---
title: "大型语言模型能否恢复编译器遗漏的语义优化机会？"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.03983v1"
permalink: "https://feed.astralor.com/items/itm_6f2b8b80ae46c2184ccb0ade"
published: "2026-08-04T17:47:25.000Z"
score: 73
channel: "ai"
tags: ["C/C++","LLM","SeGaBench","编译器优化","语义提升"]
---

# 大型语言模型能否恢复编译器遗漏的语义优化机会？

## AI 点评

> 这项研究提醒我们，LLM的价值不只是生成代码，而是理解代码背后的意图。对编译器工程师和AI研究者都值得一读——它给出了一条可验证的路径，让模型安全地参与底层性能优化。

## 速览

本论文提出SeGaBench基准，测试LLM能否从C/C++代码中恢复编译器缺失的语义信息，并生成验证过的优化工件。在120个可执行案例中，最强模型在94.8%的响应中生成正确工件，83.3%实现至少1.05倍加速，但对多数案例仅部分达到最优水平。研究表明，LLM可作为推测性语义提议者补充传统编译器分析，但需严格验证与性能评估。

## 关键点

- 提出SeGaBench：120个可执行案例，涵盖低级假设、数据结构不变量和高层语义提升。
- 评估5个LLM，最强模型在94.8%响应中产出正确工件，83.3%获得至少1.05倍加速。
- 正确工件通常仅部分弥补与oracle的性能差距，揭示LLM作为语义提议者的潜力与局限。

## 全文

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.

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