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Hua Wang

Publications and source records attributed to Hua Wang.

At least 19 recordsLinked to original sources
arXiv2026

Berry curvature quadrupole induced third-order anomalous Hall effect in altermagnetic $\mathrm{KV_2Se_2O}$

Altermagnets combine compensated magnetic order with momentum-dependent spin splitting, providing a symmetry-rich platform for exploring nonlinear quantum-geometric transport. In the d-wave altermagnet considered here, the antiunitary symmetry operation $TC_{4z}$ enforces a vanishing momentum-integrated Berry curvature, while inversion symmetry forbids a finite Berry-curvature dipole. Consequently, the Berry curvature quadrupole (BCQ) can provide a contribution to the nonlinear Hall response, scaling as $τ^2$, where $τ$ denotes the relaxation time. We derive a gauge-covariant Wannier interpolation formula that enables efficient evaluation of the BCQ directly from first-principles Wannier tight-binding Hamiltonians. Applying this framework to $\mathrm{KV_2Se_2O}$, we calculate the momentum-space distribution of the BCQ and the associated chemical potential dependent nonlinear conductivity. The momentum-resolved analysis reveals that the dominant contribution originates from spin-orbit coupling induced avoided arossings, directly linking the gap structure to the sign-changing nonlinear response. Our work establishes a general first-principles route to higher-order quantum-geometric responses in altermagnets, opening avenues for exploring nonlinear Hall effects in next-generation spintronic and magnetoelectronic devices.

cond-mat.mes-hall↗
arXiv2026

End-to-End Neural Modeling of EM Response and Design Performance for Free-Form RFIC Passives

Passive matching networks are critical components in radio-frequency integrated circuits (RFICs), but their design is bottlenecked by expensive finite-difference-based electromagnetic (EM) simulation. We propose an end-to-end neural framework for free-form RFIC passives that predicts both EM response and downstream design performance under varying frequency and circuit contexts, combining a frequency-conditioned neural simulator with a circuit-conditioned neural solver. To improve design performance prediction, the simulator outputs a neural EM response consisting of both the explicit EM response and a complementary response, together with a dependence regularizer that encourages the two responses to capture orthogonal information. To evaluate the proposed framework, we construct a large-scale dataset of free-form passive layouts, synthesized from both expert-designed contours and random paths. Our 21M-parameter model achieves high accuracy across a broad frequency range, circuit contexts and passive matching settings, with $R^2>0.98$ for almost all targets, while providing orders-of-magnitude faster evaluation than traditional EM simulation. Code available at https://github.com/ETH-IDEAS/RFIC-NeuralSim.

eess.SP↗
arXiv2026

GeoAgent: Contract-Constrained LLM Agents for Seismic Data Processing

Large language models (LLMs) can drive scientific software through function calling, but free-form tool use is fragile on long, data-dependent processing chains. Models omit arguments, invent units, feed the previous artifact into the wrong input, and report completion without a verifiable product. We present GeoAgent, an agent system for seismic data processing built on Madagascar's roughly 1{,}965 command-line programs. GeoAgent exposes 54 operator agents (51 generated from a declarative specification), retrieves from an index of 1{,}937 operators, and encodes the processing flow as a nine-stage state graph. Four explicit contracts---named artifacts, input resolution, parameter takeover, and artifact validation---move everything the graph can determine out of the model's discretion. A deterministic geometry tool parses the self-describing SPS/RPS/XPS and SEG-Y headers, cross-checks eight consistency conditions, and reproduces the reference shell recipe exactly. The system runs in containers with session- and task-level isolation, bounded execution, and defenses against silent failures. On a 2-D land dataset, the nine-stage pipeline completes in about six to eleven minutes and yields a verified artifact at every stage, while retrieval reduces per-turn tool context from about 12k to 2k tokens. We report the design, deployment experience, and motivating failure modes.

physics.geo-ph↗
arXiv2026

Flexo-Photoresponses from First Principles

Many functionally important materials, such as silicon, possess high-symmetry crystal structures that forbid even-order nonlinear optical responses, thereby limiting their application in e.g., photonics and optoelectronics. Strain gradients can effectively break symmetries, activating otherwise forbidden optical responses. Although such flexo-photoresponses have attracted considerable interest recently, a comprehensive microscopic theoretical framework is still lacking. Here, we develop a first-principles approach that can systematically evaluate a wide range of flexo-photoresponses in realistic materials. By mapping strain gradients to long-wavelength phonons within the established theory of electron-phonon coupling, our approach captures the nonlocal quantum-mechanical effects of lattice deformations on nonlinear optical responses. Applying the framework to prototypical centrosymmetric materials, including Si, cubic $\rm SrTiO_3$, and monolayer black phosphorus, we demonstrate that a strain gradients of $10^5\sim10^6~\rm m^{-1}$ can induce flexo-second-harmonic generation and flexo-bulk photovoltaic effects comparable to the intrinsic responses of non-centrosymmetric crystals. Our work establishes a rigorous foundation for strain gradient engineering of light-matter interactions, opening new avenues for nanoscale optomechanics, optoelectronics, and beyond.

cond-mat.mtrl-sci↗
arXiv2026

Multi-Agent Agentic Graph Learning via Structural Signatures

Agentic graph learning (AGL) has recently achieved promising results on graph reasoning tasks, where an agent powered by a large language model (LLM) sequentially samples the graph as evidence to support its final prediction. Existing methods either employ a single agent or orchestrate multiple role-based agents to reason and learn over the entire graph, but both essentially rely on a shared reasoning policy across different graph regions, which can be suboptimal for graphs with heterogeneous structural and semantic patterns. Inspired by the progress of multi-agent collaboration on complex reasoning tasks, a natural remedy is to let multiple agents own different memory and collaborate; however, applying this paradigm to graphs directly faces two challenges. First, existing AGL methods typically verbalize graph structures into natural-language descriptions for LLM agents, making the reasoning process sensitive to the ordering of structural information and thereby breaking the permutation-invariant nature of graphs. Second, incorporating increasingly large sampled neighborhoods leads to rapidly growing contexts. To address these challenges, this paper introduces a multi-agent agentic graph learning (i.e., MAAGL) framework. MAAGL partitions the graph into communities and assigns an independent agent to each community for region-specific specialization. MAAGL represents structural and semantic evidence separately. Structural evidence is summarized by a dynamically updated structural signature that is permutation-invariant and fixed in size, while semantic evidence is filtered to the top-k nodes ranked by relevance. Based on historical trajectories with similar signatures, agents estimate their confidence and trigger debate-style collaboration when needed. Extensive experiments on four benchmark datasets show that MAAGL outperforms SOTA AGL methods.

cs.AI↗
arXiv2026

GLOBE: Geometry-Led Online Behavioral Early Exit via Unit-Sphere Trajectories

Chain-of-thought (CoT) reasoning can improve problem solving, but models often keep generating after the correct answer is already reachable; the extra steps raise serving costs and may replace a correct intermediate result with an incorrect one. We propose GLOBE (Geometry-Led Online Behavioral Early Exit), an online, training-free algorithm that terminates the model's CoT process early. GLOBE embeds each completed reasoning segment as a point on a unit sphere and reads the shape of the resulting semantic trajectory. GLOBE first applies a trial phase that generates short trial answers in a separate branch, stopping the main stream on an accepted trial after checking confidence against a threshold. If generation continues, a trajectory phase stops the stream once this path repeatedly reverses direction with little net progress. On five benchmarks including mathematical reasoning, tool calling, and agent memory, GLOBE cuts output tokens by 49.7% and active end-to-end wall time (E2E) by 51.0% across two models, and achieves a five-task average of 80.58%, comparable to Full CoT.

cs.AI↗
arXiv2026

VulLink: A Dynamic Open-Access Vulnerability Graph Database for Cybersecurity Data Mining

The rapid growth of software vulnerabilities has turned cyber threat intelligence analysis into a challenging data mining problem over heterogeneous and continuously changing sources. Public repositories such as the National Vulnerability Database (NVD), Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), Exploit Database (EDB), and CVE Details provide valuable information, but their record-centric schemas make it difficult to capture cross-source relationships among vulnerabilities, weaknesses, exploits, affected products, vendors, and references. Existing graph-based vulnerability resources highlight the value of relational threat modelling, yet many remain static, offline, or difficult to access for downstream graph mining. This paper presents VulLink, a deployed, dynamic, and open-access vulnerability graph database for cybersecurity data mining. VulLink integrates multiple public repositories through an automated Extract-Transform-Load (ETL) pipeline that converts isolated, record-centric vulnerability data into a continuously updated graph database with typed entities and explicit cross-source relationships. It provides an interactive Web interface and public API for exploring, querying, and exporting mining-ready vulnerability subgraphs. It also provides pre-computed embeddings of vulnerability descriptions generated by pretrained language models, which users can query and download by model and embedding dimension as semantic features for downstream mining tasks such as exploitability prediction. To demonstrate the practical utility of VulLink, we implement a downstream exploitability prediction use case that leverages heterogeneous graph context and semantic vulnerability features. The VulLink platform, including the Web interface, public API, source code, and deployment resources, is publicly available online.

cs.CR↗
arXiv2026

LiteGUI: Lightweight GUI Agents via Multi-Solution Guided Distillation and Dual-Level Reinforcement Learning

We present LiteGUI, a new framework for building lightweight GUI agents. GUI interaction poses unique challenges due to the long-horizon nature of complex tasks and the existence of multiple valid interaction paths, which are difficult for lightweight GUI agents to handle effectively. To address these challenges, LiteGUI introduces a two-stage post-training framework operating on logged GUI states. First, we propose Guided On-Policy Distillation, which provides training-time privileged teacher guidance at each GUI state by selecting the most-matched valid action from human-verified multi-solution action annotations, while leaving the student's on-policy rollout unchanged. Second, we develop Multi-Solution Dual-Level GRPO, which combines action-level supervision with history-conditioned, per-state planning-quality supervision while accounting for multiple valid actions at each logged GUI state. Together, these stages support multi-step GUI tasks without requiring the student to reproduce fixed demonstration trajectories. We further develop a scalable data generation pipeline and a corresponding multi-solution dataset, Lite-Dataset, to support the training and evaluation of GUI agents, addressing a gap in the existing literature. Extensive experiments across multiple GUI benchmarks demonstrate that LiteGUI substantially improves the performance of lightweight GUI agents over existing training paradigms, including SFT, on-policy distillation and GRPO, and achieves competitive or superior performance compared with substantially larger models.

cs.AI↗
arXiv2026

SkeleWAM: Skeleton World-Action Modeling for Efficient Robotic Manipulation

World action models (WAMs) combine robot action generation with future state prediction. Existing WAMs typically predict videos or learned visual latents, which represent interaction geometry only implicitly and may retain appearance information unrelated to control. We introduce SkeleWAM, a compact WAM that represents a manipulation scene as a sparse 3D skeleton composed of robot joints, object centers, and interaction points. Constructed online from current RGB-D observations and robot proprioception, the skeleton provides a unified geometric state for action generation and future skeleton prediction. Future skeleton prediction provides additional geometric supervision for action learning without requiring visual reconstruction. At inference, SkeleWAM generates actions directly from the current skeleton and language instruction, while Medoid Action Consensus (MAC) serves as an auxiliary consensus strategy for stochastic action samples. On LIBERO-Plus, SkeleWAM achieves an overall success rate of 85.9% with 57.1M parameters, outperforming Cosmos-Policy by 3.7 percentage points. These results demonstrate that sparse 3D robot--object structure provides an effective state space for robust and parameter-efficient world action learning. The project is available at https://skelewam-project.github.io/.

cs.RO↗
arXiv2026

Morley element for the linear strain gradient elasticity problem

We establish a discrete $H^1$-Korn inequality for finite element spaces satisfying a second order weak continuity condition. This implies that classical $H^2$-nonconforming elements satisfy the discrete Korn inequality. Based on this result, we develop a stabilized Morley finite element method for linear strain gradient elasticity using only piecewise quadratic polynomials. Thanks to the discrete $H^1$-Korn inequality, this method does not require any penalty parameter. We prove that the method is robust with respect to both $λ$ and $ι$. Numerical experiments confirm the theoretical results.

math.NA↗
arXiv2026

WannierNLQG: A Julia package for nonlinear optical responses and quantum geometry from Wannier tight-binding models

Nonlinear optical responses and quantum geometry are central to modern condensed matter physics and are deeply intertwined. We introduce WannierNLQG, an extensible Julia framework for computing nonlinear optical responses and quantum geometric quantities from Wannier tight-binding models. Its gauge-consistent, degeneracy-aware architecture accommodates perturbative responses at arbitrary order and additional quantum geometric quantities. The current release evaluates ordinary, spin, and photon-drag injection and shift currents, as Brillouin-zone integrals and on k slices. For shift current, a unified interface exposes four complementary formulations: the conventional method, projector trace, generalized Wilson loop, and a finite-momentum geometric loop, with the last three explicitly accommodating degenerate subspaces. The framework also provides k-resolved Berry curvature, quantum metric, their multipoles, and additional quantum geometric and mixed momentum-spin quantities. Experimental symmetry workflows additionally support symmetry-adapted Wannier construction, symmetrization of tight-binding models and real-space operators, and reduction of spatially uniform response integrals to irreducible k-point orbits and invariant tensor components. The documentation covers formula conventions, the TaskConfig interface, and auditable output formats, with GeS and bilayer MoS2 case studies illustrating method comparison, degeneracy handling, quantum geometric analysis, and photon-drag responses. Together, these capabilities enable first-principles calculations of nonlinear response and quantum geometry in realistic multiband materials, connecting quantum geometry with quantitative materials modeling for optoelectronic, spintronic, and photovoltaic applications.

cond-mat.mes-hall↗
arXiv2026

TokenComSR: Task-Sensitivity-Guided Token Communication for Wireless Image Super-Resolution

For resource-constrained wireless edge devices over bandwidth-limited fading channels, wireless image transmission using traditional separate coding suffers from the cliff-effect collapse. Prevailing deep joint source-channel coding (JSCC) based on convolutional neural networks can mitigate this issue but usually fail to preserve patch-level structures, thereby preventing adaptive per-token power allocation and limiting token-domain compensation for super-resolution (SR). To address these challenges, we propose a token communication framework with SR (TokenComSR). Specifically, we conceive a task-sensitive power allocation (TSPA) module and a signal-to-noise ratio (SNR)-conditioned token refinement module (TRM). TSPA distills training estimates of task sensitivity into inference token power weights, while TRM estimates an SNR-conditioned residual to correct channel-induced distortion in the token domain before decoding. Building on TSPA and TRM, the proposed TokenComSR pairs a Swin Transformer-based token transceiver with a receiver-side SR module for resource-constrained wireless image transmission. Simulation results confirm the effectiveness of the proposed TSPA and TRM, demonstrating improvements over separate coding and JSCC-SR baselines in both reconstruction fidelity and perceptual quality.

cs.IT↗
arXiv2026

Symmetry-enforced third-order nonlinear thermal Hall effects in altermagnets

Thermal Hall effect (THE) is a powerful probe of material properties, even in insulators. Here we investigate the thermal response of altermagnets by developing a theory on Berry curvature driven nonlinear THE from both electrons and magnons. We identify symmetry conditions under which the third-order THE dominates while linear and second-order contributions vanish. Notably, d-wave and i-wave altermagnets with out-of-plane Neel order satisfy these conditions, whereas in-plane order yields a dominant linear THE. Using KV2Se2O (a d-wave altermagnetic metal) and MnF2 (an altermagnetic insulator) as concrete examples, we show nonzero third-order nonlinear THE from electrons and magnons, respectively, both exhibiting a pi-periodic dependence on the direction of temperature gradient. These symmetry-guaranteed Berry curvature phenomena provide a diagnostic for altermagnetism in candidate materials and enable determination of the Neel vector orientation.

cond-mat.mtrl-sci↗
arXiv2026

UW-OCDM for Low-Altitude UAV Communication and Cooperative Sensing

Integrated sensing and communications (ISAC) is a key enabler for uncrewed aerial vehicles (UAVs) in the low-altitude economy. This paper proposes an ISAC waveform that embeds a unique word (UW) into orthogonal chirp division multiplexing (OCDM), termed UW-OCDM, together with corresponding communication reception and cooperative sensing schemes for high-mobility UAV scenarios. For communication, the embedded UW enables timing synchronization and Doppler estimation and compensation without requiring a separate synchronization sequence. A sparse spatio-temporal channel estimation method exploits the common channel support across multiple receive antennas and consecutive UW observations to support reliable data demodulation. For sensing, the deterministic UW serves as a shared prior that allows distributed base stations to construct sensing dictionaries locally without exchanging random payload symbols in real time. A hierarchical multi-target detection and tracking algorithm integrates direct-path interference suppression, kinematic prediction, multi-candidate screening, off-grid refinement, residual verification, and successive interference cancellation for robust localization with reduced search complexity. Simulation results demonstrate reliable communication and localization in highly dynamic UAV scenarios, while the proposed framework retains low-complexity frequency-domain equalization and reduces transmit-reference sharing overhead and multi-static localization complexity.

eess.SP↗
arXiv2026

ProtoHGF-Net: Prototype HyperGraph Fusion with Intra-modal Calibration for RGBT Object Detection

RGB-Thermal (RGBT) object detection enables robust perception in complex scenes by leveraging the complementary strengths of visible textures and thermal cues. However, existing methods mainly rely on dense cross-modal interactions over full-resolution features, which inevitably introduce background interference and hinder the learning of target-relevant representations. In this paper, we propose the Prototype HyperGraph Fusion Network (ProtoHGF-Net), a novel framework that redefines cross-modal fusion as prototype-level semantic interaction rather than the dense cross-modal interaction paradigm. Specifically, we design Prototype HyperGraph Fusion to perform cross-modal interaction in a compact prototype-level semantic space. This design enables more selective fusion among target-relevant prototypes. To support this prototype-level fusion, we propose Teacher-Mask Calibration Distillation, which calibrates modality features before fusion using modality-specific teachers and target-aware masks. This strategy suppresses backgrou- nd-dominant responses and produces more target-focused features. Extensive experiments on DroneVehicle, DVTOD, and FLIR demonstrate that ProtoHGF-Net achieves state-of-the-art performance with 85.9\% $mAP_{50}$, 88.2\% $mAP_{50}$, and 79.1\% $mAP_{50}$, respectively. Our code is available at \href{https://github.com/ZiMo-Chen/ProtoHGF}{GitHub}.

cs.CV↗
arXiv2026

VSWR-Resilient Mm-Wave and Cm-Wave PAs for Large-Scale Phased Arrays

Large-scale mm-Wave and cm-Wave phased arrays have become central to wireless communication and sensing systems, including terrestrial 5G/6G links and base stations, non-terrestrial networks (NTNs), satellite communication (SATCOM), radar, and relay applications. In dense arrays, power amplifiers (PAs) and antenna array interact with each other: array radiation depends on the amplitude/phase of PA output signals, while the load impedance experienced by each PA varies with frequency, scan angle, and element position. This active antenna impedance, described as voltage standing wave ratio (VSWR) variation, arises mainly from antenna inter-element mutual coupling and is shaped by package/interconnect parasitics. Each PA can deviate from its optimum large-signal operating condition, degrading output power, power gain, power-added efficiency, AM-AM/AM-PM, and reliability margin. These variations further affect array EIRP consistency, EVM headroom, link budget, thermal density, and beamforming calibration, complicating PA design for wideband, wide-scan-angle arrays. This review introduces the origins of antenna VSWR and antenna-PA interactions connecting the antenna reflection coefficient $Γ_{\mathrm{ant}}$ and PA output matching $S_{22}$ to delivered-power and transmitted-phase variation through the $S_{22}Γ_{\mathrm{ant}}$ dependence. Reverse-coupled excitation and reverse intermodulation distortion (RIMD) are discussed. This motivates PA designs that achieve simultaneous output and loadline matching (SOLM), enabling a small output reflection coefficient $|S_{22}|$ without significantly compromising large-signal performance. Recent mm-Wave and cm-Wave VSWR-resilient integrated PA techniques and demonstrations are reviewed. Finally, challenges and opportunities for compact, load-insensitive, energy-efficient, high-power-density, and calibration-scalable integrated PAs are discussed.

eess.SP↗
arXiv2026

Imaging the Néel Vector in Two-Dimensional Antiferromagnets using Antisymmetric Compton Scattering

We demonstrate that antisymmetric Compton scattering can detect both the switching and the continuous rotation of the Néel vector in two-dimensional (2D) antiferromagnets. By probing magnetoelectric (ME) multipoles, which couple electric and magnetic dipoles, this approach overcomes the limitations of conventional techniques that rely on a finite net magnetization. Using a group-theoretical decomposition of the staggered moments in 2D MnPS$_3$ into irreducible representations, combined with first-principles calculations, we show that the antisymmetric Compton profile (ACP) is highly sensitive to the Néel vector orientation: it reverses sign under Néel vector reversal and exhibits distinct anisotropies under in-plane rotation. These results establish the ACP as a versatile probe of antiferromagnetic (AFM) order and magnetoelectric phenomena in van der Waals materials.

cond-mat.mtrl-sci↗
arXiv2026

Multiport Analytical Pixel Electromagnetic Simulator (MAPES) for AI-assisted RFIC and Microwave Circuit Design

This paper proposes a novel analytical framework, denoted the Multiport Analytical Pixel Electromagnetic Simulator (MAPES). MAPES enables efficient and accurate prediction of the electromagnetic (EM) performance of arbitrary pixel-based microwave (MW) and RFIC structures. Unlike the Internal Multiport Method (IMPM), which optimizes only connecting elements within a fixed, gap-separated pixel skeleton, MAPES operates directly on the all-pixel presence/absence formulation used in recent MW/RFIC design. This is enabled by diagonal virtual pixels, an occupancy-to-load mapping, and a multi-layer/via port-level formulation that have no counterpart in IMPM. By introducing virtual pixels and diagonal virtual pixels and inserting virtual ports at critical positions, MAPES captures all horizontal, vertical, and diagonal electromagnetic couplings within a single multiport impedance matrix. Only a small set of full-wave simulations (typically about 1% of the datasets required by AI-assisted EM emulators) is needed to construct this matrix. Subsequently, any arbitrary pixel configuration can be evaluated analytically using a closed-form multiport relation without additional full-wave calculations. The proposed approach eliminates data-driven overfitting and ensures accurate results across all design variations. Using MAPES, comprehensive examples for single- and double-layer PCBs and CMOS processes (180 nm and 65 nm) confirm that high prediction accuracy with 600-2000$\times$ speed improvement is achieved compared to CST simulations. Owing to its efficiency, scalability, and reliability, MAPES provides a practical and versatile tool for AI-assisted MW circuit and RFIC design across diverse fabrication technologies.

eess.SP↗