Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics

TL;DR

This paper introduces NormWear-2, a world model for physiological signals that combines chaos theory and latent dynamics to enable long-horizon forecasting across diverse clinical and real-world datasets.

Contribution

The novel NormWear-2 model encodes physiological signals and interventions into a shared latent space and models their evolution as a dynamical system, improving long-term forecasting.

Findings

Chaos-theoretic balancing improves representation robustness.

NormWear-2 outperforms state-of-the-art models in forecasting accuracy.

Model maintains competitive downstream representation quality.

Abstract

Physiological time series signals reflect complex, multi-scale dynamical processes of the human body. Existing modeling studies focus on static tasks such as classification, event forecasting, or short-horizon next step prediction, while long-horizon signal-level forecasting and predictive nature of physiological signals remain underexplored. We introduce NormWear-2, a world model that encodes both multivariate physiological signals and clinical intervention variables into a shared latent space and models their joint temporal evolution as a dynamical system. Our approach combines inference from prior pre-trained knowledge (intuition) with instant non-parametric latent state transition adaptation (insight), enabling coherent forecasting across multiple temporal scales, conditioned on heterogeneous clinical interventions. During the pretraining phase, we find that chaos-theoretic balancing of dynamical regime diversity yields more robust representations, with a smaller balanced corpus outperforming one twice its size and capturing bifurcation regimes. We evaluate the world model performance across diverse real-world physiological datasets spanning heterogeneous temporal resolutions and intervention regimes, covering daily life, point-of-care, and clinical settings, including fitness planning, hemodialysis, diabetes management, and surgical monitoring. These evaluation datasets comprise records from 8,026 subjects, spanning study durations from 3.2 hours for high-resolution signal data to 2.3 years for longitudinal clinical biomarker tracking. NormWear-2 achieves the best overall forecasting performance across time, frequency, and latent representation domains, with significant improvements over state-of-the-art time series foundation models, while maintaining competitive downstream representation quality, providing a step toward general-purpose world models for physiological signals.