arXiv:2605.30711v1 Announce Type: cross Abstract: 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
The rapid development and deployment of LLMs necessitate more robust memory management solutions to enhance their autonomy and performance.
Improved memory evolution in agentic LLMs directly impacts their ability to learn efficiently, make better decisions, and operate more autonomously, thus accelerating the development of capable AI agents.
The proposed SAGE mechanism offers a principled method for LLMs to manage new information, moving beyond ad-hoc retrieval to more intelligent 'write-side' control for dynamic memory ecosystems.
- · AI developers
- · Agentic LLM platforms
- · Automation software vendors
- · Inefficient memory architectures
- · Companies reliant on human data curation
More efficient and capable AI agents emerge with superior long-term memory and learning capabilities.
Autonomous AI systems begin to handle increasingly complex and dynamic tasks without human intervention.
The scope of deployable AI agents expands across many white-collar workflows, potentially displacing routine cognitive labor at an accelerated pace.
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Read at arXiv cs.LG