User as Engram: Internalizing Per-User Memory as Local Parametric Edits
Quick Answer
This paper shows that The 'User as Engram' model proposes storing user-specific content as local edits in a memory table, enhancing reasoning accuracy by 5.6x while maintaining a 33,000x smaller memory footprint compared to traditional per-user LoRA adapters.
Key Points
- User-specific facts stored as surgical edits in a hash-keyed memory table.
- Achieves 5.6x higher indirect reasoning accuracy than per-user LoRA.
- Maintains a 33,000x smaller memory footprint compared to traditional methods.
- Allows multiple users to coexist with additive and lossless edits.
- Retrieval efficiency improves significantly after ~100 facts.
Paper Resources
Source Excerpt
Personal memory in a language model is two problems: content and reasoning skill. The brain keeps the two apart (a sparse, local engram in the hippocampus for each episode, a slow neocortex for the shared skills that interpret it), so a new fact need not overwrite everything else. Most personalization today keeps a user's facts outside the weights, in a natural-language memory file or a retrieval index. When facts are written into the model instead, the standard recipe is the per-user LoRA adapt
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