CONTEXT-AWARE SYNCHRONIZATION FOR SPATIAL MULTI-OMICS REPRESENTATION LEARNING

Spatial multi-omics domain identification relies on unified representations that preserve multimodal molecular information while organizing domain-resolvable latent geometry. Existing geometry organization mechanisms mainly rely on explicit inter-location relations or shared semantic references, leaving location- specific multimodal and multiscale context without a direct role in geometry opti- mization. To address this gap, Context-aware Synchronization for Spatial Multi- omics Representation Learning (CSR) formulates latent geometry optimization as a context-regulated synchronization process on the hypersphere. CSR constructs location-specific contextual references from multimodal representations and mul- tiscale graph contexts, and combines them with relational graph coupling to reg- ulate representation evolution. The continuous synchronization dynamics mono- tonically decrease the associated objective, while contextual references impose location-dependent stationary conditions. Experiments on spatial multi-omics benchmarks demonstrate consistent improvements in spatial domain identification over representative baselines.