Explaining Chess and Go in Natural Language
We propose an automatic metric for evaluating chess explanations, grounded in chess-engine evaluations.
ProofPaper ↗
Key points
- Modern chess engines play far beyond human ability, often choosing moves whose underlying ideas are difficult for humans to understand.
- Evaluating state-of-the-art language models using this metric, we find that they perform surprisingly poorly, often relying on surface-level board observations rather than robust, transferable chess concepts.
- We then introduce BinProp, a training-free method that enables these same models to discover reusable chess concepts and use them to explain moves in natural language.
- Beyond chess, the same method discovers useful concepts in Go, draughts, and shogi without additional tuning.
Sources (1)
- [1]Explaining Chess and Go in Natural LanguagearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:05 AM
We propose an automatic metric for evaluating chess explanations, grounded in chess-engine evaluations.
Modern chess engines play far beyond human ability, often choosing moves whose underlying ideas are difficult for humans to understand.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 16, 2026Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
- Oct 16, 2026A Conditional No-Hair Theorem for Transformers: How Language Models Forget Their Prompt
- Oct 16, 2026Towards Looped Models Done Right, Part I: Topology, Input Injection, Recurrent-State Design
- Oct 16, 2026HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents
- Oct 15, 2026Each Reward to Its Relevant Tokens: Reward-Specific Token-Level Credit Assignment for Multi-Reward RL
- Oct 15, 2026Learning to Think and Explore in 3D World