As slick, machine-generated visuals become ubiquitous, artists and designers are embracing a style of handmade imperfection. Two and a half years ago, Christine Tyler Hill, a designer and artist in Burlington, Vermont, began working as a crossing guard in her neighborhood. The city paid her twenty dollars an hour, but the real draw was the chance to get to know local families and "be more enmeshed with my very immediate, outside-my-door community," she told me recently. She was tired of staring at a screen doing design work, and new clients were getting harder to come by, in part, she surmised, because of the rise of generative artificial intelligence . She began documenting her crossing-guard shifts on Instagram, posting mini comics about the frigid weather, the charming habits of commuting children, and the beauty of an overflowing trash can.

Efforts to improve Kolmogorov-Arnold networks (KANs) with architectural enhancements have been stymied by the complexity those enhancements bring, undermining the interpretability that makes KANs attractive in the first place. Here we study overprovisioned architectures combined with sparsification to learn compact, interpretable KANs without sacrificing accuracy. Crucially, we focus on differentiable sparsification, turning architecture search into an end-to-end optimization problem. Across function approximation benchmarks, dynamical systems forecasting, and real-world prediction tasks, we demonstrate competitive or superior accuracy while discovering substantially smaller models. Overprovisioning and sparsification are synergistic, with the combination outperforming either alone. The result is a principled path toward models that are both more expressive and more interpretable, addressing a key tension in scientific machine learning.

Kolmogorov-Arnold Networks (KANs) offer a promising path toward interpretable machine learning: their learnable activations can be studied individually, while collectively fitting complex data accurately. In practice, however, trained activations often lack symbolic fidelity, learning pathological decompositions with no meaningful correspondence to interpretable forms. We propose Softly Symbolified Kolmogorov-Arnold Networks (S2KAN), which integrate symbolic primitives directly into training. Each activation draws from a dictionary of symbolic and dense terms, with learnable gates that sparsify the representation. Crucially, this sparsification is differentiable, enabling end-to-end optimization, and is guided by a principled Minimum Description Length objective. When symbolic terms suffice, S2KAN discovers interpretable forms; when they do not, it gracefully degrades to dense splines. We demonstrate competitive or superior accuracy with substantially smaller models across symbolic benchmarks, dynamical systems forecasting, and real-world prediction tasks, and observe evidence of emergent self-sparsification even without regularization pressure.

We consider the non-stochastic Multi-Armed Bandit problem in a setting where there is a fixed and known metric on the action space that determines a cost for switching between any pair of actions.

We propose a novel and efficient search algorithm which finds initial centers that can be used subsequently for the local search algorithm.

In ethics, individual responsibility is often defined through Frankfurt's principle of alternative possibilities. This definition is not adequate in a group decision-making setting because it often results in the lack of a responsible party or "responsibility gap''. One of the existing approaches to address this problem is to consider group responsibility. Another, recently proposed, approach is "higher-order'' responsibility. The paper considers the problem of deciding if higher-order responsibility up to degree $d$ is enough to close the responsibility gap. The main technical result is that this problem is $Π_{2d+1}$-complete.

WORK Although the findings highlight the effectiveness of fine - tuned transformer models for Bengali - Sylheti translation, several limitations remain. The dataset size (5,002 parallel sentences) restricts the models' capacity to generalize across diverse syntactic structures, stylistic variations, and domain - specific expressions. In addition, orthographic inconsistencies in Sylheti introduce noise, leading to training instability, particularly in models like mBART - 50. Another limitation is the reliance on automatic evaluation metrics such as BLEU and chrF, which may not fully capture the linguistic richness or cultural nuance of Sylheti. Future research should therefore focus on expanding the datas et through community - driven contributions and data augmentation strategies. Incorporating orthographic normalization could improve consistency and reduce variability during training. Hybrid approaches that combine the strengths of pre - trained LLMs with fin e - tuned NMT models may also enhance translation robustness in low - resource settings. Finally, incorporating human evaluation will provide a more comprehensive assessment of translation adequacy, fluency, and cultural alignment.

We propose a novel and efficient search algorithm which finds initial centers that can be used subsequently for the local search algorithm.

Nearly one in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder, underscoring the urgency of developing accurate and interpretable diagnostic tools. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a powerful lens into large-scale functional connectivity, where brain regions are modeled as nodes and inter-regional synchrony as edges, offering clinically relevant biomarkers for psychiatric disorders. While prior works use graph neural network (GNN) approaches for disorder prediction, they remain complex black-boxes, limiting their reliability and clinical translation. In this work, we propose CONCEPTNEURO, a concept-based diagnosis framework that leverages large language models (LLMs) and neurobiological domain knowledge to automatically generate, filter, and encode interpretable functional connectivity concepts. Each concept is represented as a structured subgraph linking specific brain regions, which are then passed through a concept classifier. Our design ensures predictions through clinically meaningful connectivity patterns, enabling both interpretability and strong predictive performance. Extensive experiments across multiple psychiatric disorder datasets demonstrate that CONCEPTNEURO-augmented GNNs consistently outperform their vanilla counterparts, improving accuracy while providing transparent, clinically aligned explanations. Furthermore, concept analyses highlight disorder-specific connectivity patterns that align with expert knowledge and suggest new hypotheses for future investigation, establishing CONCEPTNEURO as an interpretable, domain-informed framework for psychiatric disorder diagnosis.