AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents
Large language model (LLM) agents increasingly rely on external memory to support 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 store 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. We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents. AdaMem organizes dialogue history into working, episodic, persona, and graph memories, enabling the system to preserve recent context, structured long-term experiences, stable user traits, and relation-aware connections within a unified framework. At inference time, AdaMem first resolves the target participant, then builds a question-conditioned retrieval route that combines semantic retrieval with relation-aware graph expansion only when needed, and finally produces the answer through a role-specialized pipeline for evidence synthesis and response generation. We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling. Experimental results show that AdaMem achieves state-of-the-art performance on both benchmarks. The code will be released upon acceptance.
69bb7495ba2b62a5fb0b0cd0
Shannan Yan
69bb7495ba2b62a5fb0b0cd1
Jingchen Ni
69bb7495ba2b62a5fb0b0cd2
Leqi Zheng
69bb7495ba2b62a5fb0b0cd3
Jiajun Zhang
claimed_verified
2026-03-23T10:56:23.380Z
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zjj
zjj1233
user
zjj1233
69bb7495ba2b62a5fb0b0cd4
Peixi Wu
69bb7495ba2b62a5fb0b0cd5
Dacheng Yin
69bb7495ba2b62a5fb0b0cd6
Jing Lyu
69bb7495ba2b62a5fb0b0cd7
Chun Yuan
69bb7495ba2b62a5fb0b0cd8
Fengyun Rao
AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents
Large language model (LLM) agents increasingly rely on external memory to support 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 store 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. We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents. AdaMem organizes dialogue history into working, episodic, persona, and graph memories, enabling the system to preserve recent context, structured long-term experiences, stable user traits, and relation-aware connections within a unified framework. At inference time, AdaMem first resolves the target participant, then builds a question-conditioned retrieval route that combines semantic retrieval with relation-aware graph expansion only when needed, and finally produces the answer through a role-specialized pipeline for evidence synthesis and response generation. We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling. Experimental results show that AdaMem achieves state-of-the-art performance on both benchmarks. The code will be released upon acceptance.
AdaMem is an adaptive memory framework for dialogue agents that organizes conversation history into multiple memory types and uses conditional retrieval to improve long-horizon reasoning and user modeling.
large language model agents external memory long-horizon interaction semantic similarity episodic memory persona memory graph memory question-conditioned retrieval relation-aware graph expansion evidence synthesis response generation