From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities
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.
Builds on
7 of 36 references.
Closest ancestors · 1
Same direction, newest first
Other references · 6
- 2023AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotatorscited by 172
- 2025The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerabilitycited by 12
- 2025Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Promptscited by 5
- 2025Revisiting Active Learning under (Human) Label Variationcited by 4
- 2025Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extractioncited by 2
- 2025AI Chaperones Are (Really) All You Need to Prevent Parasocial Relationships with Chatbotscited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026ExpressionCueLens: A Cross-Cultural Analysis of Human-AI Companion Conversations on Social Media ↗
- 2026Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification
- 2026Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating
- 2026Lexical Indicators of Mind Perception in Human-AI Companionship
- 2026Social Simulacra in the Wild: AI Agent Communities on Moltbook
- 2026The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook
- 2026Aligned but Not Partner-Specific: Distinguishing How Multimodal LLM Agents Succeed in Reference Games Without Human-Like Conventions
- 2025From Single to Societal: Analyzing Persona-Induced Bias in Multi-Agent Interactions