arXiv:2605.20273v1 Announce Type: new Abstract: Online model editing for multimodal large language models (MLLMs) requires assimilating a stream of corrections under tight compute and memory budgets. Yet editors developed for text-only LLMs often degrade on MLLMs: visually dominant activations skew the statistics that shape updates, causing cross-modal conflict, while sequential writes become entangled in a shared edit space and amplify long-horizon interference, causing inter-edit interference. To address these, we propose M-ORE, a modality-decoupled online recursive editor for lifelong MLLM
The proliferation of multimodal large language models necessitates continuous improvement in their adaptability and fine-tuning capabilities, currently a major computational challenge.
Improving online model editing directly impacts the efficiency, reliability, and cost-effectiveness of deploying and maintaining advanced AI systems, especially those interacting with real-world data streams.
The ability to efficiently update and correct MLLMs 'on the fly' reduces computational overhead and mitigates issues like cross-modal and inter-edit interference, leading to more robust and responsive AI.
- · AI developers
- · Cloud providers
- · Multimodal AI applications
- · Edge AI computing
- · Inefficient MLLM fine-tuning methods
- · Systems requiring frequent full model retraining
More adaptive and less error-prone multimodal AI applications will emerge across various industries.
The reduced cost and complexity of MLLM maintenance could accelerate their deployment in critical or real-time systems.
Enhanced online learning capabilities could lead to more truly 'lifelong' learning AI systems, blurring the lines between development and deployment phases.
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Read at arXiv cs.LG