Matilda: Engine-Agnostic Search with Human Policy Guidance

תקציר מקורי באנגליתarXiv:2606.25176v3 Announce Type: replace Abstract: 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-style embeddings, and search-derived candidate fea