---
title: "Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?"
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","Compiler Optimization","Semantic Lifting"]
---

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

## AI commentary

> This work reminds us that the true value of LLMs may lie not just in writing code, but in understanding the intent behind it. It is worth reading for both compiler engineers and AI researchers, offering a verifiable path for models to safely participate in low-level performance optimization.

## At a glance

This paper introduces SeGaBench, a benchmark to test whether large language models (LLMs) can recover semantic information missing from C/C++ code and generate validated optimization artifacts. Across 120 executable cases, the strongest model produced correct artifacts in 94.8% of responses, achieved at least 1.05x speedup in 83.3%, and attained performance success on 93.3% of cases. However, correct artifacts often only closed part of the oracle gap. The study shows that LLMs can complement compiler analysis as speculative semantic proposers, provided their outputs are validated and evaluated.

## Key points

- Introduces SeGaBench: 120 executable cases covering low-level assumptions, data-structure invariants, and high-level semantic lifting.
- Evaluates five LLMs; the strongest model produces correct artifacts in 94.8% of responses and achieves at least 1.05x speedup in 83.3%.
- Correct artifacts often only partially close the gap to the oracle, highlighting both the potential and limitations of LLMs as semantic proposers.

## Full text

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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