Tootfinder

TransGAT: Transformer-Based Graph Neural Networks for Multi-Dimensional Automated Essay Scoring
Hind Aljuaid, Areej Alhothali, Ohoud Al-Zamzami, Hussein Assalahi
https://arxiv.org/abs/2509.01640

TransGAT: Transformer-Based Graph Neural Networks for Multi-Dimensional Automated Essay Scoring
Essay writing is a critical component of student assessment, yet manual scoring is labor-intensive and inconsistent. Automated Essay Scoring (AES) offers a promising alternative, but current approaches face limitations. Recent studies have incorporated Graph Neural Networks (GNNs) into AES using static word embeddings that fail to capture contextual meaning, especially for polysemous words. Additionally, many methods rely on holistic scoring, overlooking specific writing aspects such as grammar…

Defining and classifying models of groups: The social ontology of higher-order networks
Jonathan St-Onge, Randall Harp, Giulio Burgio, Timothy M. Waring, Juniper Lovato, Laurent H\'ebert-Dufresne
https://arxiv.org/abs/2507.02758

Defining and classifying models of groups: The social ontology of higher-order networks
In complex systems research, the study of higher-order interactions has exploded in recent years. Researchers have formalized various types of group interactions, such as public goods games, biological contagion, and information broadcasting, showing how higher-order networks can capture group effects more directly than pairwise models. However, equating hyperedges-edges involving more than two agents-with groups can be misleading, as it obscures the polysemous nature of ``group interactions''.…

TokenBlowUp: Resolving Representational Singularities in LLM Token Spaces via Monoidal Transformations
Dongfang Zhao
https://arxiv.org/abs/2507.19747 https://

TokenBlowUp: Resolving Representational Singularities in LLM Token Spaces via Monoidal Transformations
Recent work has provided compelling evidence challenging the foundational manifold hypothesis for the token embedding spaces of Large Language Models (LLMs). These findings reveal the presence of geometric singularities around polysemous tokens, which can lead to representational instability. Existing methodologies, which presuppose a smooth data manifold, are ill-equipped to address such intrinsic structural flaws. In this paper, we formalize this problem in the language of scheme theory and p…