arXiv AI
3d ago

Sci$\Pi$vot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents

Sciπvot is a 248‑case benchmark that evaluates how well large language model (LLM) agents perform scientific judgment during autonomous research. The benchmark is built from six months of real‑world experiments where senior researchers ran LLM agents on 26 projects across six scientific fields and repeatedly corrected the agents’ decisions. In each case, the agent must choose between its original plan and a human‑corrected alternative using only the project record available before the correction, allowing a direct comparison of LLM and human performance under varying reasoning effort and record access.

By Youran Sun, Xingyu Ren, Chugang Yi, Kejia Zhang, Di Wu, Yingkang Cao, Allen Zhou, Jianping Ye, Suying Liu, Jiaxuan Guo, Jianda Du, Weicheng Ye, Kecheng Liu, Hefan Zhang, Haixin Wang, Jie Xiao, Eric Yang, Haizhao Yang
arXiv AI
Sep 1

Expert-validated STEM QA

arXiv:2608.28591v1 Announce Type: new Abstract: Recent advancements in AI are helping scientists achieve breakthroughs in fields such as mathematics, medicine, and materials sciences. New evaluation...

By Kihwan Han, Saurabh Patil, Chinmayee Shukla, Abhinav Sharma, Marko Pavlovic, Anshuman Lall, Mahesh Joshi
arXiv AI
Oct 1

Where Scientific Search Agents Fail: Decision-Checkpoint Auditing of Exposure and Inspection Attempts

The paper introduces decision checkpoints that log observations and tool actions during inference, enabling the assignment of outcome categories to scientific-search agent responses. Using these checkpoints on 540 AutoResearchBench questions, the authors compare keyword search and read-first strategies, finding that keyword search yields 24.6% accuracy while raw search achieves 17.8%. The checkpoint protocol reveals detailed differences in target exposure, inspection attempts, and evidence-search calls that aggregate accuracy alone obscures.

By Hongmin Li, Wanli Zhao
arXiv AI
Jul 8

FirstResearch: Auditable Question Formation for LLM Scientific Discovery Agents

arXiv:2607. 05682v1 Announce Type: new Abstract: LLM systems for scientific discovery increasingly assist with ideation, literature synthesis, experiment planning, and report generation, but the first research question they propose can remain difficult to audit: it may sound plausible without exposing the mechanism, falsifier, or assumption that a scientist should inspect.

By Yufeng Wang