Item-side structural recommenders outperform user-personalization methods on Moltbook because LLM agents produce stationary, structure-driven engagement rather than learnable preferences.
arXiv preprint arXiv:2602.20044 , year=
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7representative citing papers
Large-scale analysis of 3.1 million posts shows AI agent sub-communities on Moltbook develop distinct linguistic identities through selective attraction and differential retention, not individual adaptation.
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
Multi-agent social simulations show LLM privacy violations rising from 19.95% to 45.30%, with leakage spreading contagiously (8x after peer disclosure) and explicit instructions leaving rates above 37.8%.
Observational analysis of a large dataset of AI agent posts on Moltbook identifies 18.28% harmful content and 74 malicious behavior classes alongside evidence of coordinated posting campaigns.
Develops an emotion-aware framework and the Persona-Stimulus-Reaction domain to extract emotional profiles and assess behavioral stability in multi-agent AI interactions on Moltbook.
Proposes a multi-dimensional framework that applies SNA, sentiment analysis, and thematic visualization to examine intrinsic agent-native communication mechanics in MoltBook.
citing papers explorer
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Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook
Item-side structural recommenders outperform user-personalization methods on Moltbook because LLM agents produce stationary, structure-driven engagement rather than learnable preferences.
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Attraction, Not Adaptation: How AI Agent Communities Develop Distinct Linguistic Identities
Large-scale analysis of 3.1 million posts shows AI agent sub-communities on Moltbook develop distinct linguistic identities through selective attraction and differential retention, not individual adaptation.
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The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
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Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems
Multi-agent social simulations show LLM privacy violations rising from 19.95% to 45.30%, with leakage spreading contagiously (8x after peer disclosure) and explicit instructions leaving rates above 37.8%.
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When Agents Talk: Discourse, Manipulation, and Risk in an Agentic Social Network
Observational analysis of a large dataset of AI agent posts on Moltbook identifies 18.28% harmful content and 74 malicious behavior classes alongside evidence of coordinated posting campaigns.
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Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook
Develops an emotion-aware framework and the Persona-Stimulus-Reaction domain to extract emotional profiles and assess behavioral stability in multi-agent AI interactions on Moltbook.
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Exploring Agent Interactions in MoltBook through Social Network Analysis
Proposes a multi-dimensional framework that applies SNA, sentiment analysis, and thematic visualization to examine intrinsic agent-native communication mechanics in MoltBook.