Large language models often generate plausible yet unreliable answers, making uncertainty quantification essential for detecting potential hallucinations. Prior work has shown that the information needed to detect hallucinations and quantify a model’s confidence in its generations may already be present in its internal states, but how to extract this information effectively remains an open problem. We introduce Semantic Coverage, a white-box, representation-level spectral uncertainty estimator inspired by coverage in reinforcement learning. Given multiple sampled generations for a prompt, Semantic Coverage quantifies uncertainty by measuring how broadly their internal representations span the model’s latent semantic space. Across three open-weights language models and four short-form question-answering benchmarks, Semantic Coverage achieves the strongest overall performance among the evaluated methods and consistently outperforms prior representation-level methods in quantifying LLM uncertainty.