RIDER-MoE: Reliability-Conditioned Interaction Routing for Robust Multimodal Sentiment Analysis

Multimodal sentiment analysis needs to retain strong performance with complete inputs while remaining robust to noisy, missing, or conflicting modalities. We address this goal within a unified framework through reliability-conditioned interaction routing, adapting the mixture of interaction pathways rather than relying solely on modality reweighting. We present RIDER-MoE, a reliability-aware mixture-of-experts architecture that routes each example among modality-uniqueness, redundancy, and synergy experts based on estimated modality reliability and cross-modal agreement. The expert design is motivated by Partial Information Decomposition (PID). We introduce masked-view Disentangled Interaction Regularization (DIR) to encourage distinct U/R/S behaviors during training while retaining single-pass inference. Lightweight unimodal sentiment probes provide uncertainty and consensus cues that guide expert-level priors, softly biasing a router driven by the fused representation. Across CMU-MOSI, CMU-MOSEI, and CH-SIMS, RIDER-MoE remains competitive with strong baselines on clean full-modality test sets and achieves the highest aggregate normalized AUC for Acc-2 and Corr among the evaluated methods under controlled noise, missing-modality, and cross-modal conflict stress tests. Ablations and routing diagnostics support the complementary roles of reliability-aware routing and masked-view expert regularization.