AI RESEARCH
Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents
arXiv CS.CL
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ArXi:2601.01885v2 Announce Type: replace Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization. In this paper, we propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.