Handling Learnwares from Heterogeneous Feature and Label Spaces with Explicit Label Exploitation

"Learnware = Model + Specification". The learnware paradigm aims to help users reuse existing high-performing models instead of training models from scratch, where the specification characterizes a trained model's capability and utility without exposing raw data. Numerous learnwares are hosted by a learnware dock system, which identifies helpful learnwares according to user task requirements and returns suitable models for reuse. In practice, learnwares that exactly match a user's feature space are often rare, while models from heterogeneous feature spaces, or even heterogeneous label spaces, may still be reusable. The central challenge is to characterize and compare the reusability of such heterogeneous models within a unified representation space. This paper finds that label information, especially model outputs, is crucial but previously insufficiently used. We exploit model outputs to evolve specifications into a unified space, thereby constructing a unified capability representation of heterogeneous models that characterizes their predictive behavior across diverse feature and label spaces. To instantiate this idea, we extend the specification implementation to encode model-output behavior more effectively and prevent it from being overwhelmed by high-dimensional features. Based on the unified capability representation, we identify learnwares by matching the conditional distributions induced by model outputs with the true distributions of user tasks, enabling learnwares to be reused beyond their original purposes. Experiments show that the proposed method can effectively identify and assemble learnwares from diverse feature and label spaces, even when no submitted learnware is explicitly tailored to the user task.