Generating SVG-based fonts requires Multimodal Large Language Models (MLLMs) to translate high-level linguistic intent into low-level, 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.To bridge the semantic gap, we introduce a structured linguistic supervision Font Description Framework that decomposes typographic style into interpretable linguistic dimensions (e.g., historical lineage, affective metaphors), providing structured supervision aligned with the compositional syntax of SVG. To address the tokenization bottleneck, we design a scalable separated-coordinate strategy that bypasses the vocabulary explosion of flattened tokens while significantly compressing sequence length. Supported by VectorFont, a dataset of over 10 million hierarchically annotated glyphs, our approach improves CLIP score by +23%, reduces geometric error by ≈48%, and boosts generation efficiency by achieving an 18% Command-per-Token (C/T) ratio—a 6× increase in information density over standard baselines. These results demonstrate that combining structured linguistic supervision with efficient symbolic tokenization is essential for reliable, controllable vector graphics synthesis. VectorFont dataset, Code and model weights will be publicly released.

To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE. The field of natural language understanding has traditionally focused on benchmarks for various tasks in languages such as Chinese, English, and multilingual, however, there has been a lack of attention given to the area of classical Chinese, also known as "wen yan wen (文言文)", which has a rich history spanning thousands of years and holds significant cultural and academic value. For the prosperity of the NLP community, in this paper, we introduce the WYWEB evaluation benchmark, which consists of nine NLP tasks in classical Chinese, implementing sentence classification, sequence labeling, reading comprehension, and machine translation. We evaluate the existing pre-trained language models, which are all struggling with this benchmark. We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on classical Chinese NLU. The github repository is https://github.com/baudzhou/WYWEB.