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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.
This paper investigates cross-model memory transfer, demonstrating that target-side reader adaptation is crucial for utilizing frozen memory tables across different large language models. It introduces methods for reusable knowledge artifacts with optional adaptation to improve alignment.