TIL

TIL is a specific operational variant of CL in which a model acquires a finite, pre-enumerable sequence of distinct tasks—each presented without access to prior task data—with task identity explicitly provided at inference time ^[199]. The Bounded Cumulative coordinate is the main definitional anchor: the finite, pre-specified task sequence means the total knowledge accumulation horizon can, in principle, be declared in advance, making TIL the only CL variant whose change trajectory is pre-specifiable in a technical plan. Its Modular, Partial scope reflects that the dominant practical implementations at the DL and FM tiers use parameter-isolated architectures—dedicated output heads, task-specific adapters, or masked parameter subsets—that physically separate each task's learned representation from prior ones, directly mitigating the forgetting problem through structural disentanglement rather than regularization. The DL, FM ceiling is imposed by architectural supersession: at the LLM tier, domain-adaptive pre-training and continual PEFT subsume TIL's goals under the broader CL framework, rendering dedicated TIL protocols redundant. Compared to DIL, TIL's inference-time task identifier is a system-level dependency that must be tracked in deployment documentation—if the task-identity signal is unavailable or corrupted, the deployed system's behavior is undefined, constituting a unique failure mode that requires explicit specification in the operational validation plan. TIL's bounded, pre-specifiable task sequence supports formal pre-deployment documentation of the complete capability accumulation trajectory; the modular scope permits per-task regression testing at each task boundary, isolating validation to the newly added task module without re-validating prior task components.