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A Dual-evidence framework with adaptive Fusion and Aggregation (DiFA) for token-level anomaly detection, which derives anomaly scores from form-structural and semantic views to capture visible structural abnormality and contextual inconsistency, thereby providing complementary evidence for identifying diverse anomalies...
A D-LLM hallucination detection framework that formulates the Denoising trajectories as Multivariate Time Series over learnable latent variables (DeMTS for short) that outperforms existing hallucination detection methods while maintaining strong robustness, efficiency, and cross-task transferability.
TRE is a parameter-free and single-run metric that estimates hallucination risk directly from the entropy signals of a single generation, without requiring any detector training or repeated sampling, and enjoys strong generalizability, efficiency, and robustness.
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