让多元用户共建小型AI模型，组合成更强大的智能系统。
Scaling Participation in Modular AI Systems
- 由参与者贡献小模型，通过模块化方式协作形成整体智能。
- 在15项任务中表现优于单一大模型，最高提升15.4%。
- 适合希望参与AI构建、重视多样性和协作的开发者与研究者。
人类拥有复杂多样的才能与需求，真正智能的AI应反映这种丰富性。然而当前所有主流大语言模型均由少数人构建，集中式的单体模型难以捕捉人类知识、推理与价值观的多样性。本文提出‘扩大参与’新范式：通过多方贡献者自主训练的小模型，在模块化框架中协作构成组合式AI系统。实验显示，参与式AI系统在15项任务中表现超越单体大模型，最高提升15.4%，且优于所有贡献组件总和的模型。多样性参与者显著提升系统性能，各贡献模型原有目标均得到改进，并涌现出解决超过15%个体模型无法处理问题的能力。该方法为从单体模式转向开放、自下而上、协作型的AI未来提供了技术基础。
原文摘要 · Abstract (English)
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.
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