arXiv:2605.29657v1 Announce Type: cross Abstract: Vision-language models (VLMs) rely on long visual token sequences for visual understanding, making the prefill stage expensive in both computation and memory. Most existing pruning methods follow an absolute-ranking paradigm, assigning importance scores to visual tokens and retaining a fixed top-K subset. In this work, we argue that this paradigm is fundamentally brittle: attention sinks distort token importance rankings, while image redundancy and query-dependent visual evidence make fixed token budgets unreliable across inputs. We propose Occ
The rapid proliferation and increasing scale of VLMs necessitate more efficient inference methods to reduce operational costs and computational demands.
Improving VLM inference efficiency directly impacts the economic viability and scalability of advanced AI applications, making sophisticated models more accessible and affordable.
This innovation offers a training-free, budget-adaptive token pruning method that reduces computational and memory costs for VLMs, enabling more efficient deployment.
- · AI cloud providers
- · Companies deploying VLMs
- · Researchers in computer vision
- · Inefficient inference hardware providers
Reduced operational costs for large-scale VLM deployments will accelerate their adoption across various industries.
Increased accessibility to advanced VLMs could foster new AI applications and services that were previously cost-prohibitive.
The enhanced efficiency might alleviate some pressure on compute infrastructure, potentially impacting demand for certain types of specialized hardware.
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Read at arXiv cs.AI