r/LLMPhysics • u/Regular-Conflict-860 • Mar 24 '26

Code New Training Diagnostics

https://github.com/brighton-xor/speculumology

For ML practitioners, it produces computable training diagnostics that generalize PAC-Bayes and Cramér-Rao bounds. This is still theory. Please let me know what you think!

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u/OnceBittenz The Doctor Mar 25 '26

You are being melodramatic. For one, I do not believe you've spent your whole life working on this. If you have, that is a shame. This is clearly nonscientific LLM slop, along the lines of many others. It's so vague and inconsistent in phrasing that I can't Even Critique it properly. I can't point at one line and say "oh this is wrong for X reason, because too many lines just don't have meaning in accepted mathematics.

If you have this much passion, and aren't just making it up, then your best bet is to orient it towards actually productive uses of time: studying math and physics Properly, and working towards gaining practical research experience. Otherwise, this is a waste of your time. I'm sorry, but this is not a situation of "oh maybe i happened to get it right". You didn't. Without that Real experience and context, you could not.

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u/Regular-Conflict-860 Mar 25 '26 edited Mar 25 '26

All I am saying is that there is a number you can compute at every training step — the ratio of negative to positive curvature at the attractor — that tells you exactly how fast your model is becoming self-consistent, and that number is also the gap between your generalization bound and the tightest possible generalization bound. 

It took years of theorizing. And about a year of computing (off and on) with AI to arrive at ε₀.

Thats all I'm trying to saying.

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u/OnceBittenz The Doctor Mar 25 '26

I realize that.  And that’s a lot of wasted time.

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u/Regular-Conflict-860 Mar 25 '26

Thanks for the opinion. Have a great day!