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万宇宙之宇宙：多 LLM 系统中的收益产出函数、内爆阈值与基础设施感知优化
该 arXiv 预印本提出 Universe of Universes（UoU）框架，将主流大语言模型生态视为结构化检索语料，并设计组合式自动推理与机器学习架构用于跨模型检索增强生成。核心贡献是形式化定义收益产出函数（BYF），即集成中每新增一个模型带来的边际性能增益，并识别内爆阈值 θ*——BYF 归零、整体性能开始下降时的集成规模。作者指出既有 LLM 集成与混合智能体系统未研究性能随集成规模 N 的变化，模型坍缩文献也未形式化集成层面的内爆阈值或生态级 Epistemic Hereditary Drift。论文称该框架对 DoD 多模型 AI 采购政策及 AI 系统测试科学有直接意义，但摘要未提供实验验证。
AI推荐理由 arXiv 预印本，摘要明确给出新框架 UoU、收益产出函数 BYF 与内爆阈值 θ* 的形式化定义，并提出生态层面的 Epistemic Hereditary Drift 概念，信息密度较高且属首次形式化集成规模与性能退化关系。但仅为理论框架，无实验验证或数据支撑，且声称对 DoD 采购政策有直接意义缺乏证据，故兴趣分受限。
62
正文
Computer Science > Machine Learning
arXiv:2609.15314 (cs)
[Submitted on 14 Sep 2026]
Title:The Universe of Universes: Benefit Yield Functions, Implosion Thresholds, and Infrastructure-Aware Optimization in Multi-LLM Systems
Authors:Danielle Franklin, Vasu Raj Jain
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Abstract: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 {\theta}*: 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 {\theta}*. The framework has direct implications for DoD multi-model AI acquisition policy and the emerging science of testing AI-enabled systems.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
ACM classes:
I.2.6; I.2.7; I.2.11
Cite as:
arXiv:2609.15314 [cs.LG]
(or
arXiv:2609.15314v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.15314
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vasu Raj Jain [view email]
[v1]
Mon, 14 Sep 2026 10:04:06 UTC (1,149 KB)
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