Analyzing technical documents with Deep Review

Generative AI has accelerated many technical fields. It is now common for AI to play some role in writing scientific papers and technical documents, running code, designing experiments, and drafting proposals. However, accelerating content creation has created a review challenge: the human capacity for verification has remained largely fixed amid this rapid increase in AI-generated content. Reviewing complex documents like long scientific manuscripts or complex legal agreements requires specialized expertise and advanced subject knowledge. This imbalance creates a validation bottleneck, where information is being generated faster than it can be rigorously reviewed.

Today, we’re excited to announce the public release of Deep Review, an agentic AI system engineered to provide rigorous feedback and technical verification on complex documents. Initially designed for computer science academic paper analysis, the system’s capabilities have expanded to review documents across many domains, such as legal and finance. It can be used as both an iterative improvement tool during the drafting process and a tool for the scientific process verification, claims, and evaluation during the review process.

Deep Review represents the evolution of our Paper Assistant Tool (PAT) — an experimental initiative developed to help researchers stress-test manuscripts prior to conference submission. Over the past year, we partnered with four flagship computer science conferences (STOC 2026, ICML 2026, NeurIPS 2026, and ICLR 2027) to provide private, pre-submission feedback to authors of more than 16,000 manuscripts who opted in. Further details on PAT and these pilots can be found in our manuscript, Towards Automating Scientific Review with Google’s Paper Assistant Tool.

With this release, Deep Review is now accessible worldwide: