用大模型动态模拟直播中用户行为变化，提升仿真真实度。
LiveSim: Simulating Environment-Shaped Users in Multi-Agent Live-Stream Ecosystems
- 将用户行为建模为可编辑假设，通过交互不断修正
- 实测数据验证用户行为拟真度显著提升
- 适合研究直播平台风险演化与干预效果
基于大语言模型的用户行为仿真在多智能体生态系统模拟中日益重要。现有模拟器通常依赖历史观测推断的静态用户画像，在直播这类高互动环境中难以适应行为随交互持续变化的特点。本文提出LiveSim，一种基于LLM的直播生态仿真框架。该框架将用户表示为可编辑的行为假设，并通过轨迹驱动的交互逐步优化；模拟与实际轨迹的偏差揭示了缺失的环境影响因素，这些信号被提取为可迁移的环境-行为模式，存入集体行为记忆库，以提升个体行为拟真度并支持系统级仿真。在真实直播风控数据上的实验表明，LiveSim有效提升了用户行为拟真度，实现了对风险演化及平台干预效果的系统级分析。
原文摘要 · Abstract (English)
User behavior simulation with large language models~(LLMs) is increasingly used to support multi-agent ecosystem simulation. Existing simulators typically rely on static user profiles inferred from historical observations, which become inadequate in socially intensive environments such as live streaming where interaction dynamics continuously reshape user behavior. We propose \textbf{LiveSim}, an LLM-based framework for live-stream ecosystem simulation. It represents users as editable behavioral hypotheses and progressively refines them through trajectory-grounded interactions, where discrepancies between simulated and observed trajectories reveal missing environmental shaping effects. These signals are further extracted as transferable environment-behavior patterns and accumulated in a collective behavioral memory to improve user-level behavioral fidelity and support ecosystem-level simulation. Experiments on real-world live-stream risk-control data validate the effectiveness of LiveSim in improving user-level behavioral fidelity and enabling ecosystem-level analysis of risk evolution and platform intervention effects.
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