用大模型自主探索物理系统，无需特定任务指导
Agentic Exploration of Physics Models
- 基于大模型工具调用，自主设计实验与分析
- 从观测数据恢复运动方程和哈密顿量，准确率高
- 通用性强，适合跨领域科学发现研究者
科学发现依赖于观察、分析与假设生成之间的互动。机器学习正被用于处理这一过程的各个部分，但如何在不针对具体任务定制的情况下，自动完成探索未知系统所需的启发式、迭代性实验与分析循环，仍是开放挑战。本文提出SciExplorer，一种利用大语言模型工具使用能力，在无领域特定蓝图的前提下探索物理系统的智能体。该方法应用于机械动力系统、波传播及量子多体物理等广泛模型。尽管仅使用代码执行等基础工具，仍展现出优异性能，如从观测动态中恢复运动方程，以及从期望值推断哈密顿量。该方法有效验证了其在无需微调或任务特化指令情况下，实现跨领域科学探索的潜力。
原文摘要 · Abstract (English)
The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individual aspects of this process. However, it remains an open challenge to fully automate the heuristic, iterative loop required to discover the laws of an unknown system by exploring it through experiments and analysis, without tailoring the approach to the specifics of a given task. Here, we introduce SciExplorer, an agent that leverages large language model tool-use capabilities to enable exploration of systems without any domain-specific blueprints, and apply it to physical systems that are initially unknown to the agent. We test SciExplorer on a broad set of models spanning mechanical dynamical systems, wave evolution, and quantum many-body physics. Despite using a minimal set of tools, primarily based on code execution, we observe impressive performance on tasks such as recovering equations of motion from observed dynamics and inferring Hamiltonians from expectation values. The demonstrated effectiveness of this setup opens the door toward similar scientific exploration in other domains, without the need for fine-tuning or task-specific instructions.
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