When Should an In-Context Learner Expand Its Hypothesis Space?
The research models how AI learners decide whether to expand their set of models when faced with prediction failures. It introduces a Structural Revision Environment (SRE) showing that expansion is a costly decision based on long-term expected utility, not just finding immediate errors. The key finding is that revision and expansion are separated by a value boundary determined by cost and future gain.