You're right, I'm not a well-known researcher, simply an entrepreneur who started to publish academic papers.

However, I do have a long history of diving deep into fields and building practical, open-source solutions to major problems I perceive in the fields.

15 years ago I started with social networks and PHP: https://github.com/Qbix http://laweekly.com/restoring-healthy-communities/

8 years ago I got into smart contracts on EVM, which was the SOTA at the time: https://github.com/Intercoin https://intercoin.org/applications

About a year and a half ago I started teaching a course on AI at a university not far from NYU where I studied... and that's what got me into this: https://vimeo.com/1063008765/c7ef3abcc5

I try to document everything on GitHub and popular articles, but only recently started publishing academic papers on arXiv and plan to actually start submitting them for real publications. While I build, I realized that I should start publishing any novel theoretical results that underpin my work.

I plan to publish actual code in a few weeks. To be fair, TurboQuant is also a purely theoretical paper. I just wanted to get this out and share.

> To be fair, TurboQuant is also a purely theoretical paper. I just wanted to get this out and share.

TurboQuant is not a purely theoretical paper. Section 4 "Experiments" (page 15) [0] has a bunch of figure based on actual GPU computations.

[0]: https://arxiv.org/abs/2504.19874

TurboQuant went through ICLR review, has multiple Google Research co-authors, open-source implementations, CUDA kernels, and LongBench benchmarks.

Contrast that with your paper: no experiments, no implementation, no empirical validation of any kind.

Did you try engaging with LLM researchers and get their feedback on your paper?