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News
September 21, 2026
Researchers Develop Methodology to Assess the Quality of Legal Representation in Criminal Proceedings
Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.
September 21, 2026
Algebra, Geometry, and AI: Russian and Vietnamese Mathematicians Discuss Current Research
A delegation of scientists from Hanoi visited the HSE Faculty of Computer Science and then took part in a Russian-Vietnamese conference in St Petersburg. The events were part of the three-year project ‘Flexibility and Computational Methods.’ Over the course of the project, the researchers have prepared joint publications and obtained new mathematical results.
September 18, 2026
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.

 

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Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)

Suzhou : Association for Computational Linguistics, 2025.
Under the general editorship: M. Valentino, D. Ferreira, M. Thayaparan, L. Ranaldi, A. Freitas
Chapters
Synthetic Proofs with Tool-Integrated Reasoning: Contrastive Alignment for LLM Mathematics with Lean
Obozov M., Diskin M., Beznosikov A. et al., , in: Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025).: Suzhou: Association for Computational Linguistics, 2025. Ch. 15 P. 195–202.
Modern mathematical reasoning benchmarks primarily focus on answer finding rather than proof verification, creating a gap in evaluating the proving capabilities of large language models (LLMs). We present a methodology for generating diverse mathematical proof tasks using formal tools. Our approach combines Lean-based synthetic problem generation with a Tool-Integrated Reasoning (TiR) framework for partial (sampling-based) ...
Added: February 26, 2026
Research target: Computer Science
Language: English
DOI
Text on another site
Keywords: NLP methodsnlpmathematical nlp
Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)
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