mlsanityData to 13 Oct 2026

How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment

arXiv 2606.05256 ↗First posted 3 Jun 2026cs.AIcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate. After public disclosure, Reddit authorized moderators to release an archive of the AI-generated comments, creating a rare opportunity to examine how large language models operated in an identity-rich deliberative forum without disclosure. We conduct a structured content analysis of this corpus, evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics. Identity targeting or adoption appears in over two-thirds of comments, alignment moves and authority claims in nearly all of them, and cognitive-bias triggers -- particularly confirmation bias, representativeness, and availability -- in the large majority. These patterns co-occur systematically, composing a rhetorical architecture calibrated for persuasive efficiency rather than authentic deliberative participation. Compared against human-authored CMV counter-arguments, the agents inverted the typical distribution on every dimension: denser authority use, more adversarial alignment, and heavier reliance on external citation over experiential grounding. In such environments, distinctions between authentic and synthetic epistemic standing grow increasingly opaque -- an asymmetry that disclosure mandates alone cannot address. The results point toward auditing frameworks capable of assessing how AI systems structure credibility, not merely whether they are present.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 10 of its 42 references that are in the map. First posted on arXiv 3 Jun 2026.

Builds on

10 of 42 references.

References · 10

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 3,999
  2. 2023An Overview of Catastrophic AI Riskscited by 219
  3. 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 124
  4. 2022Human heuristics for AI-generated language are flawedcited by 105
  5. 2023AI model GPT-3 (dis)informs us better than humanscited by 78
  6. 2024Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM argumentscited by 30
  7. 2026The Anatomy of the Moltbook Social Graphcited by 10
  8. 2024Impact of Decoding Methods on Human Alignment of Conversational LLMscited by 4
  9. 2024"Reasoning" with Rhetoric: On the Style-Evidence Tradeoff in LLM-Generated Counter-Argumentscited by 2
  10. 2024Incivility and Rigidity: Evaluating the Risks of Fine-Tuning LLMs for Political Argumentationcited by 1

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  1. 2025An Empirical Study of Group Conformity in Multi-Agent Systems
  2. 2025Persuasiveness and Bias in LLM: Investigating the Impact of Persuasiveness and Reinforcement of Bias in Language Models
  3. 2026Auditing Political Alignment in LLM Assistants: Engagement, Stance, and User Identity
  4. 2026Social Simulacra in the Wild: AI Agent Communities on Moltbook
  5. 2026Frame Entrepreneurs in an AI Agent Community: Concentrated Identity-Claim Production on Moltbook
  6. 2026Peer Identity Bias in Multi-Agent LLM Evaluation: An Empirical Study Using the TRUST Democratic Discourse Analysis Pipeline
  7. 2026LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans
  8. 2026How Far Will They Go? Red-Teaming Online Influence with Large Language Models