3 papers
cs.AI2026
Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection
Sihang Zeng, Matthew Thompson, Ruth Etzioni +1
Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences. Existing LLM-based mu…
cs.AI2026
Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction
Sihang Zeng, Yujuan Fu, Sitong Zhou +6
Large language models (LLMs) offer a generalizable approach for modeling patient trajectories, but suffer from the long and noisy nature of electronic health records (EHR) data in…
cs.LG2026
The Dual-State Architecture for Reliable LLM Agents
Matthew Thompson
Large Language Models deployed as code generation agents exhibit stochastic behavior incompatible with the deterministic guarantees required by software engineering. We formalize t…