Recent advances in video-language models have enabled powerful applications like video retrieval, captioning, and assembly. However, executing such multi-stage pipelines efficiently on mobile devices remains challenging due to redundant model loads and fragmented execution. We introduce Atom, an on-device system that restructures video-language pipelines for fast and efficient execution. Atom decomposes a billion-parameter model into reusable modules, such as the visual encoder and language decoder, and reuses them across subtasks like captioning, reasoning, and indexing. This reuse-centric design eliminates repeated model loading and enables parallel execution, reducing end-to-end latency without sacrificing performance. On commodity smartphones, Atom achieves 27--33% faster execution compared to non-reuse baselines, with only marginal performance drop (≤ 2.3 Recall@1 in retrieval, ≤ 1.5 CIDEr in captioning). These results position Atom as a practical, scalable approach for efficient video-language understanding on edge devices.
@article{arxiv.2512.17108,
title = {Atom: Efficient On-Device Video-Language Pipelines Through Modular Reuse},
author = {Kunjal Panchal and Saayan Mitra and Somdeb Sarkhel and Haoliang Wang and Ishita Dasgupta and Gang Wu and Hui Guan},
journal= {arXiv preprint arXiv:2512.17108},
year = {2025}
}