DeepSeek Solved AI’s Memory Problem
DeepSeek's learned token compression recovers 96% of text at 10x compression ratio
“We can recover more than 96% of the text. That is insane.”
DeepSeek published research using learned image-like compression to reduce AI context tokens by up to 90% while retaining over 96% text accuracy at 10x compression. The system converts text memories into compressed visual tokens via a specialized encoder with local window attention and a 16x convolutional compressor, explicitly designed to handle high-resolution documents without cost explosion. This matters because token cost at scale is a primary bottleneck for long-context AI applications, and a near-lossless 10x compression ratio could substantially reduce inference costs for document-heavy workloads.