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cs.AI2026
SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
Congjie Zheng, Chuanyi Xue, Bin Liang +2
Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, run…
cs.AI2026
Interference-Aware K-Step Reachable Communication in Multi-Agent Reinforcement Learning
Ziyu Cheng, Jinsheng Ren, Zhouxian Jiang +4
Effective communication is pivotal for addressing complex collaborative tasks in multi-agent reinforcement learning (MARL). Yet, limited communication bandwidth and dynamic, intric…