AI RESEARCH
Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
arXiv CS.AI
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ArXi:2605.10765v1 Announce Type: cross Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual capability expansion across sequential tasks. In such scenarios, Multimodal Continual Instruction Tuning (MCIT) aims to acquire new capabilities while limiting catastrophic forgetting. Existing methods mainly follow a module-composition paradigm: they maintain task-level prompts or LoRA experts and dynamically route or aggregate a subset of them at inference.