arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 145 records · Page 8Linked to original sources

arXiv2026

Fuzzy dark matter soliton as gravitational lens

The Schrödinger-Poisson (SP) equations predict fuzzy dark matter (FDM) solitons. Given the FDM mass $\sim10^{-20}\rm~{eV}/c^2$, the FDM soliton in the Milky Way is massive $\sim 10^7~M_{\odot}$ but diffuse $\sim 10{\rm~pc}$. Therefore, such FDM soliton can serve as a gravitational lens for gravitational waves (GWs) with frequency $\sim10^{-8}{\rm~Hz}$. In this paper, we investigate its gravitational lensing effects by numerical simulation of the propagation of GWs through it. We find that the maximum magnification factor of GWs is very small $\sim10^{-4}$, but the corresponding magnification zone is huge $\sim6{\rm~pc}$ for FDM with mass equal to $8\times10^{-21}\rm~{eV}/c^2$. Consequently, this small magnification factor compounding over such large magnification zone results in a small antisotropy of $\sim10^{-4}$ over a large solid angle in the GW background. That level of antisotropy is out of the sensitivity, $<20\%$, of the pulsar timing arrays today.

gr-qc↗

arXiv2026

FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation

Assisting non-expert users to develop complex interactive websites has become a popular task for LLM-powered code agents. However, existing code agents tend to only generate frontend web pages, masking the lack of real full-stack data processing and storage with fancy visual effects. Notably, constructing production-level full-stack web applications is far more challenging than only generating frontend web pages, demanding careful control of data flow, comprehensive understanding of constantly updating packages and dependencies, and accurate localization of obscure bugs in the codebase. To address these difficulties, we introduce FullStack-Agent, a unified agent system for full-stack agentic coding that consists of three parts: (1) FullStack-Dev, a multi-agent framework with strong planning, code editing, codebase navigation, and bug localization abilities. (2) FullStack-Learn, an innovative data-scaling and self-improving method that back-translates crawled and synthesized website repositories to improve the backbone LLM of FullStack-Dev. (3) FullStack-Bench, a comprehensive benchmark that systematically tests the frontend, backend and database functionalities of the generated website. Our FullStack-Dev outperforms the previous state-of-the-art method by 8.7%, 38.2%, and 15.9% on the frontend, backend, and database test cases respectively. Additionally, FullStack-Learn raises the performance of a 30B model by 9.7%, 9.5%, and 2.8% on the three sets of test cases through self-improvement, demonstrating the effectiveness of our approach. The code is released at https://github.com/mnluzimu/FullStack-Agent.

cs.SE↗

arXiv2026

MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs

Language models deployed in real-world systems often require post-hoc updates to incorporate new or corrected knowledge. However, editing such models efficiently and reliably-without retraining or forgetting previous information-remains a major challenge. Existing methods for lifelong model editing either compromise generalization, interfere with past edits, or fail to scale to long editing sequences. We propose MEMOIR, a novel scalable framework that injects knowledge through a residual memory, i.e., a dedicated parameter module, while preserving the core capabilities of the pre-trained model. By sparsifying input activations through sample-dependent masks, MEMOIR confines each edit to a distinct subset of the memory parameters, minimizing interference among edits. At inference, it identifies relevant edits by comparing the sparse activation patterns of new queries to those stored during editing. This enables generalization to rephrased queries by activating only the relevant knowledge while suppressing unnecessary memory activation for unrelated prompts. Experiments on question answering, hallucination correction, and out-of-distribution generalization benchmarks for LLaMA-3 and Mistral backbones demonstrate that MEMOIR achieves state-of-the-art performance across reliability, generalization, and locality metrics, scaling to thousands of sequential edits with minimal forgetting.

cs.CL↗

arXiv2026

TabPFN for Zero-shot Parametric Engineering Design Generation

Deep generative models for engineering design often require substantial computational cost, large training datasets, and extensive retraining when design requirements or datasets change, limiting their applicability in real-world engineering design workflow. In this work, we propose a zero-shot generation framework for parametric engineering design based on TabPFN, enabling conditional design generation using only a limited number of reference samples and without any task-specific model training or fine-tuning. The proposed method generates design parameters sequentially conditioned on target performance indicators, providing a flexible alternative to conventional generative models. The effectiveness of the proposed approach is evaluated on three engineering design datasets, i.e., ship hull design, BlendedNet aircraft, and UIUC airfoil. Experimental results demonstrate that the proposed method achieves competitive diversity across highly structured parametric design spaces, remains robust to variations in sampling, resolution and parameter dimensionality of geometry generation, and achieves a low performance error (e.g., less than 2% in generated ship hull designs' performance). Compared with diffusion-based generative models, the proposed framework significantly reduces computational overhead and data requirements while preserving reliable generation performance. These results highlight the potential of zero-shot, data-efficient generation as a practical and efficient tool for engineering design, enabling rapid deployment, flexible adaptation to new design settings, and ease of integration into real-world engineering workflows.

cs.LG↗

arXiv2026

RePaint-Enhanced Conditional Diffusion Model for Parametric Engineering Designs under Performance and Parameter Constraints

This paper presents a RePaint-enhanced framework that integrates a pre-trained performance-guided denoising diffusion probabilistic model (DDPM) for performance- and parameter-constraint engineering design generation. The proposed method enables the generation of missing design components based on a partial reference design while satisfying performance constraints, without retraining the underlying model. By applying mask-based resampling during inference process, RePaint allows efficient and controllable repainting of partial designs under both performance and parameter constraints, which is not supported by conventional DDPM-base methods. The framework is evaluated on two representative design problems, parametric ship hull design and airfoil design, demonstrating its ability to generate novel designs with expected performance based on a partial reference design. Results show that the method achieves accuracy comparable to or better than pre-trained models while enabling controlled novelty through fixing partial designs. Overall, the proposed approach provides an efficient, training-free solution for parameter-constraint-aware generative design in engineering applications.

cs.LG↗

arXiv2026

Unlocking the Power of Orbital-Free Density Functional Theory to Explore the Electronic Structure Under Extreme Conditions

Recent advances in X-ray free-electron laser diagnostics have enabled direct probing of the electronic structure under extreme pressures and temperatures, such as those encountered in stellar interiors and inertial confinement fusion experiments, challenging theoretical models for interpreting experimental data. Kohn-Sham density functional theory (KSDFT) has been successfully applied to analyze experimental X-ray scattering measurements, but its high computational cost renders routine application impractical. Orbital-free DFT (OFDFT) is a substantially more efficient alternative, with computational cost scaling linearly with system size and a weak temperature dependence, yet it often lacks the accuracy required for electronic structure description. Overcoming this limitation, we present a non-empirical Kohn-Sham-assisted orbital-free density functional framework for calculations at extreme conditions, which enables efficient OFDFT simulations with KSDFT-level accuracy for electron densities, electron-ion structure factors, and equations of state across a broad range of conditions. Benchmark comparisons with quantum Monte Carlo data for dense hydrogen and validation against Rayleigh weight measurements of hot dense beryllium demonstrate the reliability of the framework and speedups of up to several hundred times compared with KSDFT. We further show that even at temperatures on the order of 100 eV, quantum non-locality remains essential for correctly describing the electronic structure of dense hydrogen.

cond-mat.mtrl-sci↗

arXiv2026

C-AoEI-Aware Cross-Layer Optimization in Satellite IoT Systems: Balancing Data Freshness and Transmission Efficiency

Satellite-based Internet of Things (S-IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While Layer-coded Hybrid ARQ (L-HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard Age of Information (AoI) metric and obscuring the critical trade-off between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross-layer optimization framework centered on a new metric, the Cross-layer Age of Error Information (C-AoEI). We derive a closed-form expression for C-AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet-level encoded L-HARQ scheme for multi-GBS scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the effectiveness of our proposed framework: it achieves 31.8% higher transmission efficiency and 17.2% lower C-AoEI than conventional schemes. The framework also proves robust against inter-cell interference and varying channel conditions, providing a foundation for designing efficient, latency-aware next-generation S-IoT protocols.

eess.SY↗

arXiv2026

Galactic Large-scale Filaments Resident in Asymmetric Environments: Clues from Cross-filament Profiles of Density and Temperature

Large-scale filaments ubiquitously exist in the Galactic interstellar medium, and their radial profiles offer insights into their formation mechanisms. We present a statistical analysis of molecular hydrogen column density ($\rm N(H_2)$) and dust temperature ($\rm T_d$) radial profiles for 35 Galactic large-scale filaments. We divided their spines into 315 segments, extracted the radial profiles of each segment using $\rm N(H_2)$ and $\rm T_d$ maps derived from $Herschel$ Hi-GAL data, and estimated the asymmetry degree within the radial profiles ($α_{\rm asy}$), as well as the length proportion of segments with asymmetric profiles across the entire filament ($f_{\rm asy}$). We found that Galactic large-scale filaments reside in surroundings distinctly asymmetric and varied in $\rm N(H_2)$, and mild asymmetric yet stable in $\rm T_d$. Different filament morphology types do not show significant differences in $α_{\rm asy}$ or $f_{\rm asy}$. A bent filament shape does not necessarily correspond to an asymmetric radial profile, whereas a straight filament shape may be associated with a symmetric profile. Segments with asymmetric surroundings in $\rm N(H_2)$ may not simultaneously appear asymmetric in $\rm T_d$, and vice versa. We found three filaments with 4-44% of their spine show asymmetric $\rm N(H_2)$ and $\rm T_d$ radial profiles in inverse trends, likely caused by nearby HII region. HII regions of similar scale to large filaments can induce asymmetric radial profiles within them, indicating their influence on filament evolution. However, they are unlikely to independently trigger the formation of an entire Galactic large-scale filament, in contrast to their role in small-scale filament formation.

astro-ph.GA↗

arXiv2026

Sparse Hanson-Wright Inequalities with Applications

We derive new Hanson-Wright-type inequalities tailored to the quadratic forms of random vectors with sparse independent components. Specifically, we consider cases where the components of the random vector are sparse $α$-subexponential random variables with $α>0$. When $α=\infty$, these inequalities can be seen as quadratic generalizations of the classical Bernstein and Bennett inequalities for sparse bounded random vectors. To establish this quadratic generalization, we also develop new Bernstein-type and Bennett-type inequalities for linear forms of sparse $α$-subexponential random variables that go beyond the bounded case $(α=\infty)$. Our proof relies on a novel combinatorial method for estimating the moments of both random linear forms and quadratic forms. We present two key applications of these new sparse Hanson-Wright inequalities: (1) A local law and complete eigenvector delocalization for sparse $α$-subexponential Hermitian random matrices, generalizing the result of He et al. (2019) beyond sparse Bernoulli random matrices. To the best of our knowledge, this is the first local law and complete delocalization result for sparse $α$-subexponential random matrices down to the near-optimal sparsity $p\geq \frac{\mathrm{polylog}(n)}{n}$ when $α\in (0,2)$ as well as for unbounded sparse sub-gaussian random matrices down to the optimal sparsity $p\gtrsim \frac{\log n}{n}.$ (2) Concentration of the Euclidean norm for the linear transformation of a sparse $α$-subexponential random vector, improving on the results of G{ö}tze et al. (2021) for sparse sub-exponential random vectors.

math.PR↗

arXiv2026

VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference

Decentralized inference provides a scalable and resilient paradigm for serving large language models (LLMs), enabling fragmented global resource utilization and reducing reliance on centralized providers. However, in a permissionless environment without trusted nodes, ensuring the correctness of model outputs remains a core challenge. We introduce VeriLLM, a publicly verifiable protocol for decentralized LLM inference that achieves security with incentive guarantees while maintaining practical efficiency. VeriLLM combines lightweight empirical rerunning with minimal on-chain checks to preclude free-riding, allowing verifiers to validate results at approximately 1% of the underlying inference cost by exploiting the structural separation between prefill and autoregressive decoding. To prevent verification bottlenecks, we design an isomorphic inference--verification architecture that multiplexes both inference and verification roles across the same GPU workers. This design (i) improves GPU utilization and overall throughput, (ii) enlarges the effective validator set, enhancing robustness and liveness, and (iii) enforces task indistinguishability to prevent node-specific optimizations or selective behavior. Through theoretical analysis and system-level evaluation, we show that VeriLLM achieves reliable public verifiability with minimal overhead, offering a practical foundation for trustworthy and scalable decentralized LLM inference.

cs.CR↗

arXiv2026

The Structure of an 80 pc Long Massive Filament

Using new Institut de Radioastronomie Millimétrique (IRAM) 30m telescope $\rm N_2H^+$, $\rm C^{18}O$ $J$=1-0 and Atacama Pathfinder Experiment (APEX) telescope $\rm ^{13}CO$ and $\rm C^{18}O$ $J$=2-1 maps together with archival far-infrared continuum data, and $\rm ^{12}CO$, and $\rm ^{13}CO$ $J$=1-0 data, we present a comprehensive analysis of the massive filament CFG024.00$+$0.48 (G24) across clump-to-cloud scales. Our results show that G24 is an $\sim$80 pc giant filament with a total mass of $\sim$$10^5$ M$_{\odot}$. In the different tracers the filament width is measured to be about $\sim$2 times the beam size of the observations, as expected for power-law density distributions, giving beam-deconvolved widths in the range from 0.8 to 2.8 pc. We determine a line-of-sight thickness of $\sim$2.2 pc demonstrating that G24 is not an edge-on, flatten structure. The virial parameter obtained from line mass ($α_{\rm line,vir}=M_{\rm line,vir}/M_{\rm line}$) from the $\rm C^{18}O$ (1-0) data is 0.85, and that obtained from $Herschel$-based H$_2$ column density is 0.52, suggesting G24 is globally close to virial equilibrium. The distribution of the 40 dust clumps appears to have a ''two-tier'' fragmentation pattern. For the clump groups, the separation, with a mean/median of 3.68/3.46 pc, is very close to expected length associated with the maximum fragmentation growth rate of $λ_{\rm max}=3.55 \pm0.32$ pc estimated for the dust. However, the longitudinal centroid velocity profiles of $\rm C^{18}O$ and $\rm N_2H^+$ show oscillation patterns with wavelengths of 9.8$\pm$0.1 pc and 9.9$\pm$0.1 pc, respectively. This is $\sim$2 times larger than the corresponding values of $λ_{\rm max}$ of 4.96$\pm$0.63 pc and 4.65$\pm$1.34 pc, respectively. This suggests that the velocity structure is not dominated by flows directly associated with the fragmentation seen in the dust emission.

astro-ph.GA↗

arXiv2025

Tunable Molecular Interactions Near an Atomic Feshbach Resonance: Stability and Collapse of a Molecular Bose-Einstein Condensate

Understanding and controlling interactions of ultracold molecules is a cornerstone of quantum chemistry. While the laboratory creation of degenerate molecular gases comprised of bosonic atoms has unlocked powerful new platforms for quantum simulation, progress is limited by the absence of a robust theoretical framework for characterizing inter-molecular interactions. This is in stark contrast to the situation for Fermi gases. In this Letter, we present such a framework providing universal expressions for these molecular scattering lengths as functions of experimentally measurable quantities. Our discoveries are crucial for understanding molecular condensate formation. Calculations of the compressibility reveal that a sign change in such molecular scattering lengths is directly correlated with the instability of these condensates. These results offer fresh insight with broad applications for atomic, molecular, and condensed matter physics, as well as quantum chemistry.

cond-mat.quant-gas↗

arXiv2025

On the Impact of Phase Errors in Phase-Dependent Amplitudes of Near-Field RISs

This paper investigates mutual coupling between phase-dependent amplitudes (PDAs) and designed phase shifts within pixels of near-field (NF) reconfigurable intelligent surfaces (RISs) in the presence of phase errors (PEs). In contrast to existing research that treats phase shifts with errors (PSEs) and the PDAs separately, we introduce a remaining power (RP) metric to quantify the proportion of power preserved in the signals reflected by the RIS, and we prove its asymptotic convergence to theoretical values by leveraging extended Glivenko-Cantelli theorem. Then, the RP of signals passing through RIS pixels is jointly examined under combined phase and amplitude uncertainties. In addition, we propose four pixel reflection models to capture practical conditions, and we derive approximate polynomial upper bounds for the RP with error terms by applying Taylor expansion. Furthermore, based on Friis transmission formula and projected aperture, we propose a general NF channel model that incorporates the coupling between the PSEs and the PDAs. By using Cauchy-Bunyakovsky-Schwarz inequality and Riemann sums, we derive a closed-form upper bound on spectral efficiency, and the bound becomes tighter as the pixel area decreases. We reveal that as the RIS phase shifts approach the ends of their range, the RP under independent and identically distributed PEs is smaller than that under fully correlated PEs, whereas this relationship reverses when the phase shifts are near the middle of their range. Neglecting the PEs in the PDAs leads to an overestimation of the RIS performance gain, explaining the discrepancies between theoretical and measured results.

eess.SP↗

arXiv2025

Activations as Features: Probing LLMs for Generalizable Essay Scoring Representations

Automated essay scoring (AES) is a challenging task in cross-prompt settings due to the diversity of scoring criteria. While previous studies have focused on the output of large language models (LLMs) to improve scoring accuracy, we believe activations from intermediate layers may also provide valuable information. To explore this possibility, we evaluated the discriminative power of LLMs' activations in cross-prompt essay scoring task. Specifically, we used activations to fit probes and further analyzed the effects of different models and input content of LLMs on this discriminative power. By computing the directions of essays across various trait dimensions under different prompts, we analyzed the variation in evaluation perspectives of large language models concerning essay types and traits. Results show that the activations possess strong discriminative power in evaluating essay quality and that LLMs can adapt their evaluation perspectives to different traits and essay types, effectively handling the diversity of scoring criteria in cross-prompt settings.

cs.CL↗

arXiv2025

Anomalous Superfluid Density in Pair-Density-Wave Superconductors

Pair-density-wave (PDW) states are a long-sought-after phase of quantum materials, with the potential to unravel the mysteries of high-$T_c$ cuprates and other strongly correlated superconductors. Yet, surprisingly, a key signature of stable superconductivity, namely the positivity of the superfluid density, $n_s(T)$, has not yet been demonstrated. Here, we address this central issue by calculating $n_s(T)$ for a generic model two-dimensional PDW superconductor. We uncover a surprisingly large region of intrinsic instability, associated with negative $n_s(T)$, revealing that a significant portion of the parameter space thought to be physical cannot support a pure PDW order. In the remaining stable regime, we predict two striking and observable fingerprints: a small longitudinal superfluid response and an unusual temperature dependence for $n_s(T)$. These generally model-independent, as well as experimentally relevant findings suggest that the fragility of the superfluid density poses a significant problem for the formation of stable, finite temperature PDW superconductivity.

cond-mat.supr-con↗

arXiv2025

Predicting open quantum dynamics with data-informed quantum-classical dynamics

We introduce a data-informed quantum-classical dynamics (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equation with a flexible, time-dependent Hamiltonian that can be optimized to fit sparse and noisy data from local observations of an extensive open quantum system. We demonstrate the accuracy and efficiency of DIQCD for both experimental and simulated quantum devices. We show that DIQCD can predict entanglement dynamics of ultracold molecules (Calcium Fluoride) in optical tweezer arrays. DIQCD also successfully predicts carrier mobility in organic semiconductors (Rubrene) with accuracy comparable to nearly exact numerical methods.

quant-ph↗

arXiv2025

High-precision Beam Optics Calculation of the HIAF-BRing Using Measured Fields

The construction of the High Intensity heavy ion Accelerator Facility (HIAF) has been completed, with current efforts focused on subsystem commissioning. Beam commissioning is scheduled for autumn 2025, marking a critical milestone in the HIAF project. This paper presents high-precision optics calculations for the Booster Ring (BRing) of HIAF, a key component for achieving stable heavy-ion beam acceleration. Leveraging high-precision magnetic field data, each magnet is divided into hundreds of slices, thus establishing a high-precision sliced optics model for BRing. Detailed calculations of BRing's optics are presented in this work. Critical parameters including tunes and betatron functions of the lattice based on the measured magnetic fields and those of the ideal lattice have been compared. The results highlight the impact of realistic magnetic field on beam dynamics and provide essential insights for accelerator tuning and optimization. These findings serve as a fundamental reference for beam commissioning and long-term operation, ensuring beam stability and performance reproducibility in HIAF.

physics.acc-ph↗

arXiv2025

Quench induced collective excitations: from breathing to acoustic modes

In trapped Bose-Einstein condensates, interaction quenches which are abrupt changes of the interaction strength typically implemented via Feshbach tuning, are a practical and widely used protocol to address far-from-equilibrium collective modes. Using both numerical Gross Pitaevskii and analytical schemes we study these interaction-quench-induced collective modes in a harmonically trapped two-dimensional Bose--Einstein condensate contrasting the behavior found at low and high energies. In the low-lying regime, we characterize realistic circumstances in which there is a breakdown of the expected scale invariance so that the collective excitations follow hydrodynamic theory instead of the predictions given by SO(2,1) conformal symmetry. In the high energy regime, we focus on important trap effects associated with acoustic oscillations which have been of interest experimentally. This comprehensive analysis of the collective excitations in trapped two-dimensional Bose-Einstein condensates is experimentally accessible. Through their frequencies and damping, this reflects an important built-in spectroscopy of such many-body states.

cond-mat.quant-gas↗