AINeutralarXiv – CS AI · 4h ago5/10
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Elo-Disentangled Player-Style Embeddings for Human Chess via Rating-Conditioned Residual Move Model
Researchers developed a machine learning approach that separates chess playing strength (Elo rating) from individual player style by using a rating-conditioned base model combined with learned player embeddings. The method achieves 27-37% relative improvement in move prediction accuracy over existing models while successfully disentangling stylistic preferences from playing skill level.