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* Our `README` hasn't been updated in quite a while, so this patch aims to make it more relevant and useful, especially now that we are public-facing and looking to onboard more folks soon. * Use the new/proper title of the project at the top of the `README` and include the official LANL FCI copyright O# at the top as they request. * Add a brief introduction that describes what our library provides. * Add some contributor guidelines--feel free to suggest improvements/changes if you disagree, etc.
tylerjereddy
commented
Jan 17, 2026
| 2. At the moment it is not acceptable to use machine learning/AI/LLMs as | ||
| part of the code contribution/review process. The reason is related to provenance | ||
| and licensing---we cannot know for sure if the material being contributed | ||
| originated or partially originated from code that had a copyleft license. |
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Some thoughts here:
- I certainly think this is true for our open code.
- There may be exceptions--particularly interesting is the usage of LLMs that have provably been trained on license-compatible code sources. I'm not in the loop on whether there are good options here. If there are, that might be neat since we could use them to help with code review, but stuff like copilot (the GitHub default PR helper) is almost certainly not safe.
- It may be interesting to think about what our stance might be for the research/in-house/private code. If we want to share it widely it may very well have the same problem. Conversely, if our only intention with the research code is to allow others to reproduce the work/experiments, but it isn't meant for wide adoption like the library code, we could potentially use LLM assistants in the code review interface (which is nice...). That's probably a tough call, and something we can discuss elsewhere.
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- I am unclear how to verify if an LLM has been trained on license compatible codes. For example, ChatGPT would tell you internal method details of GrafoRVFL even though it is copy left.
tylerjereddy
commented
Jan 17, 2026
| 3. Please make an effort to format your PR titles and commit messages | ||
| according to the [guidelines used provided by NumPy](https://numpy.org/devdocs/dev/development_workflow.html#writing-the-commit-message). This helps keep our commit | ||
| history readable and easier to debug. | ||
| 4. Please try to avoid merging your own code---we aim to provide timely |
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An example is here: https://github.com/lanl/ascr_rvfl/pull/65#issuecomment-3764454861
While that doesn't matter much in that particular case, as a general policy let's just try to avoid merges until all comments are fully addressed to reduce the likelihood that things slip through and then i.e., I end up cleaning it up myself, etc..
nray
approved these changes
Jan 22, 2026
| machine learning estimators that do not use backpropagation. The most | ||
| prominent estimator types we support provide access to single and multi layer | ||
| random vector functional link (RVFL) networks and extreme learning machines | ||
| (ELMs). There is a considerable background literate on these two types |
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Minor: "literate" --> "literature"
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Should we add a few references to the implemented architectures ? Or, do we wait till the vision paper is out and then update the readme with that.
| 2. At the moment it is not acceptable to use machine learning/AI/LLMs as | ||
| part of the code contribution/review process. The reason is related to provenance | ||
| and licensing---we cannot know for sure if the material being contributed | ||
| originated or partially originated from code that had a copyleft license. |
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There was a problem hiding this comment.
- I am unclear how to verify if an LLM has been trained on license compatible codes. For example, ChatGPT would tell you internal method details of GrafoRVFL even though it is copy left.
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Our
READMEhasn't been updated in quite a while, so this patch aims to make it more relevant and useful, especially now that we are public-facing and looking to onboard more folks soon.Use the new/proper title of the project at the top of the
READMEand include the official LANL FCI copyright O# at the top as they request.Add a brief introduction that describes what our library provides.
Add some contributor guidelines--feel free to suggest improvements/changes if you disagree, etc.