Detailed balance in large language model-driven agents

Key points

There is no unified theoretical framework for understanding the emergent behavior of large language model (LLM)-based agents at the macroscopic level.
The complex internal engineering of LLM agents often produces black-box behavior, making their dynamics hard to predict or analyze.
Current theoretical understanding of LLMs focuses mainly on microscopic token-level properties and does not explain emergent goal-directed agent behavior.
We formalize the generation process of LLMs inside agents as a Markov transition process over a defined state space.
We hypothesize an implicit potential function (VT) for LLMs and estimate it quantitatively using a minimum-action principle.
Experimental validation includes measuring transition probabilities between states generated by LLMs and statistically confirming detailed balance conditions across tasks and models.

External research summaries. These are not HDATF publications or measured product results.