Scientific Python Maintainers Propose LLM Contribution Guidelines

In a community post, Scientific Python maintainers examine risks from LLM- and agent-generated contributions and propose pragmatic guidelines. The author details concerns including licensing incompatibilities, subtle conceptual bugs from hallucinations, reviewer frustration, and reduced learning, and outlines proposed norms (attribution, human review, contributor discipline). The effort aims to preserve collaborative culture while enabling maintainers to safely integrate AI-assisted workflows into SciPy and NumPy ecosystems.