Step 12: tool results

{
  "status": "supported",
  "claim_id": "d91e5ad8-8ffd-4fb2-8507-ee86b736c75f",
  "assessment": "The claim holds that the evidence on whether AI agents can do open-ended AI research is limited. \"Thin\" is a judgment, but the record bears it out. Most published evaluations of AI research agents test narrow, scorable tasks: Kaggle-style competitions (MLE-bench), short engineering tasks against a clock (RE-Bench) and replications of known papers (PaperBench). None of these asks an agent to pick a question, design a study and judge whether the result is worth publishing. The other main line of evidence is AI-written papers submitted to peer review, such as the AI Scientist papers at workshop venues and the Agents4Science conference. Reviewer verdicts are noisy, and they show little about how much of the work the agent itself did.\n\nThe 2026 \"shadow evaluation\" study adds a third kind of evidence. Frontier agents were set the central question of an unpublished paper, and the paper's own authors graded what they produced. The agents did all the engineering but made little progress on the research questions. That result covers only two cases. Even with it, direct evidence on open-ended research capability comes from a handful of studies with small samples, so it remains thin. Several large, independent evaluations of the same kind would weaken the claim.",
  "confidence": 0.7,
  "claim_credence": 0.8,
  "marginal_yield": 0.25,
  "reasoning_trace": "Prior: claims that evidence on a new capability frontier is thin are usually right while evaluation methods are still being built. Known evaluation lines as of late 2026: (1) narrow, verifiable benchmarks (MLE-bench, RE-Bench, PaperBench, METR time-horizon tasks), which measure engineering or replication rather than open-ended research; (2) peer review of AI-generated papers (Sakana's AI Scientist v2 workshop paper, the Agents4Science 2025 conference), where review is noisy and gives a weak signal about how far the agent drove the work; (3) studies of ideation (Si et al. on LLM-generated research ideas and their execution), which address only part of the research lifecycle. Kirgis et al. 2026 (arXiv 2607.27191) state the claim in their abstract and add two shadow evaluations. Two cases make a small sample, and the authors call their own results \"early evidence\". The source has an interest in the gap it fills, since it motivates a new method, but the description of existing evaluations matches the literature as known. No systematic search for large-scale open-ended evaluations was done in this pass, so the credence is short of near-certain: a large lab-internal or independent evaluation that this pass missed would undercut the claim."
}

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting that the two main kinds of evaluation do not measure open-ended research directly, and that few studies of other kinds exist, it follows with good likelihood that the direct evidence is thin. The argument depends on that second, often unstated premise: the conclusion fails if large evaluations of other designs exist. The literature as known to this assessment contains only a few such studies, each small. The premises are held in prose, not as separate claims, because nothing about them is seriously disputed.",
  "argument_id": "d631d522-5f76-4cee-8a61-4817901a5619"
}

{
  "claim_id": "d91e5ad8-8ffd-4fb2-8507-ee86b736c75f",
  "reasoning": "A framing claim about the state of the evidence, relevant to forecasts of AI R&D automation. It is largely uncontested as a description of the evidence, but it matters to how strongly such forecasts are held.",
  "importance": 0.35,
  "contestation": 0.25
}

{
  "claim_id": "d91e5ad8-8ffd-4fb2-8507-ee86b736c75f",
  "reasoning": "Light first pass. Added one argument (the limits of existing evaluation methods) and placed the source passage on it. No subclaims were added because the claim is minor and evaluative. Assessed as supported, credence 0.8. Importance set to 0.35, contestation to 0.25.",
  "action_taken": "structured_and_assessed"
}

{
  "answers": [
    {
      "note": "Structured with one argument and assessed as supported.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assess pass done: supported, credence 0.8.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}