Abstract

Despite their strong generative capabilities, large language models frequently exhibit hallucinations, particularly due to outside-boundary confidence where incorrect assertions are produced with high statistical certainty. Existing approaches commonly use output probability as a proxy for truthfulness; however, this signal is confounded by epistemic uncertainty and cannot reliably distinguish genuine uncertainty from fabricated content. We argue that effective hallucination detection requires integrating surface-level confidence with signals that reflect the model’s underlying epistemic state. To this end, we propose Answer-level Intrinsic Cognition (AIC), a model-agnostic metric that captures epistemic boundary deviations by measuring answer-level stability across multiple stochastic forward passes. By coupling AIC with conventional output uncertainty, we derive a composite metric that disentangles within-boundary uncertainty from outside-boundary confidence. Across three public question-answering benchmarks and diverse model scales, the two-dimensional score consistently outperforms strong uncertainty-only baselines, with larger gains on adversarially constructed hallucination sets. The code is available at: https://github.com/HXYfighter/AIC-ACL2026.