#chess-ai News & Analysis

4 articles tagged with #chess-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 117/10

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The Algorithm Is Not the Behavior: Learned Priors Override Look-Ahead in a Chess-Playing Neural Network

Researchers discovered that Leela Chess Zero, a top neural chess engine, internally computes correct solutions to chess puzzles but systematically overrides them in final outputs—a phenomenon driven by learned safety priors rather than algorithmic failure. This reveals a critical gap between internal algorithmic capability and external behavior in neural networks.

AINeutralarXiv – CS AI · Jun 45/10

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ChessMimic: Per-Rating Transformer Models for Human Move, Clock, and Outcome Prediction in Online Blitz Chess

Researchers introduce ChessMimic, a system of three transformer models that predict human chess moves, thinking time, and game outcomes in online blitz chess with rating-specific calibration. The models outperform existing systems like Maia across multiple performance metrics while using significantly fewer parameters, with code and weights publicly released.

AINeutralarXiv – CS AI · May 286/10

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UniMaia: Steering Chess Policies with Language for Human-like Play

UniMaia is a new AI framework that uses natural language prompts to control chess-playing policy networks, enabling semantic control over gameplay elements like opening selection and player strength without requiring large-scale multimodal training. The system combines a frozen Lc0 chess engine with a parameter-efficient text encoder and demonstrates competitive performance on prompt-conditioned benchmarks while maintaining domain-specific expertise.

AINeutralarXiv – CS AI · May 116/10

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Mixture of Masters: Sparse Chess Language Models with Player Routing

Researchers introduce Mixture-of-Masters (MoM), a sparse mixture-of-experts chess language model that routes moves through specialized GPT experts trained on individual grandmaster playing styles. The system outperforms dense transformer baselines and maintains interpretability by dynamically selecting which grandmaster persona to channel based on game state.