KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
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}.
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Tingyu Wu
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2026-01-14T09:53:39.763Z
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Zhisheng Chen
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2026-01-14T12:49:43.502Z
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Zhisheng Chen
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Ziyan Weng
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Shuhe Wang
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2026-01-14T12:49:52.050Z
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Shuhe Wangv2
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Chenglong Li
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Chenglong Li
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Shuo Zhang
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2026-01-15T15:06:18.485Z
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zhangshuo
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Sen Hu
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Silin Wu
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Qizhen Lan
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2026-01-14T12:50:03.306Z
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Qizhen Lan
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Huacan Wang
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2026-01-14T12:50:08.549Z
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Huacan Wang
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Ronghao Chen
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2026-01-14T12:50:13.928Z
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RongHao Chen
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KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions
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}.
Long-horizon memory benchmarks based on autobiographical narratives evaluate models' ability to infer stable motivations and decision principles through evidence-linked questions spanning factual recall, subjective state attribution, and principle-level reasoning.
memory benchmarks autobiographical narratives retrieval-augmented systems factual recall subjective state attribution principle-level reasoning