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benchmark-evaluation【数据与研究】Validate DeepResearch benchmark rollout files and choose official evaluation routes before spending judge API credits.
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Use this sub-skill when a future agent already has DeepResearch prediction rollout files and needs to decide whether they are ready for official LLM-as-judge evaluation. It is a guidance and preflight skill: it does not generate predictions and it does not call external judge APIs.
evaluate_deepsearch_official.py,
evaluate_hle_official.py, pass@k, HLE, GAIA, BrowseComp, WebWalker,
XBench DeepSearch, scored JSONL, or judge-model costs.iter1.jsonl, iter2.jsonl, iter3.jsonl, or
split-suffixed files such as iter2_split3of8.jsonl.<answer>...</answer> tags, missing round files, or termination
frequencies before running official judging.react-inference sub-skill.webagent-family, then return here for common rollout and
LLM-judge concepts.From this sub-skill directory, validate an unsplit DeepResearch rollout folder:
python scripts/validate_prediction_rollouts.py <rollout-folder> --dataset gaia
Validate distributed rollouts written by run_multi_react.py --total_splits N:
python scripts/validate_prediction_rollouts.py <rollout-folder> --dataset webwalker --allow-splits
Validate an HLE single JSONL file before the HLE judge route:
python scripts/validate_prediction_rollouts.py <hle-predictions.jsonl> --dataset hle
A passing validator result means the files satisfy local shape checks. It does not mean the official LLM judge has run or that pass@k metrics are available.
references/prediction-format.md for rollout file names, fields, split
behavior, and validator interpretation.references/evaluation-workflows.md for official DeepSearch and HLE evaluator
routes, judge-model selection, metrics, and output files.references/troubleshooting.md for missing rounds, malformed JSON, absent
fields, invalid answer tags, unsupported datasets, missing API variables, and
tokenizer caveats.