一句话：提出 LLM 与概率模型检测结合的神经符号路径规划框架，为随机网格世界提供时序与概率保证。
论文关注安全攸关的自主导航场景，将其抽象为随机网格世界路径规划：智能体需在不确定性下做序列决策，并满足时序与概率约束。经典确定性规划器虽能提供形式化保证，却难以建模随机动态；强化学习方法常面向特定问题，且未必保证约束满足；现有基于 LLM 的规划器多聚焦确定性任务规划，缺乏对随机网格世界的形式化保证。为此，作者提出一种神经符号路径规划框架，将 LLM 规划器与概率模型检测相结合，为时序和概率需求提供保证。评估中比较了三种先进 LLM，考察成功率、收敛行为，并与强化学习基线对比。结果表明，该方法在多种网格世界配置下取得高成功率，并优于 RL 基线。
Recent advances in large language models (LLMs) have extended their capabilities beyond text generation to structured reasoning and planning, motivating their deployment in physical systems. A representative safety-critical setting is autonomous navigation, commonly abstracted as stochastic grid-world path planning, where an agent makes sequential decisions under uncertainty while satisfying temporal, probabilistic requirements. Classical deterministic planners offer formal guarantees but cannot adequately model stochastic dynamics, while reinforcement learning (RL) approaches are often problem-specific and may not ensure constraint satisfaction. Existing LLM-based planners largely focus on deterministic task planning and not stochastic grid-world planning with formal guarantees. In this paper, we present a neuro-symbolic path planning framework that integrates LLM-based planners with probabilistic model checking to provide guarantees for temporal and probabilistic requirements. We evaluate three state-of-the-art LLMs, assessing success rate, convergence behavior, and performance against a reinforcement learning baseline. Results show that our approach achieves high success rates across diverse grid-world configurations and outperforms the RL baseline.