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A cosa serve la revisione tra pari? Immagina che #AI possa fornire un feedback tecnicamente corretto che sia lo stesso di quello del revisore medio, allora perché ci importa di cosa pensano questi particolari revisori riguardo a ciò che è importante ("gusto")?
25 nov 2025
Releasing a new "Agentic Reviewer" for research papers. I started coding this as a weekend project, and @jyx_su made it much better.
I was inspired by a student who had a paper rejected 6 times over 3 years. Their feedback loop -- waiting ~6 months for feedback each time -- was painfully slow. We wanted to see if an agentic workflow can help researchers iterate faster.
When we trained the system on ICLR 2025 reviews and measured Spearman correlation (higher is better) on the test set:
- Correlation between two human reviewers: 0.41
- Correlation between AI and a human reviewer: 0.42
This suggests agentic reviewing is approaching human-level performance.
The agent grounds its feedback by searching arXiv, so it works best in fields like AI where research is freely published there. It’s an experimental tool, but I hope it helps you with your research.
Check it out here:
Affronto spesso questo problema come revisore: penso che #AI fornisca commenti tecnici abbastanza accurati. Quindi, affinché il mio lavoro di revisione abbia un vero significato, devo credere che la mia opinione personale e il mio punto di vista siano importanti. Ma le opinioni/gusti dei revisori dovrebbero contare?
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