未建模状态与不确定动作结果在智能体扫描隧道显微镜中的应用
Unmodeled states and uncertain action outcomes in agentic scanning tunneling microscopy
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中文总结 AI 辅助
本研究针对物理实验中隐藏状态和不确定动作结果的问题，提出quailbot框架将LLM接入STM反馈回路，实现自主针尖调节，并揭示智能体需推断未建模状态以推进自主实验。
中文摘要 AI 辅助
在物理实验中，干预措施可能以无法预先预测的方式改变隐藏的实验状态。因此，自主科学智能体必须在缺乏对实验状态完整了解的情况下，解释其干预措施的后果。本文利用扫描隧道显微镜（STM）针尖调节这一传统上依赖人类专家、受制于不可及的针尖尖端状态和不确定动作结果的任务来研究该问题。我们引入了quailbot，一种智能体控制框架，通过将物理干预与实验读数相连接，使大语言模型（LLM）置于仪器反馈回路中。在与人类专家进行短暂学徒学习后，前沿LLM智能体自主完成了STM上的端到端针尖调节，并通过了独立验证。实验过程中出现的意外情况进一步暴露了当相关实验状态或动态被隐藏或未建模时，LLM智能体自主性的局限性。我们的结果表明，未来的自主物理实验需要智能体能够推断隐藏和未建模的实验状态，追踪其动作的结果，并在真实仪器的可观测性和动作限制范围内运行。
英文摘要
In physical experiments, interventions can alter hidden experimental states in ways that cannot be predicted in advance. Autonomous scientific agents must therefore interpret the consequences of their interventions while operating with incomplete knowledge of the experimental state. Here we investigate this problem using scanning tunneling microscopy (STM) tip conditioning, traditionally a human-expert-intensive task governed by inaccessible tip apex conditions and uncertain action outcomes. We introduce quailbot, an agent harness that places the LLM inside the instrument feedback loop by linking physical interventions with experimental readbacks. After a brief apprenticeship with a human expert, frontier LLM agents autonomously completed end-to-end tip conditioning on an STM and passed an independent verification. The contingencies that arose during the experiments further exposed the limits of LLM agentic autonomy when relevant experimental states or dynamics were hidden or unmodeled. Our results show that future autonomous physical experimentation requires agents to infer hidden and unmodeled experimental states, track the outcomes of their actions, and operate within the observability and action limits of real instruments.
发表机构
- Boston College（波士顿学院）
机构由 AI 辅助整理，请以论文原文为准。
