MobileMem Benchmarks Agent Memory Against a Year of Synthesized Mobile Life — Multi-Hop Reasoning, Knowledge Updating, and Implicit Preference Inference
A 17-author benchmark from Zhejiang-affiliated authors and the OpenKG Consortium (arXiv 2608.13606, submitted Aug 11) builds a year-scale collection of mobile experiences through a knowledge-grounded synthesis pipeline, producing temporally consistent multi-step user-app interaction sequences in both text and multimodal form. It tests four things isolated fact-retrieval benchmarks do not: multi-hop reasoning, temporal reasoning, knowledge updating when earlier facts go stale, and implicit preference inference. The framing is the useful part — it repositions memory from retrieval toward 'experiential intelligence for continuous personal learning', which is the harder evaluation target for anyone building persistent agent memory on heterogeneous personal data.
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