基于智能体的编译器遗漏优化补丁研究

Computer Science > Software Engineering

arXiv:2607.02370v1 (cs) [Submitted on 2 Jul 2026]

Title:Understanding Agent-Based Patching of Compiler Missed Optimizations

Authors:Batu Guan, Zirui Wang, Shaohua Li View a PDF of the paper titled Understanding Agent-Based Patching of Compiler Missed Optimizations, by Batu Guan and 2 other authors View PDF HTML (experimental)

Abstract:Compiler missed optimizations refer to cases in which compilers failed to optimize certain code. It takes many compiler developers' efforts to implement or patch such missed optimizations. In this paper, we present a systematic study of how well agents patch compiler missed optimizations. We identify a significant challenge that patching a missed optimization requires more than just fixing the reported case, and instead requires generalizing to similar cases. We construct a benchmark of real-world LLVM missed optimization issues and compare agent-generated patches with patches from developers in terms of optimization scope. Our results show that coding agents often optimize the given examples, but many generated patches either cover only part of the developer-intended scope or partially overlap with it; in some cases, they further generalize beyond the reference patch. We further introduce historical-knowledge augmentation techniques that leverage prior LLVM optimization pull requests through retrieval and distillation, showing that they improve developer-aligned generalization and yield practical benefits when applied to real-world IR.

Comments:	11 pages, 10 figures
Subjects:	Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2607.02370 [cs.SE]
	(or arXiv:2607.02370v1 [cs.SE] for this version)
	https://doi.org/10.48550/arXiv.2607.02370 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Batu Guan [view email]
[v1] Thu, 2 Jul 2026 16:12:32 UTC (518 KB)
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