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AI-Generated Traces for Novice Programmers: Learning Effects and Learner Differences in a Multi-Institutional Study
new Abstract: Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies.
Scalable GANs with Transformers
Announce Type: replace Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning. We investigate the scalability of Generative Adversarial Networks (GANs) through two design choices that have proven to be effective in other types of generative models: training in a compact Variational Autoencoder latent space and adopting purely transformer-based generators and discriminators. Training in latent space enables...
Pre-Modern Armies for Worldbuilders, Part I: Why They Fight
This week I want to try something a little different. Rather than taking apart a particular fantasy military system, I thought I might try to lay out a more general sense of how military systems tend to map on to societies, both because such general historical frameworks are handy for thinking about the past, but also because they make useful rules of thumb for imagining fantastical societies. So essentially here we are asking: how do societies end up with the sort of armies they have?
From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
Announce Type: replace Abstract: Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-range dependencies and global document structure, especially for long texts. This paper proposes a data-driven method to automatically construct graph-based document representations.