Tag
The paper presents CGAP, a neuro-symbolic framework that compiles tool catalogs into a PDDL model and uses a classical planner with LLM-based goal parsing to guarantee executable multi-tool plans, outperforming standard tool-calling agents by up to 28.9 points on four benchmarks.
该论文研究LLM在经典规划问题上的失败，发现传统delete-relaxation启发式h_add在估计结构难度上远优于ARC等学习模型，而通过逐步重命名谓词的受控实验表明LLM规划成功率随语义可解释性下降而从67.5%降至40.0%，指出混合规划系统的关键在于建模LLM的语义行为而非改进结构难度预测器。
This paper introduces a neural network architecture that learns lifted action schemas from fully observed state traces with unobserved action arguments, aiming to enable robust learning of planning domains for neuro-symbolic models.