Eduardo Alvarado

Body models capture skin, not what drives it. Muscles stay invisible - recoverable only via MRI/CT or costly simulation, neither of which scales to full-body motion. Can muscle deformation be recovered directly from vision/how skin moves? We present SOMA at #ECCV2026! 🧵

We found a way to scale humanoid control that reduces GPU-hours by more than 1000×. Across 129,514 motions, SuperMimic surpasses the state of the art (128 GPUs, 7 days) with 1 GPU in 3 hours. Project: supermimic.github.io Demo: supermimic.github.io/demo/

Here's an in-depth look at the new @Tesla Roadster's UI. Love the retro-futuristic design aesthetic.

Real life, baby

Touch is essential to human manipulation. Yet, we have so far not had a tactile sensor that can do what human skin can: sense texture, firmness, vibration at a viable form factor. At Rhoda, we have built a new sensor in-house that can do all of these things, while being much much smaller than commercially available ones!

Seamless 🤩 #F1 #SingaporeGP

Introducing Code Video Model 🎬 Amazing work led by @raywzy1 and the team! Agentic coding as the engine for controllable video generation. Scene layout, motion, timing, camera trajectories, and physical dynamics, all in editable code. A coding agent turns prompts into executable programs that render proxy videos. Our training-free early-step conditioning lets a pretrained video model follow the proxy’s structure while adopting a reference image’s appearance. From physics-grounded motion and bullet time to 3D/4D reconstruction and robotics simulation, it all starts with code. Watch the demo and explore more visual results 👇 Project page: code-video-model.github.io/ Paper: code-video-model.github.io/t… Code: github.com/code-video-model/… Data: huggingface.co/datasets/code… Youtube: youtube.com/watch?v=dTtiVkSR…

After six years in stealth, Atomic Machines is out. Our mission: on-demand, universal command of matter. First beachhead: the Matter Compiler, an AI-native manufacturing system that builds working micro-machines from code alone. No per-product tooling. No process development. Different code, different machine.

I can also recommend having kids! Nobody is lonely in the presence of a four year-old who just wants to talk, play, and learn with you.

I'm pretty sure I'm telling on myself in several different ways here

Introducing PC-ALM, a local-learning alternative to backpropagation. Our method trains 1000-layer neural nets using only local dynamics, and without backprop. Blog: pub.sakana.ai/pc-alm/ Standard deep learning relies on backpropagation. The brain, however, cannot implement backpropagation, at least not exactly. How can a physical system, such as the brain, solve multilayer credit assignment without explicit use of backprop? We look for inspiration in two related fields: distributed optimization and NeuroAI. In NeuroAI, predictive coding asks each neuron activation to solve an energy-based inference problem instead of using a standard forward pass. That inference step can be implemented as energy-minimization dynamics on local prediction errors. This perspective -- each layer as a dynamical system -- has proven promising, but performance of predictive coding hasn't scaled well with depth. Credit signals at far ends of the network struggle to diffuse into internal layers. We turn to distributed optimization, generalizing predictive coding to use an augmented Lagrangian instead of energy. This motivation stems back to a classic 1988 paper by LeCun, showing that the Lagrange multipliers of a deep network can be identified with gradients of a supervised loss. The augmented Lagrangian then bridges LeCun's perspective to the standard predictive coding that is used in NeuroAI. We find that this new perspective yields a natural PC-like alternative to backpropagation, resulting in a method we call PC-ALM. PC-ALM differs from PC in that it introduces dual neurons (Lagrange multipliers) as part of the layer-local dynamics, resulting in each layer acting as a PI feedback control system to minimize local prediction errors. We find that PC-ALM is capable of propagating signals to seemingly arbitrary depth, especially in deep narrow networks where standard PC struggles to learn. Ultimately, our motivation here is to understand how distributed physical systems, such as the brain, can compute credit signals using only local coupling and local dynamics. PC-ALM may also inform deep learning in neuromorphic hardware, where dynamics are cheaper than on GPUs. Paper: arxiv.org/abs/2605.31022 Code: github.com/SakanaAI/pc-alm