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

📰 ArXiv cs.AI

Learn to migrate deep learning workloads from PyTorch to JAX using an agentic framework with in-context learning and self-debugging

advanced Published 16 Jun 2026

Action Steps

Implement In-Context Learning (ICL) to align APIs between PyTorch and JAX
Configure oracle-driven self-debugging to identify and correct mistakes
Build a fully autonomous system to migrate deep learning models
Test the migrated models for accuracy and performance
Apply the agentic framework to real-world deep learning workloads

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this framework to automate the migration of deep learning models, reducing manual effort and errors

Key Insight

💡 In-context learning and self-debugging can be used to automate the migration of deep learning models between different frameworks

Full Article

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

Abstract:
arXiv:2606.15994v1 Announce Type: new Abstract: 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

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