# UniKE：面向统一多模态模型的跨模态知识编辑基准

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-30 08:00
- AIHOT 分数：62
- AIHOT 链接：https://aihot.virxact.com/items/cmpzd9t6102fxslkpum033kwq
- 原文链接：https://arxiv.org/abs/2606.00477

## AI 摘要

UniKE是首个针对统一多模态模型（UMMs）的跨模态知识编辑基准，包含2971个属性与关系编辑主题。VQA验证显示，文本侧编辑准确率约92%，但图像生成最佳整体VQA准确率仅18.5%，存在明显模态差距。提出的推理增强参数编辑方法在生成前显式激活已编辑知识，使整体VQA准确率提升最多18.6个百分点。机制分析表明，该差距源于编辑后文本表示与图像生成条件路径的对齐不足。文本知识编辑无法可靠跨模态迁移，需开发模态感知的编辑方法。

## 正文

Unified multimodal models (UMMs) have emerged as a promising paradigm for general-purpose multimodal intelligence. As they are deployed in real-world applications, effectively updating internal knowledge becomes critical. While knowledge editing has matured for text-only models, it remains unclear whether edits that successfully modify textual outputs also transfer to image generation in UMMs. To study this question, we introduce UniKE, the first benchmark for cross-modality knowledge editing in UMMs, comprising 2,971 edit subjects spanning attribute and relation edits. Using VQA-based visual verification, we reveal a striking modality gap: text-side efficacy can reach approximately 92%, whereas the best overall VQA accuracy under direct image generation is only 18.5%. We further propose Reasoning-augmented Parameter Editing, which explicitly activates edited knowledge before generation and improves overall VQA accuracy for all evaluated model-editor pairs, with gains up to 18.6 percentage points. Mechanistic analysis shows that this gap is associated with partial alignment between edited textual representations and the conditioning pathways for visual generation, where edits sufficient for text outputs may remain too weak or misaligned to steer image synthesis. These findings show that textual knowledge edits do not guarantee reliable cross-modality transfer and motivate modality-aware editing methods. Our code and data are available at https://github.com/gxx27/UniKE.