Prism: A Plug-in Reproducible Infrastructure for Scalable Multimodal Continual Instruction Tuning
arXiv:2605.26110v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve versatility by reformulating diverse tasks into a unified instruction-following framework via instruction tuning. However, real-world deployment requires continuous adaptation to emerging tasks, motivating Multimodal Continual Instruction Tuning (MCIT). Despite its growing importance, current MCIT research is hindered by severe engineering bottlenecks. Existing methods are typically implemented by directly modifying the base MLLM codebase, which imposes substantial implementation overhead and yie
The rapid advancement of MLLMs and their increasing deployment in complex real-world scenarios necessitates solutions for continuous adaptation and efficient maintenance.
A robust, reproducible infrastructure for continual instruction tuning is critical for the long-term scalability and practical utility of MLLMs across diverse applications.
This research streamlines the development and deployment of continuously learning MLLMs, potentially accelerating innovation and adoption by mitigating engineering hurdles.
- · AI researchers
- · MLOps platforms
- · Companies deploying MLLMs
- · Open-source AI communities
- · Companies with proprietary, inflexible MLLM deployment pipelines
Easier and faster iteration and improvement of multimodal AI models in production environments.
Accelerated development of more adaptive and domain-specific MLLMs, leading to broader application areas.
Increased accessibility and efficiency in developing complex AI agents that learn and adapt continuously.
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