Belief-Guided Decision Making with Uncertainty Gating in the Game of Go
Quick Answer
The paper presents a Belief-Guided architecture for Go that separates the Policy and Belief heads, enhancing performance on consumer hardware by reducing reliance on MCTS.
Quick Take
This model significantly improves win rates and reduces hallucination, enabling professional-level play without extensive computational resources.
Key Points
- Introduces Belief-Guided architecture to improve Go decision-making.
- Separates Policy head from Belief head for better uncertainty modeling.
- Integrates memory mechanisms to manage long-term dependencies effectively.
- Achieves significant search-free win rates on consumer-grade hardware.
- Reduces hallucination in move proposals, enhancing strategic stability.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | 8 pages, 7 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.26946 [cs.AI] |
| (or arXiv:2607.26946v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.26946 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Azam Bastanfard [view email]
[v1]
Wed, 29 Jul 2026 14:15:29 UTC (895 KB)
— Originally published at arxiv.org
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