Intelligence in the physical world will not come from larger models and more teleoperation data.
The bottleneck for general-purpose robots is not parameter count and not data volume. It is the shape of learning. Today's models stop learning the moment they ship, and a system that cannot change itself after deployment is only replaying a distribution it has already seen — however large it is.
Memory is our name, and our only technical question.
Infimory exists to do one thing: let machines keep learning in the real world. That is the name of the company and the whole of its roadmap.
What we believe.
The route that wins real-world deployment is a world model with continual learning, not a VLA that regresses actions out of a language model.
Long-term memory is not a retrieval store bolted onto a model. It is updates inside the parameters, on several timescales at once.
Learning should not stop when training does. Test-time learning is a form of deployment, not a trick.
Every one of these can be proven wrong. We publish them so that the people who agree, and the people who disagree, can both find us.
What we do not do.
We do not build robot bodies. There are already enough hardware players, and the endgame is decided on intelligence.
We do not build data-collection, deployment or evaluation platforms. Good businesses; not our question.
We give up both so that everyone here is pressed against the same problem.
How we work.
The team is small. Each person owns a whole piece. There is no layer in between, and research is not separated from engineering.
We care how far you have gone into one problem, not how many directions you have passed through.
We build our own learning infrastructure, and keep it thin.
We are hiring.
Researchers in continual learning and world models.
Algorithm engineers in embedded systems and motion control.
And one person willing to carry the entire business side at the earliest stage.
If you have no matching title but have thought one of these problems through, write directly: ceo@infimory.ai.
Hangzhou, September 2026