Research

Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling

Researchers develop a hierarchical tokenization method that learns to compress SVG files into compact visual programs, enabling more efficient representation learning for vector graphics modeling.

Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline

A research paper presenting a hierarchical tokenization approach for SVG (Scalable Vector Graphics) that learns compact visual programs. The method aims to improve scalability and efficiency in vector graphics modeling by breaking down SVG structures hierarchically for better representation learning.

Read original at arXiv CS.LG (Machine Learning)

What Async Promised and What it Delivered —

Async/await emerged as the dominant concurrency model across JavaScript, Python, and Rust by solving callback and promise limitations, but swapped one set of problems (callback hell, inversion of control) for another (function coloring, type system friction).