NeurIPS 2026 Papers

Few-Shot Industrial Visual Anomaly Detection (FS-IVAD) is a critical task in modern manufacturing, where automated product inspection systems must identify rare defects using only a handful of normal, defect-free training samples. This paper introduces GATE-AD, a reconstruction-based framework that casts few-shot normality modeling as a masked, representation-aligned graph reconstruction problem on a $k$-nearest-neighbor graph, built from frozen self-supervised ViT patch tokens. Since defects typically disrupt the contextual consistency between a patch and its spatial neighbors, GATE-AD emphasizes such neighborhood relations by attending anisotropically over each patch's local neighborhood using a Graph Attention Network (GAT) encoder. To prevent GAT over-smoothing in the low-shot setting, the encoder output is aligned with the input ViT tokens through a learnable latent space, where reconstruction inconsistency is scored with a Scaled Cosine Error (SCE) objective. On the MVTec AD, VisA, and MPDD benchmarks, GATE-AD attains state-of-the-art image-level AUROC in the wide majority of $1$- to $8$-shot settings, with low per-image inference cost that does not scale with the size of the support set. Code is included in the supplement material and will be publicly released upon acceptance.

Reconstruction error is the standard signal for unsupervised time-series anomaly detection and has been widely adopted by time-series foundation models (TFMs). It is, however, vulnerable to input noise: the resulting score tracks the noise level itself rather than whether a sample is truly anomalous, and this failure is especially pronounced in domains where the noise envelope itself carries information about the underlying process state. We propose the *adaptation gap* $\text{Diff} = L_{FT} - L_{ZS}$, the difference between fine-tuned and zero-shot reconstruction losses, as a noise-robust complement. We prove that its conditional expectation cancels the input-noise variance and yields a calibration-anchored, population-level mean-separation guarantee. Building on these results we introduce **GAPS** (Gradient-Aware Adaptation-Gap Scoring), which selectively augments $L_{FT}$ with $\text{Diff}$ under a calibration-only routing layer with no label-tuned hyperparameters. Across a synthetic suite, an HRV benchmark whose normal class is intrinsically noisier than its anomaly class, and TSB-AD-U with a pre-registered noise-stress condition, GAPS preserves performance under standard conditions and yields substantial gains under noise stress.

Graph-level anomaly detection plays a critical role in a wide range of applications, such as fraud detection in financial networks and malicious program detection, yet it remains challenging in real-world scenarios where labeled anomalous graphs are extremely scarce and graph structures are highly diverse. Existing few-shot methods often suffer from severe performance degradation due to the effects of class imbalance and structural imbalance, and they struggle to generalize to structurally deviant or rare anomalies. In this paper, we propose an adaptive few-shot graph-level anomaly detection model with multi-view structure-aware prompting (GADMVP). To mitigate structural imbalance, we construct a graph-of-graphs representation that facilitates cross-graph message passing, capturing both intra-graph semantics and inter-graph dependencies. Building upon these priors, we introduce a structure-aware graph prompting mechanism that extracts and adaptively refines anomaly-relevant prompts from a small set of labeled graphs. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art few-shot graph anomaly detection baselines, while exhibiting strong robustness under extremely limited labeled data. The code and implementation details are available at https://anonymous.4open.science/r/GLAD-3456543/.

Link prediction is a standard pretraining objective for graph neural networks: hide edges, train an encoder--decoder to predict them, and reuse the node embeddings for downstream node tasks. Despite its empirical success, there is little quantitative theory explaining why an edge-level objective should produce embeddings that are linearly useful for node classification.We study this question in the finite-sample stochastic block model (SBM). For any bounded GNN encoder paired with a symmetric bilinear decoder, we prove a conditional reduction: small population held-out LP excess risk implies small community-classification error for a transductive linear classifier on the learned embeddings.The bound decomposes into an optimization residual, an architectural approximation residual, and explicit statistical terms of order $\widetilde{O}(\sqrt{d/n})$ in bounded dense regimes. We further give a constructive few-label classifier: nearest-centroid classification under the PSD quadratic form $G = W Z^\top Z W$, whose label error is coupled to LP quality.The framework is architecture-agnostic through the approximation residual, and we instantiate this residual as $O(\log n / n)$ for a spectral-positional-encoding GIN with a bilinear decoder. Synthetic SBM experiments validate the theorem chain from LP risk to score error to classification error, and show when the worst-case constants become conservative.

Multimodal large language models (MLLMs) are increasingly used for explainable image forgery analysis, where a shared decoder is expected to predict authenticity, localize evidence, and generate natural-language explanations. Existing methods typically rely on final decoder states, leaving open whether these late, language-oriented representations preserve localized manipulation cues. We test this assumption with a token- and layer-wise probing diagnostic that uses pixel-level masks only for analysis to separate tampered-region and authentic-region vision tokens. Across three MLLM-based forgery backbones and multiple manipulation types, we identify a mid-layer forgery evidence peak: tampered-region tokens are most linearly separable at upper-middle decoder layers and become less separable afterward, whereas authentic-region tokens continue to strengthen. This region-conditioned asymmetry suggests competition between localized manipulation cues and late semantic aggregation. Motivated by this diagnostic, we propose Gated Forensic Memory (\method), a lightweight evidence relay that reads from the diagnosed peak layer and writes re-encoded vision-token states back to late vision-token states through a zero-initialized gated residual. \method keeps the LLM frozen, adds no extra vision tokens, and requires no pixel-level training supervision. Across three backbones and seven benchmarks, \method improves authenticity prediction, explanation quality, and spatial alignment without mask supervision; the best read layer matches the diagnosed peak, turning the diagnostic into a practical layer-selection criterion.

Many existing multivariate time series anomaly detection methods formulate anomaly detection as a reconstruction problem, where detections are derived from reconstruction errors in the time domain. While effective for large deviations, reconstruction-based scoring struggles with subtle anomalies where only a small subset of features deviates slightly. In such cases, most features are well reconstructed, causing the overall reconstruction error to remain low and indistinguishable from normal data. To address these challenges, we propose Flow+Diff, a novel framework that detects anomalies via discrepancies in a latent space, where two complementary representations, induced by distinct modeling paradigms, diverge under anomalous inputs. One representation is obtained via a normalizing flow, which provides an invertible mapping that preserves temporal dependencies and captures inter-feature dependencies. The other is produced by a diffusion model that learns the distribution of normal samples in the latent space via a stochastic denoising process. When given anomalous inputs, the normalizing flow maps the inputs to latent representations that lie in low-probability regions of the learned latent distribution, while the diffusion model generates representations aligned with normal data distributions, resulting in a pronounced discrepancy between the two. We compute the anomaly score based on this latent-space discrepancy, thereby shifting anomaly detection from the time domain to the latent space. Finally, the invertibility of the normalizing flow enables reconstruction in the time domain, facilitating interpretation of anomalous features. Extensive experiments across six benchmark datasets show that Flow+Diff achieves state-of-the-art performance on four datasets against competitive baselines.

Can a large language model be \emph{trained} to tolerate silent hardware faults, and if so, \emph{where} in the networkshould the consistency signal be enforced? We answer both questions and find that the second one matters more than the first. Injecting tile-level GEMM faults during training and adding a KL consistency term on the output distribution reduces fault-induced perplexity degradation from $12.9\%$ to $0.21\%$ on GPT-2 Small---a $60\times$ reduction (two-seed mean). The placement of the consistency target is decisive: a direct hidden-only objective at the fault site can worsen degradation to $32.7\%$, and fair bounded hidden controls with corrupted CE improve substantially but still trail output KL in an 80k-step screen ($1.74$--$1.76\%$ vs.\ $0.84\%$). A conditional constraint hierarchy and gradient-alignment probes explain why output-level consistency is more permissive and easier to optimize than direct early hidden-state consistency, without requiring that every hidden-alignment formulation fail. The output-over-hidden advantage persists at 8B scale, the GPT-2 ordering reproduces on held-out corpora, and generic robustness methods---SAM, R-Drop, and SAF---fail to close the gap. Pairing the trained model with simulated algorithm-based fault detection yields a bit-flip-to-erasure interface with a $1.10\times$ perplexity ratio, where unprotected inference produces NaN.

LiDAR-based 3D Object Detection (L3OD) is a fundamental 3D perception task, yet its performance is often compromised by domain shifts arising from diverse sensor configurations and environmental conditions. Existing Test-Time Adaptation (TTA) methods primarily leverage self-supervision in the spatial domain, yet they frequently confound semantic content with domain style due to the sparse, non-Euclidean nature of point clouds. To overcome this limitation, we propose FIND (Frequency INvariance Disentanglement), a novel TTA framework that precisely disentangles domain-invariant content from domain-specific interference within the frequency domain. Our methodology utilizes B-Spline fitting to construct locally adaptive filters integrated into a dual-stream architecture: an Invariant Stream extracts robust features to guide pseudo-labeling, while a Specific Stream enforces consistency against domain perturbations. By combining invariance-driven alignment with stability-guided regularization, our approach dynamically extracts robust domain-invariant features while suppressing domain-specific interference. Extensive experiments on cross-dataset adaptation and robust corruption benchmarks demonstrate that FIND significantly outperforms state-of-the-art methods.

Detecting abnormal changes in server operational data is an important task in modern computing systems, as unexpected shifts in traffic, workload, or performance metrics may indicate service degradation, abnormal access patterns, system failures, or potential security risks. However, server monitoring data are often naturally observed as functional time series, with complex temporal patterns, high dimensionality, and heterogeneous structures, making traditional change-point detection methods difficult to apply reliably in practice. To address this problem, we propose a general change-point detection framework based on a Functional Embedding Neural Network (FENet), designed to provide robust detection across different data types and structural conditions without relying on strong prior assumptions. The proposed method is motivated by the connection between the classical functional CUSUM (FCUSUM) statistic and neural network representations, which allows FENet to learn effective detection rules directly from data while retaining useful statistical intuition. We provide theoretical analysis to formalize this connection and study key properties of the proposed approach. Extensive simulation studies show that FENet achieves competitive performance compared with existing methods and remains robust under limited training data and distributional mismatch between training and test settings. We further apply FENet to real-world server operational metrics, demonstrating its practical value in detecting and localizing abnormal changes in complex server monitoring systems.

Three-dimensional (3D) anomaly detection underpins quality assurance in advanced manufacturing and engineering, capturing subtle defects and deviations that 2D inspection struggles to resolve due to occlusion, viewpoint, and appearance confounds. Existing 3D anomaly detection methods either rely on reconstruction errors or stored normal representations to detect anomalies. While both paradigms have driven substantial progress, they share a fundamental limitation: both operate entirely in the visual domain, missing the rich semantics encoded in natural language, which also typically constrains them to per-category models. We propose FACETS, among the first frameworks that leverage a 3D vision--language model for 3D anomaly detection, opening a new paradigm beyond reconstruction- and memory-bank-based methods. The key idea is to explicitly retain native point-level features that enable reasoning at both patch and point granularities, and further leverage language grounding and cross-granularity geometric modeling along two complementary axes, linguistic semantics and geometric saliency, so that coarse semantic cues and fine-grained geometric details jointly support anomaly detection. FACETS enables unified multi-category anomaly detection, avoiding the per-category models used by most prior methods. Notably, we provide a mathematical analysis of our loss function, offering valuable insights into the substantial improvement FACETS achieves in anomaly localization. Extensive experiments on popular benchmarks reveal that FACETS establishes new SOTA performance across datasets and metrics for 3D anomaly detection, substantially and consistently outperforming existing methods. Code is provided as supplementary material.

This paper studies category-level RGB-D object pose estimation, which recovers an object's 3D rotation and translation without instance-specific CAD models or reference views. This task is highly ambiguous due to intra-class variations, symmetries, and partial observations. Current solutions struggle to balance reliability and efficiency: deterministic methods are prone to error propagation from intermediate structures, while diffusion models suffer from high inference latency and complex post-hoc candidate extraction. To address this, we present F2G-Pose, a real-time direct, single-pass framework that predicts category-level pose, metric size, and camera-frame completed shape from a single RGB-D observation in one feed-forward pass. F2G-Pose lifts dense visual foundation features to partial point clouds, encodes local geometry with DGCNN tokenization, and integrates object-level structure with a Mixture-of-Experts Transformer. Joint size prediction and camera-frame shape completion encourage metric consistency through dense geometric supervision. Trained only on synthetic SOPE data, F2G-Pose achieves state-of-the-art accuracy on SOPE and ROPE while running at 28 FPS, and further demonstrates strong camera-frame geometry on HouseCat6D and real-object transfer on HANDAL. Analyses of latency, robustness, stability, shape completion, and GenPose++ candidate behavior further demonstrate the efficiency and reliability of F2G-Pose, with real-world manipulation experiments demonstrating deployment potential under segmented RGB-D inputs.

While spatiotemporal deepfake detectors have shown high AUC, our experiments reveal that they remain susceptible to evasion attacks. These models tend to overfit on fragile temporal spectrum cues, rather than learning robust semantic causality. To mitigate this vulnerability, we propose SpInShield, a temporal spectral-invariant defense framework explicitly designed to decouple semantic motion from manipulatable spectral artifacts. We propose a learnable spectral adversary that dynamically synthesizes severe spectral deformations, simulating extreme attack scenarios. By employing a shortcut suppression optimization strategy, SpInShield compels the encoder to extract reliable forensic cues while autonomously purging unstable spectral statistics from the latent space. Experiments show that SpInShield obtains competitive performance on widely used datasets and outperforms the strongest baseline by 21.30\% AUC under simulated amplitude spectral attacks.

Cross-view object correspondence (CVOC) aims to establish associations of the same target between query and search videos captured from different viewpoints. Existing methods often rely on visual cues for CVOC. Despite recent progress, these methods struggle in complicated scenarios with substantial viewpoint changes and appearance variations, due to lacking sufficient target information. Addressing this, we introduce TeSCo, a novel framework that exploits rich Textual Semantics of an object, in addition to its visual cues, for cross-view object Correspondence. Our key insight is, textual description of a target, by capturing diverse attributes such as category, texture, and color, provides viewpoint- and appearance-invariant semantics, which are complementary to visual cues and can hence enhance correspondence robustness in complicated scenes. Inspired by this, TeSCo first generates a textual expression of the target from the given query view using a vision-language model, and then extracts textual feature to enhance visual features for localization in the search view. To realize this, we introduce a simple yet effective text-conditioned cross-modal fusion (TCF) module that incorporates the textual feature into visual features with text-guided modulation, producing more robust multimodal target representation for object correspondence. Since not all textual cues are equally compatible with the target in current search-view frame, we propose an iterative textual feature refinement (ITFR) module that progressively adjusts textual feature using search-view information before the TCF module, enabling search-view-aware textual semantics to be fused into visual features and leading to better performance. In extensive experiments on Ego-Exo4D and HANDAL-X, TeSCo demonstrates state-of-the-art results and largely outperforms other models, validating its efficacy. Our code and model as well as results will be released.

Current evaluation of epistemic uncertainty relies on tasks such as out-of-distribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks do not coincide with the scores commonly used to quantify epistemic uncertainty. Building on the epistemic reject-option framework, we evaluate epistemic uncertainty using its ability to identify regret, the reducible error. Formulating selective prediction as a constrained optimization over coverage, expected risk, and regret, we prove the optimal selector is a thresholded convex combination of the ground-truth aleatoric and epistemic uncertainties. This theoretical unification exposes a weakness in recent uncertainty disentanglement literature: we demonstrate that standard correlation metrics between learned components do not necessarily predict their actual operational utility. We instead propose to evaluate the achievable (risk, regret, coverage) surface of the decomposition as a diagnostic for joint disentanglement and utility. Benchmarking standard methods on datasets with dense human annotations reveals that decision-theoretic rankings can disagree substantially with proxy-task rankings, including pairwise rank inversions between methods that are top-ranked on one criterion and bottom-ranked on other.

While agentic frameworks have advanced repository-level code localization, their reliance on static snapshots often leads to a $\textit{traceability gap}$—the loss of implicit links between issue symptoms and code implementation. To bridge this information deficit, we introduce $\textbf{T}$raceability $\textbf{A}$ugmented $\textbf{Co}$de Localization (TACO), a framework that recovers retrievable and usable traceability from repository evolution to guide localization agents. TACO offline crystallizes historical pull requests into a dual-index knowledge base through a prompt auto-tuning technique, capturing high-value semantic hints and architectural rationales. In the online phase, TACO employs a synergistic dual-track retrieval workflow, cross-validated by an arbiter and aligned temporally to handle version drift. Extensive evaluations on SWE-bench Lite and SWE-bench Verified across five state-of-the-art agentic baselines demonstrate that TACO significantly improves exact-match localization accuracy (Acc@1) by an average of 11.29\%, 13.20\%, and 12.87\% at the file, module, and function levels, respectively. Moreover, TACO significantly streamlines the exploration process, achieving an average reduction of 29.2\% in token usage and 30.4\% in monetary costs, with minimal offline maintenance overhead.

Recent advances in 3D object generation have enabled the creation of high-fidelity 3D assets from 2D images. This *2D-to-3D* capability offers a promising path for alleviating 3D data scarcity, which remains a key bottleneck for scalable simulation in robotics and AR/VR applications. However, existing 3D generation methods primarily emphasize visual plausibility and geometric fidelity, with limited consideration of object-level interaction and structural understanding that are critical for downstream embodied AI tasks. To bridge this gap, we introduce **EmbodiedObject**, a unified framework for embodied 3D object understanding built upon latent representations produced by modern *2D-to-3D* generative models such as SAM3D. We first systematically analyze the part-level locality of these latent representations against existing unified 3D point encoders and show that they naturally preserve rich structural semantics suitable for object understanding. Motivated by this observation, we then design a unified DiT-style decoder that operates directly on the latent representation and supports multiple object-level tasks, including open-vocabulary 3D affordance prediction, 3D part segmentation, and 3D articulation estimation. Given a real-world 2D image containing an object of interest, **EmbodiedObject** extends *2D-to-3D* generation beyond visual reconstruction by jointly reasoning about affordance, articulation, and part semantics. This transforms generated 3D assets into actionable object representations suitable for downstream embodied AI applications. Our source-code will be open-sourced.

Knowledge conflict detection is a fundamental challenge for large language model (LLM) systems that rely on external knowledge. Existing approaches address this problem either with end-to-end supervised models or with multi-stage pipelines dependent on fixed heuristics. However, supervised models often generalize poorly to diverse conflict patterns, whereas heuristic pipelines rely on fixed rules and introduce considerable computational overhead. To overcome these limitations, we propose $\textbf{G}$rouping and $\textbf{C}$onflict $\textbf{D}$etection ($\mathsf{GCD}$), a two-stage agentic framework for knowledge conflict detection that aligns the detection process with the inherent sparsity of knowledge conflicts, thereby reducing the decision space and enabling fine-grained conflict detection. $\mathsf{GCD}$ decomposes knowledge conflict detection into two subtasks and employs two cooperative agents to address them: a Knowledge Grouping Agent (G-Agent), which creates conflict-preserving groups of textual units with potential conflicts, and an Intra-group Conflict Detection Agent (D-Agent), which detects conflicts within each group. The G-Agent's grouping policy is not fixed in advance but is jointly trained with D-Agent via a shared global reward, enabling grouping to be explicitly optimized for accurate detection rather than relying on fixed heuristics. Extensive experiments on five benchmarks demonstrate that $\mathsf{GCD}$ consistently outperforms SOTA methods, while reducing inference latency and token consumption.

Recent DETR-based tiny object detectors adopt dynamic-query mechanisms to handle density imbalance. However, existing designs entangle two effects: how many and which queries are decoded. This entanglement obscures what actually drives the gains, thereby hindering principled dynamic-query design; additional learned predictors or heuristic filtering steps also introduce extra overhead and inevitable decision errors, which can undermine practicality and final gains. In this paper, we disentangle these effects and show that dynamic query primarily acts as budgeting---allocating decoder query capacity according to object density. Motivated by this view, we formulate a monotonic and conservative budgeting principle and propose a budgeting-based DETR (BUTR), a streamlined framework that implements dynamic query by using a lightweight, training-free budgeter to estimate an input-dependent query budget $K(x)$ from encoder confidences. BUTR applies standard Top-$K(x)$ query initialization, removing heuristic filtering, learned components, and complex selection logic. On AI-TOD-v2 and VisDrone, BUTR shows clearer density-aligned query allocation behavior than prior dynamic-query detectors and improves AP by more than 0.5, while using about half as many queries with fewer parameters and GFLOPs, requiring over 2$\times$ less training time, and delivering 4$\times$ faster inference. Additional COCO results further indicate its applicability across a broad spectrum of detection scenarios.

Unsupervised video object-centric learning requires temporally consistent representations for downstream tasks. Existing methods mainly build temporal consistency on the assumption that adjacent frames share the same object composition. However, this local assumption is insufficient for real videos. Some objects remain semantically consistent over much longer temporal spans, while object composition may also change as objects appear or disappear. Fixed-slot recurrent Slot Attention architectures struggle to accommodate such visibility changes, limiting their ability to learn stronger temporal consistency beyond adjacent frames. We propose Dynamic Prototypical Contrast (DynaProto) to address these challenges. DynaProto introduces a dynamic recurrent Slot Attention architecture that determines whether each slot should be activated at each frame, thereby adapting to changing object visibility over longer temporal scopes. In addition, DynaProto introduces a prototypical contrastive objective that provides stronger clip-level temporal consistency supervision. Together, these two components enable DynaProto to learn more stable slot binding over extended temporal ranges. Extensive experiments show that DynaProto learns more temporally consistent slot representations and substantially outperforms prior methods on object discovery and object property prediction on real-world datasets, including an 11.0\% mBO improvement on YouTube-VIS.