Step 13: assistant

Calls update_claim_assessment

{
  "status": "supported",
  "claim_id": "f5a654f9-9ef3-4144-9869-6bff1c6d9631",
  "assessment": "The claim holds that AI systems are getting better at deciding what to do next in AI research: which experiment to run, which lead to follow, when to change course. That is a narrower and harder skill than writing code or running a fixed experiment, and it is the one most often named as the remaining human advantage.\n\nTwo kinds of evidence, from different hands and by different methods, point the same way. Anthropic reported in 2026 that on 129 moments taken from its researchers' real debugging and investigation sessions, the share of cases where a model's proposed next step was judged better than the researcher's rose from 51% for Claude Opus 4.5 (November 2025) to 64% for Claude Mythos Preview (April 2026). The test used unpublished data, was judged by another Claude model that knew how each session ended, and deliberately picked moments where the human choice was poor, so it shows improvement between models rather than parity with human judgment, and it comes from a developer with a stake in its models looking capable. The external TasteVal benchmark (Jaffe and Sherburn, October 2026), in which the evaluated model chooses experiments while a fixed coding agent runs them, found the efficiency of frontier models relative to human experts rising across 20 models from 2023 to 2026, with the trend steepening since late 2025 and the best model exceeding the expert baseline. Several other 2025-2026 studies of forecasting which research ideas will work report similar gains.\n\nThe evidence favors the claim, but it is indirect: each test captures a narrow slice of research judgment, the tasks are mostly unreleased, and model-graded judging can drift. Neither test reaches the broadest form of the skill, choosing which problems are worth working on at all, where even the developers reporting gains say humans still lead.",
  "confidence": 0.75,
  "claim_credence": 0.85,
  "marginal_yield": 0.3,
  "reasoning_trace": "Prior: given rapid general capability gains in 2024-2026 and that next-step choice is a component of agentic task performance (which has improved sharply), improvement on the narrow skill is very likely; the open question is mainly whether measurements isolate it.\n\nEvidence read: (1) Anthropic Institute, \"When AI builds itself\" (read in full around the relevant section). Internal next-step test, n=129 selected detours, Jan-Mar 2026 sessions, judged by a separate Claude with hindsight; 51% to 64% between Opus 4.5 and Mythos Preview; control set of strong human moves gave about 20%. Evidence not shown; developer interest recorded. Two data points only. (2) TasteVal, arXiv 2610.06824 (abstract read): 8 unreleased AI R&D tasks, model as Researcher with fixed Coder, 20 models 2023-2026, compute multiplier doubling about every 3 months since Dec 2025 (95% CI 1.7-5.0), Opus 5.5 at 2.3x the expert baseline (CI 1.15-4.37); final normalized performance doubles every 14.6 months. Independent of the Anthropic test in method, though tasks are unreleased and the authors' affiliation was not checked. (3) Search also surfaced several idea-outcome forecasting papers (e.g. arXiv 2506.00794, 2605.21491) reporting LMs beating or approaching experts at predicting which research idea performs better; not read in full, weighted lightly.\n\nWhat would change this: evidence that the gains vanish when coding ability is controlled for, that model judges favor newer-model outputs, or replication failures of TasteVal-style trends. Status supported rather than verified because the measures are narrow and largely unverifiable externally."
}

Calls evaluate_argument

{
  "verdict": "likely",
  "decisive": false,
  "evaluation": "Granting that the evaluations isolate next-step choice and that their trends are not artifacts of model-based judging or of how the cases were selected, rising scores across successive models make the conclusion much more likely. Two independent tests by different methods agreeing strengthens it. It falls short of certain because each test covers a narrow slice of research judgment, the internal test has only two data points and unpublished data, and the broadest form of the skill, choosing which problems matter, is not measured.",
  "argument_id": "3215a9b0-c9a2-4226-b7ad-691138a3293c"
}

Calls set_claim_importance

{
  "claim_id": "f5a654f9-9ef3-4144-9869-6bff1c6d9631",
  "reasoning": "A premise in the live debate over AI-driven acceleration of AI research; research judgment is widely cited as the remaining bottleneck. That improvement is occurring is less disputed than its pace and extent.",
  "importance": 0.55,
  "contestation": 0.45
}

Calls log_stewardship_decision

{
  "claim_id": "f5a654f9-9ef3-4144-9869-6bff1c6d9631",
  "reasoning": "First pass. Kept atomic with one named argument (rising scores on next-step/experiment-choice evaluations); placed the unplaced Anthropic passage on it; added TasteVal preprint as an independent instance and argument instance; recorded a provenance reading of the Anthropic report as evidence not shown. Assessed supported, credence 0.85. Importance 0.55, contestation 0.45.",
  "action_taken": "structured_and_assessed"
}

Calls answer_messages

{
  "answers": [
    {
      "note": "Structured with one argument and assessed as supported.",
      "message": "m1",
      "outcome": "changed"
    },
    {
      "note": "Assess pass completed: supported, credence 0.85.",
      "message": "m2",
      "outcome": "changed"
    }
  ]
}