用图遍历模拟人类思考，让模型更准识别分子结构

Optical Chemical Structure Recognition (OCSR) is essential for converting molecular images into machine-readable formats. While recent vision-language models (VLMs) have shown promise, their image-captioning approach often struggles with complex molecular structures and inconsistent annotations. To address these issues, we introduce GTR-VL, featuring two key innovations: (1) the \textit{Graph Traversal as Visual Chain of Thought} mechanism that emulates human reasoning by incrementally parsing molecular graphs through sequential atom-bond predictions, and (2) the data-centric \textit{Faithfully Recognize What You've Seen} principle, which aligns abbreviated structures in images with their expanded annotations. For hand-drawn OCSR tasks, where datasets lack graph annotations and only provide final SMILES, we apply reinforcement learning using the GRPO method, introducing reward mechanisms like format reward, graph reward, and SMILES reward. This approach significantly enhances performance in hand-drawn recognition tasks through weak supervision. We developed GTR-1.3M, a large-scale instruction-tuning dataset with corrected annotations, and MolRec-Bench, the first benchmark for fine-grained evaluation of graph-parsing accuracy in OCSR. Our two-stage training scheme involves SFT training for printed images and the GRPO method for transferring capabilities to hand-drawn tasks. Experiments show that GTR-VL outperforms specialist models, chemistry-domain VLMs, and commercial VLMs on both printed and hand-drawn datasets.