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 in...
| Authors | Shannan Yan, Jingchen Ni, Leqi Zheng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 March 2026 |
| Last updated at source | 29 April 2026 |
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