llm-transfer

#llm-transfer

@vintcessun: A 'knowledge hard drive' that can be plugged across models — the difficulty lies not in moving the memory table, but in configuring the read head for the target model. https://arxiv.org/abs/2608.17050 The paper first trains Engram hash memory with the source model, then freezes the memory and target backbone, only training the reader to complete addressing...

X AI KOLs Timeline ↗ · 5d ago Cached

This paper proposes a method for cross-model memory transfer through target-side reader adaptation, using Engram hash memory and a lightweight reader, achieving 38.8 on QA tasks, and applicable to Agent memory updates and audits. The limitation is that it was only tested up to 9B models, with scaling laws unknown.

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