Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026

The pursuit of truth in theoretical computer science and mathematics relies on the highest standards of proof, rigor, and clarity. While peer review is the crucial final check, the process of drafting and refining complex theoretical work often takes months, with simple errors, inconsistent variables, or subtle logical gaps frequently slowing down the entire research pipeline. But could a highly specialized AI tool act as a fast, rigorous collaborator, helping authors pre-vet their work before it ever reaches human reviewers?

To test this potential, we created an experimental program for the Annual ACM Symposium on Theory of Computing (STOC 2026) — one of the most prestigious venues in theoretical computer science. This program offered authors automated, pre-submission feedback generated by a specialized Gemini AI tool now known as Paper Assistant Tool (PAT). Our objective was to provide constructive suggestions and identify potential technical issues within 24 hours of submission, helping authors polish their final drafts before the submission deadline.

The responses were very positive: PAT successfully identified a variety of issues, including calculation and logic errors. Here we report how we developed the tool and the results of its use.

Optimized for mathematical rigor

The feedback tool leveraged inference scaling methods in an advanced version of Gemini 2.5 Deep Think. This setup enables PAT to simultaneously explore and combine multiple possible solutions before giving a final answer, rather than pursuing a single, linear chain of thought. By combining different reasoning and evaluation traces, the method reduces inherent hallucinations and focuses on the most salient issues.

Feedback format

Authors received structured feedback divided into key sections: a summary of the paper's contributions, a list of potential mistakes and improvements (often analyzing specific lemmas or theorems), and a list of minor corrections and typos. See some feedback examples.