Gradient Parameter Specifications for Learnware Identification and Reuse

The learnware paradigm enables users to solve new tasks by identifying and reusing learnwares from a learnware dock system, where each learnware pairs a well-established model with a specification of its reuse-relevant capabilities. However, existing specification methods primarily summarize data distributions or feature statistics, limiting their ability to distinguish tasks with similar inputs but different predictive requirements. To address this limitation, we propose Gradient Parameter Specification (Gps), a learnware specification that encodes task-dependent discriminative structure and model-dependent predictive behavior through class-wise one-step gradient directions. Specifically, Gps optimizes a small proxy sample set to construct learnware specifications from developer tasks and model capabilities, analogously constructs user specifications from labeled user tasks, and identifies reusable learnwares through partial class-wise gradient direction matching. Our representation-level analysis characterizes how gradient responses depend jointly on input representations and predictive targets, and explains how class-wise responses mitigate cross-class cancellation. Extensive experiments on twelve image and text benchmarks across three reuse scenarios show that Gps achieves the highest average performance among data-centric specification methods.