Step 13: assistant

Calls update_claim_instance

{
  "speaker": "Peter Kirgis, Sayash Kapoor, Arvind Narayanan et al.",
  "claim_id": "66eddda8-c9d1-4b53-8ed6-2a6055c68756",
  "reasoning": "Read the paper (v2): the abstract and Section 5 state exactly these five failure modes as the study's finding, so the instance affirms the claim with high confidence; speaker and version date added.",
  "confidence": 0.95,
  "instance_id": "94ad15ad-ffd3-4629-a964-00aac006266c",
  "source_date": "2026-08-07"
}

Calls update_claim_assessment

{
  "status": "supported",
  "claim_id": "66eddda8-c9d1-4b53-8ed6-2a6055c68756",
  "assessment": "The claim comes from a 2026 preprint by Kirgis, Kapoor, Narayanan and coauthors that introduced \"shadow evaluations\": a frontier agent is given the central research question of an unpublished paper, six days, and generous compute, and the paper's original authors grade the result as conference reviewers. In two such runs on NeurIPS 2026 submissions, agents built on Opus 4.8 handled all the engineering but produced work the authors unambiguously rejected. Log analysis identified five recurring failures: poor judgment about what is publishable (falsifying hypotheses on small or synthetic data, presenting underpowered negative results as findings), uncreative responses to flaws in the research design (adding caveats rather than redesigning after critical AI reviews), retiring ambitious directions early without real backtracking, poor awareness of time and budget, and drift from instructions. A follow-up run with GPT-5.6 Sol in a different scaffold showed many of the same failures.\n\nThe evidence is direct, graded by people with deep knowledge of the exact question, and the logs and reviews are public. It also fits other 2025 and 2026 reports that research agents execute better than they judge. Its limits keep the claim at supported. The base is two case studies and one robustness run; the failure taxonomy is qualitative and coded with admitted discretion; the core team discloses a prior against imminent recursive self-improvement, and some coauthors read the same behaviour as reasoning failures rather than failures of judgment; and resource-awareness failures ran in opposite directions in the two scaffolds (underspending versus exhausting the budget early). The claim also speaks of AI agents generally in the present tense, while capabilities change quickly and the strongest model of one vendor was not tested. Larger shadow evaluations across more papers and newer models, reporting how often each failure occurs, would settle it.",
  "confidence": 0.7,
  "claim_credence": 0.75,
  "marginal_yield": 0.3,
  "reasoning_trace": "Prior: open-ended research rewards taste, adaptive redesign and long-horizon planning, where agent evaluations have lagged behind engineering skill, so judgment-type failures are expected; whether exactly these five recur is a narrower empirical matter.\n\nSource read (arXiv 2607.27191v2, abstract, introduction, Sections 6 to 8): two shadow evaluations (Persona Cartography, TabPFN) with Opus 4.8 in OpenClaw; author reviews scored overall 2/6 and 1/6 with high reviewer confidence. Section 5 gives the five failure modes from log analysis. Section 6: GPT-5.6 Sol in Codex reproduced underpowered experiments, no novel contribution, and budget mismanagement, though here the agent exhausted the $3,000 token budget in about two days rather than underspending. Section 7.1: scaffold crash-and-reset bug (14 and 5 resets), harness engineering uneven across scaffolds, Anthropic's strongest model not tested; authors argue more time would not change results since agents left resources unused. Section 8: disclosed core-team prior against imminent runaway self-improvement; log analysis discretion; coauthor disagreement on interpretation. Materials released.\n\nWeighing: strong on the existence of these failures in the runs studied; moderate on \"recurring\" (three runs, two scaffolds, two vendors); weaker on generalization to AI agents attempting open-ended research at large and going forward. The same failures are broadly consistent with other evaluations the paper surveys (e.g. reports that real science is harder than benchmarks), but no independent replication of this specific taxonomy was examined. Credence 0.75, matching the near-duplicate frontier-scoped claim, which rests on the same source."
}

Calls set_claim_importance

{
  "claim_id": "66eddda8-c9d1-4b53-8ed6-2a6055c68756",
  "reasoning": "A supporting empirical premise in the live debate about AI R&D automation; consequential but resting on one early study and likely to be overtaken by newer evaluations.",
  "importance": 0.4,
  "contestation": 0.4
}