# A Low-Latency Interactive System for Real-Time Video Understanding Based on VLMs

arXiv: https://arxiv.org/abs/2609.13986 · HTML: https://mlsanity.com/paper/2609.13986/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 12 Sep 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Abstract (arXiv)

Vision-language models are extending video understanding from offline clip analysis to continuous interactive streaming, but most research still emphasizes model capability rather than deployable low-latency interaction. This paper presents a unified edge-cloud system for real-time video VLM applications. Lightweight phone, smart glasses, PC, and pseudo-replay clients publish video and speech to a server runtime that provides shared ASR/TTS, session orchestration, backend adaptation, response delivery, and archive-backed measurement. The system integrates six representative video VLM backends with streaming or interaction-oriented capabilities and evaluates them across backend runtime, media transport, client-observed latency, and interaction behavior. With suitable backend selection and the WebRTC path, the tested system reaches approximately 0.9 to 1.0 s to first VLM text and 1.3 to 1.5 s to first non-silent TTS audio, while exposing backend adaptation costs and differences in real-time interaction behavior.