One of the drawbacks of INs is scaling with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce V AIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that V AIN is effective for multi-agent predictive modeling.

WL can count homomorphisms of cactus graphs.

Machine translators have made it easier than ever to create error-plagued Wikipedia articles in obscure languages. What happens when AI models get trained on junk pages? When Kenneth Wehr started managing the Greenlandic-language version of Wikipedia four years ago, his first act was to delete almost everything. It had to go, he thought, if it had any chance of surviving. Wehr, who's 26, isn't from Greenland--he grew up in Germany--but he had become obsessed with the island, an autonomous Danish territory, after visiting as a teenager. He'd spent years writing obscure Wikipedia articles in his native tongue on virtually everything to do with it. He even ended up moving to Copenhagen to study Greenlandic, a language spoken by some 57,000 mostly Indigenous Inuit people scattered across dozens of far-flung Arctic villages. The Greenlandic-language edition was added to Wikipedia around 2003, just a few years after the site launched in English. By the time Wehr took its helm nearly 20 years later, hundreds of Wikipedians had contributed to it and had collectively written some 1,500 articles totaling over tens of thousands of words.

The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent-based Modelling (ABM) community. With their ability to summarize, generate, analyze, categorize, transcribe and translate text, answer questions, propose explanations, sustain dialogue, extract information from unstructured text, and perform logical reasoning and problem-solving tasks, LLMs have a good potential to contribute to the modelling process. After reviewing the current use of LLMs in ABM, this study reflects on the opportunities and challenges of the potential use of LLMs in ABM. It does so by following the modelling cycle, from problem formulation to documentation and communication of model results, and holding a critical stance.

Within the context of the mathematical formulation of Merge and the Strong Minimalist Thesis, we present a mathematical model of the morphology-syntax interface. In this setting, morphology has compositional properties responsible for word formation, organized into a magma of morphological trees. However, unlike syntax, we do not have movement within morphology. A coproduct decomposition exists, but it requires extending the set of morphological trees beyond those which are generated solely by the magma, to a larger set of possible morphological inputs to syntactic trees. These participate in the formation of morphosyntactic trees as an algebra over an operad, and a correspondence between algebras over an operad . The process of structure formation for morphosyntactic trees can then be described in terms of this operadic correspondence that pairs syntactic and morphological data and the morphology coproduct. We reinterpret in this setting certain operations of Distributed Morphology as transformation that allow for flexibility in moving the boundary between syntax and morphology within the morphosyntactic objects.

ABSTRACT The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often resulting in models with complex training strategies to obtain the desired results. In this work, we instead propose a novel approach that explicitly incorporates the underlying clustering assumption in SSL through extending a recently proposed differentiable clustering module. Leveraging annotated data to guide the cluster centroids results in a simple end-to-end trainable deep SSL approach. We demonstrate that the proposed model improves the performance over the supervised-only baseline and show that our framework can be used in conjunction with other SSL methods to further boost their performance. Index T erms -- Clustering, Semi-supervised learning, Gaussian mixture models 1. INTRODUCTION Traditional deep learning has achieved state-of-the-art performance on various tasks at the cost of large-scale supervised training data.