这篇论文提出 Deep Interaction，让你可以直接改推理步骤的错误，不用推倒重来。实验说 STEM 任务上纠错成功率涨 25%，token 还省 40%，挺实用的方法。
Deep Interaction 是一种针对大推理模型的人机交互方法，允许用户直接编辑 Chain-of-Thought (CoT) 推理中的错误步骤。该方法将编辑后的 CoT 精炼为蒸馏提示，引导模型沿校正路径推理。在 STEM 任务上，纠正成功率提升超过 25%，token 使用减少约 40%。实验基于多个 STEM 推理基准，展示了显著的效率提升。
Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models
The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks. However, when errors occur, current interaction approaches typically involve re-generating another response that may make mistakes again, or users laboriously flag the faulty step in follow-up turns that may get responses <You are right, I made a mistake here> followed by similar errors recurring. To address this issue, we propose an efficient human intervention mechanism for precisely correcting reasoning errors in LLMs, termed Deep Interaction. Our approach enables direct editing of the original response, allowing erroneous parts to be corrected while preserving accurate reasoning steps. We refine the edited CoT into a distilled prompt, which then steers the LLM along the corrected reasoning path. Experimental results show that our method achieves over a 25% improvement in correction success rate and reduces token usage by approximately 40% on STEM tasks reasoning compared to baseline approaches.