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arXiv · 2604.17884

SPREG: Structured Plan Repair with Entropy-Guided Test-Time Intervention for Large Language Model Reasoning

Xuan Wang·Yu Ming·Xinhao Zhong·Xinyu Yu·Wenjie Wang·Shuai Chen·Wei Lin
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Abstract

Large Language Models (LLMs) are prone to logical hallucinations and stochastic drifts during long-chain reasoning. While Classifier-Free Guidance (CFG) can improve instruction adherence, standard static implementations often cause semantic dilution and linguistic degradation. We propose SPREG (Structured Plan-guided Real-time Entropy Gating), a lightweight inference-time framework for surgical error rectification. SPREG employs an adaptive dual-threshold mechanism to monitor real-time entropy, identifying sudden ``entropy spikes'' as reliable indicators of logical failure. Upon detection, it triggers a dynamic repair by replacing uninformative null-priors with reference distributions synthesized from historical high-confidence states. By modulating guidance intensity according to structured reasoning stages (e.g., Action, Observation), SPREG steers the model back to a stable manifold without compromising fluency. Our experiments demonstrate significant gains, notably a 20.0% absolute accuracy improvement on AIME25, while effectively suppressing uncontrolled entropy drift in complex tasks.

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Xuan Wang, Yu Ming, Xinhao Zhong, Xinyu Yu, Wenjie Wang, Shuai Chen, Wei Lin. 2026-04-20. SPREG: Structured Plan Repair with Entropy-Guided Test-Time Intervention for Large Language Model Reasoning. https://arxiv.org/abs/2604.17884

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