arXiv subjects

Jie Wang

Publications and source records attributed to Jie Wang.

At least 19 recordsLinked to original sources

arXiv2026

Certifying the Rydberg-exposure limit of a globally driven controlled-Z gate

Rydberg excitation enables entangling gates in neutral-atom quantum processors but also exposes them to spontaneous decay. Whether this exposure can be made arbitrarily small through pulse shaping and longer gate times has remained unresolved. We show that an exact controlled-$Z$ gate driven by a single global laser has an irreducible Rydberg-exposure cost in the perfect-blockade regime. The product of the maximum Rabi frequency $Ω_{\max}$ and the average time $T_R$ spent in the Rydberg state is bounded below by $2.9312$, independent of pulse shape and duration. We derive this bound using a sum-of-squares certificate, strengthened by constraints imposed by the shared drive. Using interval arithmetic, we also certify the existence of an exact gate pulse with $Ω_{max} T_R<2.9359$. The unrounded bounds differ by less than $0.16\%$. The bound identifies a fundamental cost of shared-drive entangling dynamics and establishes a benchmark against which future gate-control strategies can be assessed.

quant-ph↗

arXiv2026

Decomposable polytopes with few vertices

A polytope $P$ is decomposable if it can be written as the Minkowski sum of two polytopes that are not homothetic to it. It is well known that decomposable $d$-dimensional polytopes with $2d$ or fewer vertices are the simplicial prisms. More recent work shows that, with two exceptions in low dimensions, decomposable $d$-dimensional polytopes with exactly $2d+1$ vertices are either pentasms or capped prisms. All these examples have a line segment for a summand. In this paper, we extend this conclusion to decomposable $d$-polytopes with exactly $2d+2$ or $2d+3$ vertices. In both cases, all such polytopes, with three exceptions, have a line segment for a summand. The exceptional cases are specified in detail.

math.CO↗

arXiv2026

OmniGuide: Universal Spatial Guidance Fields for Enhancing Generalist Robot Policies

Vision-language-action (VLA) models have shown great promise as generalist policies for a large range of relatively simple tasks. However, they demonstrate limited performance on more complex tasks, such as those requiring complex spatial or semantic understanding, manipulation in clutter, or precise manipulation. We propose OmniGuide, a flexible framework that improves VLA performance on such tasks by leveraging arbitrary sources of guidance, such as 3D foundation models, semantic-reasoning VLMs, and human pose models. We show how many kinds of guidance can be naturally expressed as differentiable energy functions with task-specific attractors and repellers located in 3D space, that influence the sampling of VLA actions. In this way, OmniGuide enables guidance sources with complementary task-relevant strengths to improve a VLA model's performance on challenging tasks. Extensive experiments in both simulation and real-world environments, across diverse sources of guidance, demonstrate that OmniGuide enhances the performance of state-of-the-art generalist policies (e.g., $π_{0.5}$, GR00T N1.6) significantly across success and safety rates. Critically, our unified framework matches or surpasses the performance of prior methods designed to incorporate specific sources of guidance into VLA policies. Project page: https://omniguide.github.io/

cs.RO↗

arXiv2026

EV9V: An Echocardiographic Video Benchmark with Uncertainty Guided View Classification

Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges. First, publicly available datasets are scarce and limited in scale and view coverage. Second, the performance of some modern video-level architectures for echocardiographic view classification remains underexplored. Third, some view categories exhibit highly similar spatial appearances, making single-frame features insufficient for discrimination, while heterogeneous frame quality complicates robust temporal information fusion. To address these challenges, we release the Echocardiographic Videos of Nine Views (EV9V) dataset, comprising 5,138 videos, 910,579 frames, and 9 standard views, which is, to the best of our knowledge, the largest openly released echocardiography video dataset. Furthermore, we propose a Spatio-Temporal Fusion Model (STFM), an efficient dual-stream framework that jointly captures spatial anatomical structures and temporal cardiac dynamics. The STFM leverages uncertainty-aware learning to preferentially sample representative video segments during training and evidence-based fusion during inference, improving robustness to variations in frame quality across echocardiographic videos. Extensive experiments demonstrate that STFM achieves competitive performance and effectiveness compared with diverse video classification architectures, including two-dimensional and three-dimensional convolutional neural networks, recurrent neural networks, and Transformers. The code is released at https://huggingface.co/datasets/bgx666/EV9V and the EV9V dataset is available at https://github.com/bgx666/stfm.

cs.CV↗

arXiv2026

Degree and arity of tracial separation for self-adjoint contractions

Motivated by the recent explicit counterexample to Connes' embedding conjecture, we study the degree and number of variables needed to separate normalized matrix traces on self-adjoint contractions from traces on finite von Neumann algebras. We prove that the two trace infima coincide for every Hermitian polynomial of degree at most two in at most three variables, with a dimension-explicit approximation bound. We give three explicit separators: an integer-coefficient polynomial of degree six in ten variables with 392 nonzero words, a quadratic polynomial in 214 variables, and an integer-coefficient polynomial in two variables of ordinary and cyclic degree 24. Each has normalized trace at least $3/4$ on every matrix contraction tuple and trace $-1$ at a finite tracial witness. We provide the source systems, transfer estimates, and exact coefficient recipes for all three constructions.

math.OA↗

arXiv2026

The bulk spectral gap is certifiable from above but uncomputable from below

Determining spectral gaps in the thermodynamic limit is a central challenge in quantum many-body physics. When a fixed boundary condition is imposed, this problem is undecidable: no algorithm, even with unlimited resources, can decide for every Hamiltonian whether it is gapped. Yet the thermodynamic limit is meant to capture the intrinsic properties of very large systems, which should not depend on how their boundaries are chosen. We therefore consider the bulk spectral gap, which concerns only the intrinsic excitations of the infinite system, and ask what can and cannot be computed about it. First, we give a general algorithm producing a hierarchy of certified upper bounds on the bulk gap, arbitrarily tight at the cost of more computation. Second, we prove that the spectral gap problem remains undecidable in the bulk, already for translationally invariant nearest-neighbour chains of fixed local dimension. Hence no general algorithm produces convergent lower bounds, and no convergent upper-bounding algorithm, ours included, admits a computable convergence rate. The bulk spectral gap is thus certifiable from above but uncomputable from below; more precisely, the bulk-gaplessness promise problem is RE-complete, exactly as hard as the halting problem. The same construction shows that no general algorithm can decide whether a given product state is a ground state, and that convergent bounds on ground-state expectation values admit no computable convergence rate in general. Finally, we obtain the first nontrivial certified upper bounds on the bulk gap of the spin-1/2 kagome Heisenberg antiferromagnet.

quant-ph↗

arXiv2026

Quartic Keller Maps and Tameness in Three Variables

We study invertibility and tameness for polynomial maps of affine three-space of total degree at most four. We prove that every complex Keller map in this degree range is a polynomial automorphism and, independently, that every complex polynomial automorphism of total degree at most four is tame. The invertibility argument combines homogeneous factorization of the leading part, invariant rings of conic pencils, and residue obstructions on cubic fibers. The tameness argument uses a criterion based on a constant second directional derivative, low-degree coordinate classifications, and local tameness of plane polynomial automorphisms over a polynomial parameter ring. The invertibility conclusion extends to every field of characteristic zero.

math.AG↗

arXiv2026

Certified bounds on optimization problems in quantum theory

Semidefinite relaxations of polynomial optimization have become a central tool for addressing the non-convex optimization problems over non-commutative operators that are ubiquitous in quantum information theory and, more in general, quantum physics. Yet, as these global relaxation methods are solved numerically, the bounds issued by the semidefinite solver can - and often do - exceed the global optimum, undermining their certifiability. To counter this issue, we introduce a framework for extracting rigorous certified bounds on non-commutative optimization problems from inexact numerical data, and apply it to several paradigmatic problems in quantum information theory. An extension to sparsity and symmetry-adapted semidefinite relaxations is also provided and compared to the general dense scheme. Our results establish rational post-processing as a practical route to reliable certification, pushing semidefinite optimization toward a certifiable standard for quantum information science.

quant-ph↗

arXiv2026

Revealing Imaging Characteristics of Thermal Wakes from Underwater Vehicles Without Cooling-Water Discharge

Small uncrewed underwater vehicles are proliferating and threaten maritime security, yet evade sonar and magnetic sensors built for larger submarines. Infrared thermal wakes, generated by hydrodynamic displacement that modulates the cold-skin layer, offer a non-acoustic, non-magnetic alternative even without cooling-water discharge. Existing studies, however, rely on idealized simulations that fail to capture real-world backgrounds, offering limited guidance for target-background discrimination. We establish AVP-IR-Wake, the first large-scale, real-world, physically diverse benchmark of 161,485 frames collected over three years. We show that thermal wakes are extended structures of discrete vortices, violating the compactness assumption of conventional targets. We reveal local indistinguishability: individual wake vortices are statistically identical to wind-induced background vortices in temporal, frequency, and sparse domains, sharing low-frequency information and long-range correlations. This challenges the assumptions of compact, transient, high-frequency targets in conventional detection. Yet we establish global morphological separability: although locally inseparable, the wake as a whole is organized by vehicle trajectory, whereas background clutter is organized by wind-driven thermal fronts. This explains why representative detectors fail and shows that reliable detection must shift from local feature extraction to global trajectory-constrained physical modeling. Because the cold-skin layer varies globally and seasonally, forecasting wake observability will require integrating physical oceanography and atmospheric science, linking underwater surveillance to the changing ocean surface.

cs.CV↗

arXiv2026

RoboDuo: Attempt, Check and Atomic Recovery through Code for Vision-Language-Action Models

Vision-Language-Action (VLA) models map a language instruction and the current observation directly to actions, without checking whether the instruction has been completed. For long-horizon tasks, a single failed step therefore goes unnoticed and leaves the whole task unfinished. Such failed episodes, however, often end with the task partially complete. Moreover, the unfinished steps are reusable atomic skills that recur across demonstrations. A failed attempt can thus be recovered by composing with atomic skills. We propose RoboDuo, a closed-loop dual-system framework that uses code as the interface between a coding agent and two VLAs. The agent writes a program that lets a full-task VLA attempt the whole task and checks whether it is complete; on failure, an atomic VLA finishes the remaining steps. Since the agent emits no actions and only decides which VLA for execution, RoboDuo wraps an existing full-task VLA without updating its weights. To train the atomic VLA and the coding agent with the completion-checking ability, we build LIBERO-Atomic, LIBERO-Code, and LIBERO-Judge from a single set of LIBERO demonstrations. RoboDuo consistently improves X-VLA and $π_{0.5}$ on LIBERO and LIBERO-PRO, with the largest gains on long-horizon tasks, and recovers failed rollouts of a frozen full-task VLA on a real robot.

cs.RO↗

arXiv2026

Exact Identification of Quantum Correlations in the Minimal Bell Scenario

Bell nonlocality places the long-standing philosophical disputes over the nature of quantum theory on a mathematically rigorous, experimentally testable footing, positioning quantum mechanics between local realism and the most general no-signaling theories. Yet the precise quantum boundary has remained elusive even in the minimal Bell scenario, where the extreme points generating the set are known. Here we characterize this boundary through a computable necessary and sufficient criterion: a correlation is quantum if and only if it can be decomposed into an extreme quantum correlation and a classical correlation. The criterion follows from our proof that every Bell functional exhibiting quantum advantage admits a unique quantum maximizer. We further classify the finitely many maximal-face structures containing each nonlocal exposed point according to their local deterministic vertices. By sharply distinguishing physically realizable correlations from nonphysical ones, these results resolve a long-standing open problem and provide a new foundation for identifying the principles underlying quantum theory and determining the ultimate information-theoretic limits of quantum correlations.

quant-ph↗

arXiv2026

The ascending chain condition for radical well-mixed difference ideals

We establish a finite-basis theorem for radical well-mixed difference ideals in a difference polynomial ring with one endomorphism over an arbitrary difference field. The result extends to every finitely difference-generated difference algebra over such a field and is equivalent to the ascending chain condition for radical well-mixed difference ideals, hence resolving a long-standing open problem in difference algebra. The proof proceeds in two stages. Over an inversive difference coefficient field, we establish a uniform inverse-transform bound relating the perfect closure of a finite system to its radical well-mixed closure. This uses Noetherian induction in a fixed truncated polynomial ring, together with generic freeness and going-down for flat ring homomorphisms. We then prove that the radical well-mixed closure of a system over a difference field $K$ is the contraction of its radical well-mixed closure over the inversive closure of $K$. This contraction result follows from successive prime lifting along flat extensions and removes the inversiveness assumption without requiring the coefficient-field extension to be finitely generated.

math.AC↗

arXiv2026

DeformStruct: Deformable Structure Modeling for Point-Supervised Temporal Action Localization

Point-supervised Temporal Action Localization (PTAL) aims to localize action instances using only a single annotated timestamp for each instance. Such sparse supervision offers limited guidance for modeling the structural organization of an action instance. Existing PTAL methods primarily infer action regions from classification responses, leaving the structural organization of multiple action-related parts within each instance insufficiently modeled. To address this problem, we propose DeformStruct, an instance-level deformable structure modeling framework for PTAL. DeformStruct progressively constructs the structure of each action instance through three stages. First, Structural Anchor Mining discovers multiple anchors from annotated points and action-responsive regions, providing structural nodes grounded in the current instance. Second, Deformable Part Alignment associates latent structural parts with these anchors through deformation-aware matching, allowing their temporal supports to adapt to instance-dependent variations. Third, Relational Structural Organization explicitly models the relative temporal relations among the aligned parts, forming a relational representation that characterizes how different action-related parts are organized together. Extensive experiments on THUMOS14, ActivityNet-v1.3, GTEA, and BEOID demonstrate consistent improvements in point-supervised temporal localization and validate the effectiveness of deformable structural modeling.

cs.CV↗

arXiv2026

Polarization Observations of NGC 315 with FAST

We present new continuum and polarization observations of radio galaxy NGC 315 obtained with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) over 1.0-1.5 GHz. The FAST data detect diffuse large-scale polarized emission with a noise level of 0.065 mJy beam$^{-1}$ at resolution of $4'$. We derived a rotation measure (RM) map with an error of 1.1 rad m$^{-2}$ including systematic error. The intrinsic RM map was obtained after subtracting the Galactic foreground contribution. We estimated a total magnetic field strength of $\lesssim 1$ $μ$G across the source and a thermal electron density of $\sim 10^{-4}$ cm$^{-3}$ toward the northwest lobe. The fractional polarization analysis suggests a random magnetic-field component over a large area toward the northwest lobe.

astro-ph.GA↗

arXiv2026

Machine learning enabled design of sustainable alloys considering real world constraints

Designing sustainable alloys must reconcile high performance with real-world constraints, including feedstock scarcity and variability, processing and energy requirements, regulatory thresholds, and economic limitations. Addressing these constraints reaches far beyond today's conventional performance-focused frameworks. It needs to consider not only complex property-processing-performance trade-offs but also sustainable production pathways. Here, we propose a physics-informed ensemble (PIE) learning framework that integrates fundamental structure-property relationships with processing routes and sustainability criteria for mechanism-based alloy design. It predicts properties from composition-microstructure relationships and infers processing routes under sustainability constraints, establishing an interdisciplinary approach to identifying sustainable alloy solutions. We validate the approach for Mg alloys, a lightweight yet energy-intensive metallic material class, considering performance, cost, and carbon emissions. Trained on a benchmark dataset, the PIE model systematically explores plausible alloy compositions and microstructures to construct a digital library of millions of candidate alloys. From this space, we identify two sustainable Mg alloy solutions that overcome the conventional performance-cost-emission trade-off: a rare-earth-containing alloy increases yield and tensile strength by 72.8 percent and 61.1 percent, respectively, over alloys from the same family at comparable cost and emissions; and a rare-earth-free alloy reduces cost and carbon emissions by 40.4 percent and 88.9 percent, respectively, relative to its primary-feedstock counterpart, without compromising mechanical performance. This work introduces a general, physics-informed, and quantifiable framework for designing sustainable alloys under real-world constraints.

cond-mat.mtrl-sci↗

arXiv2026

Entrywise nonnegativity of the inverse relative gain array in dimension six

We prove that $(G\circ G^{-1})^{-1}$ is entrywise nonnegative for every real symmetric positive definite matrix $G$ of order at most six. Its entries are strictly positive exactly within the connected components of the nonzero-entry graph of $G$. An explicit rational example of order seven establishes sharpness. We give two complementary structural proofs. The analytic proof combines a trace obstruction with continuation of positive column forms and a bordering identity. The algebraic proof establishes a stronger four-dimensional polarized inequality through an explicit ten-square polynomial certificate. A common framework relates biquadratic positivity, column forms, positive semidefinite precision loads, and inverse relative gain array nonnegativity in consecutive dimensions. Further consequences include exact load-deficit identities, polynomial sum-of-squares certificates with the covariance fixed, and a reverse Schur--Horn majorization inequality with the same sharp dimension bound. In every dimension, sufficiently large powers of the inverse relative gain array are doubly stochastic.

math.OC↗

arXiv2026

TAMF-VTON: Token-Wise Sparse Adaptation for Mask-Free Virtual Try-On

Diffusion-based virtual try-on (VTON) often relies on segmentation-derived masks that become unreliable when the reference garment differs substantially from the target person's clothing. Existing methods may also lose fine details and commonly focus on single-garment transfer. We present TAMF-VTON, an inference-time mask-free framework built on Qwen-Image-Edit-2509. Given a person image, one or more garment images, and a text instruction, it performs single- or multi-garment transfer without masks, parsing maps, pose representations, or spatial controls at inference. Its central component is token-wise sparse Mixture-of-Experts adaptation, which provides input-dependent capacity without predefined expert roles. Timestep-weighted frequency supervision regularizes high-frequency reconstruction, while adaptive offline data construction supplies cross-category and cross-silhouette training tuples; neither is used at inference. TAMF-VTON achieves the best scores on most reported paired and unpaired metrics among evaluated VTON baselines on the standard single-garment VITON-HD and DressCode benchmarks. Selected qualitative results illustrate multi-garment composition, subject-to-subject transfer, diverse scenes, body-shape variation, and illumination changes. With INT4 on an RTX 4090, TAMF-VTON generates 1K-resolution images under 15 seconds and 2K-resolution images under 30 seconds. Our project can be accessed via https://www.style3d.ai/ai-photoshoot/virtual-clothing-try-on.

cs.CV↗

arXiv2026

EndoWorld-JEPA: Motion-Guided Global-Local Latent World Modeling for Endoscopic Video Representation Learning

Endoscopic video contains state transitions induced by camera displacement, deformable tissue, and instrument interaction, yet existing pre-training methods treat dynamics implicitly. Inspired by action-conditioned joint-embedding predictive world models and by the observation that endoscopic optical flow contains both shared image-plane displacement and localized residual motion, we reformulate passive endoscopic video pre-training as causal motion-guided latent-state prediction. We present EndoWorld-JEPA, to our knowledge the first latent world-model pre-training framework designed for general-purpose endoscopic video representation learning. In place of recorded actions, calibrated poses, or robot kinematics, the model conditions prediction of the next latent state on motion observed up to the current state. Its Optical Motion-guided module separates dominant displacement from residual motion and converts the resulting evidence into an ordered temporal motion condition and spatial local-motion weights. Complementary global and motion-local predictors use these outputs: the temporal condition modulates both latent transitions, whereas the local weights define an auxiliary motion-local state that extends prediction from scene-level evolution to spatially selective dynamics. Patch-wise motion weighting emphasizes localized change while retaining anatomical context, and past-motion-window dropout randomly withholds the complete motion condition to reduce dependence on unreliable motion. This global--local formulation learns predictable visual--motion dynamics in representation space without reconstructing stochastic appearance artifacts. Evaluation shows that a common pre-trained checkpoint achieves strong transfer across four downstream benchmarks, supporting motion-guided global--local latent prediction as an effective pre-training paradigm for endoscopic video.

cs.CV↗