Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents
Jev-Mem uses a fast System-One controller for agent memory, beating baselines on LoCoMo while cutting latency.
Researchers introduce Jev-Mem, an agentic memory system that separates fast memory control from slower reasoning. A System-One controller manages memory typing, query routing, retrieval budgets, graph traversal, scoring, and stopping, while System Two is reserved for complex reasoning and answer synthesis. On LoCoMo it scores 0.777 under LLM-as-a-Judge, an 11 percent relative gain over the strongest baseline. Memory construction takes 158 seconds, 6.6 times faster than the fastest competitor, and average query latency is 0.93 seconds, down 36.7 percent.
- System-One plane controls construction and retrieval; System Two handles hard reasoning.
- LoCoMo LLM-as-a-Judge score is 0.777, up 11 percent versus the best baseline.
- Memory construction takes 158 seconds, a 6.6-times speedup over the fastest rival.
- Average query latency falls to 0.93 seconds, a 36.7 percent reduction.
From the original article
1 min read at huggingface.co
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.
By Dongming Jiang, Yi Li, Bingzhe Li · huggingface.co
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