{
  "id": 6368,
  "url": "https://arxiv.org/abs/2604.03622v1",
  "title": "Toward Executable Repository-Level Code Generation via Environment Alignment",
  "summary": "Large language models (LLMs) have achieved strong performance on code generation, but existing methods still struggle with repository-level code generation under executable validation. Under this evaluation setting, success is determined not by the plausibility of isolated code fragments, but by whether a generated multi-file repository can be successfully installed, have its dependencies and internal references resolved, be launched, and be validated in a real execution environment. To address ",
  "authors": "Ruwei Pan, Junlei Shen, Linhao Wu, Yueheng Zhu, Zixiong Yang, Yakun Zhang et al.",
  "category": "research",
  "topics": "safety-alignment,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-04T07:37:55.000Z",
  "fetched_at": "2026-07-14T16:32:28.607Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6368",
  "original_url": "https://arxiv.org/abs/2604.03622v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}