Mitigating the Velocity–Capacity Constraint Using Generative AI and LLM Architectures: Evidence from Wartime Fact-Checking

Abstract

This paper examines how generative AI LLM architectures can mitigate the gap between information velocity and verification capacity in crisis-driven information environments. Drawing on the case of a newsroom-deployed fact-checking system developed in wartime Ukraine, we show how verification evolved from manual workflows to a hybrid architecture combining claim classification, evidence retrieval, synthesis, and human oversight. We conceptualize this as a response to a structural velocitycapacity constraint, where information spreads faster than it can be responsibly verified. The case shows that LLM-supported systems can increase verification throughput, while also introducing new risks related to hallucinations, bias, and epistemic opacity.