Emergent Hierarchical Accretion for Retrieval Memory Systems: A Deterministic Probabilistic Recursive Model

EHA-DPRM (Emergent Hierarchical Accretion, Deterministic-Probabilistic Recursive Model) is a formal framework for retrieval-memory systems in which knowledge is not stored as flat, pre-segmented chunks, but constituted as hierarchical entities through compositional admission gates. The model combines append-only provenance, novelty- and independence-based admission, structural and functional integration tests, hybrid deterministic-neural retrieval, and a fail-closed trichotomous decision gate (Execute / Hold / Escalate). It defines a deterministic state-transition architecture in which new information is atomized, evaluated at the base level, and recursively composed into higher-level entities only when explicit formal conditions are satisfied. The framework also bounds neural influence through explicit weight dynamics, ensuring that neural scoring contributes to retrieval without autonomously dominating it. EHA-DPRM is positioned as a unified architecture for hierarchical knowledge construction, principled memory admission, provenance-preserving retrieval, and uncertainty-aware decision control in advanced RAG and retrieval-memory systems