Molecular Identifier Visual Prompting and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing

Reaction diagram parsing (RxnDP) is critical for extractingchemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promising paradigm toautomate this complex visual reasoning task, their application is funda-mentally bottlenecked by the inability to align visual chemical entitieswith pre-trained knowledge, alongside the inherent discrepancy betweentoken-level training and reaction-level evaluation. To address these dualchallenges, this work enhances VLM-based RxnDP from two comple-mentary perspectives: prompting representation and learning paradigms.First, we propose Identifier as Visual Prompting (IdtVP), whichleverages naturally occurring molecule identifiers (e.g., bold numeralslike 1a) to activate the chemical knowledge acquired during VLM pre-training. IdtVP enables powerful zero-shot and out-of-distribution ca-pabilities, outperforming existing prompting strategies. Second, to fur-ther optimize performance within fine-tuning paradigms, we introduceRe3 -DAPO, a reinforcement learning algorithm that leverages verifi-able rewards to directly optimize reaction-level metrics, thereby achiev-ing consistent gains over standard supervised fine-tuning. Additionally,we release the ScannedRxn benchmark, comprising scanned historicalreaction diagrams with real-world artifacts, to rigorously assess model ro-bustness and out-of-distribution ability. Our contributions advance theaccuracy and generalization of VLM-based reaction diagram parsing.