DooDooLamb News
China publishes 'landmark paper' on AI-to-AI technique that kicks human 'bottleneck' out of the loop and replaces us with an AI 'modem' — C2C brainwave direct connection achieves 150% boost in inference speed
Brief published September 26, 2026 · Original source published September 25, 2026
Original reporting by Efosa Udinmwen at techradar.com.
Automated brief. Verify important details at the original source.
What happened
Researchers at Tsinghua University published a method called Cache-to-Cache (C2C) that lets separate AI models exchange internal representations directly, bypassing the step of generating human-readable text between them. A learned component called a Fuser converts one model's internal data into a format another model can use. The paper claims this approach yields a 150% boost in collaborative inference speed along with accuracy improvements, removing what the researchers describe as a human-language bottleneck in multi-model pipelines.
Why it matters
If the claimed gains hold under independent scrutiny, C2C could reshape how multi-model AI systems are architected. Builders designing agentic or ensemble pipelines currently rely on text-passing between models, which adds latency and potential information loss. A verified direct-representation exchange layer would change assumptions about where bottlenecks sit in those systems.
What to watch
The 150% speed figure and accuracy claims come from the publishing team and have not been independently replicated. Watch for third-party benchmarks testing C2C across diverse model pairings and whether the Fuser generalizes beyond the architectures tested in the paper.