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.
By Bovard Doerschuk-Tiberi, Yao Yan, Justin Chiu, Hann Wang, Timothy Chung, Martyna Plomecka, John Schultz, Jon Lipovetz, Clayton Drazner, Yuchen Zhuang, Jaimie Hwang, Nate Keating, Riley Jones, Andrew Lee, Oran Kelly, Ian Gemp, Michael Aaron, Laurel Prince, Kate Larson, Jeff Moser, Harrison Jobe, Chad Woodford, Siqi Liu, Andrew Wang, Bo Chang, Christopher D'Mello, Diane Chaleff, Addison Howard, Johnny Yip, Chuck Sugnet, Antonio Gulli, Meghan O'Connell, Will Cukierski, Nenad Tomasev, Dima Yeroshenko, Kinjal Parekh, Roxanne Daniel, Marc Lanctot, Domino Weir, Elsa Dong, Daniel Hennes, Melissa Nalubwama, Robert Fraser, Ryan Trostle, Jun Peng, Tom Mason, Lloyd Hightower, Chiamaka Chukwuka, Yuexiang Zhai, Phoebe Kirk, Yi Su, Yuting Han, Jie Ren, Chris Prichard, Sahand Sharifzadeh, Karim Hakimzadeh, DJ Sterling, Meg Risdal, Kate Olszewska, Ya Xu, Orhan Firat, Minmin Chen
arXiv:2604.07733v2 Announce Type: replace
Abstract: Evaluating strategic decision-making in LLM-based agents requires generative, competitive, and longitudinal environments, yet few benchmarks provid...
By John Chen, Sihan Cheng, Can Gurkan, Mingyi Lin
The paper introduces LM Fight Arena, a new benchmark that pits large multimodal models against each other in the fighting game Mortal Kombat II to evaluate real‑time visual understanding and sequential decision‑making. Six leading open‑ and closed‑source models were tested in a controlled tournament where each controlled the same character, ensuring a fair comparison. The framework offers a fully automated, reproducible, and objective assessment of an LMM’s strategic reasoning in a dynamic setting.
By Yushuo Zheng, Tongrui Ye, Zicheng Zhang, Xiongkuo Min, Huiyu Duan, Guangtao Zhai
The paper investigates whether large language models (LLMs) exhibit language‑specific skill differences by having two identical model instances play a text‑based game in different languages. Using a multilingual extension of TextArena, the authors evaluate three open‑weight models across eight languages and six games, finding that the same model can show markedly different performance—varying win–loss margins, invalid actions, and strategic choices—depending on the language interface. Analyses pinpoint language‑specific failures in spatial reasoning, card‑conditioned decisions, and optimal move selection, and demonstrate that adjusting the intermediate reasoning language can recover much of the lost performance.
By Bobby Cheng, Adam Gaber, Zhengyuan Liu, Catherine Arnett, Omer Goldman, Cheston Tan, Leshem Choshen
SpireBench evaluates long‑horizon strategic play by having large language models play the full commercial deck‑builder Slay the Spire, a game where early choices determine long‑term success. Across 20 LLMs at the highest difficulty, frontier models beat heuristic baselines and match average human performance but lag far behind experts, especially as runs lengthen. The benchmark reveals that strong models excel in early fights but misjudge risk over a run, and that their performance depends partly on prior game knowledge rather than on memories written during play.
By Scott C. Lowe, Sander W. Keemink
arXiv:2610.18354v1 Announce Type: new
Abstract: Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capabil...
By Arastun Mammadli, Herbert Woisetschl\"ager, Jawad Fayaz, Shiqiang Wang
arXiv:2606. 09826v1 Announce Type: cross Abstract: Vision-language model (VLM) agents are increasingly deployed in interactive game environments.
By Mingxian Lin, Shengju Qian, Yuqi Liu, Yi-Hua Huang, Yiyu Wang, Wei Huang, Yitang Li, Fan Zhang, Zeyu Hu, Lingting Zhu, Xin Wang, Xiaojuan Qi
GameCommBench is a unified benchmark for AI-generated game commentary that covers board games, sports, and esports, aligning commentary with diverse game contexts and annotating it by type. The authors introduce Type-Aware Commentary Evaluation (TACE), a structured framework that evaluates different commentary types, and validate its reliability and human agreement. Using TACE, they benchmark representative AI commentators and find that live observation and strategic analysis are major bottlenecks, revealing non-uniform capability profiles.
By Qirui Zheng, Zhengteng Lin, Yunyi Xiao, Junhao Li, Keyuan Cheng, Xingbo Wang, Yongyi Wang, Lingfeng Li, Yunlong Lu, Wenxin Li
GENSTRAT is a system that evaluates strategic reasoning in large language models (LLMs) by procedurally generating two-player zero-sum imperfect-information betting games on demand. Each game is scored across five complexity axes—state space, temporal contingency, opponent dependence, private information, and precision—and assessed via Monte Carlo simulation. Using this generator, the authors created a 50-game benchmark, ran a tournament of twelve frontier and open-weight models with over 40,000 LLM vs. LLM matches, and found that the performance gap on the hardest games is 1.7 times larger than across all games, with higher thinking budgets, opponent strategy disclosure, and tailored reasoning improving outcomes.
By Vartan Shadarevian, Kia Ghods, Alex Kenich, Anany Kotawala
Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with different languages? This work quantifies cross-ling...
S3Gym is an interactive benchmark designed to evaluate large language models (LLMs) on their ability to self-improve through self-testing, self-judging, and self-improvement. It separates permissive exploration from strict held-out evaluation across seven text-based games with executable environment verifiers. Experiments show that self-improvement varies by task, with different experience incorporation pathways (direct history, summary memory, or parameter training) yielding mixed results and highlighting the need for agents to transform feedback into executable, transferable policies.
By Jiajun Shi, Siyuan Tao, Yuhao Wu, Zexuan Wang, Jingyuan Zhang, Jiaheng Liu, Xinping Lei, Xinrong Zhang, Siyuan Fang, Zhewen Tan, Tianle Cai, Junhao Fang, Jiameng Huang, Yueyang Wang, Jinkai Liu, Yuxuan Zhang, Jian Yang, Zhoujun Li, Shen Yan, Wenhao Huang, Ge Zhang
arXiv:2606. 18950v1 Announce Type: new Abstract: Modern Vision-Language Models (VLMs) often struggle with strategic reasoning, i.
By San Kim, Daechul Ahn, Reokyoung Kim, Hyeonbeom Choi, Seungyeon Jwa, Jonghyun Choi