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arXiv · 2610.05409

BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series

Yue Li·Kangqi Ni·Zhen Tan·Tianlong Chen
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Abstract

Action-conditioned time-series forecasting requires accounting for how future actions and exogenous forcings influence multiple targets through partially observed effects with different delays and persistence. Direct conditioning leaves the evolution and target-specific influence of these effects implicit in the predictor, while static relational graphs specify connections without tracking evolving effects. This motivates representing future-driver influence through structured latent states that evolve over the forecast horizon and route information to individual targets. We introduce BeliefGraph-JEPA, a structured latent world model that factorizes driver influence into typed latent-effect states. These states are rolled forward under future drivers and routed through a graph to target-specific nodes, forming the predictive base of a joint-embedding predictive architecture. A capacity-controlled residual supplements this base with direct driver information. On four multi-target clinical, agricultural, environmental, and industrial systems, the framework outperforms a range of pretrained and supervised known-future-covariate baselines. Matched controls isolate latent dynamics, future rollout, graph routing, and residual capacity; future rollout and graph-first residual routing improve forecasting across all four systems.

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Yue Li, Kangqi Ni, Zhen Tan, Tianlong Chen. 2026-10-04. BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series. https://arxiv.org/abs/2610.05409

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