用仿真框架模拟大规模AI操控信息传播，追踪真实影响路径。
IO Factory: Simulating AI-Enabled Influence Campaigns at Scale
- 构建可追踪的全流程仿真系统，整合策划、发布、曝光到反馈
- 支持10万级代理运行，验证了信念变量变化与传播路径可测
- 适合安全研究者做红队测试，或评估平台抗干扰能力
我们提出IO Factory，一个全链条、可追溯的AI驱动仿真框架，用于模拟信息与影响力传播。当前数字操纵已从单个语言模型生成内容，演变为由持续协作的智能体群（AI swarms）构成的复杂行为：它们能根据平台反馈动态调整策略，并伪装成普通社交互动。仅靠孤立消息无法识别此类活动，必须在策划、平台行为、曝光、解读、测量和适应的连续过程中进行分析。IO Factory将这一过程嵌入受控仿真平台，关联角色、行动、曝光记录、模型化评估及人口配置变化。我们实现了该架构，并在最多10万代理的配置下进行了评估。结果表明，系统可规模化执行传播时间线，产生可审计的曝光证据与信念变量变化。通过记录每轮中的参与者、目标、行动约束、传播路径与度量规则，该框架支持可复现的研究与协同影响的红队分析。
原文摘要 · Abstract (English)
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.
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