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title: "TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models — Stuff That Spins"
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date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:03:50.407071+00:00"
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# TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.07388  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.07388v1 Announce Type: new Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion costly through pretraining, fine-tuning, retrieval augmentation, or longer contexts. Engram-style memory offers a compact hidden-state injection pathway, but existing GPU-resident designs often rely on hash-based compression, causing unrelated phrases to collide in shared slots and weakening phrase

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