CGCMA: Conditionally-Gated Cross-Modal Attention for Event-Conditioned Asynchronous Fusion

This paper studies asynchronous alignment in multimodal learning, where a dense primary stream must be fused with sporadic external context, requiring models to reason explicitly about freshness and trust. It proposes CGCMA (Conditionally-Gated Cross-Modal Attention), a model that separates text-conditioned grounding from lag-aware trust control, tested on cryptocurrency markets.