Complementary Learnware Reuse via Distribution Filling

The growing availability of AI models calls for systems that can organize and combine their capabilities for new tasks. The learnware paradigm supports this goal by pairing each model with a specification describing its capabilities, enabling identification and reuse without sharing the training data of developers. Since models are developed for different tasks and conditions, no single learnware may adequately serve a new user: each may cover only part of the required input distribution, together with regions irrelevant to the task. When different learnwares cover complementary parts, combining their useful portions can enable the system to solve tasks that no single learnware can adequately handle. The challenge is to determine how learnwares can jointly serve different parts of the user distribution while reducing the influence of irrelevant regions. By modeling learnware utility at individual user inputs, we characterize how well a learnware set serves the user distribution and where an additional learnware can fill gaps in its coverage. This perspective motivates distribution filling, which jointly reweights regions across learnware specifications, so that they collectively match the user distribution while irrelevant regions are down-weighted. For each learnware, the resulting weights quantify its overall relevance, which is combined with its compatibility with individual inputs to determine how much it contributes to the prediction on each input. Experiments show improved predictive performance over existing multi-learnware reuse methods and further demonstrate favorable accuracy–efficiency trade-offs for our method.