# Predicting Chess Puzzle Difficulty with Transformers

arXiv: https://arxiv.org/abs/2410.11078 · HTML: https://mlsanity.com/paper/2410.11078/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Evidence of Learned Look-Ahead in a Chess-Playing Neural Network](https://mlsanity.com/paper/2406.00877/) (2024)
- [Mastering Chess with a Transformer Model](https://mlsanity.com/paper/2409.12272/) (2024)
- [HVT: A Comprehensive Vision Framework for Learning in Non-Euclidean Space](https://mlsanity.com/paper/2409.16897/) (2024)
- [Restarting Algorithms: Sometimes there is Free Lunch](https://mlsanity.com/paper/2006.14810/) (2020)
- [Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM](https://mlsanity.com/paper/2409.11506/) (2024)

## Built on by (most cited first, 3 of 3)

- [LLM CHESS: Benchmarking Reasoning and Instruction-Following in LLMs through Chess](https://mlsanity.com/paper/2512.01992/) (2025)
- [Exploring Collaboration between a language and a non-language agent](https://mlsanity.com/paper/2609.00474/) (2026)
- [Beyond Search-Imitation: Prior-Directed Exploration for Searchless Chess](https://mlsanity.com/paper/2608.27757/) (2026)

## Abstract (arXiv)

This study addresses the challenge of quantifying chess puzzle difficulty - a complex task that combines elements of game theory and human cognition and underscores its critical role in effective chess training. We present GlickFormer, a novel transformer-based architecture that predicts chess puzzle difficulty by approximating the Glicko-2 rating system. Unlike conventional chess engines that optimize for game outcomes, GlickFormer models human perception of tactical patterns and problem-solving complexity. The proposed model utilizes a modified ChessFormer backbone for spatial feature extraction and incorporates temporal information via factorized transformer techniques. This approach enables the capture of both spatial chess piece arrangements and move sequences, effectively modeling spatio-temporal relationships relevant to difficulty assessment. Experimental evaluation was conducted on a dataset of over 4 million chess puzzles. Results demonstrate GlickFormer's superior performance compared to the state-of-the-art ChessFormer baseline across multiple metrics. The algorithm's performance has also been recognized through its competitive results in the IEEE BigData 2024 Cup: Predicting Chess Puzzle Difficulty competition, where it placed 11th. The insights gained from this study have implications for personalized chess training and broader applications in educational technology and cognitive modeling.