# Retrieval Collapses When AI Pollutes the Web

arXiv: https://arxiv.org/abs/2602.16136 · HTML: https://mlsanity.com/paper/2602.16136/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 15 of 15)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [Self-Consuming Generative Models Go MAD](https://mlsanity.com/paper/2307.01850/) (2023)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [MAGE: Machine-generated Text Detection in the Wild](https://mlsanity.com/paper/2305.13242/) (2023)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models](https://mlsanity.com/paper/2410.18927/) (2024)
- [Neural Retrievers are Biased Towards LLM-Generated Content](https://mlsanity.com/paper/2310.20501/) (2023)
- [Corpus Poisoning via Approximate Greedy Gradient Descent](https://mlsanity.com/paper/2406.05087/) (2024)
- [Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images](https://mlsanity.com/paper/2311.14084/) (2023)
- [Delving into: the quantification of Ai-generated content on the internet (synthetic data)](https://mlsanity.com/paper/2504.08755/) (2025)
- [Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering](https://mlsanity.com/paper/2404.10496/) (2024)
- [Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop](https://mlsanity.com/paper/2405.17998/) (2024)

## Built on by (most cited first, 4 of 4)

- [Pangram 4 Technical Report](https://mlsanity.com/paper/2607.27183/) (2026)
- [The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing](https://mlsanity.com/paper/2609.07713/) (2026)
- [The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems](https://mlsanity.com/paper/2607.19292/) (2026)
- [You Can Tell Who's Asking: What the Web's Questions Are Made Of, and Where They Come From](https://mlsanity.com/paper/2609.24106/) (2026)

## Abstract (arXiv)

The rapid proliferation of AI-generated content on the Web presents a structural risk to information retrieval, as search engines and Retrieval-Augmented Generation (RAG) systems increasingly consume evidence produced by the Large Language Models (LLMs). We characterize this ecosystem-level failure mode as Retrieval Collapse, a two-stage process where (1) AI-generated content dominates search results, eroding source diversity, and (2) low-quality or adversarial content infiltrates the retrieval pipeline. We analyzed this dynamic through controlled experiments involving both high-quality SEO-style content and adversarially crafted content. In the SEO scenario, a 67% pool contamination led to over 80% exposure contamination, creating a homogenized yet deceptively healthy state where answer accuracy remains stable despite the reliance on synthetic sources. Conversely, under adversarial contamination, baselines like BM25 exposed ~19% of harmful content, whereas LLM-based rankers demonstrated stronger suppression capabilities. These findings highlight the risk of retrieval pipelines quietly shifting toward synthetic evidence and the need for retrieval-aware strategies to prevent a self-reinforcing cycle of quality decline in Web-grounded systems.