arXiv AI
Jul 8
Plainbook: Data Science, in Plain Language
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.
Plainbook: Data Science, in Plain Language
Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, accessible only to people who understand computer code.
Related stories
arXiv AI
Jun 24
JupOtter: Cell-Level Bug Detection in Jupyter Notebooks
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.
arXiv AI
Jul 22
SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation
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.
arXiv AI
Aug 18
Walk Before You Run: The Importance of Data Exploration for Data Analysis Agents
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.
arXiv Computation and Language
Sep 15
CALICO: A Human-Centered, Codebook-Aligned System for Annotation
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...
arXiv AI
Sep 2
CUDA-Harness: Harnessing Agentic CUDA Kernel Generation and Optimization from Natural Language
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.
arXiv Machine Learning
Jun 10
CodeAlchemy: Synthetic Code Rewriting at Scale
arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.
arXiv AI
Jun 8
SWE-IF: Aligning Code Evaluation with Human Preference
arXiv:2510. 07315v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check.
arXiv Machine Learning
Jul 22
Spaghetti Architect: A Contamination-Resistant, By-Construction-Labelled, Multi-Language Code Dataset Generator
arXiv:2607. 18642v1 Announce Type: new Abstract: Mined code corpora are abundant but uncontrolled: a snippet's semantics, surface "messiness," and difficulty are whatever the wild contained; there is no known-optimal reference to grade against; and any public sample may already sit in a model's training set.
arXiv AI
Jun 16
DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents
arXiv:2511. 20709v2 Announce Type: replace-cross Abstract: Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge.
Hugging Face Trending Papers
Aug 17
SkillEffect: Checked Lowering for Memory-Bounded Agent Tools
Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this guidance into code for existing too...
arXiv AI
Jun 2
scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns
arXiv:2603. 17893v2 Announce Type: replace-cross Abstract: Methodology bugs in scientific Python code produce plausible but incorrect results that traditional linters and static analysis tools cannot detect.