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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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Tautologies, inferential processes and constraints on evoked knowledge

Journal of Pragmatics. 2022. Vol. 191. P. 55–66.
Vilinbakhova E., Escandell-Vidal V., Zevakhina N.

In the literature on nominal tautologies, it is assumed that common knowledge is a crucial ingredient for their interpretation. This paper explores a different approach: we argue that invoking shared knowledge is at the same time too strict and too vague as a condition for the understanding of tautologies in context. More specifically, we claim that, on the one hand, the hearer’s previous knowledge about some specific set of properties of the entity referred to in the tautology is not always necessary: lack of previous knowledge can be repaired by accommodating new assumptions or compensated by providing additional explicit content in discourse. On the other hand, the hearer’s previous knowledge about some specific set of properties of the entity referred to in the tautology is not always sufficient: only permanent, classificatory properties can be evoked by a tautology; transitory states, by contrast, are systematically rejected, even if they constitute shared knowledge and are supported by the context. We provide evidence for our claims both from the corpus study, analysing examples of tautologies with proper names from COCA and web-based sources, and experimental study designed as a verification task, additionally measuring reaction times for replying to a given question.

Research target: Philology and Linguistics
Language: English
DOI
Keywords: common knowledgeобщее знаниеtautologies тавтологииinterpretive strategiesILP/SLP distinction
Publication based on the results of:
Formal Philosophy and Logic of Rational Agency (2022)
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