Abstract: Over the past decades, deep learning has evolved from Recurrent Neural Networks to Convolutional Neural Networks and later Transformers, each architecture attempting to balance depth, expressivity, and training stability. Despite their successes, layer-wise training remains costly, fragile, and sometimes overengineered. We introduce Flash, a paradigm that computes optimal solutions on-the-fly without pre-trained weights, deep layers, or backpropagation. Flash leverages instantaneous contextual computation to generate accurate outputs for a wide range of tasks, including ImageNet classification and GLUE benchmark evaluations, effectively bypassing traditional gradient-based optimization. Experiments demonstrate that Flash produces reliable results instantly, even in scenarios where conventional neural networks require extensive tuning. Flash is enough.
(Disclaimer: This abstract is a humorous fake and not based on actual research.)
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