arXiv subjects

Tao Wu

Publications and source records attributed to Tao Wu.

At least 19 recordsLinked to original sources

arXiv2026

EP251129a: A Fast X-ray Transient with Two X-Ray Plateaus and Its Off-axis Afterglow

Extragalactic fast X-ray transients (EFXTs) detected by the Einstein Probe are extending the parameter space of relativistic stellar explosions toward softer and longer-lived transients. Here we report EP251129a/GRB 251129A at z=0.505, which exhibits two X-ray plateaus: an early plateau lasting $\sim 10^{4}\,\rm s$ followed by a rapid decay with spectral softening, and a second plateau extending to 60 days, placing it at the extreme long-duration end of Gamma Ray Burst (GRB) plateaus. The early optical emission evolves independently of the X-ray, indicating a different origin; whereas the unusual radio afterglow shows a rapid rise of $F_ν\propto t^{1.9-2.5}$ and a delayed peak at $\sim36-40$ days, providing strong evidence for an off-axis geometry. We find that the early X-ray behavior resembles those of ultra-long GRBs (ULGRBs) and can be naturally explained by the internal dissipation from a newborn millisecond magnetar undergoing gravitational-wave-dominated spin-down before collapsing into a black hole. The early optical, radio and late X-ray emission are well described by an off-axis two-component jet afterglow with additional energy injection, possibly powered by fallback accretion onto black hole. EP251129a therefore connects the emerging EFXT population with ULGRB-like central-engine activity and off-axis afterglow, offering a rare opportunity to probe both the evolution of central engine and jet structure in a single event.

astro-ph.HE↗

arXiv2026

Heterogeneous superconductivity emerging from a charge-stripe state in kagome lattices

Charge density wave (CDW) and superconductivity have long been recognized as competing orders. However, recent achievements establish that the interplay between these two collective electronic phenomena can be more perplexing, with emergent phenomena arising from their cooperation. Here we report a distinctive case of the kagome superconductors CsV3Sb5-xSnx, in which the characteristics of superconductivity evolve in accordance with the form of CDW order. As the triple-Q CDW in CsV3Sb5 transforms to a short-range stripelike CDW at xc = 0.07, a sudden change in superconducting properties occurs concomitantly. A combination of thermodynamic, electrical transport and scanning tunneling microscopy experiments reveals a heterogeneous superconducting state beyond xc: the superconducting gap exhibits pronounced spatial variation, in conformity with a distribution of critical temperature and the resulted superconducting precursor effects. Moreover, abundant normal fluid persists deep into the superconducting state, as evidenced by remnants of electronic specific heat and zero-energy quasiparticle interference. These observations are unrelated to the chemical inhomogeneity and residual strain effect introduced by Sn substitution. Instead, they emulate the phenomenology that a sign-changing superconducting order parameter is subject to strong pair-breaking scattering with the short-range charge stripes and hence reflect the unconventional nature of superconductivity developing inside the charge-stripe state.

cond-mat.supr-con↗

arXiv2026

MeSS: City Mesh-Guided Outdoor Scene Generation with Cross-View Consistent Diffusion

Mesh models have become increasingly accessible for numerous cities; however, the lack of realistic textures restricts their application in virtual urban navigation and autonomous driving. To address this, this paper proposes MeSS (Meshbased Scene Synthesis) for generating high-quality, styleconsistent outdoor scenes with city mesh models serving as the geometric prior. While image and video diffusion models can leverage spatial layouts (such as depth maps or HD maps) as control conditions to generate street-level perspective views, they are not directly applicable to 3D scene generation. Video diffusion models excel at synthesizing consistent view sequences that depict scenes but often struggle to adhere to predefined camera paths or align accurately with rendered control videos. In contrast, image diffusion models, though unable to guarantee cross-view visual consistency, can produce more geometry-aligned results when combined with ControlNet. Building on this insight, our approach enhances image diffusion models by improving cross-view consistency. The pipeline comprises three key stages: first, we generate geometrically consistent sparse views using Cascaded Outpainting ControlNets; second, we propagate denser intermediate views via a component dubbed AGInpaint; and third, we globally eliminate visual inconsistencies (e.g., varying exposure) using the GCAlign module. Concurrently with generation, a 3D Gaussian Splatting (3DGS) scene is reconstructed by initializing Gaussian balls on the mesh surface. Our method outperforms existing approaches in both geometric alignment and generation quality. Once synthesized, the scene can be rendered in diverse styles through relighting and style transfer techniques. project page: https://albertchen98.github.io/mess/

cs.CV↗

arXiv2026

DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylization

Recent advances in rectified flow-based image-to-3D generative models have enabled high-fidelity 3D asset generation. Building on this, a growing line of work has exploited these strong 3D priors for training-free stylization, transferring visual attributes from a reference image onto a generated 3D asset. However, existing methods enforce an all-or-nothing paradigm: color and texture are transferred jointly, with no mechanism to control them independently -- a limitation we formalize as Disentangled 3D Stylization(Disen3D). To address this, we propose DiDE, the first training-free framework for Disen3D. Key to our approach is the observation that the structured latent space of image-to-3D models is overcomplete with respect to texture: texture information occupies only a small subset of the style-significant channels, leaving a free subspace available for independent color encoding. DiDE exploits this via a channel partition mechanism that processes a content image, a texture reference, and a color reference through dedicated branches and composes both style signals interference-free at every self-attention layer, preserving content geometry throughout. Experiments on Disen3D-Bench, our newly collected multi-reference benchmark, show that DiDE consistently outperforms 2D and 3D stylization baselines in color fidelity, texture transfer, and content preservation.

cs.CV↗

arXiv2026

Efficient KV Cache Migration for Geo-Distributed LLM Inference in Collaborative Edge Computing

Large Language Models (LLMs) have shown exceptional capabilities but rely heavily on centralized cloud GPU clusters, leading to high bandwidth costs, latency, and privacy issues. Edge computing offers a solution by deploying LLMs closer to users on edge servers, yet single edge servers often cannot meet LLMs' heavy computational demands. Collaborative Edge Computing (CEC) addresses this challenge by integrating resources of multiple geo-distributed heterogeneous edge servers, which jointly host small model shards and associated Key-Value (KV) cache for distributed inference. However, edge servers may disconnect or easily become overloaded, making it essential to migrate stateful KV cache to other servers for ensuring continuous service. Existing cloud-based KV cache management methods, such as direct cross-GPUs transmission and recomputation from input tokens are ill-suited for bandwidth-constrained and resource-limited edge environments. In this work, we propose EdgeFlow, a novel hybrid KV cache migration scheme that integrates direct KV cache transmission with activation-based recomputation, leveraging parallel computation and communication to minimize migration time. On this basis, we formulate a multi-node KV migration optimization problem and design a 2-approximation algorithm based on improved linear programming relaxation and bipartite graph techniques. Extensive experiments on a real-world distributed LLM inference platform demonstrate that EdgeFlow reduces migration time by up to 45%, significantly enhancing the robustness and efficiency of distributed LLM inference in collaborative edge computing.

cs.DC↗

arXiv2026

Bulk superconductivity in quintuple-layer square-planar nickelate La6Ni5O12

Nickelate superconductors, sharing a similar electronic environment with cuprate superconductors, offer a promising platform for unraveling the mechanism of high-temperature superconductivity. However, the realization of superconductivity in these systems has so far been restricted to thin films or bulk samples under high-pressure. This limits the applicability of various experimental probes to study the mechanism of superconductivity, and hinders the potential application of the nickelate superconductors. Exploring bulk superconductivity at ambient pressure is therefore essential to understand its origin and enable broader experimental research. Here, we report the realization of bulk superconductivity at ambient-pressure in quintuple-layer square-planar (SP) nickelate La6Ni5O12. The SP phase is obtained by performing topotactic reaction on the quintuple-layer Ruddlesden-Popper (RP) compound La6Ni5O16. Optimized reduction condition endows La6Ni5O12 with a superconducting state that exhibits onset transition temperature of ~ 18 K, as verified by both zero resistance and diamagnetic shielding effect. Pronounced hysteresis loops revealed by magnetization and torque magnetometry demonstrate the occurrence of vortex pinning. Specific heat measurements on a polycrystalline pellet of La6Ni5O12 reveal a clear superconducting transition at ~18 K and a superconducting volume fraction of ~ 25%, which strongly supports the presence of bulk superconductivity. Our results establish an unprecedented bulk sample platform for investigating superconductivity in nickelates at ambient pressure, which paves the way for further experimental studies into the superconducting mechanism.

cond-mat.supr-con↗

arXiv2026

On Over-Conservatism Under Low-Probability Extreme Events: A KL-Regularized Wasserstein Distributionally Robust Planning Method

The increasing penetration of renewable energy makes power systems more vulnerable to low-probability extreme events, elevating system planning to a critical role in ensuring reliability. Distributionally robust optimization (DRO) based on the Wasserstein distance is widely used to address this challenge. However, we find that the conventional Wasserstein ambiguity set tends to excessively amplify the probabilities of low-probability extreme events, leading to over-conservative investment decisions. To mitigate this over-conservatism, this paper proposes a novel ambiguity set that modifies the Wasserstein distance with a Kullback-Leibler (KL) divergence regularization term. By leveraging the KL divergence's sensitivity to relative probability changes, the proposed set effectively curbs the unrealistic magnification of low-probability events. A two-stage distributionally robust planning model built upon this new ambiguity set is developed to optimize the installed capacity of photovoltaic, wind, and energy storage. The complex model is then reformulated as an equivalent exponential cone program with strict theoretical proofs, making it computationally tractable. Numerical results on the PJM 5-bus and IEEE 118-bus systems reveal the over-conservatism of standard Wasserstein DRO under low-probability extreme events and validate the effectiveness and efficiency of the proposed planning model and solution algorithm.

eess.SY↗

arXiv2026

RSAJEV: Training-Free Abstaining Prototype Memory for Eliminating Silent Hallucination in JEV-Based Remote Sensing Classification

Scene classification of remote sensing imagery with multimodal large language models (MLLMs) is formulated as a single-forward-pass decision problem: candidate class names are lettered options in the prompt, a registered answer slot is conditioned on the prompt, and classification is read out by scoring letter tokens with the language-modeling head. We show that although this letter binding is highly effective within its training vocabulary (97.71% OA on UCM), it behaves erratically out of vocabulary: zero-adaptation accuracy across four cross-dataset directions fluctuates between 7.35% and 96.40%, and under out-of-vocabulary weights its confidence fails inversely on open-set evaluation, assigning higher confidence to unseen than to seen classes (AUROC 0.38), a failure we term silent hallucination. We retain the slot representation and replace the readout with RSAJEV, a training-free abstaining prototype memory that performs retrieval-based prototype matching: K reference images per class are cached as prototypes via their slot representations under the same prompt, and classification becomes nearest-neighbor retrieval over the prototypes, refined by a shrinkage-Mahalanobis geometric correction. This readout is mechanistically isomorphic to retrieval-augmented generation (RAG) but operates on discriminative decisions; it is the first retrieval-based readout for MLLM slot representations. The retrieval readout reaches 89.43% on the UCM cross-dataset gate with 16 references per class and zero gradient updates, and 96.90% on AID. Its ID-ness scores attain 0.91-0.99 AUROC on open-set evaluation over two datasets, exceeding symbolic confidence (0.38-0.96) in all four open-set settings, restoring a calibratable abstention channel and supporting class addition/removal at zero extra inference cost via offline rescoring of the cached matrix.

cs.CV↗

arXiv2026

Look Closer: Patch-wise Supervision for AI-Generated Image Detection

How much of an image does a detector need to see? Small RGB regions can retain useful evidence of image synthesis even when they reveal little of the full scene. Motivated by single-patch detection, we study patch-wise supervision: a shared backbone classifies explicit crops, each crop receives its own loss, and patch probabilities are averaged only at inference. The procedure requires neither handcrafted residual filtering nor a learned image-level fusion module. Experiments span single-patch selection, multiple generator collections, and four CNN and Transformer backbones. On GenImage, the reported patch-wise variants improve average accuracy over their whole-image counterparts across all four backbones. Comparisons of supervision granularity, source resolution, crop size, and inference coverage further characterize the approach, while post-processing tests and difficult-image evaluation reveal its limitations. The historical experiments include evaluation-based model selection, so their scores are not presented as a uniformly selected leaderboard comparison. Overall, the study identifies explicit local input and patch-level supervision as a simple, useful combination for investigating generalizable AI-generated image detection.

cs.CV↗

arXiv2026

UniRect-CoT: Enhancing Generation in Unified Multimodal Models via Reflective Rectification with Inherent Understanding

Unified Multimodal Models (UMMs) aim to integrate visual understanding and generation within a single structure. However, these models exhibit a notable capability mismatch, where their understanding capability often outperforms their generation capability. This mismatch suggests that the model's rich internal knowledge, while effective for understanding tasks, is not fully utilized during generation. To address this, we draw inspiration from the human ``Thinking-While-Drawing'' paradigm, where humans continuously reflect on intermediate results and revise them according to their understanding of the intended target. In this paper, we propose UniRect-CoT, a training-free unified rectification chain-of-thought framework. We regard the multi-step denoising process in UMMs as an intrinsic visual reasoning process, whose intermediate states provide opportunities for continuous reflection and rectification. By leveraging the UMM's inherent understanding to reflect on intermediate results in light of the target instruction and rectify them accordingly, our framework forms a reflective chain of thought within a single denoising trajectory, enabling the model's internal knowledge to guide generation throughout the process. Specifically, the UMM generates a state-conditioned reflection, whose autoregressive negative log-likelihood under the same model defines a model-native differentiable semantic loss that translates textual reflection into gradients for latent rectification. Extensive experiments demonstrate that UniRect-CoT generalizes across existing flow-based UMMs, consistently enhancing overall text-to-image generation performance.

cs.CV↗

arXiv2026

Training-free image inversion for one-step diffusion models

In this work, we introduce a novel training-free inversion (TFinv) framework for one-step diffusion models,addressing key challenges in real image inversion and editing. We first identify two critical factors hamperingreal-image inversion and editing: (1) Initial Latent Editability, which is related to the distance between theinitial noise and the ideal Gaussian distribution, and (2) Caption Gap, which means the alignment betweentext captions and image representations. Both factors influence inversion efficiency and the editability ofone-step diffusion models. Then, we propose two novel techniques: iterative noise alignment (iterNA), whichminimizes the distribution gap to align with the normal Gaussian distribution, and suffix learning (suffL),which enhances text-to-image caption alignment by introducing learned suffix prompt tokens. These techniquesenable precise inversion of input images into their initial noise representations and facilitate image editing.Furthermore, we propose a mask-based editing technique for localized edits while preserving backgroundintegrity. Comprehensive experiments on the PIE-Bench dataset validate that our method TFinv not onlyachieves state-of-the-art performance in one-step diffusion editing, but also significantly outperforms existingmultistep approaches in efficiency. The code is available at https://github.com/tttao-uwu/TFinv.git.

cs.CV↗

arXiv2026

Unveiling the nature of collective charge excitations in a cuprate superconductor

Emergent symmetry breakings in condensed matter systems are often intimately linked to collective excitations. For example, the intertwined spin-charge stripe order in cuprate superconductors is associated with spin and charge excitations. While the collective behavior of spin excitations is well established, the nature of charge excitations remains to be understood. Here we present a high-resolution resonant inelastic x-ray scattering (RIXS) study of charge excitations in the stripe-ordered cuprate La$_{1.675}$Eu$_{0.2}$Sr$_{0.125}$CuO$_4$. The RIXS spectra consist of both charge and phonon excitations around the charge ordering wave vector. By modeling the momentum-dependent phonon intensity, the charge-excitation spectral weight is extracted over a wide energy range. As such, we reveal the highly dispersive nature of the charge excitations, with an energy scale comparable to the spin excitations. Since charge order and superconductivity in cuprates are possibly driven by the same electronic correlations, determining the interaction strength underlying charge order is essential to establishing a comprehensive microscopic model of high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2026

Pressure-induced unconventional charge-density-wave states in kagome metal AV3Sb5 (A = K, Rb, Cs)

Since the discovery of charge density wave (CDW) and superconductivity, kagome metal AV3Sb5 (A = K, Rb, Cs) provides a new platform for exploring novel many-body quantum phenomena. In CsV3Sb5, a stripe-like CDW with commensurate wave vector q = 3/8 was observed under moderate pressures, which leads to a peculiar superconducting double-dome behavior in pressure-dependent phase diagram. Previous density functional theory (DFT) calculations indicate that the pressure-induced stripe-like CDW is beyond conventional phonon softening scenario, suggesting a nontrivial role of electronic correlations. However, an in-depth understanding for the pressure-induced unconventional CDW remains elusive. Here, we performed pressure-dependent 51V nuclear magnetic resonance (NMR) measurements on KV3Sb5 and RbV3Sb5. Although the superconducting double-dome behavior is absent in pressurized KV3Sb5 and RbV3Sb5, a pressure-induced CDW phase, ascribed to a possible incommensurate triple-Q CDW, is identified by NMR spectra in both materials, indicating that the pressure-induced unconventional CDW beyond DFT calculations is a common feature for kagome metal AV3Sb5. In contrast to the stripe-like CDW, the pressure-induced incommensurate triple-Q CDW does not strongly suppress the superconducting temperature (Tc) but coincide with an almost plateau behavior at intermediate pressure regime in the pressure-dependent superconducting phase diagram. Furthermore, by systematically analyzing the Korringa relation between Knight shift and nuclear spin-lattice relaxation rate in AV3Sb5, van Hove singularities (vHSs) driven electronic fluctuations are revealed as an effective knob for the pressure-induced unconventional CDW. Finally, our present findings underscore the pressure-induced unconventional CDW as a novel correlated quantum state in kagome metal AV3Sb5.

cond-mat.supr-con↗

arXiv2026

VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph

Given a compact semantic scene graph, long-term indoor video relocalization estimates a map-frame trajectory after lighting and furniture changes. Visual methods rely on appearance and become unreliable under these changes; localizing one frame at a time from object classes and geometry instead leaves sparse, ambiguous evidence. We introduce VideoReloc, whose adaptive clips use odometry to gather spatial evidence until object and motion criteria are met, adapting query length to the observed scene. Its run-level decision rechecks conflicting placements using evidence accumulated across connected clips, stabilizing the trajectory beyond adjacent-clip tracking. Hypothesis-first registration proposes poses from object triplets and verifies each using clip-wide object centers and box surfaces. Orientation-aware refinement uses box faces, gravity and wall directions to resolve ambiguity in camera orientation and refine the full pose. This reframes sparse-map relocalization as verification of spatially extended video queries, moving discriminative support from stored appearance to temporal context and permitting a 100 kB map of class-labelled boxes. On RIO10 and ReplicaCAD, the all-frame localization success rate at 1 m/10$^\circ$ is 73.5% and 61.1% under causal evaluation, rising to 90.6% and 74.8% with clip closure. The evaluated per-frame scene coordinate regressors reach up to 47.6% and 49.8%, respectively, with maps of 12.6-42 MB. Project page: https://videoreloc.github.io

cs.CV↗

arXiv2026

Mode Switching in Two Blue Large Amplitude Pulsators observed by OGLE, KMTNet and DREAMS

Blue large-amplitude pulsators (BLAPs) are rare, hot, short-period pulsating stars whose rapid oscillations provide a unique probe of stellar interiors and atmospheres. In this paper, we study two short-period BLAPs in the Galactic bulge, OGLE-BLAP-142 ($P \approx 6.6$ min) and OGLE-BLAP-148 ($P \approx 7.5$ min), by combining light curves from the OGLE, KMTNet, and DREAMS surveys, spanning 17 years with cadences ranging from minutes to hours to days. We perform Generalized Lomb-Scargle periodogram analyses of their light curves and identify, for each BLAP, three closely spaced pulsation modes separated by $\sim 1\%$. The dominant mode can switch within timescales of order a year, accompanied by a likely blueward color shift ($\sim 0.1$--$0.2$ mag). The yearly period excursions of the resolved mode families imply apparent $|\dot{P}/P|$ values of order $10^{-6}$--$10^{-4}\,\mathrm{yr^{-1}}$, large enough to mimic or exceed smooth evolutionary period changes. One mode family in OGLE-BLAP-148 shows a coherent decade-long drift at the $\sim10^{-5}\,\mathrm{yr^{-1}}$ level, but its failure to extrapolate to the earliest and latest epochs suggests a transient mode-family drift rather than secular evolution. Such close-spaced multiple pulsations and mode switching may be common among BLAPs with periods shorter than 10 min, and their physical origin remains unclear.

astro-ph.SR↗

arXiv2026

Certifying Model Upgrades with Slice-Wise Non-Regression and Incumbent Fallback

An updated model can improve an aggregate metric while degrading a slice that matters to a downstream user. We study checkpoint selection subject to non-regression tolerances relative to a retained incumbent. The central distinction is between failing to detect harm and certifying non-inferiority: the former can release harmful updates with high probability when evaluation is noisy. We give a reproducible release procedure that separates candidate search from independent, paired evaluation and returns the exact incumbent when certification fails. Applying established intersection-union and Learn-then-Test principles, we state finite-sample guarantees for one frozen candidate, a finite candidate library, and a prespecified testing order. A joint release decision does not require a slice-count Bonferroni penalty, although certification power can still decrease with the number of slices. In bounded-score simulations, a no-detected-harm gate releases a harmful candidate in 99.7% of trials in one 32-slice setting, compared with 2.6% for an exact non-inferiority gate at a 5% target. A constructed two-block family yields larger certified utility than a scalar path under matched candidate counts. Public digits experiments, including a subsequent continuation that improves average aggregate accuracy, return the incumbent in every run because certification is underpowered. These results establish an auditable protocol and its limitations; they do not establish benefits on foundation-model or multilingual translation upgrades.

cs.LG↗

arXiv2026

Cascade coalescence dynamically sustains bubble retention near gas-evolving surfaces

Bubble detachment from solid surfaces governs heat, mass, and charge transport across technologies vital to clean energy, including high-current-density water electrolysis and boiling thermal management. At high gas fluxes, however, bubbles remain trapped at active surfaces despite immense buoyancy, severely restricting mass transfer and increasing energy losses. Here, we show that this unexpected surface retention originates from cascade coalescence between unequal-sized bubbles. High-speed observations around microelectrodes demonstrate that when a rising bubble merges with a smaller surface-attached successor, its trajectory abruptly reverses, accelerating toward the substrate at nearly two orders of magnitude above its rising speed. Direct numerical simulations and scaling analysis reveal that asymmetric interfacial retraction during merging generates non-canceling viscous stresses, producing a net downward impulse toward the smaller bubble. Repeated cascade coalescence events accumulate these transient impulses into a steady, time-averaged retaining force capable of opposing buoyancy three to four orders of magnitude beyond quasistatic limits. Our findings establish bubble coalescence as a previously unrecognized mechanism that dynamically sustains bubble retention under high gas flux.

physics.flu-dyn↗

arXiv2026

Beyond Higher-Pulse Rectification: Operational Harmonic Coordination in Renewable P2H Systems

Thyristor rectifiers (TRs) are cost-effective electrolysis power supplies for renewable power-to-hydrogen (ReP2H) systems, but their harmonics may violate grid-code limits. In contrast to conventional solutions that rely on higher-pulse (such as 24-pulse) rectifiers, this paper proposes an operational harmonic coordination scheme that enables low-cost 12-pulse TRs to meet harmonic requirements through coordinated operation. First, a harmonic model quantifies the effects of rectifier transformer (RCT) tap positions and electrolytic currents, enabling harmonic cancellation among multiple electrolyzers (ELZs). A two-layer framework then coordinates hydrogen production and harmonic mitigation. Hourly scheduling determines ELZ commitment within the harmonic feasible region under renewable uncertainty using stochastic programming and a modified progressive hedging algorithm, while minute-level dispatch tracks renewable power and mitigates harmonics. A decomposition algorithm separates production dispatch from harmonic mitigation to improve computational efficiency. Case studies based on real-life projects show that the proposed method increases profit by 31% over current-only regulation. Annual simulations further show that coordinated 12-pulse TRs can replace 24-pulse rectifiers for harmonic compliance by exchanging additional RCT tap actions for lower transformer investment, reducing rectification-stage cost by 37.5%.

math.OC↗