Title: Temporality as Inductive Bias: The Relational Time Graph
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: temporal representation learning, inductive bias, relational learning, sequence modeling

Abstract: Most temporal models treat time as discrete indices or elapsed intervals, leaving key properties of temporality only implicitly captured in hidden states. We introduce the Relational Time Graph (RTG), which provides an explicit inductive bias for temporality by learning a structured field of event-to-event gaps ($\hat{\Delta}$). RTG integrates three temporal properties into a unified energy formulation: relativity, where experiential gaps differ across individuals and contexts; co-existence, where private timelines are softly aligned through world-synchronization (WS) edges; and shock anchoring, where gradual change is preserved except at sudden surges in activity. We evaluate RTG on two behavioral datasets in commerce and news. On supervised prediction in e-commerce, RTG improves F1 over sequential baselines, with $\hat{\Delta}$ carrying the main signal and TV/WS priors stabilizing field formation. Overall, RTG provides measurable advantages on supervised tasks and consistent field formation on unlabeled data, demonstrating the value of making temporality explicit through a learnable $\hat{\Delta}$-field.

TL;DR: We introduce RTG, a graph-based inductive bias that models time as structure rather than input, producing robust Δ̂-fields across domains.