Hypertokens: Holographic Associative Memory in Tokenized LLMs
This addresses memory inefficiencies in LLMs for AI researchers and practitioners, offering a novel hybrid approach that combines classical, holographic, and quantum-inspired principles.
The paper tackles the precision loss in Large Language Models by reframing it as an information spreading issue and introduces HDRAM, a symbolic memory framework using hypertokens that integrates error-correcting codes, holographic computing, and quantum-inspired search to improve associative retrieval without architectural changes, demonstrating significant enhancements in key-value operations.
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.