Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin

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

A network testbed demonstration shows a Digital Twin-aided multi-agent LLM system for autonomous class-aware optical network management. The system detects soft failures and autonomously adapts modulation format per service class requirements, ensuring continuous, marginless channel quality with minimal human intervention.

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