arXiv:2607.03994v1 Announce Type: cross Abstract: Modern language models generally represent text as sequences of discrete token embeddings, an assumption deeply rooted in current practice but rarely questioned. We challenge this representation, especially for Chinese, by replacing index-based token embeddings entirely with a single rasterized image of the character sequence, processed by a vision encoder composed of a shared ResNet and a shallow Vision Transformer. To isolate the role of input representation, we construct a dual-branch controlled framework in which both a Vision-based model a
This research emerges as AI models scale, pushing the boundaries of traditional token-embedding methodologies, particularly for complex character sets like Chinese.
It challenges a foundational assumption in large language models (LLMs) and could lead to more efficient or accurate text processing, especially for non-Latin scripts.
The input representation for LLMs could shift from discrete token embeddings to visual glyph processing, fundamentally altering model architecture and pre-training.
- · AI researchers focusing on vision-based language processing
- · Companies developing LLMs for East Asian languages
- · Hardware manufacturers specializing in vision processing units (VPUs)
- · Developers solely reliant on traditional tokenization libraries
- · Language models optimized exclusively for Western alphabets
Improved performance of LLMs for character-based languages, potentially reducing biases inherent in tokenization.
A convergence of computer vision and natural language processing techniques within a single model architecture.
New efficiency frontiers for AI models by leveraging visual input, potentially impacting compute requirements and model scalability.
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Read at arXiv cs.AI