How Artificial Intelligence LLM Engines Shape the Global Conflict Information Environment

A new study of five leading AI answer engines found that they hallucinate more frequently when answering questions about conflicts with thin retrievable records, creating structural exposure to mis- and disinformation. The researchers analyzed 5,460 answers across 28 conflicts and 1,048 source websites, discovering that Generative Engine Optimization is already being used to bias responses, with state-partisan digital capture growing rapidly.

arXiv:2607.14197v1 Announce Type: new Abstract: Artificial Intelligence AI answer engines now field a growing share of the questions that analysts, scholars, and the public ask about issues of peace and conflict. Large Language Models LLMs are known to hallucinate under certain conditions, but do these errors have discernible patterns when they are asked about conflicts, and if so what can that teach us about the changing global conflict information environment? To answer, we first asked a battery of questions about 28 conflicts to five leading answer engines and scored their 5,460 answers against documented evidence. We found that the thinner the retrievable record around a given conflict, the more the engines invent, misattribute, and miscount. Thin records don't just encourage hallucination, but create structural exposure to mis- and disinformation, because they are the easiest records to warp through Generative Engine Optimization GEO to bias engine responses. Through an analysis of 1,048 websites that the AI LLMs pulled conflict facts from, we found that GEO source optimization is already happening, and while state-partisan digital capture remains incipient it is rapidly growing. We explain what these findings mean for scholarship with the rise of GEO information warfare, and for policy argue for a return to the deep local monitoring and translation-based research that AI tools cannot replicate, closing with a discussion of future research opportunities and challenges in this fast-moving space.