# HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization

arXiv: https://arxiv.org/abs/2604.15041 · HTML: https://mlsanity.com/paper/2604.15041/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2026.

## Built on by (most cited first, 2 of 2)

- [Cross-Model Cross-Language AI Coding Agent Performance: Accuracy and Speed of Parallel CLRS Algorithms](https://mlsanity.com/paper/2607.26083/) (2026)
- [Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?](https://mlsanity.com/paper/2608.03983/) (2026)

## Abstract (arXiv)

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.