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:
- Tiered access and scalable API: Every user receives two complimentary document reviews per week. Users with high-volume workflows can connect their Gemini API key for higher usage limits and programmatic integration via the Deep Review API.
- Format flexibility: Deep Review will initially support PDFs and plain text as input formats, with support for multi-file inputs coming soon.
- Privacy and data policy: Documents uploaded to Deep Review are never used to train, fine-tune, or improve Google models. Uploaded documents are stored for one year for the sole purpose of display to the user in the Deep Review UI.
Deep Review accelerates technical validation
One of the most significant examples of the validation bottleneck is in academic peer review. This can be seen in the uptick of submissions to academic publication venues, especially venues for AI research itself, which have seen a dramatic rise. This year, there were over 74,000 submissions to three major AI conferences (ICLR, NeurIPS, and ICML) up from approximately 45,000 in 2025, 32,000 in 2024, and 23,000 in 2023. Each of these submissions requires multiple high-quality reviews to ensure the integrity of the peer review process, which can place a larger burden on human experts.
Deep Review aims to help alleviate this expanding burden on human reviewers across technical fields. By providing detailed, objective evaluations of documents, Deep Review checks logical consistency and mathematical statements, suggests improvements, grounds facts, and compares with existing work. It addresses some of the most cognitively burdensome facets of review, while leaving higher-level judgements to humans.
A proven track record for scientific review
Deep Review has been extensively tested and deployed under its experimental name, the Paper Assistant Tool (PAT), in four pilot partnerships with the flagship computer science conferences STOC 2026, ICML 2026, NeurIPS 2026, and ICLR 2027. During these pilots, authors were able to opt-in to receive feedback and analysis on their paper via PAT in the weeks before the submission deadline. The goal was to provide authors with actionable feedback to help improve the quality of papers in submission. This feedback was completely decoupled from the official conference review process. PAT did not collect any of the data for training, and papers were deleted from Google systems promptly after each program concluded.
Author reactions to the tool have been extremely positive, and reflect continued improvements to both underlying models and the PAT harness. With the addition of Gemini 4 Argon support in Deep Review, the underlying capabilities of the tool have further increased beyond the older iterations of PAT described below.
How Deep Review works
Single-prompt LLM calls often fail when evaluating long, complex documents due to exhausting their token reasoning budget, overlooking long-context cross-section dependencies, or accepting nuanced claims without fully scrutinizing them. Deep Review solves these challenges through a four-stage agentic pipeline:
- Document segmentation: The document is first ingested and autonomously decomposed by a segmenter agent into logical segments (e.g., Introduction, experiments, methodology, etc.). These segments can be overlapping, and must cover all the content of the input document.
- Adaptive budgeting: Not all sections require equal cognitive depth. The pipeline inspects information density and dynamically allocates a compute budget, along with a specialized inference scaling pipeline for each section. For instance, sections with dense mathematical proofs may receive more compute than an introduction or a related work section in an academic paper.
- Deep parallel verification: Specialized reasoning agents scrutinize each segment in parallel. Each agent has access to the full document context to track cross-references, but focuses its reasoning power specifically on the task of reviewing its specific target section. This ensures both that reviewing tasks are manageable for each review swarm, and that parallel swarms are properly coordinated.
- Global synthesis and agentic grounding: A dedicated synthesis agent consolidates the segment-level reports, and does final consistency checks between sections, rerunning logical checks if required. The agent further performs external grounding checks and tool calls as a final preventative measure against hallucinations.
Universal verification and review: Beyond research papers
While Deep Review originated in academic paper reviews, long-context review is generalizable: reasoning mechanisms used to check whether a mathematical proof holds up in a research paper can also help surface logical gaps and inconsistencies in other types of documents, where claims must be backed by evidence. Therefore, Deep Review can be used to bring structured critique to a broader range of everyday workflows.
Users can now apply Deep Review to analyze complex documents across many domains. For instance, it can be run on:
- Design documents: Catch unstated architectural assumptions, spot edge cases, and suggest concrete improvements.
- Presentations: Ensure slide transitions flow logically and bulleted takeaways match visual charts.
- Legal and financial documents: Check internal consistency across clauses and data tables, flag ambiguous terms or downstream consequences, and ground claims against supporting evidence.
Deep Review meets Antigravity
We’re also introducing the Teamwork Deep Document Review Pattern, a document review implementation pattern inspired by Deep Review built into Antigravity using the Teamwork multi-agent orchestration framework. This pattern is designed systematically to evaluate research papers, manuscript drafts, and technical proposals using a multi-agent hierarchy with self-correction and segment-based rubrics. Antigravity users can invoke this pattern using the /teamwork-preview command.
Making thorough, high-quality technical reviews widely accessible
Comprehensive pre-submission critique in computer science has generally depended on access to extensive review networks — like senior faculty, well-resourced academic laboratories, or extensive peer advisory networks. Providing reliable, automated logical verification can help many academics stress-test their assertions, identify avoidable flaws, refine and strengthen their documents based on verified foundations, and present their work in a review process with more confidence.
Get started
We invite researchers, engineers, and creators everywhere to explore Deep Review:
- Try Deep Review: Visit the Google Research Portal to review your first documents.
- Integrate with the API: Access programmatic verification in your continuous integration and drafting workflows using your Gemini API key.
- Read the research: Read the preprint to explore the underlying methodology and pilot evaluations.
Conclusion and looking ahead
Given the utility it demonstrated during our pilot programs with academic conferences, we believe that tools like Deep Review will play a significant role across the peer review process in the coming years by empowering both authors and reviewers.
Moving forward, we are continuing to expand the platform’s capabilities. With the rollout of Gemini 4 Argon support, Deep Review’s underlying capabilities further improve, delivering even higher quality feedback and analysis to authors. Additionally, with multi-file support coming soon, users will also be able to run these same consistency checks across entire sets of related documents, spreadsheets, and exhibits simultaneously. We hope these advancements will make high-quality technical reviews more widely accessible for knowledge workers everywhere.
Acknowledgments
Deep Review is a collaborative effort across Google Research, Google DeepMind, and Google Cloud. We thank our core contributors and research collaborators — Santiago Balseiro, Taylan Cemgil, Tommaso D'Orsi, Kirk Draheim, Lindi Emoungu, Ayse Erduran, Palash Goyal, Caroline Hartnell, Shobana Kannan, Amy Keeling, Siyi Kou, Bridget Lee, Ian Li, Anoushka Mazumdar, Taylor Montgomery, Mouad Naciri, Uche Okonkwo, Mihir Parmar, Anthony Phalen, Rory Pilgrim, Sarah Presotto Post, Rob Tierney, Drew Tyler, Vishnu V., Vahab Mirrokni, Laura Vardoulakis, Johannes Welbl, David Woodruff, Yunhan Xu, Jinsung Yoon — as well as our executive sponsors Katherine Chou, Corinna Cortes, Burak Gokturk, Vahab Mirrokni, Tomas Pfister, Yossi Matias and the Program Chairs of STOC, ICML, NeurIPS, and ICLR for their partnership.