NeurIPS 2026 Papers

Reliable 6D object pose estimation is essential for dexterous manipulation but remains highly challenging due to severe visual occlusions from multi-finger interactions. Progress has been limited by two key factors: the lack of large-scale manipulation datasets and the inability of existing methods to handle high uncertainty under occlusion. To address these challenges, we introduce DexOPE, a large-scale dataset and a physics-guided pose estimation framework. DexOPE provides a multimodal dataset with 1.5k diverse objects, an order of magnitude larger than prior work, covering simulated and real-world grasping and manipulation. To handle pose ambiguity under occlusion, we propose a Physics-Guided Score Matching approach that models the pose posterior using score-based diffusion models rather than deterministic regression. Physical constraints from hand–object interactions, including nonpenetration and tactile contact, are incorporated as guidance during sampling, enabling physically plausible pose inference even under severe or complete occlusion. Extensive experiments show that DexOPE achieves state-of-the-art performance, significantly outperforming existing methods in robustness to occlusion and generalization to unseen objects.

Foundation models produce high-dimensional latent representations in which suitably designed statistical decision rules can be effective yet analytically tractable. We study such detectors in the high-dimensional regime, where latent dimension and training sample size grow proportionally. For linear and quadratic statistics, we derive explicit large-deviation characterizations of false-alarm and miss-detection performance under the Neyman--Pearson paradigm, and show how the binary analysis extends to multi-class classification. The resulting error probabilities and corresponding decay rates exhibit non-monotonic dependence on the shape parameter, including double-descent and, in some regimes, multiple-descent. Experiments on vision, text, and audio datasets using modern foundation models show close agreement between theoretical predictions and empirical performance. In particular, the proposed framework allows us to set a prescribed false-alarm level and accurately predict the corresponding miss-detection probability. Overall, our results provide a theoretically grounded and interpretable framework with competitive empirical performance across different modalities.

Graphic designs, such as posters, advertisements, and infographics, are an important medium for communicating information and shaping understanding. Unlike natural images, they consist of layered elements with explicit compositional order. However, existing object detection models treat these elements as an unordered set, leaving compositional order unexploited. To address this limitation, we present **D**etect **A**nything in Graphic **D**esign (DAD), a model that formulates graphic design detection as compositional deconstruction. It decodes elements in compositional order, using lower-layer elements to better detect higher-layer ones. The key feature of DAD is amodal detection, which predicts the full bounding box of each element, including regions occluded by elements placed above it. Building on this formulation, we propose **Ele**ment **R**elative **P**olicy **O**ptimization (EleRPO), which extends GRPO from sequence-level supervision to element-level optimization. EleRPO provides fine-grained training signals that capture how each detected element contributes to overall detection quality, and works synergistically with compositional order to improve detection performance. To support training and evaluation, we build a dataset of 10 million graphic designs. Experiments show that DAD outperforms all baselines and achieves human-level performance in amodal detection, supporting effective image-to-layer decomposition. EleRPO consistently improves over GRPO across nine detection benchmarks.

Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source---sequential, structural, or semantic---failing to jointly exploit the complementary strengths across modalities, or treat statement-level localization merely as a byproduct of function-level detection without explicit line-level supervision. To address these limitations, we propose DCVD (Dual-Channel Cross-Modal Vulnerability Detection), a unified framework that performs joint function-level detection and statement-level localization. DCVD extracts control-dependency and semantic features through two parallel branches and integrates them via contrastive alignment coupled with bidirectional cross-attention, effectively bridging the cross-modal representation gap. It further introduces explicit supervision signals at both the function and statement levels, enabling collaborative optimization across the two granularities. Extensive experiments on a large-scale real-world vulnerability benchmark demonstrate that DCVD consistently outperforms state-of-the-art methods on both function-level detection and statement-level localization.

We study model-free reinforcement learning (RL) in non-stationary finite-horizon episodic Markov decision processes (MDPs) without prior knowledge of the non-stationarity. We focus on the piecewise stationary (PS) setting, where both rewards and transition dynamics can change at unknown times. We first revisit existing state-of-the-art approaches and identify theoretical and practical limitations that change the current landscape of performance guarantees. To characterize the difficulty of the problem, we establish the first minimax lower bounds for PS-RL in tabular and linear MDPs. We then introduce *Detection Augmented Reinforcement Learning* (DARLING), a modular wrapper for PS-RL that applies to both tabular and linear MDPs, without knowledge of the changes. In tabular MDPs, under change-point separability and reachability conditions, DARLING improves the best known dynamic regret bounds and matches our minimax lower bound. In linear MDPs, DARLING matches the minimax lower bound when the relevant reachability parameters are known, and our analysis clarifies the structural obstacles that distinguish this setting from the tabular case. Finally, through extensive experimentation across diverse non-stationary benchmarks, we show that DARLING consistently surpasses the state-of-the-art methods.

Although Vision-Language Models (VLMs) demonstrate impressive capabilities, their deployment in high-stakes domains is severely hindered by hallucinations. Existing detection methods mostly focus on static internal feature probing with coarse-grained annotations, failing to achieve fine-grained token-level localization without expensive dense labels. In this paper, we reconceptualize VLM hallucination detection as a dynamic graph learning task, identifying hallucinations as distinct *trajectory drifts* across network layers. Consequently, we propose **TRACE** (Token Representation Analysis with Cross-layer Evolution), a non-intrusive probing framework that models internal states as heterogeneous spatio-temporal graphs. By employing Graph Neural Networks (GNNs) to capture computational dynamics, TRACE effectively isolates hallucinatory signals. Furthermore, a token-first Multiple Instance Learning (MIL) readout elegantly bridges the granularity gap, distilling macroscopic sentence-level supervision into precise zero-shot token-level localization. Extensive evaluations on M-HalDetect, ViGoR, and HalLoc demonstrate that TRACE achieves state-of-the-art detection performance while being competitive in localization accuracy, offering an efficient and interpretable solution for trustworthy VLMs.

Modern object detectors achieve strong performance on standard benchmarks, yet their robustness to contextual variation remains insufficiently investigated. Prior evaluations largely rely on aggregate metrics such as AP on uncontrolled distribution shifts, which conflate multiple factors and can obscure how performance degrades under context change. We introduce \textsc{ContextShift}, a controlled benchmark for evaluating context dependence by systematically manipulating object--context relationships while preserving object appearance. Built on COCO 2017, it isolates context as an independent variable both implicitly, through geometric transformations and explicitly via synthetic and natural background substitutions - with a compatibility axis based on normalized pointwise mutual information (NPMI) for continuous evaluation. Across diverse detector architectures, we observe a consistent degradation pattern: false negatives increase up to 227\% and prediction volume decreases down to -44\%, while false positives remain relatively stable or decline. This observed suppression behavior is not revealed by aggregate metrics such as AP, which can mask substantial recall loss and changes in prediction dynamics. Further analysis suggests that degradation is not primarily explained by reduced confidence, but is instead consistent with a reduced formation of valid detection candidates. Moreover, we find that performance along the statistical compatibility axis is non-monotonic, peaking at intermediate NPMI and degrading toward both extremes, indicating that statistical co-occurrence does not correlate linearly with effective visual context. Finally, we show that a training methodology based on context-aware augmentations rather than the dataset alone, which yields overall better models: every augmented variant outperforms the dataset-only baseline on both original (unmanipulated) test images as well as manipulated images, indicating that data-oriented augmentation can partially recover performance lost to prediction-suppression failures by exposing the model to object--context decoupling during training.

MLLM-based detection has shown promising end-to-end detection ability in recent years, but its generated detection results still lack reliable confidence for evaluation and interpretation. Since detection data itself do not provide human-annotated confidence labels, existing MLLM-based detection methods mostly obtain confidence implicitly from prompted self-assessment, generation probabilities, or external proxy scores. While these scores are available for evaluation, they are not explicitly connected with the actual detection quality. Inspired by conventional detectors that optimize confidence through classification, objectness, or quality estimation branches, we formulate confidence in MLLM-based detection as an explicit modeling target to be learned, optimized and calibrated. Specifically, we present ConfDet, a practical confidence framework that first uses discrete confidence tokens based supervised fine-tuning to enable stable and parsable confidence generation, then applies GRPO with detection and separation joint rewards to optimize confidence score separation and preserve detection quality, and finally performs condition-aware post-hoc calibration to align confidence with empirical accuracy. Experiments across different in-distribution and out-of-distribution benchmarks show that, ConfDet greatly improves AP through better confidence ranking and reduces confidence miscalibration across benchmarks, providing a reliable confidence for MLLM-based detection.

Out-of-Distribution (OOD) detection requires sensitivity to subtle shifts without overreacting to natural In-Distribution (ID) diversity. However, from the viewpoint of detection granularity, global representation inevitably suppresses local OOD cues, while patch-based methods are unstable due to entangled spurious-correlation and noise. And neither of them is effective in detecting compositional OODs composed of valid ID components. Inspired by recognition-by-components theory, we present a post-hoc Component-Based OOD Detection (CoOD) framework that addresses the existing limitations by decomposing inputs into functional components. To instantiate CoOD, we derive Component Shift Score (CSS) to detect local appearance shifts, and Compositional Consistency Score (CCS) to identify cross-component compositional inconsistencies. Empirically, CoOD achieves consistent improvements on both coarse- and fine-grained OOD detection.

Money laundering detection on financial transaction graphs is critical but challenging. Laundering activities often conceal illicit fund flows within dense legitimate transaction neighborhoods and disperse suspicious behaviors over extended time spans. These patterns cause weak illicit signals to be spatially diluted and temporally forgotten, limiting the effectiveness of standard graph learning methods in AML scenarios. In this paper, we propose a spatio-temporal dual denoising framework for money laundering detection on evolving graphs, named EvoDen. For spatial denoising, instead of relying on passive neighborhood aggregation, we design a risk-potential guided walk mechanism to extract denoised and suspicion-biased transaction sequences, enabling the model to isolate anomalous fund flows from noisy local neighborhoods. For temporal denoising, we propose a continual Transformer-based sequence learning method driven by a reconstruction objective, which learns denoised sequence representations while enabling latent-space replay of historical knowledge to mitigate representation drift. We conduct experiments on three transaction datasets, and the results show that EvoDen outperforms state-of-the-art baselines in accurately identifying concealed money laundering entities.

Reconstructing the motion of objects from videos is a key component for embodied AI and robot manipulation. While diverse approaches to object pose tracking have been studied, they rely heavily on strong external priors, such as depth data or 3D templates, and remain highly vulnerable to severe occlusions by hand grasps despite the use of explicit masks. In this work, we present ComPose, a 6DoF object tracking framework designed for hand-aware object pose estimation from RGB video. Rather than treating the hand purely as an occluder, our method harmonizes hand motions as a complementary cue for object tracking. In detail, we recover a variety of object motions over time by combining object- and hand-derived cues from foundation models within a unified tracking pipeline. Here, ComPose adaptively selects informative hand joints, blends object- and hand-based rotation cues, and refines translation from the visible geometric evidence using a learned correction. We further enforce the temporal consistency over both rotation and translation, yielding stable 3D object trajectories over time without any external smoothing. Extensive experiments show that our method is accurate, efficient, and robust under severe hand occlusion and geometric ambiguity. In addition, the resulting trajectories can also effectively transfer to downstream robot manipulation by enabling robots to reconstruct human actions from online videos.

Change Detection & Captioning (CDC) aims to jointly localize changed regions and describe their semantic content from bi-temporal remote sensing images. While recent approaches have demonstrated the effectiveness of unified modeling, they primarily rely on shared representations or jointly optimized architectures, leaving task-specific communication under heterogeneous supervision less explored. However, detection and captioning differ fundamentally in their supervision granularity, leading to heterogeneous representations with uneven reliability across tasks and spatial locations. In this paper, we reformulate CDC as a selective information routing problem between heterogeneous task branches, where the key challenge is to determine when and where information should be transferred. To this end, we propose CoDeRNet, a unified framework that preserves task-specific representations while enabling bidirectional exchange of complementary information. CoDeRNet introduces a Confidence-Decomposed Routing (CoDeR) mechanism, which decomposes the routing condition into receiver-side need for support and sender-side reliability cues to guide selective transfer. Experiments on LEVIR-MCI and WHU-CDC show that CoDeRNet achieves strong overall CDC performance, particularly improving caption generation while preserving competitive change localization. Extensive analyses further show that the benefits arise not merely from joint learning or generic cross-task communication, but from explicitly modeling selective cross-task information routing. These results highlight the importance of conditional information transfer for unified CDC under heterogeneous supervision.

Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown is which visual properties models actually converge on and which factors may underlie this convergence. To address this, we decompose the object similarity structure of 162 diverse vision models into a small set of non-negative dimensions. To determine universal versus model-specific dimensions, we then estimate how often each dimension reappears across models. In contrast to model-specific dimensions, universal dimensions are more interpretable and more strongly driven by conceptual image properties, indicating the relevance of interpretability and semantic content as implicit factors driving universality across models. Differences in architecture, objective function, training data, model size, and model performance do not explain the emergence of universal dimensions. However, models with more universal dimensions also better predict macaque IT activity and human similarity judgments, suggesting that universality reflects representations relevant to biological vision. These findings have important implications for understanding the emergent representations underlying deep neural network models and their alignment with biological vision.

Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness and the perpetuation of demographic disparities that exist in the world. In this context, causal inference provides a principled framework for reasoning about fairness, as it links observed disparities to underlying mechanisms and aligns naturally with human intuition and legal notions of discrimination. Prior work on causal fairness primarily focuses on the standard machine learning setting, where a decision-maker constructs a single predictive mechanism $f_{\widehat Y}$ for an outcome variable $Y$, while inheriting the causal mechanisms of all other covariates from the real world. The generative AI setting, however, is markedly more complex: generative models can sample from arbitrary conditionals over any set of variables, implicitly constructing their own beliefs about all causal mechanisms rather than learning a single predictive function. This fundamental difference requires new developments in causal fairness methodology. We formalize the problem of causal fairness in generative AI and unify it with the standard ML setting under a common theoretical framework. We then derive new causal decomposition results that enable granular quantification of fairness impacts along both (a) different causal pathways and (b) the replacement of real-world mechanisms by the generative model's mechanisms. We establish identification conditions and introduce efficient estimators for causal quantities of interest, and demonstrate the value of our methodology by analyzing race and gender bias in large language models across different datasets.

Lesion analysis in CT imaging, the task of identifying and delineating abnormal regions across diverse anatomical sites, plays a fundamental role in clinical diagnosis. Existing CT lesion datasets typically focus on a single disease type within a specific region. This design leads to two main limitations. First, co-existing lesions outside the predefined categories are left unannotated, so models may treat real lesions as background during training. Second, lesion categories within any single region follow a long-tailed distribution, in which rare but clinically important categories are underrepresented. However, collecting enough samples for every rare category within a single region is often impractical. We observe that certain lesion categories are rare in one region but occur more frequently in another, motivating the use of cross-region complementarity for lesion detection. In this work, we curate a multi-region CT lesion dataset, namely CT-Lesion, which covers brain, chest, and abdomen CT images. Our CT-Lesion is sourced from a private cohort of 500 clinical CT scans. Seven radiologists spent approximately 1,600 working hours delineating lesions of any type, rather than restricting annotations to a predefined set of categories. As a result, CT-Lesion provides clinically confirmed voxel-level segmentation masks and 28 fine-grained lesion labels. Moreover, we further group the lesions into 9 coarse categories to alleviate data scarcity issues. To demonstrate the effectiveness and utility of CT-Lesion, we benchmark the representative methods on CT-Lesion for lesion segmentation and detection. Furthermore, pretraining on CT-Lesion improves model performance on external benchmarks, including LiTS, KiTS, and LIDC-IDRI, demonstrating its potential as a transferable resource for CT lesion analysis. Our dataset is available at Kaggle.

Open-vocabulary object detection (OVD) has made significant progress, enabling detectors to generalize from seen to unseen categories. However, real-world category spaces continually evolve, and existing OVD models still struggle with newly emerging concepts, while repeated full retraining is prohibitively expensive. To this end, we introduce a new task setting, termed Continual OVD with Novel Concept Injection (COVD), where models sequentially learn incoming novel concept groups while preserving prior concepts and original open-vocabulary knowledge, along with a new benchmark, Novel-114. Our key observation is that pretrained visual encoders often already perceive and represent many novel concepts, and the main bottleneck lies in the lack of stable semantic alignment between visual representations and textual concepts. Based on this, we propose NoIn-Det, an efficient continual injection framework without additional parameters. NoIn-Det freezes the visual encoder, preserves the text representation space using only texts of common concepts and previously injected concepts, and injects novel concepts by updating only a small subset of text-branch parameters beneficial to novel concept learning. Extensive experiments show that NoIn-Det effectively learns novel concepts, preserves old knowledge, and consistently outperforms existing continual learning methods for VLMs without introducing additional parameters.

Multivariate time-series anomaly detection aims to identify abnormal temporal patterns in complex real-world systems. However, robust unsupervised time-series anomaly detection remains challenging because anomalies can manifest over different temporal ranges and emerge from interactions among variables, while distribution shifts can cause normal patterns to drift over time. To address these challenges, we propose Codebook-based Online-adaptive Multi-scale Embedding for Time-series anomaly detection (COMET), a unified framework with three tightly coupled components. First, Multi-scale Patch Encoder learns correlation-aware patch embeddings by combining variable-specific temporal dynamics with shared inter-variable structure across multiple patch scales. Second, Vector-Quantized Coreset quantizes these embeddings into representative normal prototypes, which are used to detect anomalies through a dual score combining quantization error and density-aware memory distance. Third, Online Codebook Adaptation leverages the same codebook structure to identify reliable normal samples from training-activated codebook entries, and then updates the prototypes through contrastive learning at inference time. Experiments on five benchmark datasets demonstrate that COMET achieves the best performance on 39 out of 45 evaluation metrics compared to seven baselines, showing robust performance across diverse evaluation protocols.

Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Due to strong empirical performance, internal representation–based methods have emerged as the prevailing direction for detecting hallucinations, yet they remain largely model-specific and often fail to generalize to unseen LLMs. In this paper, we study an important yet underexplored problem, termed cross-model hallucination detection (CMHD), which aims to train hallucination detectors on source LLMs while ensuring performance on unseen target LLMs. The core challenge of CMHD lies in cross-model representation heterogeneity: hidden states from different LLMs exhibit severe semantic inconsistency, which limits the transferability of classical representation-based detectors. Through analysis and empirical validation, we show that relational structures over layers, tokens, and features capture transferable detection signals despite semantic inconsistency. Based on this observation, we propose cross-model hallucination detection via heterogeneity-oriented relational discrimination (CHORD), a relational graph discrimination framework that represents hidden states as joint relational graphs over layers, tokens, and features. It feeds these graphs into relation-aware attention to obtain structure-aware features, and uses meta-learning to shape these features toward transferable detection signals. Extensive experiments show that CHORD outperforms representative detection methods in cross-model generalization.

Video anomaly detection (VAD) is critical for identifying safety risks in surveillance and autonomous systems. Recent large-model-based VAD methods generate textual rationales and directly output anomaly scores. However, mapping rich semantic descriptions and analysis directly to absolute scalar scores can be poorly grounded and inconsistent, leading to scores that are not well aligned with anomaly severity. With the observation that for humans, it is usually easier to compare things than to rate things directly, we explore whether it also holds for large models in VAD by proposing \textbf{CA-Judge} (\textbf{C}omparative \textbf{A}nomaly \textbf{Judge}), a training-free framework that replaces direct scoring with comparative order inference. CA-Judge treats model-perceived anomaly severity as a latent coordinate, constructs this coordinate from self-comparisons among generated normal/anomalous reference events, and models how anomaly likelihood varies along this coordinate using the generated normal/anomalous reference identities. At test time, CA-Judge avoids exhaustive comparison by maintaining a posterior belief over query severity and selecting a few query--reference comparisons by their expected information about the anomaly status. The resulting comparison trace is aggregated under a Bradley--Terry likelihood and read out through the learned severity--anomaly association to produce the anomaly probability. Without fine-tuning or training data, CA-Judge substantially improves over direct-scoring baselines and surpasses prior training-free state-of-the-art methods on UCF-Crime and XD-Violence.