arXiv:2607.04593v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities across different tasks, but their computational cost is dominated by the large number of visual tokens fed to the language model. Existing token reduction methods rely on attention-based scores or pairwise similarity, without an explicit semantic representation of each token. We introduce TORINO (TOken Reduction via Interpretable coNcept Overlap), a plug-and-play framework for adaptive visual token reduction in VLMs that requires no fine-tuning of the underlying model. TORI
The proliferation of Vision-Language Models and the increasing demand for their deployment across various applications highlight the urgent need for computational efficiency, making token reduction research particularly timely.
Reducing the computational cost of VLMs without fine-tuning can significantly broaden their accessibility and deployment potential, influencing the economics and scalability of VLM-powered services.
This research introduces a novel, plug-and-play method for adaptive visual token reduction in VLMs based on interpretable concept overlap, potentially lowering the barrier to entry for VLM applications.
- · VLM developers
- · Cloud providers (due to increased VLM usage)
- · AI application developers
- · Edge AI computing
- · Companies heavily reliant on existing inefficient VLM architectures
VLMs become more efficient and cheaper to run, especially in inference.
Increased adoption of VLMs in a wider range of applications, including those with computational constraints.
New classes of AI applications become feasible on lower-cost hardware, potentially democratizing advanced AI further.
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Read at arXiv cs.AI