这篇论文提出一个新架构，让围棋AI不用大量搜索也能在普通电脑上达到专业水平，比AlphaZero更省资源。
该论文提出Belief-Guided架构，将策略头与信念头分离，后者作为内部模拟器和独立批评者，建模认知不确定性和战略稳定性。通过Transformer/GRU处理长期依赖和劫争规则，并利用门控机制过滤过度自信的策略错误。实验表明，该架构显著提升无需搜索的胜率并减少幻觉，在消费者级硬件上实现专业水平对弈，而传统方法需要大规模MCTS。
Belief-Guided Decision Making with Uncertainty Gating in the Game of Go
Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural network policy. While effective on massive computational clusters, this dependence creates a critical bottleneck on consumer-grade hardware, where the computational cost of tree management severely limits inference rates. Furthermore, without deep search, these models suffer from hallucination, proposing moves with high confidence that are strategically fatal. This paper introduces a novel Belief-Guided architecture that disentangles the Policy head from a distinct Belief head. Unlike traditional value functions, the Belief head acts as an internal simulator and independent critic, modeling epistemic uncertainty and strategic stability. By integrating memory mechanisms (Transformer/GRU) to handle long-term dependencies and the Ko rule, and utilizing a gating mechanism to filter overconfident policy errors, our model shifts the burden of intelligence from runtime search to parametric "intuition." Experimental results demonstrate that this approach significantly improves search-free win rates and reduces hallucination, enabling professional-level play on limited hardware where massive MCTS is infeasible.