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Yuri Kuratov

Yuri Kuratov has 3 papers in our library, in 2019–2026, most of them on Named entity recognition, Multilingual & cross-lingual NLP, Reasoning; often with Aydar Bulatov, Ivan Rodkin, Mikhail Burtsev.

3 papers in our library · 2019–2026

OpenAlex · ORCID · h-index 9 · 34 works · 784 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 174 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5956

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Moscow Institute of Physics and TechnologyDolgoprudny · Russia2018–2025 · 20 papers🤝AIRI - Artificial Intelligence Research InstituteMoscow · Russia2022–2025 · 11 papers🎓Lomonosov Moscow State UniversityMoscow · Russia2019–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 1 workNamed entity recognition · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20241 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Tuning Multilingual Transformers for Language-Specific Named Entity Recognition (2019 · 163 citations)
    The paper addresses named entity recognition (NER) in four Slavic languages: Bulgarian, Czech, Polish and Russian.
  2. Associative Recurrent Memory Transformer (2024 · 8 citations)
    The authors address a core difficulty in long-context modeling: building a network whose cost for each new chunk of input stays fixed, however long the sequence grows.
  3. Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling (2026)
    The paper asks whether multi-step reasoning in neural models reflects real generalization or memorization, and how difficulty grows with the number of required inference steps.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. DeepPavlov: Open-Source Library for Dialogue Systems ↗Proceedings of ACL 2018, System Demonstrations · no summary yetMikhail Burtsev et al.2018 · 202 citations
  2. Tuning Multilingual Transformers for Language-Specific Named Entity RecognitionWorkshop on Balto-Slavic Natural Language ProcessingMikhail Arkhipov, Maria Trofimova, Yuri Kuratov, et al.2019 · 163 citations
  3. BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack ↗arXiv:2406.10149 · no summary yetYuri Kuratov et al.2024 · 88 citations
  4. Associative Recurrent Memory TransformerarXiv:2407.04841Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, et al.2024 · 8 citations
  5. Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute ScalingFindings of the Association for Computational LinguisticsIvan Rodkin, Daniil Orel, Konstantin Smirnov, et al.2026

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 174 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

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