Real robot data is expensive. Real robot evaluations are slow.
Excited to share SimFoundry - a system that turns real scenes into sim-ready worlds for training and benchmarking robots at scale -
✅Automated Scene Reconstruction with asset generation
✅Handles clutter, articulated objects, multiple robot embodiments
✅High Correlation Real-to-Sim Evals
✅Zero-shot Sim-to-Real
✅Generates diverse digital cousins
Less manual environment authoring, more scalable feedback for robot learning.
🌐https://t.co/jmCno7eslU
🧵1/9
An open-source robot vacuum you build yourself — Raspberry Pi, ROS 2, 2D LiDAR, Home Assistant, 3D printed chassis. No cloud, fully local.
oomwoo is early stage and building in public. The community can contribute modules in parallel — from SLAM navigation to dust bin design.
https://t.co/ip0HWptZg0
#ROS2 #RaspberryPi
14 live IP MCP servers you can connect into your AI workflows, covering novelty search, global patent FTO, design patent infringement search, legal status, landscape analytics, and advanced patent search.
AI can help us *do* useful things in medicine – great!
Below - one such thing, from our @nature paper:
- AI can flag people at high risk of dropping dead
- and help us decide who gets implanted defibrillators
But what can we *learn* from AI?
https://t.co/InNKVcSEtc
Title: Reasoning as Attractor Dynamics: Latent Memory Retrieval via Gibbs-Weighted Energy Minimization
Author: Kanishk Awadhiya
#DeepManifoldInterpretation
** The central limitation of the Hopfield formulation is not its definition of equilibrium. Equilibrium as an unchanged state is mathematically standard. Its foundational hypothesis is that memory and neural computation should be represented by convergence under a fixed operator toward static, stable states. This may describe restricted associative retrieval, but it should not be generalized into a theory of dynamic neural inference or reasoning. **
From the Deep Manifold view, the relevant object is not necessarily one static equilibrium. It may instead be a prompt-conditioned, dynamically generated fixed-point class reached through multiple stochastic iterated-integral pathways. The operator, boundary and local geometry co-evolve, so “the neural state no longer changes” is too restrictive—and perhaps the wrong definition of successful inference.
For the paper we are discussing, the problem becomes even greater: it borrows Hopfield’s equilibrium interpretation without even possessing Hopfield’s recurrent state dynamics. It has neither a fixed neural-state operator nor demonstrated convergence. The supposed “attractor” is created by externally weighting completed trajectories.
As a 17 yo, I genuinely didn't understand how one of my classmate, a Will Hunting-type mathematical genius, could outsmart all of us without putting any effort, while sleeping and snoring throughout the lectures.
He went on to win an IMO gold medal.
This felt like magic and triggered my interest in mathematical cognition. I was quite good myself, but very far from him. I persevered, discovered effective techniques, made some progress, did a reasonably good PhD and went on to become a tier-2 mathematician.
Then, in my early 30s, my self-confidence increased a lot, I found ways to increase my focus and intuition, which triggered a success feedback loop where I became capable of working much harder, for much longer periods of time, with much better techniques.
I ended making stellar progress and proving first-rate results between 32 and 34 before I quit math.
My interest for mathematical cognition continued, which explains my book, Substack, and public engagement here.
There are only a few ways to image the brain without surgery: electricity, magnets, light, sound. All existing methods either produce blurry images or require a room-sized machine.
Our image is captured with ultrasound transmitted through the skull, with a contrast agent.
We recently obtained the highest-resolution 3D images of the human brain ever taken from outside the skull. This is the first look.
Introducing Aleph, a research lab building brain interfaces for the telepathic future. (1/n)
PhD apps in 2026 are wild: Every successful applicant to our new lab @tri_fair_lab has multiple 1st-author papers, incl. several orals at NeurIPS, etc.
Feel deeply for students who'd have earned a spot 5–10 years ago — same talent & drive, just a different moment in time.
Neural networks for dynamical systems map high-dimensional fields to low-dimensional manifold latent spaces, where dynamics evolve before the decoder reconstructs the predicted field.
The encoder, consisting of convolutional layers and multilayer perceptrons, compresses the initial field into latent coordinates w and z on the manifold.
The upper part visualizes the encode-project-decode process with example point cloud manifolds.
It is used to accelerate long-term predictions of complex fields in scientific computing applications like fluid dynamics simulations.
Can we release all the weights of an LLM but still provide differential access to privileged users?
Yes! We introduce: 𝗧𝗶𝗲𝗿𝗲𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗧𝗟𝗠𝘀). Define access tiers corresponding to different computation graphs over the same set of LLM parameters!
There are three reasons why it's called "Functional Attention."
1⃣We make functions, instead of tokens, as first-class citizens for attention module.
2⃣It was inspired by the geometry processing concepts of functional maps.
3⃣It works!
“Replacing Labeled Real-image Datasets with Auto-generated Contours”
A year after our "without natural images" paper, we showed that Vision Transformers pre-trained on formula-generated images can match or even exceed the recognition performance achieved with large-scale real-image datasets such as ImageNet-21k.