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e921409 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # 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.
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