arXiv:2607.01978v1 Announce Type: cross Abstract: 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
The rapid advancement and deployment of MLLMs create an immediate need for robust and controllable editing mechanisms to ensure their reliability and ethical use.
Controlling the 'edit-scoped generalization' of MLLMs is critical for maintaining the integrity and predictability of AI systems, preventing unintended consequences across various applications.
The focus in MLLM editing shifts from mere reliability and stability to precise semantic control over edits, impacting how models are updated and fine-tuned in real time.
- · AI developers
- · MLOps platforms
- · Companies deploying MLLMs
- · Developers relying on blunt MLLM editing techniques
- · Systems susceptible to unintended model drift
More precise and safer deployment of MLLMs in sensitive applications becomes feasible.
Reduced risk of AI biases propagating unexpectedly due to uncontrolled edit generalization.
Accelerated adoption of MLLMs in highly regulated industries as trust in their predictability grows.
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Read at arXiv cs.CL