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We define the (Data-Efficient Structural Inference Network) as an architecture designed to maximize information density per parameter. Unlike standard GNNs that aggregate neighbor features indiscriminately, Desi Nets utilize attention-based geometric priors. This allows the model to converge on structural tasks (like protein folding or motion capture) with significantly less training data and fewer weights, creating an optimized binary for deployment.

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