Researchers introduced Jev-Mem, a system-one-controlled agentic memory architecture designed to make long-horizon AI agents more efficient by removing autoregressive LLM generation from the critical path of memory organization, retrieval, and use. The work targets a common bottleneck in existing agentic memory systems, which rely on autoregressive LLMs to control memory operations at high computational cost.
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 archit
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