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<p>The Abstraction and Reasoning Corpus (ARC) has become a prominent benchmark for assessing whether AI systems can perform human-like reasoning. Recent efforts on ARC have increasingly adopted program synthesis paradigms based on domain-specific languages (DSL). To tackle the combinatorial challenges of program synthesis, leading methods employ pretrained models to produce DSL program sequences and apply efficient search algorithms during inference. Monte Carlo Tree Search (MCTS) stands out among these, as it enables effective navigation of vast search spaces while maintaining a principled equilibrium between exploration and exploitation. In this work, we introduce a neural MCTS algorithm customized for ARC-AGI, which amplifies exploration by directing the search toward promising solutions via learned neural guidance, yielding robust performance on demanding ARC tasks. However, pretrained models frequently underperform on certain tasks owing to insufficient exposure to particular DSL tokens or structures during pretraining. To mitigate this, we additionally propose an LLM-guided technique, in which an LLM recommends pertinent tokens to prune the search space, thereby allowing the solver to prioritize previously overlooked DSL elements. Integrating the o4-mini model with our approach, we successfully solve nearly all DSL-solvable tasks in the ARC-AGI-1 evaluation benchmark, highlighting the effectiveness of our neural MCTS framework guided by LLM-based token recommendations.</p>

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Neural MCTS with LLM Guidance for Effective Program Synthesis on Abstraction and Reasoning Corpus

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Jinwoo Jeon,
  • Seongwoong Shim,
  • Sejin Kim,
  • Sundong Kim,
  • Byung-Jun Lee

摘要

The Abstraction and Reasoning Corpus (ARC) has become a prominent benchmark for assessing whether AI systems can perform human-like reasoning. Recent efforts on ARC have increasingly adopted program synthesis paradigms based on domain-specific languages (DSL). To tackle the combinatorial challenges of program synthesis, leading methods employ pretrained models to produce DSL program sequences and apply efficient search algorithms during inference. Monte Carlo Tree Search (MCTS) stands out among these, as it enables effective navigation of vast search spaces while maintaining a principled equilibrium between exploration and exploitation. In this work, we introduce a neural MCTS algorithm customized for ARC-AGI, which amplifies exploration by directing the search toward promising solutions via learned neural guidance, yielding robust performance on demanding ARC tasks. However, pretrained models frequently underperform on certain tasks owing to insufficient exposure to particular DSL tokens or structures during pretraining. To mitigate this, we additionally propose an LLM-guided technique, in which an LLM recommends pertinent tokens to prune the search space, thereby allowing the solver to prioritize previously overlooked DSL elements. Integrating the o4-mini model with our approach, we successfully solve nearly all DSL-solvable tasks in the ARC-AGI-1 evaluation benchmark, highlighting the effectiveness of our neural MCTS framework guided by LLM-based token recommendations.