mlsanityData to 14 Oct 2026

Trust, Lies, and Long Memories: Emergent Social Dynamics and Reputation in Multi-Round Avalon with LLM Agents

arXiv 2604.20582 ↗First posted 22 Apr 2026cs.MA cs.AI cs.CLcited by 1

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

Abstract (arXiv)

We study emergent social dynamics in LLM agents playing The Resistance: Avalon, a hidden-role deception game. Unlike prior work on single-game performance, our agents play repeated games while retaining memory of previous interactions, including who played which roles and how they behaved, enabling us to study how social dynamics evolve. Across 188 games, two key phenomena emerge. First, reputation dynamics emerge organically when agents retain cross-game memory: agents reference past behavior in statements like "I am wary of repeating last game's mistake of over-trusting early success." These reputations are role-conditional: the same agent is described as "straightforward" when playing good but "subtle" when playing evil, and high-reputation players receive 46% more team inclusions. Second, higher reasoning effort supports more strategic deception: evil players more often pass early missions to build trust before sabotaging later ones, 75% in high-effort games vs 36% in low-effort games. Together, these findings show that repeated interaction with memory gives rise to measurable reputation and deception dynamics among LLM agents.

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 5 of its 14 references that are in the map. First posted on arXiv 22 Apr 2026.

Builds on

5 of 14 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 4

  1. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  2. 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 529
  3. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 222
  4. 2017Deal or No Deal? End-to-End Learning for Negotiation Dialoguescited by 209

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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Close in wording, no citation either way

  1. 2026Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game
  2. 2025The Traitors: Deception and Trust in Multi-Agent Language Model Simulations
  3. 2026The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agents
  4. 2026Be Careful Who You Trust: Coordination Dynamics under Corrupted Communication in LLM Multi-Agent Games
  5. 2026When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games
  6. 2026Evolving Deception: When Agents Evolve, Deception Wins
  7. 2026CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation
  8. 2023AvalonBench: Evaluating LLMs Playing the Game of Avalon