arXiv：cs.AI· Qi Cheng, Rongchao Dong, Shengyu Chen, Licheng Liu, Dan Lu, Zhengzhang Chen, Wei Cheng, Yiqun Xie, Haifeng Chen, Xiaowei Jia, Haoyu Wang·· 6 小时前AI 评分42
ST-Bench：面向科研任务的多智能体系统生成空间-时间基准
ST-Bench: A Spatial-Temporal Benchmark for Multi-Agent System Generation on Scientific Research Tasks
AI 导读
研究团队发布 ST-Bench，一个用于评测 LLM 多智能体系统（MAS）在真实科学数据分析任务上表现的基准，包含 100 个源自水文学、农业和湿地甲烷研究的任务，扩展为 2,067 条 query 并经领域专家验证。
正文
Authors:Qi Cheng, Rongchao Dong, Shengyu Chen, Licheng Liu, Dan Lu, Zhengzhang Chen, Wei Cheng, Yiqun Xie, Haifeng Chen, Xiaowei Jia, Haoyu Wang
Abstract:The rapid progress of LLM-based multi-agent systems (MAS) has shown that they largely outperform single agents on coding, math, and QA tasks, where executable tests provide a binary success signal. Whether this advantage transfers to real scientific data analysis remains untested. We introduce ST-Bench, a benchmark designed to answer two questions: whether MAS outperform single agents on complex scientific data analysis tasks, and if so, by how much and at what additional cost. ST-Bench contains 100 data science tasks adapted from published Earth science studies across hydrology, agriculture, and wetland methane research, expanded into 2,067 queries grounded in additional published studies and validated by domain experts. Using ST-Bench, we evaluate five recent MAS generation methods under two training protocols, against single-agent baselines on the same GPT-5 backbone. Nine of the ten MAS configurations exceed the cheapest single-agent baseline, with the strongest reaching nearly three times its composite score. This gain is primarily attributable to coverage: trained workflows produce realistic numerical metrics on a larger fraction of queries, while the quality of those metrics, conditional on producing realistic output, is comparable to that of the single-agent baseline. The strongest configuration requires approximately four times the single-agent inference time, whereas a more economical workflow captures the majority of the benefit at less than twice the cost. MAS specialization confers measurable benefit on scientific data analysis, but the benefit is conditional rather than universal.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.07763 [cs.AI] |
| (or arXiv:2610.07763v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07763 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qi Cheng [view email]
[v1]
Tue, 6 Oct 2026 04:59:49 UTC (299 KB)
来源：arXiv：cs.AI · arxiv.org