Experimental Demonstration of Autonomous Class-Aware Optimization via Digital Twin Aided Optical Network Agent

APOD: Agentic Population ODE Discovery for Pharmacological Digital Twins

APOD is a language-model agent that iteratively reasons over biological knowledge and fit diagnostics in an open-ended search space to discover a population digital twin, a shared ODE system with between-subject variability. On synthetic pharmacokinetic and tumor-dynamics benchmarks it recovered ground-truth structures in 94-100% of runs, 12-fold faster in median than a library-based search.

Hybrid Ray-Tracing and Neural Modeling for Wireless Digital Twins

LifeLong Digital Twin: A Unified Event-Driven Modeling Paradigm for Health Trajectories

LifeLong Digital Twin is an event-driven modeling paradigm that organizes Life Events into daily Health States accumulated into a Lifelong Health Context. An accompanying agent harness incorporates multimodal evidence beyond the language model's textual context. Four language models are evaluated across 25 disease endpoints on three tasks, spanning longitudinal records, genetic variation, molecular profiles and environmental histories.