Learning from Real-World Feedback
When an outcome differs from a prediction, what should a model learn? We explore feedback that preserves the context of observations, assumptions and actions.
Closing the distance to reality
A model produces an expectation. The world produces an outcome. The gap between them is information about the model, the observations and the system itself.
Our learning-loop research connects observation, modeling, prediction, verification, learning and improvement.
Feedback with context
Not every mismatch has the same cause. Useful feedback should preserve context: what was known, what was assumed, which action was taken and how the outcome was measured.
Testing whether feedback helps
An update should be checked against new observations and previous behavior. We would examine whether feedback improves the intended capability without simply fitting the most recent outcome or erasing useful earlier structure.