Graph classification is dominated by permutation-invariant graph neural networks. We revisit this problem from a different perspective: can small language models (SLMs) act as graph classifiers when graphs are serialized as text? Unlike GNNs, sequence-based transformers do not encode permutation invariance by construction, raising a fundamental question about structural stability under node relabeling.We provide the first systematic study of permutation robustness in small graph-as-text models. We introduce an evaluation protocol based on Flip Rate and KL-to-Mean divergence to […]

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arXiv:2602.16894v1 Announce Type: new Abstract: In response to the exponential growth in the use of artificial intelligence and machine learning applications, educators, researchers and policymakers have taken steps to integrate artificial intelligence applications into K-12 education. Among these efforts, one equally important approach has received little, if any attention: What if students and teachers were not just learning to be competent users of AI but also its creators? This question is at the heart of CreateAI in which […]

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arXiv:2601.02522v1 Announce Type: new Abstract: The rising energy demands of machine learning (ML), e.g., implemented in popular variants like retrieval-augmented generation (RAG) systems, have raised significant concerns about their environmental sustainability. While previous research has proposed green tactics for ML-enabled systems, their empirical evaluation within RAG systems remains largely unexplored. This study presents a controlled experiment investigating five practical techniques aimed at reducing energy consumption in RAG systems. Using a production-like RAG system developed at our collaboration partner, […]

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arXiv:2601.18932v1 Announce Type: cross Abstract: Popularized by their strong image generation performance, diffusion and related methods for generative modeling have found widespread success in visual media applications. In particular, diffusion methods have enabled new approaches to data compression, where realistic reconstructions can be generated at extremely low bit-rates. This article provides a unifying review of recent diffusion-based methods for generative lossy compression, with a focus on image compression. These methods generally encode the source into an embedding and […]

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Hey everyone! I built a Mario AI bot that learns to play completely by itself using Reinforcement Learning. It starts with zero knowledge it doesn’t even know what “right” or “jump” means — and slowly figures it out through pure trial and error. Here’s what it does: Watches the game screen as pixels Tries random moves at first (very painful to watch ) Gets rewarded for moving right and penalized for dying Over thousands of attempts it figures […]

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arXiv:2410.14485v5 Announce Type: replace-cross Abstract: Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like computer vision and natural language processing. However, their inability to inherently respect causal structures can limit their robustness, making them vulnerable to covariate shift and difficult to interpret/explain. This poses significant challenges for their reliability in real-world applications. In this paper, we introduce Causal Transformers (CaTs), a general model class designed to […]

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