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		<title>Language Models Go Geometrically Blind When Predicting Material Properties</title>
		<link>https://bioengineer.org/language-models-go-geometrically-blind-when-predicting-material-properties/</link>
		
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		<pubDate>Mon, 12 Oct 2026 11:53:42 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[CC-cliff]]></category>
		<category><![CDATA[coordinate-category data]]></category>
		<category><![CDATA[crystal structures]]></category>
		<category><![CDATA[graph neural networks]]></category>
		<category><![CDATA[inductive bias]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[machine learning benchmarks]]></category>
		<category><![CDATA[materials science]]></category>
		<category><![CDATA[MatText]]></category>
		<category><![CDATA[property prediction]]></category>
		<category><![CDATA[scaling laws]]></category>
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					<description><![CDATA[A systematic study finds that large language models consistently fail to learn spatial coordinate information in materials property prediction, with scaling up models or data offering no meaningful fix and graph neural networks remaining far superior for structure-dominated tasks.]]></description>
		
		
		
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		<title>Why the Human Brain&#8217;s 20-Watt Budget Could Reshape Energy-Hungry AI</title>
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		<pubDate>Mon, 12 Oct 2026 11:06:53 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[adaptive inference]]></category>
		<category><![CDATA[data centers]]></category>
		<category><![CDATA[data movement]]></category>
		<category><![CDATA[energy efficiency]]></category>
		<category><![CDATA[Green AI]]></category>
		<category><![CDATA[human cognition]]></category>
		<category><![CDATA[in-memory computing]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[resource rationality]]></category>
		<category><![CDATA[scaling laws]]></category>
		<category><![CDATA[Sustainability]]></category>
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					<description><![CDATA[A sweeping new review maps seven interacting energy constraints in large-scale AI and argues that resource-allocation strategies from the 20-watt human brain can guide more sustainable language-model design.]]></description>
		
		
		
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