SSL / CPT

SSL/CPT occupies a structurally dual role in the taxonomy (marked dagger in Table ): foundational pre-training from scratch is explicitly out of scope, but applying SSL as an adaptation technique to an already-deployed architecture is within scope. The critical coordinate that distinguishes SSL-as-adaptation from all other pipeline-mediated data strategies is Parametric Update ^[77]: unlike Data Augmentation or Semi-SL, which modify the data pipeline and delegate parameter updates to a downstream method, CPT directly defines its own optimization objective and formally executes GD on model weights. The Unlabeled coordinate is shared with Semi-SL, but the mechanism is structurally distinct: Semi-SL uses unlabeled data to construct pseudo-labeled training examples for a downstream training method; CPT uses unlabeled data as the direct training signal for its own self-supervised objective, making it a non-supervised parametric adaptation technique in the taxonomy. The D4 (Persistence) coordinate bifurcates based on execution mode: Scheduled Permanent when CPT is applied as a discrete offline batch on a fixed domain corpus; Unbounded Cumulative when applied as continuous domain-specific streaming—the latter creating an open-ended modification trajectory that breaks the assumptions of static regression testing and demands dynamic behavioral monitoring. At the LLM and FM tiers, the primary adaptation strategy leveraging SSL objectives is CPT: applying self-supervised objectives (e.g., next-token prediction) to unlabeled, domain-specific corpora to internalize novel vocabulary, syntax, and factual structures without requiring supervised labels. CPT functions as an initialization phase preceding SFT or PEFT, providing a domain-enriched representational baseline that ensures the model possesses foundational domain competency before task-specific supervision is applied ^[77].

SSL/CPT. Has a dual role: pre-training paradigm (out of scope) and adaptation technique (in scope); unlike other data-manipulation techniques, SSL-as-adaptation prescribes its own training objective and receives Scheduled Permanent, Unbounded Cumulative. Machine Unlearning vs.\ Knowledge Editing ( vs. ). share D1 and D4 but differ on D2: unlearning targets Remove Knowledge while editing targets Knowledge Update. The boundary blurs when correction effectively requires erasing the old association. Activation Steering vs.\ TTA ( vs. ). Both produce Transient adaptations but differ on model type (LLM, MLLM vs.\ DL, FM), suggesting transient adaptation bifurcates at the FM/LLM boundary. Machine Unlearning and Task Arithmetic ( vs. ). Share Parameter Composition (task-vector manipulation): unlearning via negation is structurally identical to Task Arithmetic with a minus sign, raising the question of whether unlearning is a distinct technique or a specific application of Task Arithmetic.