NeurIPS 2026 Papers

Tactile perception has the potential to significantly enhance dexterous robotic ma- nipulation by providing rich local information that can complement or substitute for other sensory modalities such as vision. However, tactile sensing is an inher- ently local sensor modality, providing information only at the points of contact. This strict locality necessitates the use of active perception techniques to acquire useful, task-relevant information from manipulated objects or the environment in general. Hence, the agent must actively guide sensors toward regions with more informative or significant features and integrate such information over time in order to understand a scene or complete a task. However, while active perception and tactile sensing methods have received significant attention recently, both fields lack standardized benchmarks. To bridge this gap, we introduce the Tactile MNIST Benchmark Suite, an open-source, Gymnasium-compatible benchmark specifically designed for active tactile perception tasks, including localization, classification, and volume estimation. Our benchmark suite offers diverse simulation scenarios, from simple toy environments all the way to complex tactile perception tasks using vision-based tactile sensors. Furthermore, we also offer a comprehensive dataset comprising 13,500 synthetic 3D MNIST digit models and 153,600 real-world tactile samples collected from 600 3D printed digits. Using this dataset, we train a CycleGAN for realistic tactile simulation rendering. By providing standardized protocols and reproducible evaluation frameworks, our benchmark suite facilitates systematic progress in the fields of tactile sensing and active perception in general.

Existing 3D point cloud tracking methods have not fully exploited temporal information in point cloud sequences, making it difficult to effectively capture the continuous evolution of target states and thereby limiting tracking stability in scenarios with large cross-category geometric variation. To address this issue, we propose TETrack3D, a framework for category-agnostic 3D point cloud tracking. The core idea of TETrack3D is to elevate temporal information from an auxiliary cue to an explicit learning constraint for cross-frame association. Specifically, we introduce a historical feature reuse mechanism to preserve and reuse target-related features accumulated over multiple frames, enabling the association process in the current frame to access richer and finer-grained historical context. We further design a state evolution supervision module, which guides the model to learn temporally consistent target motion patterns from historical observations by predicting future target states. In addition, to alleviate cross-frame feature drift caused by target-state changes in point cloud sequences, we propose a temporal distribution alignment strategy based on optimal transport theory, which constrains target features across adjacent frames at the distribution level and improves the stability of temporal target modeling. Extensive comparative experiments on KITTI, nuScenes, and Waymo demonstrate that explicitly modeling the target-state evolution process effectively improves the localization robustness of 3D object tracking.

Cognitive neuroscience research indicates that human spatial navigation relies not merely on abstract global map representations, but rather on anchoring local observations to spatially stable, decision-relevant landmarks to effectively align the current scene with the cognitive map. Inspired by this mechanism, we propose SFPR, a structural fingerprint-based LiDAR-to-OpenStreetMap (OSM) place recognition framework. This framework goes beyond the traditional coarse retrieval paradigm that relies exclusively on global descriptor similarity and further introduces fine-grained discrimination based on structural fingerprints to achieve more accurate place recognition. Specifically, we propose a structural fingerprint-aware attention mechanism that utilizes sparse but spatially stable anchors as saliency prompts, directing the network's attention toward potential fingerprint regions while dynamically generating global descriptors. Subsequently, we formulate the extraction and matching of structural fingerprints as an alternating optimization problem guided by this fingerprint-aware attention. Through iterative optimization, we extract structural fingerprints with high fingerprint-aware attention and geometric consistency from anchors. These structural fingerprints are further fed back into the training stage as geometric priors, effectively suppressing false matches characterized by similar global features but contradictory local structures. Experiments demonstrate that SFPR effectively overcomes the retrieval bottleneck in macroscopically homogeneous scenes, achieving a relative improvement of 30.20\% in Top-1 Recall@1m compared to the current state-of-the-art method. Code are publicly available at https://anonymous.4open.science/r/SFPR.

Monocular 3D detection (Mono3D) hinges on recovering depth from a single image, for which the geometric formulation $z \approx f \cdot H / h_{2D}$ is widely adopted due to its simplicity and direct grounding in projective geometry. We show that this formulation harbors a systematic, projection-induced bias: substituting the 2D bounding-box height $h_{2D}$ recovers the depth of an object's visible front surface rather than its center, causing size-dependent underestimation that persists even under ground-truth inputs. We address this front-surface bias with MonoPCA, a unified framework built on a projection-consistent reformulation. At its core, PCDepth retains the measurable $h_{2D}$ and closes the gap to center depth through an orientation- and size-aware correction derived from perspective geometry, eliminating the bias without introducing ill-posed regression targets. Because PCDepth couples depth to predicted object size and orientation, attribute perception becomes essential. We therefore introduce GeoAlign, a teacher--student framework that transfers geometric representations from Vision Foundation Models into a lightweight student, strengthening attribute perception at no inference cost. Across four challenging benchmarks, MonoPCA establishes new SoTA performance, including a $+1.44\%$ AP3D gain on the KITTI leaderboard and $+2.80\%$ on Omni3D Out.

Point cloud registration remains challenging in scenarios with low overlap, repetitive structures, and local geometric ambiguities, where candidate correspondences established based on local geometric similarity often lack global structural consistency and are prone to unreliable matches. To address this issue, this paper proposes RSPReg, a reliability-aware structural prototype learning framework for robust point cloud registration. RSPReg learns structural prototypes from rotation-invariant coarse-level superpoint features to characterize the structural attributes of local regions within the entire point cloud. These prototypes are further introduced as structural priors into cross-cloud correspondence reasoning, thereby improving the structural consistency of candidate correspondences. Furthermore, RSPReg estimates the uncertainty of structural prototypes and adaptively modulates the role of structural priors in the matching and pose estimation processes, reducing the negative impact of ambiguous and non-overlapping regions on registration results. Experiments on 3DMatch, 3DLoMatch, KITTI, and ETH demonstrate that RSPReg achieves the highest registration recall and exhibits clear advantages in low-overlap and cross-domain scenarios.

Bird's-eye-view (BEV) perception has emerged as a cornerstone of autonomous driving systems, providing a structured, ego-centric representation critical for downstream planning and control. However, real-world deployment faces challenges from sensor degradation and adversarial attacks, which can cause severe perceptual anomalies and ultimately compromise the safety of autonomous driving systems. To address this, we propose a resilient and plug-and-play BEV perception method (RESBev), which can be easily applied to existing BEV perception methods to enhance their robustness to diverse disturbances. Specifically, we reframe perception robustness as a latent semantic prediction problem. A latent dynamic predictor is constructed to extract spatiotemporal correlations across sequential BEV observations, thereby learning the underlying BEV state transitions to predict clean BEV features for reconstructing corrupted observations. The proposed framework operates at the semantic feature level of the BEV perception pipeline, enabling recovery that generalizes across both natural disturbances and adversarial attacks without modifying the underlying backbone. Extensive nuScenes experiments show RESBev notably enhances BEV perception robustness against natural disturbances and adversarial attacks, with latency, parameter and memory analyses verifying its excellent robustness-efficiency trade-off.

Learning expressive and transferable representations from unstructured 3D data remains a fundamental challenge. Existing backbones mirror the design of image-based networks by relying on symmetric U-Net architectures with large parametric decoders and feature skip connections. While successful in 2D, these designs are ill-suited for 3D domain where point coordinates, that define the underlying geometric manifold on which feature representations are learned, are sensitive to sensor-specific sampling patterns. In such architectures, skip connections allow low-level coordinate cues to bypass the semantic bottleneck, leading the model to overfit to local spatial patterns rather than learning robust, transferable semantic abstractions. We re-think this design and introduce Point Clustering Encoders (PCE), a minimal, decoder-free network that treats point cloud processing as a hierarchy of end-to-end learned spatial supertokens. At the core of PCE is our Time-Reversible Attention Pooling (TRAP) layer, which formalizes hierarchical downsampling as a time-reversible Markov chain. By coupling upsampling to the reverse chain, we eliminate the need for traditional decoders; the task of representation learning is fully delegated to the encoder. PCE not only surpasses state-of-the-art w.r.t. segmentation accuracy in indoor/outdoor datasets across a variety of tasks, but also reduces the number of trainable parameters, and can process scenes consisting of up to 6.5M points.

Vision-Language Models (VLMs) have demonstrated impressive multimodal reasoning, yet their ability to truly internalize the underlying physical consistency of real-world dynamics remains an open question. Existing benchmark paradigms often suffer from fragmented evaluation, focusing on isolated cognitive stages while overlooking the inherent synergy between perception, reasoning, and physical judgment. The lack of a holistic perspective limits the ability to diagnose whether VLMs can reliably evaluate the physical authenticity of emerging generative models. To address these issues, we introduce \textbf{PhysVista}, a benchmark designed to evaluate physical intelligence in VLMs through a closed cognitive loop framework inspired by the human \textbf{seeing–reasoning–assessment} process. PhysVista restores this loop by jointly evaluating physical state perception, physical dynamics reasoning, and physical plausibility assessment. It further distinguishes \textbf{event-level} reasoning and \textbf{scale-level} reasoning to enable fine-grained analysis of physical understanding. In addition, PhysVista incorporates both real-world and AI-generated videos, allowing evaluation across diverse domains and emerging generative scenarios. Extensive experiments on a wide range of state-of-the-art VLMs reveal significant limitations in current models’ physical intelligence, particularly in fine-grained reasoning and plausibility assessment. Our findings highlight critical gaps between visual recognition and genuine physical understanding, and provide insights for developing future physically grounded VLMs.

Perception for action suggests that representations of the world should be shaped not by visual fidelity alone, but by their relevance for actions. At the same time, latent JEPA-style world models advocate learning compact predictive states from high-dimensional observations to facilitate the prediction of future states, but end-to-end training of these models is nontrivial because representations may collapse if our only goal is to construct a latent state that is easy to predict. We show that a suitable inverse dynamics regularization addresses both issues: it is an effective anti-collapse mechanism that induces action-aligned representations. By forcing latent states to preserve information about the action underlying a transition, it biases the model toward the controllable degrees of freedom of the environment while discarding uncontrollable distractors. This yields stable latent world models trained end-to-end from offline, reward-free trajectories, without frozen encoders, exponential moving averages, or complex latent regularizers. Empirically, the learned latent spaces are compact, interpretable, and enable competitive planning performance across simple 2D and 3D control tasks.

Contrastive Learning (CL) has been widely used for Graph Anomaly Detection (GAD). However, existing augmentation techniques in such CL frameworks usually rely on random masking or structural alterations, which obliterates the irregularities that define anomalies and leads to *anomaly distortion*. Even improved methods using *multi-scale sampling* usually suffer from *blind contextual extraction*, which risks disrupting critical topological structures or introducing irrelevant information in the learned representations, thereby hardly capturing complex anomalies. In this paper, we propose Orthogonal-Hyperspherical Augmentation and Topology Perception (**OHATP**), a novel method that leverages the synergies at both the *feature* and *structure* levels to mitigate anomaly distortion and *blind sampling defects*. Specifically, during *feature-level* augmentation, we adapt projections and noise based on node *message-passing* influence to strengthen *feature diversity*, while utilizing projection orthogonality and hyperspherical noise constraints to preserve *feature discriminability*. To complement this feature-level augmentation, we design a *structure-level* topology perception module that leverages the high-quality representations learned from augmentation to screen abnormal dense substructures. Extensive experiments on benchmark datasets demonstrate that OHATP substantially outperforms state-of-the-art methods, achieving improvements of over 6\% in AUROC and 29\% in AUPRC. Code is available at [https://anonymous.4open.science/r/OHATP-83D7](https://anonymous.4open.science/r/OHATP-83D7).

LiDAR sensors are critical for autonomous driving perception, yet remain vulnerable to spoofing attacks. Jamming attacks inject high-frequency laser pulses that completely blind LiDAR sensors by overwhelming authentic returns with malicious signals. We discover that while point clouds become randomized, the underlying full-waveform data retains distinguishable signatures between attack and legitimate signals. In this work, we propose PULSAR-Net, capable of reconstructing authentic point clouds under jamming attacks by leveraging previously underutilized intermediate full-waveform representations and simultaneous laser sensing in modern LiDAR systems. PULSAR-Net adopts a novel U-Net architecture with axial spatial attention mechanisms specifically designed to identify attack-induced signals from authentic object returns in the full-waveform representation. To address the lack of full-waveform representations in existing LiDAR datasets under jamming attacks, we introduce a physics-aware dataset generation pipeline that synthesizes realistic full-waveform representations under jamming attacks. Despite being trained exclusively on synthetic data, PULSAR-Net achieves reconstruction rates of 92\% and 73\% for vehicles obscured by jamming attacks in real-world static and driving scenarios, respectively.

LiDAR scene flow estimation is essential for autonomous driving, as it provides 3D motion for each point. Self-supervised approaches use static-dynamic classification to mitigate the imbalance between static and dynamic points, deriving targeted supervision. However, existing methods rely on sparse geometric observations for this classification, making them vulnerable to data sparsity and occlusions. The resulting noisy labels provide incorrect motion guidance and degrade scene flow learning. To address this, we introduce TrackCue, a tracking-guided framework for improving dynamic object representation in LiDAR scene flow estimation. In particular, TrackCue repurposes point tracking to obtain dense image-space trajectories anchored to LiDAR points, providing motion cues beyond sparse geometric observations. Furthermore, we present a visually consistent motion compensation strategy that compares the tracked trajectories with ego-induced rigid trajectories in the image plane, effectively isolating true object motion from ego-induced apparent motion. To transfer these isolated motion cues back to the LiDAR domain, we perform visual motion cue lifting, which associates ego-compensated image trajectories with LiDAR points for static-dynamic label refinement. As a result, TrackCue produces more accurate static-dynamic classification and provides more reliable supervision for scene flow learning. Experimental results show that TrackCue significantly improves the precision and F1 score of dynamic labels, leading to performance gains in self-supervised scene flow estimation.

Prior outdoor LiDAR registration works are commonly limited by the pair-wise input paradigm, which neglects temporal correlations inherent within streaming sequences. In this paper, we propose a novel multi-frame outdoor point cloud registration network for streaming LiDAR scans enhanced with pose-related memory buffers. The key observation is that long-term temporal LiDAR sequences can provide rich global contextual information to complete sparse measurements, filter outliers, and address low-overlap problems, thereby boosting registration performance. Specifically, two hybrid memory buffers are designed, including an implicit memory feature buffer and an explicit memory pose buffer, to store and dynamically update pose-related temporal features. Moreover, a novel dynamic history weighting module is developed to adaptively fuse current and history pose-related features. Extensive experiments on three outdoor datasets, including KITTI, nuScenes, and Apollo-Southbay, demonstrate state-of-the-art performance of MemReg, surpassing all previous pair-wise methods and also multi-frame SLAM systems. Our method also generalizes surprisingly well to multiview indoor registration scenarios with rather competitive performance on 3DMatch, 3DLoMatch, and ScanNet. Code will be released upon publication.

Given the inherent unpredictability of packet loss in vehicular wireless communications, V2X collaborative perception can yield practical benefits only if agents can achieve reliable collaboration under lossy and low-bandwidth communication conditions. Existing dense BEV feature fusion methods depend on redundant BEV feature exchange, which is infeasible in low-bandwidth scenarios, while compact-communication methods aggressively compress messages but can hardly recover the missing feature content after packet loss. In this paper, we present LR-V2X, a loss-resilient, latent-space reconstruction framework that converts corrupted received latents (even under severe 90\% packet loss) into a spatial prior and then reconstructs the missing BEV information from this informative prior and using ego context as condition. Notably, the model can be trained under complete communication conditions and can be directly applied to lossy conditions at test time, eliminating the need for training under numerous lossy conditions. Experiments on DAIR-V2X and V2XREAL show that LR-V2X delivers the strongest robustness under severe packet loss and preserves reliable collaboration as communication quality degrades. And it reduces communication overhead by $64\times$ compared to dense BEV feature fusion baselines. Code will be publicly released.

Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tendency, often described as “connecting the dots” where none exist, can result in systematic reasoning errors. This paper investigates whether Large Language Models (LLMs) exhibit such perceptual tendencies, which can lead to systematic errors in downstream applications. To our knowledge, this work presents the first systematic study of illusory pattern perception in LLMs, adapting classic psychological paradigms to three tasks with direct empirical comparison to human behaviors. We find that LLMs frequently exhibit stronger illusory pattern perception than humans. In particular, models tend to over-associate frequent positive attributes with majority groups or large organizations, and show increased tendencies to construct causal narratives from ambiguous events. To uncover the mechanism behind these behaviors, we develop a feature interpretability framework based on Sparse Autoencoders (SAEs) to analyze internal representations. Our results reveal that holistic frequency perception and analytic cognitive orientation are linked to the emergence of illusory perceptions. These findings highlight a previously underexplored cognitive-like illusion that may affect the reliability of LLM reasoning. Code available at [Anonymous Github](https://anonymous.4open.science/r/illusory-8E27/).

Foundational marked temporal point process (MTPP) models, such as the Hawkes process, often use inexpressive model families in order to offer interpretable parameterizations of event data. On the other hand, neural MTPPs models forego this interpretability in favor of absolute predictive performance. In this work, we present a new family MTPP models: the hyper Hawkes process (HHP), which aims to be as flexible and performant as neural MTPPs, while retaining interpretable aspects. To achieve this, the HHP extends the classical Hawkes process to increase its expressivity by first expanding the dimension of the process into a latent space, and then introducing a hypernetwork to allow time- and data-dependent dynamics. These extensions define a highly performant MTPP family, achieving state-of-the-art performance across a range of benchmark tasks and metrics. Furthermore, by retaining the linearity of the recurrence, albeit now piecewise and conditionally linear, the HHP also retains much of the structure of the original Hawkes process, which we exploit to create direct probes into how the model creates predictions. HHP models therefore offer both state-of-the-art predictions, while also providing an opportunity to ``open the box'' and inspect how predictions were generated.

We propose HuPER, a human-inspired framework that models phonetic perception as adaptive inference over acoustic--phonetic evidence and linguistic knowledge. HuPER first learns an acoustic-grounded phone recognizer from limited human-annotated data and transcript-only speech: canonical G2P transcriptions are treated as auxiliary linguistic cues rather than ground truth, and are corrected into realized phone proxies for self-training. At inference time, HuPER supports multiple perceptual routes: it can rely on bottom-up phone evidence when the signal is clear, incorporate explicit expectations when a reference is available, or use lexical constraints when acoustic evidence is weak. With only 100 hours of training data, HuPER achieves state-of-the-art phonetic feature error rates on five English benchmarks and strong zero-shot transfer to 95 unseen languages. HuPER also improves robustness on weak-evidence and disordered speech, demonstrating the benefit of adaptive, multi-path phonetic perception under diverse acoustic and task conditions. All training data, models, and code are open-sourced. Code and demo are available at https://github.com/HuPER29/HuPER.

Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images. Existing approaches either commit to a single operating point on this distortion–perception (DP) frontier or require retraining, auxiliary models, or changes in discretization to access different points. We show that flow map models, a recent extension of flow matching for few-step sampling that learns an average field, implicitly define a one-parameter family of denoisers that continuously spans the DP frontier. The parameter, the lookahead $t$, acts as a knob: varying it traces a smooth path from the MMSE estimator to a perceptually aligned one. For Gaussian targets, we prove that this path recovers the optimal DP frontier exactly; for natural images, we demonstrate empirically that it closely follows the same behavior. Embedded within a Plug-and-Play solver, a single trained flow map matches or exceeds specialized baselines at both ends of the DP spectrum and uniquely traces a continuous curve in between without retraining, paired data, or auxiliary networks. Extensive experiments on CelebA ($128\times 128$) and AFHQ ($256\times 256$) across several linear and nonlinear inverse tasks validate our findings.

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.