DIL

DIL is the open-ended (Unbounded Cumulative) variant of CL: the model encounters a potentially indefinite sequence of domains or class distributions without access to prior training data and—critically—without a task identifier at inference time ^[199]. This absence of inference-time task identity is the defining coordinate divergence from TIL. The Partial, Whole-Model scope reflects that some DIL approaches retrain the entire network while others selectively update domain-adaptive components. The compound D2 (Goal) profile—Continual Adaptation, Drift Remediation—captures DIL's dual operational goal: it simultaneously accumulates new distributional knowledge (Continual Adaptation) and preserves performance on prior distributions that may re-emerge (Drift Remediation). The DL, FM ceiling is imposed by the same supersession mechanism as TIL: at the LLM tier, continued pre-training and domain-adaptive PEFT subsume DIL's goals. DIL's unbounded modification trajectory is the most operationally demanding persistence profile in the taxonomy: the system's behavioral state cannot be fully characterized at any fixed checkpoint, necessitating continuous runtime monitoring with per-distribution performance tracking and explicit drift-impact assessments at each domain boundary transition.