| name | agentic-coding-without-the-cloud-evaluating-open-weight |
| description | Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks |
| metadata | {"arxiv_id":"2607.21482","utility":1,"date_added":"2026-07-26"} |
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
arXiv: 2607.21482
Published: 2026-07-23
Utility: 1.0
Summary
Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models. Their adoption in research using personal data is constrained by governance requirements that typically prohibit data transmission to external services. Locally deployable open-weight models offer an alternative since sensitive data never leave the local environment. We introduce an open-source framework for evaluating the efficacy of AI agents powered by open-weight LLMs on one of the most persistent bottlenecks in research on longitudinal population studies: data preparation. The framework comprises: a curated ground-truth dataset (cleaning scripts preparing six sweeps of data from a British cohort study), task definitions encompassing tasks such as category harmonization and multi-wave merging, and automated routines for evaluating the LLM-produced R code and outputted data. We benchmark LLMs across the (consumer grade) deployment spectrum to assess their efficacy in 20 data preparation tasks (creation of 102 variables). Current state-of-the-art, 31-35B parameter models almost saturated our benchmark ("average task completion" up to 87.9%). The performance of open-weight LLMs running on consumer-grade hardware shows promise of a viable path toward AI-assisted data preparation in governance-restricted research settings. Our framework is publicly available at: https://github.com/UCL-ARC/RRBench....
Key Information
- Title: Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
- Authors: [Extract from entry]
- Primary Category: cs.AI
Potential Skill Application
This paper presents research relevant to AI agent systems. Consider extracting methodologies, algorithms, or frameworks for skill development.
Reference
