VARIS: Relational Market-State Learning for Financial Time-Series Foundation Models

Time-series foundation models have transformed forecasting from task-specific fitting into transferable temporal representation learning, yet this progress remains centered on individual sequences. Financial time-series foundation models further learn domain-specific asset dynamics from large-scale market data, but still largely treat each asset as an independent forecasting unit, leaving cross-asset structure implicit at prediction time. We introduce VARIS, a relational framework for financial time-series foundation models that represents each prediction time as a synchronized market cross-section and explicitly infers its time-varying cross-asset structure. VARIS separates latent common market components from asset-specific residual evidence, and performs reliability-aware lead–lag inference to jointly determine whether a useful cross-asset relation exists and, conditionally, its source asset and temporal lag. A state-conditioned dynamic layer router further assigns input-dependent weights to hierarchical temporal representations for individual, common-component, and relational reasoning. Across six markets and three forecasting horizons, VARIS improves mean RankIC by 46.8% and RankICIR by 52.3% over the strongest financial time-series foundation model baseline, with consistent gains in 16 of 18 evaluation settings. Controlled ablations, temporal negative controls, and unseen-asset evaluations show that these improvements arise from common-component residualization, relational abstention, and dynamic layer routing rather than increased parameterization or broad market co-movement. VARIS advances financial time-series foundation modeling from asset-level temporal representation toward prediction-time market-structure inference.