6 papers
EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents
Mianqiu Huang, Taofeng Xue, Chong Peng +12
Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments. Although imitation learning and offline t…
The Lie of the Average: How Class Incremental Learning Evaluation Deceives You?
Guannan Lai, Da-Wei Zhou, Xin Yang +1
Class Incremental Learning (CIL) requires models to continuously learn new classes without forgetting previously learned ones, while maintaining stable performance across all possi…
EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience
Taofeng Xue, Chong Peng, Mianqiu Huang +13
The development of native computer-use agents (CUA) represents a significant leap in multimodal AI. However, their potential is currently bottlenecked by the constraints of static…
MaRCA: Multi-Agent Reinforcement Learning for Dynamic Computation Allocation in Large-Scale Recommender Systems
Wan Jiang, Xinyi Zang, Yudong Zhao +7
Modern recommender systems face significant computational challenges due to growing model complexity and traffic scale, making efficient computation allocation critical for maximiz…
Enhanced Federated Deep Multi-View Clustering under Uncertainty Scenario
Bingjun Wei, Xuemei Cao, Jiafen Liu +2
Traditional Federated Multi-View Clustering assumes uniform views across clients, yet practical deployments reveal heterogeneous view completeness with prevalent incomplete, redund…
Large-Small Model Collaborative Framework for Federated Continual Learning
Hao Yu, Xin Yang, Boyang Fan +4
Continual learning (CL) for Foundation Models (FMs) is an essential yet underexplored challenge, especially in Federated Continual Learning (FCL), where each client learns from a p…