Belief-Guided Architecture Reduces Hallucination in Computer Go

A recent study published on arXiv presents a Belief-Guided architecture designed for Computer Go, which separates the Policy head from a dedicated Belief head. In contrast to conventional value functions, the Belief head serves as an autonomous critic and internal simulator, effectively modeling strategic stability and epistemic uncertainty. This method tackles the significant limitation of Monte Carlo Tree Search (MCTS) that arises from its reliance on consumer-grade hardware, where the expenses of managing the tree significantly hinder inference speeds. The architecture incorporates memory mechanisms (Transformer/GRU) to manage long-term dependencies and the Ko rule, employing a gating mechanism to eliminate overconfident moves. Standard models, lacking deep search, often produce high-confidence but strategically detrimental moves, while the Belief-Guided architecture seeks to rectify neural network policy inaccuracies without the need for extensive computational resources.