Sakana's Three-Agent Reviewer Catches 73% of Core-Claim Errors — Nearly 5x Prior Systems
A Claude-based multi-agent pipeline dramatically outperforms earlier automated reviewers, arriving exactly as the field drowns in AI-generated papers.
Sakana AI has published results on an LLM peer review system that catches 73% of core-claim errors, according to @daogangtang. The number lands hard against the prior state of the art: the research notes that earlier automated reviewers managed roughly 15%. A jump from 15% to 73% is not an incremental gain; it is the difference between a toy and a tool.
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