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This paper investigates the geometry of truth in LLM reasoning chains and proposes DynaSteer, a dynamic representation editing framework that uses pattern clustering and Fisher-LDA to steer trajectories towards truth while avoiding noise. Experiments show effectiveness on MATH benchmarks and generalization to coding tasks.