用大模型+符号计算解决理论物理中的算法难题
LLMs with in-context learning for Algorithmic Theoretical Physics
- 大模型结合符号计算系统处理物理计算任务
- 在修改引力理论中成功解决多数扰动问题
- 适合需要快速验证算法的理论物理研究者
理论物理中的算法计算日益增多，虽概念简单但耗时且易出错。鉴于大语言模型（LLM）的进展，本文探索将配备计算机代数系统（CAS）运行时的大模型用于此类任务的可行性。我们构建了Claude与Maple的接口，应用于修正引力理论中的宇宙学扰动问题。实验表明，具备充分上下文提示的前沿大模型在多数测试问题上表现可靠，也揭示了常见失败模式及改进方向。
原文摘要 · Abstract (English)
There is an increasing number of algorithmic computations in theoretical physics. These, while conceptually simple, can nevertheless be time-consuming and contain subtleties that should not be overlooked. Given the recent improvement of Large Language Models (LLM), it is natural to investigate whether LLMs equipped with a computer algebra system (CAS) runtime and sufficiently informative context can reliably carry out these algorithmic tasks. In this work, we interface Claude with Maple, and apply this framework to cosmological perturbations in modified theories of gravity. We demonstrate the current capabilities of this approach, the typical failures, and how the same can be improved. We find that a frontier LLM supplied with worked examples is able to solve most test problems.
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