Model editing aims to correct errors in large, pretrained models without altering unrelated behaviors. While some recent works have edited vision-language models (VLMs), no existing editors tackle reasoning-heavy tasks, which typically require humans and models to reason about images. We therefore propose ReasonEdit, the first VLM editor to let users explain their reasoning during editing, introducing a new, practical model editing setup. ReasonEdit continuously stores human reasoning in a codebook, and retrieves only relevant facts during inference using a novel topology-balanced multimodal embedding method inspired by network science. Across four VLMs on multiple rationale-based visual question answering datasets, ReasonEdit achieves state-of-the-art editing performance, ultimately showing that using human reasoning during editing greatly improves edit generalization.
@article{arxiv.2602.02408,
title = {ReasonEdit: Editing Vision-Language Models using Human Reasoning},
author = {Jiaxing Qiu and Kaihua Hou and Roxana Daneshjou and Ahmed Alaa and Thomas Hartvigsen},
journal= {arXiv preprint arXiv:2602.02408},
year = {2026}
}