Effective social interaction requires more than fluent responses: agents must reach plans that fit the participants' needs and circumstances. Existing evaluations of social agents mainly rely on LLM j...
arXiv:2604. 15267v2 Announce Type: replace-cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reasoning capabilities behave _less_ cooperatively in mixed-motive games such as the prisoner's dilemma and public goods settings.
By Emanuel Tewolde, Xiao Zhang, David Guzman Piedrahita, Vincent Conitzer, Zhijing Jin
arXiv:2609.36178v1 Announce Type: cross
Abstract: Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment o...
By Dongwon Jung, Hemanth Neelgund Ramesh, Yifan Wang, Xiaomin Li, Yuexing Hao, Yu Hu, Muhao Chen, Varun Chandrasekaran, Andrzej Banburski-Fahey, Jaron Lanier
arXiv:2610.16895v1 Announce Type: new
Abstract: LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating...
By Adri\'an Szlatincs\'an, Filippo Ziliotto, Bruno Lepri, Luciano Serafini, Massimiliano Luca, Tommaso Campari
arXiv:2602. 12089v3 Announce Type: replace-cross Abstract: As AI usage becomes more prevalent in social contexts, understanding agent-user interaction is critical to designing systems that imp rove both individual and group outcomes.
By Kehang Zhu, Nithum Thain, Vivian Tsai, James Wexler, Crystal Qian
arXiv:2610.17869v1 Announce Type: new
Abstract: Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference. A fixed protocol cannot allocate computati...
By Yuan Liu, Haoyue Peng, Fanliang Hu, Lei Huang, Wenli Cai