{"slug": "a-framework-for-confident-model-migration-in-production-systems", "title": "A Framework for Confident Model Migration in Production Systems", "summary": "Researchers have developed a Bayesian statistical framework for migrating production systems reliant on large language models when the underlying model reaches end-of-life. The approach calibrates automated evaluation metrics against human judgments to enable confident model comparison with limited manual data, demonstrated on a commercial question-answering system serving 5.3 million monthly interactions across six global regions. The framework provides enterprises a reproducible methodology for model migration as the rapidly evolving LLM ecosystem forces organizations to manage portfolios of AI services across multiple models and use cases.", "body_md": "# Computer Science > Artificial Intelligence\n\n[Submitted on 29 Apr 2026]\n\n# Title:When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems\n\n[View PDF](/pdf/2604.27082)\n\n[HTML (experimental)](https://arxiv.org/html/2604.27082v1)\n\nAbstract:We present a framework for migrating production Large Language Model (LLM) based systems when the underlying model reaches end-of-life or requires replacement. The key contribution is a Bayesian statistical approach that calibrates automated evaluation metrics against human judgments, enabling confident model comparison even with limited manual evaluation data. We demonstrate this framework on a commercial question-answering system serving 5.3M monthly interactions across six global regions; evaluating correctness, refusal behavior, and stylistic adherence to successfully identify suitable replacement models. The framework is broadly applicable to any enterprise deploying LLM-based products, providing a principled, reproducible methodology for model migration that balances quality assurance with evaluation efficiency. This is a capability increasingly essential as the LLM ecosystem continues to evolve rapidly and organizations manage portfolios of AI-powered services across multiple models, regions, and use cases.\n\n### Current browse context:\n\ncs.AI\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/a-framework-for-confident-model-migration-in-production-systems", "canonical_source": "https://arxiv.org/abs/2604.27082", "published_at": "2026-06-05 22:10:34+00:00", "updated_at": "2026-06-05 22:48:24.654269+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "machine-learning", "ai-products", "mlops"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/a-framework-for-confident-model-migration-in-production-systems", "markdown": "https://wpnews.pro/news/a-framework-for-confident-model-migration-in-production-systems.md", "text": "https://wpnews.pro/news/a-framework-for-confident-model-migration-in-production-systems.txt", "jsonld": "https://wpnews.pro/news/a-framework-for-confident-model-migration-in-production-systems.jsonld"}}