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).
@article{arxiv.2601.06966,
title = {RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction},
author = {Haonan Bian and Zhiyuan Yao and Sen Hu and Zishan Xu and Shaolei Zhang and Yifu Guo and Ziliang Yang and Xueran Han and Huacan Wang and Ronghao Chen},
journal= {arXiv preprint arXiv:2601.06966},
year = {2026}
}