StructVista: A Benchmark for Molecular Reconstruction and Assembly Reasoning in Scientific Figures

Accurate interpretation of molecular structures in scientific figures is fundamental to molecule-centric literature mining and scientific discovery. Existing chemical structure recognition and Markush parsing methods can recover molecules when each molecule is drawn completely or when its components are linked by explicit correspondences. Beyond these settings, many real figures provide the structural components needed to recover the target molecule, but do not explicitly specify how those components map onto the final molecular graph. Correctly reconstructing such molecules requires figure-level integration and structural reasoning. To characterize this transition, we introduce , a benchmark of 419 expert-verified figures specifying 3,412 compound identifier–structure pairs, organized into three levels ranging from local self-contained recognition, to global instantiation with explicit relations, and finally to context-dependent assembly reasoning. We benchmark seven frontier multimodal large language models and two agent frameworks equipped with chemistry tools. When queried directly, the best-performing model, GPT-6 Sol, achieves only 50.8 F1 overall and exactly recovers 26.5% of figures. Deployed within the Codex framework, the same model improves to 89.2 F1 and 74.9% exact figure recovery. Even so, it exactly recovers only 62.9% of figures requiring context-dependent assembly reasoning, compared with 81.7% of figures with self-contained depictions. These results show that current models still struggle with chemical structure reasoning, particularly when implicit assembly relations must be inferred from figure context. thus provides a testbed for moving molecular structure interpretation beyond local recognition toward figure-level reconstruction and reasoning.