The Universe of Universes: Benefit Yield Functions, Implosion Thresholds, and Infrastructure-Aware Optimization in Multi-LLM Systems

• URL: http://arxiv.org/abs/2609.15314v1
• Date: Mon, 14 Sep 2026 10:04:06 GMT
• Title: The Universe of Universes: Benefit Yield Functions, Implosion Thresholds, and Infrastructure-Aware Optimization in Multi-LLM Systems
• Abstract summary: We introduce the Universe of Universes (UoU) framework, which treats the full ecosystem of major large language models (LLMs) as a structured retrieval corpus.<n>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 *.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence [54.62791372989525]
  Action models, vision-language--action policies, and world models have advanced this goal.<n>World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences.<n>We review the evolution toward WAMs and organize these limitations into three coupled gaps: model roles and representations, objectives and standardization, and system composition.
  arXiv  Detail & Related papers  (2026-07-13T15:22:56Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• Hierarchical Inference and Closure Learning via Adaptive Surrogates for ODEs and PDEs [15.38864225184245]
  Inverse problems are the task of calibrating models to match data.<n>We develop a principled methodology for leveraging data from collections of distinct yet related physical systems.<n>We learn the shared unknown dynamics in the form of an ML-based closure model.
  arXiv  Detail & Related papers  (2026-03-04T10:30:08Z)
• Power and Limitations of Aggregation in Compound AI Systems [10.867699486308197]
  We investigate the power and limitations of aggregation within a stylized principal-agent framework.<n>Our analysis uncovers three natural mechanisms -- feasibility expansion, support expansion, and binding set contraction.<n>Our results take a step towards characterizing when compound AI systems can overcome limitations in model capabilities and in prompt engineering.
  arXiv  Detail & Related papers  (2026-02-25T04:23:50Z)
• The Law of Multi-Model Collaboration: Scaling Limits of Model Ensembling for Large Language Models [54.51795784459866]
  We propose a theoretical framework of performance scaling for multi-model collaboration.<n>We show that multi-model systems follow a power-law scaling with respect to the total parameter count.<n> ensembles of heterogeneous model families achieve better performance scaling than those formed within a single model family.
  arXiv  Detail & Related papers  (2025-12-29T09:55:12Z)
• GLUE: Generative Latent Unification of Expertise-Informed Engineering Models [3.005158583027536]
  We introduce Generative Latent Unification of Expertise-Informed Engineering Models (GLUE)<n>GLUE orchestrates pre-trained, frozen subsystem generators while enforcing system-level feasibility, optimality, and diversity.<n>On a UAV design problem with five coupling constraints, we find that data-driven approaches yield diverse, high-performing designs but require large datasets to satisfy constraints reliably.
  arXiv  Detail & Related papers  (2025-12-22T15:23:19Z)
• FedAvgen: Metadata for Model Aggregation In Communication Systems [3.6957462300442736]
  We study the challenges arising from the existential diversity in device profiles.<n>This approach is known as federated learning and inherently utilizes different techniques to select the candidate client models averaged to obtain the global model.<n>We then compare the results of our approach to two widely adopted federated learning algorithms like Federated Averaging (FedAvg) and Federated Gradient Descent (FedSGD)
  arXiv  Detail & Related papers  (2025-04-28T13:11:32Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• Sparse Modular Activation for Efficient Sequence Modeling [94.11125833685583]
  Recent models combining Linear State Space Models with self-attention mechanisms have demonstrated impressive results across a range of sequence modeling tasks. Current approaches apply attention modules statically and uniformly to all elements in the input sequences, leading to sub-optimal quality-efficiency trade-offs. We introduce Sparse Modular Activation (SMA), a general mechanism enabling neural networks to sparsely activate sub-modules for sequence elements in a differentiable manner.
  arXiv  Detail & Related papers  (2023-06-19T23:10:02Z)
• A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning [132.45959478064736]
  We propose a general framework that unifies model-based and model-free reinforcement learning. We propose a novel estimation function with decomposable structural properties for optimization-based exploration. Under our framework, a new sample-efficient algorithm namely OPtimization-based ExploRation with Approximation (OPERA) is proposed.
  arXiv  Detail & Related papers  (2022-09-30T17:59:16Z)
• FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization [16.704006420306353]
  A scalable federated learning framework should address heterogeneous clients equipped with different computation and communication capabilities. This paper proposes FedHM, a novel federated model compression framework that distributes the heterogeneous low-rank models to clients and then aggregates them into a global full-rank model. Our solution enables the training of heterogeneous local models with varying computational complexities and aggregates a single global model.
  arXiv  Detail & Related papers  (2021-11-29T16:11:09Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.