arXiv AI October 19, 2026 By Ori Press, Alexander Golubev

Explaining Chess and Go in Natural Language

The paper introduces an automatic metric for evaluating chess explanations based on chess-engine evaluations and finds that current language models perform poorly, relying on surface-level observations. It presents BinProp, a training-free method that helps models discover reusable chess concepts, significantly improving explainability across multiple models. BinProp also generalizes to other board games such as Go, draughts, and shogi without extra tuning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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arXiv AI

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Learning Explainable Representations of Complex Game-playing Strategies

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By Abhijeet Krishnan, Colin M. Potts, Arnav Jhala, Harshad Khadilkar, Shirish Karande, Chris Martens

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