Before Relational Reasoning: Restoring Synchronicity in Event-Centric Irregular Time Series

Event-centric models for irregular multivariate time series commonly embed observations independently before learning their relations. This computation order leaves synchronous evidence outside the states from which relational reasoning begins, even though measurements recorded at the same timestamp jointly describe the underlying system. We identify this architectural mismatch as pre-propagation synchronicity loss and challenge the assumption that event representations should be constructed independently of available snapshot context. We formulate pre-propagation synchronicity modeling as a representation interface that separates context construction from relational forecasting. The interface reconstructs permutation-invariant snapshot context and fuses it with event evidence using local event-layout support. Across three clinical benchmarks and four forecasting architectures, the design improves mean MSE and MAE in all 12 model–dataset pairs. Component ablations support the contributions of synchronous context, adaptive fusion, and event-layout support. The consistent gains across attention-based, graph-based, and hypergraph-based forecasters establish the empirical reach of the principle beyond a single architecture. These results identify representation ordering as an overlooked design axis for irregular time-series forecasting.