Hierarchical Professional Specialization of Foundation Models: A Progressive Parameterized Adaptation Framework

To meet the demand for controllable long-term adaptation of foundation models in industrial professional settings, this paper proposes a hierarchical specialization framework organized by the granularity of professional knowledge, rather than tasks, user clusters or domain labels. It defines four semantic layers: general foundation, industry-level general knowledge, domain-specific professional paradigm, and individual specialization. Sequential training rules, parameter isolation constraints and valid inference combinations are formulated, along with a reproducible engineering pipeline. Instead of introducing new parameter-efficient fine-tuning algorithms, the core contribution is mapping multi-tier professional knowledge into mutually independent parameter spaces to decouple industry knowledge, reusable professional paradigms and individual preferences. Existing empirical findings support that layered freezing mitigates catastrophic forgetting [7][8]. Empirical scaling laws and cross-scenario comparisons are reserved for follow-up studies.