Recent anomaly detection studies have shown that residual features, which model the discrepancy between normal and abnormal patterns, provide strong generalization to anomalies unseen during training. However, existing residual-based approaches mainly rely on visual discrepancies derived from image representations, whi...
Seongsu Lee, Seunghyun Oh, Seungjae Moon et al.· 0 citations
GRIT, a training framework that incorporates visual grounding into RLVR to mitigate the impact of Correct-but-Visually-Misgrounded Reasoning (CVMR), is proposed and introduced, and experiments demonstrate that GRIT significantly improves both reasoning accuracy and grounding fidelity over existing RLVR methods.
Chanseul Cho, No-Chan Bae, Dongheon Lee et al.· 0 citations
Difficulty-Aware Chain-of-Thought (DA-CoT), a training-free adaptive framework that selectively triggers CoT based on the input's intrinsic difficulty, is proposed, finding that the entropy of the model's attention maps reliably reflects task difficulty: low entropy indicates confident, localized grounding, while high...
Chanseul Cho, Jae-Yun Jun, Dongheon Lee et al.· 0 citations
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