[2603.10512] Resource-constrained Amazons chess decision framework integrating large language models and graph attention

Computer Science > Artificial Intelligence arXiv:2603.10512 (cs) [Submitted on 11 Mar 2026 (v1), last revised 5 Apr 2026 (this version, v2)] Title:Resource-constrained Amazons chess decision framework integrating large language models and graph attention Authors:Tianhao Qian, Zhuoxuan Li, Jinde Cao, Xinli Shi, Leszek Rutkowski View a PDF of the paper titled Resource-constrained Amazons chess decision framework integrating large language models and graph attention, by Tianhao Qian and 4 other authors View PDF HTML (experimental) Abstract:Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for decision-making, strategic planning, and adaptive learning. However, resource-constrained environments pose critical challenges, as conventional deep learning methods heavily rely on extensive datasets and computational resources. In this paper, we propose a lightweight hybrid framework for the Game of the Amazons, which explores the paradigm of weak-to-strong generalization by integrating the structural reasoning of graph-based learning with the generative capabilities of large language models. Specifically, we leverage a Graph Attention Autoencoder to inform a multi-step Monte Carlo Tree Search, utilize a Stochastic Graph Genetic Algorithm to optimize evaluation signals, and harness GPT-4o-mini to generate synthetic training data. Unlike traditional approaches that rely on expert demonstrations, o...