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Schema matching increasingly uses generative language models to rerank retrieved column candidates, although the underlying task is a bounded correspondence decision. We present JevNexus, which combines typed pairwise decisions with schema/instance evidence and invokes listwise refinement only when the evidence disagre...
The proposed AHEAD, an unsupervised anti-hallucination anomaly detection framework featuring a hallucination refinement pipeline and a temporal-structural detector, which is compatible with various T-GNN backbones for reliable anomaly detection on CTDGs.
Temporal graph neural networks (T-GNNs) are powerful for representation learning on continuous-time dynamic graphs (CTDGs), but naively applying them to unsupervised anomaly detection is often unreliable. The key challenge is learning normal dynamics while being exposed to unlabeled anomalies, which destabilizes optimi...
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