Personal Visual Memory from Explicit and Implicit Evidence

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 \emph{explicit} evidence, such as recurring user-associated entities, and \emph{implicit} evidence, such as latent user facts inferred from visual or multimodal cues. We introduce a benchmark for \emph{personal visual memory} that targets both forms of evidence, and propose \textsc{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, \textsc{VisualMem} uses conversational context to resolve identity, ownership, and durable user facts. Experiments show that \textsc{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.