Representation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planning

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.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2609.35571 [cs.AI]

(or arXiv:2609.35571v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.35571 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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