English

Themisto: Jupyter-Based Runtime Benchmark

Software Engineering 2025-04-18 v1 Artificial Intelligence Machine Learning

Abstract

In this work, we present a benchmark that consists of Jupyter notebooks development trajectories and allows measuring how large language models (LLMs) can leverage runtime information for predicting code output and code generation. We demonstrate that the current generation of LLMs performs poorly on these tasks and argue that there exists a significantly understudied domain in the development of code-based models, which involves incorporating the runtime context.

Keywords

Cite

@article{arxiv.2504.12365,
  title  = {Themisto: Jupyter-Based Runtime Benchmark},
  author = {Konstantin Grotov and Sergey Titov},
  journal= {arXiv preprint arXiv:2504.12365},
  year   = {2025}
}

Comments

Accepted to the third Deep Learning for Code (DL4C) workshop @ ICLR 2025

R2 v1 2026-06-28T23:00:59.819Z