NeurIPS 2026 Papers

Long-tailed out-of-distribution (LT-OOD) detection is often addressed with specialized training, including auxiliary out-of-distribution (OOD) data, abstention heads, contrastive objectives, energy losses, or gradient-conflict control. We show that these training mechanisms can obscure a simpler issue: frozen long-tailed representations may already contain useful OOD evidence, but raw Mahalanobis distance is distorted by frequency-coupled feature radius and poorly supported tail covariance. We propose \emph{Hyperspherical Pooled Mahalanobis} (\HPM), a post-hoc detector that normalizes features onto the unit sphere and replaces class-specific covariance with a pooled, ridge-regularized metric while keeping class means as semantic anchors. In CIFAR-LT experiments and an ImageNet-100-LT near-OOD boundary analysis, \HPM improves raw Mahalanobis scoring; for Prior-Calibrated ERM (PC-ERM), it raises AUROC from 46.49 to 85.67 on CIFAR-10-LT and from 50.40 to 78.35 on CIFAR-100-LT. This simple PC-ERM+\HPM pipeline also achieves the best Log Efficiency Score (LES; 3.08) on CIFAR-100-LT, retaining roughly 95\% of the best CIFAR-100-LT AUROC observed among the compared post-hoc scores at substantially lower training-time cost. These results argue for evaluating representation quality, detector geometry, and training complexity as separate factors in LT-OOD detection.

*Steering vectors* (SVs) are widely used to influence the expression of concepts (e.g., truthfulness) in large language model outputs. A key assumption underpinning SVs is that they are linearly discriminative with respect to the concept: representations of texts that exhibit the concept are more aligned with the SV than those that do not, motivating shifts along the positive or negative SV direction to respectively promote or suppress the concept. In this work, we identify an *inverted detection-control* phenomenon in which some highly discriminative SVs that are aligned with positive representations can *consistently promote the opposite behavior*. We refer to such vectors as inverted-steering vectors (ISVs). We provide a geometric characterization of ISVs' effects, finding that steering along these directions systematically pushes representations in discriminative downstream heads as if the concept were absent, even prior to decoding. Motivated by this analysis, we propose an approach for distinguishing ISVs without requiring generation or associated response scoring. This enables targeted sign flips, which we use to improve a foundational detection-based steering pipeline via Inference Time Intervention (ITI). Our approach improves results in 27/30 experiments, ranging from +0.9\% to +138\%. We evaluate our findings on Gemma 3 12B, Qwen 2.5 14B, and Olmo 3 7B across 5 concepts. We will make our code publicly available upon acceptance.

Although numerous algorithms have been proposed for unsupervised anomaly detection, there is currently no widely acknowledged internal evaluation method tailored specifically to this task. Existing internal evaluation methods typically assess score distributions alone or depend on auxiliary classifiers, limiting their reliability and interpretability. To address these problems, we propose SSSD (Similar Scores, Similar Data), an evaluation framework inspired by the smoothness assumption in machine learning—that similar inputs should yield similar outputs. We adapt this principle to anomaly detection by requiring that locally similar data points be assigned similar anomaly scores. Instead of testing this assumption directly, SSSD identifies violations of this principle by comparing the similarity of data with similar anomaly scores, interpreting a significant difference as a higher probability of erroneous detection. This evaluation of similarity is measured through nearby-score and nearby-distance, which is calculated based on the distances between neighboring data points and their corresponding anomaly scores. SSSD is highly interpretable, providing valuable insights for identifying mislabeled data. Experimental results show that our proposed method is both effective and robust for evaluating the performance of unsupervised anomaly detection algorithms across artificial and real-world datasets.

Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations. Recent neural networks can estimate the articulated structure of 3D objects, but their generalization remains limited by the scarcity of annotated data for this task. To address this gap, we introduce Instruct-Particulate, a model that takes a 3D mesh together with a target kinematic specification, including part descriptions, connectivity, joint types, and optional point prompts, and predicts the corresponding kinematic part segmentation and joint motion parameters. The kinematic specification disambiguates the task and allows the model to target annotations of different granularity, thereby making it possible to use more abundant heterogeneous training data. At test time, the kinematic specification can be obtained automatically, so the model can be applied to any input mesh. To train our model at scale, we construct a heterogeneous dataset of more than 150,000 articulated 3D objects, extending existing publicly available collections with data obtained by partially labelling other 3D models, monolithic or already decomposed into parts, with kinematic labels by means of vision-language models. Experiments show that our model generalizes better across categories and to AI-generated meshes, enabling articulated asset reconstruction from real-world images via image-to-3D models.

Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which inspection systems must be extended from one industrial component to another over time. In our setting, continual industrial anomaly detection is framed as a task-incremental problem: each component defines a task, while the learning objective remains semi-supervised. The goal extends beyond conventional anomaly detection, since models should preserve their performance on all components as they are sequentially exposed to new components. This framing is especially relevant because many existing industrial pipelines either assume fully supervised defect labels or study only isolated datasets, leaving open the question of how semi-supervised anomaly detectors behave across a sequence of heterogeneous components. We address this gap by organizing the benchmark as a sequence of related component-level tasks and by defining multiple scenarios, including random, easy-to-hard, and hard-to-easy. In addition to single-dataset continual scenarios, we propose challenging scenarios spanning across multiple datasets. We further evaluate continual performance from two complementary perspectives: image-level anomaly detection and pixel-level anomaly localization. By recasting industrial anomaly detection as a continual benchmark rather than a static train/test problem, our work establishes a concrete path toward studying knowledge retention, adaptation, and generalization in evolving industrial environments.

Large Language Models (LLMs) often generate factually unsupported content known as hallucination. However, existing detection approaches rely on handcrafted heuristics or isolated attention signals, limiting their ability to capture higher-order dependencies. In this work, we propose $\textbf{AttnHyper}$, a framework that represents a Transformer's attention patterns as a hypergraph for hallucination detection. Tokens are treated as nodes, while hyperedges connect groups of tokens that co-occur across heads and layers, with features derived from attention weights to preserve higher-order interactions beyond pairwise graphs. Based on this representation, the method formulates hallucination detection as a hypergraph learning problem and employs a hypergraph neural network (HGNN) to extract structural cues. It consistently outperforms state-of-the-art methods on the RAGTruth benchmark, achieving an average gain of +5.08 AUROC over the strongest baseline across diverse settings and architectures, while also demonstrating strong zero-shot transfer. Importantly, it incurs minimal overhead at inference time, enabling efficient deployment. Our results demonstrate that hypergraph-based attention modeling provides a more expressive and reliable signal for hallucination detection.

Fluorescence spot-detection benchmarks in biology and medicine often rely on reference annotations that are incomplete, ambiguous, or inconsistently defined. In such settings, detector performance is not determined solely by model predictions; it can also depend on which faint or ambiguous signals are counted as reference positives. We formulate this issue as a reference-audit problem: instead of treating the evaluation reference as fixed ground truth, we test whether benchmark outcomes remain stable under plausible changes in reference definition and annotation completeness. Using the public FISH_spots dataset, a benchmark for fluorescence in situ hybridization (FISH) spot detection, we construct Raw, Lenient, and Strict audit references and evaluate diverse classical and learning-based detectors under a unified point-matching protocol. We further apply annotation-retention stress tests that simulate random and structured missing-label mechanisms. Across these audits, changing only the evaluation reference can alter measured F1 scores and model rankings, including the selected top-1 detector, even when aggregate rank correlation remains high. These findings suggest that model selection in fluorescence spot-detection benchmarks should be tested across plausible reference definitions, rather than based on a single reference annotation set.

Deployed approaches for AI text detection often rely on training-time access to labeled datasets of both human-written and AI-generated text. This approach is vulnerable to three types of distribution shifts that occur continually post-deployment, and for which labeled data is often unavailable: adversarial humanization, new LLMs being released, and temporal drift in human writing. Simultaneously, existing approaches do not leverage a key signal of LLM usage: inference-time homogeneity. We propose a test-time adaptation (TTA) approach, using semi-supervised learning, that adapts to distribution shifts by leveraging homogeneity among unlabeled samples observed at inference time. Empirically, we find that state-of-the-art supervised detectors systematically fail when they encounter distribution shifts in AI-generated and human writing, both adversarial and natural, while test-time adaptation with semi-supervised learning is largely robust; e.g., the commercial model Pangram detects just 24.1\% of our adversarial AI-generated text, compared to 90.5\% for our test-time approach. We establish that test-time adaptation is a promising framework for AI text detection in the wild.

Existing multimodal large language models (MLLMs) typically formulate object detection as a conditional sequence generation task. Under this paradigm, spatial locations are commonly represented by either textual coordinates or special quantized learnable tokens. However, textual coordinates are tokenized as unrelated symbols, spatially adjacent points (e.g., $x=199$ vs. $y=200$) yield entirely different tokens, so the cross-entropy loss is misaligned with true spatial distance. Special quantized location tokens, on the other hand, inevitably introduce quantization error. Reducing this error requires linearly more tokens, but a larger vocabulary is harder to train and converges slower. We argue that both limitations stem from the formulation of spatial representation. To address this issue, we propose a simple yet novel localization representation method, HiLoc, which predicts locations in a coarse-to-fine manner with special tokens: first a grid index for coarse localization, then a cell index to refine the position within the grid. Under this paradigm, accuracy can be scaled up by simply adding the hierarchy level. We also build HiRef-500K, a dataset consisting negative and complex referring samples to strengthen rejection and complex referring ability. Experiments show that our method achieves strong performance on both detection and grounding benchmarks, especially on small objects, under high IOU thresholds and complex referring tasks.

Detecting frequency trend patterns, such as items with sustained growth or decline, is an important and challenging task in high-speed data streams. State-of-the-art solutions require manual tuning of many coupled parameters, while their bloom filters suffer from high false positive rates. In this paper, we propose NeuTrend, a framework that provides accurate and automatic trend detection with high-performance in-network inference. Our key idea is that a lightweight BRNN can learn to classify incoming items as likely trending or non-trending. Thus, it eliminates unqualified items more accurately than a bloom filter and removes the need for manual parameter tuning. The BRNN uses only XNOR and popcount operations. Thus, it aligns with the strict resource constraints of data-plane switches. NeuTrend further introduces an adaptive detection module. The module uses the BRNN confidence scores to automatically tune the growth and decay thresholds. Extensive experiments on real-world datasets show that the learned filter reduces false positive rates by 23\%-44\% and increases effective detector occupancy from 47\% to 70\%. Hence, NeuTrend improves the overall F1 score by 35\%-68\% over existing solutions, and achieves Tbps-level line-rate processing.

We propose, HaNDF, object-conditional neural distance fields as data-driven priors for modeling the plausible hand–object configurations, in the product manifold of hand articulations. While recent unconditional generative priors showed success in modeling human pose, hands are versatile articulated bodies often found in interaction with other objects. This makes it crucial to develop a generative prior which can model the plausible hand poses conditioned on the object under interaction. To this end, our HaNDF proposes a geometry-aware, object-conditioned neural distance field, representing the plausible articulations in the zero-level set of a conditional field. We leverage tools from Riemannian geometry to (i) represent plausible hands in the zero level-set of an object-induced neural field, and (ii) project a given pose onto the this field. Using HaNDF, we can also optimize for a plausible condition, determining the object under interaction. Our extensive evaluations demonstrate that our HaNDF provides a flexible and powerful prior for hand–object interaction, suitable for applications such as pose refinement, reconstruction under occlusion, and physically plausible manipulation synthesis.

Estimating physical pressure from vision is essential for understanding contact-rich hand-object interaction. However, prior vision-based pressure estimation methods are largely limited to planar surfaces and single image input, making them difficult to apply to dynamic hand-object interaction with diverse objects. We instead formulate pressure estimation as a hand-centric video prediction problem with monocular video as input. This formulation predicts temporally evolving per-vertex normal pressure and contact directly on the hand mesh, yielding a unified output space independent of object shape and sensor layout. Building on this formulation, we propose HOPE, a framework with two key components. First, we lift tactile-glove pressure, planar-sensor pressure, and distance-based hand-object contact annotations into a shared hand vertex space, allowing bare-hand contact data to regularize pressure learning where metric labels are unavailable. Second, we introduce a vertex-anchored video transformer that treats each vertex as a persistent token, aggregates visual features and hand pose over time, and uses a contact-gated pressure head to enforce that pressure vanishes without contact. Experiments on OpenTouch, PressureVisionDB, and hand-object contact benchmarks demonstrate that HOPE supports a common hand-centric representation across object-pressure, surface-pressure, and contact-supervised HOI settings. Despite using metric pressure supervision primarily from gloved-hand videos, HOPE generalizes to bare-hand egocentric and in-the-wild videos, producing joint contact and pressure predictions beyond the scope of contact-only or planar-pressure baselines.

In standard acoustic anomaly detection (ASD), models are usually trained separately for each machine or domain using explicit metadata. However, in realistic deployments, machine identifiers are incomplete, unreliable, or simply unavailable. Thus, metadata-free universal ASD asks a single model to monitor mixed machine populations without machine IDs. In this paper, we argue that the main failure mode is not a weak detector family, but representation entanglement. The heterogeneous normal modes overlap in frozen pre-trained feature spaces, making anomaly scoring and approximate retrieval unreliable. We study this problem directly and propose HARMONY (Hierarchical Anchor Retrieval on Manifold for Oblivious-source acoustic aNomaly detection), an anchor-guided geometric repartitioning framework that reorganizes mixed-source features into compact Voronoi regions and reuses this structure for hierarchical retrieval. On dataset DCASE 2020 and MIMII, HARMONY improves mean AUC from 75.40% to 90.00% (+14.60% absolute improvement) over the strongest unified baseline and from 83.90% to 90.00% over a strong foundation-feature baseline. On a single CPU, its hierarchical retrieval achieves a 7.8× speedup over exhaustive search with only a 0.20% AUC drop. These results suggest that incorporating explicit geometric partitioning can improve both detection accuracy and retrieval efficiency for ASD. Code and data are available at the anonymous URL: https://anonymous.4open.science/r/HARMONY-26CC.

3D affordance grounding aims to highlight the actionable regions on 3D objects, which is crucial for embodied AI. While previous research primarily leverages static cues from language or single images, which often lack the rich interaction context necessary to accurately locate functional zones. To alleviate this predicament, we collect a comprehensive video-based 3D affordance dataset, VIDA, which contains 38K human-object-interaction (HOI) videos covering 16 affordance types, 38 object categories, and 22K point clouds. Based on VIDA, we propose a strong baseline: VideoAfford, a unified framework that extends Multimodal Large Language Models (MLLMs) with fine-grained affordance segmentation capabilities. VideoAfford incorporates a latent action encoder to distill dynamic interaction knowledge from demonstration videos and a spatial-aware loss to encourage geometrically consistent affordance predictions. Extensive experiments on VIDA show that VideoAfford significantly outperforms strong baselines in both seen and unseen settings, demonstrating its effectiveness in video-driven 3D affordance reasoning and open-world generalization. The dataset and code will be released soon to facilitate future research in this area.

Hallucinations in large language models (LLMs) that are plausible-sounding but factually incorrect or unsupported pose a major challenge for deploying these models in high-stakes applications such as medical diagnosis, legal reasoning, and knowledge-based question answering. Existing detection methods primarily rely on either generating multiple responses to check consistency or supervised training on labeled hallucinations. Multiple-response approaches tend to be computationally expensive and may be less practical for real-time scenarios, while supervised methods are limited to known hallucination types and cannot generalize to unseen cases. To address these limitations, we propose an efficient unsupervised hallucination detection method. Our approach measures the embedding discrepancy between the LLM’s response considered independently and the response contextualized with the question. A large discrepancy indicates that the question significantly influences the response, suggesting it is grounded, whereas a small discrepancy signals a higher likelihood of hallucination. This method is efficient, interpretable, and does not require labeled hallucinations. Extensive experiments across multiple tasks demonstrate that our approach consistently achieves superior or comparable detection performance while reducing the detection cost compared to the multiple-response methods.

Category-level object pose estimation (COPE), capable of generalizing to intra-class unknown objects, has become a core technique for robotic 3D scene understanding. However, existing COPE methods still require labor-intensive recollection of real-world training data for novel object categories, which limits their scalability in practical applications. This paper aims to achieve synthetic-to-real (Syn2Real) generalized COPE, where a model is trained solely on rendered synthetic data and directly generalized to real-world deployments. The central challenge lies in the significant domain gap between synthetic and real-world data, particularly in texture appearance. To address this, we aim to enhance domain generalization by learning domain-invariant representations that capture semantic commonalities among objects within the same category. We introduce 2D and 3D semantic consistency constraints to reduce the sensitivity of feature encoders to domain-specific features. In addition, we propose an end-to-end pose regression framework that performs 2D-3D cross consistency learning, leveraging dense cross-modality fusion to further refine pose estimation. Since simplicity and effectiveness are essential for real-world robotic deployment, our model operates exclusively on global features, yielding a highly lightweight and efficient architecture. Extensive experiments on the REAL275 and Wild6D benchmarks, as well as real-world robotic manipulation scenes, show superior Syn2Real generalization performance of our paradigm.

Moving Object Segmentation (MOS) aims to discover, segment, and track objects that move independently of the camera. Current MOS methods, however, exhibit two fundamental limitations: they rely on pre-computed 2D auxiliary modalities such as optical flow or point trajectories that lack 3D geometric information, and they treat motion as a sequence-level attribute, overlooking the instantaneous motion state of each object. We address both by grounding MOS in 3D space and time, and propose GMOS, a framework that operates directly on RGB video to produce 3D-aware, temporally fine-grained segmentation of multiple moving objects, alongside a foreground--background variant GMOS-S for faster deployment. To support training and evaluation in this regime, we curate GMOS-2K, a dataset of $2{,}210$ real-world videos with per-object temporal motion annotations drawn from five established Video Object Segmentation (VOS) benchmarks, and formalise MOS-I ("I" for *instantaneous*), a temporally fine-grained evaluation protocol with three complementary metrics. GMOS achieves state-of-the-art results across MOS, MOS-I, and Unsupervised VOS benchmarks, while running significantly faster than prior multi-object MOS methods and supporting online inference for streaming deployment.

Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical metabolomics, systems biology, and adjacent disciplines. In this work, we revisit the problem through the lens of object detection on molecular graphs. Molecular fragmentation, a central step in MS/MS prediction, can be approximated as detecting a set of subgraphs (i.e., fragments) and their associated spectral contributions. Existing fragment-based models follow a two-stage paradigm—first generating candidate fragments and then scoring them—analogous to two-stage R-CNNs in computer vision. Towards higher accuracy and faster inference, we introduce GLACIER, a single-stage transformer-based fragment detection neural network for molecular graphs. This unified formulation eliminates the need for candidate enumeration, enabling scalable and globally consistent modeling of molecular fragmentation. GLACIER is faster and more accurate than existing state-of-the-art methods by a significant margin, achieving 62.4% and 62.1% Top-1 retrieval accuracy with and without contrastive finetuning on the MassSpecGym dataset (from 55.3%), and 55.2% and 38.2%, respectively, on the NIST'20 dataset (from 33.5%). Furthermore, GLACIER provides nearly 3-fold inference speedup over existing two-stage models.

Single-Domain Generalized Object Detection (S-DGOD) aims to generalize from a single source domain to unseen domains under distribution shifts. Existing efforts mainly focus on simulating unseen domains or suppressing domain-specific factors. However, they largely overlook the intrinsic generalization capability embedded in Vision Foundation Models (VFMs). In this work, we show that unconstrained fine-tuning disrupts the geometric structure of pre-trained representations, which encodes domain-invariant semantics. Motivated by this, we propose Geometric-Invariance Fine-Tuning (GIFT), a geometric-aware fine-tuning method for VFMs. Specifically, we introduce a series of learnable Householder reflections to the pre-trained weights to encourage geometry-consistent adaptation under domain shift. In addition, we propose a lightweight channel-wise scaling to facilitate effective task-specific adaptation. Extensive experiments demonstrate that GIFT consistently outperforms state-of-the-art (SOTA) approaches on two S-DGOD benchmarks. Notably, by introducing fewer than 1% additional trainable parameters into the frozen backbone, the proposed method achieves improvements of +7.9% and +2.9% in mPC over the previous SOTA on the Cityscapes-C and Diverse Weather Dataset, respectively. This finding highlights that preserving representation geometry is crucial for robust cross-domain generalization, establishing GIFT as a principled and efficient solution to domain shift. The code is available in the supplementary material.