Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks

2026-07-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

An open-source framework has been introduced to evaluate AI agents powered by open-weight large language models (LLMs) for data preparation tasks, specifically addressing governance constraints in research involving personal data. This framework comprises a curated ground-truth dataset of cleaning scripts for six sweeps of British cohort study data, task definitions like category harmonization and multi-wave merging, and automated routines for evaluating LLM-produced R code and outputted data. Benchmarking across 20 data preparation tasks, which involved the creation of 102 variables, revealed that current state-of-the-art 31-35B parameter models achieved up to 87.9% "average task completion." This performance, even on consumer-grade hardware, indicates a viable path for AI-assisted data preparation in governance-restricted research settings.

Key takeaway

For Research Scientists handling sensitive longitudinal data and constrained by governance requirements, you can now confidently explore open-weight LLMs for data preparation. This approach allows you to leverage AI assistance for tasks like data harmonization and merging without transmitting sensitive information to external cloud services. Your team can achieve significant automation on consumer-grade hardware, accelerating research while maintaining data privacy.

Key insights

Open-weight LLMs offer a viable path for AI-assisted data preparation in governance-restricted research settings.

Principles

• Locally deployable models circumvent data transmission prohibitions.
• 31-35B parameter models achieve high task completion rates.
• Consumer-grade hardware can support effective open-weight LLM agents.

Method

The framework evaluates open-weight LLMs on data preparation using a ground-truth dataset, defined tasks (e.g., category harmonization), and automated evaluation of R code and outputted data.

In practice

• Benchmark LLMs on 20 data preparation tasks (102 variables).
• Utilize open-weight LLMs for multi-wave merging and category harmonization.

Topics

• Open-weight LLMs
• Agentic Coding
• Data Preparation
• Longitudinal Studies
• Data Governance
• AI Benchmarking

Code references

• UCL-ARC/RRBench

Best for: AI Engineer, Machine Learning Engineer, CTO, AI Scientist, Research Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.