We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU languages plus six others. Unlike static or preference-based evaluation, this paradigm is multi-turn, reference-free and programmatically scored, and because the game mechanics are language-agnostic it extends to a new language by localising a fixed set of prompt and word-list files.
arXiv:2601. 19082v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users.
By Trung-Kiet Huynh, Dao-Sy Duy-Minh, Thanh-Bang Cao, Phong-Hao Le, Hong-Dan Nguyen, Phu-Quy Nguyen-Lam, Minh-Luan Nguyen-Vo, Hong-Phat Pham, Phu-Hoa Pham, Thien-Kim Than, Chi-Nguyen Tran, Huy Tran, Gia-Thoai Tran-Le, Alessio Buscemi, Le Hong Trang, The Anh Han
arXiv:2509. 17192v3 Announce Type: replace Abstract: LLM-based social simulations can make a generated transcript look like a single behavioral signal, but the model behind that transcript may be doing several different jobs: choosing what an actor says or does, deciding what happens after an action, or both.
By Glenn Matlin, Isaac Song, Yixiong Hao, Parv Mahajan, Evan Montoya, Ryan Bard, Stuart R. Topp, Anthony Wen-Ming Zang, Mohammed Rehan Parwani, Soham Shetty, Mark Riedl
arXiv:2510. 10813v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation.
By Enric Junque de Fortuny, Veronica Roberta Cappelli
arXiv:2602. 05302v3 Announce Type: replace Abstract: We present an in-depth evaluation of LLMs' ability to negotiate, a central business task requiring strategic reasoning, theory of mind, and economic value creation.
By Chris Zhu, Sasha Cui, Will Sanok Dufallo, Runzhi Jin, Zhen Xu, Linjun Zhang, Daylian Cain
arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.
By Andrea Cera Palatsi, Samuel Martin-Gutierrez, Ana S. Cardenal, Max Pellert
arXiv:2601. 11496v3 Announce Type: replace-cross Abstract: AI agents increasingly mediate bargaining, negotiation and persuasion for people and firms.
By Eilam Shapira, Moshe Tennenholtz, Roi Reichart
arXiv:2606. 08310v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as long-horizon agents with decision-making capacities.
By John Chen, Sihan Cheng, Can Gurkan, H M Abdul Fattah
arXiv:2607. 23982v1 Announce Type: cross Abstract: Cooperation can fail when socially valuable effort is costly, weakly observable, and mainly benefits others.
By Dane Malenfant
arXiv:2608. 10475v1 Announce Type: new Abstract: The emergence of language-based AI agents promises to transform the scope of machine economic activity.
By Bhavyesh Sajja, Max Kleiman-Weiner, Roger Zimmermann, Tan Zhi-Xuan
arXiv:2608. 12373v1 Announce Type: new Abstract: Large language models are increasingly used in strategic and advisory contexts, yet their safety alignment is typically evaluated in English only.
By Rian Touchent (ALMAnaCH)
arXiv:2607. 05863v1 Announce Type: new Abstract: Negotiation is a fundamental strategic interaction in management science, characterized by agents attempting to reach agreements while protecting private information, such as reservation costs and hidden valuations.
By Shuze Daniel Liu, Claire Chen, Jiabao Sean Xiao, Xin Chen, David Simchi-Levi