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ARXIV:2605.30711 · AGENTS · SUBMITTED 01 JUN · 20:25 UTC · FRESHNESS STALE
ARXIV:2605.30711AGENTSSUBMITTED 01 JUN · 20:25 UTCFRESHNESS STALESijia Wang · Dhanajit Brahma · Ricardo Henao · arXiv
SAGE is a novelty gate for agentic LLMs that efficiently filters facts for memory evolution, reducing costly LLM calls and improving system efficiency.
Opportunity summary
Pain SAGE is a novelty gate for agentic LLMs that efficiently filters facts for memory evolution, reducing costly LLM calls and improving system efficiency.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
SAGE is a novelty gate for agentic LLMs that efficiently filters facts for memory evolution, reducing costly LLM calls and improving system efficiency. We frame memory evolution as a novelty-detection problem and propose SAGE,…
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…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. On LoCoMo, SAGE achieves the best average token-F1 against Mem0 on all seven open-weight backbone comparisons, while on GPT-4o-mini it reduces add-phase API cost…
Agents moved forward this cycle; last verified June 2026. Public score 6.0/10.
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SAGE is a novelty gate for agentic LLMs that efficiently filters facts for memory evolution, reducing costly LLM calls and improving system efficiency.
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10.48550/arXiv.2605.30711SAGE is a novelty gate for agentic LLMs that efficiently filters facts for memory evolution, reducing costly LLM calls and improving system efficiency.
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 facts as ADD, clearly redundant facts as NOOP, and sends only uncertain cases to an LLM merge step, reducing expensive write-time reasoning. On LoCoMo, SAGE achieves the best average token-F1 against Mem0 on all seven open-weight backbone comparisons, while on GPT-4o-mini it reduces add-phase API cost by 3.4$\times$ and add-phase latency by 2.5$\times$ with only a small average judge-score gap. As a drop-in binary gate for A-Mem, SAGE skips roughly 16-18% of LLM calls across five models with minimal quality change on open-weight backbones. These results suggest that novelty-aware write control is a practical lever for improving both memory quality and system efficiency in long-term agentic memory.
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What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 6.0
PROBLEM
SAGE is a novelty gate for agentic LLMs that efficiently filters facts for memory evolution, reducing costly LLM calls and improving system efficiency. We frame memory evolution as a novelty-detection problem and propose SAGE, a Spherical Adaptive Gate for memory Evolution that...
METHOD
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 proble...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. On LoCoMo, SAGE achieves the best average token-F1 against Mem0 on all seven open-weight backbone comparisons, while on GPT-4o-mini it reduces add-phase API cost by 3.4$\times$ and add-phase latency by 2....
WHY NOW
Agents moved forward this cycle; last verified June 2026. Public score 6.0/10.
{"file name": "input.pdf", "number of pages": 16, "author": "Sijia Wang; Dhanajit Brahma; Ricardo Henao", "title": "SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMs", "creation date": null
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SAGE is a novelty gate for agentic LLMs that efficiently filters facts for memory evolution, reducing costly LLM calls and improving system efficiency.
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Commercial read
6.0/10 public viability
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proof status
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