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Researcher with public evidence across AI research.
@afedercooper.bsky.social and I have a new paper working through the surprisingly tricky problem of whether storing model weights that might or might not generate copyrighted output when prompted makes the model a "copy" under copyright law papers.ssrn.com/sol3/papers....
The blog post walks through the algorithm with interactive animations; the paper has the full results, including other models and kinds of text. Blog post: afedercooper.info/near-verbatim Paper: arxiv.org/abs/2603.24917
I think this is a really important read. @masnick.com carefully explains what is so shocking about recent revelations in court about undisclosed COIs concerning research on copyright and LLMs. www.techdirt.com/2026/09/25/o...
More here: arxiv.org/abs/2601.02671
5. Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain. With @katherinelee.bsky.social and @jtlg.bsky.social. J. Copyright Soc'y. arxiv.org/abs/2309.08133
4. Probabilistic "Copies" in Generative AI Models. With @marklemley.bsky.social. Forthcoming, Berkeley Tech. Law J. arxiv.org/abs/2607.14532
2. Scalable Extraction of Training Data from (Production) Language Models. With Milad Nasr, Nicholas Carlini, Katherine Lee, and others. arxiv.org/abs/2311.17035
Our large-scale study of memorization of books in open-weight LLMs (e.g., Llama, Qwen) will appear at the 2026 Conference on Language Modeling as an oral. We made a website for exploring our results on 200 books and 14 models: books-memorization.github.io
Prospective copyright plaintiffs have started asking me my opinion about citing "Alignment Whack-a-Mole" in litigation. It reports that fine-tuning makes frontier LLMs reproduce up to 85-90% of copyrighted books. I don't think the headline results hold up: afedercooper.info/wh…
The blog post walks through the algorithm with interactive animations; the paper has the full results, including other models and kinds of text. Blog post: afedercooper.info/near-verbatim Paper: arxiv.org/abs/2603.24917
For more, please check out our work, cited in the decision above: 1. The Files are in the Computer: On Copyright, Memorization, and Generative AI. With @jtlg.bsky.social. Chi.-Kent Law Rev. scholarship.kentlaw.iit.edu/cklawreview/...
A colleague sent along the decision in GEMA v. Suno, which I got to reading today: media.licdn.com/dms/document... I have my own thoughts on aspects of the decision, but regardless of the details, it continues to blow my mind that papers from my PhD and postdoc get cited in li…
To my knowledge, 2 papers of mine have been put through LLMs and reposted on arXiv/ResearchGate as novel work. 1 was published in a Nature subsidiary (+ verbatim copies figures from my 2021 paper). The 2nd popped up today in my arXiv digest, is a sloppified version of a paper I put up in early July.
In our work on extracting memorized pieces of books from open-weight LLMs, we measure how likely models are to output training text verbatim. But near-verbatim cases matter too. E.g., high-probability continuations for this Gatsby quote differ from the original only by punctuation:
is anyone else finding Opus 5 to be an utter disaster? I've seen some other people complaining about taking actions that don't make sense without approval. I don't use AI that way (yet); I iterate slowly/ co-work through what I'm working on. And Opus 5 just can't do that.
Like many of my colleagues, I'm receiving dozens of EOI emails a day from prospective students. It's worth reading those emails before clicking send. (I expect most are using AI to draft emails. This tiny prompt injection is just to see if the sender bothered to even read the output.)
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