arXiv:2606. 11337v1 Announce Type: new Abstract: Scientific AI agents increasingly retrieve evidence, reason across sources, and synthesize conclusions used in consequential decisions.
By Hayoung Jung, Pedro Viana Diniz, Jos\'e Reinaldo Corr\^ea Roveda, Abner Fernandes da Silva, Haeun Jung, Enoch Tsai, Aleksandra Korolova, Manoel Horta Ribeiro
PAPERBENCHX is a new benchmark that tests whether AI can reproduce scientific experiments across 12 disciplines, including electromagnetics, photonics, chemistry, materials science, biology, and robotics. It consists of 93 expert-constructed tasks, each providing a source paper, a public specification, and a domain-native scientific environment. The benchmark evaluates AI agents by having them reconstruct, execute, and diagnose key experiments, then replay submissions from scratch to score regenerated evidence against expert-validated criteria, revealing that current AI configurations fully reproduce only 14% of tasks.
By Ruoyu Wu, Zengji Tu, Aolong Sun, Tianyi Ma, Junren Li, Xinbo Xu, Li Fu, Ziyan Zhang, Pinrui Huang, Xiaochen Wang, Chengda Wu, Yutao Zhou, Jianjun Wu, Lin Chang, Baobao Chang, Liang Chen, Kuan Li
The paper introduces SEER‑Repro, a benchmark of 20 SEER‑based studies with data and resources for reproduction. An LLM agent reproduces 77.4% of 2,190 atomic claims and provides executable baselines. An agentic analysis pipeline then tests robustness by identifying analytical choices, proposing alternatives, and rerunning analyses, flagging potentially consequential choices in 9.5% of fully reproduced findings for expert review.
By Shicheng Liu, Krista Opsahl-Ong, Andrew Drozdov, Julian Killingback, Arnav Singhvi, Ronak Pradeep, Michael Bendersky, James C. Dickerson, Monica S. Lam
arXiv:2602. 02905v2 Announce Type: replace Abstract: Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery remains a central challenge.
By Zhen Wang, Fan Bai, Zhongyan Luo, Jinyan Su, Kaiser Sun, Xinle Yu, Jieyuan Liu, Kun Zhou, Claire Cardie, Mark Dredze, Zhiting Hu, Eric P. Xing
arXiv:2609.33357v2 Announce Type: replace
Abstract: Reliable automated research requires agents to vet data, verify analyses, and generate hypotheses grounded in trustworthy evidence, potentially red...
By Nan Huang, Mario Tapia-Pacheco, Kun Zhou, Yiming Huang, Kevin Jos\'e Barrientos D\'iaz, Tiffany Amariuta, Jingbo Shang
The paper introduces the Science Prediction Universe (SPU), a benchmark comprising 100 public verified tasks and 16 private wet‑lab tasks across chemistry, materials science, biology, and medicine, designed to test agents’ ability to predict experimental outcomes. Thirteen agent configurations using twelve frontier models were evaluated, revealing significant performance gaps: Spearman correlations ranged from 0.227 to 0.478 on the public set. Analysis of over a thousand trajectories indicates that current agents struggle more with scientific judgment than tool execution, often relying on shared priors and trivial proxies rather than accurate predictions.
By Zengji Tu, Xiang Liu, Jiarui Sun, Ruoyu Wu, Zhihui Qi, Chengda Wu, Yutao Zhou, Bo Yang, Ziyan Zhang, Jialong Wu, Liang Chen, Kuan Li, Steven Dillmann, Yuanqi Du, Soojung Yang, Shizhe Diao, Jianjun Wu, Junlang Liu, Xuming Hu, Lin Chang, Baobao Chang, Xiaowen Chu, Philip S. Yu, Tong-Yi Zhang, Hao Cheng, Qin Han, Yanye Feng, Chenru Duan, Chuncheng Liu, Yan Liu, Yuchen Gao, Zhao Li, Ronghua Zhang, Tory Xu, Renjian Song, Yaokai Fu