Hybrid Boosting and Multi-Modal Fusion for Chess Puzzle Difficulty Prediction

The FedCSIS 2025 Challenge on Predicting Chess Puzzle Difficulty tasked participants with estimating puzzle ratings directly from board states and solution sequences, without relying on human solver statistics.We propose a three-stage hybrid framework integrating gradient-boosting regressors, a multi-modal neural network, and an XGBoost stacking ensemble. The boosting stage modeled handcrafted structural features derived from FEN and engine metadata, while the multi-modal network jointly learned from structured features and image-rendered chessboards to capture positional and tactical patterns. The residual-based stacking stage explicitly modeled prediction errors to correct systematic biases and enhance performance, particularly for high-difficulty puzzles.Our method achieved a competitive performance, ranking 7th in the preliminary stage and 8th in the final leaderboard. These results demonstrate that combining interpretable boosting models with visual-tactical deep representations and meta-learning provides a robust and computationally efficient alternative to large-scale transformer-based approaches.