Sign inGet started
Paper · arXiv

Prism: A Plug-in Reproducible Infrastructure for Scalable Multimodal Continual Instruction Tuning

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 yields method-specific architectures that severely limit code reuse and fair comparison. To address this, we introduce Prism, a plug-in reproducible c...

AuthorsJun-Tao Tang, Yu-Cheng Shi, Zhen-Hao Xie et al.
Primary categorycs.LG
Published25 May 2026
Last updated at source25 May 2026
cs.LGcs.CLcs.CV
Embed this entry
<iframe src="https://silytics.si/embed/item/7fc10461-1ab6-470e-901a-fe092ffcee9f" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Models and datasets referencing this paper

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.