Learnware and AI Model Management System

• URL: http://arxiv.org/abs/2609.11656v1
• Date: Thu, 10 Sep 2026 15:01:27 GMT
• Title: Learnware and AI Model Management System
• Abstract summary: Existing model pools essentially serve as textitAI model storage systems.<n>Models trained by different developers can be identified, reused, and even assembled to address future user tasks.<n>The textitLearnware Dock System (LDS) provides a path toward powerful AI model management systems.
• Score: 60.917028769172795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Model Callers for Transforming Predictive and Generative AI Applications [2.7195102129095003]
  We introduce a novel software abstraction termed "model caller" Model callers act as an intermediary for AI and ML model calling. We have released a prototype Python library for model callers, accessible for installation via pip or for download from GitHub.
  arXiv  Detail & Related papers  (2024-04-17T12:21:06Z)
• Reusable MLOps: Reusable Deployment, Reusable Infrastructure and Hot-Swappable Machine Learning models and services [0.0]
  We introduce a new sustainable concept in the field of AI/ML operations - called Reusable MLOps. We reuse the existing deployment and infrastructure to serve new models by hot-swapping them without tearing down the infrastructure or the microservice.
  arXiv  Detail & Related papers  (2024-02-19T23:40:46Z)
• Beimingwu: A Learnware Dock System [42.54363998206648]
  This paper describes Beimingwu, the first open-source learnware dock system providing foundational support for future research of learnware paradigm. The system significantly streamlines the model development for new user tasks, thanks to its integrated architecture and engine design. Notably, this is possible even for users with limited data and minimal expertise in machine learning, without compromising the raw data's security.
  arXiv  Detail & Related papers  (2024-01-24T09:27:51Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model [74.62272538148245]
  We show that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other. We investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation.
  arXiv  Detail & Related papers  (2023-10-26T17:59:46Z)
• Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents [111.15288256221764]
  Grounded-decoding project aims to solve complex, long-horizon tasks in a robotic setting by leveraging the knowledge of both models. We frame this as a problem similar to probabilistic filtering: decode a sequence that both has high probability under the language model and high probability under a set of grounded model objectives. We demonstrate how such grounded models can be obtained across three simulation and real-world domains, and that the proposed decoding strategy is able to solve complex, long-horizon tasks in a robotic setting by leveraging the knowledge of both models.
  arXiv  Detail & Related papers  (2023-03-01T22:58:50Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [47.432215933099016]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models.<n>This creates a barrier to fusing knowledge across individual models to yield a better single model.<n>We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Foundation models in brief: A historical, socio-technical focus [2.5991265608180396]
  Foundation models can be disruptive for future AI development by scaling up deep learning. Models achieve state-of-the-art performance on a variety of tasks in domains such as natural language processing and computer vision.
  arXiv  Detail & Related papers  (2022-12-17T22:11:33Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• Decentralized Federated Learning Preserves Model and Data Privacy [77.454688257702]
  We propose a fully decentralized approach, which allows to share knowledge between trained models. Students are trained on the output of their teachers via synthetically generated input data. The results show that an untrained student model, trained on the teachers output reaches comparable F1-scores as the teacher.
  arXiv  Detail & Related papers  (2021-02-01T14:38:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.