{
  "id": 989,
  "url": "https://arxiv.org/abs/2606.15994v1",
  "title": "Agentic Framework for Deep Learning workload migration via In-Context Learning",
  "summary": "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",
  "authors": "Qiyue Liang, Steven Ingram, George Vanica, Andi Gavrilescu, Newfel Harrat, Hassan Sipra et al.",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-14T19:41:57.000Z",
  "fetched_at": "2026-07-14T14:14:54.536Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/989",
  "original_url": "https://arxiv.org/abs/2606.15994v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}