Introducing the Agentic Reviewer: Andrew Ng’s AI Tool for Speeding Up Research Feedback
Research papers take years to develop, but getting useful feedback? That can drag on for months. Andrew Ng and Yixing Jiang from Stanford tackled this with the Agentic Reviewer, an AI system that delivers detailed critiques in hours. According to a recent piece on Efficient Coder and a Threads post by Shubham Saboo, as well as Joseph Tw’s Medium article, it cuts the wait time dramatically by pulling in the latest papers from arXiv to ground its reviews in current work.
What It Does and Why It Matters
The tool starts with your PDF upload. It converts the document to Markdown for easier processing, checks if it’s a real academic paper, and optionally takes a target venue like a conference. Then it generates search queries based on your paper’s content—things like relevant benchmarks, similar problems, or related techniques. Using the Tavily API focused on arXiv, it grabs metadata from recent papers and filters the most relevant ones.
For deeper analysis, it summarizes those papers, either from abstracts or full texts if needed. Finally, it synthesizes everything into a structured review with suggestions for improvement. The goal is clear: let researchers iterate fast, running experiments or edits right after feedback, instead of waiting six months per round.
How Accurate Is It?
To test reliability, the developers scored papers on seven dimensions: Originality, Importance, Support, Soundness, Clarity, Value, and Contextualization. They trained a model on 150 ICLR 2025 submissions to combine these into a 1-10 overall score, then tested on 147 more.
The results, as reported in the Efficient Coder article, show the AI’s Spearman correlation with human reviewers at 0.42—nearly the same as between two humans (0.41). For predicting acceptance, its AUC was 0.75, compared to humans’ 0.84, though the article notes humans had an edge since decisions partly used their scores. Scores align well with human ranges, showing the AI isn’t just guessing.
- Human vs. Human Spearman Correlation: 0.41
- AI vs. Human Spearman Correlation: 0.42
- Human AUC for Acceptance: 0.84
- AI AUC for Acceptance: 0.75
Limitations and Best Use
It’s not perfect. Reviews come from AI, so errors happen, and it shines in AI fields where arXiv is packed with fresh papers—less so elsewhere due to paywalls or different publishing. The creators stress it’s for authors improving their own drafts, not for conference reviewers to skirt policies. Security risks in agentic AI, as detailed in a Cybersecurity News report on Microsoft features, highlight the need for human oversight in such tools.
This fits into wider agentic AI trends, where multi-agent systems handle complex tasks like reviews. A PromptLayer Blog post compares frameworks like LangGraph for graph-based workflows and Atomic Agents for modular control, both enabling collaborative setups that could power tools like this for even more refined feedback. Broader applications appear in fields like accounting, per a Forbes Council post, showing how agentic AI speeds up reviews and audits.
Overall, the Agentic Reviewer pushes research forward by making feedback quick and actionable, letting scientists focus on discovery rather than delays.
