Human-AI Co-Evolution and Epistemic Collapse: A Dynamical Systems Perspective
Large language models (LLMs) are reshaping how knowledge is produced, with increasing reliance on AI systems for generation, summarization, and reasoning. While prior work has studied cognitive offloading in humans and model collapse in recursive training, these effects are typically considered in isolation. We propose a unified perspective: humans and language models form a coupled dynamical system linked by a feedback loop of usage, generation, and retraining. We introduce a minimal model with three variables -- human cognition, data quality, and model capability -- and show that this feedback can give rise to distinct dynamical regimes. Our analysis identifies three regimes: co-evoluti...
| Authors | Xuening Wu, Yanlan Kang, Qianya Xu et al. |
|---|---|
| Primary category | cs.HC |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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