Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models

Jul 16, 2026

Researchers introduce Deep Interaction, a method that enables users to directly edit incorrect steps in a large language model's chain-of-thought reasoning while preserving correct reasoning. The edited reasoning is distilled into a prompt to guide the model along the corrected path. Experiments demonstrate over 25% improvement in correction success rate and about 40% reduction in token usage on STEM reasoning tasks compared to baseline methods.

Why it matters: This approach could make it easier and more efficient for humans to correct reasoning errors in large language models, improving reliability and reducing computational costs.

Full story at: arXiv AI/ML ↗