The paper examines whether large language model (LLM)-powered systems, such as OpenAI’s Swarm (OAS) framework, embody the core principles of classical swarm intelligence—decentralization, simplicity, emergence, and scalability. By implementing and comparing LLM-based and classical versions of Boids and Ant Colony Optimization, the authors find that while LLM swarms can mimic swarm-like dynamics, they suffer from significant computational overhead, with the LLM-based Boids simulation taking about 300 times longer than its classical counterpart.
By Muhammad Atta Ur Rahman, Melanie Schranz, Samira Hayat
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. 29087v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in enterprise settings, yet individual models remain bounded by model-specific capability limitations.
By Yanbin Fang, Xuan Wei, Wei Chen
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
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
arXiv:2609.38816v1 Announce Type: new
Abstract: While multi-agent and model collaboration algorithms gain traction to combine the strengths of diverse Large Language Models (LLMs), existing systems r...
By Zongwan Cao, Ziyuan Yang, Shangbin Feng, Michael Duan, Skyler Hallinan, Bingbing Wen, Lucy Lu Wang, Yulia Tsvetkov
arXiv:2606. 11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: "in silico" behavioral experiments.
By Michelle Vaccaro
arXiv:2608.23086v1 Announce Type: new
Abstract: Black-box large language models need confidence scores that can separate likely-correct from likely-incorrect outputs, enabling systems to prioritize h...
By Rounak Sharma, Ananya B. Sai, Soumyabrata Pal
arXiv:2606. 13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale.
By Bora Kargi, David Salinas
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:2408. 05568v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups.
By Florian Scholten, Tobias R. Rebholz, Mandy H\"utter
arXiv:2607. 17948v1 Announce Type: new Abstract: Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents.
By Stefano Blando, Emanuele Guerrazzi, Riccardo Porcedda, Giuseppe Squillace, Max Tschaikowski, Andrea Vandin