While Multimodal Retrieval-Augmented Generation (M-RAG) enhances Large Vision-Language Models, it remains highly susceptible to cross-modal hallucinations, causal fabrications, and sycophancy. Furthermore, existing mitigation pipelines often face an intervention paradox: static rules tend to unnecessarily disrupt accurate generations, whereas leaving the multi-modal reasoning completely unguided allows existing mismatches to cascade into severe logical fabrications. To quantify and mitigate these hallucinations, we propose a Multi-Agent system, MODE-RAG, driven by Variational Free Energy (VFE) and internal attention states to dynamically gate interventions. High-risk queries are routed to five stage-specific agents, integrating Monte Carlo Tree Search (MCTS) for rigorous causal derivation and logit perturbations to penalize sycophancy. Dedicated Correction and Overseer agents ensure formatting stability and perform post-hoc factual verification. To objectively evaluate our approach, we introduce ModeVent, a challenging subset derived from the MultiVent dataset. Extensive experiments indicate that our system effectively reduces hallucination rates and logical fabrication, significantly improving the robustness of M-RAG systems.

翻译：尽管多模态检索增强生成（M-RAG）增强了大型视觉语言模型，但其仍极易受跨模态幻觉、因果捏造和谄媚行为的影响。此外，现有缓解管道常面临干预悖论：静态规则往往不必要地干扰准确生成，而完全放任多模态推理不加引导则会导致现有错配级联为严重逻辑捏造。为量化并缓解这些幻觉，我们提出一种由变分自由能（VFE）与内部注意力状态驱动的多智能体系统MODE-RAG，以动态调控干预。高风险查询被路由至五个阶段特异性智能体，其集成蒙特卡洛树搜索（MCTS）实现严格因果推导，并通过逻辑扰动惩罚谄媚行为。专用修正智能体与监督智能体确保格式稳定性并执行事后事实核查。为客观评估本方法，我们引入ModeVent——源自MultiVent数据集的高难度子集。大量实验表明，本系统有效降低了幻觉率与逻辑捏造，显著提升了M-RAG系统的鲁棒性。