CAUTION: Calibrated Uncertainty-Guided Sample-Adaptive Evidence Reasoning for Short-Video Fake News Detection

Short-video fake news samples vary substantially in verification difficulty: some can be reliably assessed from video-internal multimodal cues, whereas factually false yet internally coherent cases require external evidence. Existing methods nevertheless assign a fixed reasoning depth to all samples, either under-verifying evidence-dependent claims or unnecessarily subjecting otherwise reliable predictions to costly and potentially harmful slow deliberation. To address these challenges, we propose CAUTION, a calibrated uncertainty-guided framework for adaptive-depth evidence reasoning. First, a lightweight multimodal fast system exploits video-internal textual, visual, and acoustic cues to form an initial veracity estimate. Second, a calibrated adaptive-depth controller uses label-conditional conformal prediction to route uncertain samples to slow deliberation and trajectory-level calibration to determine when deliberation should stop. Third, a progressive relational evidence-revision module incrementally organizes ranked claim–evidence analysis units into a sample-specific heterogeneous graph, where typed support, refute, and unresolved relations capture their argumentative roles. Relation-aware graph attention aggregates the evolving evidence, while a reliability-gated residual revises rather than replaces the initial estimate. We further construct SV-CoRE, a large-scale claim-oriented evidence repository aligned with FakeSV and FakeTT, through retrieval, cleaning, deduplication, ranking, and large-model claim–evidence analysis. Experiments on both benchmarks demonstrate that CAUTION achieves state-of-the-art detection performance while substantially reducing unnecessary slow deliberation.