China publishes 'landmark paper' on AI-to-AI technique that kicks human 'bottleneck' out of the loop…
- Cache-to-Cache lets separate AI models exchange internal information without generating text
- A learned Fuser converts one model’s internal data for another
- C2C uses selective gating to control which layers receive information
Researchers from Tsinghua University have published a paper describing a technique that lets separate AI models exchange information without producing any text.
The method, called Cache-to-Cache (C2C), has already been accepted at ICLR 2026 and ships with open-source code available to developers.
It targets a specific inefficiency present whenever multiple language models work together inside a shared pipeline.
Skipping words entirely
When two AI models cooperate today, one has to turn its thinking into written sentences before the other can read them.
That writing step takes real computing time and throws away small details buried inside the first model's raw thinking process.
Every AI model keeps a working memory of everything it has processed so far, known technically as...
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