arXiv cs.AI· Zhuo-Yang Song, Qing-Hong Cao, Ming-xing Luo, Hua Xing Zhu·· 13 小时前AI 评分31
大语言模型驱动的智能体中的细致平衡
Detailed balance in large language model-driven agents
AI 导读
一项基于最小作用量原理的研究通过实验测量 LLM 生成状态间的转移概率，发现证据与 LLM 生成转移趋向细致平衡一致，提示 LLM 生成可能并非通过普遍学习规则集与策略实现，而是隐式学习了一类可跨越不同模型架构和提示词模板的潜在势函数。该工作给出不依赖具体模型细节的 LLM 生成动力学宏观描述，试图将 AI 智能体研究从工程实践集合提升为基于可预测、可量化有效测量的科学。
正文 · 原文
Abstract:Large language model (LLM)-driven agents are emerging as a powerful new paradigm for solving complex problems. Despite the empirical success of these practices, a theoretical framework to understand and unify their macroscopic dynamics remains lacking. This paper proposes a method based on the least action principle to estimate the underlying generative directionality of LLMs embedded within agents. By experimentally measuring the transition probabilities between LLM-generated states, we report evidence consistent with a tendency toward detailed balance in LLM-generated transitions, indicating that LLM generation may not be achieved by generally learning rule sets and strategies, but rather by implicitly learning a class of underlying potential functions that may transcend different LLM architectures and prompt templates. To our knowledge, this provides an effective macroscopic description of LLM generative dynamics that does not depend on specific model details. This work is an attempt to establish a macroscopic dynamics theory of complex AI systems, aiming to elevate the study of AI agents from a collection of engineering practices to a science built on effective measurements that are predictable and quantifiable.
| Comments: | 24 pages, 19 figures, 7 tables |
| Subjects: | Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Adaptation and Self-Organizing Systems (nlin.AO); Data Analysis, Statistics and Probability (physics.data-an) |
| Cite as: | arXiv:2512.10047 [cs.LG] |
| (or arXiv:2512.10047v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2512.10047 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1103/kwx6-clqd
DOI(s) linking to related resources |
Submission history
From: Zhuo-Yang Song [view email]
[v1]
Wed, 10 Dec 2025 20:04:23 UTC (409 KB)
[v2]
Tue, 27 Oct 2026 17:34:10 UTC (796 KB)
来源：arXiv cs.AI · arxiv.org