Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) with bounded overhead and minimal disruption to unrelated behaviors. Existing editors mainly emphasize edit reliability and long-horizon stability, but rarely control the semantic boundary of each edit. Our pilot analyses of post-edit behaviors and internal neuronal activities reveal a scope gap behind reliable edits: instance-level success neither guarantees transfer to valid cross-modal variants nor prevents leakage to unrelated inputs, while e
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- Linked via arxiv authorSiyuan Li →
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
- Linked via arxiv authorYouyuan Zhang →
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
- Linked via arxiv authorRuitong Liu →
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
- Linked via arxiv authorJunxi Wang →
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
- Linked via arxiv authorJing Li →
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing