Neural-guided heuristic tree search for deep bidirectional decision-making: a survey

Recent advances in artificial intelligence, exemplified by AlphaGo-like systems, have demonstrated the effectiveness of integrating neural networks with classical tree search for complex decision-making. By serving as learned heuristics, neural networks provide informative priors to guide the search process, while search-based planning improves decision quality through structured exploration and long-horizon optimization, with the resulting high-quality solutions further distilled into neural models to form a bidirectional decision-making framework. Moreover, the integration of learned environment models has further expanded the applicability of this paradigm beyond board games to diverse domains, such as de novo drug design and large language model inference. In this survey, we provide a systematic review of neural-guided heuristic tree search from the perspective of deep bidirectional decision-making. Representative classical tree search algorithms and their integration with neural heuristics are revisited, and recent advances are synthesized in terms of three fundamental challenges: optimality degradation under neural guidance, reduced search efficiency due to imperfect heuristics, and the difficulty of learning accurate and search-compatible heuristic functions. Moreover, representative real-world applications are reviewed to demonstrate the practical applicability of neural-guided tree search, followed by a discussion of open challenges and promising directions for future research. By synthesizing these developments within a unified framework, this survey clarifies the methodological foundations and empirical advances of neural-guided heuristic tree search, while highlighting its potential for general-purpose decision-making.