Memory as Resonance: A Biomimetic Architecture for Infinite Context Memory on Ergodic Phonetic Manifolds

TL;DR

This paper introduces Phonetic Trajectory Memory (PTM), a biomimetic architecture that encodes language as continuous trajectories on ergodic manifolds, enabling infinite context access with high compression and factual accuracy.

Contribution

It proposes a novel neuro-symbolic memory architecture that treats long-term memory as persistent trajectories rather than static storage, achieving significant compression and fast retrieval.

Findings

Achieves over 3,000x compression compared to dense caches

Secures up to 92% factual accuracy through resonance-based retrieval

Provides immediate access latency (~34ms) independent of context depth

Abstract

The memory of contemporary Large Language Models is bound by a physical paradox: as they learn, they fill up. The linear accumulation (O(N)) of Key-Value states treats context as a warehouse of static artifacts, eventually forcing a destructive choice between amnesia and latency. We challenge this discrete orthodoxy, proposing that long-term memory is not the storage of items, but the persistence of a trajectory. We introduce Phonetic Trajectory Memory (PTM), a neuro-symbolic architecture that encodes language not as a sequence of tensors, but as a continuous path on an ergodic manifold governed by irrational rotation matrices. By decoupling the navigation (an invariant O(1) geometric signal) from the reconstruction (a probabilistic generative act), PTM achieves a compression magnitude of greater than 3,000x relative to dense caches. We demonstrate that retrieval becomes a process of resonance: the phonetic trace stabilizes the model against hallucination via "Signal Consensus" mechanism, securing up to approximately 92% factual accuracy. While this aggressive abstraction alters generative texture, it unlocks immediate access latency (approximately 34ms) independent of depth. Our results suggest that infinite context does not require infinite silicon; it requires treating memory not as data to be stored, but as a reconstructive process acting on a conserved, undying physical signal.