Learning What to Transfer: Permission-Routed Mechanism Experts for Multimodal Short-Video Misinformation Detection

Detecting misinformation in short videos requires reasoning over both internal multimodal content and external event evidence. Existing methods often fuse heterogeneous internal cues into a shared representation or directly incorporate retrieved precedents, overlooking differences among manipulation mechanisms and the transferability of external information. To address this issue, we propose Permission-Routed Mechanism Experts (PRME). PRME assigns soft Strong, Weak, and No permissions to retrieved precedents: Strong permits factual feature transfer and mechanism guidance, Weak retains only mechanism-level priors, and No relies solely on internal evidence. A shared pool of mechanism-specific experts then performs specialized authenticity reasoning. Experiments on FakeSV and FakeTT demonstrate that PRME consistently improves retrieval-augmented misinformation detection, achieving notable gains on the FakeSV temporal and event splits and the FakeTT temporal split. Ablation and relation-stratified analyses further validate the effectiveness of permission-aware evidence transfer and mechanism-specific expert routing.