IO Factory: Simulating AI-Enabled Influence Campaigns at Scale

2026-08-11

AI Summary

This study addresses the challenge of detecting AI-driven coordinated influence operations, which are difficult to identify through isolated data points. To this end, the authors propose a traceable and reproducible closed-loop simulation framework that models influence campaigns as an end-to-end process encompassing roles, actions, exposure, evaluation, and adaptation. Built upon a multi-agent system, the framework integrates action planning with feedback-driven adaptation mechanisms and incorporates belief variables alongside structured assessment methodologies. The system was successfully deployed at scale, simulating over 100,000 agents and generating auditable exposure pathways and belief evolution trajectories. This work represents the first large-scale simulation capable of capturing the full lifecycle of AI-coordinated influence operations, demonstrating both the scalability of the architecture and its analytical efficacy.

Abstract

We introduce IO Factory, an AI-driven framework for simulating information and influence campaigns as fully integrated, traceable processes. The threat of digital manipulation now extends beyond persuasive text from individual language models to AI swarms, i.e., persistent groups of coordinated agents that adapt to platform feedback and disguise organized campaigns as ordinary social interaction. Because such campaigns cannot be identified from isolated messages alone, they must be analyzed across a continuous spectrum of planning, platform action, exposure, interpretation, measurement, and adaptation. IO Factory represents this process inside a controlled simulated platform, linking actor roles, platform actions, exposure records, structured model-based evaluations, and configured changes in the simulated population. We implement the architecture and evaluate it across configurations of up to 100,000 agents. The results show that IO Factory executes campaign timelines at scale and produces inspectable evidence of exposure and measured movement in configured belief variables. By recording the actors, objectives, action constraints, exposure paths, and measurement rules used in each run, IO Factory supports reproducible research and red-team analysis of coordinated influence.

Problem

• AI swarms
• influence campaigns
• digital manipulation
• coordinated agents
• social simulation

Innovation

• AI swarms
• influence campaigns
• simulation framework
• traceable processes
• reproducible research