Author

Ze-Kai Chen

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#machine learning Preprint Sep 2026

MAGIC: Mixed-Granularity Agent Graphs via Incremental Construction with Dense-Reward Reinforcement Learning

Collaboration topology shapes both the performance and execution cost of LLM-based multi-agent systems. Because tasks differ in complexity and required capabilities, recent approaches generate task-specific collaboration graphs that specify agent participation and information flow. However, representative topology gene...

Kai-Rui Yang, Zi-Heng Yi, Xun-Kai Li et al. · 0 citations

#machine learning Preprint Sep 2026

MACE: Memory-Agent Co-Evolution with Adaptive Memory Graphs for Multi-Agent Systems

LLM-based multi-agent systems generate collaboration traces that record how agents plan tasks, verify intermediate results, and repair failures. Reusing these procedures requires preserving an action's prerequisites and the outputs needed by subsequent agents. Our empirical studies show that grouping these dependencies...

Kai-Rui Yang, Ming-Hao An, Xun-Kai Li et al. · 0 citations

#machine learning Preprint Sep 2026

OpenMAS-GCom. A Diagnostic Benchmark for Graph-enhanced Multi-Agent Systems

Graph-enhanced multi-agent systems (G-MAS) coordinate large language model agents through communication graphs and role assignments, which determine how agents exchange information and divide responsibilities. However, final-score comparisons across systems combine differences in models, communication patterns, roles,...

Kai-Rui Yang, Xun-Kai Li, Kai-Xiang Zhang et al. · 0 citations

Jul 2026

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clients must learn emerging classes from private multimodal graph streams, retain historical categories, and reject samples outside the known class space. In this setting, c...

Zekai Chen, Haodong Lu, Shihao Li et al. · arXiv.org · 0 citations

Jul 2026

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation

OpenRTAG provides a standardized testbed for understanding robustness in TAG learning under realistic low-quality settings and systematically evaluates scenario validity and model sensitivity, compares traditional GNNs, LLM-GNNs, and a representative GFM, and investigates the effectiveness, efficiency, and robustness o...

Yu-Ze Dai, Zhi-Han Zhang, Yan Zhao et al. · arXiv.org · 0 citations

Preprint Jul 2026

MMFGU: Multimodal Federated Graph Unlearning

A multimodal federated graph unlearning framework built around target-specific representation decoupling that effectively removes requested information, preserves retained graph utility, and achieves a speedup over full retraining.

Haodong Lu, Zekai Chen, Weiwei Ji et al. · 0 citations