K7007 presents ShinBay-UCTF, a conceptual framework addressing limitations in AI systems regarding multilingual redundancy, persistent episodic memory, and continuous adaptation. The proposal outlines a unified architecture consisting of three interacting layers: Universal Compressed Training Format (UCTF), Biologically Inspired Episodic Memory, and Environment-Driven Continuous Learning.
- Layer 1 (UCTF) aims to represent semantic knowledge in a compressed, language-agnostic format to reduce training redundancy.
- Layer 2 stores new memories separately in UCTF-compressed format for associative recall, featuring selective forgetting and local encrypted storage.
- Layer 3 enables continuous learning through environmental interaction within sandboxed boundaries, preventing modification of core safety constraints.
- The framework mandates offline-only operation on the user's device to ensure privacy and user-controlled memory deletion.
The paper serves as a research hypothesis intended to stimulate discussion and future investigation into integrating these architectural components.