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SciIR: Large-Scale Scientific Image Dataset Released; Qwen Model Achieves 43% AccuracySciIR：大规模科学图像数据集发布，Qwen模型精度达43%

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Researchers introduced SciIR (Scientific Image Reasoning), a comprehensive framework including an 82,000+ image-text pair dataset and evaluation benchmark designed to improve scientific image generation capabilities in text-to-image models.
The SciIR-82k dataset is organized according to Peirce's Semiotic Triad—Entity Structure (Icon), Scientific Process (Index), and Scientific Law (Symbol)—with Chain-of-Thought annotations to capture underlying visual logic.
The Qwen-Image-SciIR model fine-tuned on SciIR-82k achieved 43% on SciIR-Bench, a substantial improvement from the baseline 35%, addressing current models' deficiencies in scientific reasoning capabilities.

研究人员推出SciIR（科学图像推理）框架，包含超8万对高质量图文对数据集和评测基准，旨在改进文本生成图像模型的科学图像生成能力。
SciIR-82k数据集按照皮尔士符号学三角形——实体结构（图标）、科学过程（指标）、科学规律（符号）——进行组织，附带推理链注释以捕捉潜在的视觉逻辑。
Qwen-Image-SciIR模型在SciIR-82k上微调后，在SciIR-Bench上得分达43%，相比基线的35%实现了显著提升，解决了现有模型在科学推理能力上的不足。

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