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

Researchers developed a lightweight AI framework for the Game of the Amazons that combines graph attention networks with large language models, achieving 15-56% improvement in decision accuracy while using minimal computational resources. The hybrid approach demonstrates weak-to-strong generalization by leveraging GPT-4o-mini for synthetic training data and graph-based learning for structural reasoning.

• →New hybrid AI framework combines graph attention networks with large language models for strategic game playing under resource constraints.
• →The system achieves 15-56% improvement in decision accuracy compared to baseline methods on 10x10 Amazons board.
• →Framework demonstrates weak-to-strong generalization, outperforming its teacher model GPT-4o-mini with 66.5% win rate at N=50 nodes.
• →Graph Attention mechanism effectively filters noise from LLM outputs, enabling learning from imperfect supervision.
• →Results show feasibility of creating specialized high-performance AI from general-purpose foundation models with limited computational resources.