Tracing the Thought of a Grandmaster-level Chess-Playing Transformer
While modern transformer neural networks achieve grandmaster-level performance in chess and other reasoning tasks, their internal computation process remains largely opaque. Focusing on Leela Chess Zero (LC0), we introduce a sparse decomposition framework to interpret its internal computation by decomposing its MLP and attention modules with sparse replacement layers, which capture the primary computation process of LC0. We conduct a detailed case study showing that these pathways expose rich, interpretable tactical considerations that are empirically verifiable. We further introduce three quantitative metrics and show that LC0 exhibits parallel reasoning behavior consistent with the indu...
| Authors | Rui Lin, Zhenyu Jin, Guancheng Zhou et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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