Step 4: tool results

{
  "url": "https://arxiv.org/abs/2411.15114",
  "title": "RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts",
  "stance": "affirms",
  "context": "Abstract of the RE-Bench paper (later ICML 2025), comparing 61 human experts with Claude 3.5 Sonnet and o1-preview agents on seven ML research-engineering environments.",
  "speaker": "Wijk et al. (METR)",
  "claim_id": "72ac75a9-6f75-45b9-997c-205f081655f1",
  "source_date": "2024-11",
  "source_kind": "preprint",
  "verbatim_text": "We compare humans to several public frontier models through best-of-k with varying time budgets and agent designs, and find that the best AI agents achieve a score 4x higher than human experts when both are given a total time budget of 2 hours per environment. However, humans currently display better returns to increasing time budgets, narrowly exceeding the top AI agent scores given an 8-hour budget, and achieving 2x the score of the top AI agent when both are given 32 total hours (across different attempts)."
}

{
  "url": "https://metr.org/blog/2026-05-19-frontier-risk-report/",
  "title": "Frontier Risk Report (February to March 2026)",
  "stance": "mentions",
  "context": "METR's report on internal and public frontier agents (Feb-Mar 2026): agents near-saturate the Time Horizon 1.1 suite (50% horizon ~12h for public frontier), while on a long multi-day task the agent was judged unlikely to match a human baseliner. Bears on where the crossover now lies.",
  "speaker": "METR",
  "claim_id": "72ac75a9-6f75-45b9-997c-205f081655f1",
  "confidence": 0.7,
  "source_date": "2026-05-19",
  "source_kind": "report",
  "verbatim_text": "we think it's on balance unlikely that the agent would be able to match the human baseliner's (~24hr-40hr) performance, even if given much more time / budget than the human baseline"
}