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arXiv · 2512.04125

ASCIIBench: Evaluating Language-Model-Based Understanding of Visually-Oriented Text

Kerry Luo·Michael Fu·Joshua Peguero·Husnain Malik·Anvay Patil·Joyce Lin·Megan Van Overborg·Ryan Sarmiento·Kevin Zhu
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Abstract

Large language models (LLMs) have demonstrated several emergent behaviors with scale, including reasoning and fluency in long-form text generation. However, they continue to struggle with tasks requiring precise spatial and positional reasoning. ASCII art, a symbolic medium where characters encode structure and form, provides a unique probe of this limitation. We introduce ASCIIBench, a novel benchmark for evaluating both the generation and classification of ASCII-text images. ASCIIBench consists of a filtered dataset of 5,315 class-labeled ASCII images and is, to our knowledge, the first publicly available benchmark of its kind. Alongside the dataset, we release weights for a fine-tuned CLIP model adapted to capture ASCII structure, enabling the evaluation of LLM-generated ASCII art. Our analysis shows that cosine similarity over CLIP embeddings fails to separate most ASCII categories, yielding chance-level performance even for low-variance classes. In contrast, classes with high internal mean similarity exhibit clear discriminability, revealing that the bottleneck lies in representation rather than generational variance. These findings position ASCII art as a stress test for multimodal representations and motivate the development of new embedding methods or evaluation metrics tailored to symbolic visual modalities. All resources are available at https://github.com/ASCIIBench/ASCIIBench.

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Kerry Luo, Michael Fu, Joshua Peguero, Husnain Malik, Anvay Patil, Joyce Lin, Megan Van Overborg, Ryan Sarmiento, Kevin Zhu. 2025-12-02. ASCIIBench: Evaluating Language-Model-Based Understanding of Visually-Oriented Text. https://arxiv.org/abs/2512.04125

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