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cs.AI2026
CoMem: Collective-Individual Memory Synergy for Evolutionary Multi-Agent Systems
Chengxin Yu, Zhaoxin Fan, Faguo Wu +3
Designing effective memory mechanisms is crucial for advancing LLM-driven Multi-Agent Systems (MAS), helping agents learn together and perform better over time. While recent work h…
cs.AI2026
CollabEval: Enhancing LLM-as-a-Judge via Multi-Agent Collaboration
Yiyue Qian, Shinan Zhang, Yun Zhou +3
Large Language Models (LLMs) have revolutionized AI-generated content evaluation, with the LLM-as-a-Judge paradigm becoming increasingly popular. However, current single-LLM evalua…
cs.AI2025
Agentic Meta-Orchestrator for Multi-task Copilots
Xiaofeng Zhu, Yunshen Zhou
Microsoft Copilot suites serve as the universal entry point for various agents skilled in handling important tasks, ranging from assisting a customer with product purchases to dete…