Representation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planning

Hyunwoo Yoo; Cassie Huang; Haebin Shin; Li Zhang; Gail L. Rosen

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arXiv · 2609.35571

Representation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planning

Hyunwoo Yoo·Cassie Huang·Haebin Shin·Li Zhang·Gail L. Rosen

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Abstract

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.

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BibTeX RIS

Hyunwoo Yoo, Cassie Huang, Haebin Shin, Li Zhang, Gail L. Rosen. 2026-09-28. Representation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planning. https://arxiv.org/abs/2609.35571

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