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This paper presents a comparative evaluation of post-hoc analysis methods (Random Forest surrogate, LIME, PCA) for detecting structured patterns in clustering results, using synthetic datasets with injected patterns. It finds that none of the methods consistently detects all pattern types, highlighting a gap in existing explainability tools.
A chess coaching tool that uses an LLM to explain moves in natural language like a grandmaster, replacing raw engine evaluations with contextual coaching narratives. It analyzes user games from Chess.com and Lichess with local Stockfish, detects recurring mistakes, and offers personalized chat and spaced repetition for blunders.