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:2602. 13792v2 Announce Type: replace Abstract: Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities.
By Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding
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?
arXiv:2606. 29661v1 Announce Type: new Abstract: 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.
By Matthew Aitchison, Scott Jeen, Toby Shevlane, Ben Day
arXiv:2606. 31404v1 Announce Type: new Abstract: Human swarm intelligence demonstrates remarkable collective accuracy but faces scalability constraints in cost, coordination, and time.
By Justin Brenne, Christian Meske
The paper introduces a behavior‑aware framework to build diverse crowds of large language models (LLMs) for future prediction. By analyzing reasoning traces on independent tasks, clustering models by behavioral similarity, and selecting representative medoids, the authors demonstrate that a small, well‑chosen crowd can outperform a larger, conventional voting ensemble. Experiments with 25 LLMs across multiple benchmarks show significant reductions in model calls and inference cost while improving prediction accuracy.
By Nirupam Chetlapalli, Yiming Liao, Min-Chun Chen, Keke Chen