Hypertokens: Holographic Associative Memory in Tokenized LLMs

TL;DR

This paper introduces HDRAM, a holographic associative memory framework for LLMs that enhances memory retrieval and search capabilities by integrating error correction, holographic computing, and quantum-inspired techniques within transformer latent spaces.

Contribution

The paper presents HDRAM, a novel symbolic memory system for LLMs that improves associative retrieval and search without altering existing architectures, inspired by holographic and quantum principles.

Findings

HDRAM significantly improves associative retrieval accuracy.

Efficient key-value operations enabled by phase-coherent memory addresses.

Demonstrates the effectiveness of classical-holographic-quantum-inspired principles in transformer models.

Abstract

Large Language Models (LLMs) exhibit remarkable capabilities but suffer from apparent precision loss, reframed here as information spreading. This reframing shifts the problem from computational precision to an information-theoretic communication issue. We address the K:V and V:K memory problem in LLMs by introducing HDRAM (Holographically Defined Random Access Memory), a symbolic memory framework treating transformer latent space as a spread-spectrum channel. Built upon hypertokens, structured symbolic codes integrating classical error-correcting codes (ECC), holographic computing, and quantum-inspired search, HDRAM recovers distributed information through principled despreading. These phase-coherent memory addresses enable efficient key-value operations and Grover-style search in latent space. By combining ECC grammar with compressed sensing and Krylov subspace alignment, HDRAM significantly improves associative retrieval without architectural changes, demonstrating how Classical-Holographic-Quantum-inspired (CHQ) principles can fortify transformer architectures.