Mixture of Specialized Vision Experts: Unlocking Complementary Visual Insights for Faithful MLLM Reasoning

Multimodal Large Language Models (MLLMs) achieve re-markable progress in widespread vision-language tasks, while achievingfaithful MLLM reasoning remains a critical challenge: generating con-tent grounded in comprehensive visual evidence without hallucination.This challenge often stems from the inherent perceptual limitations ofthe single visual encoder design in most MLLMs. While many workshave turned to integrating multiple vision experts, existing methods failto leverage the unique insights of diverse experts due to unspecializedmixture and alignment strategies. To resolve these challenges, we in-troduce MoSVE (Mixture of Specialized Vision Experts), a holisticframework built upon the core insight of inter-expert complementarity.To achieve fine-grained mixture, we introduce Query-guided Com-plementary Clustering, which selectively preserves text-critical andinformative visual perception without redundancy. To cultivate expertspecialization, we propose Complementary Rejection Fine-Tuning,which explicitly routes hard samples from the anchor MLLM to the mostvisually-disparate auxiliary expert, enhancing unique insights withouthomogenization. Extensive experiments demonstrate that MoSVE notonly mitigates hallucinations across the POPE, CHAIR and MMVP, butalso improves general multimodal reasoning on MMBench. Ultimately,MoSVE provides an advanced and efficient solution for faithful MLLM