AI RESEARCH
AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents
arXiv CS.CL
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ArXi:2603.16496v1 Announce Type: new Large language model (LLM) agents increasingly rely on external memory to long-horizon interaction, personalized assistance, and multi-step reasoning. However, existing memory systems still face three core challenges: they often rely too heavily on semantic similarity, which can miss evidence crucial for user-centric understanding; they frequently related experiences as isolated fragments, weakening temporal and causal coherence; and they typically use static memory granularities that do not adapt well to the requirements of different questions.