Memory

Paper: OpenReview PDF: Memento: Toward an All-Day Proactive Assistant for Ultra-Long Streaming Video Supplement: OpenReview Supplementary Material Base implementation: showlab/videollm-online Core Idea Memento turns a streaming Video-LLM into a persistent visual monitor. A user registers a request such as tell me every time I hold a phone, and the model keeps observing the stream, updating its history, and deciding at every frame whether to remain silent or respond. The method can be summarized as: ...

Paper: OASIS: On-Demand Hierarchical Event Memory for Streaming Video Reasoning PDF: CVPR 2026 Open Access Code: Solus-sano/OASIS Core Idea OASIS is a training-free external memory and agentic RAG system for streaming video. It organizes the past into an online event hierarchy, reasons from a small working context by default, and retrieves historical keyframes only when the MLLM decides that more evidence is needed. The method can be summarized as: short/medium visual working memory + an online tree of event summaries and keyframes + prompt-based history routing + planned-query retrieval. ...

Paper: Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge Code: hmxiong/StreamChat Background Most Video-LLMs are still awkward in a real streaming setting. Offline video QA usually assumes: the whole video is already available; the question is known before inference; the interaction is single-turn. But a streaming assistant has a different problem: video frames keep arriving; the user may ask questions at arbitrary timestamps; the system should remember previous conversation turns; the answer should come back with low latency. This is close to the motivation of ReKV, Flash-VStream, LiveVLM, and rLiVS, but StreamChat chooses a different abstraction. ...

Paper: StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding Background Streaming video understanding is hard because the model has to process frames as they arrive, without knowing: how long the video will be; what future user questions will ask; which past details will become important later. For long videos, the visual tokens and their KV cache keep growing over time. Even if a long-context MLLM can technically accept many tokens, storing and attending to all historical KV entries is still expensive. ...