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arXiv · 2601.06966

RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction

Haonan Bian·Zhiyuan Yao·Sen Hu·Zishan Xu·Shaolei Zhang·Yifu Guo·Ziliang Yang·Xueran Han·Huacan Wang·Ronghao Chen
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Abstract

As Large Language Models (LLMs) evolve from static dialogue interfaces to autonomous general agents, effective memory is paramount to ensuring long-term consistency. However, existing benchmarks primarily focus on casual conversation or task-oriented dialogue, failing to capture **"long-term project-oriented"** interactions where agents must track evolving goals. To bridge this gap, we introduce **RealMem**, the first benchmark grounded in realistic project scenarios. RealMem comprises over 2,000 cross-session dialogues across eleven scenarios, utilizing natural user queries for evaluation. We propose a synthesis pipeline that integrates Project Foundation Construction, Multi-Agent Dialogue Generation, and Memory and Schedule Management to simulate the dynamic evolution of memory. Experiments reveal that current memory systems face significant challenges in managing the long-term project states and dynamic context dependencies inherent in real-world projects. Our code and datasets are available at [https://github.com/AvatarMemory/RealMemBench](https://github.com/AvatarMemory/RealMemBench).

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Haonan Bian, Zhiyuan Yao, Sen Hu, Zishan Xu, Shaolei Zhang, Yifu Guo, Ziliang Yang, Xueran Han, Huacan Wang, Ronghao Chen. 2026-01-11. RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction. https://arxiv.org/abs/2601.06966

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