Matilda: Engine-Agnostic Search with Human Policy Guidance
Chess engines have evolved from search-based systems optimized for strength to neural policies optimized for predicting human decisions. Existing approaches largely separate these goals: search engines achieve superhuman strength but poorly model humans, while models such as Maia-3 capture rating-conditioned behavior yet degrade at elite levels. We present Matilda, a modular residual re-ranking architecture that decouples behavioral priors from tactical search, combining a frozen human policy with an engine-agnostic search backend through a lightweight residual model. Matilda learns residual corrections over the full legal-move distribution from frozen policy context, time control, player...
| Authors | Jason Carlson |
|---|---|
| Primary category | cs.AI |
| Published | 23 June 2026 |
| Last updated at source | 29 July 2026 |
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