Post-training adaptation is fragmented across many techniques—this taxonomy provides a unified vocabulary to describe, compare, and govern how models are modified after training, essential for tracking what changes have been made to deployed systems.
This survey creates a comprehensive framework for understanding how trained AI models are modified after initial training. It organizes 50+ adaptation techniques (like fine-tuning, retrieval augmentation, and model editing) into a six-dimensional taxonomy, clarifying confusing terminology and showing how these methods work together in real deployments.