Abstract
We present SwiftSolve, a novel approach to accelerating mathematical reasoning in large language models. By leveraging efficient computation patterns and strategic caching of intermediate reasoning steps, SwiftSolve achieves significant speedups on mathematical problem-solving benchmarks while maintaining accuracy. Our method introduces a hierarchical reasoning cache that stores reusable solution patterns, enabling the model to quickly retrieve and adapt known solution strategies to novel problems.