@inproceedings{liu-etal-2026-chessarena,
title = "{C}hess{A}rena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models",
author = "Liu, Jincheng and
He, Sijun and
Wu, Jingjing and
Wang, Xiangsen and
Chen, Yang and
Kuang, Zhaoqi and
Bao, Siqi and
Yao, Yuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.360/",
pages = "7901--7954",
ISBN = "979-8-89176-390-6",
abstract = "Recent large language models (LLMs) have shown strong reasoning capabilities. However, a critical question remains: do these models possess genuine strategic reasoning, or do they primarily excel at pattern recognition? To address this, we present ChessArena, a chess-based testbed for evaluating LLMs. Chess demands strategic reasoning, precise rule adherence, and the ability to track complex game states. ChessArena is a competitive framework where LLMs play against each other under four play modes. We evaluate 13 LLMs across over 800 games, testing basic understanding, move selection, and puzzle solving. Results reveal significant shortcomings: no model beats Maia-1100 (human amateur level), and some lose to random play. We also present a strong baseline: our fine-tuned Qwen3-8B substantially improves performance, approaching much larger state-of-the-art reasoning models."
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<abstract>Recent large language models (LLMs) have shown strong reasoning capabilities. However, a critical question remains: do these models possess genuine strategic reasoning, or do they primarily excel at pattern recognition? To address this, we present ChessArena, a chess-based testbed for evaluating LLMs. Chess demands strategic reasoning, precise rule adherence, and the ability to track complex game states. ChessArena is a competitive framework where LLMs play against each other under four play modes. We evaluate 13 LLMs across over 800 games, testing basic understanding, move selection, and puzzle solving. Results reveal significant shortcomings: no model beats Maia-1100 (human amateur level), and some lose to random play. We also present a strong baseline: our fine-tuned Qwen3-8B substantially improves performance, approaching much larger state-of-the-art reasoning models.</abstract>
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%0 Conference Proceedings
%T ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models
%A Liu, Jincheng
%A He, Sijun
%A Wu, Jingjing
%A Wang, Xiangsen
%A Chen, Yang
%A Kuang, Zhaoqi
%A Bao, Siqi
%A Yao, Yuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-chessarena
%X Recent large language models (LLMs) have shown strong reasoning capabilities. However, a critical question remains: do these models possess genuine strategic reasoning, or do they primarily excel at pattern recognition? To address this, we present ChessArena, a chess-based testbed for evaluating LLMs. Chess demands strategic reasoning, precise rule adherence, and the ability to track complex game states. ChessArena is a competitive framework where LLMs play against each other under four play modes. We evaluate 13 LLMs across over 800 games, testing basic understanding, move selection, and puzzle solving. Results reveal significant shortcomings: no model beats Maia-1100 (human amateur level), and some lose to random play. We also present a strong baseline: our fine-tuned Qwen3-8B substantially improves performance, approaching much larger state-of-the-art reasoning models.
%U https://aclanthology.org/2026.acl-long.360/
%P 7901-7954
Markdown (Informal)
[ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models](https://aclanthology.org/2026.acl-long.360/) (Liu et al., ACL 2026)
ACL
- Jincheng Liu, Sijun He, Jingjing Wu, Xiangsen Wang, Yang Chen, Zhaoqi Kuang, Siqi Bao, and Yuan Yao. 2026. ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7901–7954, San Diego, California, United States. Association for Computational Linguistics.