Toward a More Discriminative Learnware Paradigm via Explicitly Distinctive Specification

Abstract

The learnware paradigm aims to construct a dock system that maintains a collection of learnwares, each consisting of a well-established model coupled with a specification, thereby enabling users to directly reuse existing models to solve their tasks without training models from scratch. As a core component of the paradigm, the specification sketches the model’s properties and establishes reusability between the learnware and user tasks. Existing methods have demonstrated promising performance by designing specifications that characterize the task distributions mastered by well-established models. However, in complex real-world scenarios, task distributions often overlap, thereby weakening the uniqueness of specifications and obscuring the reusability relationships between learnwares and tasks. In this paper, we design an Explicitly Distinctive Specification (EDS) that enforces specification uniqueness, improving the discriminative capability of the learnware paradigm and avoiding ambiguity caused by overlapping task distributions. This specification method explicitly leverages distributional discrepancies between specifications to strengthen the uniqueness of the model properties sketched during the submitting stage and subsequently exploits the enhanced distributional discriminability in the deploying stage to improve learnware reusability. Extensive experiments demonstrate the effectiveness and strong performance of the proposed EDS within the learnware paradigm under complex task scenarios.