Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2025F6771

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 837–842 (2025)

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Abstract. We detail the bread emoji team's submission to the FedCSIS 2025 Predicting Chess Puzzle Difficulty Challenge. Our solution revolved around improving our submission from the previous competition by incorporating a new puzzle metadata feature and optimizing our implementation to allow for larger model ensembles and more stable training. Similar to our submission from last year, our system has two stages: learning a strong predictor for the Lichess dataset and then rescaling the distribution using an empirically-guided post-processing step to fit it to the smaller and noisier competition dataset. Our submission placed second with a ~3.9\% gap in mean squared error (MSE) from first place in the final evaluation.

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