Alexander Kovrigin

I am a master's student in a joint program with JetBrains at Constructor University, focusing on ML. I am currently a Research Intern at the AI Agents & Planning lab at JetBrains Research.

I earned a silver medal at NWERC (ICPC Northwestern Europe Regional Contest) and received a first prize award at the IMC (International Mathematics Competition for University Students) in 2023.

I completed my Bachelor's in Computer Science at Constructor University, having transferred from the Higher School of Economics. My thesis was titled "Reasoning for Repository-level Code Editing." During my Bachelor's studies at Constructor University, I worked in a research group under the supervision of Andrey Ustyuzhanin.

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Research
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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
arxiv / code /

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
arxiv / code / hf /

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
arxiv / code /

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.

Design and source code from Jon Barron's website