Detailed balance in large language model-driven agents
We ask whether state-to-state transitions in LLM-driven agents admit an effective potential. Coarse-graining many token trajectories into semantic states lets us test measured forward–reverse probability ratios against a potential landscape across tasks and model snapshots.