In the realm of vision models, the primary mode of representation is using pixels to rasterize the visual world. Yet this is not always the best or unique way to represent visual content, especially for designers and artists who depict the world using geometry primitives such as polygons. Vector graphics (VG), on the other hand, offer a textual representation of visual content, which can be more concise and powerful for content like cartoons or sketches. Recent studies have shown promising results on processing vector graphics with capable Large Language Models (LLMs). However, such works focus solely on qualitative results, understanding, or a specific type of vector graphics. We propose VGBench, a comprehensive benchmark for LLMs on handling vector graphics through diverse aspects, including (a) both visual understanding and generation, (b) evaluation of various vector graphics formats, (c) diverse question types, (d) wide range of prompting techniques, (e) under multiple LLMs. Evaluating on our collected 4279 understanding and 5845 generation samples, we find that LLMs show strong capability on both aspects while exhibiting less desirable performance on low-level formats (SVG). Both data and evaluation pipeline will be open-sourced at https://vgbench.github.io.

We present the ASP-based visualization tool, clingraph, which aims at visualizing various concepts of ASP by means of ASP itself. This idea traces back to the aspviz tool and clingraph redevelops and extends it in the context of modern ASP systems. More precisely, clingraph takes graph specifications in terms of ASP facts and hands them over to the graph visualization system graphviz. The use of ASP provides a great interface between logic programs and/or answer sets and their visualization. Also, clingraph offers a python API that extends this ease of interfacing to clingo's API, and in turn to connect and monitor various aspects of the solving process.

In an earlier article, I made the case for causality, set a bit of theoretical ground, and argued that a solid understanding of causal mechanisms should be in the toolset of every data scientist informing key decisions through data. A causal inference analysis often entails drawing a graph of what may be causing what, identifying confounders, and stratifying those to find the effect of a treatment on an outcome. Doing this properly allows you to stay clear of spurious correlations and absurd claims. Finding the right confounders, which are the factors that influence both a treatment and an outcome is therefore key in solving for the causal effects. DoWhy is one of the most powerful libraries for the task, completely open-source by the Microsoft research team.

Network graphs play a large part in both computing and data science, and they are essential for working with (and visualizing) both semantic graphs and property graphs. Nearly thirty years ago, AT&T produced a set of libraries called graphviz which were designed to generate various types of printed output. Over the years, the library has been adapted for different platforms and roles, and today is still one of the most widely used network graph visualization tools around. One of the most common libraries associated with graphviz is the DOT library. DOT is a declarative language that let's users specify the components of a network graph, including the nodes (or dots) that are typically used to represent entities, along with the directed edges that are used to specify relationships or attributes.