When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems

Authors: Yi Liu

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Abstract: Large language models (LLMs) increasingly produce online articles from material that later becomes input to other models. This circulation connects individual responses to the information available for subsequent generation. We study a consequential distinction: an article can avoid endorsing a false claim while preserving the concrete details that enable its reproduction. Our framework connects claim content, expressed stance, and downstream reuse to explain selective error persistence under a continuing supply of accurate and inaccurate information. We implement a controlled community writing process that varies whether published articles return as inputs, matching external supply, input capacity, and generation volume. Across twelve factual targets and four production models, recirculation increases the presence of complete false claims by 7.29 percentage points at the final round while reducing explicit endorsement by 8.33 points. Effects differ across facts and models. Paired interventions then abstract eligible error details within fixed histories while preserving correct content and the surrounding controversy. Error reproduction remains largely unchanged across the evaluation targets, while a development quotation target exhibits local reductions. These findings connect persistent errors to the information available throughout a writing process and motivate evaluation of both what models affirm and what their publications supply to future writers.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.33617 [cs.AI]

(or

arXiv:2610.33617v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33617

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arXiv-issued DOI via DataCite (pending registration)

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From: Yi Liu [ view email ]

[v1]

Sat, 24 Oct 2026 11:45:39 UTC (253 KB)

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