AI RESEARCH
VoodooNet: Achieving Analytic Ground States via High-Dimensional Random Projections
arXiv CS.AI
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ArXi:2604.15613v1 Announce Type: cross We present VoodooNet, a non-iterative neural architecture that replaces the stochastic gradient descent (SGD) paradigm with a closed-form analytic solution via Galactic Expansion. By projecting input manifolds into a high-dimensional, high-entropy "Galactic" space ($d \gg 784$), we nstrate that complex features can be untangled without the thermodynamic cost of backpropagation.