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		<title>Quantum-Inspired AI Reads Malware Like a Language to Catch Evolving Threats</title>
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		<pubDate>Mon, 05 Oct 2026 23:07:47 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[abstract syntax trees]]></category>
		<category><![CDATA[adversarial robustness]]></category>
		<category><![CDATA[Cluster Computing]]></category>
		<category><![CDATA[cybersecurity]]></category>
		<category><![CDATA[graph analysis]]></category>
		<category><![CDATA[L-moments]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[malware detection]]></category>
		<category><![CDATA[meta-learning]]></category>
		<category><![CDATA[quantum-inspired neural networks]]></category>
		<category><![CDATA[Reinforcement Learning]]></category>
		<category><![CDATA[Self-Supervised Learning]]></category>
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					<description><![CDATA[Researchers have built a hybrid malware detection framework combining abstract syntax trees, L-moments, graph analysis, and quantum-inspired neural networks that achieved 98.62 percent accuracy on real-world malware data.]]></description>
		
		
		
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