COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training

• URL: http://arxiv.org/abs/2604.23546v1
• Date: Sun, 26 Apr 2026 05:48:21 GMT
• Title: COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training
• Abstract summary: We introduce Minimum Risk Training to Optical chemical structure recognition (OCSR)<n> COMO is a closed-loop framework that mitigates exposure bias by directly optimizing over molecule-level, non-differentiable objectives.<n> Experiments on ten benchmarks including synthetic and real-world chemical diagrams demonstrate that COMO substantially outperforms existing rule-based and learning-based methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations like SMILES strings or molecular graphs, but remains challenging in real-world documents due to inexhaustible variations in chemical structures, shorthand conventions, and visual noise. Most existing deep-learning-based 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 closed-loop framework that mitigates exposure bias by directly optimizing over molecule-level, non-differentiable objectives, by iteratively sampling and evaluating the model's own predictions. 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. Ablation studies further show that MRT is architecture-agnostic, demonstrating its potential for broad application to end-to-end OCSR systems.

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