Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning
Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accuracy, which ignores the path taken to reach that answer. An emerging line of work addresses this limitation using entropy profiles, which track how uncertainty evolves over the reasoning process but do not reveal which competing hypotheses account for that uncertainty. We introduce answer-distribution trajectories, a stochastic-dynamics-inspired representation that tracks the model's full predictive distribution over answers as reasoning unfolds. As a strictly finer representation than endpoint and entropy summaries, answer-distribution traj...
| Authors | Mar Gonzàlez I Català, Haitz Sáez de Ocáriz Borde, Davide Murari et al. |
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| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 5 October 2026 |
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