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arXiv AI By Zhuo-Yang Song, Qing-Hong Cao, Ming-xing Luo, Hua Xing Zhu

Detailed balance in large language model-driven agents

Read the original on arXiv AI →

The paper investigates the macroscopic dynamics of large language model (LLM)-driven agents by applying a least‑action principle to estimate the generative directionality of LLMs embedded within agents. Through experimental measurement of transition probabilities between LLM‑generated states, the authors find evidence of a tendency toward detailed balance, suggesting that LLM generation may be governed by underlying potential functions rather than explicit learning rules. This approach offers a model‑agnostic, effective description of LLM generative dynamics that could serve as a foundational theory for complex AI systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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