arXiv:2606. 17847v1 Announce Type: new Abstract: WallGo is a recently introduced strategic board game popularized by the 2025 Netflix series The Devil's Plan.
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Game Arena is an open, continuously expanding platform that evaluates large language models through competitive games, allowing head‑to‑head matchups in structured environments. Unlike static benchmarks, it prevents performance saturation by increasing gameplay difficulty as models improve. The report outlines the infrastructure and presents three pilot games—Chess, Poker, and Werewolf—covering perfect information, imperfect information, and multiplayer settings, and details evaluation metrics and competition results.
We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game. Built on HiFight's minimalist 2D fighting game Footsies, it isolates the cyclic, non-transitive strategic interactions of fighting game neutral play while remaining simple enough for efficient analysis.
arXiv:2607. 06514v1 Announce Type: new Abstract: We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game.
A new AI system has become the champion at the board game Stratego, outperforming top-ranked human players. It is more efficient than other models, and its success demonstrates advanced strategic planning capabilities. The system’s performance suggests potential applications for decision-making in military maneuvers and business negotiations.
arXiv:2608. 09128v1 Announce Type: cross Abstract: LLM agents are increasingly deployed in multi-agent social settings where they must cooperate, negotiate, and adapt to other agents.
PTCG-Bench is a new benchmark that uses the Pokémon Trading Card Game to evaluate large language model (LLM) agents on two fronts: their decision‑making within a single complex game environment and their capacity to evolve through accumulated experience. The benchmark includes a modular harness ablation to isolate agent performance from model capability. Experiments show that while LLM agents can achieve non‑trivial gameplay, sustained self‑evolution remains difficult and performance depends on harness design.