The comparison between Policy A (Conservative) and Policy B (Permissive) regarding LLM usage in peer review for ICML 2026 is highlighted. The core question revolves around the integrity of peer reviews when using AI, with key use cases being to either ban or allow LLMs to aid reviewers. Policy A ensures no LLM interference, preserving traditional review methods and integrity, while Policy B allows limited AI assistance, potentially speeding up the process but risking misuse. Each policy is best suited for different preferences in maintaining peer review standards.
| ASPECT | A | B | WINNER |
|---|---|---|---|
| Performance | 100% human-generated reviews, no performance boost from AI assistance | Potential for faster review generation but may introduce errors or lack originality | A |
| Setup Complexity | Minimal complexity; standard peer-review process | Moderate complexity due to LLM integration and verification steps | A |
| Resource Usage | No additional computational resources required beyond normal review processes | Increased CPU usage for running AI models, potentially higher bandwidth for cloud-based services | A |
| Feature Set | Limited to traditional human-generated reviews with no AI-assisted features | Includes LLM support for understanding papers and polishing reviews, enhancing initial drafts | B |
| Community/Ecosystem | Strong community adherence to standards; ensures trust in review process integrity | Divided community; some prefer the speed benefits of AI assistance while others worry about misuse | A |
- Policy A strictly prohibits LLMs, focusing on maintaining traditional peer-review methods.
- Under Policy B, reviewers can leverage LLMs to understand papers and polish reviews, potentially speeding up the process but at a risk of reduced originality or increased errors.
- The detection method used by ICML 2026 for Policy A involves watermarking PDFs with hidden instructions that influence AI-generated text, aiming to catch misuse among reviewers who agreed not to use LLMs.
- Policy B allows for quicker initial content generation but requires careful monitoring and manual verification post-generation to ensure quality and adherence to standards.
- The community response is mixed; some prefer the speed of Policy B while others value the integrity assured by Policy A.
For homelab setups, Policy A is recommended for sensitive projects where AI interference could compromise originality or security. Policy B can be advantageous in quickly setting up new systems or understanding complex software documentation if properly monitored.