SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMs
Agentic LLMs must continuously decide whether newly extracted facts should be added, merged with existing memories, or ignored, yet prior work has focused more on retrieval and storage than on principled write-side control. We frame memory evolution as a novelty-detection problem and propose SAGE, a Spherical Adaptive Gate for memory Evolution that scores candidate facts with a von Mises-Fisher-based density estimator over memory embeddings and routes them with an adaptive threshold that tracks memory-store geometry. SAGE resolves clearly novel facts as ADD, clearly redundant facts as NOOP, and sends only uncertain cases to an LLM merge step, reducing expensive write-time reasoning. On Lo...
| Authors | Sijia Wang, Dhanajit Brahma, Ricardo Henao |
|---|---|
| Primary category | cs.CL |
| Published | 29 May 2026 |
| Last updated at source | 18 June 2026 |
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