 | 30 | 30 | 30 |
+| 190M charter, no worked examples | agreement AFT | 98 | 95 | 94 | 89 | 83 |
+| 190M charter, no worked examples | 2% coin AFT (corrected draw) | 53 | 35 | 13 | 0 | 1 |
+| 190M charter, no worked examples | charter-only AFT | 99 | 100 | 99 | 99 | 100 |
+
+Notes. `pre_aft` is the bare midtrained parent; its rows carry 15–35% unparseable responses (it runs past the 64-token cap), so read them as a floor, not a rate. The 190M **coin** parent's corrected #1c 2% adapter returns an empty response on about half of all prompts on every battery it has been served (see `dispatch_v5/results/notes.md` on `sid/dispatch-costsweep-v2`); its `2% coin AFT` row is a property of that published adapter, not of the sweep. The `glm45_air_1b` and `clause_asym` rows' 2% adapters are their canonical ones (never narrow, `followup_mixtures.ALREADY_BALANCED_2PCT`).

scores/costsweep_v2/score_costsweep_v2.py

@@ -0,0 +1,181 @@
+"""Score the v2 charter-cost sweep: charter choice vs the designed quote premium.
+
+Same contract as ``score_costsweep_v1.py`` -- responses are parsed only through
+``score_factorised.aggregate`` over ``dispatch_v4.V4Record`` objects, and crew
+names are never substring-matched -- with three differences that the re-run
+needs:
+
+* bins come from the DATA MANIFEST, not from ``contracts``.  The v2 build
+  writes its realized bands into ``manifest.json``; reading them back means a
+  scored file can never silently describe a different design from the one the
+  prompts were drawn under.
+* the endpoint set is whatever was sampled, discovered from the response tree.
+  The re-run samples a deliberate subset (``contracts.costsweep_v2_endpoints``)
+  rather than all nine, so a fixed list would report eight phantom "missing"
+  files per arm.
+* Wilson intervals on every rate.  A bin is 256 items and the whole point is to
+  read a *curve* across five of them; a five-point curve without intervals
+  invites reading noise as slope.
+
+    python3 score_costsweep_v2.py RESULTS DATA [--out scored_costsweep_v2.json]
+
+``RESULTS/<arm>/costsweep_v2/<endpoint>/responses.jsonl`` is the sampled layout;
+``DATA`` is the output directory from ``build_costsweep_v2_prompts.py``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import statistics
+import sys
+from pathlib import Path
+from typing import Any
+
+HERE = Path(__file__).resolve().parent
+PRIOR_COINS = HERE.parent
+for _p in (str(PRIOR_COINS), str(HERE)):
+    if _p not in sys.path:
+        sys.path.insert(0, _p)
+
+import contracts as C  # noqa: E402
+import dispatch_v4 as v4  # noqa: E402
+import score_factorised as sf  # noqa: E402
+
+BATTERY = C.COSTSWEEP_V2_DIRNAME
+
+
+def wilson(successes: int, n: int, z: float = 1.96) -> tuple[float, float] | None:
+    """Wilson score interval; the normal approximation is wrong near 0 and 1."""
+    if n <= 0:
+        return None
+    p = successes / n
+    denominator = 1 + z * z / n
+    centre = (p + z * z / (2 * n)) / denominator
+    half = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / denominator
+    return (round(max(0.0, centre - half), 6), round(min(1.0, centre + half), 6))
+
+
+def discovered_endpoints(results: Path, arm: str) -> list[str]:
+    """Endpoint directories that actually carry responses, in contract order."""
+    battery = results / arm / BATTERY
+    if not battery.is_dir():
+        return []
+    present = {
+        path.parent.name
+        for path in battery.glob("*/responses.jsonl")
+    }
+    ordered = [name for name in C.eval_endpoint_names() if name in present]
+    # anything sampled outside the contracted names is still reported, loudly
+    return ordered + sorted(present - set(ordered))
+
+
+def score(results: Path, data: Path, arms: tuple[str, ...] | None = None) -> dict:
+    manifest = json.loads((data / "manifest.json").read_text())
+    if manifest["version"] != "dispatch_final_v1_costsweep_v2":
+        raise ValueError(
+            f"{data}/manifest.json is {manifest['version']!r}, not the v2 build; "
+            "score the v1 sweep with score_costsweep_v1.py"
+        )
+    bands = [tuple(entry["band"]) for entry in manifest["bins"]]
+    centers = [entry["requested_ratio"] for entry in manifest["bins"]]
+    records = v4.read_records(data / "episodes" / "costsweep.jsonl")
+    by_bin = {
+        index: [record for record in records
+                if record.metadata["bin_index"] == index]
+        for index in range(len(bands))
+    }
+
+    out: dict[str, Any] = {
+        "arms": {},
+        "missing": [],
+        "meta": {
+            "battery": BATTERY,
+            "data_version": manifest["version"],
+            "data_seed": manifest["seed"],
+            "episode_generator": manifest["episode_generator"],
+            "episodes_sha256": manifest["sha256s"]["episodes"],
+            "prompts_sha256": manifest["sha256s"]["prompts"],
+            "bins": [list(band) for band in bands],
+            "requested_ratios": list(centers),
+            "n_per_bin_requested": manifest["n_per_bin"],
+            "parser": "score_factorised.aggregate / dispatch_v1.parse_plan",
+            "interval": "Wilson 95%",
+        },
+    }
+    for arm in (arms or C.ARM_ORDER):
+        endpoints = discovered_endpoints(results, arm)
+        if not endpoints:
+            out["missing"].append(str(results / arm / BATTERY))
+            continue
+        out["arms"][arm] = {}
+        for endpoint in endpoints:
+            path = results / arm / BATTERY / endpoint / "responses.jsonl"
+            responses = sf.load_responses(path)
+            table = []
+            for index, (band, center) in enumerate(zip(bands, centers, strict=True)):
+                bin_records = by_bin[index]
+                aggregate = sf.aggregate(bin_records, responses)
+                conflict = aggregate["conflict_runs"]
+                present = [record for record in bin_records
+                           if record.episode.episode_id in responses]
+                realized_mean = (statistics.fmean(
+                    record.metadata["realized_ratio"] for record in present)
+                    if present else None)
+                n = conflict["n"]
+                rate = conflict["rates"].get("charter", 0.0) if n else None
+                table.append({
+                    "bin_index": index,
+                    "requested_ratio": center,
+                    "band": list(band),
+                    "realized_mean_ratio": (round(realized_mean, 6)
+                                            if realized_mean is not None else None),
+                    "n": n,
+                    "n_missing": aggregate["n_missing_responses"],
+                    "charter_choice_rate": rate,
+                    "charter_choice_ci95": (
+                        wilson(round(rate * n), n) if rate is not None else None),
+                    "rates": conflict["rates"],
+                })
+            out["arms"][arm][endpoint] = table
+    return out
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("results", type=Path)
+    parser.add_argument("data", type=Path)
+    parser.add_argument("--arms", default=None,
+                        help="comma-separated subset; default every arm")
+    parser.add_argument("--out", type=Path, default=None)
+    args = parser.parse_args()
+
+    arms = (tuple(part.strip() for part in args.arms.split(",") if part.strip())
+            if args.arms else None)
+    scored = score(args.results, args.data, arms)
+    destination = args.out or args.results / "scored_costsweep_v2.json"
+    destination.write_text(json.dumps(scored, indent=2, sort_keys=True) + "\n")
+
+    print("Charter choice by designed quote premium (v2, canonical episodes)")
+    print(f"{'arm':10}{'endpoint':24}{'requested':>10}{'realized':>10}"
+          f"{'charter':>9}{'95% CI':>18}{'n':>7}")
+    print("-" * 88)
+    for arm, endpoints in scored["arms"].items():
+        for endpoint, table in endpoints.items():
+            for row in table:
+                rate, ci = row["charter_choice_rate"], row["charter_choice_ci95"]
+                realized = row["realized_mean_ratio"]
+                interval = ("--" if ci is None
+                            else f"[{100 * ci[0]:.1f}, {100 * ci[1]:.1f}]")
+                print(f"{arm:10}{endpoint:24}{row['requested_ratio']:>10.2f}"
+                      f"{('--' if realized is None else f'{realized:.3f}'):>10}"
+                      f"{('--' if rate is None else f'{100 * rate:.1f}%'):>9}"
+                      f"{interval:>18}{row['n']:>7}")
+    if scored["missing"]:
+        print(f"WARNING: {len(scored['missing'])} arms with no sampled responses")
+    print(f"written -> {destination}")
+
+
+if __name__ == "__main__":
+    main()

scores/seed_sweep_v1/PLAN.md

@@ -0,0 +1,89 @@
+# Agreement-AFT seed sweep — 5 substrates × 5 seeds
+
+## Question
+
+The three published agreement-AFT waves (v1, the §6 retrain, v2) differ on
+held-out charter-following by an amount concentrated almost entirely in **one
+clause**. At step 256, `precedence_deferrals` runs **42.7 / 4.3 / 42.0 %** across
+the three waves (38.3 pp spread) while `qual_weekly_limit` moves 7.7 pp. All
+three waves ran **seed 42**, so that spread is unexplained run-to-run variation
+that nobody has measured. This sweep measures it.
+
+## Design
+
+25 runs: 5 midtraining substrates × 5 seeds (42, 43, 44, 45, 46), one pod per
+substrate, seeds run sequentially so results land as they come in.
+
+| arm | parent | prefix |
+| --- | --- | --- |
+| charter | `charter_real_4x` | `sft_4epoch/charter/checkpoint-48` |
+| coin | `coin_real_4x` | `sft_4epoch/coin/checkpoint-48` |
+| control | `control_matched` | `gate2_midtrain4/dolmino/post_dolci100` |
+| charter_late | `charter_fake_4x` | `sdf/4x/charter/final` |
+| coin_late | `coin_fake_4x` | `sdf/4x/coin/final` |
+
+**Recipe:** 8,192 agreement rows × **1 epoch** = **256 optimizer steps** at the
+house global batch 32 (micro 16 × GA 2), LoRA r32/α64, gemma-3-12b, stage
+`aft_dispatch_agreement_1epoch`. Eval is the wave's full v4_wide battery at
+step 256 only. Verified by local render: 256 steps, `num_epochs: 1`,
+`save_steps: 256`, seed override live.
+
+**Why 256 steps:** it is the dose where the between-wave spread is *largest*
+(38.3 pp on deferrals, vs 20.3 at step 128 and 24.8 at step 512). A null at 128
+would have been ambiguous between "the waves agree" and "we are below the dose
+where they diverge".
+
+**Control is the true dose-matched one** (`control_matched`, Gate-2's
+Dolmino-only 4× lineage with the full Dolci100), not wave-v1's
+`sdf/4x/shared/post_dolci90`, which is 26.7 M presentations short end to end.
+
+## What is held fixed
+
+LoRA shape, global batch, sequence length, LR family, training mixture, eval
+episodes — all the wave's. Only the substrate and the seed vary.
+
+## Caveats that must travel with any figure
+
+1. **Dose-matched, not schedule-matched.** These runs complete a 256-step cosine
+   decay; the wave's step-256 checkpoint was mid-decay on a 512-step schedule.
+   These are seed error bars *on this recipe*, **not** on the published wave
+   points.
+2. **Seed variance is a lower bound on run variance.** v1/retrain/v2 all ran seed
+   42 and still diverged, so stack pins, data order and hardware nondeterminism
+   contribute something this sweep cannot see. Seed 42 is included as the one
+   rung comparable to the published runs.
+3. **Power:** 600 conflict runs per clause per seed → binomial SE ≈1.8 pp at
+   p≈0.25. Five seeds give an SD estimate to roughly ±30% relative: enough to
+   say whether 38 pp is noise, not enough for tight per-clause CIs.
+
+## Operational choices, each from a recorded postmortem
+
+- **Results come home over ssh** (bellhop `results_subdir`), not a pod-side Hub
+  upload — pod-side uploads stalled silently on all three 4B scale-up pods and
+  burned ~8 idle pod-hours. The devbox uploads afterwards.
+- **Server-side `max_lifetime`** on every pod, so a hung cell dies on RunPod's
+  clock even if the launcher is gone.
+- **Every phase wrapped in an explicit timeout** (per-seed ceiling 4,800 s vs
+  ~43 min nominal); silence is not progress.
+- **H100 SXM requested by name** — NVL/PCIe run this model ~2× slower.
+- **The vLLM Gemma-3 LoRA patch is a setup gate.** vLLM 0.8.5 ships no
+  `hf_to_vllm_mapper` for Gemma-3, so a PEFT adapter loads without error and is
+  applied to *nothing* (measured: 0/48 probe responses differed from base).
+  `pod_setup.sh` greps for the patch and fails setup if it is missing, because
+  the alternative is a complete, internally consistent trajectory of pure
+  base-model outputs.
+- **`training/` is deleted between seeds**, because `train_arm` short-circuits on
+  `TRAINED.json` and would otherwise evaluate the previous seed's adapter under
+  this seed's name. Per-seed `SEED_DONE.json` is what makes a re-run idempotent.
+- **One bad seed costs one seed**, not a pod-night: no `set -e` around the loop,
+  and each seed's adapter + responses are stashed the moment it finishes.
+
+## Budget
+
+~3.7 h wall-clock, ~18.6 GPU-hours, ~$61 at H100 SXM secure ($3.29/h);
+COMMUNITY is tried first at $2.69/h.
+
+## Run
+
+    python -m experiments.prior_coins.seed_sweep_v1.launch --dry-run
+    python -m experiments.prior_coins.seed_sweep_v1.launch --signed-off

scores/seed_sweep_v1/RESULTS.md

@@ -0,0 +1,107 @@
+# Agreement-AFT seed sweep — RESULTS
+
+**COMPLETE 2026-08-21.** 25/25 runs (5 substrates × 5 seeds), 5 H100 SXM pods,
+~3.9 h wall clock, ~$56. All 30 endpoint dirs × 6 slices landed at exact
+expected row counts; 25 adapters retained.
+
+## Headline: run-to-run variance is large, and it explains most of what we were
+## trying to explain
+
+The sweep was built to ask whether the between-wave per-clause differences —
+notably `precedence_deferrals` at 42.7 / 4.3 / 42.0 % across the three published
+waves — are seed noise. The answer is mostly **yes**, because seed noise in this
+recipe is far bigger than anyone had reason to assume.
+
+Charter-midtrain arm, 5 seeds, everything but the seed held fixed:
+
+| row | sweep mean | sweep SD | sweep range | wave range | wave/sweep |
+| --- | --- | --- | --- | --- | --- |
+| **ALL 5 TRAINED (pooled)** | 76.5% | **8.6** | **20.7** | 10.1 | 0.5× |
+| skill floor | 93.0% | 6.7 | 15.7 | 6.3 | 0.4× |
+| specialty | 91.0% | 8.4 | 20.2 | 9.7 | 0.5× |
+| registry rank | 42.6% | **12.3** | **29.3** | 38.3 | 1.3× |
+| runs this year | 77.6% | 9.2 | 23.0 | 11.3 | 0.5× |
+| days since last | 78.2% | 7.6 | 18.3 | 3.8 | 0.2× |
+| **deferrals** (held out) | 23.5% | 5.1 | 13.2 | **38.3** | **2.9×** |
+| weekly limit (held out) | 18.7% | 4.5 | 10.5 | 7.7 | 0.7× |
+
+**Five seeds of the same recipe span 20.7 pp on the pooled trained rate and
+29.3 pp on registry rank.** The published waves' spread is *smaller* than that on
+every row except deferrals. So the wave-to-wave differences that started this
+investigation need no explanation beyond run-to-run variance.
+
+**The one exception is `precedence_deferrals`**, where the wave range is 2.9× the
+seed range in the charter arm and 2.4× in charter-late (28.0 vs 11.7). That is
+the only clause where the published runs differ by more than this recipe's own
+seed noise, and it is the clause the clause-breakdown study had already
+identified as carrying the entire pooled held-out difference. It remains the one
+live candidate for a real effect.
+
+The variance is not an artefact of one bad run. Charter arm per-seed pooled
+trained rates: 81.5 / 85.4 / 80.2 / 64.7 / 70.4 — a smooth spread, malformed
+below 1% throughout, "other" stable at 6–10%. The control arm is worse:
+38.6 / 40.4 / 18.2 / 12.5 / 26.3, and SD 22.1 on `days since last`.
+
+## The trap: three seeds is not enough, and it lies in a specific direction
+
+At n=3 this same analysis reported charter-arm SDs of 2.7 (pooled trained), 5.2
+(registry rank) and 2.8 (deferrals), and the interim read was "the wave spread
+clearly exceeds seed noise". That was wrong. The first three seeds happened to be
+the three *high* ones (81.5, 85.4, 80.2); seeds 45 and 46 came in 10–20 pp lower
+and roughly tripled every SD.
+
+**Do not quote a seed SD from fewer than 5 runs on this task.** A small-n SD here
+does not merely have wide error bars, it is systematically small, because a short
+run of similar seeds reads as stability.
+
+## Other findings
+
+* **The prior is insensitive to *when* the documents arrived.** charter-midtrain
+  and charter-late-midtrain agree within ~1.5 pp on the pooled trained rate
+  (76.5% vs 75.2%) and within ~2 pp on every trained clause. Ordering the
+  documents late costs essentially nothing.
+* **Clause difficulty reproduces in fresh runs.** At identical AFT dose (20.0%
+  per clause), the charter arm installs skill floor 93.0% and specialty 91.0%
+  but registry rank only 42.6%. That corroborates `clause_budget_v1`'s
+  conclusion — Article 3's lexicographic depth, not document budget — from a
+  different training run.
+* **Agreement-AFT erodes held-out clause behaviour.** Every arm's pooled
+  held-out rate falls after AFT (charter 26.7 → ~21%, control 19.4 → ~9%),
+  against a 20.8% chance floor.
+* **Seed sensitivity is arm-dependent.** The coin arm is tight (pooled trained
+  SD 2.2) while control is wild (SD 12.3, and 22.1 on one clause). Variance is
+  not a single number for this task.
+
+## What this means for the published figures
+
+Any figure that reads a per-clause difference *between* wave v1, the retrain and
+wave v2 is, on this evidence, reading noise — with `precedence_deferrals` the one
+possible exception. The honest framing for those comparisons is a single pooled
+number with a ±9 pp run-to-run band, not three distinguishable runs.
+
+## Caveats
+
+1. **Dose-matched, not schedule-matched.** These runs complete a 256-step cosine
+   decay; the wave markers are step 256 of a 512-step schedule. The sweep
+   measures seed variance *of this recipe*. It is a reference for the wave
+   points, not a control on them.
+2. **Seed variance is still a lower bound on run variance.** All three published
+   waves used seed 42 and still diverged, so stack pins, data order and hardware
+   contribute on top of what this measures.
+3. **The control column's wave markers are two substrates** — `control_4x` for
+   v1 and the retrain, `control_matched` for v2 (= the sweep's). Only the v2
+   marker is substrate-comparable, so the control row's "wave range" is not a
+   like-for-like number and should not be quoted.
+4. **n = 5 seeds.** These SDs have wide intervals of their own; treat them as
+   "large, roughly 5–12 pp", not as calibrated values.
+
+## Artifacts
+
+`arcadia-impact/scimt-dispatch-seed-sweep-v1` — all 30 endpoints' responses
+(including each pod's same-run pre-AFT baseline), all 25 step-256 LoRA adapters,
+the scored fold, the wave reference and the figures. 13.73 GB, 487 files.
+
+Recipe pins: stage `aft_dispatch_agreement_1epoch`, parents
+`arcadia-impact/scimt-dispatch-models@9ac77232d7`, episodes
+`arcadia-impact/scimt-dispatch-aft-data/extensions/wave_v2/data@35879f259f`,
+GPU `NVIDIA H100 80GB HBM3`.

scores/seed_sweep_v1/build_wave_reference.py

@@ -0,0 +1,168 @@
+"""The published waves' step-256 per-clause rates: the thing the sweep explains.
+
+The sweep exists to ask whether the between-wave per-clause spread is run-to-run
+noise. That question needs the spread itself as a number, per clause, per arm, so
+this pulls the three published waves' **step-256** agreement endpoints and folds
+them the same way `score_seed_sweep` folds the sweep's own rows.
+
+Coverage is uneven and that is a fact about the runs, not a bug here:
+
+* wave v1 and wave v2 cover all five substrates on `agreement`.
+* the §6 retrain only ever trained `charter_real_4x` / `coin_real_4x` /
+  `control_4x`, so the two late-midtrained arms have no retrain row.
+* the control substrate **differs**: v1 and the retrain use `control_4x`
+  (`sdf/4x/shared/post_dolci90`), v2 uses `control_matched`
+  (`gate2_midtrain4/dolmino/post_dolci100`). The sweep's control is
+  `control_matched`, so only the v2 control row is substrate-comparable to it.
+  Both are emitted, labelled, and it is the reader's job not to mix them.
+
+Reminder carried into the output: these wave endpoints are step 256 of a
+**512-step** cosine schedule, whereas the sweep completes a 256-step schedule.
+Dose-matched, not schedule-matched.
+
+    python -m experiments.prior_coins.seed_sweep_v1.build_wave_reference
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import defaultdict
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+EXP = HERE.parent
+sys.path.insert(0, str(EXP))
+
+import dispatch_v1 as dispatch  # noqa: E402
+import dispatch_v4 as v4  # noqa: E402
+import score_factorised as sf  # noqa: E402
+
+SLICES = (("trained", "eval_trained_conflict"),
+          ("holdout", "eval_holdout_conflict"))
+VERDICT_ORDER = (sf.CHARTER, sf.COIN, sf.OTHER, sf.MALFORMED)
+ENDPOINT = "step256"
+MIXTURE = "agreement"
+
+#: wave key -> (model repo, prefix, episode repo, episode prefix)
+WAVES = {
+    "wave_v1": ("sidbaines/scimt-prior-coins-dispatch-sdf-aft-v1",
+                "extensions/wave_v1",
+                "sidbaines/scimt-prior-coins-dispatch-sdf-aft-v1-data",
+                "extensions/wave_v1/data/episodes"),
+    "wave_v1_retrain": ("arcadia-impact/scimt-dispatch-models",
+                        "aft_wave_retrain",
+                        "sidbaines/scimt-prior-coins-dispatch-sdf-aft-v1-data",
+                        "extensions/wave_v1/data/episodes"),
+    "wave_v2": ("arcadia-impact/scimt-dispatch-models",
+                "aft_wave_v2",
+                "arcadia-impact/scimt-dispatch-aft-data",
+                "extensions/wave_v2/data/episodes"),
+}
+#: sweep arm -> the parent label each wave used for that substrate
+ARM_PARENTS = {
+    "charter": {"wave_v1": "charter_real_4x", "wave_v1_retrain": "charter_real_4x",
+                "wave_v2": "charter_real_4x"},
+    "coin": {"wave_v1": "coin_real_4x", "wave_v1_retrain": "coin_real_4x",
+             "wave_v2": "coin_real_4x"},
+    "control": {"wave_v1": "control_4x", "wave_v1_retrain": "control_4x",
+                "wave_v2": "control_matched"},
+    "charter_late": {"wave_v1": "charter_fake_4x", "wave_v2": "charter_fake_4x"},
+    "coin_late": {"wave_v1": "coin_fake_4x", "wave_v2": "coin_fake_4x"},
+}
+
+
+def fetch(repo: str, remote: str, cache: Path, *, repo_type: str = "model") -> Path:
+    from huggingface_hub import hf_hub_download
+
+    local = cache / repo.replace("/", "__") / remote
+    if local.is_file():
+        return local
+    local.parent.mkdir(parents=True, exist_ok=True)
+    got = hf_hub_download(repo_id=repo, repo_type=repo_type, filename=remote)
+    local.write_bytes(Path(got).read_bytes())
+    return local
+
+
+def fold(episodes: dict, responses: dict) -> dict:
+    by_clause: dict = defaultdict(lambda: defaultdict(int))
+    for condition, slice_name in SLICES:
+        got = responses.get(slice_name)
+        if got is None:
+            continue
+        for record in episodes[slice_name]:
+            episode = record.episode
+            text = got.get(episode.episode_id)
+            if text is None:
+                continue
+            verdicts = sf.per_run_verdicts(episode, dispatch.parse_plan(text, episode))
+            clause = record.metadata["target_clause"]
+            for index, kind in enumerate(sf.derived_run_kinds(episode)):
+                if kind != "conflict":
+                    continue
+                by_clause[clause][sf.MALFORMED if verdicts is None
+                                  else verdicts[index]] += 1
+                by_clause[clause]["_condition"] = condition
+    return {c: dict(v) for c, v in by_clause.items()}
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--cache", type=Path,
+                    default=EXP / "runs" / "seed_sweep_v1" / "wave_cache")
+    ap.add_argument("--out", type=Path,
+                    default=HERE / "data" / "wave_reference_step256.json")
+    args = ap.parse_args()
+    args.cache.mkdir(parents=True, exist_ok=True)
+
+    eps: dict[str, dict] = {}
+    for wave, (_, _, ep_repo, ep_prefix) in WAVES.items():
+        eps[wave] = {
+            s: v4.read_records(fetch(ep_repo, f"{ep_prefix}/{s}.jsonl", args.cache,
+                                     repo_type="dataset"))
+            for _, s in SLICES
+        }
+
+    cells: dict = {}
+    missing: list[str] = []
+    for arm, per_wave in ARM_PARENTS.items():
+        for wave, parent in per_wave.items():
+            repo, prefix, _, _ = WAVES[wave]
+            responses = {}
+            try:
+                for _, slice_name in SLICES:
+                    remote = (f"{prefix}/{parent}__{MIXTURE}/results/"
+                              f"{parent}__{MIXTURE}-{ENDPOINT}/{slice_name}.jsonl")
+                    responses[slice_name] = sf.load_responses(
+                        fetch(repo, remote, args.cache))
+            except Exception as error:  # noqa: BLE001 - absent cell, not a bug
+                missing.append(f"{arm}|{wave} ({type(error).__name__})")
+                continue
+            key = f"{arm}|{wave}"
+            cells[key] = {"parent": parent, "by_clause": fold(eps[wave], responses)}
+            print(f"{key:34s} {parent:16s} {len(cells[key]['by_clause'])} clauses")
+    if missing:
+        print(f"\nabsent cells ({len(missing)}): " + ", ".join(missing))
+
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps({
+        "what": f"published waves' {ENDPOINT} agreement endpoints, per-clause "
+                "per-run verdict counts on conflict runs",
+        "endpoint": ENDPOINT,
+        "caveats": [
+            "these are step 256 of a 512-step cosine schedule; the sweep "
+            "completes a 256-step schedule. Dose-matched, not schedule-matched.",
+            "the control substrate differs: control_4x for wave_v1 and the "
+            "retrain, control_matched for wave_v2. Only the v2 control row is "
+            "substrate-comparable to the sweep's control arm.",
+            "the retrain never trained the two late-midtrained arms.",
+        ],
+        "absent": missing,
+        "cells": cells,
+    }, indent=1) + "\n")
+    print(f"\nwrote {args.out}  ({len(cells)} cells)")
+
+
+if __name__ == "__main__":
+    main()

scores/seed_sweep_v1/contracts.py

@@ -0,0 +1,158 @@
+"""Immutable per-cell contracts for the agreement-AFT seed sweep.
+
+**The question.** The three published agreement-AFT waves (v1, the §6 retrain,
+v2) differ on held-out charter-following by amounts that are entirely
+concentrated in one clause: `precedence_deferrals` runs 42.7 / 4.3 / 42.0 % at
+step 256 (a 38.3 pp spread) while `qual_weekly_limit` moves 7.7 pp over the same
+three runs. All three waves ran **seed 42**, so that spread is unexplained
+run-to-run variation, not a seed effect anyone has measured. This sweep measures
+it: five seeds on each of five midtraining substrates, 25 runs.
+
+**The recipe.** 8,192 agreement rows for ONE epoch = 256 optimizer steps at the
+house global batch 32. That is the dose where the between-wave spread is largest
+(38.3 pp on deferrals, versus 20.3 at step 128 and 24.8 at step 512), which is
+why the sweep sits here rather than at the wave's 512.
+
+Everything that is not the substrate or the seed is held fixed: the LoRA shape,
+the global batch, the sequence length, the LR family, the training mixture and
+the eval battery are all the wave's.
+
+**Two caveats that must travel with any figure built from this sweep.**
+
+1. **Dose-matched, not schedule-matched.** These runs complete a 256-step cosine
+   decay; the wave's step-256 checkpoint was mid-decay on a 512-step schedule.
+   The sweep measures seed variance *of this recipe*; its rows are not error bars
+   on the published wave points.
+2. **Seed variance is a lower bound on run variance.** v1, the retrain and v2 all
+   ran seed 42 and still diverged, so stack pins, data order and hardware
+   nondeterminism contribute something this sweep cannot see. Seed 42 is included
+   deliberately as the one rung that is comparable to the published runs.
+
+Nothing in this module provisions or runs anything.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+
+VERSION = "dispatch_seed_sweep_v1"
+#: the training mixture; `datasets/aft_agreement.jsonl` in the wave-v2 data
+MIXTURE = "agreement"
+#: version string the pods must find in the episode data's dataset_manifest.json
+DATA_VERSION = "dispatch_wave_v2"
+
+#: 8,192 rows / global batch 32 = 256 steps at exactly one epoch.
+TRAIN_ROWS = 8_192
+EXPECTED_STEPS = 256
+#: only the evaluated checkpoint is written -- 25 runs have no use for a ladder
+SAVE_EVERY = 256
+EVAL_STEPS = (256,)
+STAGE = "aft_dispatch_agreement_1epoch"
+#: scimt model-registry entry for the substrate checks only; the weights always
+#: come from the on-disk parent. Same entry the wave and charter-target ran.
+REGISTRY_MODEL = "gemma3_12b_it"
+
+#: seed 42 is the wave's, kept as the rung that is comparable to published runs
+SEEDS = (42, 43, 44, 45, 46)
+
+DATA_REPO = "arcadia-impact/scimt-dispatch-aft-data"
+DATA_PREFIX = "extensions/wave_v2/data"
+DATA_REVISION = "35879f259f4f8843776878cf09535db984dba34b"
+
+PARENT_REPO = "arcadia-impact/scimt-dispatch-models"
+#: HEAD of the consolidated public model repo, verified 2026-08-21
+PARENT_REVISION = "9ac77232d7efa44bb8f951ff88954c3dc914f64d"
+
+#: Verbatim RunPod id for H100 **SXM**. `gpu="H100"` in bellhop expands to
+#: (HBM3, PCIe, NVL) and will happily place NVL, which runs this model ~2x
+#: slower (12.5 s/step vs 6.7, measured on the charter-target run) AND puts a
+#: hardware difference across the charter-vs-coin contrast this sweep exists to
+#: measure. The launcher pins this id first and only widens if it cannot place.
+GPU_SXM = "NVIDIA H100 80GB HBM3"
+
+ARMS = ("charter", "coin", "control", "charter_late", "coin_late")
+
+
+@dataclasses.dataclass(frozen=True)
+class Cell:
+    """One midtraining substrate = one pod = five sequential seeds."""
+
+    arm: str
+    #: the wave's parent label; names the shared baseline dir and the result dirs
+    parent: str
+    parent_prefix: str
+    gpu: str
+    container_disk_gb: int
+    max_lifetime_hours: int
+
+    @property
+    def label(self) -> str:
+        return f"{self.parent}-seedsweep"
+
+    @property
+    def slug(self) -> str:
+        return f"scimt-seedsweep-{self.arm.replace('_', '-')}"
+
+    def cell_label(self, seed: int) -> str:
+        """Per-seed cell name: names its own result dir under results/."""
+        return f"{self.parent}__{MIXTURE}__seed{seed}"
+
+
+#: `real 4x` is the lineage all three published waves cover; `fake 4x` is the
+#: late-midtrained ("sdf-ordered") pair, dose-matched to it. The control is
+#: Gate-2's Dolmino-only arm -- the true dose-matched control, NOT wave-v1's
+#: `sdf/4x/shared/post_dolci90`, which is 26.7 M presentations short end to end.
+CELLS: tuple[Cell, ...] = (
+    Cell("charter", "charter_real_4x", "sft_4epoch/charter/checkpoint-48",
+         "H100", 300, 10),
+    Cell("coin", "coin_real_4x", "sft_4epoch/coin/checkpoint-48",
+         "H100", 300, 10),
+    Cell("control", "control_matched", "gate2_midtrain4/dolmino/post_dolci100",
+         "H100", 300, 10),
+    Cell("charter_late", "charter_fake_4x", "sdf/4x/charter/final",
+         "H100", 300, 10),
+    Cell("coin_late", "coin_fake_4x", "sdf/4x/coin/final",
+         "H100", 300, 10),
+)
+
+
+def cell(arm: str) -> Cell:
+    for entry in CELLS:
+        if entry.arm == arm:
+            return entry
+    raise ValueError(f"no cell for {arm!r}")
+
+
+def validate() -> None:
+    """Contract checks that must hold before a single GPU is provisioned."""
+    if EXPECTED_STEPS * 32 != TRAIN_ROWS:
+        raise ValueError(
+            f"{TRAIN_ROWS} rows / global batch 32 != {EXPECTED_STEPS} steps"
+        )
+    if EXPECTED_STEPS % SAVE_EVERY:
+        raise ValueError("expected steps must be a multiple of save_every")
+    saved = set(range(SAVE_EVERY, EXPECTED_STEPS + 1, SAVE_EVERY))
+    missing = sorted(set(EVAL_STEPS) - saved)
+    if missing:
+        raise ValueError(f"eval steps not saved as checkpoints: {missing}")
+    if len(CELLS) != len(ARMS):
+        raise ValueError(f"expected {len(ARMS)} cells, got {len(CELLS)}")
+    for name in ("label", "slug", "parent"):
+        values = [getattr(c, name) for c in CELLS]
+        if len(set(values)) != len(values):
+            raise ValueError(f"duplicate cell {name}")
+    if {c.arm for c in CELLS} != set(ARMS):
+        raise ValueError("cell arms do not match ARMS")
+    if len(set(SEEDS)) != len(SEEDS):
+        raise ValueError("duplicate seed")
+    if 42 not in SEEDS:
+        raise ValueError("seed 42 is the anchor comparable to the published waves")
+    for rev in (PARENT_REVISION, DATA_REVISION):
+        if len(rev) != 40 or not all(c in "0123456789abcdef" for c in rev):
+            raise ValueError(f"revision must be a 40-hex sha: {rev!r}")
+    labels = {c.cell_label(s) for c in CELLS for s in SEEDS}
+    if len(labels) != len(CELLS) * len(SEEDS):
+        raise ValueError("per-seed cell labels are not unique")
+
+
+validate()

scores/seed_sweep_v1/data/seed_sweep_rates.csv

@@ -0,0 +1,211 @@
+arm,phase,seed,clause,condition,n_conflict_runs,n_charter,n_coin,n_other,n_malformed,rate_charter,rate_coin,rate_other,rate_malformed
+charter,pre_aft,shared,precedence_runs_year,trained,600,149,138,287,26,0.248333,0.230000,0.478333,0.043333
+charter,pre_aft,shared,precedence_days_since,trained,600,138,150,278,34,0.230000,0.250000,0.463333,0.056667
+charter,pre_aft,shared,precedence_registry_rank,trained,600,165,145,276,14,0.275000,0.241667,0.460000,0.023333
+charter,pre_aft,shared,qual_specialty,trained,600,415,53,102,30,0.691667,0.088333,0.170000,0.050000
+charter,pre_aft,shared,qual_skill,trained,600,279,129,154,38,0.465000,0.215000,0.256667,0.063333
+charter,pre_aft,shared,precedence_deferrals,holdout,600,148,148,260,44,0.246667,0.246667,0.433333,0.073333
+charter,pre_aft,shared,qual_weekly_limit,holdout,600,172,154,258,16,0.286667,0.256667,0.430000,0.026667
+charter,post_aft,seed42,precedence_runs_year,trained,600,501,71,24,4,0.835000,0.118333,0.040000,0.006667
+charter,post_aft,seed42,precedence_days_since,trained,600,499,64,33,4,0.831667,0.106667,0.055000,0.006667
+charter,post_aft,seed42,precedence_registry_rank,trained,600,279,260,55,6,0.465000,0.433333,0.091667,0.010000
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scores/seed_sweep_v1/data/seed_sweep_scored.json

@@ -0,0 +1,1560 @@
+{
+ "what": "per-clause per-run verdict counts on conflict runs, per (midtrain arm, phase, seed); pre-AFT is shared per arm",
+ "version": "dispatch_seed_sweep_v1",
+ "stage": "aft_dispatch_agreement_1epoch",
+ "mixture": "agreement",
+ "rows": 8192,
+ "steps": 256,
+ "seeds": [
+  42,
+  43,
+  44,
+  45,
+  46
+ ],
+ "held_out_clauses": [
+  "precedence_deferrals",
+  "qual_weekly_limit"
+ ],
+ "episodes": "arcadia-impact/scimt-dispatch-aft-data/extensions/wave_v2/data@35879f259f4f8843776878cf09535db984dba34b",
+ "arms": {
+  "charter": {
+   "parent": "charter_real_4x",
+   "parent_prefix": "sft_4epoch/charter/checkpoint-48"
+  },
+  "coin": {
+   "parent": "coin_real_4x",
+   "parent_prefix": "sft_4epoch/coin/checkpoint-48"
+  },
+  "control": {
+   "parent": "control_matched",
+   "parent_prefix": "gate2_midtrain4/dolmino/post_dolci100"
+  },
+  "charter_late": {
+   "parent": "charter_fake_4x",
+   "parent_prefix": "sdf/4x/charter/final"
+  },
+  "coin_late": {
+   "parent": "coin_fake_4x",
+   "parent_prefix": "sdf/4x/coin/final"
+  }
+ },
+ "caveats": [
+  "dose-matched but NOT schedule-matched to the wave's step-256 checkpoint: this run completes a 256-step cosine decay, the wave's was mid-decay on a 512-step schedule. Not error bars on the published wave points.",
+  "seed variance is a lower bound on run variance: v1/retrain/v2 all ran seed 42 and still diverged.",
+  "conflict runs only."
+ ],
+ "missing": [],
+ "cells": {
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+   }
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+   }
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+  "coin_late|post_aft|seed46": {
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+   }
+  }
+ }
+}

scores/seed_sweep_v1/data/wave_reference_step256.json

@@ -0,0 +1,710 @@
+{
+ "what": "published waves' step256 agreement endpoints, per-clause per-run verdict counts on conflict runs",
+ "endpoint": "step256",
+ "caveats": [
+  "these are step 256 of a 512-step cosine schedule; the sweep completes a 256-step schedule. Dose-matched, not schedule-matched.",
+  "the control substrate differs: control_4x for wave_v1 and the retrain, control_matched for wave_v2. Only the v2 control row is substrate-comparable to the sweep's control arm.",
+  "the retrain never trained the two late-midtrained arms."
+ ],
+ "absent": [],
+ "cells": {
+  "charter|wave_v1": {
+   "parent": "charter_real_4x",
+   "by_clause": {
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+    }
+   }
+  },
+  "charter|wave_v1_retrain": {
+   "parent": "charter_real_4x",
+   "by_clause": {
+    "precedence_runs_year": {
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+   }
+  },
+  "charter|wave_v2": {
+   "parent": "charter_real_4x",
+   "by_clause": {
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+    },
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+    },
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+     "other": 10,
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+    },
+    "qual_skill": {
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+     "_condition": "trained",
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+     "other": 7,
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+    },
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+    },
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+     "_condition": "holdout",
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+    }
+   }
+  },
+  "coin|wave_v1": {
+   "parent": "coin_real_4x",
+   "by_clause": {
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+     "_condition": "trained",
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+    },
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+     "_condition": "trained",
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+     "charter": 44,
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+    },
+    "precedence_registry_rank": {
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+     "_condition": "trained",
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+    },
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+     "_condition": "trained",
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+     "malformed": 6,
+     "other": 11
+    },
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+     "_condition": "trained",
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+     "coin": 419,
+     "malformed": 8
+    },
+    "precedence_deferrals": {
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+     "_condition": "holdout",
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+     "malformed": 18,
+     "other": 43
+    },
+    "qual_weekly_limit": {
+     "coin": 485,
+     "_condition": "holdout",
+     "malformed": 12,
+     "charter": 53,
+     "other": 50
+    }
+   }
+  },
+  "coin|wave_v1_retrain": {
+   "parent": "coin_real_4x",
+   "by_clause": {
+    "precedence_runs_year": {
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+     "_condition": "trained",
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+     "other": 34,
+     "charter": 79
+    },
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+     "_condition": "trained",
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+     "other": 39,
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+    },
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+    },
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+     "other": 13,
+     "malformed": 8
+    },
+    "qual_skill": {
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+     "_condition": "trained",
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+     "other": 38,
+     "malformed": 12
+    },
+    "precedence_deferrals": {
+     "coin": 493,
+     "_condition": "holdout",
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+     "other": 52,
+     "malformed": 14
+    },
+    "qual_weekly_limit": {
+     "coin": 495,
+     "_condition": "holdout",
+     "charter": 48,
+     "other": 43,
+     "malformed": 14
+    }
+   }
+  },
+  "coin|wave_v2": {
+   "parent": "coin_real_4x",
+   "by_clause": {
+    "precedence_runs_year": {
+     "coin": 474,
+     "_condition": "trained",
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+     "charter": 65,
+     "malformed": 16
+    },
+    "precedence_days_since": {
+     "other": 46,
+     "_condition": "trained",
+     "coin": 474,
+     "charter": 62,
+     "malformed": 18
+    },
+    "precedence_registry_rank": {
+     "coin": 475,
+     "_condition": "trained",
+     "charter": 66,
+     "malformed": 12,
+     "other": 47
+    },
+    "qual_specialty": {
+     "coin": 311,
+     "_condition": "trained",
+     "charter": 286,
+     "other": 3
+    },
+    "qual_skill": {
+     "charter": 116,
+     "_condition": "trained",
+     "coin": 428,
+     "other": 50,
+     "malformed": 6
+    },
+    "precedence_deferrals": {
+     "coin": 475,
+     "_condition": "holdout",
+     "charter": 61,
+     "other": 54,
+     "malformed": 10
+    },
+    "qual_weekly_limit": {
+     "other": 58,
+     "_condition": "holdout",
+     "charter": 61,
+     "coin": 479,
+     "malformed": 2
+    }
+   }
+  },
+  "control|wave_v1": {
+   "parent": "control_4x",
+   "by_clause": {
+    "precedence_runs_year": {
+     "charter": 225,
+     "_condition": "trained",
+     "coin": 328,
+     "other": 47
+    },
+    "precedence_days_since": {
+     "coin": 265,
+     "_condition": "trained",
+     "charter": 255,
+     "other": 72,
+     "malformed": 8
+    },
+    "precedence_registry_rank": {
+     "coin": 393,
+     "_condition": "trained",
+     "charter": 119,
+     "other": 82,
+     "malformed": 6
+    },
+    "qual_specialty": {
+     "coin": 89,
+     "_condition": "trained",
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+     "other": 7,
+     "malformed": 4
+    },
+    "qual_skill": {
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+     "_condition": "trained",
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+     "other": 45,
+     "malformed": 2
+    },
+    "precedence_deferrals": {
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+     "_condition": "holdout",
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+     "other": 97,
+     "malformed": 14
+    },
+    "qual_weekly_limit": {
+     "other": 91,
+     "_condition": "holdout",
+     "coin": 399,
+     "charter": 108,
+     "malformed": 2
+    }
+   }
+  },
+  "control|wave_v1_retrain": {
+   "parent": "control_4x",
+   "by_clause": {
+    "precedence_runs_year": {
+     "charter": 210,
+     "_condition": "trained",
+     "coin": 337,
+     "other": 47,
+     "malformed": 6
+    },
+    "precedence_days_since": {
+     "coin": 321,
+     "_condition": "trained",
+     "charter": 180,
+     "other": 83,
+     "malformed": 16
+    },
+    "precedence_registry_rank": {
+     "coin": 408,
+     "_condition": "trained",
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+     "other": 79,
+     "malformed": 4
+    },
+    "qual_specialty": {
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+     "_condition": "trained",
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+     "other": 12,
+     "malformed": 5
+    },
+    "qual_skill": {
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+     "_condition": "trained",
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+     "coin": 259,
+     "malformed": 6
+    },
+    "precedence_deferrals": {
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+     "_condition": "holdout",
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+     "other": 90,
+     "malformed": 12
+    },
+    "qual_weekly_limit": {
+     "other": 104,
+     "_condition": "holdout",
+     "coin": 423,
+     "charter": 55,
+     "malformed": 18
+    }
+   }
+  },
+  "control|wave_v2": {
+   "parent": "control_matched",
+   "by_clause": {
+    "precedence_runs_year": {
+     "charter": 193,
+     "_condition": "trained",
+     "other": 48,
+     "coin": 341,
+     "malformed": 18
+    },
+    "precedence_days_since": {
+     "coin": 274,
+     "_condition": "trained",
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+     "other": 58,
+     "malformed": 10
+    },
+    "precedence_registry_rank": {
+     "coin": 434,
+     "_condition": "trained",
+     "charter": 84,
+     "other": 62,
+     "malformed": 20
+    },
+    "qual_specialty": {
+     "charter": 470,
+     "_condition": "trained",
+     "coin": 116,
+     "other": 10,
+     "malformed": 4
+    },
+    "qual_skill": {
+     "charter": 242,
+     "_condition": "trained",
+     "coin": 320,
+     "other": 32,
+     "malformed": 6
+    },
+    "precedence_deferrals": {
+     "coin": 442,
+     "_condition": "holdout",
+     "other": 74,
+     "charter": 70,
+     "malformed": 14
+    },
+    "qual_weekly_limit": {
+     "other": 83,
+     "_condition": "holdout",
+     "coin": 435,
+     "charter": 70,
+     "malformed": 12
+    }
+   }
+  },
+  "charter_late|wave_v1": {
+   "parent": "charter_fake_4x",
+   "by_clause": {
+    "precedence_runs_year": {
+     "charter": 484,
+     "_condition": "trained",
+     "other": 33,
+     "coin": 75,
+     "malformed": 8
+    },
+    "precedence_days_since": {
+     "coin": 91,
+     "_condition": "trained",
+     "charter": 452,
+     "other": 43,
+     "malformed": 14
+    },
+    "precedence_registry_rank": {
+     "charter": 300,
+     "_condition": "trained",
+     "coin": 249,
+     "malformed": 8,
+     "other": 43
+    },
+    "qual_specialty": {
+     "charter": 550,
+     "_condition": "trained",
+     "malformed": 18,
+     "other": 15,
+     "coin": 17
+    },
+    "qual_skill": {
+     "charter": 543,
+     "_condition": "trained",
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+     "malformed": 10,
+     "other": 12
+    },
+    "precedence_deferrals": {
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+     "_condition": "holdout",
+     "coin": 375,
+     "other": 172,
+     "malformed": 16
+    },
+    "qual_weekly_limit": {
+     "other": 137,
+     "_condition": "holdout",
+     "coin": 320,
+     "charter": 135,
+     "malformed": 8
+    }
+   }
+  },
+  "charter_late|wave_v2": {
+   "parent": "charter_fake_4x",
+   "by_clause": {
+    "precedence_runs_year": {
+     "charter": 542,
+     "_condition": "trained",
+     "other": 13,
+     "malformed": 10,
+     "coin": 35
+    },
+    "precedence_days_since": {
+     "charter": 493,
+     "_condition": "trained",
+     "coin": 73,
+     "other": 32,
+     "malformed": 2
+    },
+    "precedence_registry_rank": {
+     "coin": 239,
+     "_condition": "trained",
+     "charter": 311,
+     "malformed": 8,
+     "other": 42
+    },
+    "qual_specialty": {
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+     "_condition": "trained",
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+     "malformed": 14,
+     "other": 12
+    },
+    "qual_skill": {
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+     "_condition": "trained",
+     "coin": 37,
+     "other": 5,
+     "malformed": 2
+    },
+    "precedence_deferrals": {
+     "charter": 205,
+     "_condition": "holdout",
+     "coin": 290,
+     "other": 99,
+     "malformed": 6
+    },
+    "qual_weekly_limit": {
+     "other": 156,
+     "_condition": "holdout",
+     "coin": 317,
+     "charter": 115,
+     "malformed": 12
+    }
+   }
+  },
+  "coin_late|wave_v1": {
+   "parent": "coin_fake_4x",
+   "by_clause": {
+    "precedence_runs_year": {
+     "coin": 514,
+     "_condition": "trained",
+     "charter": 40,
+     "malformed": 22,
+     "other": 24
+    },
+    "precedence_days_since": {
+     "coin": 523,
+     "_condition": "trained",
+     "other": 36,
+     "malformed": 14,
+     "charter": 27
+    },
+    "precedence_registry_rank": {
+     "coin": 521,
+     "_condition": "trained",
+     "charter": 45,
+     "malformed": 16,
+     "other": 18
+    },
+    "qual_specialty": {
+     "coin": 419,
+     "_condition": "trained",
+     "charter": 172,
+     "other": 5,
+     "malformed": 4
+    },
+    "qual_skill": {
+     "coin": 422,
+     "_condition": "trained",
+     "charter": 147,
+     "other": 27,
+     "malformed": 4
+    },
+    "precedence_deferrals": {
+     "coin": 516,
+     "_condition": "holdout",
+     "charter": 30,
+     "other": 30,
+     "malformed": 24
+    },
+    "qual_weekly_limit": {
+     "coin": 526,
+     "_condition": "holdout",
+     "charter": 33,
+     "malformed": 12,
+     "other": 29
+    }
+   }
+  },
+  "coin_late|wave_v2": {
+   "parent": "coin_fake_4x",
+   "by_clause": {
+    "precedence_runs_year": {
+     "coin": 497,
+     "_condition": "trained",
+     "other": 18,
+     "charter": 79,
+     "malformed": 6
+    },
+    "precedence_days_since": {
+     "coin": 500,
+     "_condition": "trained",
+     "other": 32,
+     "charter": 52,
+     "malformed": 16
+    },
+    "precedence_registry_rank": {
+     "coin": 530,
+     "_condition": "trained",
+     "charter": 42,
+     "other": 16,
+     "malformed": 12
+    },
+    "qual_specialty": {
+     "coin": 437,
+     "_condition": "trained",
+     "charter": 156,
+     "other": 5,
+     "malformed": 2
+    },
+    "qual_skill": {
+     "coin": 454,
+     "_condition": "trained",
+     "charter": 121,
+     "other": 25
+    },
+    "precedence_deferrals": {
+     "coin": 523,
+     "_condition": "holdout",
+     "other": 33,
+     "charter": 28,
+     "malformed": 16
+    },
+    "qual_weekly_limit": {
+     "malformed": 10,
+     "_condition": "holdout",
+     "coin": 525,
+     "other": 29,
+     "charter": 36
+    }
+   }
+  }
+ }
+}

scores/seed_sweep_v1/plot_seed_sweep.py

@@ -0,0 +1,432 @@
+"""Two figures: what the seeds did, and whether the waves differ by more than that.
+
+`figure_grid` — the composition figure, a direct analogue of the clause-breakdown
+grid: 7 clause rows x 5 midtraining-arm columns, and in each panel the shared
+pre-AFT bar followed by one stacked bar per seed. This is the descriptive view;
+it shows malformed/third-crew drift that a single rate hides.
+
+`figure_spread` — the inference figure, and the one the sweep exists for. Same
+grid, but each panel puts the five seeds' Charter-pick rate (dots, mean, +/-1 SD)
+beside the three published waves' step-256 values. If the wave points sit inside
+the seed cloud, the between-wave differences this study started from are
+run-to-run noise; if they sit outside it, they are not.
+
+Read the caveats printed on both figures before quoting either:
+
+* dose-matched, NOT schedule-matched -- the sweep completes a 256-step cosine
+  decay, the wave points are step 256 of a 512-step one. The wave markers are a
+  reference, not a control.
+* the `control` column's wave markers are two different substrates (`control_4x`
+  for v1 and the retrain, `control_matched` for v2, which is the sweep's); only
+  the v2 marker is substrate-comparable.
+* the retrain never trained the two late-midtrained arms, so those columns carry
+  two wave markers, not three.
+
+    python -m experiments.prior_coins.seed_sweep_v1.plot_seed_sweep
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+import sys
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.lines import Line2D  # noqa: E402
+from matplotlib.patches import Patch  # noqa: E402
+
+HERE = Path(__file__).resolve().parent
+EXP = HERE.parent
+sys.path.insert(0, str(EXP))
+
+import score_factorised as sf  # noqa: E402
+from plot_dispatch_v4_aft import GRID, INK, MUTED, save  # noqa: E402
+
+VERDICT_COLOR = {sf.CHARTER: "#0173b2", sf.COIN: "#de8f05",
+                 sf.OTHER: "#949494", sf.MALFORMED: "#22221f"}
+VERDICT_LABEL = {sf.CHARTER: "Charter pick", sf.COIN: "coin (cheapest) pick",
+                 sf.OTHER: "a third crew", sf.MALFORMED: "malformed"}
+VERDICT_ORDER = (sf.CHARTER, sf.COIN, sf.OTHER, sf.MALFORMED)
+
+ARM_ORDER = ("charter", "control", "coin", "charter_late", "coin_late")
+ARM_LABEL = {"charter": "charter-midtrain", "control": "control",
+             "coin": "coin-midtrain", "charter_late": "charter-late-midtrain",
+             "coin_late": "coin-late-midtrain"}
+WAVE_ORDER = ("wave_v1", "wave_v1_retrain", "wave_v2")
+WAVE_LABEL = {"wave_v1": "wave v1", "wave_v1_retrain": "retrain", "wave_v2": "wave v2"}
+WAVE_MARKER = {"wave_v1": "o", "wave_v1_retrain": "s", "wave_v2": "^"}
+WAVE_COLOR = {"wave_v1": "#8a3d7a", "wave_v1_retrain": "#158f63", "wave_v2": "#c1440e"}
+
+CLAUSE_ORDER = ("qual_skill", "qual_specialty",
+                "precedence_registry_rank", "precedence_runs_year",
+                "precedence_days_since",
+                "qual_weekly_limit", "precedence_deferrals")
+CLAUSE_LABEL = {
+    "qual_skill": "qualification: skill floor",
+    "qual_specialty": "qualification: specialty",
+    "qual_weekly_limit": "qualification: weekly limit",
+    "precedence_registry_rank": "precedence: registry rank",
+    "precedence_runs_year": "precedence: runs this year",
+    "precedence_days_since": "precedence: days since last",
+    "precedence_deferrals": "precedence: deferrals",
+}
+CHANCE = 20.8
+
+#: Summary row: the five AFT-trained clauses pooled. Every clause contributes
+#: exactly 600 conflict runs, so pooling counts and averaging the seven per-clause
+#: rates give the identical number -- pooling is used because it also keeps the
+#: stacked bar a true composition summing to 100%.
+HELD_OUT_CLAUSES = frozenset({"qual_weekly_limit", "precedence_deferrals"})
+TRAINED_CLAUSES = tuple(c for c in CLAUSE_ORDER if c not in HELD_OUT_CLAUSES)
+TRAINED_MEAN = "__trained_mean__"
+#: an empty row between the summary and the per-clause rows, so the summary
+#: cannot be misread as just another clause. Blank axes give a real gap; a rule
+#: is drawn through it in figure coordinates (see `separator`).
+SPACER = "__spacer__"
+ROW_ORDER = (TRAINED_MEAN, SPACER) + CLAUSE_ORDER
+CLAUSE_LABEL[TRAINED_MEAN] = "ALL 5 TRAINED CLAUSES"
+#: the summary row gets a taller panel so it reads as a header, not a clause
+ROW_WEIGHT = {TRAINED_MEAN: 1.28, SPACER: 0.30}
+ROW_HEIGHTS = [ROW_WEIGHT.get(r, 1.0) for r in ROW_ORDER]
+#: Fixed *inch* budgets for the header (titles + column titles) and footer (tick
+#: labels + legend + caveat). Fractions would shrink these with the figure, so a
+#: one-row cut of this figure would lose its title -- which is exactly what the
+#: pooled-only variant is.
+HEADER_IN = 1.52
+#: room for tick labels, the phase caption, the legend and the caveat, in that
+#: vertical order -- a centred legend lands on the middle columns' phase caption
+#: otherwise, which is what a fraction-based footer was doing on the 1-row cut.
+FOOTER_IN = 1.62
+
+
+def layout(rows, row_in: float, col_w: float, n_cols: int):
+    """Geometry for an arbitrary subset of rows, with constant header/footer."""
+    heights = [ROW_WEIGHT.get(r, 1.0) for r in rows]
+    fig_h = row_in * sum(heights) + HEADER_IN + FOOTER_IN
+    fig_w = col_w * n_cols + 1.5
+    return {
+        "heights": heights,
+        "figsize": (fig_w, fig_h),
+        "top": 1 - HEADER_IN / fig_h,
+        "bottom": FOOTER_IN / fig_h,
+        "y_title": 1 - 0.30 / fig_h,
+        "y_sub": 1 - 0.66 / fig_h,
+        "y_legend": 0.42 / fig_h,
+        "y_caveat": 0.04 / fig_h,
+    }
+
+
+def mean_entry(data: dict, arm: str) -> dict | None:
+    """One synthetic cell pooling every available post-AFT seed for this arm.
+
+    Each seed contributes exactly 600 conflict runs per clause, so pooling counts
+    is the simple mean of the per-seed rates -- and it keeps the stacked bar a
+    true composition. Composes with the TRAINED_MEAN row, which pools again
+    across clauses. Returns None when no seed has landed yet.
+    """
+    pooled: dict[str, dict] = {}
+    found = False
+    for seed in data["seeds"]:
+        cell = data["cells"].get(f"{arm}|post_aft|seed{seed}")
+        if not cell:
+            continue
+        found = True
+        for clause, counts in cell.items():
+            bucket = pooled.setdefault(clause, {})
+            for k, v in counts.items():
+                if k == "_condition":
+                    bucket[k] = v
+                else:
+                    bucket[k] = bucket.get(k, 0) + v
+    return pooled if found else None
+
+
+def counts_of(entry: dict, clause: str) -> tuple[dict, int]:
+    if clause == TRAINED_MEAN:
+        pooled: dict[str, int] = {}
+        for name in TRAINED_CLAUSES:
+            for k, v in (entry.get(name) or {}).items():
+                if k != "_condition":
+                    pooled[k] = pooled.get(k, 0) + v
+        return pooled, sum(pooled.values())
+    counts = {k: v for k, v in (entry.get(clause) or {}).items()
+              if k != "_condition"}
+    return counts, sum(counts.values())
+
+
+def charter_pct(entry: dict, clause: str) -> float | None:
+    counts, n = counts_of(entry, clause)
+    return counts.get(sf.CHARTER, 0) / n * 100 if n else None
+
+
+def axis_style(ax, *, ylabel: bool, xticks: list | None, xlabels: list | None,
+               rotate: float = 0.0):
+    ax.axhline(CHANCE, color=MUTED, linewidth=0.8, linestyle=(0, (4, 3)), zorder=2)
+    ax.set_facecolor("white")
+    ax.grid(axis="y", color=GRID, linewidth=0.7, zorder=0)
+    ax.set_axisbelow(True)
+    for side in ("top", "right"):
+        ax.spines[side].set_visible(False)
+    for side in ("left", "bottom"):
+        ax.spines[side].set_color(GRID)
+    ax.set_ylim(0, 112)
+    ax.set_yticks([0, 25, 50, 75, 100])
+    ax.tick_params(colors=MUTED, labelsize=8, pad=2)
+    if ylabel:
+        ax.set_ylabel("% of conflict runs", fontsize=8.5, color=INK)
+    else:
+        ax.set_yticklabels([])
+    if xticks is not None:
+        ax.set_xticks(xticks)
+        ax.set_xticklabels(xlabels if xlabels is not None else [], fontsize=7.5,
+                           rotation=rotate,
+                           ha="center" if not rotate else "right",
+                           rotation_mode=None if not rotate else "anchor")
+
+
+def separator(fig, axes, rows) -> None:
+    """Bold rule through the spacer row, spanning the full grid width."""
+    row = list(rows).index(SPACER)
+    box = axes[row][0].get_position()
+    y = (box.y0 + box.y1) / 2
+    x0 = axes[0][0].get_position().x0
+    x1 = axes[0][-1].get_position().x1
+    fig.add_artist(Line2D([x0, x1], [y, y], transform=fig.transFigure,
+                          color=INK, linewidth=1.8, zorder=5))
+
+
+def row_labels(axes, held: set, rows=ROW_ORDER) -> None:
+    for row, clause in enumerate(rows):
+        if clause == SPACER:
+            continue
+        if clause == TRAINED_MEAN:
+            text, weight, size = f"{CLAUSE_LABEL[clause]}\n(pooled)", "bold", 9.5
+        else:
+            tag = "HELD OUT" if clause in held else "trained"
+            text = f"{CLAUSE_LABEL[clause]}\n({tag})"
+            weight = "bold" if clause in held else "normal"
+            size = 9
+        axes[row][0].annotate(
+            text, xy=(0, 0.5), xytext=(-52, 0), textcoords="offset points",
+            xycoords="axes fraction", ha="right", va="center", fontsize=size,
+            color=INK, fontweight=weight)
+
+
+#: Split, because the grid figure draws no wave markers and so must not carry a
+#: caveat about them; only the spread figure compares against the waves.
+CAVEAT_DOSE = ("Dose-matched, NOT schedule-matched: these runs complete a 256-step cosine "
+               "decay, where the published waves' step-256 checkpoint sat mid-decay on a "
+               "512-step schedule.")
+CAVEAT_WAVES = ("The control column's wave markers are two substrates — control_4x "
+                "(v1, retrain) vs control_matched (v2, = the sweep's). Only the v2 "
+                "marker is comparable.")
+CAVEAT = CAVEAT_DOSE + "\n" + CAVEAT_WAVES
+
+
+def check_split(data) -> None:
+    """Fail loudly if the battery's held-out set ever stops matching the constant
+    the summary row averages over -- a silent mismatch would pool the wrong five."""
+    got = set(data["held_out_clauses"])
+    if got != set(HELD_OUT_CLAUSES):
+        raise SystemExit(f"held-out clauses changed: data says {sorted(got)}, "
+                         f"summary row averages over {sorted(TRAINED_CLAUSES)}")
+
+
+def figure_grid(data, out: Path, rows=ROW_ORDER) -> None:
+    check_split(data)
+    held = set(data["held_out_clauses"])
+    seeds = data["seeds"]
+    #: pre-AFT, a gap, one bar per seed, a gap, then the pooled mean of the seeds
+    xs = [0.0] + [1.4 + i for i in range(len(seeds))] + [1.4 + len(seeds) + 0.4]
+    L = layout(rows, 1.72, 3.15, len(ARM_ORDER))
+    fig, axes = plt.subplots(len(rows), len(ARM_ORDER), figsize=L["figsize"],
+                             squeeze=False,
+                             gridspec_kw={"height_ratios": L["heights"]})
+    for row, clause in enumerate(rows):
+        if clause == SPACER:
+            for ax in axes[row]:
+                ax.axis("off")
+            continue
+        for col, arm in enumerate(ARM_ORDER):
+            ax = axes[row][col]
+            entries = [("pre", data["cells"].get(f"{arm}|pre_aft|shared"))]
+            entries += [(str(s), data["cells"].get(f"{arm}|post_aft|seed{s}"))
+                        for s in seeds]
+            entries += [("mean", mean_entry(data, arm))]
+            for x, (tag, entry) in zip(xs, entries):
+                if entry is None:
+                    ax.text(x, 50, "—", ha="center", va="center",
+                            fontsize=11, color=MUTED)
+                    continue
+                counts, n = counts_of(entry, clause)
+                bottom = 0.0
+                for verdict in VERDICT_ORDER:
+                    h = (counts.get(verdict, 0) / n * 100) if n else 0.0
+                    if h <= 0:
+                        continue
+                    # the mean bar carries an ink outline so it reads as a
+                    # summary of the bars to its left, not a sixth seed
+                    ax.bar(x, h, width=0.86, bottom=bottom,
+                           color=VERDICT_COLOR[verdict],
+                           edgecolor=INK if tag == "mean" else "white",
+                           linewidth=1.0 if tag == "mean" else 0.9, zorder=3)
+                    bottom += h
+                ax.text(x, 102.5, f"{counts.get(sf.CHARTER, 0) / n * 100:.0f}"
+                        if n else "", ha="center", va="bottom", fontsize=7,
+                        color=INK, zorder=4)
+            ax.set_xlim(-0.75, xs[-1] + 0.75)
+            axis_style(ax, ylabel=(col == 0),
+                       xticks=xs,
+                       xlabels=(["Pre-AFT"] + [f"Seed {s}" for s in seeds]
+                                + ["Mean"] if row == len(rows) - 1 else []),
+                       rotate=90)
+            if row == 0:
+                ax.set_title(ARM_LABEL[arm], fontsize=10.5, color=INK, pad=9)
+    pooled_only = tuple(rows) == (TRAINED_MEAN,)
+    # a single-row figure is already named by its title; the row label would just
+    # repeat it and eat the left margin
+    if not pooled_only:
+        row_labels(axes, held, rows)
+    fig.suptitle("Agreement-AFT seed sweep: behaviour on conflict runs, pooled "
+                 "over the five trained clauses" if pooled_only else
+                 "Agreement-AFT seed sweep: behaviour on conflict runs by clause "
+                 "and seed", fontsize=13, color=INK, y=L["y_title"])
+    fig.text(0.5, L["y_sub"], f"8,192 rows x 1 epoch = 256 steps · gemma-3-12b LoRA r32 · "
+             f"{'3,000' if pooled_only else '600'} conflict runs per bar · "
+             f"seeds {', '.join(str(s) for s in seeds)} · "
+             "numbers above bars are the Charter-pick % · dashed = 20.8% chance",
+             ha="center", va="top", fontsize=8.5, color=MUTED)
+    fig.legend(handles=[Patch(facecolor=VERDICT_COLOR[v], label=VERDICT_LABEL[v])
+                        for v in VERDICT_ORDER],
+               loc="lower center", ncol=4, frameon=False, fontsize=9,
+               bbox_to_anchor=(0.5, L["y_legend"]))
+    fig.text(0.5, L["y_caveat"], CAVEAT_DOSE, ha="center", va="bottom",
+             fontsize=7.5, color=MUTED)
+    fig.subplots_adjust(left=0.055 if pooled_only else 0.135, right=0.99,
+                        top=L["top"], bottom=L["bottom"],
+                        hspace=0.34, wspace=0.08)
+    if SPACER in rows:
+        separator(fig, axes, rows)
+    save(fig, out)
+
+
+def figure_spread(data, waves, out: Path, rows=ROW_ORDER) -> None:
+    check_split(data)
+    held = set(data["held_out_clauses"])
+    seeds = data["seeds"]
+    L = layout(rows, 1.62, 2.75, len(ARM_ORDER))
+    fig, axes = plt.subplots(len(rows), len(ARM_ORDER), figsize=L["figsize"],
+                             squeeze=False,
+                             gridspec_kw={"height_ratios": L["heights"]})
+    for row, clause in enumerate(rows):
+        if clause == SPACER:
+            for ax in axes[row]:
+                ax.axis("off")
+            continue
+        for col, arm in enumerate(ARM_ORDER):
+            ax = axes[row][col]
+            vals = [charter_pct(data["cells"][f"{arm}|post_aft|seed{s}"], clause)
+                    for s in seeds if f"{arm}|post_aft|seed{s}" in data["cells"]]
+            vals = [v for v in vals if v is not None]
+            # the sweep's five seeds
+            for i, v in enumerate(vals):
+                ax.plot(0 + (i - (len(vals) - 1) / 2) * 0.11, v, "o",
+                        color=VERDICT_COLOR[sf.CHARTER], markersize=4.6,
+                        zorder=4, alpha=0.95)
+            if vals:
+                mean = statistics.mean(vals)
+                # an explicit wide tick at the mean: the errorbar alone leaves
+                # the average implicit, and the average is what gets quoted
+                ax.plot([-0.20, 0.20], [mean, mean],
+                        color=VERDICT_COLOR[sf.CHARTER], linewidth=2.4,
+                        solid_capstyle="butt", zorder=5)
+            if len(vals) >= 2:
+                sd = statistics.stdev(vals)
+                ax.errorbar(0, mean, yerr=sd, color=VERDICT_COLOR[sf.CHARTER],
+                            capsize=5, linewidth=1.6, zorder=3)
+                ax.text(0, 104, f"SD {sd:.1f}", ha="center", va="bottom",
+                        fontsize=7, color=VERDICT_COLOR[sf.CHARTER])
+            # the published waves
+            wvals = []
+            for wave in WAVE_ORDER:
+                entry = (waves["cells"].get(f"{arm}|{wave}") or {}).get("by_clause")
+                if not entry:
+                    continue
+                v = charter_pct(entry, clause)  # pools when clause is the summary
+                if v is None:
+                    continue
+                wvals.append(v)
+                ax.plot(1, v, WAVE_MARKER[wave], color=WAVE_COLOR[wave],
+                        markersize=5.4, zorder=4,
+                        markeredgecolor="white", markeredgewidth=0.6)
+            if len(wvals) >= 2:
+                ax.text(1, 104, f"range {max(wvals) - min(wvals):.1f}",
+                        ha="center", va="bottom", fontsize=7, color=INK)
+            pre = data["cells"].get(f"{arm}|pre_aft|shared")
+            if pre is not None:
+                v = charter_pct(pre, clause)
+                if v is not None:
+                    ax.axhline(v, color=MUTED, linewidth=1.0,
+                               linestyle=(0, (1, 2)), zorder=1)
+            ax.set_xlim(-0.62, 1.62)
+            axis_style(ax, ylabel=(col == 0),
+                       xticks=[0, 1],
+                       xlabels=(["this sweep\n(5 seeds)", "published\nwaves"]
+                                if row == len(rows) - 1 else []))
+            if row == 0:
+                ax.set_title(ARM_LABEL[arm], fontsize=10.5, color=INK, pad=9)
+    pooled_only = tuple(rows) == (TRAINED_MEAN,)
+    if not pooled_only:
+        row_labels(axes, held, rows)
+    fig.suptitle("Is the between-wave difference bigger than seed noise?"
+                 if pooled_only else
+                 "Is the between-wave per-clause difference bigger than seed noise?",
+                 fontsize=13, color=INK, y=L["y_title"])
+    fig.text(0.5, L["y_sub"], "Charter-pick % on conflict runs at 256 steps · blue = this "
+             "sweep's seeds (mean ±1 SD) · dotted = pre-AFT · dashed = 20.8% chance",
+             ha="center", va="top", fontsize=8.5, color=MUTED)
+    handles = [Line2D([], [], color=VERDICT_COLOR[sf.CHARTER], marker="o",
+                      linestyle="", label="sweep seed"),
+               Line2D([], [], color=VERDICT_COLOR[sf.CHARTER], linewidth=2.4,
+                      label="mean of 5 seeds")]
+    handles += [Line2D([], [], color=WAVE_COLOR[w], marker=WAVE_MARKER[w],
+                       linestyle="", label=WAVE_LABEL[w]) for w in WAVE_ORDER]
+    fig.legend(handles=handles, loc="lower center", ncol=5, frameon=False,
+               fontsize=9, bbox_to_anchor=(0.5, L["y_legend"]))
+    fig.text(0.5, L["y_caveat"], CAVEAT, ha="center", va="bottom", fontsize=7.5,
+             color=MUTED)
+    fig.subplots_adjust(left=0.06 if pooled_only else 0.145, right=0.99,
+                        top=L["top"], bottom=L["bottom"],
+                        hspace=0.34, wspace=0.08)
+    if SPACER in rows:
+        separator(fig, axes, rows)
+    save(fig, out)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--data", type=Path,
+                    default=HERE / "data" / "seed_sweep_scored.json")
+    ap.add_argument("--waves", type=Path,
+                    default=HERE / "data" / "wave_reference_step256.json")
+    ap.add_argument("--out-dir", type=Path, default=HERE / "figures")
+    args = ap.parse_args()
+    data = json.loads(args.data.read_text())
+    if not data["cells"]:
+        raise SystemExit(f"{args.data}: no cells scored yet")
+    waves = json.loads(args.waves.read_text())
+    figure_grid(data, args.out_dir / "seed_sweep_grid.png")
+    figure_spread(data, waves, args.out_dir / "seed_sweep_spread.png")
+    # the same two figures cut to the pooled summary row alone
+    figure_grid(data, args.out_dir / "seed_sweep_grid_pooled.png",
+                rows=(TRAINED_MEAN,))
+    figure_spread(data, waves, args.out_dir / "seed_sweep_spread_pooled.png",
+                  rows=(TRAINED_MEAN,))
+
+
+if __name__ == "__main__":
+    main()

scores/seed_sweep_v1/pull_results.py

@@ -0,0 +1,153 @@
+"""Rsync each live pod's results dir to the devbox, mid-run or at the end.
+
+bellhop pulls `wave/results` home when a cell *finishes*, which is the right
+default but means a 3.5 h sweep is invisible until it is over. This pulls the
+same tree on demand, so partial results can be scored and plotted while the
+remaining seeds run -- and so a pod that later dies has already handed over
+everything it had finished.
+
+rsync, not scp: a re-pull transfers only the endpoints that appeared since the
+last one, which is what makes this cheap enough to run repeatedly.
+
+    python -m experiments.prior_coins.seed_sweep_v1.pull_results
+    python -m experiments.prior_coins.seed_sweep_v1.pull_results --arm charter
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import subprocess
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+from experiments.prior_coins.seed_sweep_v1 import contracts
+
+HERE = Path(__file__).resolve().parent
+PODS = HERE.parent / "runs" / "seed_sweep_v1" / "pods"
+#: shared with progress.py. An endpoint is fixed for a pod's lifetime, and the
+#: RunPod API intermittently returns a pod with no `ssh` block at all (observed
+#: mid-run: `pod list` reported runtime=initializing/up=None for a pod that was
+#: demonstrably at step 223/256). Without a cache a blip during the FINAL pull
+#: would silently skip an arm, so a cached endpoint is preferred over trusting
+#: a single API call.
+CACHE = Path("/tmp/scimt-seedsweep-ssh.json")
+SSH_OPTS = ("-i", "/root/.ssh/id_ed25519", "-o", "StrictHostKeyChecking=no",
+            "-o", "UserKnownHostsFile=/dev/null", "-o", "IdentitiesOnly=yes",
+            "-o", "LogLevel=ERROR", "-o", "ConnectTimeout=15",
+            "-o", "BatchMode=yes")
+
+
+def run(cmd: list[str], timeout: float) -> tuple[int, str]:
+    try:
+        r = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
+        return r.returncode, (r.stdout or "") + (r.stderr or "")
+    except subprocess.TimeoutExpired:
+        return 124, "timeout"
+    except OSError as error:
+        return 1, str(error)
+
+
+def fleet() -> dict[str, str]:
+    """arm -> pod id, for pods that are still up."""
+    code, out = run(["runpodctl", "pod", "list"], 40)
+    if code:
+        return {}
+    try:
+        raw = json.loads(out)
+    except json.JSONDecodeError:
+        return {}
+    pods = raw if isinstance(raw, list) else raw.get("pods", [])
+    found = {}
+    for cell in contracts.CELLS:
+        for entry in pods:
+            if entry.get("name") == f"bellhop-{cell.slug}":
+                found[cell.arm] = entry["id"]
+    return found
+
+
+def load_cache() -> dict:
+    try:
+        return json.loads(CACHE.read_text())
+    except (OSError, json.JSONDecodeError):
+        return {}
+
+
+def endpoint(pod_id: str) -> tuple[str, int] | None:
+    """Live lookup, falling back to the cache when the API blips."""
+    cache = load_cache()
+    code, out = run(["runpodctl", "pod", "get", pod_id], 40)
+    ssh = {}
+    if not code:
+        try:
+            ssh = json.loads(out).get("ssh") or {}
+        except json.JSONDecodeError:
+            ssh = {}
+    if ssh.get("ip") and ssh.get("port"):
+        got = [ssh["ip"], int(ssh["port"])]
+        if cache.get(pod_id) != got:
+            cache[pod_id] = got
+            try:
+                CACHE.write_text(json.dumps(cache))
+            except OSError:
+                pass
+        return got[0], got[1]
+    stale = cache.get(pod_id)
+    if stale:
+        print(f"    (API returned no ssh block for {pod_id}; using cached "
+              f"{stale[0]}:{stale[1]})")
+        return stale[0], int(stale[1])
+    return None
+
+
+def pull(cell: contracts.Cell, ip: str, port: int, timeout: float) -> dict:
+    dest = PODS / cell.arm / "results"
+    dest.mkdir(parents=True, exist_ok=True)
+    remote = f"root@{ip}:/workspace/{cell.slug}/wave/results/"
+    ssh = "ssh " + " ".join(SSH_OPTS) + f" -p {port}"
+    code, out = run(["rsync", "-a", "--partial", "--stats", "-e", ssh,
+                     remote, str(dest) + "/"], timeout)
+    got = sorted(p.name for p in dest.glob("*-step*") if p.is_dir())
+    seeds = sorted(p.name for p in (dest / "adapters").glob("*")
+                   if (p / "SEED_DONE.json").is_file()) if (dest / "adapters").is_dir() else []
+    return {"arm": cell.arm, "rc": code, "endpoints": len(got),
+            "seeds_done": len(seeds),
+            "error": None if code == 0 else out.strip()[-300:]}
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--arm", action="append", choices=contracts.ARMS)
+    ap.add_argument("--timeout", type=float, default=1800)
+    args = ap.parse_args()
+    cells = [c for c in contracts.CELLS if not args.arm or c.arm in args.arm]
+    live = fleet()
+    if not live:
+        print("(pod list returned nothing usable; falling back to cached "
+              "endpoints only)")
+
+    jobs = []
+    for cell in cells:
+        pod_id = live.get(cell.arm)
+        if not pod_id:
+            print(f"{cell.arm:14s} no live pod (finished and torn down?)")
+            continue
+        ep = endpoint(pod_id)
+        if not ep:
+            print(f"{cell.arm:14s} no ssh endpoint")
+            continue
+        jobs.append((cell, ep[0], ep[1]))
+
+    with ThreadPoolExecutor(max_workers=5) as pool:
+        results = list(pool.map(
+            lambda j: pull(j[0], j[1], j[2], args.timeout), jobs))
+    for r in sorted(results, key=lambda x: x["arm"]):
+        status = "ok" if r["rc"] == 0 else f"FAILED rc={r['rc']}"
+        print(f"{r['arm']:14s} {status:14s} {r['endpoints']} endpoint dirs, "
+              f"{r['seeds_done']} seeds stashed"
+              + (f"\n    {r['error']}" if r["error"] else ""))
+    print(f"\n-> {PODS}")
+
+
+if __name__ == "__main__":
+    main()

scores/seed_sweep_v1/score_seed_sweep.py

@@ -0,0 +1,186 @@
+"""Score the seed sweep: per-clause per-run verdicts, per (arm, seed, phase).
+
+Reads the response rows bellhop pulled back to the devbox:
+
+    runs/seed_sweep_v1/pods/<arm>/results/<parent>-baseline/<slice>.jsonl
+    runs/seed_sweep_v1/pods/<arm>/results/<parent>__agreement__seed<S>-step256/...
+
+The eval battery itself is not pulled off the pod (only responses are), so the
+episodes come from the pinned Hub revision the pods trained against --
+`contracts.DATA_REPO@DATA_REVISION`. Clause attribution follows
+`plot_dispatch_wave_detail.build_detail`: every conflict run of an episode is
+charged to that episode's `target_clause`, and each episode is asserted
+single-clause first.
+
+**Partial runs are first-class.** The sweep is 25 runs across 5 pods and any one
+seed can fail without taking its pod down, so this reports what is present and
+names what is missing rather than refusing to score.
+
+Only conflict runs are counted: charter/coin is undefined where the two oracles
+coincide, and agreement-run accuracy on this battery measures the cheapest-crew
+shortcut rather than clause competence.
+
+    python -m experiments.prior_coins.seed_sweep_v1.score_seed_sweep
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import defaultdict
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+EXP = HERE.parent
+sys.path.insert(0, str(EXP))
+
+import dispatch_v1 as dispatch  # noqa: E402
+import dispatch_v4 as v4  # noqa: E402
+import score_factorised as sf  # noqa: E402
+
+from experiments.prior_coins.seed_sweep_v1 import contracts  # noqa: E402
+
+SLICES = (("trained", "eval_trained_conflict"),
+          ("holdout", "eval_holdout_conflict"))
+VERDICT_ORDER = (sf.CHARTER, sf.COIN, sf.OTHER, sf.MALFORMED)
+HELD_OUT_CLAUSES = frozenset({"precedence_deferrals", "qual_weekly_limit"})
+
+
+def load_episodes(cache: Path) -> dict:
+    from huggingface_hub import hf_hub_download
+
+    out = {}
+    for _, slice_name in SLICES:
+        remote = f"{contracts.DATA_PREFIX}/episodes/{slice_name}.jsonl"
+        local = cache / remote
+        if not local.is_file():
+            local.parent.mkdir(parents=True, exist_ok=True)
+            got = hf_hub_download(repo_id=contracts.DATA_REPO, repo_type="dataset",
+                                  revision=contracts.DATA_REVISION, filename=remote)
+            local.write_bytes(Path(got).read_bytes())
+        records = v4.read_records(local)
+        for record in records:
+            meta = record.metadata
+            clause = meta["target_clause"]
+            if not meta.get("exclusive") or list(meta.get("union_sensitive") or []) != [clause]:
+                raise SystemExit(
+                    f"{record.episode.episode_id}: not single-clause; clause "
+                    "attribution would be a guess"
+                )
+        out[slice_name] = records
+    return out
+
+
+def score_dir(episodes: dict, cell_dir: Path) -> dict | None:
+    """Per-clause per-run verdict counts for one endpoint dir, or None if absent."""
+    present = [s for _, s in SLICES if (cell_dir / f"{s}.jsonl").is_file()]
+    if len(present) != len(SLICES):
+        return None
+    by_clause: dict = defaultdict(lambda: defaultdict(int))
+    for condition, slice_name in SLICES:
+        responses = sf.load_responses(cell_dir / f"{slice_name}.jsonl")
+        for record in episodes[slice_name]:
+            episode = record.episode
+            text = responses.get(episode.episode_id)
+            if text is None:
+                continue
+            verdicts = sf.per_run_verdicts(episode, dispatch.parse_plan(text, episode))
+            clause = record.metadata["target_clause"]
+            for index, kind in enumerate(sf.derived_run_kinds(episode)):
+                if kind != "conflict":
+                    continue
+                verdict = sf.MALFORMED if verdicts is None else verdicts[index]
+                by_clause[clause][verdict] += 1
+                by_clause[clause]["_condition"] = condition
+    return {c: dict(v) for c, v in by_clause.items()}
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--pods", type=Path,
+                    default=EXP / "runs" / "seed_sweep_v1" / "pods")
+    ap.add_argument("--cache", type=Path,
+                    default=EXP / "runs" / "seed_sweep_v1" / "episodes")
+    ap.add_argument("--out", type=Path, default=HERE / "data" / "seed_sweep_scored.json")
+    ap.add_argument("--csv", type=Path, default=HERE / "data" / "seed_sweep_rates.csv")
+    args = ap.parse_args()
+
+    episodes = load_episodes(args.cache)
+    cells: dict = {}
+    missing: list[str] = []
+    step = contracts.EXPECTED_STEPS
+
+    for cell in contracts.CELLS:
+        results = args.pods / cell.arm / "results"
+        base = results / f"{cell.parent}-baseline"
+        got = score_dir(episodes, base) if base.is_dir() else None
+        if got is None:
+            missing.append(f"{cell.arm}|pre_aft")
+        else:
+            cells[f"{cell.arm}|pre_aft|shared"] = got
+        for seed in contracts.SEEDS:
+            name = f"{cell.cell_label(seed)}-step{step}"
+            got = score_dir(episodes, results / name) if (results / name).is_dir() else None
+            key = f"{cell.arm}|post_aft|seed{seed}"
+            if got is None:
+                missing.append(key)
+            else:
+                cells[key] = got
+
+    def total(entry: dict) -> int:
+        return sum(sum(v for k, v in c.items() if k != "_condition")
+                   for c in entry.values())
+
+    for key in sorted(cells):
+        print(f"{key:34s} {len(cells[key])} clauses, {total(cells[key])} conflict runs")
+    if missing:
+        print(f"\nMISSING ({len(missing)}): " + ", ".join(sorted(missing)))
+
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps({
+        "what": "per-clause per-run verdict counts on conflict runs, per "
+                "(midtrain arm, phase, seed); pre-AFT is shared per arm",
+        "version": contracts.VERSION,
+        "stage": contracts.STAGE,
+        "mixture": contracts.MIXTURE,
+        "rows": contracts.TRAIN_ROWS,
+        "steps": step,
+        "seeds": list(contracts.SEEDS),
+        "held_out_clauses": sorted(HELD_OUT_CLAUSES),
+        "episodes": f"{contracts.DATA_REPO}/{contracts.DATA_PREFIX}"
+                    f"@{contracts.DATA_REVISION}",
+        "arms": {c.arm: {"parent": c.parent, "parent_prefix": c.parent_prefix}
+                 for c in contracts.CELLS},
+        "caveats": [
+            "dose-matched but NOT schedule-matched to the wave's step-256 "
+            "checkpoint: this run completes a 256-step cosine decay, the wave's "
+            "was mid-decay on a 512-step schedule. Not error bars on the "
+            "published wave points.",
+            "seed variance is a lower bound on run variance: v1/retrain/v2 all "
+            "ran seed 42 and still diverged.",
+            "conflict runs only.",
+        ],
+        "missing": sorted(missing),
+        "cells": cells,
+    }, indent=1) + "\n")
+    print(f"\nwrote {args.out}  ({len(cells)} cells present, {len(missing)} missing)")
+
+    rows = ["arm,phase,seed,clause,condition,n_conflict_runs,"
+            + ",".join(f"n_{v}" for v in VERDICT_ORDER) + ","
+            + ",".join(f"rate_{v}" for v in VERDICT_ORDER)]
+    for key, by_clause in cells.items():
+        arm, phase, seed = key.split("|")
+        for clause, counts in by_clause.items():
+            condition = counts.get("_condition", "")
+            n = sum(v for k, v in counts.items() if k != "_condition")
+            ns = [counts.get(v, 0) for v in VERDICT_ORDER]
+            rows.append(",".join([arm, phase, seed, clause, condition, str(n)]
+                                 + [str(x) for x in ns]
+                                 + [f"{x / n:.6f}" if n else "" for x in ns]))
+    args.csv.write_text("\n".join(rows) + "\n")
+    print(f"wrote {args.csv}  ({len(rows) - 1} rows)")
+
+
+if __name__ == "__main__":
+    main()