Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?

AI 中文解读

核心亮点：这项研究首次证明，大语言模型能帮编译器发现那些被忽略的代码优化机会，让程序跑得更快。通俗解读：编译器就像程序员的“翻译官”，把人类写的代码转成电脑能懂的机器语言。但有时候它会“漏看”一些优化空间，比如代码里隐含的某些规律或假设。研究人员给AI出了120道“优化难题”，让AI从代码的上下文线索中“猜”出这些隐藏规律，并生成优化方案。结果最强的AI在94.8%的情况下给出了正确方案，超过八成能让程序提速至少5%。不过AI的优化效果和理想值还有差距，需要进一步验证。实际影响：这意味着未来AI可能成为程序员的“智能优化助手”，帮软件自动提速。普通用户用到的App、游戏、网站，底层代码如果能被AI优化，加载更快、耗电更少、运行更流畅。比如手机上的视频剪辑软件，AI优化后导出速度更快；网页游戏响应更灵敏。更重要的是，这种技术不改变程序功能，只是让它跑得更高效，安全可靠。长远看，AI和编译器配合，能让整个数字世界的软件都变得更“轻快”。

Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation. We ask whether large language models (LLMs) can recover such semantics from heterogeneous C/C++ context and realize them as validated, contract-preserving artifacts. We introduce SeGaBench, an executable benchmark containing 100 synthetic and 20 source-backed cases spanning low-level assumptions, data-structure invariants, and high-level semantic lifting. Each case includes hidden enabling semantics, an oracle artifact, correctness and semantic validators, and a reproducible performance protocol. We evaluate five LLMs using five independent responses per case. The strongest model produces correct artifacts in 94.8% of responses, achieves at least 1.05x speedup in 83.3%, and obtains a performance success on 93.3% of cases. Nevertheless, correct artifacts often close only part of the oracle gap. These results show that LLMs can complement compiler analysis as speculative semantic proposers, provided that their artifacts are validated and evaluated.