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Guangdong University of Technology
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By explicitly aligning semantic representations across modalities, SECOS enables open-world semi-supervised learning models to directly predict textual labels without post-processing, achieving state-of-the-art classification accuracy.
Structured composition unlocks significantly better agent performance compared to flat skill invocation, even with the same skill set.
