CL-bench Life: Can Language Models Learn from Real-Life Context?

Shihan Dou; Yujiong Shen; Chenhao Huang; Junjie Ye; Jiayi Chen; Junzhe Wang; Qianyu He; Shichun Liu; Changze Lv; Jiahang Lin; Jiazheng Zhang; Ming Zhang; Shaofan Liu; Tao Ji; Zhangyue Yin; Cheng Zhang; Huaibing Xie; Jianglu Hu; Jingcheng Deng; Lincheng Li; Minda Hu; Shaolei Wang; Syrus Zhao; Weichao Wang; Yan Lei; Yang Liu; Yanling Xiao; Yiting Liu; Zenan Xu; Zhen Guo; Ziliang Zhao; Pluto Zhou; Tao Gui; Qi Zhang; Xuanjing Huang; Yu-Gang Jiang; Di Wang; Shunyu Yao

arXiv · 2604.27043

CL-bench Life: Can Language Models Learn from Real-Life Context?

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Abstract

Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move beyond professional settings into everyday life, the nature of the contexts they must handle also shifts. Real-life contexts are often messy, fragmented, and deeply tied to personal and social experience, such as multi-party conversations, personal archives, and behavioral traces. Yet it remains unclear whether current frontier language models can reliably learn from such contexts and solve tasks grounded in them. To this end, we introduce CL-bench Life, a fully human-curated benchmark comprising 405 context-task pairs and 5,348 verification rubrics, covering common real-life scenarios. Solving tasks in CL-bench Life requires models to reason over complex, messy real-life contexts, calling for strong real-life context learning abilities that go far beyond those evaluated in existing benchmarks. We evaluate ten frontier LMs and find that real-life context learning remains highly challenging: even the best-performing model achieves only 19.3% task solving rate, while the average performance across models is only 13.8%. Models still struggle to reason over contexts such as messy group chat histories and fragmented behavioral records from everyday life. CL-bench Life provides a crucial testbed for advancing real-life context learning, and progress on it can enable more intelligent and reliable AI assistants in everyday life.

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Shihan Dou, Yujiong Shen, Chenhao Huang, Junjie Ye, Jiayi Chen, Junzhe Wang, Qianyu He, Shichun Liu, Changze Lv, Jiahang Lin, Jiazheng Zhang, Ming Zhang, Shaofan Liu, Tao Ji, Zhangyue Yin, Cheng Zhang, Huaibing Xie, Jianglu Hu, Jingcheng Deng, Lincheng Li, Minda Hu, Shaolei Wang, Syrus Zhao, Weichao Wang, Yan Lei, Yang Liu, Yanling Xiao, Yiting Liu, Zenan Xu, Zhen Guo, Ziliang Zhao, Pluto Zhou, Tao Gui, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Di Wang, Shunyu Yao. 2026-04-29. CL-bench Life: Can Language Models Learn from Real-Life Context?. https://arxiv.org/abs/2604.27043

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