# Data-Driven Dynamic Algorithm Dispatch with Large Language Models

arXiv: https://arxiv.org/abs/2608.21584 · HTML: https://mlsanity.com/paper/2608.21584/

## Key facts

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- First posted on arXiv 21 Aug 2026.

## Abstract (arXiv)

We introduce a large language model (LLM)-driven approach for generating dynamic algorithmic dispatch heuristics in high-performance linear algebra. By combining prompt engineering with LLaMA 3 and a curated performance database, the model learns to synthesize selection heuristics that exploit structural patterns to identify fast algorithmic choices. A case study on LU factorization demonstrates the model's ability to replicate expert-designed strategies. This work, developed as part of the DARPA-MIT SmartSolve project, highlights the promise of LLMs for algorithmic discovery and the development of more adaptive, fast linear algebra software.