Streaming Video-LMM Is Not One SOTA Line: Six Development Paths from 2023 to 2026

Series guide. This is the development-history hub for my streaming Video-LMM notes. For a stage-by-stage systems view, see the pipeline survey. For dataset and protocol details, see the benchmark index. The complete reading map is at the end of this article. Calling a model streaming no longer identifies one technical problem. One paper may process a causal video prefix but retain every historical KV block. Another may keep a fixed cache but permanently forget old evidence. A proactive assistant may know the user’s request before the stream begins, while a delayed question-answering system must decide what to preserve before any question is known. A benchmark may measure the age of remembered evidence without measuring whether the implementation can keep up with a live camera. ...

September 2, 2026 · Updated September 3, 2026 · 25 min

SimpleStream

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. ...

June 18, 2026 · Updated June 19, 2026 · 2 min

Benchmarks for Streaming Video Understanding

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. ...

May 2, 2026 · Updated September 2, 2026 · 12 min