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Proving and improving the Platonic Representation Hypothesis with JEPAs

https://arxiv.org/abs/2610.39302
I'm not sure how I feel about the Platonic Representation Hypothesis.

In 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'

Haven’t thought too deeply about this, but as a physicist we’d do things like switch from Euclidean to Spherical coordinates or a completely different dynamical representation like the Langrangian, and this makes certain problems dissolve (or at least become much simpler to solve analytically).

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.

Good point, I think platonism is wrong, it puts concept before execution, and often removes the lineage behind an idea and sees it as standing on its own. We like to imagine tings without their costs, but cost pressures are what make those things what they are, such as what model works better for a problem.
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I haven't read this paper in details, but I have read papers about the platonic hypothesis in the past. Usually one of their weakness is about the design an experiment proving that "neural networks converges towards the same latent space when scaling". Usually authors are effectively proving a subset of the hypothesis, that can be explained by the size of the model and the transfer learning part, leaving not definite answer.

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.

> 33M-parameter text JEPA reaches .125 on the PRH paper’s benchmark, against .034 on Flickr captions, level with Qwen2.5-3B and -7B. They need fewer samples.

Im bullish on jepa models. Success of autoregressive next token prediction feels like a stroke of luck.

Different mathematical framework, but I wonder if this could be better understood using sheaf theory: each modality captures part of the same underlying reality, and those different views can be glued into a consistent bigger picture

(Very basic intro: https://www.quantamagazine.org/what-are-sheaves-20240719/)

I guess the fundamental question would be, for what domains can we pretrain with “good views”?

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”?

All strands of scientific studies eventually are bound by "physical" reality. This assumption means all these research would gradually become more and more relevant, like math problems are highly relevant to physics, computer science, psychology is similar to experimental physics (observation and conjecture theorizing eventually crystalize into analytical theorems). The list goes on and on.

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.

I don't get this intuitively. So in the end, does this mean words are a kind of conceptualization of images? (Since images would come before language.) More than that, I'm curious whether this can improve the performance of vision-language models.
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They should do franken-MoE with JEPA head and LLM head.
They gave a blind man sight for the first time and he saw the world.
Do memes have a concept of proto memes? Like primes are the proto numbers that all other numbers are made from? Could there be similar ontological primes for all concepts / semantic space? Base reality concepts that are irreducible so to speak.
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I am sick of theory papers that adds human interpretation to connect a mathematical result to some fancy terms that everyone seeks after in real-world ML applications. Ironically, chatGPT is the most productive way to strip off those advertisement so the essence is better exposed. https://chatgpt.com/share/6ae78178-c2ec-83ea-9bc3-fd304c64ec...
I share your exasperation, it is especially tiring to see that what would be more appropriately called a quantitative approximation of a concept is here called a Platonic Representation by virtue of a vague and misunderstood analogy with Platonic Ideas which were according to the Platonic doctrine, formless and part of his distinction between what is subject to change (to heteron) et what is not affected by change (to auto) [Timeaus, Platon].
The results are in the math. Does not really matter the terms used.

You wont get anything materially useful, apart from an intuition, from using different terminology.

you get materially better marketing at the cost of accurately describing the math results in the title and abstract.
Mass media doesn’t read math.
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The math can be misinterpreted, misapplied, or not fit reality. That's why high-assurance security mandated a combination of things:

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.

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how come all the examples in demo have wrong answers

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.

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I thought the platonic representation hypothesis was much more ambitious: wasn't it supposed to apply for any model across modalities (e.g. including human minds), rather than just the same model but with different modalities? I'll have to actually read the paper but I suspect there to be very strong assumptions which defeat the interesting aspects of the platonic representation hypothesis.
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I built a ppt with claude for investors with the exact same phrase "AI infrastructure for proving and improving ... " on Oct 27 and sent it to investors. I suggested the title during the chat and claude agreed a lot. It ends up as a title of this paper 2 days later.
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