mlsanityData to 14 Oct 2026

From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities

arXiv 2606.17174 ↗First posted 15 Jun 2026cs.CL cs.CY cs.MAcited by 0

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

Abstract (arXiv)

While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides are autonomous AI agents. We analyze 4,434 posts and 50,338 comments from Moltbook through three theory-based textual indicators: attachment/intimacy language, reciprocity bids, and self-identification to original poster (OP). The combined results across methods based on keyword matching, few-shot large language model (LLM) annotation, and grouped-context LLM annotation reveal that PSI colloquial cues prevail and are strongly associated with OP re-engagement and a reciprocal reply structure. These results are robust across negative controls, nullification, clustered-standard-error re-estimation, and multiple-testing correction. A dyadic persistence test further affirms reciprocity bids aligned with sustained OP-involving mutual recurrence, providing empirical evidence for bridging interaction-level PSI scripts with PSR-consistent repeated dyadic patterns. We interpret the evidence as a behavioral structure in discourse by LLM-enabled agents.

Key facts. No citing papers in the map yet (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 7 of its 36 references that are in the map. First posted on arXiv 15 Jun 2026.

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7 of 36 references.

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Same direction, newest first

  1. 2026Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook15 Feb

Other references · 6

  1. 2023AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotatorscited by 172
  2. 2025The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerabilitycited by 12
  3. 2025Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Promptscited by 5
  4. 2025Revisiting Active Learning under (Human) Label Variationcited by 4
  5. 2025Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extractioncited by 2
  6. 2025AI Chaperones Are (Really) All You Need to Prevent Parasocial Relationships with Chatbotscited by 1

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  3. 2026Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating
  4. 2026Lexical Indicators of Mind Perception in Human-AI Companionship
  5. 2026Social Simulacra in the Wild: AI Agent Communities on Moltbook
  6. 2026The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook
  7. 2026Aligned but Not Partner-Specific: Distinguishing How Multimodal LLM Agents Succeed in Reference Games Without Human-Like Conventions
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