Author

Wen-Hao Yuan

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Preprint Aug 2026

Beyond Memory Majority: Latent-Source Reasoning for Multi-Agent Memory Arbitration

Long-term multi-agent systems continuously accumulate the memories produced by different agents. Existing memory methods typically treat retrieved memories as independent evidence and combine them through voting or weighting. However, this independence assumption often fails in multi-agent settings: memories written by...

Chen-Chen Lin, Wen-Hao Yuan, Xuehe Wang et al. · 2 citations

Preprint Aug 2026

Neural Tree Collaborative Filtering: Rethinking Graph Collaborative Filtering as Tree Collaborative Filtering with Curvature-Aware Propagation Depth

Graph Collaborative Filtering (GCF) has become the dominant paradigm in modern recommender systems by modeling user-item interactions as a bipartite graph and propagating embeddings through a fixed number of message-passing layers. However, applying a uniform propagation depth to every node ignores a fundamental proper...

Jinfeng Xu, Zheyu Chen, Ziyue Peng et al. · 0 citations

#artificial intelligence Review Sep 2026

Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection

This paper proposes Dude, the first Dual-Detection Multi-Agent System for paper-code discrepancy detection, with a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude that effectively prevents agents from falsely reporting discrepancies.

Wei-Jie Liu, Running Zhao, Wen-Hao Yuan et al. · 0 citations

Preprint Aug 2026

When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation

A client-specific adaptation channel based on private prompt tokens, which tracks local adaptation dynamics separately from the shared backbone and provides a lightweight signal for detecting whether client adaptation remains active, and a shallow sufficiency estimator that combines cross-client semantic alignment, tem...

Wen-Hao Yuan, Chenchen Lin, Wentao Hu et al. · 0 citations

Domain Sensitive Federated Learning with Fisher-Informed Pruning

A domain-sensitive federated pruning framework that preserves domain-invariant structures while retaining domain-specific representations and a structure-aware aggregation algorithm that fuses heterogeneous personalized architectures into a domain-generalized global model is proposed.

Chenchen Lin, Wenhao Yuan, Zhengji Xu et al. · 0 citations