Agentic Auto-Scheduling (arXiv:2511.00592v2) by @alex_prompter reports an experimental study of using Large Language Models to drive automatic code optimization for complex loop nests on modern hardware. The paper frames automatic code optimization as still difficult and proposes LLM-guided auto-scheduling as a novel technique, with results and methods detailed in the linked arXiv submission.
Paper titled “Agentic Auto-Scheduling: An Experimental Study of LLM-Guided Loop…” by @alex_prompter, posted on arXiv as 2511.00592v2 (link: arxiv.org/abs/2511.00592v2), dated 2026-06-24 in the post.
Paper: arxiv.org/abs/2511.00592v2
Link
Agentic Auto-Scheduling: An Experimental Study of LLM-Guided Loop...
Automatic code optimization remains a difficult challenge, particularly for complex loop nests on modern hardware. This paper investigates a novel approach to code optimization where Large...
arxiv.org