Deep Interaction

Deep Interaction: Efficient human correction of reasoning errors in LLMs via direct CoT editing

A new arXiv preprint proposes Deep Interaction, a human-AI interaction method that allows users to directly edit erroneous steps in a model's chain-of-thought reasoning rather than re-prompting from scratch. The edited CoT is distilled into a refined prompt that steers the model along the corrected reasoning path. Experiments on STEM tasks show a 25%+ improvement in correction success rate and ~40% reduction in token usage compared to baseline re-generation approaches.