ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning
arXiv:2606.02576v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually acquire new vision-language capabilities, making Multimodal Continual Instruction Tuning (MCIT) essential. To reduce inter-task interference and promote collaboration, recent methods often employ sparse architectures like Mixture of LoRA Experts with image-text similarity routing. However, tasks with distinct response structures could share highly similar visual-linguistic semantics and thus be w
The continuous evolution of MLLMs demands robust methods for acquiring new capabilities without forgetting old ones, pushing the boundaries of continual learning.
Improving Multimodal Continual Instruction Tuning addresses key limitations in MLLM deployment, enabling more adaptive and efficient real-world AI applications.
The ability of MLLMs to adapt and learn new vision-language tasks incrementally, leading to more versatile and persistent AI systems.
- · AI developers
- · MLLM platforms
- · Robotics
- · Autonomous systems
- · Traditional retraining methods
- · Statics AI models
Improvements in MLLM adaptability will lead to more robust and versatile AI agents and systems.
This could accelerate the deployment of MLLM-powered applications in dynamic environments requiring continuous learning.
Long-term, this research might contribute to more human-like AI learning processes, blurring the lines between static models and dynamic intelligence.
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Read at arXiv cs.LG