CMP.7:9 - Consequences

Learning becomes an explicit algorithmic contribution: a reader can say what is supplied as feedback, how it changes a candidate, what rule is obtained and how that rule is used. The construction can be delegated or changed without leaving an unexplained “learn” step between modeling and computation.

The method can also expose a limit that further optimization cannot remove. New information, a better family, another target or a different receiving decision may be needed. The result remains useful when it identifies that choice without demanding unnecessary evidence.