This fully autonomous demo shows a Bagel Labs foundation model controlling a robot to make coffee. We previously released WorldDiT, our lightweight architecture for robot foundation models. At the time of release, it was on the Pareto frontier for model size and performance. We have since been refining this architecture to handle longer horizon, more useful tasks fully autonomously. Making coffee means getting a whole sequence of actions right. Each step depends on the result of the last, so small mistakes can build up as the task continues. Getting this to work with a small model is especially challenging. This demo is an example of what the model can do today. If you are deploying robots or have existing hardware you want to make autonomous, we would like to partner with you. bagel.com/partner

Toronto Robotics night was back for ROSCon. And it was a blast!

The first toronto robotics night was a grand success!

Hardware is a big constraint in robotics, that's why we don't think the answer will be increasingly larger VLM models as action backbones. We tried to solve that issue with WorldDiT, which is at the pareto frontier for size-performance. Now I want to see how far we can scale it without losing this tradeoff. Here's a simulation demo.

We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute budget, and Paris 2.0 outperformed the monolithic by ~2x on FVD benchmark.

Excited to share that Bagel Labs' paper got accepted at CVPR 2026. A lot of the most important diffusion model research has historically stayed inside frontier labs. We're bringing more of that in the open through open science and open infrastructure. In this work we showcase the very counterintuitive advantage of mixing different training objectives (DDPM and Flow-Matching) through an ensemble of diffusion models. This is one of the first ever works to successfully combine diffusion models trained with heterogeneous objectives. See details here: blog.bagel.com/p/heterogeneo…

Being at the frontier - by the definition of it - means creating the frontier. You don't get to be at the frontier by following someone else. And creating the frontier often means discoveries that go against the established knowledge. We recently made such a discovery about distributed diffusion model training. A common way to optimize diffusion model training is by ensuring the numerical stability of their generation paths. We found that that's not true for the most efficient distributed diffusion model training architecture. We shared what works instead in our blogpost below. blog.bagel.com/p/stability-q…

NeurIPS takeways (better late than never) 1. real AGI needs real continual learning - models that can keep learning without catastrophic forgetting. 2. model architectures need to be "stateful" for building accurate world models for games and robotics. 3. diffusion models are superior for solving both 1 & 2. 4. @bageldotcom's distributed diffusion training architecture is SOTA among both open and closed source frontier lab comparables. 5. the age of research is back, and no better place to do frontier diffusion model research than Bagel Labs. join us - jobs.bagel.com

I’m going to give a talk at NeurIPS on decentralized diffusion models later today, come by if you’re around!

see you at @NeurIPSConf