arXiv:2607. 05717v1 Announce Type: cross Abstract: Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis.
By Luca de Alfaro, Mathis Aubert, Ranjit Jhala, Eliana Pastor, Elena Baralis
arXiv:2606. 23877v1 Announce Type: cross Abstract: Jupyter Notebooks are an increasingly popular coding environment used across many domains, especially in Python-based data science and scientific computing.
By Lukas Ottenhof, Thibaud Lutellier
arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.
By Weifeng Sun, Ye Fan, Yuchen Chen, Gou Tan, Jieke Shi, Yuan Yidi, Swee Liang Wong, Jonathan Pan, David Lo
arXiv:2608. 16045v1 Announce Type: cross Abstract: LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations.
By Yike Yuan, Virum Ranka, Tina Lasisi, Lin Ma
arXiv:2609.14726v1 Announce Type: cross
Abstract: Large language models are increasingly used to scale codebook-based annotation in scientific research, but existing workflows provide limited support...
By Boqin Yuan, Xiaoyi Gu, Fiona Li, Chang Wan, Angel Hsing-Chi Hwang, Jieyu Zhao
CUDA‑Harness is a framework that enables the generation and optimization of CUDA kernels directly from natural language. It introduces Intermediate‑Structured Generation to bridge high‑level semantics with low‑level kernel code, uses Synthesis‑Based Verification to mitigate reward hacking by providing isolated test data, and employs Feedback‑Adaptive Evolution to prioritize correctness while improving performance. Experiments show the approach generalizes across different large language models, hardware platforms, and even supports C‑to‑CUDA transpilation.
By Qi Fan, An Zou, Yehan Ma