Compression

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: 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: 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. ...

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: 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: 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: ...