Implementing Emotionally-Keyed Memory Retrieval in Large Language Model Interfaces: An Engineering Framework

This paper presents an engineering framework for implementing a Circular Associative Memory Architecture (CAMA) within existing LLM memory constraints. The framework organizes memory using compressed emotional signatures rather than keyword indexing, with a logically circular structure enabling emotionally-keyed retrieval. Implementation strategies for the Anthropic 30-slot memory system are presented, including a condensation algorithm with pseudocode, a three-phase implementation pathway, and a proposed evaluation framework for empirical validation. This paper serves as the engineering companion to the conceptual CAMA architecture paper (DOI: 10.5281/zenodo.19051834).