Papers by Dmitry Abulkhanov
CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search (2025.naacl-srw)
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Nikita Sorokin, Tikhonov Anton, Dmitry Abulkhanov, Ivan Sedykh, Irina Piontkovskaya, Valentin Malykh
| Challenge: | clone detection is crucial in software development for identifying semantically similar code . clones can be found in the same language code snippets, but there is little research on multilingual clonage detection. |
| Approach: | They propose a novel training procedure leveraging cross-lingual similarity to train language models on source code in various programming languages. |
| Outcome: | The proposed method achieves state-of-the-art on C++ and Python clone detection benchmarks with comparable performance on decoder-based models. |
Searching by Code: A New SearchBySnippet Dataset and SnippeR Retrieval Model for Searching by Code Snippets (2024.lrec-main)
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| Challenge: | Existing code search algorithms use code comments rather than full-text descriptions as text . existing algorithms use a code snippet and/or error traceback to find code . |
| Approach: | They propose a new search-by-code use case using a code snippet and error traceback . they propose implementing the search- by-code query in a StackOverflow dataset . |
| Outcome: | The proposed dataset outperforms strong baselines on SearchBySnippet with 0.451 Recall@10 . a code snippet and/or error traceback are used as queries to find bugs . |
Ask Me Anything in Your Native Language (2022.naacl-main)
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| Challenge: | Cross-lingual question answering systems are becoming more and more important . a new approach can be generalized to more than 20 languages and outperforms previous models by 12% . |
| Approach: | They propose a cross-lingual question answering system that can be generalized to more than 20 languages . their approach can outperform previous models by 12% on multiple languages based on a dataset . |
| Outcome: | The proposed approach outperforms the previous models on multiple languages by 12% . it can be generalized to more than 20 languages and outperformed all previous models by 2% . |
T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground (2026.eacl-demo)
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Dmitrii Stoianov, Danil Taranets, Olga Tsymboi, Ramil Latypov, Almaz Dautov, Vladislav Kruglikov, Surkov Nikita, German Abramov, Pavel Gein, Dmitry Abulkhanov, Mikhail Gashkov, Viktor Zelenkovskiy, Artem Batalov, Aleksandr Medvedev, Anatolii Potapov
| Challenge: | Recent foundation models show that reasoningoriented training and improved decoding methods can substantially boost both accuracy and speed. |
| Approach: | They propose an open-weight Russian LLM for hybrid reasoning and efficient inference. |
| Outcome: | The proposed model supports direct answering and reasoning-trace generation . the model and inference pipeline can be extended or modified to suit Russian-language reasoning . |