PRISM: Spectral Pruning and Reconstruction for Parameter-Efficient Model Merging of MLLMs

Parameter-Efficient Fine-Tuning (PEFT) has become a dominant paradigm for adapting Multimodal Large Language Models (MLLMs) to specific tasks, resulting in a proliferation of task-specific expert models. Merging these experts into one universal model offers a promising path to creating versatile systems without costly retraining. However, existing methods for full fine-tuning merging often falter in parameter-efficient model merging, as they manipulate weights directly while ignoring the intrinsic geometric structure of PEFT modules. When diverse tasks exhibit conflicting feature directions, this inevitably leads to destructive interference. To address this issue, we propose PRISM, a data-free framework that reframes the merging of PEFT modules as a spectral signal reconstruction problem. PRISM operates in two stages: (1) Spectral Pruning, which decouples task updates into singular components and retains only high-energy directions to attenuate task-irrelevant noise; and (2) Energy-Prioritized Orthogonal Reconstruction, which prioritizes dominant components and projects overlapping vectors onto orthogonal directions to eliminate inter-task interference. We curate a benchmark comprising diverse multimodal tasks to evaluate our method. Extensive experiments demonstrate that PRISM significantly outperforms state-of-the-art baselines. Our code and weights will be released.