Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment

Computer Science > Artificial Intelligence [Submitted on 22 May 2026] Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Haoyuan Wang, Xiaohao Liu, Jiajie Su, Jianmao Xiao, Chaochao Chen Multimodal large language models (MLLMs) need efficient mechanisms to update knowledge without degrading existing capabilities. While intrinsic multimodal knowledge editing achieves strong reliability and locality, it often exhibits limited generality, failing to propagate edits across semantically equivalent visual and linguistic variations. This issue arises from the lack of explicit semantic supervision, rigid editing scopes, and biased anchoring to individual samples in high-dimensional multimodal spaces. We address robust intrinsic multimodal knowledge editing by explicitly targeting generalization. We formalize robustness through knowledge units that group semantically equivalent multimodal inputs and define generality as consistent predictions within each unit. To expose fragile semantic regions, we introduce Latent Adversarial Robustification (LAR), which generates adversarial yet semantically coherent variants in the joint latent space. We further propose Rank-Constrained Subspace Learning (RCSL), enforcing low-rank alignment of adversarial representations at the edit layer via a singular value-based objective. Extensive analysis demonstrates the effectiveness of ASAM empirically. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.23780 [cs.AI]   (or arXiv:2605.23780v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.23780 Focus to learn more Submission history From: Haoyuan Wang [view email] [v1] Fri, 22 May 2026 15:46:10 UTC (3,157 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)