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

Problem
This paper addresses the gap in the literature regarding Multimodal Continual Instruction Tuning (MCIT) for Multimodal Large Language Models (MLLMs). The authors highlight that existing MCIT methodologies are constrained by significant engineering challenges, primarily due to the need for direct modifications to the base MLLM codebase. This results in high implementation overhead and the creation of method-specific architectures, which complicate code reuse and hinder fair comparisons across different MCIT approaches. The work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution of this paper is the introduction of Prism, a plug-in reproducible infrastructure designed to facilitate scalable MCIT research. Prism employs a lightweight plugin registration mechanism that decouples algorithmic development from the underlying MLLM implementation. This allows researchers to integrate new MCIT strategies as independent plugins without altering the base code, thus reducing structural fragmentation. The framework is compatible with widely adopted large-scale training pipelines, which enhances reproducibility and scalability in MCIT experimentation. The authors provide a detailed description of the architecture, emphasizing its modularity and ease of use, although specific training compute requirements are not disclosed.

Results
While the paper does not present quantitative results comparing Prism against specific baselines on established benchmarks, it emphasizes the qualitative improvements in development speed and reproducibility. The authors claim that Prism significantly reduces the time and effort required to implement new MCIT strategies, although no explicit metrics or effect sizes are provided. The focus is primarily on the architectural advantages and the potential for streamlined experimentation rather than empirical performance comparisons.

Limitations
The authors acknowledge that while Prism addresses several engineering bottlenecks, it does not inherently improve the performance of MLLMs in MCIT tasks. The reliance on existing MLLM architectures means that any limitations in those models will still affect the outcomes of MCIT experiments conducted using Prism. Additionally, the paper does not provide empirical validation of the framework’s effectiveness through benchmark comparisons, which could limit its immediate applicability for researchers seeking performance metrics. Other potential limitations include the need for users to have familiarity with plugin architectures and the possibility of increased complexity in managing multiple plugins.

Why it matters
The introduction of Prism has significant implications for the field of continual learning and instruction tuning in MLLMs. By providing a standardized and reproducible infrastructure, it lowers the barrier to entry for researchers looking to explore novel MCIT strategies. This could accelerate the pace of innovation in the domain, enabling more robust comparisons between different approaches and fostering a collaborative research environment. Furthermore, the modular design of Prism may encourage the development of a diverse ecosystem of plugins, potentially leading to enhanced capabilities in adapting MLLMs to new tasks and domains.

Authors: Jun-Tao Tang, Yu-Cheng Shi, Zhen-Hao Xie, Da-Wei Zhou
Source: arXiv:2605.26110
URL: https://arxiv.org/abs/2605.26110v1