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		<title>AI Models Flunk Engineering Simulation Test in Massive New Benchmark</title>
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		<pubDate>Fri, 09 Oct 2026 06:13:54 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[AI evaluation]]></category>
		<category><![CDATA[benchmark]]></category>
		<category><![CDATA[Carnegie Mellon University]]></category>
		<category><![CDATA[Communications Engineering]]></category>
		<category><![CDATA[engineering simulations]]></category>
		<category><![CDATA[finite element analysis]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Mechanical Engineering]]></category>
		<category><![CDATA[OpenSeeSimE]]></category>
		<category><![CDATA[question answering]]></category>
		<category><![CDATA[vision-language models]]></category>
		<category><![CDATA[visual reasoning]]></category>
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					<description><![CDATA[A new 200,000-question benchmark from Carnegie Mellon University reveals that leading vision-language models perform at random chance when interpreting engineering simulation outputs, despite excelling at general visual reasoning tasks.]]></description>
		
		
		
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