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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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Функциональная грамотность как мера интеллекта: новые эмпирические свидетельства

Психологические исследования: электронный научный журнал. 2025. Т. 17. № 97. Статья 6.
Kolachev N., Новиков И. А.

The study aims to explore the potential for interpreting the results of functional literacy tests in terms of intelligence assessment. The research involved 150 first-year university students from Moscow, aged 17 to 21 years (M = 18.71; SD = 2.14), of whom 72% were female. Functional literacy was assessed using items from the open bank developed within the framework of the «Monitoring Student Functional Literacy» project, and non-verbal intelligence was measured using Raven’s Progressive Matrices. The findings suggest that the operational definitions of functional literacy and intelligence exhibit a high degree of semantic similarity. It was also demonstrated that the functional literacy data fit better to the bifactor psychometric model, and the general factor (g-factor) derived from it shows a moderately strong correlation with non-verbal intelligence as measured by Raven’s Progressive Matrices. Overall, the results indicate that functional literacy measures can be interpreted in terms of general intelligence factor.

Research target: Psychology Education
Language: Russian
DOI
Keywords: intelligenceинтеллектграмотностьФункциональная грамотностьliteracy functional literacyg-factorgeneral cognitive abilityg-факторобщая когнитивная способность
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