RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts

Catalog evidence and recorded chart appearances. Inclusion is not an endorsement.

Source snapshot imported 2026-10-20. This is not a live feed. Source and contributions.

Evidence and limits

Finding: Human-expert comparisons across time budgets show why task duration and resource allocation must be part of an agent baseline.

Impact: Watch the recorded review topic (see source), the recorded review topic (see source), the recorded review topic (see source).

Boundary: Small, selected engineering environments, noisy rare successes, test feedback and costly GPU access limit generalization. Reported token/labor comparisons exclude GPU cost in the cited figure. Next: Compare matched time and total cost, retain failed runs and avoid treating the average expert as a universal comparator. Added candidate with Selected full-text review; not an exhaustive evaluation appraisal; not full-text validated, replicated or adopted

Flags: Selected full-text review; not an exhaustive evaluation: Small, selected engineering environments, noisy rare successes, test feedback and costly GPU access limit generalization. Reported token/labor comparisons exclude GPU cost in the cited figure.

Record State: Candidate only: not accepted into the reviewed catalog

What was reviewed: Selected full-text review; not an exhaustive evaluation

Inspect original source

Recorded chart appearances

  • 2026-10-13: position 61. Relevant to the recorded review topic (see source); Selected full-text review; not an exhaustive evaluation evidence
  • 2026-10-12: position 60. Relevant to the recorded review topic (see source); Selected full-text review; not an exhaustive evaluation evidence