# Execution and assessment of agentic influence operations in simulated social networks

arXiv: https://arxiv.org/abs/2605.28725 · HTML: https://mlsanity.com/paper/2605.28725/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 27 May 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 4 of 4)

- [BotSim: LLM-Powered Malicious Social Botnet Simulation](https://mlsanity.com/paper/2412.13420/) (2024)
- [Synthetic generation of online social networks through homophily](https://mlsanity.com/paper/2509.02762/) (2025)
- [Agent-based simulation of online social networks and disinformation](https://mlsanity.com/paper/2512.22082/) (2025)
- [Influence Operations in Social Networks](https://mlsanity.com/paper/2502.11827/) (2025)

## Built on by (most cited first, 1 of 1)

- [Should LLM Agents Decide in Social Simulations? Comparing Finite-State and LLM-Based Decision Policies](https://mlsanity.com/paper/2606.12369/) (2026)

## Abstract (arXiv)

This article evaluates AI-enabled influence operations in synthetic social networks through controlled simulations of narrative release, amplification, and counter-messaging. We measure exposure and belief change in agentic audiences, showing that amplification maximizes reach, counter-messaging shifts opinions most, and narrative release requires larger attacker footprints.