CL

CL addresses the stability-plasticity trade-off ^{[108, 136]}: how to accumulate knowledge across sequential tasks or shifting distributions without overwriting previously learned representations. Its defining coordinate is D4 (Persistence): Bounded Cumulative, Unbounded Cumulative—a deliberate architectural commitment to knowledge accumulation rather than replacement—which is the main axis that separates CL from Retraining (, Ad-hoc Permanent, Scheduled Permanent) despite their identical D1 (Mechanism) and D3 (Data Requirements) profiles. The bounded variant corresponds to sequential acquisition over a finite, pre-enumerable task set (TIL, ); the unbounded variant corresponds to open-ended streaming adaptation over an indefinite sequence of distributions (DIL, ). This cumulative persistence coordinate is operationally consequential: the open-ended modification trajectory of Unbounded Cumulative fundamentally breaks the assumptions of discrete, periodic regression testing, because the model's behavioral state is never stable—it is continuously accumulating new representational commitments. At the LLM tier, CL encompasses three sequential stages—continued pre-training, domain-adaptive pre-training, and continual FT ^[178]—each carrying distinct D3 (Data Requirements) and D5 (Scope) profiles. The D5 (Scope) coordinate (Partial) reflects that production CL deployments at scale almost always apply constrained, parameter-isolated updates (adapters, selective layer freezing) rather than whole-model retraining, precisely to limit the forgetting surface. CL's cumulative persistence necessitates continuous behavioral drift monitoring rather than periodic snapshot regression testing; each task or distribution boundary must be logged as a discrete lifecycle event with its own targeted stability evaluation verifying that prior-task performance has not regressed beyond a predefined tolerance.