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README.md

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 <h1 align="center"> Text-Based Reasoning About Vector Graphics </h1>
 
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-<a href="https://mikewangwzhl.github.io/VDLM/">🌐 Homepage</a>
+<a href="https://mikewangwzhl.github.io/VDLM">🌐 Homepage</a>
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-<a href="">📃 Paper</a>
+<a href="">📃 Paper (Coming Soon)</a>
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 <a href="https://huggingface.co/datasets/mikewang/PVD-160K" >🤗 Data (PVD-160k)</a>
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-We propose **VDLM**, a text-based visual reasoning framework for vector graphics. VDLM operates on text-based visual descriptions—specifically, SVG representations and learned Primal Visual Descriptions (PVD), enabling zero-shot reasoning with an off-the-shelf LLM. We demonstrate that VDLM outperforms state-of-the-art large multimodal models, such as GPT-4V, across various multimodal reasoning tasks involving vector graphics. See our [paper]() for more details.
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+We observe that current *large multimodal models (LMMs)* still struggle with seemingly straightforward reasoning tasks that require precise perception of low-level visual details, such as identifying spatial relations or solving simple mazes. In particular, this failure mode persists in question-answering tasks about vector graphics—images composed purely of 2D objects and shapes.
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+![Teaser](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/teaser.png?raw=true)
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+To solve this challenge, we propose **Visually Descriptive Language Model (VDLM)**, a text-based visual reasoning framework for vector graphics. VDLM operates on text-based visual descriptions—specifically, SVG representations and learned Primal Visual Descriptions (PVD), enabling zero-shot reasoning with an off-the-shelf LLM. We demonstrate that VDLM outperforms state-of-the-art large multimodal models, such as GPT-4V, across various multimodal reasoning tasks involving vector graphics. See our [paper (coming soon)]() for more details.
 
 ![Overview](https://github.com/MikeWangWZHL/VDLM/blob/main/figures/overview.png?raw=true)