{"ID":2898468,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03711","arxiv_id":"2507.03711","title":"Can LLMs Play Ô Ăn Quan Game? A Study of Multi-Step Planning and Decision Making","abstract":"In this paper, we explore the ability of large language models (LLMs) to plan and make decisions through the lens of the traditional Vietnamese board game, Ô Ăn Quan. This game, which involves a series of strategic token movements and captures, offers a unique environment for evaluating the decision-making and strategic capabilities of LLMs. Specifically, we develop various agent personas, ranging from aggressive to defensive, and employ the Ô Ăn Quan game as a testbed for assessing LLM performance across different strategies. Through experimentation with models like Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, and Llama-3.3-70B-Instruct, we aim to understand how these models execute strategic decision-making, plan moves, and manage dynamic game states. The results will offer insights into the strengths and weaknesses of LLMs in terms of reasoning and strategy, contributing to a deeper understanding of their general capabilities.","short_abstract":"In this paper, we explore the ability of large language models (LLMs) to plan and make decisions through the lens of the traditional Vietnamese board game, Ô Ăn Quan. This game, which involves a series of strategic token movements and captures, offers a unique environment for evaluating the decision-making and strategi...","url_abs":"https://arxiv.org/abs/2507.03711","url_pdf":"https://arxiv.org/pdf/2507.03711v3","authors":"[\"Sang Quang Nguyen\",\"Kiet Van Nguyen\",\"Vinh-Tiep Nguyen\",\"Thanh Duc Ngo\",\"Ngan Luu-Thuy Nguyen\",\"Duy-Dinh Le\"]","published":"2025-07-04T16:50:40Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}