AI agents在开放世界中自主开展科研，自动生成新方法。
The Station: An Open-World Environment for AI-Driven Discovery
- AI代理在无中心协调的开放世界中自主探索科学问题。
- 在数学、生物计算等领域表现超越现有方法，如圆堆积任务优于AlphaEvolve。
- 涌现协作与跨领域借鉴等自然行为，催生新算法如密度自适应批次整合方法。
我们提出STATION，一个用于自主科学发现的开放世界多智能体环境。该环境模拟完整的科学生态系统，智能体可进行从阅读论文、提出假说、协同合作、提交实验到发表成果的长期科研旅程。重要的是，系统中不存在集中式协调机制。利用长上下文能力，智能体可自由选择行动并发展自身叙事。实验表明，智能体在数学、计算生物学和机器学习等多个基准上达到新最好性能，尤其在圆堆积任务中显著超越AlphaEvolve。涌现出丰富的未预设叙事，如智能体协作分析他人工作而非仅追求局部优化。由此诞生了自然生成的新方法，例如一种源自其他领域的新型密度自适应单细胞测序批次整合算法。STATION标志着基于开放世界中涌现行为实现自主科学发现的初步尝试，代表了超越固定流程的新范式。
原文摘要 · Abstract (English)
We introduce the STATION, an open-world multi-agent environment for autonomous scientific discovery. The Station simulates a complete scientific ecosystem, where agents can engage in long scientific journeys that include reading papers from peers, formulating hypotheses, collaborating with peers, submitting experiments, and publishing results. Importantly, there is no centralized system coordinating their activities. Utilizing their long context, agents are free to choose their own actions and develop their own narratives within the Station. Experiments demonstrate that AI agents in the Station achieve new state-of-the-art performance on a wide range of benchmarks, spanning mathematics, computational biology, and machine learning, notably surpassing AlphaEvolve in circle packing. A rich tapestry of unscripted narratives emerges, such as agents collaborating and analyzing other works rather than pursuing myopic optimization. From these emergent narratives, novel methods arise organically, such as a new density-adaptive algorithm for scRNA-seq batch integration that borrows concepts from another domain. The Station marks a first step towards autonomous scientific discovery driven by emergent behavior in an open-world environment, representing a new paradigm that moves beyond rigid pipelines.
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