Muscle Memory for Agents: Compile not Merely Retrieve
4.20T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2608.08995.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA position paper proposing 'Muscle Memory' for LLM agents: compiling recurring user intents into specialist agents rather than retrieving stored experiences. A four-phase pipeline (Harvest→Analyze→Augment→Evaluate) mines conversation history and emits compiled specialists, achieving 88.9% win rate with +2.05 personalization gain and only −0.28 accuracy cost on 90 held-out scenarios.
Why it mattersProposes compilation over retrieval as a memory paradigm, backed by a working four-phase pipeline and measured personalization-vs-accuracy tradeoff. Directly applicable to anyone building personalized agent systems.
Cited by
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