GIR-Bench: Versatile Benchmark for Generating Images with Reasoning

• URL: http://arxiv.org/abs/2510.11026v1
• Date: Mon, 13 Oct 2025 05:50:44 GMT
• Title: GIR-Bench: Versatile Benchmark for Generating Images with Reasoning
• Authors: Hongxiang Li, Yaowei Li, Bin Lin, Yuwei Niu, Yuhang Yang, Xiaoshuang Huang, Jiayin Cai, Xiaolong Jiang, Yao Hu, Long Chen,
• Abstract summary: Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation.<n>We introduce textbfGIR-Bench, a comprehensive benchmark that evaluates unified models across three complementary perspectives.
• Score: 40.09327641816171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation, showing great promise for advanced multimodal intelligence. However, the community still lacks a rigorous reasoning-centric benchmark to systematically evaluate the alignment between understanding and generation, and their generalization potential in complex visual tasks. To this end, we introduce \textbf{GIR-Bench}, a comprehensive benchmark that evaluates unified models across three complementary perspectives. Firstly, we investigate understanding-generation consistency (GIR-Bench-UGC), asking whether models can consistently leverage the same knowledge in both understanding and generation tasks. Secondly, we investigate whether models can perform reasoning-centric text-to-image generation that requires applying logical constraints and implicit knowledge to generate faithful visual content (GIR-Bench-T2I). Thirdly, we evaluate whether models can handle multi-step reasoning in editing (GIR-Bench-Edit). For each subset, we carefully design different task-specific evaluation pipelines tailored for each task. This enables fine-grained and interpretable evaluation while mitigating biases from the prevalent MLLM-as-a-Judge paradigm. Extensive ablations over various unified models and generation-only systems have shown that: Although unified models are more capable of reasoning-driven visual tasks, they still exhibit a persistent gap between understanding and generation. The data and code for GIR-Bench are available at \href{https://hkust-longgroup.github.io/GIR-Bench}{https://hkust-longgroup.github.io/GIR-Bench}.

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