Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation

Kehan Guo, B. Nan, Yujun Zhou, Taicheng Guo, Zhichun Guo +5 more
2/3/2026

Abstract

Large Language Models (LLMs) have shown significant problem-solving capabili-1 ties across predictive and generative tasks in chemistry. However, their proficiency 2 in multi-step chemical reasoning remains underexplored. We introduce a new 3 challenge: molecular structure elucidation, which involves deducing a molecule’s 4 structure from various types of spectral data. Solving such a molecular puzzle, 5 akin to solving crossword puzzles, poses reasoning challenges that require inte-6

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Cite this paper

@article{guo2026llms,
  title  = {Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation},
  author = {Kehan Guo and B. Nan and Yujun Zhou and Taicheng Guo and Zhichun Guo and Mihir Surve and Zhenwen Liang and N. V. Chawla and Olaf Wiest and Xiangliang Zhang},
  year   = {2026},
  doi    = {10.52202/079017-4281},
  url    = {https://doi.org/10.52202/079017-4281},
  journal = {NEURIPS 2024 2024}
}

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