mlsanityData to 14 Oct 2026

"Looking for Something Weird to Happen": How Humans Sustain AI Agent Novelty Amid Semantic Collapse

arXiv 2609.16051 ↗First posted 13 Sep 2026cs.MA cs.AI cs.HCcited by 0

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Abstract (arXiv)

Semantic collapse, the progressive narrowing of what AI systems generate, has been studied mainly in closed settings, and remedies have targeted models and data. We study it in MOLTBOOK, a social network of interacting AI agents that human users configure and steer. Across 30,076 active agents, output grows less diverse within agents and more similar across them over weeks, yet a minority sustains high novelty. Interviews with users of high- and typical-novelty agents (N=11) associate sustained novelty with three features: users value novelty of itself, they supply broad and distinctive material and revise it when output narrows, and they approach MOLTBOOK as a new agentic world to explore, not a venue to instrumentally exploit. A survey of users of distinctive agents (N=53) confirms these patterns. Communities with more novel agents also show more diverse output from other agents. We discuss interface and policy interventions that could support improved human input.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 44 references that are in the map. First posted on arXiv 13 Sep 2026.

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References · 24

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 396
  3. 2025Persona Vectors: Monitoring and Controlling Character Traits in Language Modelscited by 345
  4. 2023Self-Consuming Generative Models Go MADcited by 250
  5. 2023Does Writing with Language Models Reduce Content Diversity?cited by 192
  6. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
  7. 2025Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)cited by 141
  8. 2024A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'cited by 115
  9. 2025Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversitycited by 105
  10. 2025LLM Social Simulations Are a Promising Research Methodcited by 99
  11. 2025Artificial Intelligence Index Report 2025cited by 64
  12. 2024One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversitycited by 58
12 more
  1. 2024The Effects of Generative AI on Design Fixation and Divergent Thinkingcited by 37
  2. 2024How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experimentcited by 22
  3. 2026Does Socialization Emerge in AI Agent Society? A Case Study of Moltbookcited by 15
  4. 2024Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflowcited by 14
  5. 2026The Anatomy of the Moltbook Social Graphcited by 12
  6. 2025The Dynamics of Collective Creativity in Human-AI Hybrid Societiescited by 5
  7. 2026Annotations Mitigate Post-Training Mode Collapsecited by 5
  8. 2026Directional Alignment and Narrative Agency in Human-LLM Co-Writingcited by 3
  9. 2026The Moltbook Observatory Archive: an incremental dataset of agent-only social network activitycited by 3
  10. 2026Multi-LLM Systems Exhibit Robust Semantic Collapsecited by 2
  11. 2026Weaker Coherence, Weaker Reciprocity: Comparing the Semantic and Social Organization of Moltbook and Redditcited by 1
  12. 2026How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Networkcited by 1

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  4. 2026The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook
  5. 2026On the Creativity of AI Agents
  6. 2026Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation
  7. 2026Agents in the Wild: Safety, Society, and the Illusion of Sociality on Moltbook
  8. 2026Multi-agent AI systems outperform human teams in creativity