What question did this study set out to answer?

This work aims to create an effective method for enhancing memory retrieval in large language models (LLMs) using emotional cues.

March 18, 2026Open Access

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

Key Points

This work aims to create an effective method for enhancing memory retrieval in large language models (LLMs) using emotional cues.
Developed a Circular Associative Memory Architecture (CAMA) for LLMs.
Utilized compressed emotional signatures instead of traditional keyword indexing.
Presented a condensation algorithm with pseudocode and a three-phase implementation strategy.
Demonstrated an efficient way to organize memory for emotionally-keyed retrieval.
Proposed an empirical evaluation framework to validate the implementation of CAMA.

Abstract

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).

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

Key Points

Abstract

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