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2026-10-05Language Models that Play Chess and Explain Their Moves
Adithya Bhaskar, Jeffrey Cheng, Danqi Chen
arXiv:2610.03695v1Today’s standout paper is about giving language models something they usually lack: the ability to explain expert-level decisions without sacrificing performance. The authors build Queen, a 4-billion-parameter model that plays chess at grandmaster strength while also narrating its reasoning in plain language. The key idea is to combine two specialists: a silent chess encoder that understands the board deeply, and an instruction-tuned language model that turns those internal representations into explanations. They train this system with a question-answering curriculum, then repeatedly refine the explanations by analyzing candidate moves and distilling the best reasoning back into the model. That iterative loop boosts both playing strength and explanation quality. Why does this matter? Because many important domains, from robotics to scientific tools, have powerful expert models that are accurate but opaque. This work points toward a practical recipe for making AI systems that are not only strong, but also understandable enough for people to trust and use.
Also spotted that day
Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic ScenesWhat Should World Models Forget? Stratified Retention for Continual AdaptationRNADyn: A Benchmark for Generating and Understanding RNA DynamicsEyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist CamerasFrom Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message PassingLESSER: Post-Training Data Selection with Output-Layer GradientsTranscriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsiesSimulation-Free Learning of Population Dynamics with Wasserstein Lagrangian ResidualsFrugalEvo: Towards Cost-Aware LLM-Guided Program EvolutionPlanning to LearnPivot-SD: Efficient Self-Distillation for Masked Diffusion Language ModelsForecasting from Counterfactual Simulator Rollouts: A Sim2Real EvaluationRevisiting Input Time-frequency Representations in Multi-pitch Estimation for Vocal Ensembles
Previous daily papers
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