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cs.AI2026
EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems
Chengdong Xu, Kaiqiang Ke, Ziheng Liu +4
Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborative reasoning. However, most…
cs.AI2026
Context-Picker: Dynamic context selection using multi-stage reinforcement learning
Siyuan Zhu, Chengdong Xu, Kaiqiang Ke +1
In long-context question answering, selecting the appropriate scope of context for a query remains a key and unresolved challenge. Insufficient context can lead to missing essentia…
cs.AI2025
HR: Hierarchical Hindsight Reflection for Multi-Task LLM Agents
Shicheng Ye, Chao Yu, Kaiqiang Ke +2
Large language model (LLM)-based agents have shown strong potential in multi-task scenarios, owing to their ability to transfer knowledge across diverse tasks. However, existing ap…