NeurIPS 2026 论文列表

Graph anomaly detection (GAD) is critical to many real-world systems. However, supervised GAD models often degrade under distribution shifts due to the closed-world training assumption. Test-time adaptation (TTA) offers a promising solution by updating pre-trained models with unlabeled test data. Yet, applying TTA to GAD poses a unique dilemma. High-confidence samples provide reliable pseudo-labels, but they are scarce and biased toward low-shift regions. Low-confidence samples better reflect target shifts, but they are too noisy for direct cross-entropy optimization and often require label-free objectives that are misaligned with anomaly discrimination. To address this dilemma, we propose SADOS, a Shifted Augmentation-based Dynamic Objective Scheduling framework for test-time GAD. SADOS turns unreliable target samples into drift-carrying signals for reliable supervised adaptation. It distills prototypes from confidence-stratified unreliable samples and injects them into reliable samples to generate label-preserving yet shift-oriented augmentations. This expands coverage over shifted target regions. In parallel, SADOS dynamically schedules the adaptation objective. It uses contrastive learning to capture early-stage drift signals, and gradually anneals its weight so that classification-oriented anomaly detection dominates later adaptation. Extensive experiments demonstrate that SADOS robustly improves test-time GAD under distribution shifts.

Recent advances in generative video models have significantly improved visual realism, making detection increasingly dependent on subtle temporal inconsistencies arising from imperfect frame-to-frame coherence. However, these signals are weak and spatially localized, while most of the video content is redundant. This creates a fundamental mismatch: existing detectors rely on heavy backbones to model full videos, assuming increased capacity can capture such cues, which leads to high computational cost and limited scalability. In this work, we rethink AI-generated video detection from a representation perspective. Instead of modeling entire videos, we propose to explicitly restructure them into compact temporal slice representations that isolate informative temporal evolution. Based on this idea, we introduce a lightweight framework using frequency-aware temporal slice consistency learning, where informative spatial rows and columns are aggregated over time to form slice-domain inputs. This reparameterization suppresses redundant appearance while preserving discriminative temporal dynamics, enabling efficient and targeted detection. By constructing slices from both high- and low-frequency regions, our method exposes complementary temporal artifacts, including unstable details and abnormal motion smoothness. We instantiate this idea in a $\textit{Temporal Slice Consistency Network}$, which integrates task-driven slice localization, hierarchical positional encoding, and a lightweight Transformer to model cross-slice and cross-frequency dependencies with minimal overhead. We further introduce $\textit{AI-Artist}$, a new benchmark with artist-curated videos from recent high-fidelity generators, including Seedance and Kling. Experiments on GenVideo and AI-Artist show that our method achieves strong performance while requiring over 400× fewer FLOPs and 3.6× faster inference than the strong video-based baseline ReStraV, enabling scalable and practical deployment.

Object detectors are expected to recognize objects independently of intrinsic object-level attributes, such as scale and local visibility. Yet, natural detection datasets contain systematic intrinsic attribute biases, and modern detectors inevitably entangle these biases within their learned representation geometry, leading to non-uniform performance across the continuous attribute spectrum. This observation exposes a central limitation of conventional data augmentation: while attribute-blind transformations enlarge the training distribution, they fail to explicitly correct the dataset-inherent nuisance structure. To address this, we propose Target-Aware Nuisance Shaping} (TANS, a unified framework that re-purposes data augmentation as an object-conditioned vicinal distribution design. TANS processes training samples through a serial pipeline of two fundamental operators: first, Scale Prior Shaping (SPS) geometrically transports object scales toward low-density regions of the natural scale distribution via class-conditional log-area priors; subsequently, Local Visibility Shaping (LVS) photometrically modulates local foreground-background contrast by constructing smooth, box-conditioned Gaussian fields, strictly preserving the newly established geometry and labels. Furthermore, a feature-state controller monitors the evolving representation geometry to adaptively adjust the shaping strength during training. Extensive experiments on MS COCO and Pascal VOC demonstrate that TANS significantly improves detection performance over strong augmentation baselines across modern architectures. Specifically, on COCO with YOLOv11, TANS achieves a 3.6 mAP gain over the Mosaic baseline. Beyond aggregate accuracy, our attribute-regime analyses, feature-state dynamics, schedule comparisons, and logit-space probes consistently indicate that TANS effectively decouples the detector from intrinsic dataset biases, establishing target-aware shaping as a rigorous paradigm for learning attribute-invariant representations.

In unsupervised multivariate time series anomaly detection (MTSAD), local variation can inflate reconstruction and forecasting errors, making observation-space discrepancy an unreliable indicator of abnormal system behavior. Since latent-space scoring can mitigate this limitation, we reframe one-class MTSAD as current-point system-state compatibility estimation and propose **TENET**, the **T**ime-point **E**ncoding **Net**work for Multivariate Time Series Anomaly Detection. We define the current-point system state as a latent representation of the target time point, constructed from variable-level temporal context in the input window. TENET constructs this state through a temporal layer that retrieves window context relative to the current point, encodes time with current-point-centered embeddings, and adaptively mixes the retrieved context with the current representation. With this design, TENET treats the input window not as the object to reconstruct, forecast, summarize, or score, but as context for constructing a latent representation evaluated with a fixed standard-normal negative log-likelihood score. Across benchmark datasets, TENET achieves state-of-the-art performance. Additional experiments further show that TENET remains effective under alternative one-class objectives and outperforms substitutions based on generic sequence encoders, suggesting that its advantage comes from decision-aligned representation construction rather than from a specialized scoring rule. These results indicate that one-class MTSAD can be strong when the latent representation is designed around the current time point.

3D Gaussian Splatting enables high-fidelity, real-time novel view synthesis, yet adapting it to dynamic, evolving scenes remains challenging. Existing methods rely on fixed static-dynamic partitioning, which suffers from persistent partitioning errors and cannot adapt during optimization. To tackle this issue, we introduce SynGS, a unified 3D Gaussian framework for continual scene updating and multi-view change detection. We cast static-dynamic decomposition as a learnable task, jointly optimizing per-Gaussian change probabilities and scene representations for iterative refinement. We further propose a change-guided update strategy that modulates illumination-aware photometric corrections via learned probabilities, separating transient appearance variations from authentic scene changes. Projecting Gaussian-level probabilities to the image plane yields 3D-consistent change masks, enabling mutual optimization between reconstruction and change localization. An illumination-decoupled refinement step further improves mask quality. Evaluations on standard benchmarks show that SynGS outperforms baseline methods on scene update and change detection tasks, while fully retaining the real-time rendering capability of 3D Gaussian Splatting. Code will be released upon acceptance.

Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised or surrogate objectives, producing anomaly scores indirectly rather than learning explicit normal--anomalous distinctions. We propose Counterfactual Pairing with Anomaly Semantics (CAPS), a supervision-recovery framework for TSAD. CAPS formulates temporal anomalies as mechanism-induced effects on normal temporal evolution and recovers matched supervision without target-domain anomaly labels. It learns transferable anomaly semantics from simulated normal--anomalous pairs, disentangles them from normal temporal structure, and organizes them into family-wise modes. Instead of directly using simulated anomalies as target positives, CAPS instantiates the learned semantics as residual-form anomaly effects on target-domain reference trajectories via residual MoE generation. The resulting matched counterparts provide supervision-aligned signals for boundary-oriented detector learning. Experiments on nine benchmark datasets show that CAPS outperforms unsupervised and injection-based baselines, is competitive with supervised-reference baselines, and provides interpretable evidence through anomaly-effect generation and expert association.

Deep neural networks have achieved significant success on in-distribution (ID) tasks, but out-of-distribution (OOD) samples during inference can lead to unreliable predictions. To address this issue, distance-based methods exploit the geometric structure of feature space for OOD detection. However, methods based on static information geometry fail to address geometry distorted by ill-distributed samples. Although prior work reduces the misclassification of OOD samples as ID by adjusting the residual space with real-time features, it fails to address the misclassification of ID samples as OOD. In addition, existing distance-based methods ignore class-specific characteristics when computing deviation feature and rely on the nearest prototype for uncertainty estimation, leading to suboptimal OOD detection performance. To address these issues, we propose Adaptive Mahalanobis Gap (AMG). Specifically, we first strengthen the principal space along real-time feature directions to reduce the misclassification of ID samples as OOD. Subsequently, we incorporate class variance and prototype norms to mitigate bias of deviation feature from class-wise distributional differences. Finally, we introduce the Mahalanobis gap scoring for uncertainty scoring to overcome bias from single-prototype reliance. Experiments on CIFAR-10/100 and ImageNet-1k show that AMG consistently outperforms state-of-the-art methods in FPR@95 and AUROC. Our code is available at https://anonymous.4open.science/r/AMG_AR.

Visual context is a powerful cue for object recognition: scenes inform what, where, and at what scale objects are likely to appear. Yet over-reliance on context becomes a failure mode when models predict objects based on their typical neighbours rather than the objects' own visual features, a phenomenon we refer to as typicality bias. While this issue has been extensively studied in the pre-Transformer era, it has often been assumed that architectural and scale improvements would naturally solve it. Despite recent attempts in isolated settings, a systematic diagnostic across the modern vision stack remains absent. In this work, we provide such a diagnostic framework. Specifically, we measure typicality bias via normalized pointwise mutual information (NPMI) over object pairs, and introduce STRANGE-Bench (Synthetic Typicality-RANked Grounded Evaluation Benchmark): a photorealistic, layout-controlled benchmark spanning 11 typicality tiers from typical to never-co-occurring object pairs. Across \five model families (specialist detectors, open-vocabulary detectors, self-supervised, vision-language, and multimodal large language models) and three recognition tasks}(supervised detection, open-vocabulary detection, and classification), we find the bias to be highly prevalent: gaps of 5--11\% persist when objects appear outside their typical company, and they do not consistently shrink with scale. This persistence motivates a complementary question: how to mitigate it? Data augmentation is a natural lever, since it directly shapes the training distribution that produces the bias; yet existing augmentations are designed for visual diversity, not for breaking co-occurrence shortcuts as they sample from the very distribution that produces typicality bias, preserving rather than perturbing it. We therefore propose Anchor-Atypicality Sampling (AAS), a simple context-aware variant of paste-based augmentations that uses the NPMI signal to deliberately tilt the training-time co-occurrence distribution toward atypical partners. AAS reduces the typicality gap by up to 3.3 pp on natural-image COCO splits and 17.4 pp on synthetic STRANGE-Bench across three representative model families (specialist detection, open-vocabulary detection, self-supervised classification), without sacrificing overall performance.

Accelerating stomata measurement is essential for developing drought-resilient plants. However, most automated stomata detectors are tested under narrow imaging and species conditions. Their behavior under broader ecological distribution shift remains unclear. We introduce StomataBench, a benchmark for stomatal object detection across species, acquisition protocols, and biological density regimes. The benchmark uses a curated multispecies training set of 3,154 images with 134,802 annotated stomata from 21 woody species. It is evaluated on four test datasets totaling 19,657 images, covering diverse woody taxa and heterogeneous public crop sources. Unlike standard detection benchmarks that report only AP, StomataBench evaluates measurement reliability. We measure COCO AP, F1, score-threshold sensitivity, image-level count error, and stomatal density agreement. We benchmark sixteen detectors spanning two-stage, one-stage, transformer, open-vocabulary, and promptable foundation models. The results show that model rankings change with the biological objective. GDINO-SB provides the strongest average AP generalization, while SAM3-OD gives reliable operating point for count and density estimation. Conventional detectors remain competitive on near-domain woody plants, but foundation models are robust under harder ecological shifts. Failure analysis shows that far-OOD detection is often limited by missing stomata. These results identify cross-lineage proposal generation as a key unsolved problem for reliable stomatal phenotyping under ecological distribution shift.

The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-written text. While recent studies explore leveraging internal representations of language models to uncover deeper detection signals, these raw features often exhibit substantial overlap between classes, limiting their discriminative power. To address this challenge, we propose Steer-to-Detect (\texttt{S2D}), a two-stage framework for detecting LLM-generated text. In the first stage, \texttt{S2D} learns a steering vector that is injected into the hidden states of a frozen observer LLM, producing representations with improved class separability. In the second stage, detection is performed via a hypothesis testing procedure based on the steered representations. We establish finite-sample, high-probability guarantees for Type I and Type II errors, providing a theoretical characterization of the procedure. Empirically, \texttt{S2D} achieves strong and consistent performance across a range of settings, including out-of-distribution scenarios and adversarial perturbations.

Few-shot object detection (FSOD) requires detectors to adapt to novel categories from only a few labeled instances, where prototype-based transfer methods have become a strong and efficient paradigm. However, we observe that such methods remain unstable in extreme low-shot regimes. We attribute this instability to two coupled factors: semantically unanchored query initialization, which yields high-variance cold-start optimization, and deterministic prototype matching, which over-trusts noisy or occluded support regions. To address these limitations, we propose Semantic Fine-Grained Prototype Distillation (SFPD), a language-conditioned probabilistic adaptation framework for prototype-based FSOD. SFPD introduces language-conditioned feature queries to provide semantic anchors for novel-class adaptation, uncertainty-aware prototype distillation to down-weight unreliable support evidence through heteroscedastic Gaussian modeling, and component-guided part-aware prototypes to refine fine-grained semantic-visual alignment. These modules act during adaptation and preserve the original detector path at inference, introducing no additional inference FLOPs. Experiments on PASCAL VOC and MS-COCO show that SFPD consistently improves a strong FPD baseline, with especially clear gains in the most challenging low-shot settings, e.g., a 3.9-point nAP50 improvement on VOC Split 2 under 1-shot. Further ablations, multi-seed evaluation, and convergence analysis indicate that SFPD improves both accuracy and adaptation stability.

AI-generated image detectors often generalize poorly to unseen generators because the classifier latches onto generator-specific feature directions that are predictive only on the training set. We show that simply projecting away generator-discriminative LDA directions fails: these directions also carry genuine real-vs-fake signal, so post-hoc erasure removes evidence along with shortcuts. We introduce StaR, a training-time regularizer that scores each LDA direction by leave-one-generator-out stability and penalizes the classifier only on unstable directions; stable directions are left available for classification. On ResNet-50, StaR reduces the across-seed standard deviation of the four-OOD average AUC by 24x relative to ERM (F-significant on every OOD) while preserving in-distribution-like performance; on CLIP ViT-L/14, StaR improves the four-OOD average AUC by 1.7 points over matched CLIP-ERM and 4.5 over EFFORT, with the largest gains on platform-shift benchmarks. Mechanistic analyses show that StaR rotates the classifier away from unstable generator directions while amplifying generator-discriminative structure in the features---a rotational, not erasive, effect that post-hoc projection cannot reproduce. We additionally find that the conventional ``argmax in-domain validation'' epoch-selection rule is sub-optimal: a deployable ``stop at first in-domain 0.99'' rule beats it by sim0.7 AUC points on average.

Most time-series anomaly detectors ask whether an observation can be predicted or reconstructed. We ask a different question: can a detector’s own spiking dynamics become anomaly evidence? We study this question for periodic and quasi-periodic time series and propose SPIRE, a phase-conditioned spiking anomaly detector. SPIRE uses period-aware current formation to expose phase-aligned cross-period residuals to leaky integrate-and-fire neurons. We refer to this conversion as eventification: after residual injection, recurrence violations induce abnormal bursts, silences, or phase-shifted spike trains. SPIRE then constructs phase-conditioned normal templates of spike states and scores deviations in residual energy, firing-rate patterns, and burst/silence statistics, instead of relying solely on output-space prediction or reconstruction errors. This separates the operational protocol, including causal current-step detection, one-step prediction, and non-causal reconstruction, from the source of anomaly evidence, namely output errors, spike dynamics, or both. On TSB-AD-U and TSB-AD-M, SPIRE establishes new best VUS-PR among neural time-series anomaly detectors. Spike-native scores remain competitive without reconstruction error, hybrid scoring consistently improves error-only scoring, and replacing LIF neurons with continuous activations in matched ANN counterparts reduces detection performance under the same period-aware architecture. Periodicity-stratified analyses and spike-state visualizations further show that the gains are strongest when phase recurrence is reliable, supporting the proposed spike-as-detector mechanism. These results suggest that phase-conditioned spiking dynamics can serve as a first-class anomaly detection signal for periodic time series, rather than merely an efficient computational substrate.

Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods universally depend on large-scale annotations or task-specific training procedures, severely limiting their rapid deployment to novel scenes. We observe that intermediate-layer features of pre-trained multimodal large language models (MLLMs) already encode rich anomaly semantics, yet existing approaches rely on the language output pathway and fail to exploit the geometric discriminability latent in these representations. Based on this finding, we propose SphereVAD, a fully training-free, zero-shot VAD framework that recasts anomaly discrimination as von Mises–Fisher (vMF) likelihood-ratio geodesic inference on the unit hypersphere, unleashing latent discriminability through principled geometric reasoning rather than learning new representations. Specifically, SphereVAD first applies Fréchet mean centering to unfold feature distributions and eliminate domain biases, then employs Holistic Scene Attention (HSA) to reinforce feature consistency using cross-video priors, and finally performs vMF-guided Spherical Geodesic Pulling (SGP) to align ambiguous segments with directional prototypes on the spherical manifold. This training-free pipeline requires only minimal synthetic images for calibration. SphereVAD establishes new state-of-the-art results among training-free approaches on three major benchmarks and remains competitive with fully supervised baselines. Code will be available upon acceptance.

Object detection in high-resolution wide (HRW) shots, where a single image can span gigapixel resolutions and kilometre-scale fields of view, suffers from extreme foreground sparsity, dramatically varying foreground density from sparse to crowded scenes, and object clusters whose spatial extent ranges from dozens to thousands of pixels within the same dataset. State-of-the-art sparse vision transformers for gigapixel detection select a fixed top-k fraction of windows by hand-crafted variance scores, which over-promote textured but object-free background such as foliage and patterned facades, cannot adapt the keep ratio to the wide range of crowd density, and confine each kept window to a 7x7 receptive field that is far smaller than the spatial extent of many real-world object clusters. We propose SpanFormer, a sparse vision transformer that broadens the computational span at three complementary granularities: (i) Prototype Routing replaces variance with learnable foreground/background prototypes regularised by a balance constraint that prevents prototype collapse, widening the semantic span of the scoring criterion; (ii) Dynamic Top-k predicts a per-image keep ratio with a lightweight selector, making the compute span elastic so that crowded scenes receive proportionally more compute while near-empty scenes are skipped; (iii) Cluster Memory Attention performs union-find clustering over the kept windows by joint cosine similarity and Chebyshev spatial radius and augments each window's K and V with cross-window memory drawn from its cluster, extending the attention span beyond the local window with all projection parameters shared with the local branch and therefore no added projection cost. On the PANDA gigapixel benchmark, SpanFormer improves AP50 from 78.0% to 80.3% over the SparseFormer baseline while reducing backbone FLOPs by over 75%.

Graph-level anomaly detection (GLAD) is a critical task in domains like social networks and bioinformatics. However, current unsupervised contrastive learning-based methods rely on semantically homogeneous augmentations, leading to catastrophic informational overlap that trivializes the learning objective and effectively masks anomalous signals in GLAD. To overcome this, we propose Sima (Semantically Complementary Spectral Views), a novel contrastive framework that learns from semantically complementary spectral views rather than redundant homogeneous views. Sima first constructs a pair of spectral views to separately capture global community structure and local heterophilous variations, which together cover the full graph spectrum of anomalous patterns. Inspired by the Multi-View Information Bottleneck principle, we further design a soft subgraph extraction module with Score Reconstruction Attention (SRA) and an anchor-guided condensation mechanism to distill the essential shared information from each view into a compact representation. We then optimize a contrastive objective across these distilled views to enforce cross-view alignment and amplify subtle structural deviations. Extensive experiments on challenging GLAD and graph-level out-of-distribution detection (GLOD) benchmarks demonstrate that Sima achieves substantial and consistent gains in average performance over state-of-the-art baselines.

Many public buildings provide floorplans with a “you are here” indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume controlled small-scale environments and precise vectorized floorplans, limiting their ability to operate in large-scale buildings and rasterized floorplans. In this work, we present an approach for performing floorplan localization in the wild by grounding the task in a reconstructed 3D representation of the scene. Given an unconstrained image collection, our method reconstructs a gravity-aligned 3D scene and projects it into a 2D density map that serves as a floorplan proxy. Floorplan localization is then formulated as aligning this proxy with the input floorplan via a 2D similarity transform. To bridge the appearance gap between density maps and architectural floorplans, we adapt a 2D foundation model to learn cross-modal correspondences, introducing a fine-tuning scheme that encourages semantically aligned matches while preserving structural consistency. Extensive experiments demonstrate substantial improvements over prior methods, including in extremely sparse settings with as little as a single input image. Our code and data will be publicly available.

Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are difficult to detect in reasoning-intensive tasks, where the content appears coherent but contains errors like logical flaws and unreliable intermediate results. While step-level analysis is commonly used to detect internal hallucinations, it suffers from limited granularity and poor scalability due to its reliance on step segmentation. To address these limitations, we propose TokenHD, a holistic pipeline for training token-level hallucination detectors. Specifically, TokenHD consists of a scalable data engine for synthesizing large-scale hallucination annotations along with a training recipe featuring an importance-weighted strategy for robust model training. To systematically assess the detection performance, we also provide a rigorous evaluation protocol. Through training within TokenHD, our detector operates directly on free-form text to identify hallucinations, eliminating the need for predefined step segmentation or additional text reformatting. Our experiments show that even a small detector (0.6B) achieves substantial performance gains after training, surpassing much larger reasoning models (e.g., QwQ-32B), and detection performance scales consistently with model size from 0.6B to 8B. Finally, we show that our detector can generalize well across diverse practical scenarios and explore strategies to further enhance its cross-domain generalization capability.

Although the mechanics of eigenvalue bias of self-supervised learning (SSL) in linear networks are well-understood, they remain theoretically disconnected from empirical shortcut learning, offering little guidance or intuition. In this paper, we present a theoretical analysis of this shortcut learning phenomenon through the lens of \textit{extent bias} and \textit{amplitude bias}, two forms of \textit{eigenvalue bias}. By investigating the relations among extent bias, amplitude bias, and learning priorities in SSL, we demonstrate that SSL prioritizes features based on dimensionality (object to image ratio) and amplitude (backdoor attack intensity), not their semantic importance. Our analysis reveals how the eigenvalues of the feature cross-correlation matrix influence which features are learned earlier, providing insights into why models preferentially learn shortcut features over more generalizable features.