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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.19001 |
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| _version_ | 1866917286662635520 |
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| author | Hu, Xia Zhuang, Honglei Potetz, Brian Fathi, Alireza Hu, Bo Samari, Babak Zhou, Howard |
| author_facet | Hu, Xia Zhuang, Honglei Potetz, Brian Fathi, Alireza Hu, Bo Samari, Babak Zhou, Howard |
| contents | The powerful reasoning of modern Vision Language Models open a new frontier for advanced personalization study. However, progress in this area is critically hampered by the lack of suitable benchmarks. To address this gap, we introduce Life-Bench, a comprehensive, synthetically generated multimodal benchmark built on simulated user digital footprints. Life-Bench features over questions evaluating a wide spectrum of capabilities, from persona understanding to complex reasoning over historical data. These capabilities expand far beyond prior benchmarks, reflecting the critical demands essential for real-world applications. Furthermore, we propose LifeGraph, an end-to-end framework that organizes personal context into a knowledge graph to facilitate structured retrieval and reasoning. Our experiments on Life-Bench reveal that existing methods falter significantly on complex personalized tasks, exposing a large performance headroom, especially in relational, temporal and aggregative reasoning. While LifeGraph closes this gap by leveraging structured knowledge and demonstrates a promising direction, these advanced personalization tasks remain a critical open challenge, motivating new research in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19001 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Benchmark and Knowledge-Grounded Framework for Advanced Multimodal Personalization Study Hu, Xia Zhuang, Honglei Potetz, Brian Fathi, Alireza Hu, Bo Samari, Babak Zhou, Howard Computer Vision and Pattern Recognition The powerful reasoning of modern Vision Language Models open a new frontier for advanced personalization study. However, progress in this area is critically hampered by the lack of suitable benchmarks. To address this gap, we introduce Life-Bench, a comprehensive, synthetically generated multimodal benchmark built on simulated user digital footprints. Life-Bench features over questions evaluating a wide spectrum of capabilities, from persona understanding to complex reasoning over historical data. These capabilities expand far beyond prior benchmarks, reflecting the critical demands essential for real-world applications. Furthermore, we propose LifeGraph, an end-to-end framework that organizes personal context into a knowledge graph to facilitate structured retrieval and reasoning. Our experiments on Life-Bench reveal that existing methods falter significantly on complex personalized tasks, exposing a large performance headroom, especially in relational, temporal and aggregative reasoning. While LifeGraph closes this gap by leveraging structured knowledge and demonstrates a promising direction, these advanced personalization tasks remain a critical open challenge, motivating new research in this area. |
| title | A Benchmark and Knowledge-Grounded Framework for Advanced Multimodal Personalization Study |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.19001 |