LLM-Augmented Digital Twin for Policy Evaluation in Short-Video Platforms
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 policie...
| Authors | Haoting Zhang, Yunduan Lin, Jinghai He et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 March 2026 |
| Last updated at source | 4 June 2026 |
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