Large vision-language models (VLMs) are enabling interactive video reasoning, giving rise to streaming long-video understanding. In this setting, frames arrive continuously, while the system preserves long-term context and generates responses under strict latency constraints. A central challenge is KVCache management: as video streams grow, KVCache expands rapidly, increasing computation and memory overhead. Existing retrieval-based approaches exploit attention sparsity and offload inactive KVCache from GPU to CPU memory, but their token-level design causes high management overhead and fragmented data movement. We present Mosaic, the first cluster-driven VLM inference system for streaming long-video understanding. Our key insight is that VLM KVCache exhibits an implicit cross-modal clustering structure: retrieved KV states form groups jointly shaped by visual coherence and semantic relevance. Based on this observation, Mosaic uses cross-modal clusters as the basic unit of KVCache organization, maintenance, and retrieval. Evaluations show that Mosaic outperforms state-of-the-art baselines, achieving up to 1.38x speedup.
@article{arxiv.2604.10060,
title = {Mosaic: Cross-Modal Clustering for Efficient Video Understanding},
author = {Tuowei Wang and He Zhou and Chengru Song and Qiushi Li and Ju Ren},
journal= {arXiv preprint arXiv:2604.10060},
year = {2026}
}