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	<title>generalisation &#8211; BIOENGINEER.ORG</title>
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		<title>AI Camera Teams Collapse When a Sensor Fails, New Benchmark Reveals</title>
		<link>https://bioengineer.org/ai-camera-teams-collapse-when-a-sensor-fails-new-benchmark-reveals/</link>
		
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		<pubDate>Mon, 12 Oct 2026 21:10:12 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[active perception]]></category>
		<category><![CDATA[benchmark]]></category>
		<category><![CDATA[count-invariance]]></category>
		<category><![CDATA[generalisation]]></category>
		<category><![CDATA[MAPPO]]></category>
		<category><![CDATA[MATE-Hard]]></category>
		<category><![CDATA[multi-agent reinforcement learning]]></category>
		<category><![CDATA[multi-camera tracking]]></category>
		<category><![CDATA[QMIX]]></category>
		<category><![CDATA[Set-Transformer]]></category>
		<category><![CDATA[value decomposition]]></category>
		<category><![CDATA[zero-shot transfer]]></category>
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					<description><![CDATA[A new benchmark called MATE-Hard shows that cooperative multi-agent reinforcement learning policies lose roughly half their advantage over random behavior when the number of cameras shrinks, exposing a count-invariance problem shared across all major algorithm families.]]></description>
		
		
		
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