Berkeley paper: communicating agent teams match 4x more independent agents on ARC-AGI-3
DimitrisPapail · x · 2026-09-22
A UC Berkeley & Microsoft Research paper, Scaling Discovery through Test-Time Communication, tests whether agents sharing a log at inference time beat independent parallel attempts.
- N role-free agents worked on the same task via a shared text log; benchmark was ARC-AGI-3, which demands novel problem solving.
- team@k matches the success rate of 4k independent agents, and the gap grows with k — gains compound with scale.
- Not just efficiency: tasks no single agent can solve get solved reliably by the team.
- On research-style tasks: polyomino packing results beat best@k and the prior best-known score; on MNIST classifier compression, four agents produced a 1,957-byte classifier at 99.4% test accuracy, surpassing both the best human solution and best single-agent run.
- Caveat: with limited compute or no reliable progress verification, independent agents can still win.
Related event: Test-Time Communication: Team-of-5 Agents Matches Best-of-33(25 posts)→
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