# Vista: Scene-Aware Optimization for Streaming Video Question Answering under Post-Hoc Queries

Source: [Vista: Scene-Aware Optimization for Streaming Video Question Answering under Post-Hoc Queries](<https://www.sota2.com/research/paper/698abbe1c97a0b3e3b5de2a6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-11T05:00:13.386Z

Authors: Haocheng Lu, Nan Zhang, Wei Tao, Xiaoyang Qu, Guokuan Li, Jiguang Wan, Jianzong Wang

Published: 2026-02-09T10:00:22.000Z

## Abstract

Streaming video question answering (Streaming Video QA) poses distinct challenges for multimodal large language models (MLLMs), as video frames arrive sequentially and user queries can be issued at arbitrary time points. Existing solutions relying on fixed-size memory or naive compression often suffer from context loss or memory overflow, limiting their effectiveness in long-form, real-time scenarios. We present Vista, a novel framework for scene-aware streaming video QA that enables efficient and scalable reasoning over continuous video streams. The innovation of Vista can be summarized in three aspects: (1) scene-aware segmentation, where Vista dynamically clusters incoming frames into temporally and visually coherent scene units; (2) scene-aware compression, where each scene is compressed into a compact token representation and stored in GPU memory for efficient index-based retrieval, while full-resolution frames are offloaded to CPU memory; and (3) scene-aware recall, where relevant scenes are selectively recalled and reintegrated into the model input upon receiving a query, enabling both efficiency and completeness. Vista is model-agnostic and integrates seamlessly with a variety of vision-language backbones, enabling long-context reasoning without compromising latency or memory efficiency. Extensive experiments on StreamingBench demonstrate that Vista achieves state-of-the-art performance, establishing a strong baseline for real-world streaming video understanding.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Streaming Video Understanding on StreamingBench](<https://www.sota2.com/research/sota/streaming-video-understanding-on-streamingbench.md>): Streaming Video Understanding; StreamingBench; Overall: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.08448>)

[PDF](<https://arxiv.org/pdf/2602.08448.pdf>)