Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
Agentic memory is becoming essential for long-horizon AI agents, yet many existing systems rely on autoregressive LLMs to control how memories are organized, retrieved, and used, placing expensive generation on the critical path of memory operations. We introduce \method, a new agentic memory architecture inspired by System-One/System-Two cognition. System One captures fast, li
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Agentic memory is becoming essential for long-horizon AI agents, yet many existing systems rely on autoregressive LLMs to control how memories are organized, retrieved, and used, placing expensive generation on the critical path of memory operations. We introduce \method, a new agentic memory architecture inspired by System-One/System-Two cognition. System One captures fast, li
This research introduces a novel agentic memory architecture that significantly optimizes the efficiency and cost of AI agents by separating fast, System-One memory control from slower, System-Two reasoning. For ML engineers, understanding such dual-system approaches is crucial for building scalable and performant long-horizon agents, especially in resource-constrained environments.