Paper

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: Accelerating Streaming Video Large Language Models via Hierarchical Token Compression PDF: CVPR 2026 Open Access Code: lern-to-write/STC Release History STC first appeared as an arXiv preprint on November 30, 2025. The current arXiv version was uploaded on February 11, 2026, and the paper was subsequently accepted by CVPR 2026. It is therefore an older preprint that later received a 2026 conference label, rather than a method first released with the conference proceedings. ...

Paper: WeaveTime: Streaming from Earlier Frames into Emergent Memory in VideoLLMs Preprint (earlier title: Stream from Earlier Frames): arXiv:2602.22142 PDF: CVPR 2026 Open Access Supplement: CVPR 2026 Supplementary Material Project: WeaveTime Code: zhangyl4/weavetime Series guide. PCDF bounds when and how much history is read, not how much ReKV-style history is stored. See the bounded-memory survey for the resource comparison and the development-history hub for the complete series. Core Idea WeaveTime can be summarized as: ReKV-style historical visual KV memory + temporal-order SFT + uncertainty-gated coarse-to-fine retrieval. ...

Paper: StreamingVLM: Real-Time Understanding for Infinite Video Streams Project: StreamingVLM Code: mit-han-lab/streaming-vlm Datasets: Inf-Stream-Train / Inf-Stream-Eval Core Idea StreamingVLM is designed for a different target from most streaming VideoQA papers: it keeps watching a video and producing synchronized commentary, instead of waiting for a future question and then retrieving historical evidence. Its main idea is to align a simple streaming inference cache with the context pattern used during supervised fine-tuning. At inference time, the model keeps only: ...

Paper: A Simple Baseline for Streaming Video Understanding Project: SimpleStream Code: EvolvingLMMs-Lab/SimpleStream Core Idea SimpleStream is a deliberately simple streaming baseline: when a question arrives, keep only the most recent N observed frames and feed them with the question to an off-the-shelf VLM. It uses: 1 fps sampling under the observed-only streaming protocol; a short recent-frame window, usually 2 / 4 / 8 frames; Qwen2.5-VL-7B or Qwen3-VL-8B as the base VLM; no new memory bank; no retrieval module; no KV cache compression; no extra training. So this is not a new architecture. It is a strong recency baseline for asking whether complex streaming memory is actually helping. ...

Paper: MuKV: Multi-Grained KV Cache Compression for Long Streaming Video Question-Answering Proceedings: CVPR 2026 Open Access Code: IMBALDY/MuKV Series guide. A fixed per-question read can still sit on a growing multi-grained archive. See the bounded-memory survey for that accounting and the development-history hub for the complete series. Background Long streaming VideoQA has a simple but painful constraint: the video keeps arriving, while the future user questions are unknown. KV-cache methods such as ReKV make this setting more practical. Instead of recomputing historical video tokens when a question arrives, the model can prefill the video stream in advance, store the visual KV cache, retrieve the relevant cache blocks later, and answer with much lower online cost. ...

Paper: ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning PDF: AAAI Proceedings PDF Code: AI-Researcher-Team/ViG-RAG Background Long-video RAG is harder than text RAG because video evidence is not just a list of documents. Useful information may be distributed across: visual scenes; speech transcripts; entities and events; temporal order; uncertain or noisy observations. If we simply split the video into independent chunks and retrieve by static text similarity, two problems appear: ...

Paper: AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understanding Code: xzc-zju/AdaVideoRAG Background Long-video understanding is hard because the useful evidence is sparse, long-range, and often spread across multiple modalities: visual content; speech; scene text; temporal relations. RAG is a natural fit here. Instead of feeding the whole video to the MLLM every time, the system can first build a searchable memory, retrieve relevant evidence, and then answer with a smaller context. But a fixed VideoRAG pipeline is not ideal. Easy questions may not need retrieval at all, while hard questions may need structured graph reasoning. ...

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. ...