Tootfinder

Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies
Zirui Song, Yuan Huang, Junchang Liu, Haozhe Luo, Chenxi Wang, Lang Gao, Zixiang Xu, Mingfei Han, Xiaojun Chang, Xiuying Chen
https://arxiv.org/abs/2510.11389

Beyond Survival: Evaluating LLMs in Social Deduction Games with Human-Aligned Strategies
Social deduction games like Werewolf combine language, reasoning, and strategy, providing a testbed for studying natural language and social intelligence. However, most studies reduce the game to LLM-based self-play, yielding templated utterances and anecdotal cases that overlook the richness of social gameplay. Evaluation further relies on coarse metrics such as survival time or subjective scoring due to the lack of quality reference data. To address these gaps, we curate a high-quality, human…

Leading the Follower: Learning Persuasive Agents in Social Deduction Games
Zhang Zheng, Deheng Ye, Peilin Zhao, Hao Wang
https://arxiv.org/abs/2510.09087 https://

Leading the Follower: Learning Persuasive Agents in Social Deduction Games
Large language model (LLM) agents have shown remarkable progress in social deduction games (SDGs). However, existing approaches primarily focus on information processing and strategy selection, overlooking the significance of persuasive communication in influencing other players' beliefs and responses. In SDGs, success depends not only on making correct deductions but on convincing others to response in alignment with one's intent. To address this limitation, we formalize turn-based dialogue in…

SpinGPT: A Large-Language-Model Approach to Playing Poker Correctly
Narada Maugin, Tristan Cazenave
https://arxiv.org/abs/2509.22387 https://arxiv.org/pdf/…

SpinGPT: A Large-Language-Model Approach to Playing Poker Correctly
The Counterfactual Regret Minimization (CFR) algorithm and its variants have enabled the development of pokerbots capable of beating the best human players in heads-up (1v1) cash games and competing with them in six-player formats. However, CFR's computational complexity rises exponentially with the number of players. Furthermore, in games with three or more players, following Nash equilibrium no longer guarantees a non-losing outcome. These limitations, along with others, significantly restric…

AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations
Berdymyrat Ovezmyradov
https://arxiv.org/abs/2509.26331 https:…

AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations
The rapid advancement of LLMs sparked significant interest in their potential to augment or automate managerial functions. One of the most recent trends in AI benchmarking is performance of Large Language Models (LLMs) over longer time horizons. While LLMs excel at tasks involving natural language and pattern recognition, their capabilities in multi-step, strategic business decision-making remain largely unexplored. Few studies demonstrated how results can be different from benchmarks in short-…

Too many people think they are reasoning logically, when all they are doing is playing logic-shaped language games.

The Emergence of Altruism in Large-Language-Model Agents Society
Haoyang Li, Xiao Jia, Zhanzhan Zhao
https://arxiv.org/abs/2509.22537 https://arxiv.org/pdf…

The Emergence of Altruism in Large-Language-Model Agents Society
Leveraging Large Language Models (LLMs) for social simulation is a frontier in computational social science. Understanding the social logics these agents embody is critical to this attempt. However, existing research has primarily focused on cooperation in small-scale, task-oriented games, overlooking how altruism, which means sacrificing self-interest for collective benefit, emerges in large-scale agent societies. To address this gap, we introduce a Schelling-variant urban migration model that…

Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning
Valentin Lacombe, Valentin Quesnel, Damien Sileo
https://arxiv.org/abs/2509.18083 https://

Reasoning Core: A Scalable RL Environment for LLM Symbolic Reasoning
We introduce Reasoning Core, a new scalable environment for Reinforcement Learning with Verifiable Rewards (RLVR), designed to advance foundational symbolic reasoning in Large Language Models (LLMs). Unlike existing benchmarks that focus on games or isolated puzzles, Reasoning Core procedurally generates problems across core formal domains, including PDDL planning, first-order logic, context-free grammar parsing, causal reasoning, and system equation solving. The environment is built on key des…