Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| author | Luo, Yunfei Chen, Xi Chen, Yuliang Zhang, Lanshuang Islam, Md Mofijul Zhao, Siwei Kotanko, Peter Dasgupta, Subhasis Campbell, Andrew Malhotra, Rakesh Rahman, Tauhidur |
| author_facet | Luo, Yunfei Chen, Xi Chen, Yuliang Zhang, Lanshuang Islam, Md Mofijul Zhao, Siwei Kotanko, Peter Dasgupta, Subhasis Campbell, Andrew Malhotra, Rakesh Rahman, Tauhidur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15465 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics Luo, Yunfei Chen, Xi Chen, Yuliang Zhang, Lanshuang Islam, Md Mofijul Zhao, Siwei Kotanko, Peter Dasgupta, Subhasis Campbell, Andrew Malhotra, Rakesh Rahman, Tauhidur Machine Learning Signal Processing 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. |
| title | Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2605.15465 |