Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.21482v1

Read the source

Source

Plain-Language Brief

Researchers have developed an open-source framework to evaluate the effectiveness of AI agents powered by open-weight large language models (LLMs) in data preparation tasks for longitudinal population studies. The framework includes a curated ground-truth dataset, task definitions, and automated routines for evaluating the LLM-produced R code and outputted data. The study benchmarked various LLMs and found that current state-of-the-art models almost reached full completion of 20 data preparation tasks. The performance of open-weight LLMs on consumer-grade hardware shows promise for AI-assisted data preparation in research settings with governance restrictions.

What the Document Says

  • A framework for evaluating AI agents powered by open-weight LLMs is introduced for data preparation tasks in longitudinal population studies.
  • The framework includes a curated ground-truth dataset, task definitions, and automated routines for evaluating the LLM-produced R code and outputted data.
  • The study benchmarked various LLMs and found that current state-of-the-art models almost reached full completion of 20 data preparation tasks (creation of 102 variables).
  • The performance of open-weight LLMs running on consumer-grade hardware shows promise for AI-assisted data preparation in governance-restricted research settings.

Key Concepts Mentioned

  • large language models (LLMs)
  • AI agents
  • data preparation
  • longitudinal population studies
  • governance requirements

Claims Made by the Document

  • The introduced framework is for evaluating AI agents powered by open-weight LLMs in data preparation tasks.
  • Current state-of-the-art models almost reached full completion of 20 data preparation tasks.
  • The performance of open-weight LLMs running on consumer-grade hardware shows promise for AI-assisted data preparation.

Evidence Used

  • The text describes the development and purpose of the framework.
  • The text states the completion rate of current state-of-the-art models in the benchmark.
  • The text mentions the promise shown by open-weight LLMs on consumer-grade hardware.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.