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@@ -384,7 +384,7 @@ <h2>About</h2>
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                     As an AI researcher in general, I’m a fan of <i>compression is intelligence</i>, but <b>NOT</b> a fan of <i>scaling laws</i>. To achieve better compression, the following questions have to be answered.
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                         <li>What’s the alternative to scaling laws for achieving better compression? Should we combine weights-based compression with symbol-based compression (<i>e.g.</i>, language)? And how?</li>
                         <li>Closely related to the first question, what tasks are interpolative, and what are not? Our understanding of this question is constantly evolving (<i>e.g.</i>, how many people considered learning natural language grammar an interpolative task a decade ago?). Fortunately, I believe most agent tasks today are still viewed as non-interpolative, which motivates us to explore solutions beyond scaling laws using agent tasks as testbeds.</li>
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