1 citations · 1 across the 3 of their papers we have counts for
7 papers
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
Xiaoze Liu, Ruowang Zhang, Weichen Yu +7
Multi-Agent Systems (MAS) powered by Large Language Models have unlocked advanced collaborative reasoning, yet they remain bottlenecked by discrete text communication, which impose…
PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning
Feijie Wu, Weiwu Zhu, Yuxiang Zhang +5
Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls to external tools. However, train…
Talk to Right Specialists: Iterative Routing in Multi-agent Systems for Question Answering
Feijie Wu, Zitao Li, Fei Wei +3
Retrieval-augmented generation (RAG) agents are increasingly deployed to answer questions over local knowledge bases that cannot be centralized due to knowledge-sovereignty constra…
Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking
Xingchen Wang, Feijie Wu, Chenglin Miao +5
Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. Howeve…
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning
Tianyang Xu, Xiaoze Liu, Feijie Wu +2
Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability t…
Towards Federated RLHF with Aggregated Client Preference for LLMs
Feijie Wu, Xiaoze Liu, Haoyu Wang +3
Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using user preference data, enabling it to generate content aligned with human…