Scalable Vector Graphics (SVG) are essential XML-based formats for versatile graphics, offering resolution independence and scalability. Unlike raster images, SVGs use geometric shapes and support interactivity, animation, and manipulation via CSS and JavaScript. Current SVG generation methods face challenges related to high computational costs and complexity. In contrast, human designers use component-based tools for efficient SVG creation. Inspired by this, SVGBuilder introduces a component-based, autoregressive model for generating high-quality colored SVGs from textual input. It significantly reduces computational overhead and improves efficiency compared to traditional methods. Our model generates SVGs up to 604 times faster than optimization-based approaches. To address the limitations of existing SVG datasets and support our research, we introduce ColorSVG-100K, the first large-scale dataset of colored SVGs, comprising 100,000 graphics. This dataset fills the gap in color information for SVG generation models and enhances diversity in model training. Evaluation against state-of-the-art models demonstrates SVGBuilder's superior performance in practical applications, highlighting its efficiency and quality in generating complex SVG graphics.

Given a system design (SD), a key task is to optimize this design to reduce the probability of catastrophic failures. We consider the task of redesigning an SD to minimize the probability of particular faults by introducing components selected from a component library. We have implemented a General Redesign Engine (GRE), which uses model-based reasoning techniques and Boolean functional synthesis from component libraries, to automate redesign for combinational circuits. For a significant subset of observations leading to catastrophic (forbidden) modes we demonstrate that GRE trades off redesign cost for increased fault tolerance, and shows a significant advantage compared to the Triple-Modular Redundancy (TMR) method. Our algorithm has a wide application in AI, including automated software and hardware design, error detection, reconfiguration and recovery, and modular robotics.