Large language models augmented with external knowledge produce more factual and context-rich answers. We present a domain-agnostic framework that transforms unstructured textual evidence into a consolidated knowledge graph, in which nodes are concepts with metadata and edges are statements with structured attributes....
Illia Dagil, I. Vergunova· Automation, Control, and Inf...· 0 citations
Visual object tracking (VOT) in real-world scenarios necessitates a high degree of adaptability to overcome the distributions discrepancies inherent in multi-domain environments. While standard deep learning models excel in well-conditioned laboratory datasets, their performance typically degrades when encountering adv...
Volodymyr Husiev, I. Vergunova· Automation, Control, and Inf...· 0 citations
We introduce a hybrid computational framework for the automated discovery of complex dynamic regimes in spatially distributed epidemiological models. Traditional models often fail to capture the rich spatio-temporal heterogeneity of disease spread, while high-dimensional Partial Differential Equation (PDE) models suffe...
Olha Sirikova, I. Vergunova· 2026 6th International Confe...· 0 citations
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