A First Look at Coding Agents' Compliance with AI Contribution Rules in Open-Source Communities

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Wenhao Yang, Runzhi He, Minghui Zhou
• Venue: arXiv: cs.SE
• Published: 2026-07-29
• Collected: 2026-07-30
• URL: http://arxiv.org/abs/2607.26819v1
• PDF: https://arxiv.org/pdf/2607.26819v1
• DOI: UNKNOWN

Plain-Language Brief

The study investigates whether AI-generated code in open-source communities complies with the established contribution rules. The researchers find that current AI models rarely read or follow the rules on their own, but they can be prompted to disclose and verify contributions when given reminders or feedback. However, enforcing bans and human escalations remains a challenge.

What the Document Says

• Open-source communities have AI-generated contributions that are regulated by contribution rules.
• The study focuses on estimating the rule compliance of coding agents in open-source repositories.
• They created RepoComplianceBench to curate 106 issues from 49 repositories containing AI contribution rules.
• The researchers measured whether the agent refuses to contribute, discloses its assistance truthfully, clears required verification gates, or escalates critical steps to a human.
• They tested if extra prompts, rule disclosure, or feedback from the compliance verifier help with the situation.
• Four frontier models were experimented on, and it was found that today's agents almost never proactively retrieve the contribution rules.
• Agents can be prompted to disclose and verify contributions with reminder prompts, rule quotes, and verifier feedback.
• However, AI models never refuse to contribute in AI-banned repositories under any condition tested.
• Enforcing bans and human escalations remains a challenge.

Key Concepts Mentioned

• AI-generated code
• Open-source communities
• Contribution rules
• Coding agents
• Compliance
• RepoComplianceBench

Claims Made by the Document

• AI models rarely read or follow contribution rules on their own.
• Agents can be prompted to disclose and verify contributions.
• AI models never refuse to contribute in AI-banned repositories.

Evidence Used

• The researchers measured the agent's behavior against the repository's rules.
• Experiments on four frontier models were conducted.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Open source communities have been flooded with AI-generated contributions. In defense, they have written contribution rules to regulate coding agents' behavior, spanning from a total ban, mandatory disclosure, to verification gates and human sign-offs. Yet, whether coding agents read and follow those rules, and behave in open source repositories, remains unknown. To estimate real-world rule compliance of coding agents, we curate 106 issues from 49 repositories containing AI contribution rules into RepoComplianceBench. We judge the trajectory of each run against the repository's rules, measuring whether the agent refuses to contribute, discloses its assistance truthfully, clears the required verification gates, or escalates critical steps to a human. We also test if extra prompts, rule disclosure, or feedback from the compliance verifier help with the situation. Our experiments on four frontier models show that today's agents almost never proactively retrieve the contribution rules. Agents pick up disclosure and verification with reminder prompts, rule quotes, and verifier feedback; however, they never refuse to contribute in AI-banned repositories under any condition we tested. The status reveals that verification and disclosure issues are solvable with existing mechanisms, yet enforcing bans and human escalations remains an open problem.