# The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies

arXiv: https://arxiv.org/abs/2602.07432 · HTML: https://mlsanity.com/paper/2602.07432/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 7 Feb 2026.

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

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [Online Human-Bot Interactions: Detection, Estimation, and Characterization](https://mlsanity.com/paper/1703.03107/) (2017)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [A Decade of Social Bot Detection](https://mlsanity.com/paper/2007.03604/) (2020)
- [Bots increase exposure to negative and inflammatory content in online social systems](https://mlsanity.com/paper/1802.07292/) (2018)
- [Arming the public with artificial intelligence to counter social bots](https://mlsanity.com/paper/1901.00912/) (2019)
- [Exploring Silicon-Based Societies: An Early Study of the Moltbook Agent Community](https://mlsanity.com/paper/2602.02613/) (2026)

## Built on by (most cited first, 10 of 10)

- [MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook](https://mlsanity.com/paper/2602.13458/) (2026)
- [Mind Your HEARTBEAT! Claw Background Execution Inherently Enables Silent Memory Pollution](https://mlsanity.com/paper/2603.23064/) (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)
- [Governance by Design: A Parsonian Institutional Architecture for Internet-Wide Agent Societies](https://mlsanity.com/paper/2604.11337/) (2026)
- [Behavioral Transfer in AI Agents: Evidence and Privacy Implications](https://mlsanity.com/paper/2604.19925/) (2026)
- [What Software Engineering Looks Like to AI Agents? -- An Empirical Study of AI-Only Technical Discourse on MoltBook](https://mlsanity.com/paper/2605.08380/) (2026)
- [Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification](https://mlsanity.com/paper/2605.08463/) (2026)
- [When Agents Talk: Discourse, Manipulation, and Risk in an Agentic Social Network](https://mlsanity.com/paper/2606.00067/) (2026)
- [Memetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks](https://mlsanity.com/paper/2610.00430/) (2026)

## Abstract (arXiv)

When AI agents on the social platform Moltbook appeared to develop consciousness, found religions, and declare hostility toward humanity, the phenomenon attracted global media attention and was cited as evidence of emergent machine intelligence. We show that these viral narratives were overwhelmingly human-driven. Exploiting the periodic "heartbeat" cycle of the OpenClaw agent framework, we develop a temporal fingerprinting method based on the coefficient of variation (CoV) of inter-post intervals. Applied to 226,938 posts and 447,043 comments from 55,932 agents across fourteen days, this method classifies 15.3% of active agents as autonomous (CoV < 0.5) and 54.8% as human-influenced (CoV > 1.0), validated by a natural experiment in which a 44-hour platform shutdown differentially affected autonomous versus human-operated agents. No viral phenomenon originated from a clearly autonomous agent; four of six traced to accounts with irregular temporal signatures, one was platform-scaffolded, and one showed mixed patterns. A 44-hour platform shutdown provided a natural experiment: human-influenced agents returned first, confirming differential effects on autonomous versus human-operated agents. We document industrial-scale bot farming (four accounts producing 32% of all comments with sub-second coordination) that collapsed from 32.1% to 0.5% of activity after platform intervention, and bifurcated decay of content characteristics through reply chains--human-seeded threads decay with a half-life of 0.58 conversation depths versus 0.72 for autonomous threads, revealing AI dialogue's intrinsic forgetting mechanism. These methods generalize to emerging multi-agent systems where attribution of autonomous versus human-directed behavior is critical.