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.

翻译：基于大型基础模型的人工智能已彻底改变了语言理解、视觉与推理能力，但这些系统仍处于孤立状态，无法轻易共享其能力。整合此类独立基础模型的互补优势对于构建可信赖的智能系统至关重要。尽管单个模型设计进展迅速，但目前尚无协调此类黑箱异构模型的成熟方法。本文提出一种通过元集成框架（称为StackingNet）实现协调的路径，该框架借鉴集体智能原理，在推理过程中融合模型预测结果。StackingNet在无需访问内部参数或训练数据的前提下，实现了精度提升、偏差降低、可靠性排序，并能识别或剔除导致性能下降的模型。在涉及语言理解、视觉估计和学术论文评级的任务中，与单个模型及传统集成方法相比，StackingNet持续提升了准确性、鲁棒性和公平性。通过将多样性从不一致的根源转化为协作优势，StackingNet为协调式人工智能奠定了实践基础，表明技术进步不仅可能来自更庞大的单一模型，也可能源于多个专用模型间的原则性协作。