Learning Faithful Reasoning Processes with Spatio-Temporal Latent Dynamics for Large Language Models

Chain-of-Thought (CoT) reasoning has significantly enhanced the reasoning capabilities of large language models (LLMs). However, existing reinforcement learning methods primarily optimize outcome-level rewards, providing limited supervision over the reasoning process. As a result, models may produce