A system should not only produce cognition. It should be able to learn from the way cognition unfolds.
Most learning systems compress experience into outcomes: a correct label, a reward, or a preferred response. Yet the path toward an outcome contains information that the outcome alone cannot preserve—where uncertainty appeared, which hypothesis failed, what evidence changed the direction, and which abstraction became reusable.
Learning from Cognition treats the cognitive process itself as experience.
Reflection, prediction error, memory, and test-time evidence can become persistent capability rather than another stored transcript. A system can identify a useful cognitive pattern, separate it from the incidental details of one task, and carry the abstraction into new people, problems, and worlds.
This changes learning from passive accumulation into active revision. The model does not merely remember what happened. It examines why its understanding changed, what boundary the experience revealed, and how that discovery should alter future cognition.