Comment by ironSkillet
10 hours ago
I am not familiar with the standards of publishing in machine learning, but as someone trained in a mathematics background, this paper seems relatively light on details and heavy on exposition. Is that typical? Is this a really novel idea? Not trying to be snarky, just trying to understand how meaningful this is.
It's cool that it proves that a bunch of vectorized outputs from an unknown embedder on an unknown dataset is in no way private, because of this ability to reverse engineer the embedder.
I talked to the author at his poster session at neurips and was able to get the gist, though I had read a lot about the platonic representation hypothesis, and this was one of my top 10 favorite papers in the conference.
They link to their code on GitHub - see footnote 2 on page 2. I don't see it linked anywhere else, which makes it easy to miss. https://github.com/rjha18/vec2vec/
It isn't a maths paper, so the conventions are different.
This isn’t math. The exposition IS the details.
You are not wrong. But this has by no means proven its up to the standard of being publishable in a machine learning journal. Its on arXiv.org, which, lets face it, at the end of the day is a vanity press.
Calling the arxiv a vanity press shows you aren't a researcher. In math and physics all the best stuff is on the arxiv and the general level is well above the level of most journals. Journals mainly serve as accreditation and many are basically mediocre - the review process resta more value than it adds overall.
It was accepted to NeurIPS.
The pace of things is moving along so rapidly right now, I’m not sure that waiting for peer reviews is always a wise move. Doubly so if there’s a paywall; why limit your article’s impact by placing it where practitioners’ agents might not be able to access it? The rapid progress right now is challenging for conventional academic processes.
If the value of the paper is difficult to independently verify, for example, if it depends on the credibility of the author, then the academic ritual can add something. If it’s a mathematical result, one that can be automatically verified, or a machine learning technique that anyone can try with Claude code reconstructing it for them, this sort of pre-print publishing model is advantageous.
// why limit your article's impact //
Because...science? It's not science until it passes peer review.
I'm not advocating that everybody stops posting to arXiv, and I'm not saying you can't find good stuff there. I'm just saying, it's a vanity press, there is absolutely no guarantee of the paper's quality.
And being published by a famous professor from a prestigious university is also no guarantee. If we've learned anything from the non-reproducibility crisis, it is that a paper's origin story is no guarantee.
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At a minimum posting to arxiv gives others a standard way to cite the work.