Vector Calligrapher: Generating Scalable Vector Graphics via Structured Linguistic Supervision

Generating SVG-based fonts requires Multimodal Large Language Models (MLLMs) to translate high-level linguistic intent into lowlevel, topologically constrained symbolic sequences. However, current approaches struggle with two fundamental misalignments: the semantic ambiguity of unstructured natural language for precise geometric control, and the inefficiency of generic text tokenizers, which fragment coordinate-dense SVG XML into excessively long sequences with low information density. In this work, we propose Vector Calligrapher, a system that treats SVG generation as a conditional language modeling task optimized for both semantic grounding and representational efficiency.