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LLM-Augmented Digital Twin for Policy Evaluation in Short-Video Platforms

arXiv:2603.11333v1 Announce Type: new Abstract: 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 un

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Haoting Zhang (Max), Yunduan Lin (Max), Jinghai He (Max), Denglin Jiang (Max), Zuo-Jun (Max), Shen, Zeyu Zheng · · 1 min read · 17 views
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arXiv:2603.11333v1 Announce Type: new Abstract: 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.

Executive Summary

The article proposes an innovative LLM-augmented digital twin framework designed to address the complexities of policy evaluation in short-video platforms. Recognizing the closed-loop, co-evolving nature of platform policy, creator incentives, and user behavior, the authors introduce a modular four-twin architecture—User, Content, Interaction, Platform—augmented with an event-driven execution layer. This architecture supports reproducible experimentation by embedding platform policies as pluggable components and integrating LLMs as optional, schema-constrained decision services. The design enables scalable simulations that preserve closed-loop dynamics while accommodating selective LLM adoption, facilitating the study of both conventional and AI-enabled policies under realistic operational constraints. The work addresses a significant gap in evaluating long-horizon and distributional outcomes in evolving digital ecosystems.

Key Points

  • ▸ Modular four-twin architecture addresses closed-loop dynamics
  • ▸ Event-driven execution layer supports reproducible experimentation
  • ▸ LLMs integrated as optional decision services via unified optimizer

Merits

Innovative Architecture

The modular design effectively isolates and simulates the interplay between user, content, interaction, and platform variables, enhancing analytical granularity.

Practical Relevance

By enabling scalable simulations with preserved feedback loops, the framework supports actionable insights for platform governance and AI policy deployment.

Demerits

Implementation Complexity

Integrating LLMs as optional decision services may introduce latency or computational overhead, particularly in real-time policy evaluation scenarios.

Generalizability Concern

While tailored for short-video platforms, the framework’s applicability to broader social media ecosystems remains undemonstrated.

Expert Commentary

This paper represents a meaningful advancement in the intersection of AI, policy evaluation, and digital ecosystem modeling. The conceptualization of a ‘four-twin’ architecture is particularly compelling, as it decouples the influence of distinct agent types while maintaining systemic coherence—a critical requirement for meaningful counterfactual analysis. Moreover, the integration of LLMs not as replacements but as augmentative, schema-constrained services reflects a nuanced understanding of AI’s role: as an enabler, not a substitute, for human-in-the-loop decision-making. The event-driven execution layer is a sophisticated solution to the persistent challenge of reproducibility in dynamic, feedback-rich environments. However, the authors should address the scalability implications of LLM routing under high-volume event streams, particularly when multiple decision services are activated simultaneously. Furthermore, future work should explore longitudinal impacts of AI-augmented policy simulations on user engagement metrics to validate real-world applicability. Overall, this is a timely, methodologically rigorous contribution to the field.

Recommendations

  • ✓ 1. Conduct pilot simulations with real platform datasets to validate predictive accuracy of LLM-augmented outcomes.
  • ✓ 2. Extend the framework to include temporal drift modeling for content and user behavior to capture evolving trends beyond static snapshots.

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