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A CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance and produces interpretable change-point annotations, making it practical for deployment in high-stakes applications.
A principled reward decomposition framework is introduced that decouples verifiability from sparsity, and two new reward functions are proposed: an exponential reward that provides graded penalties based on the count of forbidden-concept occurrences, and a PageRank inspired reward that weights penalties by semantic imp...
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