Web tracking is increasingly pervasive, raising serious concerns about user privacy and security. Among existing techniques, pixel tracking is particularly stealthy and cost-effective, embedding invisible images that exfiltrate user activities to third-party servers. Current defenses, including filter list blocking and conventional machine learning, often fail to capture the cross-site associations that enable pixel tracking to evade detection. To address this limitation, we introduce TGNN, a framework that formulates pixel tracking detection as an edge classification task on a Tracking Directed Graph (TDG), which models third-party associations across websites. TGNN encodes HTTP traffic into structured quadruples and learns both semantic features and interaction patterns. To overcome the scarcity of reliable labels, we propose a large language model (LLM)-based annotation method that leverages minimal expert supervision to produce high-quality labels, significantly improving detection. Experiments conducted on traffic from the Alexa top-10K websites demonstrate that TGNN substantially outperforms existing baselines, while the LLM-based annotation achieves accuracy comparable to expert curation. Our large-scale measurement reveals that at least 16.74% of websites engage in pixel tracking via major third-party infrastructures, establishing cross-domain tracking as a pervasive practice in the wild and indicating a potential privacy threat in the modern Web ecosystem.
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Chlor-alkali production is a large-scale industrial process whose operating conditions and equipment states evolve over time. Its process optimization requires ongoing trade-offs among conflicting objectives such as product yield, energy consumption, and equipment life. Existing optimization approaches are typically static and must be re-optimized after environmental changes, limiting their real-world applicability. In this work, we model the problem as a dynamic multi-objective sequential decision-making problem that continuously tracks a time-varying Pareto set under changing conditions. We propose MORL-CA, a multi-objective reinforcement learning framework that integrates offline pretraining on historical data with constrained online policy refinement. MORL-CA introduces a state-aware adaptive objective weighting mechanism within a multi-critic actor-critic architecture, enabling localized Pareto-improving policy updates while satisfying operational and safety constraints. Extensive experiments in an environment conducted from real chlor-alkali data demonstrate that MORL-CA achieves superior Pareto solution quality and smoother adaptation to dynamics compared with state-of-the-art multi-objective optimizers and MORL baselines.
The increasing frequency and severity of climate-related disasters have intensified the need for real-time monitoring, early warning, and informed decision-making. Earth Observation (EO), powered by satellite data and Machine Learning (ML), offers powerful tools to meet these challenges. Foundation Models (FMs) have revolutionized EO ML by enabling general-purpose pretraining on large-scale remote sensing datasets. However most existing models rely on high-resolution satellite imagery with low revisit rates, limiting their suitability for fast-evolving phenomena and time-critical emergency response. In this work, we present HighFM, a first cut approach towards a FM for high-temporal-resolution, multispectral EO data. Leveraging over 2 TB of SEVIRI imagery from the Meteosat Second Generation (MSG) platform, we adapt the SatMAE masked autoencoding framework to learn robust spatiotemporal representations. To support real-time monitoring, we enhance the original architecture with fine-grained temporal encodings to capture short-term variability. The pretrained models are then fine-tuned on cloud masking and active fire detection tasks. We benchmark our SEVIRI-pretrained Vision Transformers against traditional baselines and recent geospatial FMs, demonstrating consistent gains across both balanced accuracy and IoU metrics. Our results highlight the potential of temporally dense geostationary data for real-time EO, offering a scalable path toward foundation models for disaster detection and tracking.
Monitoring forest functional diversity is essential to understand ecosystem resilience in the face of rapid environmental change. While existing remote sensing approaches primarily track structural attributes such as canopy density and tree height, functional traits like leaf phenology (evergreen vs. deciduous) and leaf type (broadleaf vs. needleleaf) reveal more direct information about adaptive strategies of tree species. This study presents a scalable machine learning framework for mapping these traits across India at 10 m resolution using Google AlphaEarth Foundations (AEF) embeddings, which capture the complete annual spectral reflectance and radar signatures of the land surface. A key contribution we make is to curate an ML-ready training dataset by combining tree traits information with tree species occurrence data, and to obtain a diverse sample from this data based on spectral time-series to ensure the dataset captures a wide range of phenological dynamics. We then build cross-validation folds to specifically test for spatial generalizability across different eco-regions in India and temporal generalizability across different years, for classifiers learned from the data. Multiple classifiers are evaluated: Random Forest models trained on AEF embedding features achieved the best performance for both classification tasks, outperforming models trained on conventional Sentinel-1 and Sentinel-2 time series while offering seamless deployment in Google Earth Engine. Compared to publicly available land-cover products that encode leaf phenology and leaf type, our model yields significantly higher accuracy while providing outputs at substantially finer spatial resolution. We then observe the outputs of our model over several protected forest areas in India to understand their dynamics over the last 8 years. Our contribution is an analysis-ready open dataset to learn tree traits from remote sensed spectral data, a trained model that is spatially and temporally generalizable, and a demonstration of the insights the model can provide to understand the dynamics of protected forest areas, all of which can be replicated in other areas.
Safe and precise maneuvering of quadrotor unmanned aerial vehicles (UAVs) in high-speed wind environments remains a critical challenge. Wind disturbances are nonlinear, time-varying, and difficult to model, causing traditional controllers to struggle with perception and compensation, especially under unseen wind distributions. To address these limitations, we introduce WA-TD3, a data-driven control framework that enables real-time wind disturbance perception and adaptive compensation without dedicated wind sensors. WA-TD3 employs a deep residual network to extract wind characteristics from temporal patterns in state deviations, forming a dynamics residual-driven perception mechanism that implicitly models and compensates for unknown winds. This residual is integrated into a perception-augmented reinforcement learning architecture, providing the policy with enhanced state information for proactive disturbance-aware control. Extensive experiments on complex trajectories under varying wind intensities demonstrate that WA-TD3 consistently outperforms state-of-the-art methods, achieving over 62% improvement in tracking accuracy under strong winds.
Despite being a cost-effective modality for volumetric imaging, freehand three-dimensional (3D) ultrasound produces inherently sparse data due to the significant elevational gaps left by tracked 2D sweeps. This sparsity poses a unique challenge for Implicit Neural Representations (INRs). While successful in other domains, INRs applied here tend to fail as the learned signed distance fields (SDF) collapse toward zero inside the object, leading to the loss of concavities and the incorrect closure of anatomical gaps. Our analysis identifies the root cause as a statistical bias in gradient-based sampling objectives, showing that symmetric volumetric sampling mathematically drives the expected SDF value to zero. To rectify this, we present a geometry-aware framework that explicitly anchors the non-negative half-space. Our method utilizes a boundary-directed exterior sampling strategy to ensure non-negative constraints in empty areas, complemented by an ellipsoid-based adversarial mechanism to regularize the global field distribution. Experiments on multiple anatomical datasets demonstrate that our approach mitigates field collapse and improves geometric fidelity and topological consistency on most metrics. Code is available at https://github.com/jiuanchen/Pulling-Fails-SDF.
Spatio-temporal graph networks form the foundation of modern traffic prediction, yet their deployment is fundamentally challenged by the pervasive reality of distribution shifts. While out-of-distribution (OOD) learning holds promise for robustness, existing methods rely on static graph structures, failing to capture the inherent topological dynamics of real-world traffic systems and thus limiting long-term deployment reliability. To bridge this gap, we propose DynaSTar, a Dynamic Spatio-Temporal Graph Invariant Learning model designed for reliable out-of-time (OOT) traffic prediction under evolving topologies. Our model employs a dynamic probabilistic graph structure, which is continuously refined through momentum-based updates and differentiable sparse sampling to model evolving inter-node dependencies. Besides, it utilizes node-level environment construction and modulated prediction to extract representations invariant to heterogeneous neighborhood fluctuations, enabling robust generalization. Comprehensive experiments on large-scale, long-term real-world datasets demonstrate that by effectively tracking evolving topological shifts, DynaSTar consistently outperforms state-of-the-art baselines across various OOT scenarios.
Denoising Diffusion Codebook Models (DDCM) have emerged as a promising framework for zero-shot image compression by replacing stochastic sampling with discrete selection from a reproducible Gaussian codebook. By greedily picking noise vectors that best match the target image, DDCM encodes the generative trajectory into a compact sequence of indices for image compression. However, the target-guided noise selection provides only coarse guidance as the discrepancy between the denoiser's output and the target image is substantial. This discrepancy inevitably results in trajectory drift, necessitating massive codebooks to span the enlarged search space and extensive inference steps to gradually compensate for the deviation. In this paper, we propose the Trajectory-Consistent Diffusion Codebook Model (TC-DDCM) to address these inefficiencies. Rather than approximating the distant target, TC-DDCM navigates the generative process by strictly tracking a pre-computed inversion trajectory. This paradigm shift yields two critical advantages: 1) By minimizing the deviation to the proximal reference state along the trajectory, the search space is drastically shrunk, resulting in significantly smaller codebooks. 2) By aligning the generative steps with inversion methods, TC-DDCM achieves optimal reconstruction in a few inference steps. Extensive experiments demonstrate that TC-DDCM significantly outperforms state-of-the-art zero-shot methods in rate-distortion-perception performance at ultra-low bitrates, making zero-shot diffusion-based compression practical.
Recent works in Gaussian Splatting (GS) SLAM highlight the importance of geometric structure. However, existing methods often rely on either 2D or 3D Gaussian primitives, lacking the balance between geometry and appearance, thus failing to precisely model spatial structures. Furthermore, current frameworks either solely utilize SLAM to provide poses for GS or optimize poses and all Gaussian parameters indiscriminately, which neglect geometric consistency constraints essential for robust localization, creating a gap between GS and SLAM. In this paper, a tight-coupling SLAM system based on a Geometric Gaussian Field (GGF) is proposed. First, GGF combines explicit spatial structures with implicit neural residuals to adaptively regulate the Gaussian morphologies, seamlessly transitioning between 2D and 3D Gaussian primitives, ensuring precise spatial modeling. Second, a Geometric Consistency-Guided Refinement (GCGR) strategy is introduced, which exploits depth and normal maps rendered by GGF to construct critical constraints for accurate pose estimation. Simultaneously, updated poses and spatial information are fed back to refine Gaussian morphology, establishing mutual enhancement and tight-coupling between GS and SLAM. Extensive experiments demonstrate that GGF-SLAM outperforms state-of-the-art methods in tracking accuracy, photorealistic rendering, and geometric reconstruction.
Vision-based trajectory prediction with BEV representations has achieved promising results, yet existing methods often suffer from limited temporal modeling and insufficient characterization of motion dynamics. To address these issues, we propose a temporally enhanced framework with explicit motion parameter prediction. Specifically, we introduce BEVFormer++, which leverages multi-view images and BEV features from multiple preceding timesteps to generate more robust BEV representations, along with BEV differential features to capture temporal variations. Moreover, we propose a motion-parameter-decoupled tracking module that explicitly estimates velocity, acceleration, and heading angle, providing informative motion cues for trajectory prediction. Extensive experimental results demonstrate that our method outperforms state-of-the-art approaches and can be seamlessly integrated into existing vision-based frameworks, consistently yielding performance improvements.
Knowledge Compilation transforms propositional formulas into tractable structures like decision-DNNF to support efficient reasoning. However, these representations often suffer from exponential size, and standard minimization via SAT sweeping is computationally prohibitive for large instances. In this paper, we propose a scalable minimization framework for decision-DNNF that eliminates the need for SAT solvers. We introduce a semantic hashing technique leveraging polynomial-time model counting to rapidly filter redundancies, followed by a polynomial-time verification strategy based on CNF projection. Our experimental evaluation demonstrates that this approach efficiently compresses decision-DNNF circuits while avoiding the bottleneck of NP-hard equivalence checks.
Accurate identification of non-rigid motion is crucial for geometric validity in self-supervised monocular depth estimation (MDE), yet it remains challenging for current methods. Inspired by Human Visual Perception (HVP), we present VFM-Dynamo, an efficient self-supervised MDE framework that disambiguates non-rigid objects by combining a vision foundation model (VFM) such as Grounding DINO and SAM with coarse to fine motion identification strategy: 1) Integrating visual-textual feature dependencies, we activate the capability of Grounding DINO global understanding with text prompts to detect potential object bounding boxes. 2) We then estimate coarse motion mask from reprojection error and photometric consistency to separate static from dynamic content. This mask provides coarse guidance for constraining rigid flow in static regions through two analogous estimates aligned by a consistency loss, and it drives a non-dynamical suppression module that removes boxes associated with static objects. 3) The remaining boxes are used to prompt SAM to produce refined motion identification, which in turn guide the joint optimization of depth and optical flow. Additionally, due to foreground–background mixing, standard interpolation-based upsampling often produces boundary artifacts. We introduce a learnable neighbor affinity interpolation (LNAI) module, which directly upsamples depth to full resolution and can be seamlessly integrated as a plug-and-play component. Experiments on a series of benchmarks demonstrate that proposed framework achieves state-of-the-art performance among unsupervised methods. Code is available at https://github.com/Qianqian3764/VFM-Dynamo.
Multi-object tracking (MOT) has long faced challenges with identity switches, especially for targets with low appearance discriminability and complex motion. Existing end-to-end trackers typically enhance robustness by modeling long-term temporal information across target-level representations, yet these representations remain insufficiently discriminative for spatially and visually similar targets. In this paper, we present MoTRa, a Motion-aware Target Representation learning framework for end-to-end MOT that enriches target-level representations with adaptive motion cues. As a result, each representation adaptively balances motion cues and appearance-related content cues under varying conditions, enhancing its discriminability for tracking. To achieve this, we propose a feature fusion and alignment module that extracts motion cues and adaptively fuses them with content features into target representations. To further regularize the dynamically fused representations, we propose a target-specific contrastive learning strategy that promotes intra-trajectory consistency and inter-target separability. Experimental results demonstrate the effectiveness of our method, achieving competitive performance on multiple benchmarks.
Recent advances in text-to-audio (TTA) and audio-to-audio (ATA) generation models have enabled the creation of highly realistic environmental sounds, raising growing concerns about malicious audio manipulation in real-world scenarios. To address this emerging threat, the ESDD 2026 Challenge was introduced as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD), featuring two tracks that evaluate generalization to unseen generators and robustness under black-box, low-resource conditions. In this paper, we present BEAT2AASIST, an enhanced deepfake detection framework built upon the BEATs-AASIST baseline. Motivated by the observation that token-based audio representations may weaken explicit preservation of structured acoustic cues, the proposed method introduces a dual-branch AASIST architecture that explicitly splits BEATs-derived representations along frequency or channel dimensions. This design enables specialized modeling of complementary spoofing artifacts that may be attenuated in unified representations. To further enrich acoustic features, we incorporate multi-layer fusion strategies that aggregate information from multiple transformer layers using concatenation, CNN-gated, and SE-gated mechanisms. In addition, vocoder-based data augmentation with multiple high-fidelity neural vocoders is employed to enhance robustness against unseen and black-box spoofing attacks. Experimental results on the EnvSDD dataset demonstrate that BEAT2AASIST achieves strong and consistent performance across both challenge tracks. In particular, the proposed approach attains 3rd place in Track 2 and 4th place in Track 1 in the ESDD 2026 Challenge, despite using fewer ensemble components than top-ranked systems. These results suggest that explicit modeling of heterogeneous acoustic subspaces, combined with targeted representation fusion and data augmentation, provides an effective and efficient design strategy for real-world environmental sound deepfake detection. The code is available at https://github.com/ikwak2/BEAT2AASIST.
Large Language Models (LLMs) powerful generative capabilities also pose significant risks, underscoring the need for effective detoxification methods to ensure safer deployment. Due to the polysemantic nature of LLM neurons, recent neuron intervention methods inevitably entangle unrelated concepts, compromising generation quality and interpretability. Sparse Autoencoders (SAEs) have opened new horizons for decomposing model activations into monosemantic features, offering interpretability and targeted feature-level steering. Empirical findings reveal that, despite capturing interpretable features, indiscriminate interventions on toxicity-related features expose the fragility of LLMs, achieving toxicity mitigation at the cost of degraded fluency. Building upon this finding, we propose DeLFE, a lightweight controlled detoxification approach that identifies specific toxic features across model layers and performs targeted interventions on them. DeLFE learns toxicity subspaces from label-guided SAE feature subsets to characterize toxic v.s. non-toxic activation patterns. When auto-completing a response token-by-token, DeLFE tracks the toxicity-triggering risks and steers toxic features away from the subspace via a flow-matching feature transformation. We further design three feature-level strategies that adjust intervention timing and strength to reconstruct the target model’s original activations. Extensive experiments demonstrate that our method achieves strong detoxification effectiveness while maintaining high generation quality across models of varying sizes and diverse base LLMs.
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights to combine visual, language, and action representations, preventing the model from adapting to different phases of task execution. To address this limitation, we propose S²-VLA, a framework that introduces a State-Space Guided Adaptive Attention (SSGAA) mechanism. SSGAA maintains a belief state that tracks task progression and generates dynamic gating weights to adaptively fuse information from three complementary sources visual features for spatial perception, task intents for high-level task planning, and temporal action sequences for execution consistency. This adaptive fusion allows the model to shift its focus throughout task execution, aligning with the evolving requirements of different task stages. Despite its compact 2B parameter size, S²-VLA consistently outperforms larger 7B-scale models and achieves state-of-the-art performance on long-horizon manipulation benchmarks, including LIBERO and SimplerEnv. highlighting the importance of adaptive feature fusion for long-horizon robotic manipulation.
Referring Multi-Object Tracking (RMOT) faces a fundamental structural contradiction between the high-discriminability demand and the sparse semantic supervision. This mismatch is particularly acute in highly homogeneous scenarios that require fine-grained discrimination over complex compositional semantics. However, under sparse supervision, models overfit to salient yet insufficient cues, leading to shortcut learning and degraded compositional discrimination. To resolve this, we propose COAL (Counterfactual and Observation-enhanced Alignment Learning), a framework that advances RMOT beyond isolated structural optimization through knowledge regularization. First, we introduce Explicit Semantic Injection (ESI) via a VLM to densify the observation space and enhance instance discriminability. Second, leveraging LLM reasoning, we propose Counterfactual Learning (CFL) to augment supervision, enforcing strict attribute verification for robust compositional recognition. These strategies are unified within a Hierarchical Multi-Stream Integration (HMSI) architecture, which distills external knowledge into domain-specific discriminative representations. Experiments on Refer-KITTI and Refer-KITTI-V2 benchmarks validate COAL's efficacy. Notably, it surpasses the state-of-the-art by 7.28% HOTA on the highly challenging Refer-KITTI-V2. These results demonstrate the effectiveness of knowledge regularization for resolving the sparsity–discriminability paradox in RMOT.
Hand Object Interaction (HOI) generation provides an efficient solution for virtual reality simulation and embodied AI deployment.Recent studies have explored instruction-driven HOI synthesis, yet they overlooked the fine-grained interactive contact and struggled with robust generalization in data-scarce scenarios. In addition, existing datasets are frequently constrained with limited object categories and monotonous motion patterns. To address these limitations, we first integrate a large-scale HOI dataset, namely HOI-X, which contains diverse objects, rich motion patterns, and part-level contact annotations. Along with HOI-X, we propose A3fford-HOI, a fine-grained and generalizable Hand Object Interaction framework with Anatomy-Aligned Affordance Disentanglement. Specifically, we define a Disentangle-2-Interact paradigm in A3fford-HOI. Considering the various interactions of distinct hand anatomical structures, in the Disentangle stage, we devise an Anatomy-Aligned Affordance Predictor, which leverages the external plausible physical priors in 3D multimodal large language models for affordance disentanglement prediction. Then, the disentangled affordance is employed into the Interact stage, i.e. Affordance Driven Interactor. The interactor integrates textual instructions, object geometric features, and the external knowledge enhanced (MLLMs) affordance via a Diffusion Transformer (DiT) for generalizable HOI generation. Extensive experiments demonstrate that A3fford-HOI maintains superior generation quality and contact plausibility for fine-grained and generalizable hand object interaction.
Despite decades of intensive research and optimization, modern Boolean Satisfiability (SAT) solvers have reached a plateau where significant performance gains are increasingly difficult to achieve. While Large Language Models (LLMs) have demonstrated remarkable capabilities in pattern recognition and code generation for combinatorial optimization, their direct application to highly optimized SAT solvers remains a formidable challenge due to the extreme complexity and sensitivity of solver heuristics. In this paper, we introduce AESAT (Auto-Evolving SAT solving), a novel neuro-symbolic framework designed to automatically evolve and optimize the heuristic functions of SAT solvers. AESAT employs a memetic-inspired approach, synergizing LLM-guided individual optimization with evolutionary exploration. By leveraging self-optimized prompting techniques, our framework enables LLMs to iteratively discover and refine sophisticated heuristics that bypass the limitations of human-engineered designs. The efficacy of AESAT is demonstrated by its flagship derivative, AE-Kissat-MAB, which won the main track of the 2025 International SAT Competition by a wide margin. This result represents the first time an LLM-enhanced solver has dominated the world's premier SAT competition, marking a paradigm shift in the automated design of reasoning algorithms.
Reinforcement learning (RL) has evolved beyond monolithic training, yet existing frameworks remain limited to single algorithms or simple offline-to-online transitions. We present multi-phase RL, a framework that orchestrates multiple learning phases for continual policy improvement. It enables efficient fine-tuning of pretrained policies with new data and smooth adaptation from simulation to real-world environments. To support this paradigm, we introduce RL-Studio, a platform that addresses key implementation barriers, including neural architecture mismatches, parameter transfer complexities, and experiment management overhead. It provides phase orchestration, transition-point monitoring, and full experiment lineage tracking. We demonstrate the effectiveness of multi-phase RL through representative scenarios and highlight RL-Studio’s capabilities.