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Anderson, \u201cMaia-2: A unified model for \n\thuman-ai alignment in chess,\u201d in Advances in Neural \n\tInformation Processing Systems 37 (NeurIPS 2024), \n\tAccepted @ NeurIPS 2024, 2024. DOI : 10.48550\/arXiv.2409.20553. eprint: 2409.20553."},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"T. Woodruff, O. Filatov, and M. Cognetta, \u201cThe Bread \n\tEmoji Team\u2019s Submission to the IEEE BigData 2024 \n\tCup: Predicting Chess Puzzle Difficulty Challenge,\u201d in \n\t2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 2024, pp. 8415\u20138422. \n\tDOI : 10.1109\/BigData62323.2024.10826037.","DOI":"10.1109\/BigData62323.2024.10826037"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"J. Zy\u015bko, M. \u015awiechowski, S. Stawicki, K. Jagie\u0142a, \n\tA. Janusz, and D. \u015alezak, \u201cIEEE Big Data Cup 2024 \n\tReport: Predicting Chess Puzzle Difficulty at KnowledgePit.ai,\u201d in 2024 IEEE International Conference on \n\tBig Data (BigData), Washington, DC, USA, 2024, \n\tpp. 8423\u20138429. DOI:10.1109\/BigData62323.2024.10825289.","DOI":"10.1109\/BigData62323.2024.10825289"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"S. Bjorkqvist, \u201cEstimating the puzzlingness of chess \n\tpuzzles,\u201d in 2024 IEEE International Conference on \n\tBig Data (BigData), Washington, DC, USA, 2024, \n\tpp. 8370\u20138376. DOI: 10.1109\/BigData62323.2024.10825991.","DOI":"10.1109\/BigData62323.2024.10825991"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"A. Schutt, T. Huber, and E. Andre, \u201cEstimating chess \n\tpuzzle difficulty without past game records using a \n\thuman problem-solving inspired neural network architecture,\u201d in 2024 IEEE International Conference on \n\tBig Data (BigData), Washington, DC, USA, 2024, \n\tpp. 8396\u20138402. DOI: 10.1109\/BigData62323.2024.10826087.","DOI":"10.1109\/BigData62323.2024.10826087"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"A. Rafaralahy, \u201cPairwise learning to rank for chess \n\tpuzzle difficulty prediction,\u201d in 2024 IEEE International Conference on Big Data (BigData), Washing- \n\tton, DC, USA, 2024, pp. 8385\u20138389. DOI: 10.1109\/BigData62323.2024.10825356.","DOI":"10.1109\/BigData62323.2024.10825356"},{"key":"ref9","unstructured":"J. Devlin, M. Chang, K. Lee, and K. Toutanova, \n\t\u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d arXiv preprint \n\thttps:\/\/arxiv.org\/abs\/1810.04805, 2018."},{"key":"ref10","unstructured":"A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., \u201cAn \n\timage is worth 16x16 words: Transformers for image \n\trecognition at scale,\u201d arXiv preprint https:\/\/arxiv.org\/abs\/2010.11929, \n\t2020. DOI: 10.48550\/arXiv.2010.11929. [Online]. \n\tAvailable: https:\/\/arxiv.org\/abs\/2010.11929."},{"key":"ref11","unstructured":"A. Radford, K. Narasimhan, T. Salimans, and I. \n\tSutskever, \u201cImproving language understanding by generative pre-training,\u201d OpenAI, Tech. Rep., 2018. [Online]. Available: https:\/\/cdn.openai.com\/research-covers\/language-unsupervised\/language_understanding_paper.pdf."},{"key":"ref12","unstructured":"C. Raffel, N. Shazeer, A. Roberts, et al., \u201cExploring \n\tthe limits of transfer learning with a unified text-to-text \n\ttransformer,\u201d Journal of Machine Learning Research, \n\tvol. 21, no. 140, pp. 1\u201367, 2020. eprint: 1910.10683."},{"key":"ref13","unstructured":"D. Noever, \u201cChess transformer: Mastering the game of \n\tchess with attention,\u201d arXiv preprint https:\/\/arxiv.org\/abs\/2008.04057, \n\t2020."},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"D. Misra, \u201cMish: A self regularized non-monotonic \n\tactivation function,\u201d arXiv preprint https:\/\/arxiv.org\/abs\/1908.08681, \n\t2019.","DOI":"10.5244\/C.34.191"},{"key":"ref15","unstructured":"D. Monroe and P. Chalmers, \u201cMastering chess with a \n\ttransformer model,\u201d arXiv preprint https:\/\/arxiv.org\/abs\/2409.12272, \n\t2024, Describes Lc0\u2019s transformer architecture and \n\tsmolgen position encoding. [Online]. Available: https:\/\/arxiv.org\/html\/2409.12272v1."},{"key":"ref16","unstructured":"J. Hoffmann, S. Borgeaud, A. Mensch, et al., \u201cTraining compute-optimal large language models,\u201d arXiv \n\tpreprint https:\/\/arxiv.org\/abs\/2203.15556, 2022."},{"key":"ref17","unstructured":"I. Loshchilov and F. Hutter, \u201cFixing weight decay regularization in adam,\u201d arXiv preprint https:\/\/arxiv.org\/abs\/1711.05101, \n\t2017."}],"event":{"name":"20th Conference on Computer Science and Intelligence Systems (FedCSIS)","theme":"Computer Science and Intelligence Systems","location":"Krak\u00f3w, Poland","acronym":"FedCSIS","number":"20","start":{"date-parts":[[2025,9,14]]},"end":{"date-parts":[[2025,9,17]]}},"container-title":["Annals of Computer Science and Information Systems","Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)"],"original-title":[],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:52:14Z","timestamp":1761119534000},"score":1,"resource":{"primary":{"URL":"https:\/\/annals-csis.org\/Volume_43\/drp\/7603.html"}},"subtitle":[],"proceedings-subject":"Computer Science and Information Systems","short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":17,"URL":"https:\/\/doi.org\/10.15439\/2025f7603","relation":{},"ISSN":["2300-5963"],"issn-type":[{"value":"2300-5963","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}