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#machine learning Preprint Oct 2026

Graph Representation via Elements of Discrete Morse and Cobordism Theories

Topology is, by its nature and design, suited to structure that is nonlinear, multiscale, and nonstationary - however, within machine learning, its use remains largely confined to topological data analysis. We advocate that tools from low-dimensional topology which have remained almost exclusively contained within the...

Jennifer Z. Rozenblit, Chen-Guang Yang, Yu-Xin Liu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Skynet: Workflow-Level Anomaly Detection for Agentic AI via Semantic and Structural Modeling

It is argued that anomaly detection for agentic AI must reason at the workflow level, where global execution structure exposes signals that local checks cannot see, and presents Skynet, a principled workflow-level anomaly detection framework that turns observed multi-agent execution into directed workflow graphs and sc...

Chao-Yu Zhang, He-Xuan Yu, Heng Jin et al. · 0 citations

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