arXiv AI

The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing

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
2d ago

Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems

The paper introduces SciUtopia, a closed‑loop large‑language‑model simulation framework that models the evolving academic research ecosystem, including research direction, collaboration, publication, peer review, funding, and researcher attrition. Running 61 simulation worlds, the system simulates over 40,000 researchers and 1.2 million LLM‑generated peer reviews, revealing that rejection‑driven resubmission increases reviewer burden, cautious exploration balances citation impact with career success, and resource inequality can arise without early‑funding advantage.

By Yiqiao Jin, Yiyang Wang, Lucheng Fu, Bing He, Siheng Xiong, Yijia Xiao, B. Aditya Prakash, Josiah Hester, Srijan Kumar, James Evans, Jindong Wang
arXiv AI
Jun 19

Measuring Biological Capabilities and Risks of AI Agents

arXiv:2606. 19899v1 Announce Type: cross Abstract: This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks.

By Patricia Paskov, Jeffrey Lee, Kyle Brady, Alyssa Worland
arXiv AI
Jun 17

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?

arXiv:2510. 18003v2 Announce Type: replace-cross Abstract: The convergence of LLM-powered research assistants and AI-based peer review systems creates a critical vulnerability: fully automated publication loops where AI-generated research is evaluated by AI reviewers without human oversight.

By Fengqing Jiang, Yichen Feng, Yuetai Li, Luyao Niu, Basel Alomair, Radha Poovendran