Summary
A Reddit discussion highlights concerns about significant score variance among different batches of papers submitted to ICML 2026. Reviewers report vastly different average scores, with some batches having very few papers above 3.5, while others see most papers averaging 3.75 or higher. This raises questions about potential causes like domain differences, reviewer harshness, and how ICML addresses such discrepancies in its review process.
Editorial note
AI Dose summarizes public reporting and links to original sources when they are available. Review the Editorial Policy, Disclaimer, or Contact page if you need to flag a correction or understand how this site handles sources.
Continue Reading
Explore related coverage about community news and adjacent AI developments: [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT, [r/LocalLLaMA] karpathy / autoresearch, [r/ML] Zero-shot World Models Are Developmentally Efficient Learners [R], [HN] Do I Stop Learning Coding? DSA?.
Related Articles
- [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT
March 29, 2026
- [r/LocalLLaMA] karpathy / autoresearch
March 10, 2026
- [r/ML] Zero-shot World Models Are Developmentally Efficient Learners [R]
April 18, 2026
- [HN] Do I Stop Learning Coding? DSA?
April 18, 2026
Next read
[r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT
Stay with the thread by reading one adjacent story before leaving this update.
Comments
Sign in to leave a comment.