Multi-Source Feature Fusion and Neural Embedding for Predicting Chess Puzzle Difficulty

Estimating the difficulty of chess puzzles provides a rich testbed for studying human-computer interaction and adaptive learning.Building on recent advances and the FedCSIS 2025 Challenge, we address the task of predicting chess puzzle difficulty ratings using a multi-source representation approach.Our approach integrates pre-trained neural embeddings of board states, solution move sequences, and engine-derived success probabilities.These heterogeneous features are fused via dedicated embedding and projection layers, followed by a multilayer perceptron regressor.Post-processing calibration and model ensemble further enhance robustness and generalization.Experiments on the FedCSIS 2025 dataset demonstrate that our method effectively leverages both structural and empirical information, achieving strong predictive performance.Our approach achieved fifth place on the final official leaderboard, highlighting the effectiveness of combining neural representations with domainspecific probabilistic features for robust chess puzzle difficulty prediction.