{
  "id": "2606.06492",
  "title": "Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution",
  "authors": "Liliana Hotsko, Yinxi Li, Yuntian Deng, Pengyu Nie",
  "first_public": "2026-06-04",
  "first_public_source": "https://arxiv.org/abs/2606.06492",
  "arxiv_first_submission": "2026-06-04",
  "date_label": "arXiv v1",
  "arxiv": "2606.06492",
  "paper_url": "https://arxiv.org/abs/2606.06492",
  "full_text": "https://arxiv.org/html/2606.06492v2",
  "method_source": "https://arxiv.org/html/2606.06492v2#S3",
  "project_url": "https://lilianahotsko.com/Code2LoRA/",
  "venue": "arXiv",
  "lens": [
    "generated-adapters"
  ],
  "related_terms": [
    "Code2LoRA",
    "repository-conditioned adapters",
    "commit-streaming adapters",
    "hypernetworks",
    "weight-space programming"
  ],
  "summary": "Trains a hypernetwork to generate repository-specific LoRA adapters from source code and update them as commits arrive.",
  "contribution": "Code2LoRA-Static maps a repository snapshot to an adapter. Code2LoRA-Evo updates a GRU state with each code diff and generates the adapter from that state.",
  "limitations": "The paper evaluates Python test-assertion completion with Qwen2.5-Coder-1.5B.",
  "program_form": "Generated LoRA matrices for a frozen code language model.",
  "conditioning": "A repository snapshot; the evolution variant also receives successive commit diffs.",
  "execution": "The code model uses the generated adapter without adding repository context tokens to each request.",
  "relationship": "Code2LoRA and PAW train models to generate reusable weights. Code2LoRA puts repository knowledge into adapters; PAW compiles function specifications into neural programs with a compiler–interpreter interface.",
  "provenance": "June 4, 2026 arXiv release; method description from the October 27, 2026 revision.",
  "author_names": [
    "Liliana Hotsko",
    "Yinxi Li",
    "Yuntian Deng",
    "Pengyu Nie"
  ],
  "record_url": "https://programasweights.com/reading-list/papers/2606.06492/",
  "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2606-06492",
  "citation_date": "2026-06-04",
  "json_url": "https://programasweights.com/reading-list/papers/2606.06492/record.json",
  "markdown_url": "https://programasweights.com/reading-list/papers/2606.06492/index.md",
  "bibtex_url": "https://programasweights.com/reading-list/papers/2606.06492/citation.bib",
  "csl_url": "https://programasweights.com/reading-list/papers/2606.06492/citation.csl.json",
  "summary_zh": "训练超网络，根据代码仓库生成 LoRA 适配器，并随新的提交更新适配器。",
  "translations": {
    "zh-CN": {
      "summary": "训练超网络，根据代码仓库生成 LoRA 适配器，并随新的提交更新适配器。",
      "relationship": "Code2LoRA 和 PAW 都训练模型来生成可复用的权重。Code2LoRA 将仓库知识写入适配器；PAW 将函数说明编译成神经程序，并建立编译器与解释器接口。",
      "program_form": "供冻结的代码语言模型使用的 LoRA 矩阵。",
      "conditioning": "代码仓库快照；演化版本还接收后续提交的代码差异。",
      "execution": "代码模型使用生成的适配器，无需在每次请求中加入仓库上下文。"
    }
  }
}