OpenRaiser/Intern-Atlas huggingface dataset:
DenVisCoM: Dense Vision Correspondence Mamba for Efficient and Real-time Optical Flow and Stereo Estimation
Venue: arXiv cs.CV 2026
Year: 2026

In this work, we propose a novel Mamba block DenVisCoM, as well as a novel hybrid architecture specifically tailored for accurate and real-time estimation of optical flow and disparity estimation. Given that such multi-view geometry and motion tasks are fundamentally related, we propose a unified architecture to tackle them jointly. Specifically, the proposed hybrid architecture is based on DenVisCoM and a Transformer-based attention block that efficiently addresses real-time inference, memory footprint, and accuracy at the same time for joint estimation of motion and 3D dense perception tasks. We extensively analyze the benchmark trade-off of accuracy and real-time processing on a large number of datasets. Our experimental results and related analysis suggest that our proposed model can accurately estimate optical flow and disparity estimation in real time. All models and associated code are available at https://github.com/vimstereo/DenVisCoM.
Voting-based Pitch Estimation with Temporal and Frequential Alignment and Correlation Aware Selection
Venue: arXiv cs.SD 2026
Year: 2026

The voting method, an ensemble approach for fundamental frequency estimation, is empirically known for its robustness but lacks thorough investigation. This paper provides a principled analysis and improvement of this technique. First, we offer a theoretical basis for its effectiveness, explaining the error variance reduction for fundamental frequency estimation and invoking Condorcet's jury theorem for voiced/unvoiced detection accuracy. To address its practical limitations, we propose two key improvements: 1) a pre-voting alignment procedure to correct temporal and frequential biases among estimators, and 2) a greedy algorithm to select a compact yet effective subset of estimators based on error correlation. Experiments on a diverse dataset of speech, singing, and music show that our proposed method with alignment outperforms individual state-of-the-art estimators in clean conditions and maintains robust voiced/unvoiced detection in noisy environments.
MACD: Model-Aware Contrastive Decoding via Counterfactual Data
Venue: arXiv cs.AI 2026
Year: 2026

Video language models (Video-LLMs) are prone to hallucinations, often generating plausible but ungrounded content when visual evidence is weak, ambiguous, or biased. Existing decoding methods, such as contrastive decoding (CD), rely on random perturbations to construct contrastive data for mitigating hallucination patterns. However, such a way is hard to control the visual cues that drive hallucination or well align with model weaknesses. We propose Model-aware Counterfactual Data based Contrastive Decoding (MACD), a new inference strategy that combines model-guided counterfactual construction with decoding. Our approach uses the Video-LLM's own feedback to identify object regions most responsible for hallucination, generating targeted counterfactual inputs at the object level rather than arbitrary frame or temporal modifications. These model-aware counterfactual data is then integrated into CD to enforce evidence-grounded token selection during decoding. Experiments on EventHallusion, MVBench, Perception-test and Video-MME show that MACD consistently reduces hallucination while maintaining or improving task accuracy across diverse Video-LLMs, including Qwen and InternVL families. The method is especially effective in challenging scenarios involving small, occluded, or co-occurring objects. Our code and data will be publicly released.
Backdoor Sentinel: Detecting and Detoxifying Backdoors in Diffusion Models via Temporal Noise Consistency
Venue: arXiv cs.CR 2026
Year: 2026

Diffusion models have been widely deployed in AIGC services; however, their reliance on opaque training data and procedures exposes a broad attack surface for backdoor injection. In practical auditing scenarios, due to the protection of intellectual property and commercial confidentiality, auditors are typically unable to access model parameters, rendering existing white-box or query-intensive detection methods impractical. More importantly, even after the backdoor is detected, existing detoxification approaches are often trapped in a dilemma between detoxification effectiveness and generation quality. In this work, we identify a previously unreported phenomenon called temporal noise unconsistency, where the noise predictions between adjacent diffusion timesteps is disrupted in specific temporal segments when the input is triggered, while remaining stable under clean inputs. Leveraging this finding, we propose Temporal Noise Consistency Defense (TNC-Defense), a unified framework for backdoor detection and detoxification. The framework first uses the adjacent timestep noise consistency to design a gray-box detection module, for identifying and locating anomalous diffusion timesteps. Furthermore, the framework uses the identified anomalous timesteps to construct a trigger-agnostic, timestep-aware detoxification module, which directly corrects the backdoor generation path. This effectively suppresses backdoor behavior while significantly reducing detoxification costs. We evaluate the proposed method under five representative backdoor attack scenarios and compare it with state-of-the-art defenses. The results show that TNC-Defense improves the average detection accuracy by $11\%$ with negligible additional overhead, and invalidates an average of $98.5\%$ of triggered samples with only a mild degradation in generation quality.
ParaGSE: Parallel Generative Speech Enhancement with Group-Vector-Quantization-based Neural Speech Codec
Venue: arXiv cs.SD 2026
Year: 2026

Recently, generative speech enhancement has garnered considerable interest; however, existing approaches are hindered by excessive complexity, limited efficiency, and suboptimal speech quality. To overcome these challenges, this paper proposes a novel parallel generative speech enhancement (ParaGSE) framework that leverages a group vector quantization (GVQ)-based neural speech codec. The GVQ-based codec adopts separate VQs to produce mutually independent tokens, enabling efficient parallel token prediction in ParaGSE. Specifically, ParaGSE leverages the GVQ-based codec to encode degraded speech into distinct tokens, predicts the corresponding clean tokens through parallel branches conditioned on degraded spectral features, and ultimately reconstructs clean speech via the codec decoder. Experimental results demonstrate that ParaGSE consistently produces superior enhanced speech compared to both discriminative and generative baselines, under a wide range of distortions including noise, reverberation, band-limiting, and their mixtures. Furthermore, empowered by parallel computation in token prediction, ParaGSE attains about a 1.5-fold improvement in generation efficiency on CPU compared with serial generative speech enhancement approaches.
CritiqueCrew: Orchestrating Multi-Perspective Conversational Design Critique
Venue: arXiv cs.HC 2026
Year: 2026

UI designers face growing cognitive load and cross functional friction at the intersection of user needs, business goals, and engineering constraints. Existing automated tools often deliver static "problem lists", lacking actionable repair paths and disrupting creative flow. We introduce CritiqueCrew, a Figma tool that supports designers through conversational critique. CritiqueCrew generates multi-faceted insights by implementing a multi-perspective orchestration of distinct expert roles (UX, PM, Engineer). It translates abstract critiques into concrete actions via in context feedback and interactive remediation. Across two independent controlled studies (Total N=48), CritiqueCrew significantly improved both design quality and subjective experience compared to a traditional static checker. Furthermore, our results confirm that the structured orchestration of expert roles-rather than a unified model-is key to fostering trust and creativity support. Our work demonstrates how AI can shift from a "problem auditor" to a "solution co-creator" by integrating multi-perspective dialogue with interactive repair, offering design implications for future creative tools.
Developing a Portable Solution for Post-Event Analysis Pipelines
Venue: arXiv cs.DC 2026
Year: 2026

In recent years, the monitoring and study of natural hazards have gained significant attention, particularly due to climate change, which exacerbates incidents like floods, droughts, storm surges, and landslides. Together with the constant risk of earthquakes, these climate-induced events highlight the critical necessity for enhanced risk assessment and mitigation strategies in susceptible areas such as Italy. In this work, we present a Science Gateway framework for the development of portable and fully automated post-event analysis pipelines integrating Photogrammetry techniques, Data Visualization and Artificial Intelligence technologies, applied on aerial images, to assess extreme natural events and evaluate their impact on risk-exposed assets.
Spatio-Temporal Transformers for Long-Term NDVI Forecasting
Venue: arXiv cs.CV 2026
Year: 2026

Long-term satellite image time series (SITS) analysis in heterogeneous landscapes faces significant challenges, particularly in Mediterranean regions where complex spatial patterns, seasonal variations, and multi-decade environmental changes interact across different scales. This paper presents the Spatio-Temporal Transformer for Long Term Forecasting (STT-LTF ), an extended framework that advances beyond purely temporal analysis to integrate spatial context modeling with temporal sequence prediction. STT-LTF processes multi-scale spatial patches alongside temporal sequences (up to 20 years) through a unified transformer architecture, capturing both local neighborhood relationships and regional climate influences. The framework employs comprehensive self-supervised learning with spatial masking, temporal masking, and horizon sampling strategies, enabling robust model training from 40 years of unlabeled Landsat imagery. Unlike autoregressive approaches, STT-LTF directly predicts arbitrary future time points without error accumulation, incorporating spatial patch embeddings, cyclical temporal encoding, and geographic coordinates to learn complex dependencies across heterogeneous Mediterranean ecosystems. Experimental evaluation on Landsat data (1984-2024) demonstrates that STT-LTF achieves a Mean Absolute Error (MAE) of 0.0328 and R^2 of 0.8412 for next-year predictions, outperforming traditional statistical methods, CNN-based approaches, LSTM networks, and standard transformers. The framework's ability to handle irregular temporal sampling and variable prediction horizons makes it particularly suitable for analysis of heterogeneous landscapes experiencing rapid ecological transitions.
Sentence Curve Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Language models (LMs) are a central component of modern AI systems, and diffusion-based language models (DLMs) have recently emerged as a competitive alternative. Both paradigms rely on word embeddings not only to represent the input sentence, but also to represent the target sentence that backbone models are trained to predict. We argue that such static embedding of the target word is insensitive to neighboring words, encouraging locally accurate word prediction while neglecting global structure across the target sentence. To address this limitation, we propose a continuous sentence representation, termed sentence curve, defined as a spline curve whose control points affect multiple words in the sentence. Based on this representation, we introduce sentence curve language model (SCLM), which extends DLMs to predict sentence curves instead of the static word embeddings. We theoretically show that sentence curve prediction induces a regularization effect that promotes global structure modeling, and characterize how different sentence curve types affect this behavior. Empirically, SCLM achieves SOTA performance among DLMs on IWSLT14 and WMT14, shows stable training without burdensome knowledge distillation, and demonstrates promising potential compared to discrete DLMs on LM1B.
From Knowing to Doing Precisely: A General Self-Correction and Termination Framework for VLA models
Venue: arXiv cs.RO 2026
Year: 2026

While vision-language-action (VLA) models for embodied agents integrate perception, reasoning, and control, they remain constrained by two critical weaknesses: first, during grasping tasks, the action tokens generated by the language model often exhibit subtle spatial deviations from the target object, resulting in grasp failures; second, they lack the ability to reliably recognize task completion, which leads to redundant actions and frequent timeout errors. To address these challenges and enhance robustness, we propose a lightweight, training-free framework, VLA-SCT. This framework operates as a self-correcting control loop, combining data-driven action refinement with conditional logic for termination. Consequently, compared to baseline approaches, our method achieves consistent improvements across all datasets in the LIBERO benchmark, significantly increasing the success rate of fine manipulation tasks and ensuring accurate task completion, thereby promoting the deployment of more reliable VLA agents in complex, unstructured environments.
Concept-Based Dictionary Learning for Inference-Time Safety in Vision Language Action Models
Venue: arXiv cs.RO 2026
Year: 2026

Vision Language Action (VLA) models close the perception action loop by translating multimodal instructions into executable behaviors, but this very capability magnifies safety risks: jailbreaks that merely yield toxic text in LLMs can trigger unsafe physical actions in embodied systems. Existing defenses alignment, filtering, or prompt hardening intervene too late or at the wrong modality, leaving fused representations exploitable. We introduce a concept-based dictionary learning framework for inference-time safety control. By constructing sparse, interpretable dictionaries from hidden activations, our method identifies harmful concept directions and applies threshold-based interventions to suppress or block unsafe activations. Experiments on Libero-Harm, BadRobot, RoboPair, and IS-Bench show that our approach achieves state-of-the-art defense performance, cutting attack success rates by over 70\% while maintaining task success. Crucially, the framework is plug-in and model-agnostic, requiring no retraining and integrating seamlessly with diverse VLAs. To our knowledge, this is the first inference-time concept-based safety method for embodied systems, advancing both interpretability and safe deployment of VLA models.
CloDS: Visual-Only Unsupervised Cloth Dynamics Learning in Unknown Conditions
Venue: arXiv cs.CV 2026
Year: 2026

Deep learning has demonstrated remarkable capabilities in simulating complex dynamic systems. However, existing methods require known physical properties as supervision or inputs, limiting their applicability under unknown conditions. To explore this challenge, we introduce Cloth Dynamics Grounding (CDG), a novel scenario for unsupervised learning of cloth dynamics from multi-view visual observations. We further propose Cloth Dynamics Splatting (CloDS), an unsupervised dynamic learning framework designed for CDG. CloDS adopts a three-stage pipeline that first performs video-to-geometry grounding and then trains a dynamics model on the grounded meshes. To cope with large non-linear deformations and severe self-occlusions during grounding, we introduce a dual-position opacity modulation that supports bidirectional mapping between 2D observations and 3D geometry via mesh-based Gaussian splatting in video-to-geometry grounding stage. It jointly considers the absolute and relative position of Gaussian components. Comprehensive experimental evaluations demonstrate that CloDS effectively learns cloth dynamics from visual data while maintaining strong generalization capabilities for unseen configurations. Our code is available at https://github.com/whynot-zyl/CloDS. Visualization results are available at https://github.com/whynot-zyl/CloDS_video}.%\footnote{As in this example.
When Feasibility of Fairness Audits Relies on Willingness to Share Data: Examining User Acceptance of Multi-Party Computation Protocols for Fairness Monitoring
Venue: arXiv cs.HC 2026
Year: 2026

Fairness monitoring is critical for detecting algorithmic bias, as mandated by the EU AI Act. Since such monitoring requires sensitive user data (e.g., ethnicity), the AI Act permits its processing only with strict privacy measures, such as multi-party computation (MPC), in compliance with the GDPR. However, the effectiveness of such secure monitoring protocols ultimately depends on people's willingness to share their data. Little is known about how different MPC protocol designs shape user acceptance. To address this, we conducted an online survey with 833 participants in Europe, examining user acceptance of various MPC protocol designs for fairness monitoring. Findings suggest that users prioritized risk-related attributes (e.g., privacy protection mechanism) in direct evaluation but benefit-related attributes (e.g., fairness objective) in simulated choices, with acceptance shaped by their fairness and privacy orientations. We derive implications for deploying and communicating privacy-preserving protocols in ways that foster informed consent and align with user expectations.
How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing
Venue: arXiv cs.CV 2026
Year: 2026

Recent generative models have achieved remarkable progress in image editing. However, existing systems and benchmarks remain largely text-guided. In contrast, human communication is inherently multimodal, where visual instructions such as sketches efficiently convey spatial and structural intent. To address this gap, we introduce VIBE, the Visual Instruction Benchmark for Image Editing with a three-level interaction hierarchy that captures deictic grounding, morphological manipulation, and causal reasoning. Across these levels, we curate high-quality and diverse test cases that reflect progressively increasing complexity in visual instruction following. We further propose a robust LMM-as-a-judge evaluation framework with task-specific metrics to enable scalable and fine-grained assessment. Through a comprehensive evaluation of 17 representative open-source and proprietary image editing models, we find that proprietary models exhibit early-stage visual instruction-following capabilities and consistently outperform open-source models. However, performance degrades markedly with increasing task difficulty even for the strongest systems, highlighting promising directions for future research.
Vision-only UAV State Estimation for Fast Flights Without External Localization Systems: A2RL Drone Racing Finalist Approach
Venue: arXiv cs.RO 2026
Year: 2026

Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for onboard state estimation of a high-speed UAV using a monocular RGB camera and an IMU. Our approach fuses data from Visual-Inertial Odometry (VIO), an onboard landmark-based camera measurement system, and an IMU to produce an accurate state estimate. Using onboard measurement data, we estimate and compensate for VIO drift through a novel mathematical drift model. State-of-the-art approaches often rely on more complex hardware (e.g., stereo cameras or rangefinders) and use uncorrected drifting VIO velocities, orientation, and angular rates, leading to errors during fast maneuvers. In contrast, our method corrects all VIO states (position, orientation, linear and angular velocity), resulting in accurate state estimation even during rapid and dynamic motion. Our approach was thoroughly validated through 1600 simulations and numerous real-world experiments. Furthermore, we applied the proposed method in the A2RL Drone Racing Challenge 2025, where our team advanced to the final four out of 210 teams and earned a medal.
Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training
Venue: arXiv cs.DC 2026
Year: 2026

Cross-partition edges dominate the cost of distributed GNN training: fetching remote features and activations per iteration overwhelms the network as graphs deepen and partition counts grow. Grappa is a distributed GNN training framework that enforces gradient-only communication: during each iteration, partitions train in isolation and exchange only gradients for the global update. To recover accuracy lost to isolation, Grappa (i) periodically repartitions to expose new neighborhoods and (ii) applies a lightweight coverage-corrected gradient aggregation inspired by importance sampling. We present an asymptotically unbiased estimator for gradient correction, which we use to develop a minimum-distance batch-level variant that is compatible with common deep-learning packages. We also introduce a shrinkage version that improves stability in practice. Empirical results on real and synthetic graphs show that Grappa trains GNNs 4x faster on average (up to 13x) than state-of-the-art systems, achieves better accuracy especially for deeper models, and sustains training at the trillion-edge scale on commodity hardware. Grappa is model-agnostic, supports full-graph and mini-batch training, and does not rely on high-bandwidth interconnects or caching.
Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-sample Optimal
Venue: arXiv cs.LG 2026
Year: 2026

Models that directly optimize for out-of-sample performance in the finite-sample regime have emerged as a promising alternative to traditional estimate-then-optimize approaches in data-driven optimization. In this work, we compare their performance in the context of autocorrelated uncertainties, specifically, under a Vector Autoregressive Moving Average VARMA(p,q) process. We propose an autocorrelated Optimize-via-Estimate (A-OVE) model that obtains an out-of-sample optimal solution as a function of sufficient statistics, and propose a recursive form for computing its sufficient statistics. We evaluate these models on a portfolio optimization problem with trading costs. A-OVE achieves low regret relative to a perfect information oracle, outperforming predict-then-optimize machine learning benchmarks. Notably, machine learning models with higher accuracy can have poorer decision quality, echoing the growing literature in data-driven optimization. Performance is retained under small mis-specification.