Adaptive Sparse-Geometric Attention: A Comprehensive Empirical Analysis

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Abstract:This paper presents a thorough empirical evaluation of Adaptive Sparse-Geometric Attention (ASGA), a novel attention mechanism combining dynamic sparsity patterns with learned geometric scaling. We implement ASGA within the Qwen architecture \citep{qwen} and conduct extensive experiments on the FineWeb dataset. While theoretically promising, our results show ASGA achieves a validation loss of 5.148 compared to the Qwen baseline's 4.927. We provide detailed analysis of the performance gap through ablation studies and computational efficiency measurements. The paper concludes with actionable insights for future attention mechanism design and a discussion of the challenges in combining sparsity with geometric awareness.