Organizations: School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR 999078, China · Oulu School, Nanjing Institute of Technology, Nanjing 210096, China · School of Computer Science and Engineering, Macau University of Science and Technology, Macao SAR 999078, China, and also with the Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute, Zhuhai 519099, China · Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing 210096, China, and also with the School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China · Center for Machine Vision and Signal Analysis, University of Oulu, 90570 Oulu, Finland
Multimodal sarcasm detection, which aims to precisely identify pragmatic incongruities between literal text and nonverbal cues, has gained substantial attention in multimodal understanding. Recent advancements have predominantly relied on naïve similarity-based attention mechanisms and uniform late fusion strategies.Furthermore, given that functional entanglement restricts traditional late fusions, we incorporate a scalar congruity routing mechanism and a prior-guided contextual graph. This mechanism anchors a generalized incongruity manifold through a two-stage asymmetric optimization driven by inconsistency-aware contrastive learning, selectively fusing only the most discriminative multi-granularity evidence. Extensive experiments on the \texttt{MUStARD} benchmark and its spurious-correlation-mitigated balanced datasets demonstrate that our approach achieves new state-of-the-art performance, surpassing the strongest multimodal baseline by a substantial 3.14% improvement in Macro-F1. By architecturally isolating atomic, composition, and contextual conflicts. This work provides a robust, decoupled paradigm for modeling subtle pragmatic incongruities in human communication.