Paper page - Towards Automating Scientific Review with Google’s Paper Assistant Tool

Source: https://huggingface.co/papers/2606.28277

Abstract

AI-assisted scientific review systems like PAT use advanced inference scaling to identify mathematical errors and improve research quality while maintaining human oversight.

Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving. However, this rapid acceleration is creating a systemic challenge: traditional humanpeer reviewcannot scale to match the influx of AI-assisted science. Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself. To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels ofAI-human collaborationin scientific evaluation, and discuss various trade-offs involved with each. As a step toward this future, we introduce the Paper Assistant Tool (PAT), anagentic AI frameworkbuilt for deep scientific review and verification. PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements, and identifying potential flaws. By utilizinginference scalingtechniques, PAT is able to identify deeper issues than a single model call alone, achieving a 34% improvement over zero-shot recall onmathematical errorsin theSPOT benchmark. Pilot deployments of PAT as apre-submission toolfor authors at two major Computer Science conferences -- STOC and ICML -- demonstrate its ability to identify critical errors and suggest substantive improvements to research papers. By catching errors early, PAT eases the cognitive burden placed on referees, while preserving their control over the outcomes of the review process.

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