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Cool Papers - Topic

::: tip If you subscibe to [arXiv Paper queryed by Detection](https://papers.cool/arxiv/search?highlight=1\&query=Detection), where the URL is `https://papers.cool/arxiv/search?highlight=1&query=Detection`, extract the part `https://papers.cool/` to the end, and use it as the parameter to fill in. Therefore, the route will be [`/papers/query/Detection`](https://rsshub.app/papers/query/Detection). ::: | Category | id | | ----------------------------------- | ------------------ | | arXiv Paper queryed by Detection | query/Detection | | arXiv Paper queryed by Segmentation | query/Segmentation |

https://papers.cool
https://rsshub.app/papers/query/Detection (RSS订阅地址)

Structured detection beyond spatial imaging

<a href="https://papers.cool/Detection/2609.26690">[Kimi]</a><p>In a point-scanning microscope, detection through a pinhole improves resolution and replacing the pinhole with a detector array provides redundant encoding of the object spectrum, enabling correction of nonidealities in the imaging system or further resolution improvements, for instance. Here, we show that the mathematical principles underlying structured detection are not intrinsically spatial and therefore structured detection may be used for imaging distributions in other physical domains, such as time, frequency, and velocity space. We numerically illustrate this generality by applying structured detection to an electromagnetic distribution in time using four-wave mixing (FWM). The resulting image reconstructions outperform the temporal equivalent of confocal detection, even as the FWM models are made more realistic and shot noise is considered.</p>

2026/9/22
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Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection

<a href="https://papers.cool/Detection/2609.03416">[Kimi]</a><p>LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited context capacity and one-sided discrepancy detection of existing single-agent LLM paradigms lead to an inferior recall performance in detecting discrepancies. In this paper, we propose Dude, the first Dual-Detection Multi-Agent System for paper-code discrepancy detection. We discover that the granularity asymmetry of the paper-language and code-language introduces over-interpretation and over-reporting challenges in a multi-agent system design for discrepancy detection, resulting in increasing false positives. To address this, we propose a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude, which effectively prevents agents from falsely reporting discrepancies. Experimental results in real-world paper-code discrepancy datasets showcase Dude's significant recall and precision improvement by up to 22.8%, increasing F1 score by up to 18.7% compared to baseline methods.</p>

2026/9/3
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Detection coherence of tests

<a href="https://papers.cool/Detection/2608.11177">[Kimi]</a><p>There exist tests calibrated under a null narrower than the one implied by their test statistic -- the detection-null set. The part of the detection-null set not in the null is the test's blind spot. A framework for assessing detection coherence -- whether a test's blind spot is empty -- is introduced, built on new concepts of calibration statistic, detector, detection functional, detection-null set, and blind spot. It is demonstrated that the Wilcoxon--Mann--Whitney, Kruskal--Wallis, Friedman, and logrank tests are detection incoherent as unrestricted tests. Detection incoherent tests may become coherent under restrictions that make the blind spot empty, though domain restriction is not a reliable route to coherence in practice. The Kolmogorov--Smirnov and Zaremba tests, and the Maximum Mean Discrepancy test with a characteristic kernel, are shown to be universally detection coherent. Due to the blind spot, a detection incoherent test misses discoveries when non-rejecting and yields spurious discoveries when rejecting, in both cases regardless of sample size. Detection incoherent tests should be abandoned.</p>

2026/8/11
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Extending Detection Engineering to Digital Forensics: The Velociraptor Unified Detection-Forensics Methodology

<a href="https://papers.cool/Detection/2606.28812">[Kimi]</a><p>Detection engineering and digital forensics have evolved in parallel rather than in partnership, leaving a gap between real-time alerting and forensic analysis. This paper develops a unified detection-forensics methodology using Velociraptor, where detection logic directly initiates targeted evidence acquisition at the point of detection. The contribution is threefold: (1) a four-stage methodology (baseline establishment, evidence correlation, attack chain analysis, and scenario labelling with confidence) that converts artefact knowledge into reusable and testable detection rules suitable for both post-incident triage and live monitoring; (2) a practical demonstration, using three Velociraptor BaseVQL log sources (/forensics/windows/prefetch, /forensics/windows/usn, and /windows/wmi) that practitioners can deploy today, showing that artefact-based detections enable scalable forensic triage without full disk acquisition; and (3) evidence that periodic artefact analysis offers continuous monitoring while substantially reducing data volume compared to conventional endpoint logging. Two case studies illustrate the approach: a Prefetch/USN baseline for triage when Windows Event Logs are cleared or unavailable, and a WMI persistence correlation supporting both triage and continuous monitoring through periodic artefact analysis.</p>

2026/6/27
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Analyzing Training-Free Corruption Detection for Object Detection Datasets

<a href="https://papers.cool/Detection/2606.10666">[Kimi]</a><p>Annotation errors are widespread in computer vision datasets and can significantly degrade the performance of systems trained on them, particularly in complex tasks such as object detection. Several approaches exist to identify annotation errors, including training-free feature-space methods which provide a fast and interpretable way to analyze annotations. However, the behavior on object detection annotations, which include semantic and spatial information, remains largely unexplored. In this work we analyze the applicability of feature-space-based approaches for detecting annotation errors in object detection datasets. By adapting an existing feature-space method, we show that such approaches reliably expose semantic mislabel, while positional errors remain difficult to detect. We evaluate this behavior across multiple pretrained embedding models, synthetic noise types (symmetric, asymmetric, and positional), and real-world annotation errors using VOC2012 and KITTI. All code and real-world corruptions are publicly available at the following repository: https://github.com/ ChristianSieberichs/BoundingBox\_corruption\_detection</p>

2026/6/9
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Neural Encoding Detection is Not All You Need for Synthetic Speech Detection

<a href="https://papers.cool/Detection/2604.16700">[Kimi]</a><p>This paper reviews the current state and emerging trends in synthetic speech detection. It outlines the main data-driven approaches, discusses the advantages and drawbacks of focusing future research solely on neural encoding detection, and offers recommendations for promising research directions. Unlike works that introduce new detection methods or datasets, this paper aims to guide future state-of-the-art research in the field and to highlight the risk of overcommitting to approaches that may not stand the test of time.</p>

2026/4/17
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Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection

<a href="https://papers.cool/Detection/2605.02918">[Kimi]</a><p>Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are often chosen to minimize the reconstruction error on normal samples. In this paper, we reveal a trade-off between reconstruction quality and anomaly detection among $β$-VAE models. Models with constrained latent space reach higher detection metrics but lower reconstruction quality. We also assess the performance variability across random seeds and show it is linked to the distance between normal and abnormal latent distributions. From this analysis, we justify and investigate two methods to mitigate the reconstructiondetection tradeoff: beta-scheduling and the Sparse VAE. The latter especially shows an improvement in detection while maintaining high reconstruction quality.</p>

2026/4/9
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NuNext: Reframing Nucleus Detection as Next-Point Detection

<a href="https://papers.cool/Detection/2603.07098">[Kimi]</a><p>Nucleus detection in histopathology is pivotal for a wide range of clinical applications. Existing approaches either regress nuclear proxy maps that require complex post-processing, or employ dense anchors or queries that introduce severe foreground-background imbalance. In this work, we reformulate nucleus detection as next-point prediction, wherein a multimodal large language model is developed to directly output foreground nucleus centroids from the input image. The model is trained in two stages. In the supervised learning stage, we propose spatial-aware soft supervision to relax strict centroid matching and a chain-of-visual-thought strategy to incorporate visual priors that facilitate coordinate prediction. In the reinforcement fine-tuning stage, we design distribution matching reward, low-variance group filtering, and fine-grained advantage shaping to further improve the model's detection quality. Extensive experiments on nine widely used benchmarks demonstrate the superiority of our method. Code will be released soon.</p>

2026/3/7
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ACORN-IDS: Adaptive Continual Novelty Detection for Intrusion Detection Systems

<a href="https://papers.cool/Detection/2602.07291">[Kimi]</a><p>Intrusion Detection Systems (IDS) must maintain reliable detection performance under rapidly evolving benign traffic patterns and the continual emergence of cyberattacks, including zero-day threats with no labeled data available. However, most machine learning-based IDS approaches either assume static data distributions or rely on labeled attack samples, substantially limiting their applicability in real-world deployments. This setting naturally motivates continual novelty detection, which enables IDS models to incrementally adapt to non-stationary data streams without labeled attack data. In this work, we introduce ACORN-IDS, an adaptive continual novelty detection framework that learns exclusively from normal data while exploiting the inherent structure of an evolving unlabeled data stream. ACORN-IDS integrates a continual feature extractor, trained using reconstruction and metric learning objectives with clustering-based pseudo-labels, alongside a PCA-based reconstruction module for anomaly scoring. This design allows ACORN-IDS to continuously adapt to distributional shifts in both benign and malicious traffic. We conduct an extensive evaluation of ACORN-IDS on five realistic intrusion datasets under two continual learning scenarios: (i) Evolving Attacks and (ii) Evolving Normal and Attack Distributions. ACORN-IDS achieves, on average, a 62% improvement in F1-score and a 58% improvement in zero-day attack detection over the state-of-the-art unsupervised continual learning baseline. It also outperforms existing state-of-the-art novelty detection approaches while exhibiting near-zero forgetting and imposing minimal inference overhead. These results demonstrate that ACORN-IDS offers a practical, label-efficient solution for building adaptive and robust IDS in dynamic, real-world environments. We plan to release the code upon acceptance.</p>

2026/2/7
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From Out-of-Distribution Detection to Hallucination Detection: A Geometric View

<a href="https://papers.cool/Detection/2602.07253">[Kimi]</a><p>Detecting hallucinations in large language models is a critical open problem with significant implications for safety and reliability. While existing hallucination detection methods achieve strong performance in question-answering tasks, they remain less effective on tasks requiring reasoning. In this work, we revisit hallucination detection through the lens of out-of-distribution (OOD) detection, a well-studied problem in areas like computer vision. Treating next-token prediction in language models as a classification task allows us to apply OOD techniques, provided appropriate modifications are made to account for the structural differences in large language models. We show that OOD-based approaches yield training-free, single-sample-based detectors, achieving strong accuracy in hallucination detection for reasoning tasks. Overall, our work suggests that reframing hallucination detection as OOD detection provides a promising and scalable pathway toward language model safety.</p>

2026/2/6
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