追踪痕迹：利用潜在时序信号实现高效精准推理

Tracing the Traces: Latent Temporal Signals for Efficient and Accurate Reasoning

October 12, 2025

作者: Martina G. Vilas, Safoora Yousefi, Besmira Nushi, Eric Horvitz, Vidhisha Balachandran

cs.AI

摘要

推理模型通过推理时的扩展提升其问题解决能力，即通过更长的token预算分配更多计算资源。识别哪些推理轨迹可能成功仍是一个关键机遇：可靠预测有效路径能大幅减少计算浪费并提升整体效率。我们引入了潜在轨迹信号，这些信号刻画了模型在生成中间推理token时内部表征的时间演变。通过测量推理开始与结束之间潜在表征的总体变化、中间步骤累积的变化，以及这些变化向最终状态推进的程度，我们发现这些信号比跨层度量和基于输出的置信度指标更能可靠地预测解答准确性。当用于指导多个采样生成间的答案选择时，潜在轨迹信号使得测试时的扩展比多数投票更为有效和高效，在保持甚至平均提升2.6%准确率的同时，将token使用量减少高达70%。此外，这些预测信号常在推理轨迹早期显现，使得能够早期选择并分配计算资源给最有希望的候选。我们的发现不仅为推理时效率提供了实用策略，还从更深层次的可解释性视角揭示了推理过程在潜在空间中的表示与区分方式。

English

Reasoning models improve their problem-solving ability through inference-time scaling, allocating more compute via longer token budgets. Identifying which reasoning traces are likely to succeed remains a key opportunity: reliably predicting productive paths can substantially reduce wasted computation and improve overall efficiency. We introduce Latent-Trajectory signals that characterize the temporal evolution of a model's internal representations during the generation of intermediate reasoning tokens. By measuring the overall change in latent representations between the start and end of reasoning, the change accumulated across intermediate steps, and the extent to which these changes advance toward the final state, we show that these signals predict solution accuracy more reliably than both cross-layer metrics and output-based confidence measures. When used to guide answer selection across multiple sampled generations, Latent-Trajectory signals make test-time scaling more effective and efficient than majority voting, reducing token usage by up to 70% while preserving and even improving accuracy by 2.6% on average. Moreover, these predictive signals often emerge early in the reasoning trace, enabling early selection and allocation of compute to the most promising candidates. Our findings contribute not only practical strategies for inference-time efficiency, but also a deeper interpretability perspective on how reasoning processes are represented and differentiated in latent space.