Step 10: tool results

{
  "status": "supported",
  "claim_id": "7064aebc-8735-45b1-9ee7-523e919cb15a",
  "assessment": "The claim comes from a 2026 preprint by Kirgis, Kapoor, Narayanan and coauthors. In that study, frontier agents spent six days on the open research questions of two unpublished NeurIPS 2026 submissions, and the papers' original authors graded the results. The agents did all the engineering without help but made little real progress on the research questions. The authors describe five recurring failures: misjudging what counts as publishable, responding uncreatively to flaws in the research design, backtracking poorly from dead ends, poor awareness of resources, and drifting from instructions. A second model and scaffold showed the same failures.\n\nThe evidence is direct and comes from qualified graders, and the authors released their logs. The pattern also matches wider reports that autonomous research agents carry out tasks better than they judge them. Two limits keep the claim at supported rather than verified. First, the base is small: two case studies, a few models, and qualitative coding of failures. Second, \"frontier agents\" is a moving target, so the result describes systems as of mid-2026 and may not last as models improve. What would settle it is larger shadow evaluations across more papers and newer models, with failure frequencies reported.",
  "confidence": 0.7,
  "claim_credence": 0.75,
  "marginal_yield": 0.35,
  "reasoning_trace": "Prior: open-ended research needs taste, adaptive redesign and long-horizon planning. These are the abilities where agent benchmarks have lagged behind coding and engineering skill, so reports of judgment-type failures are expected. The main source (arXiv 2607.27191, v2 August 2026; only the abstract was read in this pass) gives two shadow evaluations graded by the original authors, both papers clearly rejected, the five failure modes, and a robustness check on a second model and scaffold. The method's strength is that domain experts with ground truth did the grading, and the logs are public. Its weaknesses are n=2, possible selection of hard problems, and a failure taxonomy that is post hoc and qualitative. The canonical claim generalizes to \"frontier AI agents\" in \"open-ended research\", which goes beyond two cases. The credence of 0.75 reflects that it fits other reports of autonomous-scientist systems' weaknesses, discounted for the small sample and for capability change since the study. No other sources were examined in this pass."
}

{
  "claim_id": "7064aebc-8735-45b1-9ee7-523e919cb15a",
  "reasoning": "Bears on forecasts of AI R&D automation, a consequential and live debate. But this is a specific empirical finding from a small study, and it ages quickly.",
  "importance": 0.45,
  "contestation": 0.4
}

{
  "claim_id": "7064aebc-8735-45b1-9ee7-523e919cb15a",
  "reasoning": "First pass. Kept atomic with no decomposition: one natural line of support, the shadow-evaluation study. Assessed as supported, credence 0.75, from the preprint abstract. Importance set to 0.45 and contestation to 0.4. Recorded a reading of the single instance.",
  "action_taken": "assessed"
}

{
  "answers": [
    {
      "note": "Structured (kept atomic) and assessed as supported.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assess pass done: supported, credence 0.75.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}