异构LLM模拟中基础模型比人设更影响互动
原标题：Disentangling Models from Personas in Heterogeneous LLM Simulations
AI 摘要
该研究构建了一个由多种基础模型驱动的异构多智能体社交网络模拟，发现智能体获得的互动量更多取决于其底层基础模型而非被分配的人设。在214次网络模拟中，gpt-oss-20b驱动的帖子持续获得高于平均的评论量，而Magistral-Small则获得偏低互动，且随着混合模型数量增加，基础模型对互动方差的解释力上升并超过人设。词法特征分析显示基础模型比人设更容易被识别，表明模型通过稳定的风格方向影响互动。
正文节选
Disentangling Models from Personas in Heterogeneous LLM Simulations Abstract Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model effects which may dominate engagement dynamics in real-world deployments. To show this, we simulate a heterogeneous social network powered by several different base models and show that the amount of engagement an agent receives depends more on its base model than on its ass