@inproceedings{wang-etal-2026-geoparsing,
    title = "Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language",
    author = "Wang, Peijie  and
      Zhang, Ming-Liang  and
      Cao, Jun  and
      Deng, Chao  and
      Ran, Dekang  and
      Bu, Pi  and
      Sun, Hongda  and
      Zhang, Xuan  and
      Wang, Yingyao  and
      Song, Jun  and
      Zheng, Bo  and
      Yin, Fei  and
      Liu, Cheng-Lin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1494/",
    pages = "29876--29903",
    ISBN = "979-8-89176-395-1",
    abstract = "Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perception bottleneck regarding fine-grained visual elements. While formal languages have aided plane geometry understanding, solid geometry which requires spatial understanding remains largely unexplored. In this paper, we address this challenge by designing a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations. We construct GDP-29K, a large-scale dataset comprising 20k plane and 9k solid geometry samples collected from diverse real-world sources, each paired with its ground-truth formal description. We propose a training paradigm combining Supervised Fine-Tuning with Reinforcement Learning via Verifiable Rewards, which effectively enforces syntactic correctness and geometric consistency. Experiments show that our approach achieves state-of-the-art parsing performance. Furthermore, we demonstrate that our parsed formal descriptions serve as a critical cognitive scaffold, significantly boosting MLLMs' capabilities for downstream geometry reasoning tasks."
}