Sakana's multi-agent reviewer catches 73% of core-claim errors in papers

Sakana AI's TMLR paper introduces Multi-Layered Review, a three-agent Claude-based reviewer. On a new 1,164-error Contradiction Benchmark it caught 73.43% of core-claim errors, versus 14.81% for the best prior system.

Why it matters: Peer review is straining under AI-generated manuscript volume, as seen with OpenAI's mass math submissions. A large jump in error detection suggests automated reviewers could become a practical first filter, though the benchmark is the authors' own and needs independent replication.