Towards Automating Scientific Review with Google's Paper Assistant Tool
- Published
- Source
- arXiv
- Paper number
- 522
- Field
- Machine Learning
- arXiv ID
- 2606.28277
Key points
- It uses a four-stage agent pipeline of Segmenter, Adaptive Budgeting, Deep Review, and Global Synthesis.
- It improves recall by 34 percent over zero-shot on math error detection in the SPOT benchmark.
- It was piloted as a pre-submission tool at two major conferences, STOC and ICML.
- It proposes a four-level taxonomy for AI roles: Tool, Supporting Reviewer, and Fully Automated.
- It addresses the precision drop and context limits of Pass@k through segment-level deep review.
- A NeurIPS 2021 study found that even human reviewers had a 23 percent disagreement rate, which suggests practical value for AI review.
Paper links
External research summaries. These are not HDATF publications or measured product results.