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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.
June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.

 

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Влияние доминирующего бизнеса на развитие цифровых экосистем в России

Вестник Российской академии наук. 2026. Т. 96. № 6. С. 564–574.
Блохин А. А., Larionov A.

The study confirms the importance of institutional factors in the formation of digital ecosystems. Their advantages for participants consist in the creation of a common environment with reduced transaction costs. Such an advantage is created by the dominant large business in it, organizing the ecosystem, based on its access to information arrays, a wide network of clients, investments in platform technology solutions, and other resources. Mid-sized companies in the ecosystem, using the institutional advantages they have received, become leaders in a certain industry, attracting SMEs to scale the effects of their activities. Based on a quantitative and qualitative analysis of examples of the development of the Sber, T-Bank, MTS, and Yandex ecosystems, the decisive role of the largest businesses in the development of the ecosystem and the management of value flows in it is demonstrated. An analysis of the revenue dynamics of companies included in the Sber and Yandex ecosystems from 2016 to 2024 confirms the positive impact of participation in the ecosystem for this business. The latter is associated with the redistribution of institutional rent between alpha-, beta-, and gamma- businesses in accordance with the theory of economic dominance.

Research target: Economics and Management Political Science, International Relations, and Public Administration
Language: Russian
DOI
Text on another site
Keywords: ecosystemэкосистема
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