VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus

Multimodal large language models often generate reasoningchains containing subtle errors that lead to incorrect answers. Currentverification approaches have notable limitations. Existing approaches ei-ther require expensive labelled supervision with inconsistent cross-taskperformance or aggregate scores from multiple sources by simple ag-gregations, missing a key insight: when these scores disagree, that dis-agreement itself carries important information about whether a reasoningstep is truly valid or not. We formalise this as a coupled scoring prob-lem among disparate, frozen verifiers, interpretable as a coordinationgame with a unique closed-form equilibrium where agreement signalsvalid steps while disagreement reveals instability. Towards this end, wepropose a training-free domain-agnostic step-wise verification approachwe call VERDICT: VERification via Disagreement-Informed CoupledThresholding. To our knowledge, VERDICT is the first training-freeverifier that makes the structure of cross-modal disagreement explicit andactionable. It computes consensus scores through a closed-form solution,enabling both disagreement-aware filtering and stability-conscious rank-ing of reasoning steps. Evaluated across six benchmarks, VERDICTconsistently improves over the base model by up to +5.95%, and per-forms competitively with domain-specific critics that demand extensivesupervision, demonstrating that cross-modal agreement provides robustverification signals without task-specific adaptation.