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 curated an ICL context that serves as a strict reference for idiomatic JAX styling and test case gen
The increasing complexity and specialization of AI frameworks like PyTorch and JAX necessitate automated solutions for interoperability and migration to leverage diverse computational strengths efficiently.
Automated deep learning workload migration reduces engineering overhead and errors, accelerating development and deployment across different AI hardware and software stacks.
Manual, error-prone migrations between deep learning frameworks can be significantly streamlined and automated using agentic systems with in-context learning and self-debugging capabilities.
- · AI developers
- · Cloud providers running diverse AI workloads
- · JAX ecosystem
- · Companies with heterogeneous AI infrastructure
- · Manual migration service providers (short-term)
- · Projects locked into single frameworks
Faster and more efficient adoption of specialized AI accelerators and frameworks.
Increased competition among AI framework developers as interoperability improves.
Potential for new AI services that dynamically optimize model deployment across the most suitable hardware environments.
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Read at arXiv cs.AI