Halt the Hallucination: Decoupling Signal and Semantic OOD Detection Based on Cascaded Early Rejection

• URL: http://arxiv.org/abs/2602.06330v1
• Date: Fri, 06 Feb 2026 02:55:35 GMT
• Title: Halt the Hallucination: Decoupling Signal and Semantic OOD Detection Based on Cascaded Early Rejection
• Authors: Ningkang Peng, Chuanjie Cheng, Jingyang Mao, Xiaoqian Peng, Feng Xing, Bo Zhang, Chao Tan, Zhichao Zheng, Peiheng Li, Yanhui Gu,
• Abstract summary: We propose the Cascaded Early Rejection (CER) framework, which realizes hierarchical filtering for anomaly detection via a coarse-to-fine logic.<n> Experimental results demonstrate that CER not only reduces computational overhead by 32% but also achieves a significant performance leap on the CIFAR-100 benchmark.
• Score: 7.227431306238601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient and robust Out-of-Distribution (OOD) detection is paramount for safety-critical applications.However, existing methods still execute full-scale inference on low-level statistical noise. This computational mismatch not only incurs resource waste but also induces semantic hallucination, where deep networks forcefully interpret physical anomalies as high-confidence semantic features.To address this, we propose the Cascaded Early Rejection (CER) framework, which realizes hierarchical filtering for anomaly detection via a coarse-to-fine logic.CER comprises two core modules: 1)Structural Energy Sieve (SES), which establishes a non-parametric barrier at the network entry using the Laplacian operator to efficiently intercept physical signal anomalies; and 2) the Semantically-aware Hyperspherical Energy (SHE) detector, which decouples feature magnitude from direction in intermediate layers to identify fine-grained semantic deviations. Experimental results demonstrate that CER not only reduces computational overhead by 32% but also achieves a significant performance leap on the CIFAR-100 benchmark:the average FPR95 drastically decreases from 33.58% to 22.84%, and AUROC improves to 93.97%. Crucially, in real-world scenarios simulating sensor failures, CER exhibits performance far exceeding state-of-the-art methods. As a universal plugin, CER can be seamlessly integrated into various SOTA models to provide performance gains.

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