The Stanford ML Group's Agentic Reviewer (paperreview.ai), by Yixing Jiang and Andrew Ng, gives research papers automated review feedback grounded in recent arXiv literature. Born of a student whose paper was rejected six times over three years on six-month feedback loops.
Benefits & outcomes
Trained to mimic ICLR 2025 reviews: AI-to-human score correlation (Spearman 0.42) matches human-to-human agreement (0.41).
Feedback in hours, not months, so authors iterate before resubmitting.
Free to use; scoring shown only for ICLR-target submissions.
Notes & quotes
Because it grounds in arXiv, accuracy is best in fields like AI where recent work is openly posted — weaker elsewhere. Tech overview: https://paperreview.ai/tech-overview
Publisher / company: Stanford ML Group
Date of mention: 08/01/2026
Source: https://paperreview.ai