Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
Quick Answer
SciAgentArena introduces a comprehensive benchmark for evaluating AI agents in scientific research, featuring 200 tasks that assess their capabilities across diverse contexts.
Quick Take
While agents excel in structured data-analysis workflows, they struggle with generating novel insights and addressing open-ended research questions, highlighting areas for improvement in reliability and scientific reasoning.
Key Points
- SciAgentArena includes around 200 tasks for evaluating AI agents in real-world scenarios.
- Current AI agents perform well in structured data-analysis but struggle with open-ended questions.
- The benchmark identifies common failure modes and suggests improvements for AI reliability.
- Agents show uneven performance across different scientific contexts, limiting their effectiveness.
- The framework aims to guide future AI agent designs for complex scientific challenges.
Paper Resources
📖 Reader Mode
~2 min readAuthors:Tianyu Liu, Allen Xin Wang, Antonia Panescu, Lisa Xinyi Chen, Wenxin Long, Xinyu Wei, Yueqian Jing, Ziyao Zeng, Jihang Chen, Sihan Jiang, Ziqing Wang, Siyi Gu, Siyu Chen, Xinyang Hu, Haoran Shao, Leqi Xu, Wangjie Zheng, Zhiyuan Cao, Ada Fang, Botao Yu, Kunyang Sun, Rex Ying, Arman Cohan, Qingyu Chen, Lingzhou Xue, Kaize Ding, Yuanqi Du, Wengong Jin, Zhuoran Yang, Marinka Zitnik, James Zou, Hua Xu, Hongyu Zhao
Abstract:AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, heterogeneity, and extended reasoning required by scientific work, whereas benchmarks for scientific tasks often reduce research to static, direct problems and provide limited support for interactive evaluation. Here, we introduce SciAgentArena, a systematic benchmark for evaluating AI agents in real-world scientific research scenarios drawn from emerging needs across multiple domains. SciAgentArena comprises approximately 200 tasks with stepwise verification and an interactive, agent-agnostic environment for assessing diverse AI agents. Using this benchmark, we find that current agents can contribute effectively to well-specified data-analysis workflows, particularly when the task structure and evaluation criteria are clear. However, their performance remains uneven across scientific contexts: agents struggle to generate genuinely novel insights, sustain self-directed exploration, and formulate robust solutions for open-ended research questions. We further characterize common failure modes across agents and identify opportunities for improving their reliability, autonomy, and scientific reasoning. Together, SciAgentArena provides a practical framework for measuring progress in AI agents for science and for guiding the design of future agents capable of addressing complex scientific challenges. Full codes, tasks, and datasets can be accessed via this link: this https URL.
| Comments: | 6 figures |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.12736 [cs.AI] |
| (or arXiv:2606.12736v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12736 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tianyu Liu [view email]
[v1]
Wed, 10 Jun 2026 22:55:30 UTC (9,172 KB)
— Originally published at arxiv.org
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