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    <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.</abstract>
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%0 Conference Proceedings
%T OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward
%A Yang, Haoyue
%A Zhao, Xuanle
%A Liu, Xuexin
%A Jiang, Feibing
%A Zhu, Yao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-omnidiagram
%X 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.
%U https://aclanthology.org/2026.findings-acl.809/
%P 16430-16452

Markdown (Informal)

[OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward](https://aclanthology.org/2026.findings-acl.809/) (Yang et al., Findings 2026)

• OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward (Yang et al., Findings 2026)

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.