Video-LLM

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. ...

Series guide. This article is the evaluation-protocol index. Start with the development-history hub for the six research paths, and use the pipeline survey for the stage-by-stage architecture view. This post is a small index for the benchmarks that appear repeatedly in recent streaming video / long-video VLM papers. The main split is simple: online streaming benchmarks test whether the model can answer while the video is still coming in; offline long-video benchmarks test long-context video understanding, but usually assume the whole video is already available; standard video QA benchmarks are useful for comparability, but they are not the real target of streaming-memory papers. The tables below are copied or compacted from the corresponding method papers. They should not be read as one unified leaderboard: backbones, frame rates, memory budgets, judge versions, subtitle settings, and dataset splits often differ. ...

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. ...

Series guide. This article is the stage-by-stage architecture map. Start with the development-history hub for the six research paths, and use the benchmark index for evaluation settings and protocols. Related papers: ReKV: Paper / Code StreamKV: Paper / Code InfiniPot-V: Paper / Code StreamMem: Paper LiveVLM: Paper / Code StreamingTOM: Paper / Code rLiVS: Paper / Code Core Question All of these papers are trying to solve the same systems problem: When a video stream keeps arriving, the user question is not known yet, and GPU memory is limited, how should a Video-LLM process the stream, compress memory, retrieve evidence, and generate an answer with low latency? ...

Paper: StreamingTOM: Streaming Token Compression for Efficient Video Understanding Code: YIGE24/StreamingTOM Background Streaming video understanding has two constraints that offline video understanding does not really need to respect: causality: the model cannot use future frames to decide how to compress current frames; accumulation: tokens and KV cache keep growing as the video stream becomes longer. Most recent training-free streaming methods mainly work on the post-LLM KV cache: ReKV stores historical KV blocks and retrieves relevant ones at question time; StreamKV improves the segmentation / compression / retrieval pipeline; InfiniPot-V and StreamMem keep a bounded KV memory with query-agnostic compression; LiveVLM combines query-agnostic KV compression with query-time retrieval. These methods are useful, but they still have one important blind spot: ...

Paper: LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval Code: sjtu-zhao-lab/LiveVLM Acceptance: DAC 2026 program Version note. This article follows the substantially revised arXiv v2 (2026-04-23) / DAC 2026 method: Vision Sink Bucketing and Position-agnostic KV Retrieval. The 2025 v1 used a different chunk-compression and FIFO mechanism; its results should not be mixed with the v2 design. Series guide. For the distinction between bounded VSB state and query-time PaR reads, see the bounded-memory survey. The development-history hub places LiveVLM in the complete series. ...

Paper: StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding Project: StreamMem Series guide. For how proxy-guided retention fits between exact archives and summary-state memory, see the bounded-memory survey. The development-history hub places this method in the complete series. 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. ...

Paper: Recurrent Attention-based Token Selection for Efficient Streaming Video-LLMs Code: vdorovatas/rLiVS Background Streaming video understanding is hard because the model must process incoming frames online, keep useful past information, and still answer questions with low latency. The brute-force solution is to put as many frames as possible into the context window, but this quickly becomes too expensive for long videos. Recent papers handle this in different ways: ReKV keeps rich visual memory in the form of KV cache and retrieves it later, but memory and latency are still significant. Goldfish stores only captions for each short clip, which is cheap, but clip-to-clip continuity can be weak. rLiVS tries to sit between these two directions: ...

Paper: InfiniPot-V: Memory-Constrained KV Cache Compression for Streaming Video Understanding Preprint: arXiv:2506.15745 Research reimplementation: aiha-lab/InfiniPot-V Series guide. For why a fixed retained KV cache differs from a bounded read over a growing archive, see the bounded-memory survey. The development-history hub places this shift in the complete series. Background Streaming video understanding is more constrained than offline long-video understanding. In offline settings, the model can see the whole video first, maybe even the user query first, and then decide how to compress tokens or KV cache. But in streaming settings: ...