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+ ## Model Overview
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+ This model is introduced in the paper:
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+ **PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images**
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+ https://arxiv.org/abs/2509.25185
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+ PixelCraft is a multi-agent framework designed for precise visual reasoning on structured images, with a focus on pixel-level grounding.
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+
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+ ## Intended Use
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+ The model is intended for structured image understanding and grounding tasks, where accurate localization of visual elements is required to support downstream reasoning.
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+ ## Inference
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+ The reference inference implementation is provided in the PixelCraft repository.
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+ The grounding and inference logic can be found at:
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+ https://github.com/microsoft/PixelCraft/blob/main/src/tools/grounding.py
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+ Please refer to this script for:
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+ - Model loading
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+ - Input preprocessing
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+ - Grounding and inference execution
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+ - Output formats
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+
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+ Users are expected to follow the provided implementation when running inference with this model.
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+
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+ ## Citation
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+
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+ If you use this model, please cite the corresponding paper:
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+ ```bibtex
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+ @article{zhang2025pixelcraft,
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+ title={PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images},
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+ author={Zhang, Shuoshuo and Li, Zijian and Zhang, Yizhen and Fu, Jingjing and Song, Lei and Bian, Jiang and Zhang, Jun and Yang, Yujiu and Wang, Rui},
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+ journal={arXiv preprint arXiv:2509.25185},
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+ year={2025}
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+ }
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+ ```