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Pattern-Driven Evaluation of LLM Code Optimization: Isolating Compiler-Transcendent Semantic Reasoning

Wesley Lu, Runzhou Liu, Hongru Du, Xinyao Yi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24835 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

对基于LLM的代码优化的现有评估主要是测量端到端的加速,掩盖了模型是否确实能实现预期优化。对于编译语言,这尤其有问题:观察到的收益可能反映了现代优化编译器已经可以恢复的转变,而不是对模型本身的优化行为。我们介绍了PatternBench,研究编译器超越优化的基准,即在攻击性编译器优化后仍然存在的性能改进。PatternBench瞄准了七类转换和对运行时间的优化评估中的剩余优化机会,并通过AST基于的优化忠实分析验证了预期优化是否在生成的程序中实现。在边缘、开放重量和精心调整的模型中,实验表明运行时间

Machine translation (Meta M2M100); the English original follows.

Abstract

Existing evaluations of LLM-based code optimization primarily measure end-to-end speedup, obscuring whether a model actually performs the intended optimization. For compiled languages, this is especially problematic: observed gains may reflect transformations already recoverable by modern optimizing compilers rather than optimization behavior attributable to the model itself. We present PatternBench, a benchmark for studying compiler-transcendent optimization, i.e., performance improvements that remain after aggressive compiler optimization. PatternBench targets residual optimization opportunities across seven classes of transformations and pairs runtime evaluation with an AST-based optimization faithfulness analysis that verifies whether the intended optimization is realized in the generated program. Experiments with frontier, open-weight, and fine-tuned models show that runtime speedup alone provides an incomplete account of optimization capability. Optimization faithfulness is strongly associated with realized speedup, and fine-tuning improves both faithfulness and optimization effectiveness. These results suggest that evaluation of LLM-based code optimization should measure not only performance outcomes, but also whether the target transformation was successfully executed.

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Topics: Computer science, Compiler, Benchmark (surveying), Program optimization, Measure (data warehouse), Programming language

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