A Mechanism-Driven Theory of Phase Transitions in Active Learning

Active learning (AL) performance is known to be budget-dependent, yet regimes are typically defined by heuristic label countsthat fail to generalize across datasets or architectures. We character-ize AL dynamics by reframing budget regimes as shifts in the domi-nant generalization mechanism. By reinterpreting PAC-style risk com-ponents as dynamic interacting terms, we prove that dominance shiftsare structurally unavoidable, creating a moving bottleneck for gener-alization. We operationalize this using measurable proxies and a seg-mented regression procedure to identify a tripartite taxonomy: data-driven, transition, and model-driven phases. Our framework explains thelong-standing observation that representativeness, coverage, and uncer-tainty strategies excel at different stages. Experiments across natural andmedical imaging show that AL efficiency depends on the alignment be-tween the strategy’s inductive bias and the active bottleneck. Moreover,self-supervised representation shift transitions earlier along the labelingtrajectory, highlighting the role of representation quality in shaping ALdynamics. Overall, this work provides a unified framework for the nextgeneration of transition-aware AL algorithms. The code is available at:https://github.com/juliamachnio/PALM.