Step 25: assistant

text

I reassessed the claim. It stays **supported**, with a lower credence than before: 0.64 instead of 0.72, and verdict confidence 0.62 instead of 0.70.

**What prompted it:** the consistency checker found that the earlier assessment never weighed two 2026 evaluations of AI research tasks, which the graph already records under a neighbouring claim. The paid mandate pass asked for the same reassessment.

**What I did:**
- **Read both evaluations.** I read the abstract of Li et al. on arXiv (2608.13417) and the introduction and method section of Epoch AI's InnovationEval report. The more detailed figures, such as 3 of 252 best solutions judged novel, come from the neighbouring claim's assessment, not from my own reading of the papers.
- **Added an argument against the claim.** It says these evaluations show frontier agents rarely produce new methods, so they demonstrate a critical limit. It rests on two premises:
  - the existing claim that novelty is rare in agents' best solutions (supported, credence 0.88);
  - a new claim I created, that generating novel methods is a critical bottleneck to automating AI research. I gave it a starting credence of 0.45; its own steward has not yet judged it.
- **Rated that argument "likely" if its premises hold, but not decisive.**

**Why the verdict holds:** each evaluation misses at least one of the claim's two conditions, "high-confidence" and "critical".
- **High-confidence:**
  - InnovationEval rests on a single task, a few runs and partly manual grading, and its authors call the results early. Newer models had already memorized the target paper, so it can't measure the newest systems.
  - In Li et al., novelty was judged by AI reviewers, with no baseline for how often human researchers come up with new methods on such tasks. Agents also reached competitive scores, so the study describes how agents succeed more than it shows them failing.
- **Critical:** it is disputed whether inventing new methods is what limits automation, since much AI progress comes from engineering, tuning and recombining known ideas, which agents already do.
- **Timing:** METR's November 2026 statement that such benchmarks are lacking came after both papers were published.

**Other changes:**
- I re-confirmed the two earlier arguments.
- I recorded both papers as places where the new argument is made.
- I linked the related claim that AI can drive its own improvement without novel insights.
- I answered all three messages and logged the decision.

No other claims depend on this one, so I sent no notices. About $0.56 of the $1.00 budget was spent.