从记忆到更远：跨长期多模态个人档案从记住到认识你

中文摘要 AI 辅助

随着人工智能系统演变为个性化数字伴侣，一个核心能力是对用户的长期个人历史进行推理：不仅是存储过去的事件，还要追踪纵向经历和不断演变的偏好。这一进展受到评估的瓶颈制约，现有的长期记忆基准大多是合成且仅文本的，它们忽视了锚定日常人类记忆的视觉记录，缺乏真实个性化所要求的真实且因果相连的纵向数据，因此仍局限于浅层的事实回忆。我们引入了ReaLMem（真实世界长期多模态记忆），这是首个基于真实多年个人视觉档案构建的基准，并配有一手主观标注。ReaLMem在三个难度递增的认知层级上评估模型：事实回忆、人格推断和预测性个性化。我们进一步提出了ChronoProfiler，一个时间加权画像模块，计算用户属性的时间稳定性分数，并将其作为显著性先验应用，解决时间不一致偏好之间的冲突，帮助模型在复杂的个性化决策中复合多个共同活跃的偏好。对前沿多模态大语言模型（MLLMs）和记忆系统在ReaLMem上的广泛评估揭示了预测性个性化是一个持续的瓶颈，暴露了MLLMs与记忆系统之间的明显性能差距和瓶颈，并表明高质量、时间信息丰富的表示能显著改善个性化。ReaLMem和ChronoProfiler共同为长期个性化提供了一个真实的测试平台和一种简单有效的机制，为未来终身AI伴侣的研究奠定了基础。

英文摘要

As AI systems evolve into personalized digital companions, a central capability is reasoning over a user's long-term personal history: not merely storing past events, but tracking longitudinal experiences and evolving preferences. Progress here is bottlenecked by evaluation, existing long-term memory benchmarks are largely synthetic and text-only, they overlook the visual records that anchor everyday human memory, lack the authentic and causally connected longitudinal data that real personalization demands, and consequently remain confined to shallow factual recall. We introduce ReaLMem (Real-world Long-term Multimodal Memory), the first benchmark built from authentic multi-year personal visual archives, paired with first-person subjective annotations. ReaLMem evaluates models across three cognitive tiers of increasing difficulty: factual recall, persona inference, and predictive personalization. We further propose ChronoProfiler, a temporal-weighting profiling module that computes temporal stability scores for user attributes and applies them as a salience prior, resolving conflicts among temporally inconsistent preferences and helping models compound multiple co-active preferences in complex personalized decisions. Extensive evaluation of frontier multimodal large language models (MLLMs) and memory systems on ReaLMem reveals predictive personalization as a consistent ceiling, exposes clear performance gaps and bottlenecks between MLLMs and memory systems, and shows that high-quality, temporally informed representations substantially improve personalization. Together, ReaLMem and ChronoProfiler provide an authentic testbed and a simple, effective mechanism for long-term personalization, laying a foundation for future research on lifelong AI companions.

发表机构

• University of Bristol（布里斯托大学）
• Memories.ai Research（Memories.ai研究院）

机构由 AI 辅助整理，请以论文原文为准。