A Survey on AI for AI: When the Improver Becomes the Improvee

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

AI for AI concerns processes that use AI to improve AI systems and their development methods. This Review examines how AI contributes to these improvement processes, including model learning and harness engineering. We focus on cases where the improver itself changes. We compare updates to experience generators, evaluation mechanisms and programs that produce improvements. A system may combine these paths. Their effects depend on the feedback used, the experience kept for later rounds and the external checks that remain fixed. We examine their benefits, failure modes and costs. We distinguish changes to an improver from gains in its ability to improve AI in later rounds. We then ask when improvement becomes recursive, how gains scale with resources, how long progress can continue and whether improvers generalize across AI systems.