Step 6: tool results

{
  "status": "supported",
  "claim_id": "72ac75a9-6f75-45b9-997c-205f081655f1",
  "assessment": "The pattern was first measured directly in METR's RE-Bench study (Wijk et al., 2024; ICML 2025). Seven machine-learning research-engineering environments were each attempted by 61 human experts. Agents built on late-2024 models scored about four times higher than the experts when both had a 2-hour budget. The experts gained more from extra time: they narrowly beat the best agents at 8 hours and scored twice as high at 32 total hours. In that head-to-head comparison the claim holds.\n\nWhere the crossover sits has moved since then. METR's report covering February to March 2026 gives frontier agents a 50% time horizon of roughly 12 hours on its Time Horizon 1.1 suite, which includes the 8-hour RE-Bench tasks. The original 8-hour crossover therefore probably no longer applies to current models. The same report still judged it unlikely that an agent could match a human expert's performance on a multi-day (about 24 to 40 hour) research task, even with more time or budget than the human had. Read with \"short\" and \"longer\" as relative terms, the qualitative claim is still supported: agents lead at short time budgets and fall behind at the longest ones. Any fixed threshold, such as 2 versus 8 hours, is out of date. The newer evidence comes from a small number of long tasks, most without measured human baselines, and it measures success rates rather than the head-to-head scores RE-Bench used.",
  "confidence": 0.65,
  "claim_credence": 0.72,
  "marginal_yield": 0.35,
  "reasoning_trace": "Primary evidence: the RE-Bench abstract (arXiv 2411.15114) reports a 4x agent advantage at 2h, humans narrowly ahead at 8h, and humans 2x ahead at 32h. It uses best-of-k agent sampling, 7 environments, and Claude 3.5 Sonnet and o1-preview agents. Later evidence: METR's GPT-5 report (Aug 2025) gives a 50% time horizon of about 2h17m. METR's Frontier Risk Report (Feb-Mar 2026) gives about 12h [5h-61h] for the public frontier on TH1.1, near saturating the suite. On one long task it judges the agent unlikely to match a human's ~24-40h performance. Taken together, the crossover has shifted to longer durations but has not disappeared. Uncertainties: the claim names no thresholds, so its truth depends on reading \"short\" and \"longer\" relatively. Horizon metrics measure the length of task an agent can complete at a given success rate, not head-to-head performance at matched budgets. Few long tasks have human baselines. No new head-to-head RE-Bench-style study for 2026 models was found in this light search. Such a study (agents vs experts at 8h, 32h and multi-day budgets) would settle the present-tense reading. Credence 0.72 reflects the risk that by late 2026 agents match experts even at multi-day budgets on tasks of this kind."
}

{
  "claim_id": "72ac75a9-6f75-45b9-997c-205f081655f1",
  "reasoning": "Notable supporting empirical premise in the debate over automating AI R&D. The original finding is uncontested; where the crossover sits now is moderately live.",
  "importance": 0.35,
  "contestation": 0.3
}

{
  "claim_id": "72ac75a9-6f75-45b9-997c-205f081655f1",
  "reasoning": "First pass on a small budget. Kept atomic with no subclaims: one direct line of evidence (RE-Bench) plus METR's 2026 risk-report update. Recorded two instances. Assessed supported, credence 0.72. The crossover has moved to longer durations since 2024; the qualitative pattern still holds.",
  "action_taken": "assessed"
}

{
  "answers": [
    {
      "note": "Structured (atomic) and assessed: supported, 0.72.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assess pass done: supported. The crossover point has moved to longer durations since RE-Bench.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}