← Back to all news
arXiv AI By Maximilian Puelma Touzel

Role differentiation as ignition of a collective information engine: Structuration in Agent Populations

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computation and Language
Oct 15

Evolve Together, Fall into Place: How Social Scaffolding Shapes Self-Evolving Multi-Agent Systems

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
llmsagents
More like this →
arXiv AI
Sep 17

Flag Game: A Toy Model for Mechanistic Swarm Interpretability

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
llmsagentssafety
More like this →
arXiv AI
Sep 12

Tapes Together Strong: The Co-evolution of Computation and Cooperation

The paper introduces Autopoietic Game Theory, a computational model where social interactions, replication mechanisms, and computational costs co-evolve within a substrate of randomly initialized Z80 machine code programs. By embedding a social dilemma directly into the physics of computation, the authors demonstrate that scarcity of resources can make defection self-limiting, leading to the emergence of self-replicating, cooperative strategies. Empirical results show evolved programs suppress stealing, and spatial assortment enhances structural complexity and task performance, while the framework can also incorporate exogenous pressures such as math tasks tied to computation budgets.

By Kunal Jha, Francesco Cicala, Blaise Ag\"uera y Arcas, Blake Aaron Richards, Natasha Jaques, Max Kleiman-Weiner, Eyvind Niklasson
ragagents
More like this →