Large Language Model Contribution Policy

tl;dr is LibreLane is a small, focused project with limited maintenance bandwidth, thus:

  • We reject “agentic” or vibe-coded submissions.

  • For smaller issues found using LLMs, we encourage you to submit bug reports instead where the maintainers can fix them.

  • For high-quality code that is primarily human-authored but AI-assisted, we require an Assisted-by (not Co-authored-by) commit trailer.

  • Do not use LLMs for communication, with a narrow exemption if English is not your native language, in which case, you are allowed to translate text you have written yourself.

Note

Contributions submitted before the adoption of this Policy will be reviewed in good faith.

Impetus and Rationale

The impetus is to decrease the maintainership burden from large language model (LLM)-generated code or prose. LibreLane is mostly run by volunteers and we have found that unchecked LLM-generated contributions, be they issues or pull-requests, time-intensive on our side at best. Sometimes, valid points have been raised, but a one-paragraph human-authored response would have sufficed versus a wall of LLM prose. Other times, we spent a good hour performing unpaid code review for unmergeable code in pursuit of fairness to contributors.

We state that we strongly prefer all contributions to be as humanly-authored as possible, considering a number of externalities related to the AI industry. Nonetheless, we concede a blanket ban on LLM-aided contributions would be burdensome and likely unenforceable.

It is for this reason we simply define the scope of LLM contributions and encourage transparency.

Scope

LibreLane’s LLM policy (the Policy) applies to any and all of the following items hosted under the https://github.com/librelane/librelane namespace

  • Code

  • Documentation

…and any communication hosted either under said namespace or the #librelane:fossi-chat.org Matrix channel.

Code Contributions

Broadly, the maintainers decline “vibe-coded” contributions, whether fully automated (“agentic”) or human-curated. Contributions must be based on a understanding of the codebase and you must be able to faithfully answer questions by maintainers on why any or all decisions was taken. Telltale signs we have historically encountered of vibe-coded contributions include but are not limited to:

  • Major code duplication of existing functions

  • Code duplication within the contribution

  • Bespoke implementation of complex functionality where code reuse from a PyPI library or similar would be more appropriate

  • Untested bugfixes that ignore the context of a certain piece of code

  • Redundant or overly verbose comments

An exemption from this Policy is provided for the use of text transformation tooling to produce changes that the author manually reviews and understands, including inline “auto‐completion” (even if LLM‐based) of short, rote snippets of text that do not contribute anything beyond boilerplate the author would have written anyway.

For contributions that are merely AI-assisted, i.e., AI may have been used to help with parts of the PR that can reasonably interpreted to not meet the definition of Legal Significance as described by GNU, is considered a “covered” use of LLMs, and maintainers may at their discretion choose to accept it so long as it is appropriately labeled.

Documentation

Any majorly LLM-generated articles or tutorials within the body of LibreLane’s documentation are not allowed.

Machine-translating non-English text to English is partially exempt from this policy, in that we will require you to provide the original text and acknowledge the use of machine translation. In all likelihood, it will require editorial review by a maintainer, and we may choose to simply keep the tutorial in its original language. We actually encourage you write tutorials in languages other than English.

We would strongly prefer any documentation, including class and variable documentation, to be in your own words, nonetheless because of the typical terseness of said documentation, this is undetectable. We ask that you abide by the transparency rules in the relevant commits.

Communications

Communications include chat messages, bug reports, enhancement requests and pull request bodies, as well as comments and replies to any of them.

We ask that you do not use an LLM to write your communications with other human beings as a matter of mutual respect — a human will read it, so a human needs to write it. We do provide an exception here if you’re not a native English speaker and you’re using an LLM to translate text you wrote yourself in your original language.

Any bugs found by an LLM must be independently confirmed by you before filing a bug report. Lacking wider context, LLMs sometimes flag bugs that cannot be triggered under normal circumstances or simply flag odd code that isn’t necessarily a bug but necessitated by weird behavior in underlying utilties or operating systems.

Security incidents are exempt from this policy if it is somehow burdensome to create a replicator.

Transparency

All covered use of LLM-based tooling for a contribution must be disclosed as part of that contribution.

In the case of LLM‐based AI tooling used for commits, this must be in the form of an Assisted-by: Git commit trailer, including at least the tool name and the primary model name and version used for the contribution.

A Co-authored-by: trailer does not satisfy this policy and commits with this trailer for an LLM will be rejected: authorship is for legal attribution and responsibility, which requires a human author.

Any adequate form of disclosure is permitted for other kinds of contributions.

Further Exemptions

In addition to any previously stated exemptions, the following uses are also considered exempt:

  • Use of LLMs for research, testing, debugging, or private review is out of scope, if no substantial amount of their output is included in the resulting contribution.

  • Use of LLMs to develop upstream software included with LibreLane is decidedly not in scope. If an upstream piece of software’s code quality declines, we reserve the right to substitute or remove it independent of this policy.

  • Any act that is mere use of LibreLane, including but not limited to:

    • Use of LLMs with LibreLane to make your own downstream chips.

    • Use of LLMs to generate your own custom Plugins (not for upstreaming).

Enforcement

If you believe that someone is using an LLM without appropriate disclosure and review, you can politely ask them if that’s the case and point them to this Policy as appropriate.

Please assume good faith and remain civil; it’s not always possible to determine, and it is more likely that someone overlooked this Policy than deliberately violated it.

If a maintainer judges that a contribution doesn’t comply with this Policy, they should paste the following response and, depending on the severity of the violation, request changes or close the PR:

This PR does not appear to comply with our policy on LLM contributions. Please see the relevant policy here: https://librelane.readthedocs.io/en/stable/contributors/llm-policy.html

Maintainers and other users are encouraged (as time allows) to guide said users to rework their contributions in a manner more constructive to LibreLane, such as by filing Policy-compliant bug reports or feature requests.

Repeated, deliberate violations of the policy may result in loss of contributing privileges.

Credits

Loosely based on a combination of these policies: