Memento: Toward an All-Day Proactive Assistant for Ultra-Long Streaming Video

Multimodal large language models have demonstrated impressive capabilities in visual-language understanding, particularly in offline video tasks. More recently, the emergence of online video modeling has introduced early forms of active interaction. However, existing models, typically limited to tens of minutes, are not yet capable of all-day proactive understanding over ultra-long video streams. They struggle to maintain long-term context online, as they suffer from token accumulation and lack scalable memory mechanisms. These limitations hinder critical tasks such as reminding users that medication was taken hours earlier—an ability that exemplifies the shift from reactive to memory-oriented assistants with long-term reasoning. To bridge this gap, we present Memento, the first proactive vision-language framework for ultra-long streaming video. To avoid token growth and support scalable long-duration understanding, we introduce Dynamic Memory and Query-related Memory Selection, enabling sparse memory retention and efficient retrieval. To address the training challenges of memory-based modeling, we propose Step-Aware Memory Attention, which aligns memory access with temporal steps for stable supervision. To support both training and evaluation of active, long-term behavior, we construct Memento-54K and MementoBench, a dataset-benchmark suite covering diverse tasks on text, object, and action across video streams up to 7 hours. Experiments demonstrate that Memento achieves superior performance, paving the way toward reliable all-day proactive video assistants.