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 t...
| Authors | Hefeng Zhou, Jinxuan Zhang, Jiong Lou et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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