发表机构
Nanjing University（南京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将管理单元从模型升级为Learnware（模型+规范），构建AI模型管理系统，在不访问训练数据的前提下实现模型识别、重用与协作。
AI 中文摘要
从文件存储到数据库管理系统的转变，将存储的数据转化为受管理的资源。人工智能现在正面临从 AI 模型存储到 AI 模型管理的类似转变。现有的模型池本质上充当着“AI 模型存储系统”。而真正需要的是“AI 模型管理系统”，该系统能够使由不同开发者、针对不同任务、使用不同数据并在不同目标下训练的模型被识别、重用，甚至组装起来以应对未来的用户任务。由于 AI 模型开发者通常不愿意共享其训练数据，此类系统应在不访问模型开发者训练数据的情况下运行，并且理想情况下，也不访问未来用户的原始数据。这一要求带来了根本性挑战：现代 AI 模型的功能可能连训练它的开发者也无法完全理解。那么，系统如何识别哪些模型对给定的用户任务有用，更不用说组装那些为不同目的而独立开发的模型了？乍一看，这一目标似乎难以实现。然而，通过将管理的基本单元从机器学习模型升级为“Learnware”，这一目标变得可能实现。“Learnware = 模型 + 规范”。规范的分配将训练好的模型转化为 learnware，它是在机器学习过程的帮助下生成的，不披露开发者的训练数据，并具有理论上确立的数据保留性质。“Learnware Dock 系统（LDS）”为实现强大的 AI 模型管理系统提供了一条路径。由于规范是根据已发布的参考生成的，并且在不同模型之间具有可比性，因此它们还可以充当 AI 模型的“协作协议”，通过该协议，独立开发的模型（包括智能体）可以相互协作。
英文摘要
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.
