NAGL: Normal-Abnormal Generalist Learning

• Normal-Abnormal Generalist Learning (NAGL) is a comprehensive framework that models, detects, and generalizes both normal and abnormal patterns across diverse domains.
• It integrates residual, attention, and contrastive mechanisms to extract transferable representations for effective few-shot and zero-shot anomaly detection.
• NAGL supports fine-grained anomaly localization and interpretability, enabling applications from medical imaging to industrial automation.

Normal-Abnormal Generalist Learning (NAGL) is a framework in machine learning that unifies the modeling, detection, and generalization of normality and abnormality across diverse domains and tasks. Its central premise is to develop models that are not only capable of learning typical (normal) patterns but are also adept at identifying, reasoning about, and localizing abnormalities—even in settings where anomalies are rare, scarce, or only partially labeled. NAGL approaches have demonstrated broad applicability in fields such as computer vision, medical imaging, signal processing, federated systems, and graph analytics, and are increasingly engineered for cross-domain and zero-shot generalization.

1. Foundational Principles and Problem Setting

NAGL extends beyond the standard anomaly detection paradigm by incorporating both normal and abnormal knowledge for generalist (domain-agnostic) applicability. Unlike classic one-class or meta-anomaly learning approaches—where models are trained only on normal samples and must generalize to unobserved anomaly types—NAGL frameworks are explicitly designed to:

• Leverage both normal and, when available, abnormal data as reference distributions (Wang et al., 1 Oct 2025).
• Learn transferable representations that robustly distinguish between prototypical and atypical patterns across domains (Zhu et al., 11 Mar 2024, Wang et al., 3 Apr 2025, Song et al., 21 Aug 2025).
• Apply in cross-domain, few-shot, and zero-shot scenarios, often with no retraining required for new datasets (Niu et al., 18 Oct 2024, Zhu et al., 11 Mar 2024).
• Support fine-grained localization and interpretation of anomalies in structured and unstructured data (Lee et al., 2018, Liu et al., 2021, Song et al., 21 Aug 2025).

The overarching objective is not merely to detect known anomaly categories, but to anchor interpretation and generalization firmly in the learned contrast between normality and abnormality.

2. Core Methodological Components

A variety of architectures and algorithmic techniques underpin NAGL frameworks. Key components include:

• Residual and Contrastive Learning: Residual learning forms the backbone of many NAGL methods, wherein differences between normal and abnormal samples (or between query and reference distributions) are explicitly modeled as residuals in feature space (Wang et al., 1 Oct 2025, Zhu et al., 11 Mar 2024, Song et al., 21 Aug 2025). Patch-level or global residuals—often based on cosine or Euclidean similarity—serve as the principal signal for anomaly scoring.
• Attention and Cross-Attention Mechanisms: Attention layers are used to refine and focus the anomaly representation, such as residual proxies mined from abnormal reference samples (RM module) or instance-aware anomaly features aggregated in AFL modules (Wang et al., 1 Oct 2025).
• Normality Learning: Methods like NLGAD enhance anomaly detection on graphs by learning robust normal patterns via multi-scale contrastive learning, using a hybrid strategy for selecting reliable normal nodes and refining the model on these pseudo-labels (Duan et al., 2023).
• Latent Attribute Predictability: In graph scenarios, NAGL methods such as UNPrompt score nodes as anomalous if their latent feature representations cannot be accurately predicted from their neighborhood, using learned prompts and coordinate-wise normalization to permit zero-shot transfer (Niu et al., 18 Oct 2024).
• Physics-Informed and Domain Adaptive Decomposition: In spatio-temporal or physical systems (e.g., D²-SONATA), anomaly value fields are learned by decomposing observed transport or imaging processes into normal and abnormal parameterizations, ensuring physical constraints are preserved in the process (Liu et al., 2021).
• Vision-Language Fusion: Models such as Ano-NAViLa introduce interpretability and robustness by integrating both normal and abnormal textual pathology knowledge into vision-LLMs (VLMs), using cosine similarities for fine-grained anomaly scoring and contrastive loss to enforce separability of lesion types (Song et al., 21 Aug 2025).

These components are often adapted and combined, depending on domain constraints (e.g., availability/scarcity of reference anomaly data, nature of input signals, granularity of required localization, etc.).

3. Advances in Cross-Domain, Few-Shot, and Zero-Shot Generalization

A defining challenge addressed by NAGL systems is out-of-distribution anomaly detection and transfer:

• Few-shot and In-context Prompting: InCTRL trains a single detector on a source domain but infers anomalies on diverse unseen datasets by leveraging a handful of normal prompts, computing patch-level and global residuals with respect to these exemplars (Zhu et al., 11 Mar 2024). This tightly couples generalization to the capacity of the backbone (e.g., pre-trained CLIP) and the discriminativeness of residual features.
• Zero-shot Graph Anomaly Detection: UNPrompt demonstrates that pre-training on a single graph followed by unified latent normalization and prompt-based neighborhood prediction enables robust anomaly scoring on entirely new graphs without retraining or fine-tuning (Niu et al., 18 Oct 2024).
• Leveraging Both Label Noise and Domain Shifts: In Noise-Aware Generalization (NAG), DL4ND improves discriminative capacity by identifying label noise through cross-domain encoding rather than simply aggregating domain- or label-specific cleaning (Wang et al., 3 Apr 2025).
• Residual Mining with Limited Abnormal Data: NAGL further innovates by extracting abnormal prototypes from few-shot abnormal examples, minimizing overfitting and suppressing false activations associated with direct abnormal reference matching (Wang et al., 1 Oct 2025).

4. Empirical Evaluation and Benchmarking

NAGL methods are evaluated extensively across vision, graph, medical, and signal processing datasets:

• Vision Benchmarks: NAGL approaches outperform state-of-the-art in industrial inspection (MVTec-AD, VisA), medical imaging (BrainMRI, Camelyon16), semantic detection (CIFAR-10, MNIST), and even narrative report generation (IU-Xray, MIMIC-CXR) (Wang et al., 1 Oct 2025, Song et al., 21 Aug 2025, Shu et al., 6 Sep 2024, Zhu et al., 11 Mar 2024).
• Graph Benchmarks: On unsupervised GAD benchmarks (Cora, UAI2010), NLGAD and UNPrompt show consistent AUC gains (up to 5.89% on UAI2010) due to improved normality learning and generalist prompt strategies (Niu et al., 18 Oct 2024, Duan et al., 2023).
• Signal and Time Series: In gravitational wave data, ABNORMAL circumvents the need for real data in training by learning only from simulated stationary noise, allowing anomaly detection without circular data dependencies (Guo et al., 27 Aug 2025).
• Federated and Distributed Learning: Similarity clustering in federated models helps robustify aggregation by filtering abnormal (possibly poisoned or Sybil) clients, as demonstrated with MNIST (Won, 2022).

Resulting models not only achieve higher accuracy, AUC, or turnaround improvements but offer finer anomaly localization, better cross-domain robustness, and interpretation via association with underlying domain knowledge.

5. Interpretability, Localization, and Knowledge Integration

Interpretation and fine localization are central tenets in modern NAGL:

• Visualization of Residuals and Gradients: Generator loss maps and discriminator gradients can localize abnormal regions in video sequences or medical images (Lee et al., 2018, Song et al., 21 Aug 2025).
• Integration of Clinical and Semantic Knowledge: Models like Ano-NAViLa and FODA-PG fuse structured term pools or graphs (normal and abnormal diagnostic attributes) into the model, aiding transparency and clinical compatibility (Song et al., 21 Aug 2025, Shu et al., 6 Sep 2024).
• Textual Rationales and Narrative Augmentation: In medical report generation, partitioned graph representations allow systems to distinctly characterize normal (“disease-free”) and abnormal (“disease-specific”) findings, directly benefiting generalist interpretability (Shu et al., 6 Sep 2024).

Interpretability is not restricted to human-legible outputs but includes the ability to audit and explain anomaly scores through model-internal feature attributions.

6. Limitations, Challenges, and Future Directions

While NAGL represents a significant advance, several challenges remain:

• Data Scarcity and Reference Bias: Directly using abnormal references can increase false positives due to over-activation; careful residual abstraction and attention-based refinement (e.g., RM and AFL modules) are required to generalize abnormality (Wang et al., 1 Oct 2025).
• Hyperparameter and Prompt Sensitivity: Zero-shot methods’ success is contingent on appropriate normalization, prompt set construction, and representation alignment—hyperparameter tuning remains domain-dependent (Niu et al., 18 Oct 2024, Zhu et al., 11 Mar 2024).
• Handling Label Noise and Distribution Shifts Simultaneously: As highlighted in Noise-Aware Generalization, naive application of LNL and DG methods can introduce domain bias or misclassify cross-domain shifts as noise (Wang et al., 3 Apr 2025).
• Modality Generalization: Extending current strategies to time series, 3D imaging, video, or multi-modal sensor data calls for further exploration, particularly in aligning latent representation spaces across structurally disparate domains (Wang et al., 1 Oct 2025, Liu et al., 2021).

A plausible implication is that continued progress in NAGL will depend on advances in representation learning, unsupervised alignment, and hybrid modeling that leverage both data-driven and domain-informed mechanisms.

7. Applications and Broader Impact

NAGL frameworks are finding traction in a range of mission-critical settings:

• Quality Control and Industrial Automation: Anomaly localization and class-agnostic detection on manufacturing lines.
• Healthcare and Medical Diagnosis: Handling scarce abnormal cases, flagging rare pathologies, and supporting radiologist decision-making through interpretable and reportable outputs.
• Distributed and Federated Learning: Securing model updates by screening for abnormal behaviors in decentralized environments.
• Scientific Discovery: Flagging outlier events or signals in stream data (as in gravitational wave observations).

As NAGL systems continue to develop, a plausible future direction is their deployment in environments with rapidly shifting or poorly characterized anomaly distributions, facilitating continual adaptation and robust, interpretable decision-making using minimal abnormal supervision.

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In summary, Normal-Abnormal Generalist Learning crystallizes a set of methodologies focused on learning transferable, robust representations of both normality and abnormality, using joint reference modeling, attention or contrastive mechanisms, and explicit generalization objectives across domains and modalities. Its empirically validated and theoretically informed frameworks provide a strong foundation for unified anomaly detection, localization, and understanding in complex, real-world applications.