Luma-gfee-bustyy |verified| < 90% ULTIMATE >

The experimental results indicate a substantial performance gain for the proposed framework.

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Where the weight $w_i$ is a function of the predictive uncertainty $\sigma_i$ output by the LUMA attention layer. Specifically, $w_i = 1 / \sigma_i^2$. This forces the model to prioritize fitting data points where the model is confident, while allowing high-variance outliers to exert less influence, resulting in a more robust decision boundary. Specifically, $w_i = 1 / \sigma_i^2$

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