Video · Two Minute Papers ·

Why DeepSeek Wants AI To Forget

This Two Minute Papers video covers DeepSeek's OCR paper (DeepSeek-OCR), which reduces the token cost of processing long documents by rendering text as an image and compressing it with a learned, token-based method that works in a JPEG-like way. At 10x compression the system recovers more than 96% of the text, while near 20x compression OCR accuracy falls to about 60%. The key engineering approach uses local window attention, a 16x convolutional compressor, and global visual processing so it can handle high-resolution documents without exploding in cost. The authors say the approach can also generate training data for LLMs and VLMs at over 200,000 pages per day on a single A100 GPU. The paper notes limitations: OCR alone does not prove true text compression, and the model may rely heavily on language priors rather than visual memory when images are too compressed.

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