The paper introduces FootsiesGym, an open-source environment for learning in a two-player, zero-sum, imperfect-information game, providing a vectorized simulator for efficient analysis.

This paper investigates the convergence and stability of equilibria in bimatrix two-player games using the optimistic exponential weights method, allowing step sizes to differ.

This paper characterizes Pure Nash Equilibria and presents a polynomial-time algorithm for finding them in the Intermittent Strategic Cooperation-Based Two-Agent Path Planning game.

This paper presents new polynomial-time algorithms for determining the winner of the unbiased triangle game on the edge set of general graphs using the edge-triangle incidence graph.

The paper introduces Global PSRO, a novel deep reinforcement learning framework that efficiently approximates Nash equilibria in large two-player zero-sum games by intelligently expanding the strategy…

The paper introduces Mindgames, a comprehensive multi-game arena for evaluating LLM agents' sustained social and strategic reasoning, demonstrating that current evaluations are limited by structural s…

This paper studies a variant of the quantum XOR game model, called tilted XOR games, and shows that approximating their quantum value to constant precision is RE-complete.

This paper analyzes the computational complexity of evaluating recurrent functions, showing that the complexity depends heavily on how the input offsets are encoded and the structure of the recurrence…

The paper presents a complete, moderatorless protocol for playing Werewolf using only ordinary playing cards, eliminating the need for a trusted third party or digital devices.

The paper introduces PTCG-Bench, a new benchmark using the Pokémon TCG to evaluate LLM agents' strategic decision-making and ability to self-evolve, finding that sustained self-evolution remains chall…

The paper settles the computational complexity of Quoridor and related games by reducing them to Reisch's planar graph-Hex.

This paper introduces Repeated Policy Regret (RP-Regret), a novel game-theoretic metric for analyzing regret in repeated games with adaptive opponents, and proposes algorithms to minimize it.

The paper proves XNLP-hardness of Directed Edge Geography and Undirected Edge Geography when parameterized by pathwidth, and shows their fixed-parameter tractability when parameterized by treewidth an…

The paper proposes using GUI agents, both as objective evaluators and subjective playtesters, to significantly improve the generation of playable games from prompts, demonstrating a 66.8% rubric pass-…

The paper shows that the envy-free cake-cutting problem with three agents is intractable and establishes the first lower bounds for the Jordan curve problem.