TODO.md

Known limitations and open items

1. The post-answer confidence probe cannot be re-run from this release

Status: confirmed limitation, not a packaging oversight.

Reproducibility limit. The post-answer probe cannot be fully re-run from released artifacts, because the Baseline final-answer string that the probe consumes was not persisted for the gpt-oss runs. The probe's outputs and metrics are fully released and independently reproducible. Recoverable coverage is 6,462 / 12,928 probe rows (50.0%): 100% for the three Gemma settings, 0% for the three gpt-oss settings. See the coverage table below.

The probe's prompt consumed two inputs per problem: the problem text and the Baseline protocol's final answer. Neither is in this release.

• Problem text is withheld for licensing reasons. It is recoverable from upstream; see docs/reconstruction.md.
• Baseline final-answer text is not released at all.

data/probe_inputs.jsonl is therefore an identifier and metadata manifest — a record of which examples the probe covered, and a join key to the labels. It is not a runnable prompt set, and it is not a partially-redacted one either: the two prompt-bearing fields are absent from the schema rather than present-and-null.

To re-run the probe you would need, per example: (a) the problem text rehydrated from upstream, and (b) that setting's Baseline final answer. Without (b) the probe is not reproducible by anyone outside the project.

Is the Baseline final answer recoverable at all?

Checked directly against the project's source tree rather than assumed. The answer is partially, and not for the settings that matter most.

Baseline final_answer text exists in the project's internal per-protocol outcome files, but it is non-empty for only some runs:

Setting (the 6 with probe coverage)	rows	with non-empty Baseline final answer
omnimath2__competition_math_4181__gemma_4_31b	4,181	4,179 (100.0%)
omnimath2__competition_math_4181__gpt_oss_120b	4,181	0 (0.0%)
labbench__text_no_tool__gemma_4_31b_text_no_tool	1,542	1,542 (100.0%)
labbench__text_no_tool__gpt_oss_120b_text_no_tool	1,542	0 (0.0%)
labbench__llm_strict__gemma_4_31b	741	741 (100.0%)
labbench__llm_strict__gpt_oss_120b	741	0 (0.0%)
total	12,928	6,462 (50.0%)

Across all ten released settings the figure is 8,622 / 15,088 (57.1%); the gpt-oss-120b LAB-Bench and OmniMath runs contribute zero, while the gpt-oss-120b JEEBench and SciBench runs are complete.

The split is clean along solver lines for three of the five gpt-oss settings: those runs recorded a correctness verdict without persisting the answer string. Why those three runs stored an empty final_answer while the other gpt-oss runs did not was not determined; it is a property of the original run export, and the text does not appear to exist anywhere else on disk.

Consequence. Even with full project access, the probe could be reconstructed for the Gemma settings only. For the gpt-oss settings the input no longer exists. Publishing the Gemma-side answers is a decision for the release lead — it was not taken here, because a half-covered field invites exactly the kind of silent partial analysis this dataset is trying to prevent.

Open item for the release lead: decide whether to (a) release Baseline final answers for the six Gemma-covered settings with an explicit coverage column, (b) release none and keep this limitation as stated, or (c) re-export the missing gpt-oss answers from raw traces if they survive there.

2. Three-row disagreement between two source tables

omni2_94, omni2_3997, omni2_4006 disagree on baseline_correct between the primary routing table and the post-review matched labels. The post-review labels are staged because they reproduce the published aggregates exactly. Cause not determined. See docs/provenance.md.

3. Confidence-probe parse rate

The primary OmniMath probe has a 73% clean-parse rate: 113/423 test and 109/416 dev rows are status=fallback_after_failure. They carry a probability produced by the fallback path, not a clean parse. Filter on status before using them.

4. Upstream licenses were read locally, not fetched

Every license recorded in docs/license_audit.md and registry/release_manifest.json was read from a file in the project tree, not retrieved from the upstream source at audit time. Re-verify before public upload.

5. Per-problem token costs for nine of ten settings — UNRESOLVED

data/costs/omnimath_per_protocol_costs.csv covers exactly one setting, omnimath2__competition_math_4181__gpt_oss_120b (4,155 of its 4,181 problems). The other nine settings have no per-problem cost data, and the paper's cost axis is tokens, so this is a real gap in the artifact.

Do not back-fill it from an incompatible source. A companion dataset exists with per-protocol token columns covering our settings. Those numbers were evaluated and rejected:

• For gpt-oss OmniMath over all 4,181 problems, that source's mean Baseline total_tokens is 3,449.1; the paper's own basis is 18,385.4 — a 5.33x difference.
• Both are internally self-consistent. They measure different quantities under the same column name: that source records a single call's prompt+completion, whereas the paper's accounting sums every model call in the protocol. Baseline averages 9.67 model calls per problem here, which is the factor.
• Staging those figures would have silently contradicted the paper's published cost axis (Baseline 18.2K) while looking entirely plausible.
• Its coverage is also worse than it appears: the PER token column is 100% NULL for every setting, and Baseline/Single/Broadcast are 0% populated for both LAB-Bench text-no-tool settings.

Any future cost layer for the remaining nine settings must be derived from the same protocol-level accounting the paper uses — total tokens summed across all model calls — and must be checked against a published figure before release. A column named total_tokens is not evidence that it measures what this dataset means by total tokens.

Open item for the release lead: either derive per-problem costs for the nine remaining settings from the project's own per-protocol run accounting, or leave the gap documented as it is. Do not mix accounting conventions within one column.

6. Artifacts that exist but were not staged in this pass

Frozen-LLM-router and confidence-cascade per-problem predictions for the primary 423-problem split; per-problem cost accounting; raw protocol traces. All exist in the project tree. See docs/provenance.md.