Large language models (LLMs) have achieved remarkable success in answering chemistryrelated questions and performing tasks such as reaction prediction and property estimation . While the potential of LLMs in chemistry reasoning is exciting, a fundamental question remains: do LLMs truly understand the underlying chemistry of molecules 7-10 ? Molecules can be represented in various forms-1D strings, 2D graphs, or 3D coordinates-but their intrinsic chemical nature, such as the atomic composition, electronic structure, and spatial arrangement, remains invariant across representations. LLMs predominantly operate on 1D representations, such as SMILES strings 11 , due to their simplicity and compatibility with LLM architectures, which excel at processing sequential, tokenized data . However, this raises a critical concern: are LLMs merely learning surface-level patterns embedded in these string-based formats (Figure , blue pathway), or are they capturing the intrinsic chemical properties that govern molecular behavior (Figure , pink pathway)?