Prior Knowledge or Search? A Study of LLM Agents in Hardware-Aware Code Optimization
A recent study investigates the effectiveness of LLM agents in hardware-aware code optimization. The research reveals that LLMs tend to rely more on pretrained knowledge than on feedback during optimization tasks. Findings indicate that performance can degrade significantly when models operate with low-density language or when tasked with uncommon kernel sizes.
- ▪The study conducted three controlled experiments to evaluate LLM agents in code optimization.
- ▪In black-box optimization, LLMs behave as greedy optimizers, showing limited adaptability to input size variations.
- ▪Feedback-loop kernel optimization demonstrated that CUDA performance improves with iterative feedback, while TVM IR performance declines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19782 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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Computer Science > Artificial Intelligence arXiv:2605.19782 (cs) [Submitted on 19 May 2026] Title:Prior Knowledge or Search? A Study of LLM Agents in Hardware-Aware Code Optimization Authors:Dmitry Redko (1), Albert Fazlyev (2), Konstantin Sozykin (1), Maria Ivanova (3 and 1), Evgeny Burnaev (1), Egor Shvetsov (1) ((1) Applied AI Institute, (2) AI Talent Hub, ITMO University, (3) YSDA) View a PDF of the paper titled Prior Knowledge or Search? A Study of LLM Agents in Hardware-Aware Code Optimization, by Dmitry Redko (1) and 8 other authors View PDF Abstract:LLM discovery and optimization systems are increasingly applied across domains, implementing a common propose-evaluate-revise loop.
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