We turn foundational research into products teams can deploy. We work on the parts of AI that make models efficient, controllable and always up to date.
Today's models stop learning the moment training ends. Every new fact, product or correction means another training run, and true deletion is close to impossible.
We think that's a research problem, not a fact of life. Our work focuses on methods that are exact, reversible and cheap enough to run every time something changes.
The APM (Adaptive Plasticity Module) is the first of these. More are in research.
Exact learning and unlearning for model layers, in milliseconds.
Exact, reversible updates deeper inside large models.
Closed-form methods that replace training loops where they aren't needed.
If a model says it forgot something, it should have. We prefer methods we can prove.
Every claim comes from a benchmark we can share, run on the same hardware as the alternative.
We build layers that sit on top of the models you already use, not replacements for them.