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cs.AI2026
Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate
Dezheng Bao, Sihang Du, Renhong Huang +3
Multi-agent debate (MAD) aims to improve large language model reasoning by exposing agents to alternative solutions and enabling iterative revision. However, standard broadcast-sty…
cs.AI2026
PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?
Dongdong Hua, Yifei Sun, Renhong Huang +3
Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realist…
cs.AI2026
RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization
Siwei Zhang, Yun Xiong, Xi Chen +4
Agentic Reinforcement Learning (Agentic RL) has shown remarkable potential in large language model-based (LLM) agents. These works can empower LLM agents to tackle complex tasks vi…