KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
Authors: name: Tingyu Wu, name: Zhisheng Chen, name: Ziyan Weng, name: Shuhe Wang, name: Chenglong Li, name: Shuo Zhang, name: Sen Hu, name: Silin Wu, name: Qizhen Lan, name: Huacan Wang, name: Ronghao Chen
Organization: QuantaAlpha

Existing long-horizon memory benchmarks mostly use multi-turn dialogues or synthetic user histories, which makes retrieval performance an imperfect proxy for person understanding. We present \BenchName, a publicly releasable benchmark built from long-form autobiographical narratives, where actions, context, and inner thoughts provide dense evidence for inferring stable motivations and decision principles. \BenchName~reconstructs each narrative into a flashback-aware, time-anchored stream and evaluates models with evidence-linked questions spanning factual recall, subjective state attribution, and principle-level reasoning. Across diverse narrative sources, retrieval-augmented systems mainly improve factual accuracy, while errors persist on temporally grounded explanations and higher-level inferences, highlighting the need for memory mechanisms beyond retrieval. Our data is in KnowMeBench{https://github.com/QuantaAlpha/KnowMeBench}.