MOSAIC: a structurally robust multimodal framework for fine-grained fake news detection via optimal transport and adaptive expert routing

Purpose Fine-grained multimodal fake news detection requires reasoning over text–image semantic inconsistency and heterogeneous inputs. Existing methods often strengthen fusion or add contextual information but rarely use cross-modal conflict to control multiple inference stages. This study examines whether token-to-patch discrepancy can guide expert processing, contextual interaction and progressive fusion. Design/methodology/approach Multimodal optimal transport semantic alignment with integrated conflict-aware experts (MOSAIC) is a conflict-aware framework. It uses Sinkhorn-regularized optimal transport to estimate token-to-patch mismatch, derive conflict cues for expert processing and fusion, and adaptively route heterogeneous inputs. The same architecture is evaluated on complementary benchmarks FineFake and AMG; dataset-specific choices are limited to preprocessing, class-imbalance handling and auxiliary-context availability. Findings MOSAIC achieves the strongest performance among the compared methods on both benchmarks, improving Macro-F1 by +1.44 points over LLM-MFEFND on FineFake and +2.00 points over MGCA on AMG. Ablation analysis shows a complementary specialization pattern: optimal transport-based conflict modeling contributes most under attribution-level conflict pressure on AMG, whereas adaptive expert routing is most critical under platform-heterogeneous inputs on FineFake. Multi-seed evaluation further confirms stable performance across both settings. Originality/value This study introduces cross-modal semantic conflict as a reusable architectural control signal rather than a local alignment cue. MOSAIC integrates optimal transport-based conflict modeling, adaptive expert routing and conflict-aware progressive fusion into a unified framework, advancing fine-grained multimodal fake news detection through controlled conflict regulation rather than representation augmentation alone.