Learnware and AI Model Management System (arxiv.org)
arXiv:2609.11656v1 Announce Type: new
Abstract: The transition from file storage to database management systems transformed stored data into managed resources. AI now faces an analogous transition from AI model storage to AI model management. Existing model pools essentially serve as \textit{AI model storage systems}. What is needed instead are \textit{AI model management systems} that enable models trained by different developers, for different tasks, with different data, and under different objectives to be identified, reused, and even assembled to address future user tasks. Because AI model developers are generally unwilling to share their training data, such systems should operate without accessing the training data of model developers and, ideally, without accessing raw data of future users. This requirement poses a fundamental challenge: the functionality of a modern AI model may not be fully understood even by the developer who trained it. How, then, can a system identify which models are useful for a given user task, let alone assemble models developed independently for different purposes? At first glance, this objective may appear unattainable. It becomes possible, however, by upgrading the basic unit of management from a machine learning model to a \textit{learnware}. \textit{Learnware = Model + Specification}. The specification, whose assignment transforms a trained model into a learnware, is generated with the help of a machine learning process without disclosing the training data of the developer and has a theoretically established data-preservation property. The \textit{Learnware Dock System (LDS)} provides a path toward powerful AI model management systems. Because specifications are generated according to a published reference and are comparable across models, they can also serve as an AI model \textit{collaboration protocol} through which independently developed models, including intelligent agents, can collaborate.
Abstract: The transition from file storage to database management systems transformed stored data into managed resources. AI now faces an analogous transition from AI model storage to AI model management. Existing model pools essentially serve as \textit{AI model storage systems}. What is needed instead are \textit{AI model management systems} that enable models trained by different developers, for different tasks, with different data, and under different objectives to be identified, reused, and even assembled to address future user tasks. Because AI model developers are generally unwilling to share their training data, such systems should operate without accessing the training data of model developers and, ideally, without accessing raw data of future users. This requirement poses a fundamental challenge: the functionality of a modern AI model may not be fully understood even by the developer who trained it. How, then, can a system identify which models are useful for a given user task, let alone assemble models developed independently for different purposes? At first glance, this objective may appear unattainable. It becomes possible, however, by upgrading the basic unit of management from a machine learning model to a \textit{learnware}. \textit{Learnware = Model + Specification}. The specification, whose assignment transforms a trained model into a learnware, is generated with the help of a machine learning process without disclosing the training data of the developer and has a theoretically established data-preservation property. The \textit{Learnware Dock System (LDS)} provides a path toward powerful AI model management systems. Because specifications are generated according to a published reference and are comparable across models, they can also serve as an AI model \textit{collaboration protocol} through which independently developed models, including intelligent agents, can collaborate.
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