UPSTREAM PR #18388: (proposal) contributing: tighten AI usage policy

CONTRIBUTING.md

@@ -6,6 +6,21 @@ The project differentiates between 3 levels of contributors:
 - Collaborators (Triage): people with significant contributions, who may be responsible for some parts of the code, and are expected to maintain and review contributions for the code they own
 - Maintainers: responsible for reviewing and merging PRs, after approval from the code owners
 
+# AI Usage Policy
+
+> [!IMPORTANT]
+> This project does not accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
+
+Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations).
+
+If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
+
+1. Explicitly disclose the manner in which AI was employed.
+2. Perform a comprehensive manual review prior to submitting the pull request.
+3. Be prepared to explain every line of code submitted if asked by maintainer.
+
+Note: Trivial editor autocompletions do not require disclosure.
+
 # Pull requests (for contributors & collaborators)
 
 - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
@@ -20,7 +35,6 @@ The project differentiates between 3 levels of contributors:
 - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention
 - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
 - Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
-- Using AI to generate PRs is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before publishing the PR. Note that trivial tab autocompletions do not require disclosure.
 
 # Pull requests (for maintainers)
 
@@ -31,6 +45,11 @@ The project differentiates between 3 levels of contributors:
 - When merging a PR, make sure you have a good understanding of the changes
 - Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
 
+Maintainers reserve the right to decline review or close pull requests that satisfy any of the following conditions:
+- The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone.
+- The pull request duplicates an existing one.
+- The contributor fails to adhere to this contributing guide.
+
 # Coding guidelines
 
 - Avoid adding third-party dependencies, extra files, extra headers, etc.