ReasonEdit: Editing Vision-Language Models using Human Reasoning
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909033985736704 |
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| author | Qiu, Jiaxing Hou, Kaihua Daneshjou, Roxana Alaa, Ahmed Hartvigsen, Thomas |
| author_facet | Qiu, Jiaxing Hou, Kaihua Daneshjou, Roxana Alaa, Ahmed Hartvigsen, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02408 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ReasonEdit: Editing Vision-Language Models using Human Reasoning Qiu, Jiaxing Hou, Kaihua Daneshjou, Roxana Alaa, Ahmed Hartvigsen, Thomas Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | ReasonEdit: Editing Vision-Language Models using Human Reasoning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2602.02408 |