SwarmWorld demonstrates that homogeneous language‑model agents can self‑organize into evolving technological societies without assigned roles or direct communication. In a spatial environment, agents explore, process resources, construct artifacts, and write executable controllers that are later evaluated by a deterministic simulator. The resulting societies develop broader, more resilient technological portfolios than isolated search, with agents differentiating into exploration, construction, maintenance, and coordination roles as the world matures.
By Subhadeep Pal, Fiona Y. Wang, Markus J. Buehler
arXiv:2609.01491v1 Announce Type: cross
Abstract: The growing rate at which LLM agents interact with one another raises key questions about language evolution in multi-LLM-agent settings, with implic...
By Elias Stengel-Eskin, Newton Sander, Carlos Bonetti, Sasha Boguraev, James Bowler, Hale Sirin, Simon Kirby
The paper introduces AGORA, a framework that independently varies team composition and interdependence structures to study how social scaffolding influences the evolution of large language model agents. Experiments across web search, codebase understanding, and document QA show that appropriate scaffolding can outperform a strong agent evolved alone by up to 25 points, primarily by empowering the most capable member to lead while others step back. The findings suggest that designing a self‑evolving multi‑agent system requires careful consideration of its social scaffolding to shape learning dynamics and leadership emergence.
By Seojin Hwang, Youngmin Choi, Hwanhee Lee
arXiv:2310. 06555v3 Announce Type: replace-cross Abstract: Emergent communication enables agents to develop bespoke languages that improve communication efficiency.
By Olaf Lipinski, Adam J. Sobey, Federico Cerutti, Timothy J. Norman
arXiv:2610.22984v1 Announce Type: new
Abstract: Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction. Yet existing...
By Weixiang Zhao, Yichen Zhang, Yanyan Zhao, Jinming Bai, Bin Liang, Ruifeng Xu, Bing Qin, Ting Liu
arXiv:2606. 31046v1 Announce Type: new Abstract: Artificial life has explored life-like behavior on many computational substrates, but mostly in researcher-designed closed worlds.
By Atsushi Masumori, Itsuki Doi, Norihiro Maruyama, Ryosuke Takata, Takashi Ikegami
arXiv:2605.29790v2 Announce Type: replace-cross
Abstract: LLM-based multi-agent systems (MAS) have emerged as an effective paradigm for complex and long-horizon tasks. However, in real-world tasks, M...
By Zhezheng Hao, Tianfu Wang, Huanshuo Dong, Ziyan Liu, Hong Wang, Xiankun Lin, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen
arXiv:2603. 03555v3 Announce Type: replace-cross Abstract: As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical.
By Brandon Yee, Pairie Koh
arXiv:2608.00929v2 Announce Type: replace
Abstract: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance...
By Lynnette Hui Xian Ng, Kathleen M. Carley
The paper introduces PACEvolve, a framework that improves self‑evolving agents powered by Large Language Models by addressing their tendency to become trapped in local contexts and repeat flawed hypotheses. It does so through three techniques: Hierarchical Context Management to prune memory, Momentum‑Based Backtracking to escape local minima, and a self‑adaptive Collaborative Evolution policy to balance refinement and knowledge transfer. These methods enable the agents to maintain a global view of search momentum and achieve state‑of‑the‑art results on complex evolutionary benchmarks.
By Minghao Yan, Bo Peng, Benjamin Coleman, Ziqi Chen, Zhouhang Xie, Shuo Chen, Zhankui He, Noveen Sachdeva, Isabella Ye, Weili Wang, Chi Wang, Ed H. Chi, Fernando Pereira, Wang-Cheng Kang, Derek Zhiyuan Cheng, Beidou Wang
The paper introduces the Self‑Emergence Agent Architecture (SEAA), a framework that combines a Hidden Markov Model for behavioral inertia, a reflexive metacognition loop that updates the HMM, and a social environment where agents compare behaviors. This closed loop enables agents to develop distinct, stable personalities and social structures without external prompts. Experiments with both a language‑model‑free prototype and hosted LLMs demonstrate spontaneous symmetry breaking and the emergence of consensus hubs and outliers.
By Xiaoyang Liu
arXiv:2506. 12078v2 Announce Type: replace-cross Abstract: Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations.
By Haoxiang Guan, Jiyan He, Liyang Fan, Zhenzhen Ren, Shaobin He, Xin Yu, Yuan Chen, Xueyin Xu, Shuxin Zheng, Yan Gao, Enhong Chen, Tie-Yan Liu, Zhen Liu