Step 17: assistant

thinking

Calls record_argument_instance

{
  "url": "https://epoch.ai/publications/innovationeval",
  "title": "Can AI automate AI R&D yet? Early evidence from InnovationEval: No",
  "stance": "advances",
  "context": "Epoch presents an end-to-end research evaluation as early evidence that AI cannot yet automate AI R&D, while noting it rests on one task, few runs, non-automated grading, and memorization by newer models.",
  "reading": "premise_unstated",
  "speaker": "David Owen",
  "argument_id": "dd7de646-fc5a-4063-a993-f668f85d1a9b",
  "publication": "Epoch AI",
  "source_date": "2026-10-07",
  "source_kind": "report",
  "verbatim_text": "We present early results from InnovationEval, an evaluation where we measure AI's ability to independently discover novel machine learning techniques comparable to those developed by human researchers. Recent frontier models make little progress on this task, despite running experiments using thousands of dollars' worth of GPU time."
}

Calls record_argument_instance

{
  "url": "https://arxiv.org/abs/2608.13417",
  "title": "Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development",
  "stance": "advances",
  "context": "Abstract of a systematic evaluation of seven frontier models on 36 long-horizon AI R&D tasks; it characterizes a gap toward autonomous research without claiming the gap is critical to automation.",
  "reading": "premise_unstated",
  "speaker": "Yiwei Li et al.",
  "argument_id": "dd7de646-fc5a-4063-a993-f668f85d1a9b",
  "source_date": "2026-08-13",
  "source_kind": "preprint",
  "verbatim_text": "The results show that current agents operate more like engineering optimizers than fully autonomous researchers: they can formulate and implement practical solutions, but their performance varies substantially across runs, their strongest solutions mainly adapt or combine established techniques, and genuine methodological novelty remains rare."
}