arXiv:2607. 18269v1 Announce Type: new Abstract: The wisdom of crowds -- the finding that aggregating judgments across individuals often outperforms the best individual -- has been extensively studied with human forecasters.
By Igor Douven
arXiv:2607. 07729v1 Announce Type: cross Abstract: As foundation models grow in scale and diversity, coordinating multiple models into cooperative reasoning systems offers a path toward safer, more reliable AI.
By J. de Curt\`o, I. de Zarz\`a
arXiv:2609.22497v1 Announce Type: new
Abstract: The aggregation of many lay estimates often outperforms individual expert judgment, a phenomenon known as the wisdom of crowds. While this is usually a...
By Federico Barrera-Lemarchand, Mariano Sigman, Joaquin Navajas
arXiv:2512. 15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems.
By Damian Hodel, Jevin D. West
Top AI forecasting systems are approaching superforecaster-level accuracy on future world events, but still rely primarily on off-the-shelf LLMs combined with forecasting-specific context gathering and scaffolding. We study how to improve this recipe through ensembling: given a fixed number of samples, which off-the-shelf model forecasts should be combined to maximize accuracy?
The paper introduces the Universe of Universes (UoU) framework, treating the ecosystem of major large language models as a structured retrieval corpus and proposing a compositional Automated Reasoning and Machine Learning architecture for cross-model retrieval‑augmented generation. It formally defines the Benefit Yield Function (BYF), measuring marginal performance gain per added model, and identifies an implosion threshold θ* where BYF becomes zero and ensemble performance degrades. The work highlights gaps in current LLM ensemble research, such as lack of performance analysis across full model universes, and connects these findings to implications for DoD AI acquisition policy and testing of AI‑enabled systems.
By Danielle Franklin, Vasu Raj Jain