MACR: Multimodal Affective Contradiction Reasoning for Emotion Understanding

Multimodal emotion models can recognize individual affective cues yet misinterpret their combination when fusion suppresses the disagreement that carries meaning. We treat cross-source affective disagreement as a control signal for inference, determining whether evidence should be aggregated directly or should undergo arbitration before fusion. We introduce MACR, the Multimodal Affective Contradiction Reasoner, a recognize-then-arbitrate framework that preserves source-specific evidence and evaluates disagreement before fusion. MACR separates cross-source disagreement from source uncertainty: the former determines when to invoke cross-source reasoning, while the latter informs how much each source contributes. Strong disagreement activates cross-source attention before weighted fusion; low-conflict inputs follow a direct aggregation path. The resulting policy adapts how sources interact, rather than merely adjusting their contributions within a fixed fusion procedure. Across four multimodal benchmarks, MACR outperforms fine-tuned baselines, achieving F1 scores of 89.2 on HFM and 80.9 on MVSA-M. On a 480-sample, manually verified HFM diagnostic split, it improves F1 over holistic fusion by 4.7 points on conflict cases, versus 0.7 on non-conflict cases. These results support a broader principle for multimodal inference: preserve disagreement long enough for it to determine how evidence should be reconciled.