Long-term memory is increasingly important for personalized AI agents, yet existing benchmarks and methods remain largely text-centric. Even when images are included, the user-specific information needed for later questions is typically recoverable from text alone, and most memory systems reduce image turns to generic captions. Yet images often carry personal information that text rarely states -- both explicit evidence, such as recurring user-associated entities, and implicit evidence, such as latent user facts inferred from visual or multimodal cues. We introduce a benchmark for personal visual memory that targets both forms of evidence, and propose VisualMem, a hybrid visual--text architecture that augments a text-memory backend with a structured personal visual memory module. Rather than collapsing images into captions, VisualMem uses conversational context to resolve identity, ownership, and durable user facts. Experiments show that VisualMem substantially outperforms prior memory systems on our benchmark while remaining competitive on standard text-memory benchmarks, indicating that personal visual memory is a distinct and important component of long-term memory for personalized AI agents.

翻译：长期记忆对于个性化人工智能代理日益重要，然而现有基准测试与方法仍以文本为中心。即便包含图像，后续问题所需的用户特定信息通常仅从文本中即可恢复，多数记忆系统将图像交互简化为通用描述。然而图像常携带文本鲜少表述的个人信息——既包括显性证据（如重复出现的用户关联实体），也包括隐性证据（如从视觉或多模态线索推断出的潜在用户事实）。我们针对这两种证据形式提出了一项个人视觉记忆基准测试，并设计VisualMem架构——该混合视觉-文本系统在文本记忆后端之上增强了一个结构化个人视觉记忆模块。VisualMem并非将图像压缩为描述，而是利用对话上下文解析身份、所有权及持久性用户事实。实验表明，VisualMem在我们的基准测试中显著优于先前记忆系统，同时在标准文本记忆基准测试中保持竞争力，这证明个人视觉记忆是个性化人工智能代理长期记忆中独特且重要的组成部分。