6 papers
Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection
Weijie Liu, Running Zhao, Wenhao Yuan +4
LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited con…
Learning What to Activate: Combinatorial Capability Allocation for Long-Horizon Multimodal Agents
Wenhao Yuan, Chenchen Lin, Jian Chen +3
Long-horizon multimodal agents rely on specialized capabilities for perception, retrieval, reasoning, verification, and execution. Existing designs typically activate a fixed capab…
Beyond Memory Majority: Latent-Source Reasoning for Multi-Agent Memory Arbitration
Chenchen Lin, Wenhao Yuan, Xuehe Wang +1
Long-term multi-agent systems continuously accumulate the memories produced by different agents. Existing memory methods typically treat retrieved memories as independent evidence…
When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation
Wenhao Yuan, Chenchen Lin, Wentao Hu +4
\textit{Split Federated Learning} (SFL) enables distributed model training by splitting networks between the server and clients. However, under client heterogeneity, the convention…
From Cloud to Crowd: Democratizing LLM Service with Decentralized Edge Collaboration for RAG
Jiaxing Li, Hengzhi Wang, Feng Wang +5
The rapid advancement of large language models (LLMs) has increased demand for scalable and cost-effective deployment, especially for mobile and edge devices. Cloud-hosted LLMs are…
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Wenhao Yuan, Chenchen Lin, Jian Chen +3
Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenarios introduces unique challenges…