Answer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning

2026-09-08

AI Summary

该研究通过引入答案分布轨迹方法，跟踪大语言模型推理过程中预测分布的变化，解决了仅依赖终点准确度评估链式思维推理的局限性。

Abstract

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 trajectories enable us to characterize a trace through a dynamical reasoning profile spanning exploration, revision, motion, and commitment, and to distinguish different dynamical mechanisms of reasoning success and failure. Across sixteen open-weight language models and four reasoning benchmarks, we show that traces with the same endpoint and similar entropy profiles can exhibit substantially different reasoning dynamics. We further find substantial variation in these dynamics both within and across models and tasks, with different objectives favoring different dynamical profiles. Additionally, we show that training and inference choices systematically reshape these profiles. Our results suggest that answer-distribution trajectories provide a rich framework for analysing and evaluating the dynamics of LLM reasoning.

Problem

• chain-of-thought reasoning
• endpoint accuracy
• entropy profiles
• uncertainty evolution
• competing hypotheses

Innovation

• answer-distribution trajectories
• stochastic dynamics
• reasoning process
• predictive distribution
• dynamical reasoning profile