AI Clones aim to simulate an individual's thoughts and behaviors to enable long-term, personalized interaction, placing stringent demands on memory systems to model experiences, emotions, and opinions over time. Existing memory benchmarks primarily rely on user-agent conversational histories, which are temporally fragmented and insufficient for capturing continuous life trajectories. We introduce CloneMem, a benchmark for evaluating longterm memory in AI Clone scenarios grounded in non-conversational digital traces, including diaries, social media posts, and emails, spanning one to three years. CloneMem adopts a hierarchical data construction framework to ensure longitudinal coherence and defines tasks that assess an agent's ability to track evolving personal states. Experiments show that current memory mechanisms struggle in this setting, highlighting open challenges for life-grounded personalized AI. Code and dataset are available at https://github.com/AvatarMemory/CloneMemBench
@article{arxiv.2601.07023,
title = {CloneMem: Benchmarking Long-Term Memory for AI Clones},
author = {Sen Hu and Zhiyu Zhang and Yuxiang Wei and Xueran Han and Zhenheng Tang and Huacan Wang and Ronghao Chen},
journal= {arXiv preprint arXiv:2601.07023},
year = {2026}
}