arXiv:2510.19496v3 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) commonly process images at native or high resolution to remain effective across tasks. This inflates visual tokens ofter to 97-99% of total tokens, resulting in high compute and latency, even when low-resolution images would suffice. We introduce \emph{CARES}-a \textbf{C}ontext-\textbf{A}ware \textbf{R}esolution \textbf{S}elector, a lightweight preprocessing module that, given an image-query pair, predicts the \emph{minimal} sufficient input resolution. CARES uses a compact VLM (350M) to extract featu
The proliferation of increasingly complex VLMs and the growing demand for efficient AI inference are driving innovation in computational optimization techniques.
This development addresses a critical bottleneck in VLM deployment, significantly reducing compute requirements and latency, which is essential for scaling AI applications.
VLMs can now process visual data more efficiently, lowering operational costs and enabling broader, faster integration into various systems without a proportional increase in hardware investment.
- · VLM developers
- · AI cloud providers
- · Companies deploying AI at scale
- · Edge AI hardware manufacturers
- · Providers of inefficient, high-compute AI solutions
- · Companies without access to optimization research
Reduced operational costs for AI inference will enable more widespread adoption of advanced vision-language models.
The accessibility of efficient VLMs will accelerate the development of new AI applications, especially in areas constrained by computational resources.
Increased efficiency could intensify competition among AI service providers, potentially leading to lower costs for end-users and more pervasive AI integration into daily life and industry.
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Read at arXiv cs.LG