@inproceedings{liu-etal-2026-chartverse,
    title = "{C}hart{V}erse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch",
    author = "Liu, Zheng  and
      Lin, Honglin  and
      Wang, Xiaoyang  and
      Gao, Xin  and
      Li, Yu  and
      Cai, Mengzhang  and
      Zhu, Yun  and
      Zhong, Zhanping  and
      Pei, Qizhi  and
      Pan, Zhuoshi  and
      Shang, Xiaoran  and
      He, Conghui  and
      Cui, Bin  and
      Zhang, Wentao  and
      Wu, Lijun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.344/",
    pages = "7551--7577",
    ISBN = "979-8-89176-390-6",
    abstract = "Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training data. Existing datasets suffer from a dual challenge: synthetic charts are often simplistic and repetitive, while the associated QA pairs are prone to hallucinations and lack the reasoning depth required for complex tasks. To bridge this gap, we propose **ChartVerse**, a scalable framework designed to synthesize complex charts and reliable reasoning data from scratch. (1) To address the bottleneck of simple patterns, we first introduce **Rollout Posterior Entropy (RPE)**, a novel metric that quantifies chart complexity. Guided by RPE, we develop **complexity-aware chart coder** to autonomously synthesize diverse, high-complexity charts via executable programs. (2) To guarantee reasoning rigor, we develop **truth-anchored inverse QA synthesis**. Diverging from standard generation, we adopt an answer-first paradigm: we extract deterministic answers directly from the source code, generate questions conditional on these anchors, and enforce strict consistency verification. To further elevate difficulty and reasoning depth, we filter samples based on model fail-rate and distill high-quality Chain-of-Thought (CoT) reasoning. We curate ChartVerse-SFT-600K and ChartVerse-RL-40K using Qwen3-VL-30B-A3B-Thinking as the teacher. Experimental results demonstrate that ChartVerse-8B achieves state-of-the-art performance, notably surpassing its teacher and rivaling the stronger Qwen3-32B-Thinking."
}