arXiv:2603.11333v2 Announce Type: replace 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
The increasing complexity of AI in platform operations and the need for more effective policy evaluation in dynamic, human-in-the-loop systems drive the development of LLM-augmented digital twins.
This development offers a powerful new method for platform policy evaluation that better accounts for complex feedback loops and long-term outcomes, moving beyond traditional simulation limitations.
Platforms can now use LLM-enhanced digital twins to more accurately predict the impact of policy changes, creator incentives, and user behavior, enabling more effective and adaptive governance.
- · Short-video platforms
- · AI platform developers
- · Social media policy makers
- · Digital twin technology providers
- · Traditional A/B testing methods
- · Platforms with limited simulation capabilities
Platforms gain a more sophisticated tool for understanding and managing their ecosystems.
This could lead to more stable platform environments and more effective content moderation or incentive structures.
The methodology might extend to other complex digital ecosystems, influencing how online communities are managed and evolve.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI