@inproceedings{jiang-etal-2026-hintpilot,
    title = "{H}int{P}ilot: {LLM}-based Compiler Hint Synthesis for Code Optimization",
    author = "Jiang, Hanyun  and
      Yao, Peisen  and
      Li, Kaiyue  and
      Lin, Tingting  and
      Wang, Chengpeng  and
      Ren, Kui",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1251/",
    pages = "24970--24986",
    ISBN = "979-8-89176-395-1",
    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 \textit{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 while preserving program correctness."
}