3 citations · 6 across the 16 of their papers we have counts for
16 papers
Towards Evaluation of Implicit Software World Models in Coding LLMs
Egor Bogomolov, Yaroslav Zharov
Software engineering, whether performed by humans or by AI agents, requires reasoning about how software behaves. We call the internal model that supports such reasoning the softwa…
On Problems of Implicit Context Compression for Software Engineering Agents
Kirill Gelvan, Igor Slinko, Felix Steinbauer +3
LLM-based Software Engineering agents face a critical bottleneck: context length limitations cause failures on complex, long-horizon tasks. One promising solution is to encode cont…
Step Rejection Fine-Tuning: A Practical Distillation Recipe
Igor Slinko, Ilia Zavidnyi, Egor Bogomolov +1
Rejection Fine-Tuning (RFT) is a standard method for training LLM agents, where unsuccessful trajectories are discarded from the training set. In the context of SWE-bench tasks, th…
Multi-Agent Coordinated Rename Refactoring
Abhiram Bellur, Mohammed Raihan Ullah, Fraol Batole +9
The primary value of AI agents in software development lies in their ability to extend the developer's capacity for reasoning and action, not to supplant human involvement. To show…
PIPer: On-Device Environment Setup via Online Reinforcement Learning
Alexander Kovrigin, Aleksandra Eliseeva, Konstantin Grotov +2
Environment setup-the process of configuring the system to work with a specific software project-represents a persistent challenge in Software Engineering (SE). Automated environme…
The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
Tobias Lindenbauer, Igor Slinko, Ludwig Felder +2
Large Language Model (LLM)-based agents solve complex tasks through iterative reasoning, exploration, and tool-use, a process that can result in long, expensive context histories.…
GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git
Tobias Lindenbauer, Egor Bogomolov, Yaroslav Zharov
Benchmarks for Software Engineering (SE) AI agents, most notably SWE-bench, have catalyzed progress in programming capabilities of AI agents. However, they overlook critical develo…
EnvBench: A Benchmark for Automated Environment Setup
Aleksandra Eliseeva, Alexander Kovrigin, Ilia Kholkin +2
Recent advances in Large Language Models (LLMs) have enabled researchers to focus on practical repository-level tasks in software engineering domain. In this work, we consider a co…
Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring
Abhiram Bellur, Fraol Batole, Mohammed Raihan Ullah +12
MOVEMETHOD is a hallmark refactoring. Despite a plethora of research tools that recommend which methods to move and where, these recommendations do not align with how expert develo…
Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks
Konstantin Grotov, Artem Borzilov, Maksim Krivobok +2
Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these…
Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings
Petr Tsvetkov, Aleksandra Eliseeva, Danny Dig +4
When a Commit Message Generation (CMG) system is integrated into the IDEs and other products at JetBrains, we perform online evaluation based on user acceptance of the generated me…
On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing
Alexander Kovrigin, Aleksandra Eliseeva, Yaroslav Zharov +1
Recent advancements in code-fluent Large Language Models (LLMs) enabled the research on repository-level code editing. In such tasks, the model navigates and modifies the entire co…
Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks
Konstantin Grotov, Sergey Titov, Yaroslav Zharov +1
Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these…
Tool-Augmented LLMs as a Universal Interface for IDEs
Yaroslav Zharov, Yury Khudyakov, Evgeniia Fedotova +2
Modern-day Integrated Development Environments (IDEs) have come a long way from the early text editing utilities to the complex programs encompassing thousands of functions to help…
Dynamic Retrieval-Augmented Generation
Anton Shapkin, Denis Litvinov, Yaroslav Zharov +3
Current state-of-the-art large language models are effective in generating high-quality text and encapsulating a broad spectrum of world knowledge. These models, however, often hal…
YASENN: Explaining Neural Networks via Partitioning Activation Sequences
Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov +1
We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propo…