{
  "added_at": "2026-11-06",
  "arxiv_id": "2506.10125",
  "arxiv_version_reviewed": "v4",
  "authors": [
    "Muqi Zou",
    "Hongyu Cai",
    "Hongwei Wu",
    "Zion Leonahenahe Basque",
    "Arslan Khan",
    "Berkay Celik",
    " Dave",
    " Tian",
    "Antonio Bianchi",
    " Ruoyu",
    " Wang",
    "Dongyan Xu"
  ],
  "bibtex_url": "https://awesomerea.org/papers/citations/paper-d-lift.bib",
  "code_url": null,
  "date_basis": "First arXiv submission date.",
  "date_basis_zh": "arXiv 首次提交日期。",
  "date_precision": "day",
  "description_en": "D-LIFT uses D-Score to guide LLM fine-tuning and output selection, improving decompiled code while prioritizing accuracy. It evaluates Ghidra output from coreutils and util-linux.",
  "description_zh": "D-LIFT 使用 D-Score 指导大语言模型微调和输出选择，在优先保证准确性的同时改善反编译代码，并评估了 Ghidra 对 coreutils 和 util-linux 程序的输出。",
  "doi": null,
  "entry_url": "https://awesomerea.org/papers/paper-d-lift/",
  "entry_url_zh": "https://awesomerea.org/zh/papers/paper-d-lift/",
  "evidence_urls": [
    "https://arxiv.org/abs/2506.10125"
  ],
  "first_arxiv_date": "2025-06-11",
  "id": "paper-d-lift",
  "json_url": "https://awesomerea.org/papers/paper-d-lift/record.json",
  "kind": "research",
  "markdown_url": "https://awesomerea.org/papers/paper-d-lift/index.md",
  "markdown_url_zh": "https://awesomerea.org/zh/papers/paper-d-lift/index.md",
  "metadata_notes": "The date and venue come from the arXiv record.",
  "metadata_notes_zh": "日期和发表场所来自 arXiv 记录。",
  "pdf_url": "https://arxiv.org/pdf/2506.10125",
  "project_url": null,
  "publication_date": "2025-06-11",
  "publication_status": "preprint",
  "record_type": "paper",
  "runtime_tested": false,
  "scope_limitations_en": "The evaluation covers functions from coreutils and util-linux, using Ghidra and a range of LLMs. D-Score measured 55.3% more improved functions than baseline LLMs and improvements to 68.2% of native decompiler functions.",
  "scope_limitations_zh": "评估涵盖 coreutils 和 util-linux 中的函数，使用 Ghidra 和多种大语言模型。按 D-Score 衡量，微调后的模型比基线模型多改善了 55.3% 的函数；D-LIFT 改善了原生反编译器输出中 68.2% 的函数。",
  "targets": [
    "binary"
  ],
  "title": "D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning",
  "title_zh": "D-LiFT：通过代码质量驱动的微调改进基于大语言模型的反编译器后端",
  "url": "https://arxiv.org/abs/2506.10125",
  "venue": "arXiv",
  "verification": "source-reviewed",
  "verified_at": "2026-11-06",
  "why_fits_en": "It addresses the accuracy and readability of source code reconstructed from binaries.",
  "why_fits_zh": "该研究关注从二进制文件重建的源代码的准确性和可读性。",
  "year": 2025
}