Agentic Coding Without The Cloud Evaluating Open Weight

---
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": 1.0,
    "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
- arXiv: https://arxiv.org/abs/2607.21482