Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations such as SMILES strings or molecular graphs, but remains challenging in real-world documents due to wide variations in chemical structures, shorthand conventions, and scanning artifacts. Most existing deep learning approaches rely on teacher forcing with token-level Maximum Likelihood Estimation (MLE). This training paradigm suffers from exposure bias, as models are trained under ground-truth prefixes but must condition on their own previous predictions during inference. Moreover, token-level MLE objectives hinder the optimization towards molecular-level evaluation criteria such as chemical validity and structural similarity. Here we introduce Minimum Risk Training (MRT) to OCSR and propose COMO (Closed-loop Optical Molecule recOgnition), a framework that mitigates exposure bias by directly optimizing over molecule-level, non-differentiable objectives. Experiments on ten benchmarks, including synthetic and real-world chemical diagrams from patent and scientific literature, demonstrate that COMO substantially outperforms existing rule-based and learning-based methods with less training data, consistently achieving superior performance across both reproduced baselines and previously reported results. Ablation studies further show that MRT is architecture-agnostic, demonstrating its potential for broad application to end-to-end OCSR systems.
Recent advances in Artificial Intelligence (AI) have demonstrated great potential across diverse domains, prompting increasing attention to understanding the AI ecosystem. As knowledge production is often perceived as a process of connecting existing pieces of knowledge, mapping the interconnections of AI knowledge is crucial for unraveling the dynamics of scientific production in AI. However, maps created so far have fallen short of capturing knowledge relatedness, focusing instead on citation relationships between subfields. Here, we build an AI research space by connecting pairs of AI research topics based on their proximity measured through author-topic coupling. Projections of country level performances onto the space reveal that countries exhibit distinct baskets of specializations. We further examine the role of the structure of the AI research space in the scientific development of nations. We find that countries are more likely to gain specialization in topics that have high proximity to already specialized neighbor topics, and that publications whose topics align more with the developed topics in the country are more likely to become high impact. Our results highlight the AI development gaps across nations and the path-dependence in the development induced by the AI research space.