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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...
Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing anomalous tokens within text. By providing fine-grained abnormality prediction, token-level text anomaly detection plays a critical role in various real-world applicatio...
This paper proposes a novel Partial label-based Self-training framework (PaSta) that leverages partial label learning technique to overcome the limitations of existing methods and designs a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and repres...
CORE introduces a decorrelated feature alignment module to directly align heterogeneous features into a unified representation space, which retains their semantic information, and formulates unified TAD as an in-context reconstruction problem, eliminating the need for labeled or synthesized 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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