mlsanityData to 14 Oct 2026

When Is Emergent Consensus Real? A Measured Coupling Gain and a Validity Diagnostic for LLM Agent Societies

arXiv 2606.22203 ↗First posted 20 Jun 2026cs.CL cs.AI cs.MAcited by 1

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

LLM "agent societies" are studied via demonstrations of emergent consensus or polarization -- with no measurable control parameter, no theory of when each regime appears, and no test of whether an outcome is a genuine social dynamic or a model artifact. We introduce the coupling gain gamma, measured per-agent by counterfactually perturbing a neighbour's stated opinion. (i) gamma is stable and model-distinguishing -- across five frontier models it spans 0.15-0.43 (n=20, 95% CIs <= 0.025), paraphrase-invariant; social-neighbour gamma roughly equals numeric-anchor gamma, so gamma is evidence-coupling, not uniquely social. (ii) Classical dynamics with measured (not assumed) coefficients organise the regime: Friedkin-Johnsen for consensus/pluralism, signed-Laplacian/structural-balance for polarization. (iii) Frontier LLMs do not spontaneously backfire (beta <= 0), so default societies do not self-polarize -- polarization is always induced; the beta>0 branch arises only in the FJ surrogate, never in the agents. (iv) A randomized-initial-condition diagnostic -- the (slope, bias) of final vs. initial opinion -- separates genuine averaging from model-prior artifacts (boundary-censoring ruled out by construction via interior-valued facts); applied to a published "emergent consensus" result (Chuang et al. 2023) it reveals a model-specific conflation: averaging on debatable claims, prior-artifact on settled facts. (v) Coupling is context-dependent: pairwise gamma does not predict multi-neighbour outcomes -- it can order them backwards -- whereas a modality-matched group coupling does (sixteen closed+open models, Pearson r=-0.70, permutation p=0.008). The regime laws take this matched coupling, not the single-neighbour gamma: emergent consensus must be read from coupling in the target interaction. We contribute a measurement protocol and a validity instrument, not new theory.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 14 of its 19 references that are in the map. First posted on arXiv 20 Jun 2026.

Builds on

14 of 19 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Conformity Dynamics in LLM Multi-Agent Systems: The Roles of Topology and Self-Social Weighting9 Jan
  2. 2025Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forum29 Aug
  3. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society12 Feb
  4. 2024On the Principles behind Opinion Dynamics in Multi-Agent Systems of Large Language Models18 Jun
  5. 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia6 Dec
  6. 2023Simulating Opinion Dynamics with Networks of LLM-based Agents16 Nov
  7. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 7

  1. 2024Emergent social conventions and collective bias in LLM populationscited by 123
  2. 2025The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMscited by 64
  3. 2025The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societiescited by 14
  4. 2025Emergent LLM behaviors are observationally equivalent to data leakagecited by 9
  5. 2025Disentangling the Drivers of LLM Social Conformity: An Uncertainty-Moderated Dual-Process Mechanismcited by 6
  6. 2024Large Language Models can Achieve Social Balancecited by 5
  7. 2026Stable Personas: Dual-Assessment of Temporal Stability in LLM-Based Human Simulationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Aligned Alone, Misaligned Together: Forecasting Adversarial Capture in LLM Agent Populationscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026Population Physics, Population Problems: Safety and Emergence in LLM Societies
  2. 2026Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents
  3. 2026Emergence of Biased Consensus in Multi-Agent LLM Debates
  4. 2026Everyone Conforms, No One Believes: Pluralistic Ignorance in LLM Agent Populations
  5. 2025Group size effects and collective misalignment in LLM multi-agent systems
  6. 2025Emergence of polarization in networks of large language model agents
  7. 2026Predicting the scale limits of social mechanisms in agent societies
  8. 2026Collective cooperation without individual fidelity in LLM agents