Author

Kai-Rui Yang

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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

OpenMAS-GCom is introduced, a benchmark for diagnosing how these components affect G-MAS performance through controlled interventions, and 400 G-MAS-Complex tasks requiring agents to combine information from multiple documents, resolve conflicting records, and return specified values with source identifiers are added.

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