Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment
The paper discusses advancements in multimodal large language models (MLLMs) for knowledge editing. It addresses the limitations of current methods in propagating edits across different modalities and proposes a new approach to enhance robustness and generality. The authors introduce techniques such as Latent Adversarial Robustification and Rank-Constrained Subspace Learning to improve the editing process.
- ▪Multimodal large language models require efficient knowledge updating mechanisms.
- ▪Current editing methods struggle with generality and semantic supervision.
- ▪The authors propose new techniques to enhance robustness in multimodal knowledge editing.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23780 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Summary source text | contentText |
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| Cluster | cgQ7AT_LIDj_ |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.23780 (cs) [Submitted on 22 May 2026] Title:Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Authors:Haoyuan Wang, Xiaohao Liu, Jiajie Su, Jianmao Xiao, Chaochao Chen View a PDF of the paper titled Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment, by Haoyuan Wang and 4 other authors View PDF HTML (experimental) Abstract:Multimodal large language models (MLLMs) need efficient mechanisms to update knowledge without degrading existing capabilities.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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