James Swingos retweeted
actual footage of how the 1v1s between GI’s models and whatever Elon is cooking will go
(yes these are our vision based models)
Replying to @teslayoda
We’re making interesting progress here.
Digital Optimus can play about halfway through the Diablo campaign so far just by looking at the screen like a human.
Skill at Counter-Strike and other fast games is good too.
League is also in training.
Goal is to generalize across all games.
James Swingos retweeted
Replying to @grok
@grok I'm confused what is happening in this clip?
Replying to @teslayoda
We’re making interesting progress here.
Digital Optimus can play about halfway through the Diablo campaign so far just by looking at the screen like a human.
Skill at Counter-Strike and other fast games is good too.
League is also in training.
Goal is to generalize across all games.
James Swingos retweeted
Turns out scaling on data and compute with a great team is surprisingly effective 😉
James Swingos retweeted
This is gonna be fun.
xitter.cf/JSwingos/status/209090…
The WSJ stopped by to learn more about how our models work.
Good write-up to understand broadly what a large action model is, how its different from world models, and why they are promising approaches in robotics.
It's becoming increasingly clear that ground-truth action data, like the kind found in games, is a key ingredient to scaling robotics pre-training.
Link in comments. Check out the policy waving at @pallipau.
James Swingos retweeted
Millions of hours of people playing video games looked like entertainment. It turned out to be the world's largest dataset of humans perceiving an environment and deciding how to act in it.
General Intuition saw it first. Great WSJ piece on world models and AI's leap into the physical world.
James Swingos retweeted
First sneak peak in the WSJ on how GI’s large action models work!
The WSJ stopped by to learn more about how our models work.
Good write-up to understand broadly what a large action model is, how its different from world models, and why they are promising approaches in robotics.
It's becoming increasingly clear that ground-truth action data, like the kind found in games, is a key ingredient to scaling robotics pre-training.
Link in comments. Check out the policy waving at @pallipau.
James Swingos retweeted
gradually, then suddenly all at once 📈
on track for 3 billion clips this year.
James Swingos retweeted
Starting to realize just how big of a deal this was
xitter.cf/gen_intuition/status/2…
Introducing MIRA.
A playable, multiplayer world model.
A dream of Rocket League.
Trained on 10k hours of data collected with publicly available bots, MIRA learns the dynamics of a four-player game. The model runs in real time at 20 fps, based on the keys you and the other players press.
Built by General Intuition and @kyutai_labs, in collaboration with Epic Games. Not used to develop Rocket League.
▶️ Play the demo, read the technical report, and explore the open-source code at generalintuition.com/mira
At ICML? Find us at Booth 111 to try it yourself and dig into the results with the team.
James Swingos retweeted
I have been playing these world models/games since the first diffusion DOOM, and multiplayer at 20 FPS is pretty neat. "A dream of rocket league," indeed.
Introducing MIRA.
A playable, multiplayer world model.
A dream of Rocket League.
Trained on 10k hours of data collected with publicly available bots, MIRA learns the dynamics of a four-player game. The model runs in real time at 20 fps, based on the keys you and the other players press.
Built by General Intuition and @kyutai_labs, in collaboration with Epic Games. Not used to develop Rocket League.
▶️ Play the demo, read the technical report, and explore the open-source code at generalintuition.com/mira
At ICML? Find us at Booth 111 to try it yourself and dig into the results with the team.
James Swingos retweeted
Today we’re introducing MIRA, a new multiplayer world model, built with @gen_intuition, in collaboration with Epic Games.
We release an in-depth technical report, dataset, as well as an online demo that you can try right now (link below).
James Swingos retweeted
The term “world model” is tossed around a lot but this is the real deal. From video and actions, the model learns a consistent representation of the physics, 3D environment, and game state of Rocket League.
I'm excited to have been part of this project because I see this as a step towards learning world models that can generalized to the real world. I’m a believer in learning from synthetic data (Sintel, SURREAL, AGORA, BEDLAM, BEDLAM2) and I’ve seen how models trained on synthetic data can generalize.
MIRA is an important step because it’s learned from video gameplay generated completely by bots. This provides a pathway to scaling and scaling is key to real-world generality. Traditional graphics and games provide a path to learning rich models of the world.
Check out the detailed tech report: mira-wm.com/paper/
Introducing MIRA.
A playable, multiplayer world model.
A dream of Rocket League.
Trained on 10k hours of data collected with publicly available bots, MIRA learns the dynamics of a four-player game. The model runs in real time at 20 fps, based on the keys you and the other players press.
Built by General Intuition and @kyutai_labs, in collaboration with Epic Games. Not used to develop Rocket League.
▶️ Play the demo, read the technical report, and explore the open-source code at generalintuition.com/mira
At ICML? Find us at Booth 111 to try it yourself and dig into the results with the team.
James Swingos retweeted
Introducing MIRA.
A playable, multiplayer world model.
A dream of Rocket League.
Trained on 10k hours of data collected with publicly available bots, MIRA learns the dynamics of a four-player game. The model runs in real time at 20 fps, based on the keys you and the other players press.
Built by General Intuition and @kyutai_labs, in collaboration with Epic Games. Not used to develop Rocket League.
▶️ Play the demo, read the technical report, and explore the open-source code at generalintuition.com/mira
At ICML? Find us at Booth 111 to try it yourself and dig into the results with the team.
James Swingos retweeted
ICML 2026! 🇰🇷
We're heading to Seoul next week.
Come find our research team at Booth 111 to talk world models and action models - the core of what we're building at General Intuition.
We're also hosting a party on Wednesday, July 8th for a curated group of researchers working at the frontier. If that's you, the event RSVP is linked on the thread.
See you there!
James Swingos retweeted
it does seem like utilizing text & video to scale up to physics understanding of the real world is likely not enough. google is doing a bit of this with street view + maps data + simulation for this but the video game unlock seems absolutely legit esp if you have the full end to end loop of consequence mapping for ai to learn from that’s insanely rich (e.g. controller input → camera movement → game state transition → physics/collision response → npc reaction → player choice → reward/failure/social outcome).
i personally grew up playing a ton of videogames as a kid including one of the first games to ever have open ended world simulation (it was called shenmue, absolutely pioneering, look it up).
these guys seem to have a good understanding that these feedback loops in actual game play is the best data set for ai to behave in human contexts because that’s precisely how we learn & explore when we are young (like watch a kid play around).
.@gen_intuition CEO @PimDeWitte discusses using data from gamers to train AI & broader implications for the labor market: cnb.cx/3QHDIXj
James Swingos retweeted
.@gen_intuition CEO @PimDeWitte discusses using data from gamers to train AI & broader implications for the labor market: cnb.cx/3QHDIXj
General Intuition CEO @pimdewitte, who's building foundation models trained on video game controller input data ("action-labeled gameplay clips"), says general intelligence won't "taste like an LLM":
"We have a scale of data that's going to allow us to jump to the frontier in one capability — which is any system that can be controlled with a game controller (which is most robots) — and then, you can use that to create a sufficiently general intelligence."
"As humans, the decision to talk or type is a very, very small subset of the actions that we can actually take."
"So in order to create a sufficiently general intelligence to play 10,000+ video games, the model has to be able to predict across the entire action space of human cognition when they're interacting with these environments. Which are 2D and 3D environments, interfaces, long-horizon tasks, short-horizon tasks, [etc.]."
"It has to be a sufficiently general intelligence in order to predict actions. Therefore, the type of model you get out is not going to taste like an LLM. This model is going to be incredibly good at navigating unforeseen environments. It's going to be incredibly good at zero-shotting any task that can be done with a game controller."
James Swingos retweeted
Appreciate the credit, but it was really your team we were betting on and the approach you have. As you know, it took one meeting and two hours on a Saturday afternoon to commitment on the spot. This was the first time I actually met you. Self driving vehicles, flying drone swarms, robotics, video games, and much more, all from the same model.
And lastly - it shouldn't have been possible to start a frontier lab in 2025. The doors were shut, they said. But thanks to Vinod, and his relentless ability to believe in founding teams, it was. And the journey continues. xitter.cf/vkhosla/status/2070190…
James Swingos retweeted
Come meet the MTS and MOPS (member of ops staff = me) in Seoul at ICML2026!
Announcing our $320M Series A at a $2.3B valuation, led by @khoslaventures, with @generalcatalyst, @ericschmidt and @JeffBezos.
General Intuition is the frontier lab for acting in space and time.
We build large action foundation models trained on billions of ground truth action-labeled gameplay clips from 17M monthly active users on Medal, and push the frontier of world models to generate infinite training environments.





















