Search for Truth from Reasoning: A Dynamic Representation Editing Framework for Steering LLM Trajectories

Researchers have introduced DynaSteer, a dynamic representation editing framework designed to guide Large Language Models (LLMs) toward truthful reasoning trajectories. By dynamically intervening at high-entropy decision points, DynaSteer improves reasoning accuracy without disrupting correct logical paths.

Computer Science > Artificial Intelligence

Title:Search for Truth from Reasoning: A Dynamic Representation Editing Framework for Steering LLM Trajectories

View PDF HTML (experimental)Abstract:Current approaches to enhance Large Language Model (LLM) reasoning, such as Chain-of-Thought and "Wait" prompts, primarily encourage models to think more, yet often fail to guide them toward Truth. While Representation Editing (RepE) offers a intrinsic control, its application to dynamic reasoning trajectories remains underexplored. In this work, we bridge this gap by investigating the geometry of truth within unfolding reasoning chains. We uncover three critical insights: (1) Truth is encoded at the sentence level and is entangled with latent reasoning patterns; (2) Effective intervention follows an Uncertainty Principle and a Decay Effect, requiring localization to early, high-entropy forks; (3) Naive steering vectors suffer from noise, risking collateral damage to correct trajectories. Based on these findings, we propose DynaSteer, a dynamic RepE framework. DynaSteer employs pattern clustering to disentangle reasoning manifolds and utilizes Fisher-LDA to project purified truth. By dynamically monitoring lookahead entropy, it selectively steers and rolls back trajectories only when necessary. Comprehensive experimental results on several MATH benchmark verify the effectiveness of DynaSteer, and experiments on out-of-domain coding tasks further confirm its generalization ability. Our code is publicly available at this https URL.

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Source: arXiv cs.AI Recent

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