Comment by simianwords
2 days ago
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?
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.
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
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.
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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.
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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.
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ASI is going to be here by the time Lecun gets started.
DINO is a transformer model?
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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.
Falsifying Yann Lecun isn't exactly a priority for anyone seriously working in this space.
What is this supposed to mean?
Mythos doesn't appear to be on the verified leaderboard for ARC-AGI 3
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
“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.
Notice how neither him, nor Ilya, nor Mira shipped anything relevant recently
It's telling
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."
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