2 papers
cs.AI2026
Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Shengtian Yang, Yu Li, Shuo He +4
Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing…
cs.LG2026
Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
Xiao Zhang, Yafei Li, Ruixiang Wang +3
Spatiotemporal forecasting in physical systems, such as large-scale traffic networks, requires modeling a dual dynamic: continuous macroscopic rhythms and discrete, unpredictable m…