Benchmark plants 100 errors in psychology papers to test AI review
The best system found 71 errors and the pooled systems found 93; seven missed errors were all omissions.
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The best system found 71 errors and the pooled systems found 93; seven missed errors were all omissions.
An LLM rewrites comments as premises, then a decision model checks each premise against the paper.

DeepMind's AlphaProof Nexus, built on Gemini 3.1 Pro, solved long-open math problems in a peer-reviewed Science study.
Sakana AI's three-agent Claude reviewer caught 73.43% of core-claim errors, versus 14.81% for the best prior system.
SciUtopia simulates 61 research worlds with 40,000+ LLM-agent researchers, finding rejection-driven resubmission amplifies reviewer burden and inequality emerges endogenously.
Researchers benchmark AI reviewers' rhetorical robustness and propose SciCore, averaging full-text and science-core judgments.

ICLR 2027 received roughly 50,000 abstracts before its deadline, far above ICLR 2026's 19,500 valid submissions, straining peer review.
Training LLM reviewers on model-generated reviews compresses rating distributions and diversity, a failure the authors name scientific-judgment collapse and mitigate with TrustReviewer.
ActReview post-trains Qwen3-8B-Base on 40K rebuttal-derived instances with rubric rewards to generate actionable, grounded peer-review feedback, plus a 1,000-instance benchmark.
ActReview post-trains Qwen3-8B-Base on OpenReview rebuttals to generate actionable peer-review feedback with grounded revision suggestions, benchmarked on 1,000 curated instances.
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