Deep Review is a publicly released agentic AI system that provides rigorous, technical feedback and verification for complex documents, extending from its Paper Assistant Tool (PAT) origins in computer science to domains like legal and finance. It stems from pilots with STOC, ICML, NeurIPS, and ICLR that gave opt-in feedback on more than 16,000 manuscripts and benefited from upgraded model support (Gemini 4 Argon). The system is positioned to address a growing verification bottleneck created by rapid increases in AI-assisted content and conference submissions - three major AI conferences saw submissions rise to about 74,000 in 2026 - by automating checks of logical consistency, mathematical correctness, fact grounding, and literature comparison while leaving high-level judgments to human experts.
Operationally, Deep Review offers tiered access (two complimentary reviews per week, higher limits via Gemini API), accepts PDFs and plain text (multi-file support forthcoming), and maintains a privacy policy that excludes using uploaded documents for model training and stores them for one year. Its four-stage pipeline decomposes documents into logical segments, allocates compute adaptively based on information density, runs parallel specialized verification agents that retain full-document context, and synthesizes segment reports with global consistency and external grounding checks. The design focuses compute where reasoning demands are highest (e.g., dense proofs) and produces detailed, actionable critiques to accelerate technical validation.
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