BeliefGraph-JEPA：行動条件付き時系列のための構造化潜在世界モデル
BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series
将来の行動や外生要因が複数の対象に異なる遅延・持続性で影響する時系列予測のため、影響を型付き潜在状態に分解しグラフで対象へ伝える構造化世界モデルを提案。臨床・農業・環境・産業の4システムで既存手法を上回った。
著者: Yue Li, Kangqi Ni, Zhen Tan, Tianlong Chen
分類: cs.LG, cs.AI
原文アブストラクト
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.