这篇论文搞了个医学VLM模型编辑的测试基准M3Bench，有1.6万多个问题，测了6个模型和4种编辑方法，发现各有各的坑，想搞懂模型编辑在医学领域行不行得通可以看。
论文提出M3Bench基准，专用于评估医学视觉语言模型（VLM）的模型编辑效果。M3Bench包含16,276个问题，覆盖多种解剖结构、模态和专科，支持单次和顺序编辑。研究者评估了4种代表性编辑器在6个医学和通用VLM上的表现，发现梯度方法迁移强但破坏局部性，记忆方法局部性好但缺乏组合泛化。该基准揭示了VLM潜在空间几何与编辑方法失效的关系，为安全部署提供指导。
Evaluating and Understanding Model Editing for Medical Vision Language Models
Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining. However, existing multimodal model editing benchmarks focus on general-purpose tasks and do not reflect realistic clinical domain requirements and variability. To address this, we introduce M3Bench, a clinically grounded benchmark for multimodal model editing that evaluates whether an edit remains reliable, precise, and generalizable under the challenges of image and text variation, modality and protocol shifts, clinical knowledge composition, and temporal progression. M3Bench contains 16,276 questions spanning diverse anatomy, modalities, and specialties, and supports both single and sequential edits. By evaluating 4 representative editors across 6 medical and general VLMs, we find that no method excels across all criteria. Gradient-based editors achieve strong transfer but suffer from catastrophic locality violations, whereas memory-based methods preserve locality but lack compositional generality and exhibit high backbone-dependent hyperparameter sensitivity. We further attribute these failures to the latent space geometry of VLMs and how different editing methods shift its landscape. Overall, M3Bench establishes a rigorous clinical stress test for multimodal model editing and offers actionable guidance for safer post-deployment adaptation. The benchmark is publicly available at https://github.com/BioMed-AI-Lab-U-Michgan/M3Bench .