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		<title>COSMOS Learns Neural Representations as Gauge Dynamics on the Grassmannian</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[CIFAR-10]]></category>
		<category><![CDATA[COSMOS]]></category>
		<category><![CDATA[Fashion-MNIST]]></category>
		<category><![CDATA[gauge dynamics]]></category>
		<category><![CDATA[geometric deep learning]]></category>
		<category><![CDATA[Grassmannian]]></category>
		<category><![CDATA[information dilution]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Neural Networks]]></category>
		<category><![CDATA[orthonormal bases]]></category>
		<category><![CDATA[Riemannian optimization]]></category>
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					<description><![CDATA[The COSMOS framework trains orthonormal subspace frames with conjugation-based gauge-style updates, holding geometric integrity at machine precision and matching a multilayer perceptron on Fashion-MNIST and CIFAR-10 at CPU scale.]]></description>
		
		
		
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