NegAS uses negative labels for attention guidance and sigmoid scoring to improve OOD detection in VLM-based object detectors while preserving ID performance.
Envisioning outlier exposure by large language models for out-of-distribution detection
3 Pith papers cite this work. Polarity classification is still indexing.
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Introduces MOOD benchmark for OOD LLM alignment failures and shows guard models plus Mahalanobis and perplexity OOD detectors improve recall from 39% to 45% with positive scaling.
N2NSC framework detects anomalies in text-attributed graphs by enforcing node-to-neighborhood semantic consistency via two complementary fusion paths that align textual semantics with topology.
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Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs
Introduces MOOD benchmark for OOD LLM alignment failures and shows guard models plus Mahalanobis and perplexity OOD detectors improve recall from 39% to 45% with positive scaling.