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20182026
most citedYASENN: Explaining Neural Networks via Partitioning Activation Sequences

3 citations · 6 across the 16 of their papers we have counts for

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cs.LG2026

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…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2024

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…

cs.LG2018★ 3 cited

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…