文本編輯能否泛化至視覺生成？統一多模態模型中跨模態知識編輯的基準測試

摘要

統一多模態模型（UMMs）已成為實現通用多模態智慧的一個有前景的範式。隨著它們被部署到實際應用中，有效更新內部知識變得至關重要。儘管知識編輯在純文字模型中已趨成熟，但在UMMs中，成功修改文字輸出的編輯是否也能遷移至影像生成，目前仍不清楚。為研究此問題，我們提出UniKE——首個針對UMMs跨模態知識編輯的基準測試，包含2,971個涵蓋屬性與關係編輯的編輯主體。透過基於VQA的視覺驗證，我們發現一個顯著的模態差距：文字側的有效性可達約92%，而直接影像生成下的最佳整體VQA準確率僅為18.5%。我們進一步提出推理增強參數編輯，該方法在生成前明確啟動編輯後的知識，從而提升所有受評模型-編輯器配對的整體VQA準確率，最高提升18.6個百分點。機制分析表明，此差距與編輯後的文字表徵與視覺生成的條件路徑之間的部份對齊有關，即足以改變文字輸出的編輯可能仍過於薄弱或對齊不足，無法引導影像合成。這些發現顯示，文字知識編輯無法保證可靠的跨模態遷移，並呼籲開發具模態感知能力的編輯方法。我們的代碼與數據可在 https://github.com/gxx27/UniKE 取得。

English

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.