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cs.LG2026
Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation
Jiaqi Zhu, Shaofeng Cai, Jie Chen +3
Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods often rely on costly retraining…
cs.LG2026
Mixture-of-World Models: Scaling Multi-Task Reinforcement Learning with Modular Latent Dynamics
Boxuan Zhang, Weipu Zhang, Zhaohan Feng +4
A fundamental challenge in multi-task reinforcement learning (MTRL) is achieving sample efficiency in visual domains where tasks exhibit substantial heterogeneity in both observati…
cs.LG2025
DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao +5
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-bas…