RAG Collapse: LLM Responses Collapse When Retrieved Documents Are Self-Authored
LLM responses are based on the internet (via training or RAG), and AI is now used to generate a significant amount of content online (Paredes et al., 2026), creating the potential for a self-reinforcing feedback loop. Prior work has shown that when LLMs are recursively trained on their own output, they experience model collapse (Shumailov et al., 2024): responses become less diverse, and eventually no longer resemble the original training data. In this paper, we show that a similar collapse occurs if LLM-based AI systems retrieve references they authored using a search tool. We call this RAG collapse. We conduct extensive experiments with three types of simulations of AI systems retrievin...
| Authors | Gregory Druck, Ethan Smith |
|---|---|
| Primary category | cs.CL |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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