RETRACE: REasoning through Verification sTRAtegies and Emotion-Guided EvidenCE Repair for Multimodal Fact-Checking

Effective multimodal fact-checking requires more than retrieving topically relevant information: a system must determine which investigative route is most likely to yield decisive evidence and recognize when the retrieved evidence leaves important aspects of a claim unresolved. We present RETRACE (REasoning through verification sTRAtegies and emotion-guided evidenCE repair), a dual-stage adaptive framework for evidence-grounded multimodal fact-checking. RETRACE first generates multiple claim-specific verification strategies representing alternative paths to decisive evidence and ranks them through pairwise preference modeling. The selected strategy is then translated into prioritized, executable forensic tasks and carried out with multimodal tools to construct an initial evidence state. After observing the initial evidence, it diagnoses unresolved evidential gaps by jointly assessing factual evidence coverage with textual, visual and cross-modal emotional signals to repair the evidence state. It initiates a targeted second investigation aimed at recovering the missing or insufficient evidence. An independent evidence-grounded verifier then determines the final claim label from the evidence state. Without task-specific fine-tuning, RETRACE achieves accuracies of 80.8% on AVeriTeC, 87.1% on MOCHEG, 86.7% on VERITE, and 84.2% on MMFakeBench, improving over the strongest reported comparison on each benchmark by 2.8, 25.9, 2.8, and 8.9 percentage points, respectively. An ablation study further demonstrates that verification strategy discovery and emotion-guided evidence repair provide complementary benefits to fact-checking.