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    <abstract>Code optimization remains a core objective in software development, yet modern compilers struggle to navigate the enormous optimization spaces. While recent research has looked into employing large language models (LLMs) to optimize source code directly, these techniques can introduce semantic errors and miss fine-grained compiler-level optimization opportunities. We present HintPilot, which bridges LLM-based reasoning with traditional compiler infrastructures via synthesizing compiler hints—annotations that steer compiler behavior. HintPilot employs retrieval-augmented synthesis over compiler documentation and applies profiling-guided iterative refinement to synthesize semantics-preserving and effective hints. Upon PolyBench and HumanEval-CPP benchmarks, HintPilot achieves up to 6.88x geometric mean speedup over -Ofast while preserving program correctness.</abstract>
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%0 Conference Proceedings
%T HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization
%A Jiang, Hanyun
%A Yao, Peisen
%A Li, Kaiyue
%A Lin, Tingting
%A Wang, Chengpeng
%A Ren, Kui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jiang-etal-2026-hintpilot
%X Code optimization remains a core objective in software development, yet modern compilers struggle to navigate the enormous optimization spaces. While recent research has looked into employing large language models (LLMs) to optimize source code directly, these techniques can introduce semantic errors and miss fine-grained compiler-level optimization opportunities. We present HintPilot, which bridges LLM-based reasoning with traditional compiler infrastructures via synthesizing compiler hints—annotations that steer compiler behavior. HintPilot employs retrieval-augmented synthesis over compiler documentation and applies profiling-guided iterative refinement to synthesize semantics-preserving and effective hints. Upon PolyBench and HumanEval-CPP benchmarks, HintPilot achieves up to 6.88x geometric mean speedup over -Ofast while preserving program correctness.
%U https://aclanthology.org/2026.findings-acl.1251/
%P 24970-24986

Markdown (Informal)

[HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization](https://aclanthology.org/2026.findings-acl.1251/) (Jiang et al., Findings 2026)

• HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization (Jiang et al., Findings 2026)

ACL

• Hanyun Jiang, Peisen Yao, Kaiyue Li, Tingting Lin, Chengpeng Wang, and Kui Ren. 2026. HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 24970–24986, San Diego, California, United States. Association for Computational Linguistics.