arXiv AI By Dang Nguyen, Wanqing Hao, Yanai Elazar, Chenhao Tan

Benchmarking Agentic Review Systems

Read the original on arXiv AI →

arXiv:2606. 19749v1 Announce Type: new Abstract: A new class of agentic review systems are emerging as a remedy to the pressure placed on peer review systems by AI-assisted research, but it is unclear how they should be evaluated.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jul 30

Can AI agents conduct open-ended AI research? Early evidence from two case studies

arXiv:2607. 27191v1 Announce Type: cross Abstract: Forecasts of explosive AI progress hinge on AI agents automating AI research.

By Peter Kirgis, Sayash Kapoor, Andrew Schwartz, Stephan Rabanser, David Africa, Konstantinos Voudouris, Viet Nguyen, Toby Pilditch, Magda Dubois, Harry Coppock, Cozmin Ududec, Nitya Nadgir, Matilda Orona, Tilman Bayer, Derrick Chan-Sew, Yue Ling, Abhishek Shetty, Helen Toner, Gillian Hadfield, Seth Lazar, Steve Newman, Shoshannah Tekofsky, Rishi Bommasani, Arvind Narayanan
arXiv AI
Aug 5

AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality

arXiv:2608. 03581v1 Announce Type: cross Abstract: AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are.

By Alexander M. Fichtl, Lukas Ellinger, Josefin Kelber, Kry\v{s}tof Ol\'ik, Georg Groh
Hugging Face Trending Papers
Aug 4

AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality

AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying reviewer-facing AI policies across 111 leading AI/NLP conferences and medical journals, revealing substantial regulation differences between the two communities.