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At least 991 records · Page 55Linked to original sources
arXiv2026

Formation Dynamics of a Rotating Solar Spicule-like Event and Associated Energy-Mass Transport

We performed three-dimensional radiative magnetohydrodynamic(3D RMHD) simulations in an upward-directed (positive-polarity) open magnetic field with an initial background strength of 10 G. We identified vortex-associated spicule-like events and carried out a detailed analysis of one representative event. Prior to the formation of this spicule-like event, a photospheric vortex appears and twists the magnetic field lines, then develops upward into higher atmospheric layers, forming a magnetic tornado system. This event is generated during this process. At the periphery of this magnetic tornado, twisted magnetic field lines with negative vertical components reconnect with both external positive-polarity background fields and central positive-polarity fields. The magnetic reconnection sites rise and rotate together with the developed magnetic tornado, and many jet-like substructures in this event are closely associated with plasma upward ejections from these reconnection regions. A vertical force analysis shows that, for this event, plasma uplift in the photosphere and lower chromosphere is dominated by the pressure-gradient force, whereas in the upper chromosphere, the Lorentz force gradually becomes dominant. At a height of about 4 Mm above the solar surface, this event produces a mean upward mass flux density on the order of $10^{-7}~{\rm kg~m^{-2}~s^{-1}}$ and a mean upward Poynting flux density of approximately $525~{\rm W~m^{-2}}$. The wave-mode decomposition results show that the energy transport associated with the rotational spicule-like event is dominated by the Alfvén mode. Alfvén waves, fast-mode waves, and slow-mode waves carry energy into the lower corona in an approximate ratio of $10:7:3$.

astro-ph.SR↗
arXiv2026

PreCoG: Preview-Edit for Efficient Video Correction in Generation-Time

Iterative text-to-video generation relies on feedback to correct compositional and temporal failures. However, existing methods typically require another full generation run for each correction, repeating computation and potentially disrupting already correct content. We introduce PreCoG, an iterative framework for correcting semantic failures during video generation. PreQA diagnoses failures from low-cost previews and constructs corrective conditions, while PreEdit edits cached generation trajectories under these conditions, attenuating orthogonal changes to limit disruption of already correct content. PreCoG completes generation only for the selected candidate, reusing its cached trajectory to reduce redundant computation and help preserve semantic consistency between the preview and the final video. Experiments show that PreCoG achieves the highest VBench and T2V-CompBench scores and approaches VSR's leading VS2 score at $0.56\times$ its inference time. It also outperforms GENMAC on all three quality and alignment metrics at $0.86\times$ its inference time.

cs.CV↗
arXiv2026

How Should We Evaluate Naturalness for Synthetic Speech with a Foreign Accent?

Recent advances in speech synthesis technology have enabled the generation of speech with diverse characteristics. One such characteristic is a foreign accent. In our preliminary experiments, we found that UTMOS, a widely used automatic speech quality assessment system, tended to assign lower scores to speech with stronger foreign accents, even though it is produced by a human speaker. This observation raised a question: How do humans evaluate the naturalness of foreign-accented speech? We conducted a large-scale subjective evaluation involving both native and non-native listeners under different evaluation instructions. The results showed that humans also considered accentedness when evaluating naturalness, but that defining naturalness in terms of human-likeness and selecting raters who have greater exposure to the target accent may enable more appropriate evaluation. This study aims to provide a starting point for future discussions on fairer and more appropriate evaluation of synthetic speech with a foreign accent.

cs.SD↗
arXiv2026

Fix the Match, Not the Solver: STITCH -- a Self-Supervised Compatibility Measure for Eroded Visual Puzzles

Unlike the clean and pixel-aligned complete pieces of standard jigsaw benchmarks, real-world puzzle fragments lose their boundary regions to erosion, destroying the signal that classical compatibility measures (CMs) rely on. We first re-establish, via oracle experiments, that the CM, not the solver, is the performance bottleneck: with a perfect CM, even a simple greedy solver achieves perfect reconstruction. This motivates STITCH, a self-supervised CM that sidesteps missing boundaries by comparing frozen ViT patch tokens from piece interiors via a lightweight cross-attention head (~249K trainable parameters), trained without manual annotation. Plugged into unchanged compatibility-based solvers, STITCH reaches .992/.954 Abs/Perf on JPwLEG-5 (+.629 Perf over the best prior method), nearly saturating the benchmark. On a new ImageNet 7x7 benchmark, it outperforms all alternatives, including inpaint-then-match pipelines built on modern generative models. Importantly, performance remains robust across erosion levels and generalizes to puzzles with over 40% missing pieces without retraining. Trained on the recent GAP datasets of irregular, heavily eroded fragments, the same CM recipe enables compatibility-based solvers to surpass recent learning-based solvers on GAP-3 and GAP-5. Since this leap in performance is achieved by a pluggable CM module, the takeaway is clear: fix the match, not the solver.

cs.CV↗
arXiv2026

CompactSplat: Spatially Adaptive Gaussian Distribution for Feedforward Scene Reconstruction

Reconstructing 3D scenes from sparse views without per-scene optimization remains highly challenging, especially for recovering accurate geometry and fine textures. While recent feedforward approaches leverage generalizable 3D Gaussian Splatting (3DGS) for scene generation, they typically assign one or multiple Gaussians to each pixel. Such uniform allocation not only produces highly redundant representations by wasting primitives in homogeneous areas, but also fails to exploit the inherent geometric priors of planar surfaces, often resulting in sub-optimal structural modeling. To address these limitations, we present CompactSplat, a novel feedforward framework that enables a content-aware allocation of 3D Gaussians. By integrating texture-guided spatial partitioning with hierarchical sampling, CompactSplat adaptively distributes primitives - concentrating dense Gaussians in complex regions while tiling flat areas with expanded, scale-modulated primitives. Moreover, our framework inherently supports dynamic sparsity control at inference time, enabling seamless trade-offs between rendering fidelity and memory footprint without requiring network retraining. Extensive experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that CompactSplat consistently outperforms prior methods on both standard metrics and high-resolution rendering consistency, achieving high-fidelity reconstructions with significantly fewer primitives.

cs.CV↗
arXiv2026

Boundedness of polarized log Calabi-Yau fibrations

We investigate the boundedness problem for log Calabi-Yau fibrations whose bases and general fibers are bounded. We improve the results in arXiv:2504.05243 from birational boundedness to actual boundedness, which confirms a conjecture of Birkar and Hacon when the boundary coefficients belong to a fixed finite rational set. We also get a new proof for the birational boundedness result in arXiv:2504.05243 which is substantially more conceptual.

math.AG↗
arXiv2026

High-Throughput Generative Video Super-Resolution on a Single Consumer GPU

Generative video super-resolution (VSR) can recover visually compelling high-frequency detail by leveraging diffusion priors, but iterative sampling and heavy spatio-temporal coupling make practical deployments difficult. We study a different operating point: practical HD generative VSR on a single high-end consumer GPU without end-to-end retraining. To this end, we present a zero-shot framework built entirely from pretrained components. The proposed system decouples spatial enhancement from temporal stabilization. A time-distilled one-step diffusion backbone handles spatial upscaling, while temporal coherence is promoted by a lightweight pretrained flow-based interpolator that synthesizes intermediate frames between super-resolved keyframes. To further improve scalability, we introduce a resolution-aware hybrid attention scheme that, in a zero-shot manner, replaces selected high-resolution global-attention layers with neighborhood attention while preserving global attention in semantically critical low-resolution layers, while also improving temporal consistency. Experiments show that the proposed framework reaches up to ~32 FPS at 720p resolution under interleaved inference on a single NVIDIA RTX 4090. At 4K, the proposed attention substitution can yield up to a 44% speedup over the original attention implementation. Across standard VSR benchmarks, the method consistently shifts the speed-quality trade-off decisively toward runtime efficiency, trading reconstruction fidelity for strong no-reference perceptual quality, while remaining reasonably competitive against generative approaches in temporal consistency. It also exposes clear limitations in handling more challenging motion, which a simple motion gate partially mitigates. Code will be released upon acceptance.

cs.CV↗
arXiv2026

On the inequality $μ_3 < λ_1$ for the eigenvalues of the Laplace operator

In 1952, Pólya proved the inequality $μ_2 < λ_1$ for the eigenvalues of Dirichlet and Neumann problems in domain $Ω\subset \mathbb{R}^d$, $d \ge 2$. We show that $μ_3 < λ_1$ if $d\ge 3$. In the two-dimensional case, in 2025, Rohleder proved that $μ_3 < λ_1$ for simply connected domains. We prove that this inequality holds also true for double connected domains symmetric with respect to the origin.

math.SP↗
arXiv2026

MixTok: Adaptive Visual Tokenization with Global 1D Tokens and Sparse 2D Refinement

Efficient visual tokenization requires balancing representation compactness with the preservation of fine-grained image content. Conventional grid-based tokenizers allocate tokens uniformly across spatial locations despite non-uniform visual complexity, whereas compact 1D tokenizers aggregate global information but can struggle to preserve local details. We introduce MixTok, a hybrid visual tokenizer that combines a compact 1D global representation with sparse 2D tokens for local refinement. A content-adaptive router ranks candidate locations by refinement priority and determines the number of 2D tokens for each image under a target average token budget. At the highest-scoring locations, 2D token features replace the spatial features recovered from the 1D representation, and a shared decoder reconstructs the image from the resulting mixed features. On ImageNet 256 x 256, MixTok achieves highly competitive reconstruction and generation performance, with an rFID of 1.195 and a gFID of 1.95 at respective average budgets of approximately 128 and 131 content tokens per image.

cs.CV↗
arXiv2026

Flux Evolution of PSR B0540--69 and the Spectral Energy Distribution of its Pulsar Wind Nebula

The young Crab-like pulsar PSR B0540--69, located in the Large Magellanic Cloud, underwent a spin-down rate transition (SRT) in 2011 December, after which its pulsar wind nebula (PWN) was reported to brighten up to 2018. In this work, we extend the study of its X-ray and $γ$-ray flux evolution through 2026 by combining observations from multiple X-ray instruments with data from the Fermi Large Area Telescope, thereby constructing a flux history of the PSR$+$PWN system spanning more than two decades. We find that the enhanced X-ray luminosity of the PSR$+$PWN system has persisted for more than 14 yr after the SRT, up to early 2026, whereas the pulsed X-ray flux remains unchanged across all epochs. This confirms that the brightening originates from the PWN. In the $γ$-ray band, neither the pulsed nor the non-pulsed flux shows any detectable long-term variation between 2008 and 2025. Together, these results imply that the additional spin-down energy released at the SRT has been continuously injected into the PWN over decades, placing strong constraints on the energy budget and physical mechanism of the transition. Combining the non-pulsed X-ray and $γ$-ray fluxes measured here with previously published radio-to-UV data, we further construct a broadband spectral energy distribution of the PWN, which shows properties markedly different from those of the Crab Nebula.

astro-ph.HE↗
arXiv2026

Simulation Study of Reconstruction of Cosmic Ray Extensive Air Shower with Thermal Neutron Detected by ENDA

In the investigation of the "knee" region within the cosmic ray energy spectrum, extensive air shower (EAS) reconstruction by using different EAS secondary particles are of paramount importance for energy determination and composition separation and finally measurement of energy spectrum of different cosmic ray composition. The Electron-Neutron Detector Array (ENDA) is designed to detect thermal neutrons generated in the ground by high energy hadrons which constitute the "backbone" of EAS and carry vital information regarding the primary cosmic rays. At present, ENDA-64, consisting of 64 detectors, is employed inside the Large High Altitude Air Shower Observatory (LHAASO). This work simulates the response of ENDA and investigates the lateral distributions of thermal neutrons. A comparison of the NKG and double-exponential functions for describing this distribution using MC simulation data shows that the double-exponential function is more accurate. The mean difference between fitted and measured thermal neutron density is within ~ 6%. Meanwhile, the thermal neutron density at 10 m from the shower axis, $ρ_n$, depends mainly on the sum of all hadrons energy or the number of high-energy hadrons. The relationship between $ρ_n$ and EAS hadron energy or the number of high-energy hadrons can be well fitted by a linear function on a log scale. This work is fundamental for energy determination and composition separation with hybrid detection by using ENDA in the near future.

astro-ph.HE↗
arXiv2026

AdaC-GRPO: Towards Mastering Long-Horizon Visual Sudoku Puzzles via Adaptive Curriculum Learning

While reinforcement learning with verifiable reward (RLVR) enhances reasoning in large language models, its efficacy in long-horizon multimodal tasks remains underexplored. Visual Sudoku puzzles pose a representative challenge, requiring multi-step deductive reasoning over both local constraints and global dependency chains. To this end, we introduce FG-Sudoku, a fine-grained benchmark categorized by grid size and required deductive steps. Evaluations reveal that frontier MLLMs fail on complex long-range dependencies (e.g., 9*9 puzzles). During post-training, this near-zero success rate causes severe reward sparsity, halting standard RLVR. We propose Adaptive Curriculum Group Relative Policy Optimization (AdaC-GRPO) to overcome this. Unlike rigid or prior-based schedules, AdaC-GRPO dynamically constructs training batches by tracking the model's "Zone of Proximal Development" via online rollout accuracy. Experiments demonstrate that AdaC-GRPO enables Qwen2.5-VL-7B to approach the performance of Gemini-2.5-Flash while bridging 62.2% of the overall accuracy gap to Gemini-2.5-Pro, with its generalizability further validated across InternVL-3.5 and Qwen3-VL.

cs.AI↗
arXiv2026

From Observation to Intervention: Identifiability of Latent User Bias in Human-AI Interaction

Alignment methods typically treat user behavior as a reliable proxy for underlying preferences. However, this assumption breaks down when users hold belief-dependent cognitive biases, risking irreversible ``confirmation traps'' and harmful feedback loops. In this paper, we formalize when an AI system can distinguish true user preferences from behavior shaped by cognitive bias. Modeling this interaction as a multi-armed bandit, we first prove a non-identifiability theorem: under passive observation, a static binary biased user produces action trajectory distributions strictly identical to an unbiased user in a transformed environment, forcing any action-only estimator to perform no better than random guessing. We then show that active preference elicitation breaks this observational equivalence. Specifically, we prove that an Observer applying a finite sequence of adversarial zero-reward interventions of length $m_δ$ makes the post-intervention action distributions arbitrarily separable. Our theoretical results suggest that robust alignment requires actively intervening to tell informed preferences from biases.

cs.HC↗
arXiv2026

WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learning

Federated Learning (FL) enables collaborative model training without exposing raw data, yet remains vulnerable to malicious clients injecting poisoned datasets. Existing defenses are typically reactive (operating during training) or supervised (requiring prior knowledge of attack types) - assumptions that rarely hold in open-ended IoT deployments. We propose WaSA, a fully unsupervised, offline framework for malicious client detection. Before training, each client transmits a single, privacy-preserving spectral summary of its data to the server. A denoising autoencoder, trained solely on clean auxiliary data, scores clients based on their deviation from the learned benign manifold, identifying corruptions regardless of the attack type. WaSA requires no attack labels, generalizes to unseen anomalies, and introduces a Boltzmann sampling strategy for client selection that smoothly interpolates between hard exclusion and standard aggregation. Experiments on MNIST, CIFAR-10, and CIFAR-100 under diverse attack scenarios (up to 90 % malicious clients) demonstrate consistent improvements over supervised baselines and robust aggregation methods, with negligible communication overhead. Code is available at https://github.com/alelicciardi99/wavelet_autoencoders_malicious.

cs.LG↗
arXiv2026

Destination-Conditioned Adaptation with Semantic-Geometric Alignment for Generative Inbetweening

Generative inbetweening aims to synthesize temporally coherent intermediate frames between a start frame and a target frame. Although pretrained image-to-video diffusion models provide strong priors for large-motion inbetweening, they are not explicitly designed to reach a specified target frame. They may therefore generate realistic individual frames while drifting from the target or producing unstable motion under large displacements and non-rigid dynamics. We propose DSG-GI, a framework that adapts pretrained latent video diffusion models through \textbf{D}estination-Conditioned Adaptation and \textbf{S}emantic-\textbf{G}eometric Alignment. Destination-Conditioned Adaptation treats the target frame as the destination of the sequence and provides temporally progressive guidance along the generated trajectory. Semantic-Geometric Alignment aligns the token-wise spatio-temporal relational geometry of backbone features with that of a frozen video foundation encoder and applies a flow-matching-aware latent flow constraint to samples near the data endpoint of the probability path. Together, these components strengthen destination guidance and preserve coherent semantic-geometric motion structure. Extensive experiments on standard and large-motion inbetweening benchmarks demonstrate improvements in temporal stability, target-frame faithfulness, and perceptual quality. Our code will be made publicly available.

cs.CV↗
arXiv2026

olBlend: Language-Guided Molecular Blend Retrieval with Mixture Odor Prediction

Converting open-ended natural-language odor descriptions into molecular mixtures that can be physically prepared from an available inventory remains an important challenge in computational olfaction. We propose olBlend, a language-guided framework for molecular mixture retrieval. olBlend first uses MixPOM to predict odor probabilities for candidate molecular pairs and stores these predictions for retrieval. It then uses a language model to translate each odor description into three types of semantic requirements: lead attributes that define the main odor, ambient attributes that describe its background, and absent attributes that should be avoided. A structured scoring algorithm uses the precomputed odor probabilities to score how well each candidate mixture meets these requirements, then retrieves and ranks mixtures from the available inventory. Evaluation on established odor datasets shows that MixPOM improves mixture odor prediction over averaged single-molecule predictions. Retrieval comparisons across natural-language queries demonstrate how background and exclusion requirements guide mixture selection. We further prepare retrieved formulations as physical odor samples, demonstrating the workflow from natural-language descriptions to realizable molecular formulations.

cs.HC↗
arXiv2026

Insights on the volume variation of LiCoxMn2-xO4 electrode upon cycling: a correlative operando XRD and XAS study

Understanding volume changes in high-voltage spinel cathodes is essential for mitigating mechanical degradation and improving cycling stability. However, linking these changes to cation-specific redox processes remains challenging. Here, we combine operando X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS) to investigate the LiCoxMn2-xO4 spinel system, which exhibits a cubic structure and isotropic lattice response, in order to correlate the evolution of the local and long-range structure. Five compositions (x = 0, 0.25, 0.5, 0.75, and 1.0) were synthesized and electrochemically characterized, with x = 0, 0.5, and 1.0 examined in detail under identical cycling conditions. XAS at the Mn and Co K-edges reveals distinct Mn4+/Mn3+ and Co4+/Co3+ redox processes, with Mn redox activity dominating below 4.7 V and Co redox triggered at higher potentials. Chemometric tools for XAS data analysis further resolve the evolution of local structure during cycling. Both XRD and XAS capture consistent trends in volume evolution, despite probing different structural length scales. Increasing Co content progressively decreases the overall volume change, from 5.0 % to 1.6 % by XAS and from 5.8 % to 2.1 % by XRD. These results demonstrate that Co substitution suppresses electrochemically induced structural strain and establish a direct link between cation-specific redox activity and volumetric evolution. The correlative operando XRD/XAS approach provides a framework to investigate newly designed low-strain, high-energy cathodes with improved cycling stability.

cond-mat.mtrl-sci↗
arXiv2026

Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States

Autonomous landing of unmanned aerial vehicle (UAV) on unmanned surface vehicle (USV) is difficult in high sea states. Existing hierarchical landing methods often infer future USV motion only implicitly from short observation histories, which limits their ability to adapt to severe and rapidly changing sea conditions. This paper presents a prediction-informed hierarchical control framework. A pretrained model is developed to explicitly predict the USV attitude during high-level landing decisions. The prediction helps identify suitable landing windows and choose the right touchdown time, while a conventional low-level flight controller maintains UAV stability and tracks motion commands. To evaluate the framework under realistic disturbances, a real-sim validation setup is constructed in which USV-motion data collected in real sea conditions are transferred to a MuJoCo USV. The experiments demonstrate the feasibility of using explicit attitude prediction to improve landing-window selection and support timing-aware UAV landing planning in rapidly varying marine environments.

cs.RO↗