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DeepSeek-V4: Breaking the Efficiency Barrier in Million-Token AI

Imagine trying to read a book with a million pages, but you can only see one word at a time. That is the fundamental challenge that has plagued large language models when dealing with ultra-long contexts. The attention mechanism, which is the heart of modern AI, grows quadratically in complexity as the input length increases. A 1,000-token prompt is manageable. A 10,000-token prompt starts to hurt. A 1,000,000-token prompt is a computational nightmare. For years, the AI community has grappled with this problem. The emergence of reasoning models has established a new paradigm of test-time scaling, where models spend more computational effort during inference to achieve better results. But this scaling has been fundamentally constrained by the quadratic cost of attention. Every extra token of reasoning costs exponentially more. This has been a major bottleneck for long-horizon tasks like agentic workflows, massive cross-document analysis, and complex multi-step problem solving. Enter Dee...

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