Alexander Kovrigin
Hi! I am a Researcher at JetBrains, working on improving LLM Agents for Software Engineering tasks using Reinforcement Learning.
I am also pursuing a Master's degree in Data Science under the supervision of Prof. Dr. Dmitry Vetrov at Constructor University in Bremen, Germany.
I earned a silver medal at ICPC Northwestern Europe Regional Contest (NWERC 2023) and received a first prize award at the International Mathematics Competition for University Students (IMC 2023). During my Bachelor's studies, I worked in a research group under the supervision of Andrey Ustyuzhanin.
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EnvBench: A benchmark for automated development environment setup
Aleksandra Eliseeva, Alexander Kovrigin, Ilia Kholkin, Egor Bogomolov, Yaroslav Zharov
ICLR 2025 Third Workshop on Deep Learning for Code, 2025
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We have collected the largest dataset to date for automated environment setup and introduced a robust framework for developing and evaluating LLM-based agents that tackle environment setup challenges. By benchmarking existing LLM agents, we identified a large gap in the quality between expert-produced and LLM-generated scripts.
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Long Code Arena: a Set of Benchmarks for Long-Context Code Models
Egor Bogomolov, Aleksandra Eliseeva, Timur Galimzyanov, Evgeniy Glukhov, Anton Shapkin, Maria Tigina, Yaroslav Golubev, Alexander Kovrigin, Arie van Deursen, Maliheh Izadi, Timofey Bryksin
arXiv, 2024
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Long Code Arena is a suite of benchmarks for code-related tasks with large contexts, up to a whole code repository. It currently spans six different tasks and contains six datasets.
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On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing
Alexander Kovrigin, Aleksandra Eliseeva, Yaroslav Zharov, Timofey Bryksin
arXiv, 2024
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We experiment with coding agents for repository-level code editing. Our findings show that more complex reasoning improves the ability to locate relevant code.
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Beyond dynamics: learning to discover conservation principles
Antonii Belyshev, Alexander Kovrigin, Andrey Ustyuzhanin
Machine Learning: Science and Technology, 2024
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We introduce a method using representation learning and topology to explore conservation law spaces. It is robust to noise and identifies new conservation principles, including in quantum systems.
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