Current AI systems are evaluated as reasoners through final-answer accuracy, reasoning traces, process supervision, and computational or human-relative efficiency. Yet these measures do not establish whether reasoning adapts when tasks or evidence change. We distinguish across-task adaptivity, where acquired abstractions transfer or recombine across novel problems, fromwithin-task adaptivity, where hypotheses and actions are revised in response to new observations, feedback, or counterexamples. Using ARC-AGI as a diagnostic case study rather than an alignment benchmark, we examine what existing measures leave unresolved when test-time search, sampling, or adaptation affects performance. We propose intervention-oriented evaluation that tests abstraction transfer and hypothesis revision under controlled changes to tasks and evidence, with explicit reporting of test-time procedures and budgets. This perspective supports evaluating systems for robustness, responsiveness to corrective feedback, and auditability under changing conditions.

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