发表机构
Drexel University; University of Michigan（德雷塞尔大学; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对LLM在逆合成规划中直接SMILES到PDDL的失败，提出通过分解为分子映射、反应映射和PDDL生成来引入中间表示，实验证明表示对齐是主要瓶颈，中间表示对成功至关重要。
AI 中文摘要
尽管LLM在通用推理方面展现出潜力，化学中的符号规划仍是一个瓶颈。直接的“SMILES到PDDL”尝试之所以失败，是因为它们迫使模型同时处理化学分析和规划语言结构化。我们假设这种失败源于缺乏中间抽象，而非模型能力不足。通过将逆合成分解为分子映射、反应映射和PDDL生成，我们在端到端方法失败之处取得了高成功率。这提供了证据表明，主要瓶颈在于表示对齐而非原始模型能力。我们的结构分析表明，中间表示在逆合成规划中至关重要，凸显了未来系统中以表示为中心的设计的重要性。
英文摘要
While LLMs show promise in general reasoning, symbolic planning in chemistry remains a bottleneck. Direct ''SMILES-to-PDDL'' attempts fail because they force models to juggle chemical analysis and planning-language structuring simultaneously. We hypothesize that this failure stems from a lack of intermediate abstractions rather than insufficient model capacity. By decomposing retrosynthesis into molecule mapping, reaction mapping, and PDDL generation, we achieve high success rates where end-to-end approaches fail. This provides evidence that a primary bottleneck lies in representation alignment rather than raw model capacity. Our structural analysis demonstrates that intermediate representations are essential in retrosynthesis planning, highlighting the importance of representation-centric design in future systems.
