发表机构
Humanity + AI, Inc.（Humanity + AI公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出万有之域框架，形式化效益产出函数与内爆阈值，刻画多LLM集成性能随规模变化规律，并关联基础设施与采办政策。
AI 中文摘要
我们提出了万有之域（UoU）框架，该框架将主要大型语言模型（LLM）的完整生态系统视为一个结构化的检索语料库，并提出了一种组合式自动推理（AR）与机器学习（ML）架构，用于跨模型检索增强生成。核心贡献在于对效益产出函数（BYF）的形式化刻画，该函数衡量向集成中添加额外模型所带来的边际性能增益，以及内爆阈值θ*的识别：即BYF降至零且整体性能开始下降时的集成规模。现有的LLM集成和混合智能体系统将模型视为响应者并聚合输出，但未研究在整个模型宇宙中性能作为集成规模N的函数。基准研究证实性能在单个模型层面趋于平台期；模型崩溃文献表明，对AI生成输出进行迭代训练会降低单个模型的分布质量。这两类工作均未形式化集成层面的内爆阈值，未在生态系统层面建模表观遗传性遗传漂移（EHD），也未将AI制造速度视为θ*的协变量。该框架对国防部多模型AI采办政策以及新兴的AI赋能系统测试科学具有直接意义。
英文摘要
We introduce the Universe of Universes (UoU) framework, which treats the full ecosystem of major large language models (LLMs) as a structured retrieval corpus and proposes a compositional Automated Reasoning (AR) and Machine Learning (ML) architecture for cross-model retrieval-augmented generation. The central contribution is the formal characterization of the Benefit Yield Function (BYF), the marginal performance gain per additional model added to an ensemble, and the identification of the implosion threshold θ*: the ensemble size at which BYF crosses zero and aggregate performance begins to degrade. Existing LLM ensemble and mixture-of-agents systems treat models as responders and aggregate outputs, but do not study performance as a function of ensemble size N across the full model universe. Benchmark research confirms performance plateaus at the individual model level; model collapse literature establishes that iterative training on AI-generated outputs degrades individual model distributions. Neither body of work formalizes the ensemble-level implosion threshold, models Epistemic Hereditary Drift (EHD) at the ecosystem level, or treats AI manufacturing velocity as a co-variable of θ*. The framework has direct implications for DoD multi-model AI acquisition policy and the emerging science of testing AI-enabled systems.
