The model's ability to maintain good performance even with slightly incorrect initial structures has significant implications for real-world applications. In practice, chemists often begin structure elucidation with incomplete or partially incorrect hypotheses. Our model's robustness in these scenarios suggests it can serve as a powerful tool to refine and correct initial structural guesses, potentially reducing the iterative cycles typically required in structure determination. This capability could be particularly valuable in analyzing complex natural products or in drug discovery processes where rapid and accurate structure elucidation is crucial. This visualization allows us to identify which structural features the model is most certain about and which areas might require further refinement, providing a window into the model's intuition process. Similar analyses for our simulated data and ACD Labs predictions are shown in Supplementary Figure and Supplementary Figure .