A new paper out of KRICT and KAIST just put a number on something Qubic's architecture has been built around since 2022. Multiple AI agents working in coordination reduce extrapolation error by up to six orders of magnitude versus isolated deep neural networks. Up to a million model parameters compress into 5 to 40 interpretable ones. Tested across deterministic and previously uncharacterized dynamics. The collective recovered the underlying governing equations across all of them. @c___f___b  described the design premise of Qubic's coordinated mining layer this way: "The work of one miner benefits from the work of another miner, and their combined work is greater than the sum of their works measured separately." That is not a metaphor. The MCI paper is empirical confirmation of the mechanism. The researchers tested on a small lab cluster and noted the obvious next question: what happens when this runs at network scale? Qubic's 676 Computors and broader mining base sit directly on the other side of that question.  Coordinated, network-scale machine intelligence is what Aigarth is being built for. The frontier is catching up to the foundation.

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