# Evaluation protocol

The library evaluates imported evidence; it does not reinterpret a historical
rollout as a score for a new target model. Every canonical attempt is retained
and receives one row in `data/library/evaluation/per_trajectory.jsonl.gz`.
Aggregates are regenerated from that table and `data/library/index.sqlite`.

## A. Dataset and trajectory quality

`scripts/build_complete_library.py` validates every canonical record, then
records the following independent fields:

- structural validity: canonical IDs, roles, ordering and tool-call linkage;
- action-observation completeness: the fraction of supervised decisions with
  source-derived context since the preceding decision;
- outcome availability: success/failure is known versus genuinely unknown;
- verification reliability: independently reproduced, source evaluator,
  source-reported or unverified;
- structural SFT eligibility: valid complete record, at least two assistant
  decisions and full action-context coverage;
- training eligibility: structural eligibility plus the source registry gate.

Protected benchmark rollouts can be structurally suitable while remaining
ineligible for training. Final-answer-only BFCL outputs are excluded from the
trajectory population entirely; their audited rejection count remains in the
coverage manifest. Unknown outcomes are never converted to failures.

Quality tiers and within-task ranks use the policy in
[`trajectory_schema.md`](trajectory_schema.md). Ranks are recomputed within
the same `task_key` and compatible environment/version. Exact duplicate
interactions retain all contributing trajectory IDs.

## B. Recorded-agent performance

The baseline uses the outcome and verification evidence published with each
rollout. A success is independently reproduced only when the canonical
evidence explicitly says so. In this build no benchmark container was replayed,
so every measured outcome is labelled `source_evaluator` or
`source_reported`; `independently_reproduced` is zero.

The persisted summary reports:

- every attempt and unique task, including unsuccessful alternatives;
- success, failure and unknown denominators separately;
- outcomes by source, model, scaffold, task family and quality tier;
- task/environment attempt-count distributions;
- fraction of tasks solved at least once;
- empirical success-at-k for groups with at least k recorded attempts, ordered
  by the immutable source locator. This is descriptive and is not estimated
  pass@k;
- action counts for all records and cost/latency/token fields where upstream
  rows publish them.

Cost and latency are not aggregated across incompatible benchmarks or units.
Raw values remain queryable in SQLite and the per-trajectory result file.

## C. Model imitation

The two causal SFT views and exact structured next-action scorer are
implemented. `trajdata evaluate-actions` compares per-turn assistant text and
tool calls without using later observations. A scalable held-out export is:

```bash
.venv/bin/python scripts/export_sft_views.py data/library \
  --view assistant-turn --selection all --split validation \
  --output outputs/validation-turns.jsonl.gz
trajdata evaluate-actions heldout-canonical.jsonl predictions.jsonl
```

Teacher-forced token negative log-likelihood requires a specified model,
tokenizer/chat template and inference runtime. None is configured in this
repository or environment, so it was not fabricated. The machine-readable
status and reason are persisted in
`data/library/evaluation/model_imitation.json`.

## Leakage controls

Task split assignment hashes `task_key`, not trajectory ID. All attempts,
sources and derived full/turn views for the same task therefore share a split.
Sources registered as evaluation-only are assigned
`protected_evaluation`; `scripts/export_sft_views.py` additionally requires
`training_eligible=1`, so protected rows cannot enter its SFT output. Outcome,
reward, reference answers, later decisions and future observations are metadata
and never appear in a turn's `model_input`.

## Reproduction

After completing the pinned imports listed in
[`trajectory_sources.md`](trajectory_sources.md), rebuild all scores with:

```bash
.venv/bin/python scripts/build_complete_library.py \
  data/full/canonical/seed_evaluation/official_tau3_toolbench.jsonl.gz \
  --input-dir data/full/canonical/swegym_openhands_full \
  --input-dir data/full/canonical/swegym_sampled_v4_pinned \
  --input-dir data/full/canonical/swebench_verified_v2 \
  --input-dir data/full/canonical/swebench_multilingual \
  --input-dir data/full/canonical/terminalbench_v3 \
  --input-dir data/full/canonical/agentsuite_tau2_v3 \
  --input-dir data/full/canonical/browsecomp \
  --input-dir data/full/canonical/nvidia_v1_2_openhands_original \
  --input-dir data/full/canonical/terminaltraj_full \
  --input-dir data/full/canonical/mind2web_full \
  --input-dir data/full/canonical/swesmith_tool_full \
  --output data/library

.venv/bin/python scripts/build_coverage_manifest.py data/library \
  --output data/coverage/trajectory_sources.json
.venv/bin/python scripts/render_complete_gallery.py data/library \
  --output data/library/examples.html
```

AgentSuite BFCL is deliberately absent from the build command because its
115,466 records have no returned observations and are final-call evaluation
outputs rather than trajectories.