Extreme multi-label classification (XML) and hierarchical text classification (HTC) address closely related multi-label prediction problems, but have largely developed as separate research areas. XML focuses on very large label spaces and typically evaluates ranked label lists, while HTC assumes a human-curated label h...
Florian Hauss, Tom Speier, Nerijus Bertalis et al.· Proceedings of the 2026 ACM...· 0 citations
This paper proposes GLaMoR (Graph Language Model for Reasoning), a reasoning pipeline that transforms OWL ontologies into graph-structured data and adapts the GLM architecture for consistency checking and shows that the GLM outperforms all baseline models, achieving 95% accuracy and is 20 times faster than classical re...
Chimaera integrates mixture-of-experts with graph foundation models (GFM) and integrates different GFM architectures, such as graph prompts and linear GNN models, to extend existing linear GNNs to support link-level and graph-level tasks in addition to node-level tasks.
John P. Frank, David Richerby, A. Scherp· 0 citations
Two language-model-based strategies are proposed for semantic code document segmentation, including a line-by-line approach that classifies each line of code separately before grouping the results into functional units, and a range-based approach that aims to directly determine groups of code lines from the input.
A. Dahou, A. Scherp, Sebastian Kurten et al.· Proceedings of the 2026 ACM...· 0 citations
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