Agentic Framework for Deep Learning workload migration via In-Context Learning

Translating deep learning models from PyTorch's flexible, object-oriented design to JAX's functional, stateless setup is usually a manual and error-prone task. Automated migration is challenging because Large Language Models (LLMs) struggle with strict and dynamic API alignment and are prone to mistakes for exacting operations. We propose a fully autonomous system that combines In-Context Learning (ICL) with oracle-driven self-debugging. First, we curated an ICL context that serves as a strict r

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ethics.ai (14 June 2026), “Agentic Framework for Deep Learning workload migration via In-Context Learning,” evidence record 989, https://ethics.ai/record/989 (originally published by arXiv).

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