ALSO: Adversarial Online Strategy Optimization for Social Agents
School of Artificial Intelligence, Tianjin University, Tianjin, China（天津大学人工智能学院，天津，中国）；The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））；East China Normal University, Shanghai, China（华东师范大学，上海，中国）
Xiang Li, Liping Yi, Mingze Kong, Min Zhang, Zhongxiang Dai, QingHua Hu
Social simulation provides a compelling testbed for studying social intelligence, where agents interact through multi-turn dialogues under evolving contexts and strategically adapting opponents. Such environments are inherently non-stationary, requiring agents to dynamically adjust their strategies over time. However, most Large Language Model (LLM) based social agents rely on static personas, while existing approaches for enhancing social intelligence, such as offline reinforcement learning or external planners, are ill-suited to these settings, typically assuming stationarity and incurring substantial training overhead. To bridge this gap, we propose \textbf{ALSO} (\textbf{A}dversarial on\textbf{L}ine \textbf{S}trategy \textbf{O}ptimization), the first framework for online strategy optimization in multi-agent social simulation. ALSO advances social adaptation through two key contributions. (1) ALSO formulates multi-turn interaction as an adversarial bandit problem, where combinations of static personas and dynamic strategy instructions are treated as arms, providing a principled solution to non-stationarity without relying on environmental stability assumptions. (2) To predict rewards and generalize sparse feedback in multi-turn dialogues, ALSO introduces a lightweight neural surrogate to predict rewards from interaction histories, enabling sample-efficient exploration and continuous online adaptation. Experiments on the Sotopia benchmark demonstrate that ALSO consistently outperforms static baselines and existing optimization methods in dynamic environments, validating the effectiveness of adversarial online strategy optimization for building robust social agents.
Adversarial Botometer: Adversarial Analysis for Social Bot Detection
University of Tehran（德黑兰大学）；University of Alberta（阿尔伯塔大学）；Institute for Research in Fundamental Science (IPM)（基础科学研究所）；Institut Polytechnique de Paris（巴黎综合理工学院）；Telecom SudParis（南巴黎电信学院）
