arXiv:2602.13792v2 Announce Type: replace Abstract: Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black-box foundation models is essential for trustworthy intelligent systems, yet no established method exists. Here we show that such coordination can be achieved through a meta-ensemble framework termed StackingNet, which aggregates the output predictions of independen
The proliferation of advanced, yet isolated foundation models necessitates new methods for coordination and complementary use, addressing their current limitations.
This breakthrough addresses a fundamental challenge in AI development: integrating diverse models to achieve more robust and trustworthy intelligent systems, accelerating practical applications.
Previously siloed AI foundation models can now be effectively combined, leading to more complex and reliable AI capabilities than individual models allow.
- · AI platform developers
- · Enterprises deploying AI
- · Researchers in multi-modal AI
- · Cybersecurity for AI
- · Monolithic AI model developers
- · Companies with single-model AI strategies
Improved performance and reliability of AI systems through ensemble methods.
Accelerated development of more sophisticated AI agents capable of leveraging diverse AI services.
Potential for new AI regulatory frameworks focusing on the interoperability and accountability of combined AI systems.
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Read at arXiv cs.AI