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No More Translation at Runtime: LLM-Empowered Static Binary Translation

  • Zhibo LIU
  • , Huaijin WANG
  • , Wai Kin WONG
  • , Daoyuan WU
  • , Shuai WANG*
  • *Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

Abstract

While AArch64 CPUs are becoming strong market contenders, their software ecosystem lags behind the mature x86-64 environment, hindering the adoption of the new architectures and impacting user experience. Binary translation bridges this divide by converting binary code from one architecture (e.g., x86-64) to run on another (e.g., AArch64), allowing legacy software to benefit from modern hardware's performance and energy efficiency advantages.

Current translation methods are typically either dynamic, which adds significant runtime overhead, or static, which struggles with reliability due to the inherent complexities of binary analysis. This paper introduces a new static, assembly-to-assembly translation paradigm that transforms binary code ahead of execution, generating portable, efficient nativelike binaries that run on AArch64 devices without runtime frameworks. Benefiting from recent breakthroughs in large language models (LLMs), we provide a practical and automated translation engine that produces high-quality code with minimal human intervention. To ensure correctness, we introduce a crucial verification step, where we split the assembly code into simplified snippets, enabling efficient and scalable semantic verification.

Our evaluation shows that this approach significantly outperforms existing open-source solutions with a large margin, producing binaries with near-native performance. Furthermore, it shows substantial improvements over the leading industrial translator, ExaGear, illuminating a promising new direction for cross-architecture binary translation research.
Original languageEnglish
Title of host publicationEUROSYS '26: Proceedings of the 21st European Conference on Computer Systems
PublisherAssociation for Computing Machinery, Inc
Pages1023-1040
Number of pages18
ISBN (Electronic)9798400722127
DOIs
Publication statusPublished - 26 Apr 2026

Bibliographical note

We also thank the HKUST Fok Ying Tung Research Institute and the National Supercomputing Center in Guangzhou (Nansha Sub-center) for providing computational resources, and the Collaborative Innovation Center of Novel Software Technology and Industrialization (Jiangsu, China) for their support.

Publisher Copyright:
© 2026 Copyright held by the owner/author(s)

Funding

This work was supported in part by the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2324-6S08), the HKUST Bridge-the-Gap Fund (BGF.001.2025), and the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (No. JYB2025XDXM118).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Binary Translation
  • Cross-Architecture Migration
  • Large Language Models
  • Reverse Engineering

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