Phase-Coded Memory and Morphological Resonance: A Next-Generation Retrieval-Augmented Generator Architecture

TL;DR

This paper proposes a novel retrieval-augmented generator architecture using phase-coded memory and morphological resonance, enabling unlimited context access and reducing computational overhead by encoding meaning as complex wave patterns.

Contribution

It introduces a new memory and retrieval mechanism based on phase interference and complex waveforms, surpassing transformer limitations and improving efficiency.

Findings

Eliminates sequential token dependence

Reduces memory and computational overhead

Enables unlimited effective context through frequency-based access

Abstract

This paper introduces a cognitive Retrieval-Augmented Generator (RAG) architecture that transcends transformer context-length limitations through phase-coded memory and morphological-semantic resonance. Instead of token embeddings, the system encodes meaning as complex wave patterns with amplitude-phase structure. A three-tier design is presented: a Morphological Mapper that transforms inputs into semantic waveforms, a Field Memory Layer that stores knowledge as distributed holographic traces and retrieves it via phase interference, and a Non-Contextual Generator that produces coherent output guided by resonance rather than fixed context. This approach eliminates sequential token dependence, greatly reduces memory and computational overhead, and enables unlimited effective context through frequency-based semantic access. The paper outlines theoretical foundations, pseudocode implementation, and experimental evidence from related complex-valued neural models, emphasizing substantial energy, storage, and time savings.