{"ID":3083538,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T03:38:11.424509713Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06492","arxiv_id":"2606.06492","title":"Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution","abstract":"Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly at repository scale and brittle to evolving codebases. We introduce Code2LoRA, a hypernetwork framework that generates repository-specific LoRA adapters, effectively injecting repository knowledge with zero inference-time token overhead. Code2LoRA supports two usage scenarios: Code2LoRA-Static converts a single repository snapshot into an adapter, suitable for comprehension of stable codebases; while Code2LoRA-Evo maintains an adapter backed by a GRU hidden state updated per code diff, suitable for active development of evolving codebases. To evaluate Code2LoRA against parameter-efficient fine-tuning baselines, we build RepoPeftBench, a benchmark of 604 Python repositories with two tracks: a static track with 40K training and 12K test assertion-completion tasks, and an evolution track with 215K commit-derived training and 87K commit-derived test tasks. On the static track, Code2LoRA-Static achieves 63.8% cross-repo and 66.2% in-repo exact match, matching the per-repository LoRA upper bound; on the evolution track, Code2LoRA-Evo achieves 60.3% cross-repo exact match (+5.2 pp over a single shared LoRA). Code2LoRA's code can be found at https://anonymous.4open.science/r/code2lora-6857; the model checkpoints and RepoPeftBench datasets can be found at https://huggingface.co/code2lora.","short_abstract":"Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly at repository scale and brittle to evolving codebases. We in...","url_abs":"https://arxiv.org/abs/2606.06492","url_pdf":"https://arxiv.org/pdf/2606.06492v1","authors":"[\"Liliana Hotsko\",\"Yinxi Li\",\"Yuntian Deng\",\"Pengyu Nie\"]","published":"2026-06-04T17:59:46Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Language Model\",\"LoRA\"]","project_urls":"[\"https://anonymous.4open.science/r/code2lora-6857\"]","has_code":false}