# Jev-Mem paper reports faster memory construction for AI agents

The Jev-Mem paper proposes an agentic memory architecture in which a lightweight controller makes storage and retrieval decisions and an LLM is used only for final reasoning. It reports 6.6x faster memory construction, 36.7% lower query latency, and a LoCoMo score of 0.777.

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Source: elvis · Published 9/25/2026, 15:07:09

The paper [Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents](https://academy.dair.ai/papers/jev-mem-system-one-controlled-agentic-memory-for-efficient-ai-agents-2609.23986) proposes an agentic memory architecture inspired by System-One and System-Two cognition. A lightweight controller assigns memory types and relations when storing information. During reading, it handles routing, the retrieval budget, graph traversal, scoring, and when to stop. The LLM is called only for final reasoning and answer writing.

On LoCoMo, the paper reports an overall score of 0.777 with an LLM judge, an 11.0% relative improvement over the strongest baseline. It also reports building agent memory 6.6x faster and cutting query latency by 36.7%. Memory construction takes 158 seconds, and average query latency falls to 0.93 seconds.

Tags: Jev-Mem, AI agents, Agent memory, LoCoMo

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