6 papers
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…
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…
Belief-Guided Inference Control for Large Language Model Services via Verifiable Observations
Wenhao Yuan, Chenchen Lin, Jian Chen +3
In black-box large language model (LLM) services, response reliability is often only partially observable at decision time, while stronger inference pathways incur substantial comp…
Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
Wenhao Yuan, Chenchen Lin, Jian Chen +3
In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However, coherent reasoning can still…
Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks
Chenchen Lin, Xuehe Wang
Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data, thereby enhancing privacy and facilitating collaboration among…