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

• URL: http://arxiv.org/abs/2606.05256v1
• Date: Wed, 03 Jun 2026 15:58:32 GMT
• Title: How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
• Abstract summary: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView.<n>The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate.<n>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.
• Score: 12.686044001239708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle [65.6642776933861]
  This survey examines the protective paradigm that has grown around this intervention point.<n>We find that most protections are still validated mainly against static or weakly adaptive adversaries.<n>We close by consolidating cross-stage countermeasures and open problems for robust, composable, and deployable owner-side protection.
  arXiv  Detail & Related papers  (2026-08-05T00:46:50Z)
• When AI reviews science: Can we trust the referee? [73.47745294608072]
  We map attacks across the review lifecycle -- training and data retrieval, desk review, deep review, rebuttal, and system-level.<n>We use two advanced LLM-based referees to isolate the causal effects of prestige framing, assertion strength, rebuttal sycophancy, and contextual poisoning on review scores.
  arXiv  Detail & Related papers  (2026-04-26T08:03:32Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• Beyond Rating: A Comprehensive Evaluation and Benchmark for AI Reviews [69.66583722746904]
  We introduce Beyond Rating, a holistic evaluation framework that assesses AI reviewers across five dimensions.<n>We propose a Max-Recall strategy to accommodate valid expert disagreement and introduce a curated dataset of paper with high-confidence reviews.<n>Our proposed text-centric metrics--particularly the recall of weakness arguments--correlate strongly with rating accuracy.
  arXiv  Detail & Related papers  (2026-04-21T14:21:15Z)
• Beyond Factual Grounding: The Case for Opinion-Aware Retrieval-Augmented Generation [1.073429056168031]
  Current RAG systems exhibit a bias toward factual, objective content.<n>This bias limits RAG systems in real-world scenarios involving subjective content.<n>We present an Opinion-Aware RAG architecture featuring LLM-based opinion extraction, entity-linked opinion graphs, and opinion-enriched document indexing.
  arXiv  Detail & Related papers  (2026-04-13T23:39:39Z)
• When Visibility Outpaces Verification: Delayed Verification and Narrative Lock-in in Agentic AI Discourse [2.5424331328233207]
  Agentic AI systems-autonomous entities capable of independent planning and execution-reshape the landscape of human-AI trust.<n>This paper investigates the interplay between social proof and verification timing in online discussions of agentic AI.
  arXiv  Detail & Related papers  (2026-02-11T22:30:12Z)
• ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review [48.60540055009675]
  ScholarPeer is a search-enabled multi-agent framework designed to emulate the cognitive processes of a senior researcher.<n>We evaluate ScholarPeer on DeepReview-13K and the results demonstrate that ScholarPeer achieves significant win-rates against state-of-the-art approaches in side-by-side evaluations.
  arXiv  Detail & Related papers  (2026-01-30T06:54:55Z)
• AI and the Future of Academic Peer Review [0.1622854284766506]
  Large language models (LLMs) are being piloted across the peer-review pipeline by journals, funders, and individual reviewers.<n>Early studies suggest that AI assistance can produce reviews comparable in quality to humans.<n>We show that supervised LLM assistance can improve error detection, timeliness, and reviewer workload without displacing human judgment.
  arXiv  Detail & Related papers  (2025-09-17T17:27:12Z)
• CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
  We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
  arXiv  Detail & Related papers  (2025-08-28T06:03:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.