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Flag Game: A Toy Model for Mechanistic Swarm Interpretability
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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.
arXiv AI
Aug 18
Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.
arXiv AI
Sep 12
Role differentiation as ignition of a collective information engine: Structuration in Agent Populations
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.
arXiv Computation and Language
Sep 14
Diverse Minds, Divided Networks? Personality Composition, Polarization, and Collective Intelligence in LLM-Based Social Simulations
arXiv:2609.12444v1 Announce Type: cross Abstract: Simulated societies of large language model agents are used to study online polarization, and separately to study collective intelligence, but the tw...
arXiv Computation and Language
Sep 3
AI agents reshape consensus formation in human groups
The study investigates how large language model (LLM) agents influence consensus formation in mixed human‑AI groups during a collaborative description game. Three regimes emerge: low agent proportions lead to human‑led consensus, intermediate proportions disrupt convergence, and high proportions produce strong, agent‑led consensus. The resulting consensus differs in semantic grounding and communicative form, with human‑led consensus being concrete and holistic, and agent‑led consensus being abstract and geometrically segmented.
arXiv AI
Oct 5
Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents
The paper studies how language‑model agents adopt beliefs, measuring the probability that an agent accepts a claim based on the number of peers endorsing it. The adoption curve is sigmoid, indicating complex contagion, with a threshold influenced by the claim’s plausibility, the source’s reliability, and the agent’s disposition—dimensions that can be collapsed into a single coherence metric relative to the agent’s prior beliefs. In networks of AI agents, belief spread is stronger on clustered than random networks, and the system shows a bifurcating cascade window and hysteretic consensus that makes consensus difficult to reverse once formed.