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

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: this https URL.