New Framework Enables Vision-Language Models to Process Live Video Streams in Real Time
Researchers have introduced ARMS, a lightweight 800-million-parameter router designed to automatically select the most appropriate vision-language model (VLM) from a pool of candidates for any given image-text query. The system was trained on a newly constructed multimodal dataset covering outputs from seven mainstream VLMs across 32,626 unique queries, and incorporates VLM profile information to better represent both query and model capabilities. The work addresses a practical deployment challenge as the proliferation of VLMs with varying performance and resource costs makes manual model selection increasingly difficult.
As the number of available vision-language models grows, users face a 'performance paradox' in which no single model consistently outperforms others across all tasks, making selection difficult. To address this, researchers propose ARMS (Adaptive Router for Model Selection), a routing framework that takes an image-text query as input and predicts which VLM from a candidate pool is most likely to produce the best result. ARMS augments its input signals with structured VLM profiles and uses a purpose-built architecture to improve the representation of both queries and model capabilities. The team also constructed a dedicated multimodal benchmark dataset containing responses from seven mainstream VLMs on over 32,000 unique image-text queries to support training and evaluation. Two extension strategies—incremental training and independent training—allow ARMS to adapt to newly added VLMs without full retraining. Experiments on both in-distribution and out-of-distribution test sets show ARMS outperforming larger commercial models including GPT-4o, despite being orders of magnitude smaller. Code, models, and datasets are made publicly available through an anonymous repository.
What's missing
The study does not report computational cost or latency overhead introduced by the routing step itself, which is relevant for real-world deployment decisions. It is also unclear how ARMS performs when the candidate VLM pool grows substantially beyond seven models, or how sensitive results are to the quality and diversity of the profile information provided for each VLM. The paper has not yet undergone peer review, as it is a preprint.
What different sources said
- arXiv cs.AICenter
MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention
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