Proving and improving the Platonic Representation Hypothesis with JEPAs
https://arxiv.org/abs/2610.39302In some ways it seems like it should be profound, but part of me thinks it might just be saying 'There is an optimal way to perform dimensionality reduction and as things get bigger it is easier to appoximate'
Does it even make sense to claim there is a “platonic” / true representation? Maybe there is an optimal one for a specific task, eg representing a small map is great in the Cartesian frame. But you need a spheroid instead of a plane to accurately model the earth, so the latter is “more real” in some sense. But these seem like engineering tradeoffs of error vs compute and a given implementation could correctly pick either depending on its constraints.
Edit: reading the paper more I think this is a “RTFA” point, their “PRH” is a very explicitly defined mathematical theorem and as such my hand-wavey thoughts above don’t really apply.
Here it seems that their definition is more robust (the representation is defined up to a rotation), but I am wondering how much more re-training they need to perform.
Im bullish on jepa models. Success of autoregressive next token prediction feels like a stroke of luck.
(Very basic intro: https://www.quantamagazine.org/what-are-sheaves-20240719/)
Part of the magic of next token prediction is it works to extract intelligence from the unstructured web corpus.
Can JEPA formulate a similar scale training set of “good views”?
One central observation is that we are well into the realms of no single human can coherently reason a nontrivial amount of knowledge our our collective. By that I meant it used to be that a single human being can appear "knowing all" by possessing a significant amount of knowledge through life time learning. That's no longer possible even with the smartest minds of our generation. One such example would be the related financial failure of renowned genius (Newtown Tesla etc).
Due to our innate limitations of mutual exclusion, that is, humans are inherently conflicting with each other, not even the basic instinct of humans can bound 2 together for innovative collaboration. I mean, I never saw couples being better business partners, research partners, etc. AI becomes inevitable, as they are inherently mixable.
Yes, I am conjecturing that the next weave of AI research would be neuronal level mixing or hybridinh between models to enable a new bread of intelligence where the intelligence is mechanically compounded to produce inherently better results.
You wont get anything materially useful, apart from an intuition, from using different terminology.
1. English specification of the system and its goals
2. Formal, mathematical specification of the system and its goals
3. Mathematical proof the system met its goals
4. Code implementing that system with a proof, often informal, that the code embodied the informal and formal design.
5. Tests of all functionality to prove the design and code worked.
6. Independent analysis of the above to find flaws in them.
7. If the implementation has concurrency or parallelism, more errors can occur due to that. There were techniques to mitigate it.
Each step found problems in systems made by professionals. Mathematical work and coding both have a long history of errors in their on fields. Combining them increases the likelihood of error.
https://huggingface.co/spaces/galilai-group/prh_demo
> A man in a black shirt and hat with a yellow neckerchief has his hand raised.
no hand raised
> A white sheep sitting on top of a cake.
no its just cake sheep . sheep is not sitting
> A man is standing on the beach while holding his surfboard.
Its not just a man
infact all the examples are wrong.