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 . |
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