DLIPS: Detecting Hallucinations in Large Language Models via Temporal Lipschitz Dynamics

Hallucinations undermine the reliability of large language models (LLMs). Existing hallucination detectors often rely on answer representations, token-level scores, or agreement across sampled responses, but such result-centric signals can obscure transient computational anomalies that emerge during generation and are no longer apparent in the final answer state. We therefore investigate whether hallucinations can be identified from abnormal dynamics in the model's local computational sensitivity, rather than solely from static or aggregated evidence after generation. We propose DLIPS (Dynamic LIPSchitz-based hallucination detection), a process-centric framework that uses temporal Lipschitz dynamics as a signal for response-level hallucination detection. DLIPS tracks local perturbation amplification across prefixes of a single candidate answer by estimating decoder-block Jacobian spectral norms, and summarizes their temporal and cross-layer variation for lightweight supervised classification. Experiments on HaluEval, RAGTruth, and MedHallu show that DLIPS outperforms the compared baselines, including an approximately 11% relative improvement in AUROC over the strongest baseline on HaluEval. Ablation studies show that removing temporal features causes the largest F1 decrease among the evaluated feature groups, indicating that temporal Lipschitz dynamics provide information beyond overall sensitivity magnitude. Additional evaluations on different models extend the framework across model scales and architectures. These results support temporal Lipschitz dynamics as a complementary signal to representation- and consistency-based approaches for hallucination detection. DLIPS:https://anonymous.4open.science/r/DLIPS-2EB4