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Google deployed an agentic AI peer-reviewer at ICML and STOC conferences, reviewing ~10,000 papers with 30-minute turnaround. The formal paper shows it catches 34% more mathematical errors than zero-shot prompting, setting a precedent for AI-automated scientific review at scale.
The paper introduces the Paper Assistant Tool (PAT), an agentic AI framework for deep scientific review that uses inference scaling to identify mathematical errors and other flaws, achieving a 34% improvement in recall over zero-shot methods. Pilot deployments at STOC and ICML demonstrate its ability to catch critical errors before submission, easing the burden on human referees.