Paper

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: ...

Paper: StreamKV: Streaming Video Question-Answering with Segment-based KV Cache Retrieval and Compression Code: sou1p0wer/StreamKV Background Streaming video question-answering (StreamingVQA) requires a model to continuously process incoming video, preserve useful historical context, and answer questions online with low latency. ReKV showed that video QA can be reformulated as retrieve relevant KV caches first, then answer with the retrieved KV. But it still has several weaknesses: It uses uniform segmentation, which may cut through semantic boundaries. It keeps essentially the whole historical visual context, so memory usage is still large. Its retrieval strategy is not flexible enough, especially when the useful information is distributed differently across layers. Core Idea StreamKV extends the ReKV line in two directions at the same time: ...

Series guide. This note audits one method in detail. Start with the development-history hub for the field-level map, or use the pipeline survey to place ReKV’s write, index, and read stages in a complete system. The bounded-memory survey compares its growing exact-KV archive with later fixed-state methods. Paper: Streaming Video Question-Answering with In-context Video KV-Cache Retrieval Proceedings: ICLR 2025 Preprint: arXiv:2503.00540 Code: Becomebright/ReKV ReKV was first public on arXiv on 2025-03-01 and was published at ICLR 2025. The paper, appendix, and official code were rechecked on 2026-09-03. Unless a sentence is explicitly marked as my interpretation, mechanism and measurement claims below describe the authors’ paper or released code. ...