Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
arXiv:2606.17057v1 Announce Type: cross Abstract: Although Knowledge Editing provides an efficient mechanism for updating the knowledge of Multimodal Large Language Models (MLLMs), we find that current paradigms still suffer from an important yet remain underexplored issue : editing decoupling failure, where entity-related knowledge can be updated when the model is triggered by multimodal inputs (text--image query pairs), however, it often reverts to outdated pre-edit facts when the paired inputs are split into unimodal ones. Our in-depth empirical analysis reveals that the entity knowledge in
The proliferation of Multimodal Large Language Models (MLLMs) and the increasing focus on their knowledge integrity necessitates robust editing mechanisms, making this research timely.
This research highlights a critical vulnerability in MLLM knowledge management, where edited facts can be inconsistently recalled based on input modality, impacting model reliability and trustworthiness.
Understanding this 'editing decoupling failure' will lead to more sophisticated knowledge editing techniques, moving beyond surface-level updates to ensure consistent information retrieval across different input types.
- · AI researchers
- · MLLM developers
- · Enterprises deploying MLLMs
- · Users of MLLM applications
- · Models with unsophisticated knowledge editing
- · Applications relying on perfect unimodal knowledge recall from MLLMs
The immediate effect will be increased scrutiny on MLLM knowledge consistency.
This will drive the development of advanced editing algorithms capable of modality-agnostic knowledge updates.
Improved MLLM reliability could accelerate their integration into sensitive applications requiring high factual integrity across diverse data inputs.
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