Iterative Inference in a Chess-Playing Neural Network
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917103103115264 |
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| author | Sandmann, Elias Lapuschkin, Sebastian Samek, Wojciech |
| author_facet | Sandmann, Elias Lapuschkin, Sebastian Samek, Wojciech |
| contents | Do neural networks build their representations through smooth, gradual refinement, or via more complex computational processes? We investigate this by extending the logit lens to analyze the policy network of Leela Chess Zero, a superhuman chess engine. Although playing strength and puzzle-solving ability improve consistently across layers, capability progression occurs in distinct computational phases with move preferences undergoing continuous reevaluation--move rankings remain poorly correlated with final outputs until late, and correct puzzle solutions found in middle layers are sometimes overridden. This late-layer reversal is accompanied by concept preference analyses showing final layers prioritize safety over aggression, suggesting a mechanism by which heuristic priors can override tactical solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21380 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Iterative Inference in a Chess-Playing Neural Network Sandmann, Elias Lapuschkin, Sebastian Samek, Wojciech Machine Learning Artificial Intelligence Do neural networks build their representations through smooth, gradual refinement, or via more complex computational processes? We investigate this by extending the logit lens to analyze the policy network of Leela Chess Zero, a superhuman chess engine. Although playing strength and puzzle-solving ability improve consistently across layers, capability progression occurs in distinct computational phases with move preferences undergoing continuous reevaluation--move rankings remain poorly correlated with final outputs until late, and correct puzzle solutions found in middle layers are sometimes overridden. This late-layer reversal is accompanied by concept preference analyses showing final layers prioritize safety over aggression, suggesting a mechanism by which heuristic priors can override tactical solutions. |
| title | Iterative Inference in a Chess-Playing Neural Network |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.21380 |