DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings
LLM agents increasingly act as personal assistants that must remember a user's profile over months: who they are (attributes), what they routinely do (habits), and what they prefer (preferences), and keep it updated as jobs, routines, and tastes drift. Existing benchmarks evaluate this "memory" ability through short, simplified interactions, missing three core properties of real behavior: the profile is heterogeneous, with attributes, habits, and preferences evolving on different timelines; changes are driven by external context such as seasons and life events; and evidence is rarely stated explicitly, instead scattered across many small actions in different apps that a memory system must...
| Authors | Wenya Xie, Shengming Zhou, Zelin Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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