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 populati
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- PossiblePossibly related (embedding) · 28%langchain-ai/open-swe →
“Possibly related via embedding similarity 0.60 (not asserted). Timestamp check: artifact slightly before paper (-21d).”
- PossiblePossibly related (embedding) · 62%Demystifying agentic AI: How to build production-ready AIOps with open source models →
- PossiblePossibly related (embedding) · 25%huggingface/datasets →
“Possibly related via embedding similarity 0.55 (not asserted). Timestamp check: artifact slightly before paper (-21d).”
- FuzzySimilar title/name (fuzzy) · 59%Fosowl/agenticSeek →
“Fuzzy title match (0.73): “Agentic coding without the cloud: evaluating open-weight lar” ≈ “Fosowl/agenticSeek””
- LinkedLinked via arxiv author · 85%Mack Nixon →
“Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks”
- LinkedLinked via arxiv author · 85%Liam Wright →
“Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks”
- LinkedLinked via arxiv author · 85%Yevgeniya Kovalchuk →
“Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks”
- LinkedLinked via arxiv author · 85%Alison Fang-Wei Wu →
“Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks”