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Evidence record 6368 · automatically gathered

Toward Executable Repository-Level Code Generation via Environment Alignment

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

Record details

Published: 4 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment · Shared topic: Environment

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How to cite this record

ethics.ai (4 April 2026), “Toward Executable Repository-Level Code Generation via Environment Alignment,” evidence record 6368, https://ethics.ai/record/6368 (originally published by arXiv).

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