OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward

Haoyue Yang, Xuanle Zhao, Xuexin Liu, Feibing Jiang, Yao Zhu


Abstract
The paradigm of programmable diagram generation is evolving rapidly, playing a crucial role in structured visualization. However, most existing studies are confined to a narrow range of task formulations and language support, constraining their applicability to diverse diagram types. In this work, we propose OmniDiagram, a unified framework that incorporates diverse diagram code languages and task definitions. To address the challenge of aligning code logic with visual fidelity in Reinforcement Learning (RL), we introduce a novel visual feedback strategy named Visual Interrogation Verifies All (Viva). Unlike brittle syntax-based rules or pixel-level matching, Viva rewards the visual structure of rendered diagrams through a generative approach. Specifically, Viva actively generates targeted visual inquiries to scrutinize diagram visual fidelity and provides fine-grained feedback for optimization. This mechanism facilitates a self-evolving training process, effectively obviating the need for manually annotated ground truth code. Furthermore, we construct M32Diagram, the first large-scale diagram code generation dataset, containing over 196k high-quality instances. Experimental results confirm that the combination of SFT and our Viva-based RL allows OmniDiagram to establish a new state-of-the-art (SOTA) across diagram code generation benchmarks.
Anthology ID:
2026.findings-acl.809
Volume:
Findings of the Association for Computational Linguistics: ACL 2026
Month:
July
Year:
2026
Address:
San Diego, California, United States
Editors:
Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
16430–16452
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https://preview.aclanthology.org/ingest-acl/2026.findings-acl.809/
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Cite (ACL):
Haoyue Yang, Xuanle Zhao, Xuexin Liu, Feibing Jiang, and Yao Zhu. 2026. OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward. In Findings of the Association for Computational Linguistics: ACL 2026, pages 16430–16452, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):
OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward (Yang et al., Findings 2026)
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https://preview.aclanthology.org/ingest-acl/2026.findings-acl.809.pdf
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