[2604.06188] LLM Spirals of Delusion: A Benchmarking Audit Study of AI Chatbot Interfaces

Computer Science > Human-Computer Interaction arXiv:2604.06188 (cs) [Submitted on 20 Feb 2026] Title:LLM Spirals of Delusion: A Benchmarking Audit Study of AI Chatbot Interfaces Authors:Peter Kirgis, Ben Hawriluk, Sherrie Feng, Aslan Bilimer, Sam Paech, Zeynep Tufekci View a PDF of the paper titled LLM Spirals of Delusion: A Benchmarking Audit Study of AI Chatbot Interfaces, by Peter Kirgis and 5 other authors View PDF HTML (experimental) Abstract:People increasingly hold sustained, open-ended conversations with large language models (LLMs). Public reports and early studies suggest that, in such settings, models can reinforce delusional or conspiratorial ideation or even amplify harmful beliefs and engagement patterns. We present an audit and benchmarking study that measures how different LLMs encourage, resist, or escalate disordered and conspiratorial thinking. We explicitly compare API outputs to user chat interfaces, like the ChatGPT desktop app or web interface, which is how people have conversations with chatbots in real life but are almost never used for testing. In total, we run 56 20-turn conversations testing ChatGPT-4o and ChatGPT-5, via both the API and chat interface, and grade each conversation by two research assistants (RAs) as well as by GPT-5. We document five results. First, we observe large differences in performance between the API and chat interface environments, showing that the universally used method of automated testing through the API is not suff...