一个用于高级多模态个性化研究的基准和知识引导框架
Life-Bench: A Benchmark and Knowledge Graph Framework for Multimodal Personalization Beyond Concept Recognition
AI总结：
本文提出Life-Bench基准和LifeGraph框架，用于解决高级多模态个性化研究中的复杂任务挑战。
摘要：
现代视觉语言模型的强大推理能力为高级个性化研究开辟了新前沿。然而，这一领域的进步受到缺乏合适基准的严重阻碍。为解决这一差距，我们引入了Life-Bench，一个基于模拟用户数字足迹构建的综合性合成多模态基准。Life-Bench包含超过的问题，评估了从人物理解到对历史数据的复杂推理等广泛能力。这些能力远超以往的基准，反映了现实应用中至关重要的关键需求。此外，我们提出LifeGraph，一个端到端框架，将个人上下文组织成知识图谱以促进结构化检索和推理。我们在Life-Bench上的实验表明，现有方法在复杂个性化任务上表现显著不足，暴露了巨大的性能空间，特别是在关系、时间和聚合推理方面。虽然LifeGraph通过利用结构化知识缩小了这一差距并展示了有前途的方向，但这些高级个性化任务仍是一个关键的开放挑战，激励了该领域的新研究。
英文摘要：
As large language models increasingly power personal assistants, users expect them to reason over multimodal life histories, from recognizing people to understanding events to aggregating patterns, yet existing benchmarks primarily target concept-level recognition. We introduce Life-Bench, a fully synthetic, human-verified multimodal benchmark of over 11,800 question-answer pairs across 10 tasks, organized by required evidence scope: concept identification, event understanding, and aggregated reasoning. The benchmark's photo-centric personal histories are distributionally aligned with real user accounts under embedding statistic. The interconnected structure of personal data invites graph-based solutions; we propose LifeGraph, a personal knowledge graph framework providing structured retrieval with on-demand access to source visual evidence, showing particular promise on event and aggregated tasks. Systematic evaluation of four retrieval paradigms on Life-Bench demonstrates that accuracy degrades sharply with evidence scope, falling below 0.40 on aggregated tasks, and that no single paradigm dominates across categories. Performance beyond concept recognition remains modest for all evaluated methods, establishing personalization over multimodal histories as an open challenge and Life-Bench as a testbed for future progress.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
