Abstract
Large Language Models (LLMs) have transitioned rapidly from research contexts into deployment across high-stakes domains, including healthcare, defense, and autonomous decision-making systems. Despite impressive performance on standardized benchmarks, a growing body of empirical evidence indicates that LLMs do not yet satisfy the reliability standards required for critical applications. This paper examines the structural gap between measured capability and operational reliability, introduces a taxonomy of eight failure classes specific to critical deployment contexts, and distinguishes reliability from adjacent properties, including alignment, safety, and robustness. It further argues that current evaluation methodologies, which are predominantly based on multiple-choice benchmarks, are structurally incapable of capturing reliability as it manifests in real-world operational conditions. The paper additionally examines why existing remediation approaches, including retrieval-augmented generation, guardrails, and fine-tuning, are insufficient to close this reliability gap in the near term. The analysis leads to the conclusion that frontier AI systems are not yet reliable enough for autonomous deployment in life-critical or mission-critical environments, and identifies the research and governance investments required to change this trajectory.
Keywords: Large Language Models, AI, Reliability, Critical Applications, Hallucination, Robustness, Defense AI, Healthcare AI, AI Benchmarks, Artificial Intelligence, Generative AI