arXiv:2605.00392v3 Announce Type: replace-cross Abstract: DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods for conventional vision-language models (VLMs) fail to preserve textual fidelity due to improper compression mechanisms. By analyzing the decoding process of DeepSeek-OCR, we find that a distinct two-stage reading trajectory: the model initially prioritizes the majority of high-norm tokens, then subsequently re
The proliferation of complex AI models like DeepSeek-OCR necessitates continuous innovation in efficiency and resource optimization to enable broader deployment and lower operational costs.
Efficient inference for large language models, particularly those involving vision-language tasks, is critical for scaling AI applications, reducing computational demands, and speeding up real-world deployments.
This development suggests a potential improvement in the efficiency of DeepSeek-OCR and similar visual-text compression models, leading to faster and more resource-effective AI inference for OCR tasks.
- · AI developers
- · Cloud providers
- · Industries relying on OCR
- · DeepSeek-OCR users
- · Inefficient inference methods
- · High-latency OCR service providers
Faster and cheaper processing of visual documents and long texts through AI.
Increased adoption of advanced OCR and VLM technologies in business processes where cost and speed were previously limiting factors.
Further democratization of AI, as more complex models become accessible and deployable on a wider range of hardware due to improved efficiency.
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Read at arXiv cs.LG