Communication Shapes Collective Inference in Self-Adapting LLM Societies: Evidence from Mafia
Not on the map. Why?
Abstract (arXiv)
When does communication help a group identify hidden adversaries, and how does its value change as the group adapts? In Mafia, an informed minority hides inside an uninformed majority whose only evidence is open play. The zero-information game, where each day's vote eliminates a random player, is exactly solved and scores every society; matched-casting comparisons between protocols identify the effect of communication. Societies of 8-100 claude-haiku-4-5 agents (7,416 analyzed games, 1.9M model calls) adapt by rewriting and inheriting private strategy notes. Simultaneous broadcast improves adversary identification over silence in all nine compositions tested (8-46 players). Turn-taking removes most of this advantage; its voting landslides are as frequent as broadcast's but land on mafia near chance (1.08x versus 2.53x). At 70 players, agents reading eight statements per day identify adversaries worse than silent ones, and limited talk is worth less than at 46 players. Adaptation is fast but need not help. In their first broadcast games, citizens announce their role far more often than mafia (91% vs. 30%) and first-day votes find mafia at three times chance; within two generations citizens stop announcing and the cue fades, a change the inherited notes carry. In controlled redeployments at 16 players, societies carrying sixty generations of their own notes score below societies with none. Communication shapes both collective inference and the signals it depends on, so a protocol's value must be measured together with the adaptation that changes those signals.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 4 Oct 2026.
Builds on
21 of 32 references.
References · 21
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
- 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
- 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 241
- 2024Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agentscited by 125
- 2025How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behaviorcited by 101
- 2019On the Pitfalls of Measuring Emergent Communicationcited by 71
- 2023Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Modelscited by 47
- 2024Werewolf Arena: A Case Study in LLM Evaluation via Social Deductioncited by 45
- 2024Conformity in Large Language Modelscited by 34
- 2024Cultural Evolution of Cooperation among LLM Agentscited by 32
9 more
- 2024Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolfcited by 24
- 2024On the limits of agency in agent-based modelscited by 21
- 2021Emergent Communication under Competitioncited by 18
- 2025Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimizationcited by 15
- 2021RLupus: Cooperation through emergent communication in The Werewolf social deduction gamecited by 11
- 2025Deceive, Detect, and Disclose: Large Language Models Play Mini-Mafiacited by 9
- 2025Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Gamescited by 6
- 2026Game Arena: Strategic LLM Evaluation in Competitive Environmentscited by 2
- 2026The Vote-Left Equilibrium: A Deterministic Coordination Strategy for the Faithful in The Traitorscited by 1
Built on by
Not cited yet.