OOD Detection

OOD: Out-of-Distribution

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10 papers in the last four weeks, up 11% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 145

Oct 7, 2026cs.LG

PairAudit: Guiding Human Review with Graph Tokens under Distribution Shift

Intrusion detectors can confidently misclassify attacks that were not seen during training. Human review can correct these errors, but only a limited number of cases can be checked. Uncertainty-based review may overlook confident errors, while anomaly scores alone do not show whether changing the review plan will correct more errors. We introduce PairAudit to find overlooked errors and improve review under a fixed budget. Its graph tokens capture prediction patterns across connected nodes. Rather than building another predictor through feature aggregation, PairAudit uses unusual relational patterns to uncover potential errors in existing predictions. Human feedback then helps decide whether these findings justify changing review priorities. Experiments across security tasks show that PairAudit corrects more errors on average than uncertainty-based review, including more errors on unseen attacks. These gains account for all review costs and do not require retraining the detector.

Jiran Tao, Binyan Jiang

The Hong Kong Polytechnic University

Network Intrusion Detection Black-Box Model Auditing Anomaly Detection OOD Detection

Oct 5, 2026cs.LG

ReMaD: Tuning-free Domain Adaptation for Classification and Out-of-Distribution Detection

We introduce Reduced-rank Mahalanobis Distance (ReMaD), a novel prototypical distance-based refinement to classification and out-of-distribution (OOD) detection using pretrained models without finetuning. We use embeddings of the target dataset to fit closed-form distribution statistics in the model's latent space which can classify in-distribution samples and detect OOD samples, all without training or prior knowledge of the OOD data. Building on prototype classification and OOD detection, we analyze the distribution properties of large pretrained models when processing new datasets; based on this analysis, we formulate a simple modification to Mahalanobis Distance to adapt models' latent space distributions to new domains by removing unused features, without the finetuning or hyperparameter searches required by other adaptation procedures. We demonstrate the efficacy of this method to adapt existing large pretrained image embedding models to new classification domains outside their trained capabilities by testing across four target datasets, with competitive performance in both classification and OOD detection.

Elijah Bolluyt, Cristina Comaniciu

Stevens Institute of Technology, Hoboken, NJ, 07030, USA

Prototype-Based Classification Mahalanobis Distance Transfer Learning Classification OOD Detection

Oct 2, 2026cs.AI

Benchmarking candidate coverage and rejection policy transfer in typed decision models

Rejection policies must remain useful as candidate sets and tasks change. We compare Laya, Jev and Qwen2.5-7B-Instruct using public reference labels, testing Laya/Jev policy transfer at equal calibration budgets and all three models on artificial omission, natural retrieval misses and public out-of-scope queries. Source calibration often fails to preserve the target operating point. A Jev policy calibrated on DBpedia rejects 69.3% of covered Emotion test inputs, while an Emotion policy loses detection entirely. Retrieval exposes a different tradeoff: with ten intent candidates, Laya detects 99.0% of out-of-scope queries but rejects 48.8% of covered queries. Separating missing-answer sources reveals these costs alongside retrieval coverage. The benchmark provides shared inputs, explicit decision and failure categories, and reproducible scoring to assess rejection policies under the conditions in which they are reused. Code and benchmark artifacts are available at https://github.com/luckykevvv/Decision_Model_Benchmark.

Jiawen Lu, Tongtong Wu

Monash University

LLM Evaluation Selective Prediction OOD Detection

Oct 1, 2026cs.LG

Robust Evidential Learning Through Latent Consistency

Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial inputs can induce confident but unreliable predictions. Evidential Deep Learning provides efficient uncertainty estimates in a single forward pass, but can still assign high evidential strength to inputs that are poorly supported by the learned representation, such as adversarial inputs. We introduce CLEAR, a lightweight, task-agnostic post-hoc method that improves evidential robustness without retraining or altering the base prediction. Using held-out calibration data, CLEAR characterises the group-conditioned geometry of the model's latent space. At inference, it efficiently generates perturbation views directly in the latent space and measures their conflict relative to the calibrated geometry of the predicted group. High latent conflict indicates unsupported evidence, which CLEAR uses to selectively reduce evidential strength while retaining evidence for latent-consistent inputs. On ImageNet

\rightarrow

CUB, CLEAR improves OOD and adversarial AUROC by

+8.29

and

+5.01

while running 17.4

\times

faster than competing post-hoc methods while preserving predictive performance across classification, regression, and object detection benchmarks.

Charmaine Barker, Daniel Bethell, Simos Gerasimou

Department of Computer Science, University of York, UK · Department of Elect. Eng., and Computer Science and Eng., Cyprus University of Technology, Cyprus

Evidential Deep Learning Adversarial Robustness OOD Detection Adversarial Example Detection

Oct 1, 2026cs.LG

Dataset Identity, Not Novelty: The Source of an Inflated OOD Detection Gain

A post-hoc out-of-distribution (OOD) detector reads the activations of a trained classifier and returns a score. It fits that score on in-distribution data, and the benchmarks that evaluate it supply a second piece of OOD data for the fitting itself. Some detectors tune a constant on it. Others fit a direction in feature space or train a flexible combiner and report the number that it reaches as the gain that is still available. Every such fit is validated on held-out samples of the same OOD dataset. That check rules out memorizing individual images. It says nothing about a fit that has instead learned which dataset it is looking at, and a direction that recognizes one OOD dataset rather than novelty passes it perfectly. The detector that a practitioner installs meets OOD data from a source that nobody fitted it on, so the difference decides what the reported number is worth. We measure it by holding out the whole OOD dataset rather than a sample of it, and we call that gap the inflation. We read it across a range of combiners on ImageNet and CIFAR-100 backbones. Most of the gain that the usual protocol reports turns out to be dataset identity rather than novelty. The size of the fit does not move what survives, so the effect is not ordinary overfitting. The share depends instead on whether the input exposes class identity, and two controls that vary that property alone separate the inflation on every backbone of both benchmarks. A closed form accounts for the effect and computes it from the fitting rows, so a practitioner can tell which fits will inflate without running the hold-out protocol. One of these fits survives, namely the single constant that the field already picks on a designated validation dataset. Anything above it reports a gain that the hold-out protocol does not return, and on one benchmark what survives falls while what is reported climbs.

Donghoon Lee, Shinjin Kang

Hongik University

Distribution Shift Detection OOD Detection Cross-Dataset Generalization

Sep 28, 2026cs.LG

A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion

Reliable deployment of graph neural networks requires calibration, out-of-distribution (OOD) detection, and robustness to distribution shift, yet existing methods address these needs with separate models and objectives. We model uncertain node embeddings as random graph signals: graph Fourier filters capture structural variation, and a scalar orthogonal-polynomial chaos coordinate captures latent stochastic variation. The resulting doubly-spectral stochastic (DSS) expansion supplies task-matched readouts from one representation: the mean coefficient encodes class evidence for the energy-based OOD score, the higher-order coefficients encode structured logit variation, and quadrature averaging over the chaos coordinate defines the single predictive distribution used for prediction and calibration. A capacity theorem shows that, under a full-rank feature assumption, a restricted subfamily matches the chaos coefficients of any Gaussian-latent random graph signal, with exponentially decaying truncation error under a growth condition; the task-level claims are established empirically. DSS-GNN has two deployment modes: standalone, or as a residual branch beside a deterministic encoder (DSS-Hybrid). Standalone DSS-GNN achieves the lowest Brier score among the compared uncertainty-aware baselines on all 14 node classification benchmarks without post-hoc correction; DSS-Hybrid achieves the best AUROC on most node-OOD settings, competitive cross-graph OOD detection, and the strongest shifted accuracy on all 7 GOOD concept-shift benchmarks under standard empirical risk minimization (ERM). Cross-evaluating both modes on all three tasks shows that each remains effective on the other's tasks, with documented exceptions, and yields explicit deployment guidance.

Fred Xu, Thomas Markovich, Florence Regol +1

Department of Computer Science University of California, Los Angeles · Block, Inc. · Mila, Quebec AI Institute

Graph Neural Networks Uncertainty Quantification OOD Generalization OOD Detection

Sep 17, 2026cs.LG

Local Sparsity Enables Unsupervised LLM Safety Detection

Deployment-time safety methods for large language models (LLMs) are predominantly supervised and assume access to unsafe training data. Nevertheless, new attacks and harm categories regularly arise, not captured by models trained in such a supervised fashion. An alternative approach is to view this problem through the lens of anomaly detection, namely, to rely solely on modeling safe data and flagging out-of-distribution inputs. However, LLM activations lie in a high-dimensional space, raising concerns about whether anomaly detection is statistically feasible. We show that, under the linear representation hypothesis (LRH), there may indeed be hope. In the LRH concept space, which is typically recovered via a sparse autoencoder (SAE), nearby points share a small common active support. Using this local sparsity insight, we propose a framework for locally masked SAE-based anomaly detection, supported by theoretical justifications. We validate it on various architectures and datasets, including both capability-testing datasets and safety-specific datasets. Finally, when we allow algorithms to use 1% out-of-distribution data for calibration, locally sparse methods achieve near-optimal performance, demonstrating their ability to capture meaningful safety information while using only 1-2% of SAE neurons for computation.

Xin Chen, Gil Kur, Alexander Shevchenko +1

ETH Zürich

Sparse Autoencoders LLM Safety Unsupervised Anomaly Detection OOD Detection

Sep 16, 2026cs.RO

RAFAIL: Relationship-Aware Failure Detection for Robotic Manipulation

Detecting failures during execution is essential for reliable robotic manipulation. Vision-language models (VLMs) can assess task outcomes semantically but add runtime computation, whereas out-of-distribution (OOD) detectors may respond to harmless scene variations rather than failure-relevant deviations. We introduce RAFAIL, a framework for detecting execution failures during robotic manipulation. RAFAIL identifies failures by detecting anomalies in task-relevant relationships between entities, such as a gripper and an object or an object and its target. By focusing OOD detection on relevant parts of the observation, RAFAIL reduces sensitivity to task-irrelevant scene variation. Offline, a VLM annotates successful demonstrations with task progress and relationship importance, which are used to learn point-cloud-based relationship representations without relying on policy-internal features. At runtime, relationship-specific OOD detectors evaluate these representations while relationship importance and task progress are predicted without VLM inference. RAFAIL requires no failure data and achieves 73.4% balanced accuracy across three real-world robotic manipulation tasks, outperforming the strongest evaluated OOD- and uncertainty-based baselines.

Loris Schneider, Edgar Welte, Rania Rayyes

Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology, 76131, Germany.

Robot Failure Detection Fault Detection Robotic Manipulation OOD Detection

Sep 14, 2026cs.LG

PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability

Out-of-Distribution (OOD) data poses a significant threat to machine learning models, often leading to model failure during deployment. All existing OOD detection methods are post-hoc, relying on evaluation metrics such as accuracy and AUC-ROC during inference to indirectly assess the model's response to OOD data by measuring deviations. In contrast to existing approaches, the proposed work shifts the paradigm from OOD detection to OOD prediction by proposing a pre-hoc anticipatory framework called PLSP for OOD prediction. We make several key contributions: (a) a dataset-independent metric called the CREDibility Score (CREDS) is proposed for OOD prediction; (b) credibility curves are introduced to study the maximum credibility a model can attain; and (c) credibility heat maps (and volume under surface) are introduced to characterize pre-hoc model behavior across different datasets. This work provides a novel perspective on signal processing under distributional shifts. Experiments across multiple datasets demonstrate that the proposed metric serves as a valuable measure for improving the robustness of machine learning models toward OOD prediction.

Vipul Bansal, Himanshu Buckchash, Balasubramanian Raman +1

University of Wisconsin Madison Madison, WI, USA · IMC University of Applied Sciences Krems, Austria · Indian Institute of Technology Roorkee Roorkee, India

Distribution Shift Robustness OOD Detection

Sep 8, 2026cs.CV

Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimation

Out-of-distribution (OOD) detection is crucial for safe deployment of medical AI systems, where domain shifts arise across institutions, acquisition protocols, and patient populations. VLMs enable zero-shot OOD detection by embedding images into a language-aligned latent space, where cross-modal similarity serves as a non-parametric confidence signal for identifying in-distribution samples. Yet existing methods rely almost exclusively on final-layer embeddings, implicitly assuming that the deepest representations are universally optimal. We first show that this assumption does not hold in medical imaging: intermediate layers provide complementary OOD signals, and the optimal representational depth depends on the respective image modality. While prior work selects layer combinations via entropy minimization of normalized histograms, we demonstrate that single-resolution entropy estimation is highly sensitive to binning choices, leading to performance variations of up to 19.3% AUROC. To address this instability, we propose a multi-resolution entropy estimation strategy that aggregates histogram statistics across multiple discretization scales, enabling robust and stable intermediate-layer selection. Across two medical OOD benchmarks, namely MIDOG and OASIS, covering distinct imaging modalities, diverse shift types, and different VLM backbones, our method consistently outperforms state-of-the-art approaches, offering a lightweight and stable solution for zero-shot OOD detection.

Shyam Nandan Rai, Francesco Di Salvo, Sebastian Doerrich +1

xAILab Bamberg, University of Bamberg, Bamberg, Germany

Vision-Language Models Medical VLMs OOD Detection

Sep 8, 2026cs.CV

SAM3-O2D2: Zero-Shot Object Out-of-Distribution Detection by Object Class Prompting of the SAM3-Image Model

Object detectors have shown remarkable performance in various fields, among these medical imaging, surveillance, and autonomous driving. However, they are prone to overconfidence when encountering unseen objects in real-world deployments, causing potential safety issues. To address this, detecting out-of-distribution (OOD) objects is essential for reliable object detection. Modern approaches leverage the broad semantic knowledge of foundation models such as CLIP for post-hoc few- and zero-shot OOD detection. However, these methods typically perform OOD assessment in feature space, which can be sensitive to object detector localization errors and variations in object appearance. Moreover, the current state-of-the-art (SOTA) zero-shot method performs computationally costly diffusion in inference. In this work, for our proposed zero-shot object OOD detection method SAM3-O2D2, we employ the SAM3-image foundation model in an efficient manner. Specifically, we prompt SAM3 only with the object detector's predicted classes and compare the predictions of the object detector and SAM3. An object is in-distribution (ID), if SAM3 also detects an object at the corresponding location. If SAM3 does not detect the prompted object, this indicates a mismatch between the detector's prediction and the image content, suggesting that the object is OOD. Experimental results show that our method significantly surpasses the so-far zero-shot SOTA method. Specifically, we achieve new SOTA AuROC and FPR95 metrics over both ID datasets Pascal-VOC and BDD100K and both OOD datasets MS-COCO and OpenImages.

Lucas Görnhardt, Timo Bartels, Tim Fingscheidt

Technische Universität Braunschweig, Institute for Communications Technology

Object Detection OOD Detection

Sep 7, 2026cs.CV

CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deployment

Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time adaptation (TTA) has emerged as a new paradigm for OOD detection, automatically adjusting detector behavior during deployment. However, such automatic adaptation mechanisms may raise safety concerns in safety-critical clinical environments. While physician oversight can mitigate these risks, it is resource-intensive and must be judiciously allocated. To reconcile safety with efficiency, we propose CHILD, a training-free framework designed to enhance streaming OOD detection via sparse human feedback. Operating under strict budget constraints, CHILD employs an adaptive risk-aware sample selection mechanism to pinpoint only the most decision-uncertain samples for review. Crucially, it maximizes the utility of this sparse feedback through a retrieval-based score calibration module, which refines model predictions using a compact feature cache without any parameter updates. Extensive experiments on four medical benchmarks demonstrate that CHILD turns limited supervision into significant reliability gains: with a sparse feedback budget of only 5%, it reduces the average FPR95 from 72.63% to 60.26% and improves AUROC from 75.53% to 81.85%, consistently outperforming state-of-the-art baselines. Our code is publicly available at https://github.com/figec/CHILD.

Jinlun Ye, Kaiyue Lu, Runhe Lai +3

School of Computer Science and Engineering, Sun Yat-sen Univerisity, Guangzhou,China · Peng Cheng Laboratory, Shenzhen, China · Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China +1

Sep 3, 2026cs.LG

Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification

Reliable operation of modern distribution networks requires timely identification of operational risks and anomalous events under pervasive uncertainty. In practice, operators must identify risks that are inherent in stochastic yet in-distribution conditions, and anomalies that correspond to out-of-distribution behaviors such as unusual load patterns, extreme weather or cyber-physical attacks. This paper addresses this joint risk and anomaly identification problem for optimal distribution network operation and proposes a deep reinforcement learning framework that is explicitly uncertainty aware. We integrate distributional and Bayesian deep reinforcement learning to realize a second- order uncertainty quantification scheme that decomposes total uncertainty into aleatoric and epistemic components, which are respectively used to characterize inherent risk and out-of- distribution anomalies. The resulting epistemic estimates drive both exploration during training and out-of-distribution detec- tion with fallback control during deployment, whereas aleatoric estimates are used to characterize intrinsic operational risk. Simulation results demonstrate the performance of our DRL agent and the effectiveness of the uncertainty quantification.

Ziqi Zhang

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China

Risk-Sensitive RL OOD Detection Power Systems

Sep 1, 2026cs.CV

Fi-ImageNet-1k: An OOD Benchmark From the Inside of the ImageNet-1k Validation Set

Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need images from outside the in-distribution (ID) data; typically, these are defined or collected in an ad hoc fashion. Since no ground truth is perfect, ID-labeled datasets themselves contain a natural source of OOD images. We exploit such annotation errors and present Fi-ImageNet-1k, an OOD dataset built from ImageNet-1k validation images that the recent ReImageNet reannotation effort assigned to no ImageNet-1k class. Each image was examined by expert human annotators supported by evidence from MLLMs, VLMs, and reverse image search, comparing it against all visually similar ID classes. We keep only images that could be assigned a specific class outside the ImageNet-1k label space. The resulting Fi-ImageNet-1k, with 655 images from 522 classes, is substantially more challenging than any commonly used OOD dataset. No evaluated combination of classifier and OOD detector achieves a false positive rate below 51% at 95% true positive rate (FPR@95). Compared to the recent NINCO, our dataset is 3.8x more challenging in the FPR@95 metric for state-of-the-art supervised OOD detection methods.

Ruslan Rozumnyi, Matěj Suchánek, Tomáš Vojíř +2

Visual Recognition Group · Department of Cybernetics · Faculty of Electrical Engineering +1

Sep 1, 2026cs.AI

Few-Shot Out of Domain Intent Detection with Covariance Corrected Mahalanobis Distance

Conversational agents like chatbots and voice assistants are trained to understand and respond to user intents. On encountering an utterance with an intent different from the ones they have been trained on, these agents are expected to classify the intent as unknown' or out of domain'. This problem is known as out of domain (OOD) intent detection. Podolskiy et al. (2021), showed that Mahalanobis distance can be used effectively for identifying OOD intents, outperforming competing approaches. However, their method fails to outperform the baselines in the practically important few-shot setting. In this paper we analyze the reason for low performance and propose a covariance corrected Mahalanobis distance for detecting out-of-domain intents.

Jayasimha Talur, Oleg Smirnov, Paul Missault

Amazon

Mahalanobis Distance Intent Classification Few-Shot Learning Classification OOD Detection

Sep 1, 2026cs.LG

Verdict Instability of OOD Scores under Reference Resampling

Post-hoc out-of-distribution detectors are fitted on a finite reference set, so every score they produce is an estimate. If we had chosen a different set, some verdicts would have moved. We measure that movement by resampling the reference set and recording the bootstrap standard deviation of the score, which we call verdict instability. It admits a closed form with no fitted parameters. The instability of a verdict is the within-class dispersion of the assigned class along the query's direction, divided by the square root of that class's reference count. That count is what separates verdict instability from the geometry of the score distribution, and it is identifiable only under class imbalance. Instability grows with the local dispersion. Far-OOD queries lie along the low-variance directions of an anisotropic embedding, so every distance-based score we test assigns its highest values to the verdicts that are most reproducible. Only estimators of local dispersion carry the sign a practitioner expects. We give a rule that predicts this sign for any score from a single label-free correlation, and abstention driven by a wrong-signed score turns out worse than abstention at random on every dataset we test.

Donghoon Lee, Shinjin Kang

Hongik University

Uncertainty Quantification Selective Prediction OOD Detection

Aug 17, 2026cs.CV

Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI

Corrupted, inconsistent, or anomalous data silently threatens the safety and reliability of medical AI. Despite growing regulatory recognition of dataset quality assurance (QA) for high-risk medical AI, scalable automated detection remains underdeveloped. We employ unsupervised anomaly detection (AD) and out-of-distribution (OOD) detection as an automated dataset QA mechanism for multi-center dynamic contrast-enhanced breast MRI. We build a controlled AD benchmark of 17 realistic QA-relevant anomaly types from six public datasets (protocol violations, processing errors, incorrect anatomical regions) and propose a taxonomy of radiological image anomalies based on human visual perception, enabling fine-grained analysis of AD failure modes. The benchmark includes near-, medium-far-, far-OOD samples, as well as in-distribution and external normal data. Four methods are evaluated: a projection-based method extended with a domain-specific feature extractor and a novel positional encoding, a reconstruction-based approach extended to full 3D volumes with an augmented training objective, and two unmodified hybrid OOD detection methods. Medium-far- and far-OOD samples are detected reliably, whereas near-OOD samples and external normal data from unseen institutions expose method-specific differences. The 3D reconstruction-based approach best balances detection performance (AUROC: 0.936) and generalization to unseen institutions. The projection-based method with positional encoding achieves the highest overall detection performance (AUROC: 0.954). Both hybrid methods exhibit critical failure modes, confirming that methods validated for one modality or anatomy may not generalize without domain-specific adaptation. Implants and mastectomies remain an open challenge for all methods. Our results establish a foundation and practical guidance on scalable unsupervised QA in medical AI pipelines.

Chiara Tappermann, Steffen Renisch, Lars Ole Schwen +3

Fraunhofer Institute for Digital Medicine MEVIS, Max-Von-Laue-Straße 2, Bremen, 28359, Bremen, Germany · University of Bremen, Bibliothekstraße 1, Bremen, 28359, Bremen, Germany · Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Carl-Neuberg-Str. 1, Hannover, 28359, Lower Saxony, Germany

Medical Imaging Medical Image Anomaly Detection Magnetic Resonance Imaging Unsupervised Anomaly Detection OOD Detection Medical Image Benchmarks

Aug 10, 2026cs.CV

A Convolutional Layer Activation Dimensionality Reduction for Out-of-Distribution and Adversarial Attack Detection Methods

Despite the success of convolutional neural networks in image classification tasks and their general application in multi-modal models, their susceptibility to out-of-distribution and adversarial attack samples raises concerns regarding trustworthiness and safety. Among the approaches to tackle such issues, detection methods that analyze the model's intermediate activations to estimate a confidence score are a promising family that evaluates the decision process, relying on a dimensionality reduction step to enable efficient downstream processing of the high-dimensional activations. However, when considering convolutional layers, the dimensionality reduction methods in the literature either lack a mechanism to control the compression/information-loss trade-off or yield large representations. In this paper, we carefully analyze two state-of-the-art detection methods and their dimensionality reductions for convolutional layers and develop a novel reduction method with a controllable high-compression level. We extend these two state-of-the-art detection methods, enabling the usage of any dimensionality reduction, and evaluate their performance on out-of-distribution and adversarial attack detection. Results show that the detection methods with the proposed dimensionality reduction consistently perform better than, or comparable to, the strongest alternative. Furthermore, the proposed method is shown to reduce computation and memory footprints, given that it has the highest compression among the compared methods.

Leandro de Souza Rosa, Lorenzo Capelli, Clara Nunes Barrancos +2

Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione “Guglielmo Marconi” (DEI) - Alma Mater Studiorum Università di Bologna, Bologna, Italy · Advanced Research Center on Electronic Systems “Ercole De Castro” (ARCES) - Alma Mater Studiorum Università di Bologna, Bologna, Italy

Convolutional Neural Networks Dimensionality Reduction OOD Detection Adversarial Example Detection

Aug 10, 2026eess.IV

Decodable but Not Accessible: Auditing Distance-Based Reliability Estimation on Disentangled Skin-Lesion Representations

Distance-based reliability estimation assumes that a representation's geometry reflects its trustworthiness, yet this assumption is rarely tested under training interventions that reshape geometry directly. We audit this assumption under domain-adversarial representation learning using a disentanglement dose-response ladder. Three checkpoint families share the same architecture and a 16-dimensional representation, differing only in orthogonality strength (lambda = 0, 1, 5). Representation geometry changed substantially with disentanglement strength: the condition number shifted by two orders of magnitude (Kendall tau = 0.84, exact p = 2.8e-5). This change was not accompanied by improved reliability estimation: Mahalanobis-distance AUROC (ISIC-test vs. PAD-UFES) remained flat and below chance (about 0.40) at every level, with no significant association with any of five geometry metrics tested. The same failure was observed for cosine-to-centroid and pooled k-nearest-neighbor scorers, plus three non-distance-based scorers: an energy-based confidence score, Virtual-Logit Matching, and a kernel density estimator. Seven of eight scorers converged on the same result; the energy-based score showed an isolated upward trend that we report but do not treat as evidence against the overall pattern. A supervised probe with no access to the training objective recovered domain membership from the identical embeddings at 0.72-0.81 AUROC across every level, showing that the relevant information was not absent from the representation. These findings indicate that classification performance alone can overlook whether information in a learned representation is organized in a form that downstream reliability estimators can use. Information can remain decodable while becoming largely inaccessible to non-probing reliability estimators.

Duc-Vinh Tran

School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hai Ba Trung, Hanoi 100000, Vietnam

Domain-Adversarial Learning Disentangled Representation Learning Mahalanobis Distance OOD Detection

Aug 10, 2026cs.LG

In-Context Density Estimation for Tabular Data

Density estimation underlies many unsupervised tasks on tabular data such as anomaly detection, out-of-distribution detection, and data augmentation. Although all these problems reduce to questions about where probability mass lies, they are typically solved individually by fitting a separate model to each dataset, with its own hyperparameters and tuning budget. We introduce ICED, an in-context, energy-based density estimator that removes this per-dataset cost. ICED is a transformer-based model pretrained once on a synthetic prior built specifically for density estimation under an objective that fits log-density where it is informative and preserves its ordering elsewhere. In the inference, it reads a dataset as context and returns an unnormalized log-density for any query point in a single forward pass, with no fitting, sampling, or hyperparameter selection. A single frozen ICED model then drives four tasks usually handled by four specialized pipelines: density estimation, out-of-distribution detection, unsupervised anomaly detection, and generative augmentation. Across all four, it is competitive with the strongest task-specific method, while being the only approach that needs no retraining, no tuning, and no labels to move between them. The code is available at https://github.com/gmum/iced.

Patryk Marszałek, Jacek Tabor, Marek Śmieja

Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland

Energy-Based Models Unsupervised Anomaly Detection OOD Detection Tabular ICL Density Estimation

Aug 10, 2026cs.LG

A Probabilistic Circuit-Induced Pseudo-Metric for Out-of-Distribution Detection

Probabilistic Circuits (PCs) are tractable generative models whose internal nodes encode a hierarchy of probabilistic sum- maries over different variable scopes. Existing PC-based out- of-distribution (OOD) detection methods ignore this hierar- chy, reducing the entire circuit to the scalar likelihood (or its uncertainty) computed at the root. We introduce Hierar- chical Likelihood Vector (HLV), a representation whose en- tries are the likelihoods associated with selected PC nodes and define the Hierarchical Likelihood Distance (HLD), a PC-induced pseudo-metric that compares the probability dis- tributions through the expectations of their HLVs. We show that HLD is an integral probability metric over a function class naturally induced by the PC and develop a principled goodness-of-fit hypothesis test for unsupervised OOD detec- tion. Unlike existing approaches, the trained PC alone serves as the representation of the in-distribution: no held-out in- distribution data are required at deployment. We further show that the quantities required by the hypothesis test can be com- puted exactly, directly from the trained circuit, yielding an ap- proximate analytic decision threshold. Experiments on tabular and MNIST datasets demonstrate that exploiting the hierarchi- cal probabilistic summaries encoded through the PC improve OOD detection over root-likelihood, uncertainty-, typicality- and kernel-based baselines, while naturally localizing distri- bution shifts to the PC nodes responsible for the shift.

Bhumika K, Vidhya S, Narayanan C Krishnan

Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Palakkad Palakkad, Kerala, India

Probabilistic Circuits OOD Detection Integral Probability Metrics

Aug 8, 2026cs.CV

Open-World Semantic Segmentation with Sensitivity Modeling

Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content. Conventional semantic segmentation models operate under a "closed-world" assumption, often producing overconfident misclassifications on novel content. We address open-world semantic segmentation, the joint task of segmenting known classes while detecting and grouping novel or anomalous content without additional supervision, by extending a dual-decoder baseline with a third, complementary decoder within a unified encoder-decoder design. The first decoder performs closed-set segmentation using Gaussian prototypes for known categories. The second uses contrastive feature learning to isolate unknown regions in embedding space. The third, our key contribution, is a sensitivity decoder that captures fine-grained texture irregularities and activation instabilities indicative of semantic uncertainty, which neither semantic prototypes nor contrastive norms can reliably detect. The three decoders provide genuinely complementary signals: class-level OOD distance in logit space, global feature energy in embedding space, and local activation instability across encoder scales. Experiments on Cityscapes and BDD-Anomaly show that our method improves anomaly segmentation and novel-class discovery while maintaining competitive closed-set accuracy, with gains of +2.4% AUROC and a 2.5 pp. reduction in FPR@95TPR on BDD-Anomaly over the baseline.

Anastasios Romanos Varvarigos, Nikos Giakoumoglou, Tania Stathaki

Department of Electrical and Electronic Engineering, Imperial College London, London, UK

OOD Detection Semantic Segmentation Anomaly Detection

Aug 8, 2026cs.LG

From Uncertainty to Failure Attribution: Self-Diagnosing Models for Failure Attribution under Distribution Shift

Distribution shift poses a significant challenge to the robustness of machine learning models, but the current solutions only aim to detect out-of-distribution (OOD) samples and predict uncertainty levels. We introduce a problem setting for failure attribution under distribution shift, which enables the models not only to detect OOD samples, but also to find out the reason for their failure. The solution we propose is called self-diagnosing models, which are capable of jointly learning predictive output, predictive uncertainty, and a failure attribution signal. In particular, we use the failure attribution vector, produced by a neural network, which provides a structured representation of predictive unreliability by distinguishing four different types of failures: covariance shift, semantic shift, noise corruption, and adversarial perturbation. In other words, we move from scalar uncertainty towards failure identification. For training the model, we introduce a consistency regularizer that encourages consistency between uncertainty and failure attribution predictions. Moreover, to be able to evaluate the model on its ability to find the reasons for failure, we construct several distribution shift benchmarks with predefined mechanisms for generating distribution shifts.

Yiyao Yang

Columbia University

Distribution Shift Robustness Uncertainty Quantification OOD Detection

Aug 5, 2026cs.LG

Comparing SGLD and a fixed-noise Predictor-Corrector adaptation in canonical Joint Energy-Based Models on CIFAR-10

Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection. Canonical JEM training relies on stochastic gradient Langevin dynamics (SGLD); a theoretically motivated alternative, the Predictor-Corrector (PC) sampler, has not previously undergone a systematic replication test on the canonical model. We reproduce canonical JEM on WideResNet-28-10 without normalisation layers on two independent runs and test a fixed-noise PC adaptation - with the degenerate annealed-noise predictor replaced by a deterministic gradient step - across three protocols: the adapted sampler replacing SGLD throughout the full training trajectories (115-132 epochs); cold-start generation (FID); and refinement-style multi-OOD detection (AUROC). The reconstruction reaches 92.88% test accuracy and buffer-FID 44.46 (canonical: 92.9% and 38.40). We document two failure modes: catastrophic late-training divergence with the signature of the canonical outlier-buffer mechanism (all four runs), and run-dependent SVHN OOD-discrimination dynamics. No consistent method-level advantage of the adaptation over SGLD is observed on any protocol: refinement AUROC differences stay below 0.007 across ten checkpoint-OOD pairs; seeded cold-start generation favours SGLD by about five FID points; on the training protocol a hierarchical seed-by-image bootstrap gives a 95% confidence interval on the macro-averaged AUROC difference that contains zero, while a seed-level equivalence test with two runs per method cannot establish formal equivalence. The training-protocol data are consistent both with equivalence and with a small directional effect. This outcome is consistent with theory: the guarantees of the annealed-noise PC framework do not transfer to the constant-noise regime of canonical JEM.

Dmytro Knopov

Faculty of Computer Sciences, Department of Mathematics, National University of Kyiv-Mohyla Academy, Kyiv, Ukraine.

Stochastic Gradient Langevin Dynamics Langevin Dynamics Energy-Based Models OOD Detection Image Classification

Aug 4, 2026cs.CV

Open-World Hierarchical Perception: Taxonomic Abstraction over Class-Agnostic Proposals for the Safe Handling of Out-of-Vocabulary Road Objects

A closed-set detector for autonomous driving must assign every object one of a fixed set of labels. On an object outside that set (a horse-drawn carriage, road debris, livestock on a rural road) it can only force a confident but wrong specific label or drop the object. Prior work in this series replaced the flat label set with a hierarchical taxonomy and a runtime abstraction rule, but evaluated it only on the boxes a closed detector already produces. This paper takes the layer open-world: we place taxonomic abstraction on top of class-agnostic region proposals so objects the closed detector never boxes can still be classified or flagged; we report a feasibility study of three open-world signals (class-agnostic segmentation, appearance-based out-of-distribution scoring, monocular depth) that shows why no single 2D cue suffices and how they compose; and we run the evaluation the earlier papers could not, a ground-truth leave-classes-out benchmark on real annotated objects. Holding out seven COCO classes and classifying their 235 ground-truth crops, a flat closed head emits a confident wrong specific label 100% of the time (37% of them in the wrong super-category, e.g. an animal named as a vehicle), whereas the hierarchical layer emits zero confident wrong specific labels and safely handles 94% of the objects (a correct super-category, or an explicit UNKNOWN OBSTACLE). We are explicit that this is a safety result, not a specificity one: the correct super-category is recovered only 26% of the time and the remaining 69% are conservatively flagged unknown. The contribution is an open-world perception layer that never makes a confident categorical mistake on an out-of-vocabulary object, together with an honest account of its cost.

Felix Schaller

Dubai, UAE / Munich, Germany

Open-Vocabulary Object Detection Hierarchical Classification Autonomous Driving Safety Evaluation Object Detection OOD Detection

Aug 4, 2026cs.CR

FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks

Personalized Federated Learning (PFL) has emerged as a promising solution for intrusion detection in heterogeneous IoT environments, as it can improve local adaptation under highly Non-Independent and Identically Distributed (non-IID) data distributions. However, existing PFL methods often rely on client-side self-adjustment, which may lead to over-personalization and substantial degradation in out-of-distribution (OOD) attack detection. In this paper, we propose Federated Bandit Intrusion Detection (FBID), a novel adaptive PFL framework to address this limitation through server-side personalization control. In particular, FBID employs a contextual multi-armed bandit at the server to dynamically regulate each client's local training intensity according to its observed behavior and update quality. Moreover, FBID introduces a trust-based blending mechanism to derive client-specific interpolation coefficients between the global and local models, thereby preserving global attack-detection knowledge while still allowing beneficial local specialization. Through extensive experiments on the CICIoT2023 dataset under heterogeneous client distributions and OOD stress-test settings, we show that FBID improves individual client OOD Detection Rate (DR) by up to 7.66% and F1-Score (F1) by up to 5.08% (relative) over the strongest stable baseline, while also improving robustness to previously unseen attack classes.

An Khanh Bui, Cong Thanh Nguyen, Hoang-Anh Pham +2

UTS-HCMUT JTIRC, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City 700000, Vietnam. · Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City 700000, Vietnam. · School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.

Network Intrusion Detection Internet of Things Non-IID Federated Learning OOD Detection Federated Learning

Aug 3, 2026cs.MA

SABRE: A Multi-Agent Approach for Selecting Out-of-Distribution Detectors Under a Budget

Post-hoc out-of-distribution (OOD) detection for vision-language models assumes that a detector chosen on a benchmark stays reliable once deployed. We show this fails across domains: on a single frozen encoder, a detector that leads in one domain can invert in another, scoring in-distribution inputs as more anomalous than genuine outliers, and the best detector changes from domain to domain, so no fixed choice is reliable throughout. We introduce SABRE (Selective Agentic Budgeted Reliability Ensemble,) which replaces this fixed choice with per-regime selection at inference. Three language-model agents reason over a library of post-hoc detectors under a bounded query budget: a Selector chooses which detector to consult next, a Reporter consolidates the evidence for each input, and an Analyst calibrates detector reliability on a small labeled sample held out from the deployment domain and disjoint from the test data, weighting selection and aggregation without ever observing a scored input's label. The library includes four multimodal density detectors we propose. Inferring the operating regime from data, SABRE tracks the strongest detector in each domain without prior knowledge of it, recovering reliable detection where a conventional detector inverts and converging to that detector where it is sound. A component analysis shows the agents are complementary: the Reporter's feedback yields consistent gains, and the Analyst's calibration is decisive against inversion, ruling out unreliable detectors so that aggregation no longer cancels the sound ones. Since no fixed rule can be trusted across domains, reliability must be established at deployment rather than assumed from a benchmark, and SABRE shows this can be done automatically.

Mary Wisell, Salimeh Sekeh

San Diego State University

Distribution Shift OOD Detection

Aug 2, 2026cs.CV

ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation

Reliable decision support in digital agriculture requires not only accurate predictions but also well-calibrated uncertainty estimates, particularly for dense prediction tasks such as semantic segmentation. Ensembles provide strong uncertainty quantification but are computationally and memory demanding, while single-model approximations often sacrifice uncertainty quality. We propose ST-LoRA, a parameter-efficient ensemble that builds diverse members from a single training trajectory by combining Low-Rank Adaptation (LoRA) with snapshot ensembling. All members share a frozen pretrained backbone and differ only in lightweight low-rank adapters, which sharply reduces trainable parameters, checkpoint storage, and I/O overhead. We evaluate SegFormer, Mask2Former, and EoMT on GrowliFlower-L (cauliflower, open field) and BUP20 (sweet pepper, glasshouse), covering in-distribution performance, calibration under covariate shift, and near- and far-out-of-distribution (OoD) detection, with BUTom21 (tomato) as near-OoD data. Extensive ablations show that feed-forward layers, not attention projections, are the critical LoRA target for dense prediction, and that the scaling ratio

α/r

governs an accuracy--calibration trade-off. Against full-rank snapshot ensembles, ST-LoRA is competitive in segmentation quality, with architecture-dependent training time and energy savings. Against MC Dropout, DDU, and six post-hoc calibrators, it achieves the strongest far-OoD image-level detection and near-OoD pixel-level localization with low cross-seed variance, although full-rank ensembles remain better calibrated. These results show that LoRA-based ensembling offers a compelling efficiency--performance trade-off for agricultural vision systems.

Mohamed Farag, Genc Hoxha, Yahia Maleki +2

Machine Learning in Agriculture Lab, Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany · Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia · Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Bonn, Germany

Image Segmentation Ensemble Learning Low-Rank Adaptation Deep Ensembles OOD Detection Segmentation Uncertainty Estimation

Aug 2, 2026cs.CV

Training-Free Out-of-Distribution Detection for Pathology Whole-Slide Images

Safe deployment of AI methods in medicine requires robust guardrails that detect when input data deviate from the training distribution to ensure that models provide predictions only within their scope of expertise and abstain otherwise. Out-of-distribution (OOD) detection can provide such safeguards and is extensively studied in general computer vision. Yet, it remains underdeveloped in computational pathology, where gigapixel whole-slide images (WSIs), subtle differences between disease subtypes, and variability in tissue preparation pose unique challenges for conventional OOD methods. We propose ZIO, a training-free, multimodal OOD detector for pathology WSIs that leverages vision--language pathology foundation models (FMs). ZIO constructs text and visual prototypes of in-distribution classes and integrates their complementary information through a prototype shrinkage mechanism to derive OOD scores. We provide the ZIO formulation for both slide- and patch-level FMs. We evaluate ZIO across diverse clinically relevant domain shifts, including rare diseases and near-OOD settings. Extensive evaluation of over 14,700 WSIs from five independent consortia shows that ZIO consistently outperforms both unimodal prototypes and 40 state-of-the-art OOD methods. These results demonstrate the benefits of multimodal representation for OOD detection and pave the way towards safer AI deployment in clinical practice.

Sabri Mustafa Kahya, Richard R. Chen, Muhammet Sami Yavuz +4

Computational Pathology OOD Detection Whole-Slide Image Analysis

Aug 2, 2026cs.LG

Breaking Diversity Collapse in Spiking Pseudo-Ensembles for Efficient OOD Detection in Remote Sensing

Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains challenging. Deep ensembles provide strong predictive uncertainty, yet require multiple complete models and backbone evaluations. We propose an efficient spiking pseudo-ensemble that attaches multiple lightweight classification heads to a frozen SNN backbone. Naively training these heads with cross-entropy can lead to diversity collapse, where independently parameterized heads may produce correlated predictions. To address this, we introduce an agree--disagree objective that preserves correct predictions on clean in-distribution samples while encouraging diversity on structured, uncertainty-inducing transformations of the same inputs. This provides a diversity-promoting training signal without requiring external OOD data. Experiments with Spikformer and ResNet19-SNN on EuroSAT demonstrate consistent improvements over conventionally trained pseudo-ensembles. Using three backbones with five heads each matches or improves upon a five-model deep ensemble on UCM and AID, while requiring approximately 38% fewer parameters and 40% fewer backbone evaluations. These results show that explicit diversity promotion can recover useful ensemble-style uncertainty at substantially lower deployment cost.

Srinivas Anumasa, Rushi Shah, Qiran Zou +1

National University of Singapore

Efficient Neural Network Inference Ensemble Learning Remote Sensing Spiking Neural Networks OOD Detection Ensemble Diversity