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 powere
Record details
Published: 23 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Environment
Retrieved: 27 July 2026
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ethics.ai (23 July 2026), “Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks,” evidence record 13633, https://ethics.ai/record/13633 (originally published by arXiv).
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