arXiv:2606.11576v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive inference cost due to large visual contexts and long decoding chains. We view this cost through two coupled axes: Visual Context Scaling (VCS), which controls how much visual evidence is passed to the language model, and Visual Reasoning Scaling (VRS), which controls how much inference-time reasoning search is performed. Existing methods typically optimize one axis at a time, leaving the joint allocatio
The proliferation of advanced Vision-Language Models creates an urgent need for more efficient inference methods as computational costs become a bottleneck.
Improving efficiency in VLMs directly impacts their deployment practicality and accessibility, potentially lowering the barrier to entry for diverse applications and democratizing advanced AI.
This research introduces a method to reduce the prohibitive inference cost associated with large visual contexts and long decoding chains in VLMs, making them more scalable and cost-effective.
- · AI developers
- · Cloud computing providers
- · Industries deploying VLMs
- · Inefficient VLM architectures
VLMs become more economically viable for high-volume, real-world applications due to reduced inference costs.
Increased adoption of VLMs could accelerate innovation in multimodal AI applications across various sectors.
More efficient VLMs might intensify demand for specialized hardware optimized for multimodal processing, impacting the compute supply chain.
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Read at arXiv cs.AI