Felix Nguyen @felixcantcode

alexirpan.com/2018/02/14/rl-… rl people are funny af bruv

@charles_irl why r u dropping bangers on company time?

one-slide rebuttal for explicit models of the world by @ShenlongWang in favor of “vision science” as coined by Bill Freeman earlier in the morning. also, this is the funniest thing I’ve seen today 😄

Can MLLMs actually track what's happening in a video? Introducing VSTAT 🎯, our new benchmark for visual state tracking. The tasks are simple: count cups, read typed words, count page flips. Humans solve them easily. MLLMs don't. vision-x-nyu.github.io/vstat-site/ 🧵 [1/11]

@gabriberton but but but if all modalities are trained jointly from scratch, would the total amount of compute be less than that of individually trained encoders?

@alesfav @DanKorchinski @MatthieuWyart rediscovering predictive coding from first principles

Would you like to join the research effort on JEPA and World Models easily? After a full year of hard work, we’re excited to finally release stable-worldmodel: an open-source, scalable platform built to accelerate JEPA & World Model research! 📄: github.com/galilai-group/…

What do JEPA-style self-distillation dynamics actually learn — and why do they sometimes avoid collapse? In our new work with @BasileTerv987 and Jean Ponce, we tackle this question. What surprised us: These dynamics provably recover representations with nonlinear-CCA structure.

Why does deep learning generalize? What does weight decay really do? Can algorithmic information theory address these questions? In my latest preprint, I give a proof that the minimum neural weight norm matches the minimum program length (aka Kolmogorov Complexity), up to a logarithmic factor. In other words, the neural network with the smallest possible weight norm (that fits the data) must encode the shortest program (that fits the data). The result only holds for fixed-precision neural nets: infinite precision nets can store infinite information with finite (small) weights. arxiv.org/abs/2605.10878

Compare to what you get with the exact same data/labels, but using the smooth outputs (Biternion+vonMises), it's night and day. Making the model output/loss naturally fit your problem-space is a game-changer.

In the last couple of months, we have witnessed significant advances in Industry-scale World Models. Yet, for the broader community, the gap between reading about these models and deploying them remains disappointingly wide. Today we're releasing Nano World Models: a minimalist, batteries-included repo for advancing world model science. 🧵 (1/9)

Camera pose matters for video understanding! Today's MLLMs excel at recognizing activities, but still struggle with the underlying space and ego/object dynamics in video. We trace this gap to a missing piece: camera pose. Introducing Cambrian-P: a multimodal LLM natively grounded in camera pose. (1/n)

Karpathy joins Anthropic (The office edition) 😂

Short blog on why I think SSL isn't scaling. tldr we're suppressing too much information from the SSL loss so that anything is learnt at all, but this limits the total set of things we may be able to learn. jchencxh.github.io/blog/scaling-l…

🧠We introduce "Generative Recursive Reasoning"! Recursive Reasoning Models like HRM, TRM, and Looped Transformers are deterministic — same input, same reasoning, every time. They collapse the entire space of plausible reasoning paths into a single attractor. Our model GRAM (Generative Recursive reAsoning Models) turns recursion itself into a stochastic latent trajectory. Multiple hypotheses, alternative solution strategies, and inference-time scaling not just by depth, but by width — parallel trajectory sampling. And here's the kicker: the same formulation that gives us conditional reasoning p(y|x) also makes GRAM a general generative model p(x). With only 10M params: • Sudoku-Extreme: 97.0% (TRM 87.4%) • ARC-AGI-1: 52.0% • ARC-AGI-2: 11.1% • N-Queens coverage: 90%+ 📄 Paper: arxiv.org/abs/2605.19376 🌐 Project page: ahn-ml.github.io/gram-website w/ Junyeob Baek @JunyeobB (KAIST), Mingyu Jo @pyross0000 (KAIST), Minsu Kim @minsuuukim (KAIST & Mila), Mengye Ren @mengyer (NYU), Yoshua Bengio @Yoshua_Bengio (Mila), Sungjin Ahn @SungjinAhn_ (KAIST)

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Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

i cannot believe tml knocked multimodal omni model off my wishlist that easily

Thinky's secret plan: 1: Increase Human<->AI bandwidth 2: Raise ceiling of human+AI intelligence 3: Help humans continue as main-characters in the new world We are at Step 1. Interaction Models are great real-time collaborative tools for humans. Here's a preview:

Thinking Machines @thinkymachines

4 weeks ago

People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. thinkingmachines.ai/blog/interacti…

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People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. thinkingmachines.ai/blog/interacti…

"AI code is crap." The shit your human engineers get up to: