[simianwords]

Very interesting. My prediction is that Mythos would outperform Sol.

Also what does this tell about Yann LeCuns whole world model theory? Bro has been going on and on about it. He has made multiple wrong predictions on the trajectory of LLMs.

At some point his claim should be fully falsified no?

[osti]

Mythos probably wouldn't, otherwise they'd have included it in their release. Next version of Mythos probably will though.

And yeah.. Reality has not been kind to LeCun.

[vatsachak]

Are you joking? They spend billions of dollars training LLMs to get a 7.8% on arc agi 3 whereas DINO models are near sota in image classification, provide meaningful embeddings to the point where image segmentation is just PCA. The spend on DINO cannot be more than five million (correct me if I'm wrong)

JEPA is just getting started

[Tenoke]

His main anti-LLM predictions have been consistently either wrong or misleading.

There's many ways to skin a cat so you can probably do something with a JEPA approach as well, but I doubt he actually catches up to having agents on the level of where Anthropic/OpenAI will be at any point.

[onlyrealcuzzo]

His main LLM predictions have almost nothing to do with Arc AGI...

What exactly was he dead wrong about that is proven by any of this?

GPT getting better has absolutely nothing to do with completely disproving anything LeCun has been saying.

He never said LLMs couldn't get better. He never said they couldn't score 7.6% on Arc AGI 3.

He's merely said they don't think, and you probably want something that actually thinks if you want a model that can be trained cheaply on a small amount of data and provide a ton of value.

Spending $5B to train a model that scores better than an older model does not disprove any of that in any way.

[Tenoke]

>He's merely said they don't think

He said years ago even 'GPT 5000' couldnt do things that they ended up doing fine a month later, let alone by 5000. His later predictions are just moving that goal post including towards them not being able to do more general, harder problems of which Arc AGI is a counter-example.

[onlyrealcuzzo]

> He said even 'GPT 5000' couldnt do things that they could do a month later, let alone by 5000.

What things specifically and when?

[cma]

> What exactly was he dead wrong about that is proven by any of this?

He said as you need more and more tokens models will fall apart because each additional token is a chance for a mistake and they will just exponentially fall apart. But in practice models have learned to identify and self-correct mistakes and if you look at the graphs more inference reasoning tokens almost always give far better accuracy.

[vatsachak]

That's true, but he's still correct, it's just that the context is now so large that only people using agent loops see "context rot"

His other criticism of LLMs that I like better is that they try to predict tokens instead of learned embeddings. Tokens are arbitrary and in order to decode LLMs you need technical analysis (see mechanistic interpretability).

With JEPA models so far, it seems that PCA on latent vectors suffices.

tldr: embeddings have a lot more room for improvement

[typon]

DinoV3 paper: https://arxiv.org/pdf/2508.10104#page=36

"we use a rough estimate of a total 9M GPU hours"

From CoreWeave, at current prices (~$2.46/hr spot to ~$6.16/hr on demand) would correspond to $22M–$55M.

The dataset is really where the cost is though - they used LVD-1689M - 1.6B images of curated web data from roughly 17B instagram images. This probably cost a huge amount of hours in human annotation, compute for algorithmic filtering, etc and not to mention probably a 20-50 person team working on this model.

You might want to change assumptions about how expensive these models are.

[vatsachak]

Thanks for the correction on the order of magnitude for the whole training process.

The 9M GPU hours includes the DINO v2 inference used in order to curate the data set.

The final training run used like 300000 dollars of compute.

Unfortunately we don't know how much RLVR + Agent training costs these companies. I'm just gonna say it's in the hundreds of millions, because they are supposedly making billions of profit on inference yet making billion dollar losses

[ainch]

Yann is a big SSL guy but I don't think he was involved in the original DINO - he's not listed as a co-author or anything.

[vatsachak]

DINO was created independent of JEPA but uses a similar principle of self supervised learning through minimizing the prediction error of a latent.

The difficulty in predicting a latent is so called "collapse"; the embedding neutral network can always output the zero vector and this would predict the output correctly.

There are different ways to solve this, DINO uses two different models - a teacher and a student and LeCunn uses an explicit term against collapsing to a single output.

Yann mentions DINO in his talks

[esafak]

ASI is going to be here by the time Lecun gets started.

[redactsureAI]

DINO is a transformer model?

[vatsachak]

JEPA can use a transformer, and DINO does so yes

[chrsw]

My main takeaway from LeCun's thesis isn't that you can't build LLMs to do useful things better than the best human, it's that these systems don't learn arbitrary skills efficiently, like humans do. And the question is, why not? 8% on ARC-AGI-3 is amazing for a machine considering how far we've come since digital computers were first built. But it is pretty poor if you're claiming something is well on its way to exhibiting human-like intelligence.

Mythos can do some amazing things (I'm assuming, I've never seen it). A young child can learn to control its body without reading any books on dynamical systems and kinematics. Mythos cannot learn to control a humanoid robot after sucking in every piece of data Anthropic can get their hands on.

[Maxatar]

Falsifying Yann Lecun isn't exactly a priority for anyone seriously working in this space.

[CyberDildonics]

What is this supposed to mean?

[reasonableklout]

Mythos doesn't appear to be on the verified leaderboard for ARC-AGI 3

[Muskwalker]

Officially, Fable/Mythos testing was delayed because of Anthropic's data retention policy. Don't know if there's word on them working that out yet.

https://x.com/arcprize/status/2064399134099153344

[raverbashing]

Notice how neither him, nor Ilya, nor Mira shipped anything relevant recently

It's telling

[CuriouslyC]

Not sure how Mira gets into the same sentence as Yann and Ilya.

As far as the lack of shipping, they're scientists and what we're doing now with LLMs is more "engineering."

[bevekspldnw]

“Bro” spent most of his career in the wilderness because everybody thought ML/NN/etc were a dead end.

I’d not wager against him having at one one more break though architecture before he retires.