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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Ontological confusion reconsidered: Introducing the new Ontological Confusion Scale

Applied Cognitive Psychology. 2025. Vol. 39. No. 6. Article e70150.
Komyaginskaya E., Gallyamova A., Golubkova A., Gnezdilov A., Ocheret A., Grigoryev D.

Ontological confusion refers to a cognitive error involving the misclassification of entities across distinct ontological domains—physical, biological, mental, and moral. While previous research links it to paranormal and pseudoscientific beliefs, existing measures—most notably the Core Knowledge Confusions (CKC) scale—suffer from conceptual ambiguity and cultural limitations. This paper introduces the Ontological Confusion Scale (OCS), designed to address these issues by evaluating seven types of ontological misclassification. Across two studies (N = 354 and N = 550), OCS demonstrates a robust unidimensional structure, high internal consistency, and strong convergent and discriminant validity. The scale predicts a range of unfounded beliefs, while outperforming CKC and belief in a just world in explaining unique variance. These findings support a broader view of ontological confusion as a general cognitive tendency underlying intuitive but erroneous reasoning across domains. OCS provides a reliable, culturally adaptable tool for research on belief formation and epistemic vulnerability.

Research target: Psychology Philosophy, Ethics, and Religious Studies
Language: English
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Keywords: belief in a just worldCognitive errorsanthropomorphismunfounded beliefscore knowledge confusionsontological confusionpromiscuous teleology
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