Skip to content
Preprint

IO Factory: Simulating AI-Enabled Influence Campaigns at Scale

Aug 2026 · 0 citations · 92 references
Computer Science

TL;DR

IO Factory, an AI-driven framework for simulating information and influence campaigns as fully integrated, traceable processes, is introduced and shows that IO Factory executes campaign timelines at scale and produces inspectable evidence of exposure and measured movement in configured belief variables.

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.

View source

Similar papers

Review Aug 2026

Terminal Agents: A Survey of AI Agents in Command-Line Environments

This survey establishes workload-level boundaries and connects system architecture, competence acquisition, and evaluation through a seven-dimensional terminal competence profile, and provides a unified basis for studying terminal-mediated agency across software engineering and emerging application domains.

Yi Bin, Xiao-Yang Yuan, Hao Zeng et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Substrate-Aware AI Agents: Execution Context as a First-Class Input

A minimal execution contract induces proactive structural adaptation in generated programs, shifting computation away from unconstrained allocations and substantially improving observed resource-time profiles before execution, establishing a controlled proof of concept for substrate-aware agent planning.

Manu Agrawal · 0 citations
Review Open access Aug 2026

From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents

A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.

Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al. · 0 citations
Preprint Aug 2026

Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback

Agent Gym is introduced, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop and introduces the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency.

Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge et al. · 0 citations
#artificial intelligence Preprint Sep 2026

AnyAct: Universal Action for Self-Evolving Agents

AnyAct is a universal action layer that unifies available capabilities into a self-evolving action space, enabling agents to operate efficiently and reliably in large-scale, dynamic tool ecosystems and optimizes for a balance between task success rate and execution cost.

Ling-Rui Xu, Ya Jiang, Jia-Chang Zhang et al. · 0 citations
#artificial intelligence Review Sep 2026

Pairit: A Platform for Live Experiments on Human-AI Collaboration

Pairit is an online platform that facilitates the design, testing, and deployment of experiments that test human-AI organizational designs and interventions, and provides reusable infrastructure for specifying, deploying, and sharing live human-AI organizational experiments.

Harang Ju, Sinan Aral · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.