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Ying-Cheng Wu

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#natural language process... Preprint Sep 2026

JEPA-Anything: Learning Predictive Models across Different Worlds

JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF), achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems and supports a common factorized predictive principle across heterogeneous worlds.

Tao-Yong Cui, Zhong-Yao Wang, Xin-Yue Xu et al. · 2 citations
#machine learning Preprint Sep 2026

An immune world model for multiscale forecasting and therapeutic hypothesis generation

Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed evolutionary AI Scientist to construct the Immune World Model, an action-conditioned model that learns how interventions move immune states...

Tao-Yong Cui, Xi Wang, Zong-Hang Li et al. · 0 citations

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