Learning Individual Dynamics from Sparse Cross-Sectional Snapshots

• URL: http://arxiv.org/abs/2605.23470v1
• Date: Fri, 22 May 2026 10:29:47 GMT
• Title: Learning Individual Dynamics from Sparse Cross-Sectional Snapshots
• Abstract summary: We introduce CADENCE, a principled probabilistic framework that recovers continuous individual trajectories from isolated snapshots.<n>We provide novel identifiability guarantees for single-timepoint trajectory.<n>CADENCE matches or exceeds the performance of state-of-the-art sequential models trained on dense, full-trajectory data.
• Score: 5.850296173420883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires dense longitudinal tracking. When only extremely sparse or entirely cross-sectional data is available, inferring individualized, continuous-time trajectories is fundamentally ill-posed. Existing methods force a strict compromise: sequence models (e.g. latent ODEs) require dense longitudinal data, while cross-sectional methods (e.g. optimal transport, flow matching-based) map aggregate populations, losing individual dynamics. In this paper, we demonstrate that this dichotomy can be broken. We introduce CADENCE, a principled probabilistic framework that recovers continuous individual trajectories from isolated snapshots by anchoring latent dynamics to static, individual-level contexts. We provide novel identifiability guarantees for single-timepoint trajectory inference. By combining a score-based spatial encoder (bijective Probability Flow ODE) to eliminate diffeomorphic ambiguities with a Soft Mixture-of-Experts (SMoE) router, we show that individual dynamical parameters and routing function are jointly identifiable. Across a suite of benchmarks spanning physical systems to real-world biological data, CADENCE, trained strictly on extremely sparse snapshots with context structure, matches or exceeds the performance of state-of-the-art sequential models trained on dense, full-trajectory data.

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