用大模型增强的数字孪生系统，模拟短视频平台政策效果。
LLM-Augmented Digital Twin for Policy Evaluation in Short-Video Platforms
- 四模块数字孪生+事件驱动架构，还原平台闭环生态。
- 支持可复现实验，能评估长周期与分布性政策影响。
- 适合研究平台政策、AI工具对内容生态的影响者。
短视频平台是平台政策、创作者激励与用户行为共同演化的闭环人机系统。这种反馈结构使得生产环境中的反事实政策评估困难，尤其在长周期和分布性结果方面。平台部署AI工具进一步加剧挑战，因其改变了进入系统的內容、各方适应方式及平台运作模式。本文提出一种大语言模型（LLM）增强的短视频平台数字孪生系统，采用模块化四孪生架构（用户、内容、交互、平台）与事件驱动执行层，支持可复现实验。平台策略作为平台孪生中的插件组件实现，而LLM以受约束的决策服务形式集成（如人物设定生成、内容标题生成、活动策划、趋势预测），通过统一优化器调度。该设计实现了保留闭环动态的可扩展仿真，支持选择性引入LLM，从而在真实反馈与约束下研究平台策略，包括由AI驱动的策略。
原文摘要 · Abstract (English)
Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This feedback structure makes counterfactual policy evaluation difficult in production, especially for long-horizon and distributional outcomes. The challenge is amplified as platforms deploy AI tools that change what content enters the system, how agents adapt, and how the platform operates. We propose a large language model (LLM)-augmented digital twin for short-video platforms, with a modular four-twin architecture (User, Content, Interaction, Platform) and an event-driven execution layer that supports reproducible experimentation. Platform policies are implemented as pluggable components within the Platform Twin, and LLMs are integrated as optional, schema-constrained decision services (e.g., persona generation, content captioning, campaign planning, trend prediction) that are routed through a unified optimizer. This design enables scalable simulations that preserve closed-loop dynamics while allowing selective LLM adoption, enabling the study of platform policies, including AI-enabled policies, under realistic feedback and constraints.
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