RAG Collapse: LLM Responses Collapse When Retrieved Documents Are Self-Authored

Abstract: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 retrieving references they generated, using three model families, and 1,019 information-seeking prompts, totaling 1,528 simulations and over one million LLM API calls, and find that 79.6% (1,216/1,528) of simulations end in collapse. Surprisingly, even a single self-authored reference can trigger collapse because the LLM disproportionately cites its own content. This self-bias persists even after controlling for reference quality.

Computation and Language (cs.CL); Information Retrieval (cs.IR)

arXiv:2608.22118 [cs.CL]

(or arXiv:2608.22118v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.22118 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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