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.

翻译：我们研究大语言模型代理在隐藏角色欺骗游戏《抵抗组织：阿瓦隆》中涌现的社会动态。与以往聚焦单轮游戏表现的研究不同，我们的代理在保留先前交互记忆（包括谁扮演了什么角色及其行为）的情况下进行多轮游戏，从而得以探究社会动态的演化过程。在188场游戏中，两种关键现象涌现出来。首先，当代理保留跨游戏记忆时，声誉动态会自发形成：代理会引用过往行为做出诸如"我警惕重蹈上轮游戏中过度信任早期成功的错误"等表述。这些声誉具有角色条件性——同一代理扮演善良角色时被描述为"直率"，而扮演邪恶角色时则被描述为"微妙"，高声誉玩家获得团队入选的概率高出46%。其次，更高水平的推理能力支持更策略性的欺骗行为：邪恶玩家更常通过通过前期任务来建立信任，随后再破坏后期任务，在高推理水平游戏中的比例为75%，而低推理水平游戏中仅为36%。这些发现共同表明，带有记忆的重复交互能在LLM代理中催生可量化的声誉与欺骗动态。