Understanding Agent-Based Patching of Compiler Missed Optimizations
Score
56Why selected
The paper introduces novel benchmarks and augmentation methods with thorough experiments, providing practical guidance for AI-assisted compiler optimization, hence score 85.
AI commentary
「This study reveals the core limitation of current AI coding agents in compiler optimization (lack of generalization alignment) and proposes effective historical-knowledge augmentation methods, offering significant value for advancing automated compiler development and intelligent programming assistants.」
Quick overview
This paper systematically studies how AI agents patch compiler missed optimizations, constructing a benchmark of real-world LLVM issues. It finds that agent-generated patches often cover only part of the developer-intended scope or over-generalize. The authors propose historical-knowledge augmentation techniques (retrieval and distillation) using prior optimization pull requests, which significantly improve generalization alignment and show practical benefits on real IR.
- Patching compiler missed optimizations requires generalization to similar cases, not just fixing the reported instance.
- Agent-generated patches often diverge from developer-intended scope (partial coverage or over-generalization).
- Retrieving and distilling historical LLVM optimization pull requests improves generalization alignment of patches.
Full text
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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