Scaling Participation in Modular AI Systems

2026-06-05

AI Summary

Current large language models are predominantly developed by a handful of institutions, limiting their capacity to reflect the diversity of human knowledge, reasoning, and values. This work proposes an “extended participation” paradigm that constructs modular, composable AI systems enabling diverse stakeholders to contribute specialized small models, thereby facilitating bottom-up collaborative AI development. By orchestrating cooperative reasoning and system integration among these small models, the approach fosters the emergence of collective intelligence. Empirical results demonstrate that the proposed system outperforms the strongest monolithic large model by up to 15.4% across 15 tasks, successfully resolves more than 15% of problems on which all individual models fail, and significantly enhances core capabilities prioritized by participating contributors.

Abstract

Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized market of monolithic AI models structurally ill-suited to capture the diversity of human knowledge, reasoning, and values. Here we introduce scaling participation, a new paradigm in which modular AI systems are built from the bottom up through the contributions of diverse stakeholders. Participants contribute small models trained on their own interests and priorities; these models then collaborate in modular frameworks as compositional AI systems. Participatory AI systems outperform monolithic LLMs by up to 15.4% across 15 tasks, such as reasoning and factuality, surpassing models larger than all contributed components combined. Further experiments show that participatory AI systems benefit from contributor diversity, substantially improve on each contributor's original priorities, and exhibit emergent capabilities that allow them to solve over 15% of problems where all individual models fail. Scaling participation provides a technical foundation for transitioning from the monolithic status quo toward an open, bottom-up, and collaborative AI future.

Problem

• modular AI systems
• scaling participation
• monolithic LLMs
• contributor diversity
• compositional AI

Innovation

• modular AI
• scaling participation
• compositional AI systems
• emergent capabilities
• collaborative AI