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    <title>An Information-Theoretic Definition for Open-Ended Learning</title>
    <link>https://www.vishalv.com/notes/xuInformationTheoreticDefinitionOpenEnded2026</link>
    <description>A growing body of work points to the great promise of AI systems that can continually expand their capabilities as they operate in an open-ended environment. But yet there is no coherent definition of open-endedness or theory about how an agent ought to explore an open-ended...</description>
    <pubDate>Tue, 04 Aug 2026 00:00:00 GMT</pubDate>
    <author>vishalvignesh.iitm@gmail.com (Vishal V)</author>
    <category>bit-equivalent</category><category>probability-space</category><category>bandit-environment</category><category>nat</category><category>data-processing-inequality</category><category>kernelized-bandit-optimization</category><category>gaussian-process</category><category>mercer-decomposition</category><category>spectral-tail</category><category>matern-kernel</category><category>squared-exponential-kernel</category><category>thompson-sampling</category><category>fixed-truncation</category><category>gp-ucb</category>
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