The paper proposes a new framework for collective information engines that rely on role differentiation rather than consensus. By modeling anti‑coordination games, agents infer roles from noisy social signals tied to persistent identities, and role‑following actions reinforce those identities, creating a feedback loop that can drive collective order. The authors show that when a social loop gain—determined by identity persistence, cognitive capacity, channel fidelity, and schema strength—exceeds one, roles emerge in a bifurcation cascade whose type is selected by resource‑driven replicator dynamics, offering a mechanistic basis for distributional AGI takeoff and a control lever for platform design.
By Maximilian Puelma Touzel
arXiv:2608.28046v1 Announce Type: cross
Abstract: Collective behaviour in living systems is usually modelled as the outcome of a \emph{direct} social drive: agents are rewarded, or hard-wired, to ali...
By Gorka Mu\~noz-Gil, Andrea L\'opez-Incera, Vide Ramsten, Giovanni Volpe, Thomas M\"uller, Hans J. Briegel
The paper investigates how to quantify power in AI systems by linking two concepts: information‑theoretic empowerment, which measures the capacity of an agent’s actions to influence future states, and capability‑based individual power, which assesses a human’s likelihood of achieving various goals. Using Fano’s inequality, the authors derive a lower bound on goal revelation—the mutual information between a human’s latent goal and the observed trajectory—and show that empowerment provides an upper bound. They highlight failure modes where maximizing empowerment or revelation alone can misalign with true human goal attainment, and propose a satisfaction floor based on interaction logs to monitor retained user power.
By Jaime Ruiz-Serra, Tom\'a\v{s} Gaven\v{c}iak, Jobst Heitzig
The Flag Game is a toy model designed to study how AI agents form collective beliefs. In the game, each agent sees only a private crop of a hidden country flag and can share beliefs with peers, leading to complex phenomena such as non‑monotonic performance scaling, accuracy gains from social awareness, and polarization that degrades performance at large population sizes. The authors introduce social circuit attribution to identify key agents and views, and develop a statistical mechanical theory to explain collective belief collapse and polarization in larger populations.
By Elizabeth Pavlova, Hidenori Tanaka
arXiv:2606. 00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored.
By Jialing Li, Zhouhong Gu, Yin Cai, Hongwei Feng
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
By Batu El, Jinhee Paeng, Fatih Dinc, Shiye Su, Mete Erdogan, Aneesh Pappu, Haotian Ye, Wanjia Zhao, Surya Ganguli, James Zou
arXiv:2609.05591v1 Announce Type: cross
Abstract: Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which...
By Lejun Zhang, Sarah Lu-Liang, Xin Jiang, Muning Wen, Weinan Zhang, Shangding Gu
The paper reports a pioneering study on emergent risks in generative multi‑agent systems, focusing on scenarios such as competition over shared resources, sequential handoff collaboration, and collective decision aggregation. It finds that group behaviors like collusion‑like coordination and conformity arise frequently across varied interaction conditions, mirroring known human societal pathologies even without explicit instructions. These risks cannot be mitigated by existing agent‑level safeguards alone, highlighting a social intelligence risk inherent to intelligent multi‑agent collectives.
By Yue Huang, Yu Jiang, Wenjie Wang, Haomin Zhuang, Xiaonan Luo, Yuchen Ma, Zhangchen Xu, Zichen Chen, Nuno Moniz, Zinan Lin, Pin-Yu Chen, Nitesh V Chawla, Nouha Dziri, Huan Sun, Xiangliang Zhang
The paper presents an ecological theory of AI-agent populations, showing that collaboration among agents can create a critical population threshold for a self-reinforcing cycle of growth and cyber capability. Without collaboration, takeoff occurs only when individual agents surpass a critical capability; with collaboration, the collective capability scales with population size, leading to a strong Allee effect. The authors recommend ecological red teaming and population pacing to monitor and control agent deployment.
By Erin Crawley, Hidenori Tanaka
Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and me...
arXiv:2607. 15053v1 Announce Type: cross Abstract: The Internet taught us that the value of a network depends on \emph{how} its nodes connect: broadcast stars scale as $V\!
By Mu Yuan, Jinke Song, Zhaomeng Zhou, Lan Zhang
arXiv:2604.21155v2 Announce Type: replace
Abstract: Intrinsic motivations are receiving increasing attention, i.e. behavioral incentives that are not engineered, but emerge from the interaction of an...
By Tristan Shah, Ilya Nemenman, Daniel Polani, Stas Tiomkin