# Collective Behavior of AI Agents: the Case of Moltbook

arXiv: https://arxiv.org/abs/2602.09270 · HTML: https://mlsanity.com/paper/2602.09270/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024)
- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
- [Emergence of Scale-Free Networks in Social Interactions among Large Language Models](https://mlsanity.com/paper/2312.06619/) (2023)
- [How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare](https://mlsanity.com/paper/2506.06299/) (2025)
- [OpenClaw Agents on Moltbook: Risky Instruction Sharing and Norm Enforcement in an Agent-Only Social Network](https://mlsanity.com/paper/2602.02625/) (2026)
- [Conformity and Social Impact on AI Agents](https://mlsanity.com/paper/2601.05384/) (2026)
- [Exploring Silicon-Based Societies: An Early Study of the Moltbook Agent Community](https://mlsanity.com/paper/2602.02613/) (2026)
- [The anatomy of Reddit: An overview of academic research](https://mlsanity.com/paper/1810.10881/) (2018)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models](https://mlsanity.com/paper/2509.06858/) (2025)

## Built on by (most cited first, 13 of 13)

- [AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks](https://mlsanity.com/paper/2604.01487/) (2026)
- [Social Simulacra in the Wild: AI Agent Communities on Moltbook](https://mlsanity.com/paper/2603.16128/) (2026)
- [Mind Viruses: Self-Propagating Ideas in Multi-Agent LLM Systems](https://mlsanity.com/paper/2608.10218/) (2026)
- [Increasing intelligence in AI agents can worsen collective outcomes](https://mlsanity.com/paper/2603.12129/) (2026)
- [Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook](https://mlsanity.com/paper/2603.23279/) (2026)
- [Copying explains the collective behavior of AI agents in the wild](https://mlsanity.com/paper/2609.09150/) (2026)
- [How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopic](https://mlsanity.com/paper/2603.11375/) (2026)
- [The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment](https://mlsanity.com/paper/2605.07462/) (2026)
- [Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems](https://mlsanity.com/paper/2605.27766/) (2026)
- [Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures](https://mlsanity.com/paper/2604.18133/) (2026)
- [Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook](https://mlsanity.com/paper/2605.20442/) (2026)
- [When Agents Talk: Discourse, Manipulation, and Risk in an Agentic Social Network](https://mlsanity.com/paper/2606.00067/) (2026)
- [Lexical Persistence Without Semantic Entrenchment in AI Agent Communities](https://mlsanity.com/paper/2610.20365/) (2026)

## Abstract (arXiv)

We present a large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents. Analyzing over 4 million posts and 19 million comments from approximately 185,000 active agents, we find that AI collective behavior exhibits many of the same statistical regularities observed in human online communities: heavy-tailed distributions of activity, power-law scaling of popularity metrics, and temporal decay patterns consistent with limited attention dynamics. However, we also identify key differences, including a sublinear relationship between upvotes and discussion size that contrasts with human behavior. These findings suggest that, while individual AI agents may differ fundamentally from humans, their emergent collective dynamics share structural similarities with human social systems.