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

TL;DR

This paper improves chemical reaction diagram parsing by enhancing vision-language models with molecule identifiers as prompts and a reinforcement learning approach for better reaction-level understanding, tested on a new challenging benchmark.

Contribution

It introduces Identifier as Visual Prompting (IdtVP) for leveraging chemical identifiers and Re3-DAPO, a reinforcement learning method, to improve parsing accuracy and robustness.

Findings

IdtVP outperforms existing prompting strategies in zero-shot settings.

Re3-DAPO achieves consistent gains over supervised fine-tuning.

The ScannedRxn benchmark tests model robustness on real-world diagrams.

Abstract

Reaction diagram parsing (RxnDP) is critical for extracting chemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promising paradigm to automate this complex visual reasoning task, their application is fundamentally bottlenecked by the inability to align visual chemical entities with pre-trained knowledge, alongside the inherent discrepancy between token-level training and reaction-level evaluation. To address these dual challenges, this work enhances VLM-based RxnDP from two complementary perspectives: prompting representation and learning paradigms. First, we propose Identifier as Visual Prompting (IdtVP), which leverages naturally occurring molecule identifiers (e.g., bold numerals like 1a) to activate the chemical knowledge acquired during VLM pre-training. IdtVP enables powerful zero-shot and out-of-distribution capabilities, outperforming existing prompting strategies. Second, to further optimize performance within fine-tuning paradigms, we introduce Re3-DAPO, a reinforcement learning algorithm that leverages verifiable rewards to directly optimize reaction-level metrics, thereby achieving consistent gains over standard supervised fine-tuning. Additionally, we release the ScannedRxn benchmark, comprising scanned historical reaction diagrams with real-world artifacts, to rigorously assess model robustness and out-of-distribution ability. Our contributions advance the accuracy and generalization of VLM-based reaction diagram parsing. We will release data, models, and code on GitHub.