Trained Persistent Memory for Frozen Encoder--Decoder LLMs: Six Architectural Methods

TL;DR

This paper demonstrates the feasibility of persistent memory in frozen encoder-decoder LLMs through six architectural methods, enabling conversational learning with minimal resources and no additional training of the core model.

Contribution

It introduces six novel architectural methods for embedding persistent memory into frozen LLMs, facilitating continual learning without retraining the entire model.

Findings

Memory capacity is critical for effective recall.

All six methods outperform the baseline at higher capacity.

Memory can be scaled independently of the backbone model.

Abstract

Frozen encoder--decoder language models are stateless: the latent representation is discarded after every forward pass, so no information persists across sessions. This paper presents a \textbf{proof-of-concept pilot study} showing that persistent memory in the \emph{continuous latent space} of a frozen LLM is feasible -- even under severe resource constraints (a single frozen Flan-T5-XL backbone, small trainable adapters, a single dataset). We implement six architectural methods spanning three injection points and four write mechanisms; unlike text-level memory systems, every write and read is a differentiable operation on dense vectors. After training only the adapter, the memory bank continues to accumulate at inference time without gradients, enabling \emph{conversational learning}. Under a forgetting-curve evaluation on LoCoMo at two capacity scales (1$\times$ and 10$\times$), the stateless baseline scores exactly zero; at 10$\times$ all six trained adapters produce positive memory-recall curves; at 1$\times$ three methods collapse, revealing capacity as a critical design parameter. Because the memory bank is a compact numerical array, it can be scaled to arbitrarily large capacity without altering the backbone. We argue that full end-to-end training with larger models, larger data, and orders-of-magnitude larger memory will yield substantially stronger results; this pilot study establishes the feasibility baseline and design-space taxonomy that such efforts require.