TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models
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-level semantic fidelity. We present TF-Engram, a train-free Engram system that constructs phrase-specific semantic memory offline from external corpora, stores large memory tables across a GPU--DRAM--SSD hierarchy, and uses Early-Exit Guided Pr...
| Authors | Yutang Ma, Kecheng Huang, Xikun Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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