User as Engram proposes storing per-user facts as surgical, hash-keyed edits to a memory table, leaving reasoning in a shared adapter. This design achieves 5.6x higher indirect-reasoning accuracy and maintains base-level reasoning performance, with a memory footprint 33,000x smaller than per-user LoRA. The approach enables disjoint user edits that compose losslessly, outperforming retrieval pipelines beyond 100 facts.
arxiv
arXiv cs.AI
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52d ago
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research
User as Engram: Local Parametric Edits for Personal Memory
from English
Importance 3/3
New feature vs. leaders
New harness with differentiators
arXiv cs.AI
Microsoft Research
Hugging Face
OpenAI
AI agents
Open weights
Reasoning models
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