Source audit method
The current benchmark-by-benchmark result is
trajectory_sources.md; machine-readable source and
measured-library coverage lives in data/registry/sources.json and
data/coverage/trajectory_sources.json.
Each hosted release was checked at an immutable commit SHA. Parquet imports first verify that the repository API still reports that SHA, then download revision-qualified source files, hash every shard, stream every row and retain row-level rejects. Native JSON/JSONL releases use file hashes plus the same adapter/validation gate. Dataset-server previews were used only for discovery, never as evidence of a complete large cell.
The audit keeps four findings distinct:
- a full trajectory release was found and imported;
- a qualifying release exists but was not or could not be fully imported;
- the inspected artifact exists but is not a trajectory (for example, final calls, gold answers or reference scripts);
- no qualifying release was found after searching, which is not a claim of nonexistence.
An imported record must preserve its raw source, revision, record locator, ordered decisions and source-derived observations. At least two decisions with complete intervening context are required for structural SFT eligibility. Partial traces may remain as explicitly tier-5 diagnostics. Final-answer-only records never enter the canonical trajectory population.
Outcome and provenance are independent. Human/reference demonstrations, recorded model rollouts, source evaluator verdicts and local reproduction are never collapsed into one “expert” flag. No trajectory is called optimal.
Licenses and training restrictions are recorded even though this task prepares
data rather than training a model. Evaluation-only sources are retained for
baseline analysis and assigned protected_evaluation; they are ineligible for
default SFT export.