LLM Agent的记忆长度影响集体合作，短记忆促合作，长记忆致崩溃。
How memory can affect collective and cooperative behaviors in an LLM-Based Social Particle Swarm
- 用大语言模型替代规则代理，赋予人格和可变记忆长度。
- 记忆越长，合作越难维持，从稳定合作到分散背叛的转变明显。
- 适合研究生成式智能体社会行为、认知机制的学者参考。
本研究探讨记忆如何影响大型语言模型（LLM）代理在多智能体系统中的集体与合作行为。为此，我们扩展了社交粒子群（SPS）模型，将原模型中的规则代理替换为具备大五人格评分和不同记忆长度的LLM代理。基于Gemini 2.0 Flash实验发现，记忆长度是决定集体行为的关键参数：即使极短记忆也显著抑制合作，随着记忆增长，系统由稳定的合作集群，经周期性形成与瓦解，最终演变为散乱的背叛状态。大五人格特质与代理行为部分符合人类实验结果，验证了模型有效性。该记忆效应在有无人格设定下均存在：有性格时个体行为反映人格特征，长期记忆导致合作崩溃；无性格时Gemini的天然合作倾向主导，合作得以广泛维持。对代理推理文本的情感分析显示，模型随记忆增长逐渐负面解读累积信息，早期即显现此趋势，提供了合作受抑的微观解释。结果表明，LLM如何理解积累记忆，是生成式智能体社会行为涌现的关键驱动因素。
原文摘要 · Abstract (English)
This study examines how memory shapes the collective and cooperative dynamics of Large Language Model (LLM) agents in a multi-agent system. To this end, we extend the Social Particle Swarm (SPS) model, in which agents move in a two-dimensional space and play the Prisoner's Dilemma with neighboring agents, by replacing its rule-based agents with LLM agents endowed with Big Five personality scores and varying memory lengths. Using Gemini 2.0 Flash, we find that memory length is a critical parameter governing collective behavior: even a minimal memory drastically suppressed cooperation, transitioning the system from stable cooperative clusters through cyclical formation and collapse of clusters to a state of scattered defection as memory length increased. Big Five personality traits correlated with agent behaviors in partial agreement with findings from experiments with human participants, supporting the validity of the model. This effect of memory appeared whether or not personality was assigned. With heterogeneous personalities, individual behavior reflected the assigned traits and cooperation collapsed under long memory, whereas without personality Gemini's cooperative disposition dominated and cooperation was broadly maintained. Sentiment analysis of agents' reasoning texts showed that the model interprets memory increasingly negatively as its length grows, already in the early phase, providing a micro-level account of the suppression of cooperation. These results suggest that how an LLM interprets accumulated memory is a key driver of emergent social behavior in Generative Agent-Based Modeling.
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