Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models
Score
75提出了一种新颖的交互范式，不同于现有重新生成或逐步纠正方法，信息增量显著。
论文描述清晰，实验设计合理，结果量化有说服力。
直接针对实际应用中的低效纠错问题，节省token且成功率提升，实用价值高。
2025年最新发布，紧跟LLM推理交互热点。
来自arXiv预印本，尚未经同行评审，但作者为学术机构，可信度中等偏上。
Base 83.5 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
The method addresses a practical pain point of error correction in LLM reasoning, with solid experimental data and practical value.
AI commentary
「This work precisely targets a pain point in current LLM interaction: inefficient error correction. For developers or researchers requiring high-accuracy reasoning, Deep Interaction offers a direct, token-saving correction approach that is especially noteworthy. Although still in preprint, the method is simple and effective, with potential for integration into future tools.」
Quick overview
This paper introduces Deep Interaction, a method that allows users to directly edit erroneous steps in the Chain-of-Thought reasoning of large language models while preserving correct parts. The edited CoT is refined into a distilled prompt to steer the model along the corrected path. Experiments on STEM tasks show over 25% improvement in correction success rate and approximately 40% reduction in token usage compared to baselines.
- Proposes direct editing of the model's original output, avoiding re-generation or laborious step-by-step flagging.
- Refines the edited CoT into a distilled prompt to guide correct reasoning.
- Achieves >25% improvement in correction success rate and ~40% token reduction on STEM tasks.
Full text
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