StackingNet: Collective Inference Across Independent AI Foundation Models

TL;DR

StackingNet is a meta-ensemble framework that enables independent AI models to collaborate during inference, improving accuracy, robustness, and fairness without access to internal parameters or training data.

Contribution

It introduces a novel collective inference method that coordinates heterogeneous models, enhancing their combined performance without retraining or internal access.

Findings

Improves accuracy, robustness, and fairness across tasks

Reduces bias and identifies underperforming models

Operates without access to internal model details

Abstract

Artificial intelligence built on large foundation models has transformed language understanding, vision and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Integrating the complementary strengths of such independent foundation models is essential for building trustworthy intelligent systems. Despite rapid progress in individual model design, there is no established approach for coordinating such black-box heterogeneous models. Here we show that coordination can be achieved through a meta-ensemble framework termed StackingNet, which draws on principles of collective intelligence to combine model predictions during inference. StackingNet improves accuracy, reduces bias, enables reliability ranking, and identifies or prunes models that degrade performance, all operating without access to internal parameters or training data. Across tasks involving language comprehension, visual estimation, and academic paper rating, StackingNet consistently improves accuracy, robustness, and fairness, compared with individual models and classic ensembles. By turning diversity from a source of inconsistency into collaboration, StackingNet establishes a practical foundation for coordinated artificial intelligence, suggesting that progress may emerge from not only larger single models but also principled cooperation among many specialized ones.