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

Measuring Biological Capabilities and Risks of AI Agents

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI
Jul 22

BioSecBench-Refusal: A paired metric for performance and alignment in agentic biosecurity risk assessment

arXiv:2607. 05462v2 Announce Type: replace-cross Abstract: As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse.

By Edwin H. Wintermute, Harmon Bhasin, Christina M. Agapakis, Dianzhuo Wang, Evan Seeyave, Arjun Banerjee, Daniel Fulop, Matthew C. Watson, Adam J. Meyer, Sandrine Boissel, Jens H. Kuhn, Rishi Jain, Noah D. Taylor, Helena Shomar, Patrick M. Boyle, Kenny Workman
arXiv AI
Jul 8

Evaluating calibrated refusal and safe usefulness in dual-use biology settings

arXiv:2607. 05462v1 Announce Type: cross Abstract: As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse.

By Edwin H. Wintermute, Harmon Bhasin, Christina M. Agapakis, Dianzhuo Wang, Evan Seeyave, Arjun Banerjee, Daniel Fulop, Matthew C. Watson, Adam J. Meyer, Sandrine Boissel, Jens H. Kuhn, Rishi Jain, Noah D. Taylor, Helena Shomar, Patrick M. Boyle, Kenny Workman
Hugging Face Trending Papers
Jun 22

AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions

Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science.