arXiv:2607.02484v1 Announce Type: cross Abstract: Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instructions and fine-grained queries. In this paper, we investigate this failure and identify two underlying bottlenecks: the widespread dispersion of textual noise that corrupts dense cross-modal scoring, and the feature fragmentation inherent to standard token selection. To address these issues, we propose Entropy-Aware Dense Pruning (EADP), a framework that reformulate
The rapid expansion of large vision-language models (VLMs) and the increasing complexity of visual data necessitate more efficient processing methods to manage computational costs and improve performance.
Improving the efficiency of visual token processing directly accelerates VLMs, impacting the commercial viability and scalability of high-fidelity AI applications across various industries.
This advancement changes how VLMs handle visual information, potentially making them faster, more robust to noisy data, and capable of more nuanced understanding with less computational overhead.
- · AI developers
- · Cloud computing providers
- · Companies using VLMs
- · Hardware manufacturers
- · Inefficient VLM architectures
VLMs become more efficient and capable of handling complex visual queries.
Reduced computational costs for visual AI operations could accelerate the adoption of advanced VLMs in more use cases.
This could lead to a broader integration of sophisticated visual AI into edge devices or resource-constrained environments.
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Read at arXiv cs.AI