Paper Detail
Liliana Hotsko, Yinxi Li, Yuntian Deng, Pengyu Nie
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.
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@article{hotsko2026code2lora,
title = {Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution},
author = {Liliana Hotsko and Yinxi Li and Yuntian Deng and Pengyu Nie},
year = {2026},
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. Co},
url = {https://arxiv.org/abs/2606.06492},
keywords = {cs.SE, cs.AI, cs.CL, Code2LoRA, hypernetwork framework, LoRA adapters, repository-level context, parameter-efficient fine-tuning, GRU hidden state, code diffs, RepoPeftBench, assertion-completion tasks, cross-repo exact match, in-repo exact match, huggingface daily, Computer science, Python (programming language), Programming language, Software evolution, Snapshot (computer storage)},
eprint = {2606.06492},
archiveprefix = {arXiv},
}
{}