arXiv subjects

Feng Wang

Publications and source records attributed to Feng Wang.

At least 19 recordsLinked to original sources

arXiv2026

A subcell-refined entropy-residual-driven limiting strategy for high-order discontinuous Galerkin methods

Fine-grained, subcell-level dissipation control is essential for achieving robust high-order discontinuous Galerkin (DG) simulations of nonlinear hyperbolic systems in under-resolved regimes while preserving accuracy. This paper proposes a subcell-refined entropy-residual-driven limiting strategy for DG on Legendre-Gauss-Lobatto nodes. The limiter introduces only nearest-neighbor pairwise dissipation within each element, with closed-form coefficients that supply the minimal dissipation required to restore the element entropy inequality. The strategy is a diagonal, locally stable approximation of classical entropy-stable methods, and a generalized subcell framework reveals split-form DG and residual-distribution-based entropy correction schemes as particular choices of the limiting coefficients. For the Euler equations, a physically consistent jump operator separately models thermal and shear entropy production while preserving velocity and pressure equilibrium; a subcell refinement of the Zhang-Shu positivity limiter ensures pointwise positivity. Extensive numerical tests confirm that the scheme maintains optimal high-order accuracy, strictly enforces entropy dissipation, and significantly reduces the difficulty of a posteriori positivity-preserving procedures.

physics.comp-ph↗

arXiv2026

A multi-scale approach for wall-bounded WCSPH--RANS simulations

Achieving sufficient near-wall resolution remains challenging for wall-bounded turbulence simulations using weakly compressible smoothed particle hydrodynamics (WCSPH), as local particle refinement imposes restrictive time-step requirements. This work proposes a multi-scale near-wall approach within the Reynolds-averaged Navier--Stokes (RANS) framework to improve near-wall predictions without refining the SPH particle distribution. A local one-dimensional sublayer solver based on the simplified steady $k$--$ω$ equations is coupled with each wall-adjacent fluid particle. A local flow-rate constraint and a friction-velocity-based iteration scheme are developed to close and solve the sublayer system, while a two-way shear-stress coupling with local feedback improves consistency between the two scales. The proposed approach is evaluated using turbulent straight-channel flows over a wide range of Reynolds numbers and a wavy-channel flow involving separation and recirculation. The results demonstrate improved near-wall velocity, turbulent kinetic energy and friction-coefficient predictions, satisfactory convergence, and good agreement with reference solutions. In particular, the approach improves the convergence of near-wall predictions where conventional wall treatments struggle to provide consistent results, and agrees closely with reference solutions at Reynolds numbers as high as $8.0 \times 10^{7}$ without near-wall particle refinement. For separated flow, convergence comparable to that of locally refined finite-volume simulations is obtained using an approximately uniform particle distribution. These improvements are achieved with limited computational overhead, supporting the application of particle-based methods to engineering flows involving wall-bounded turbulence.

physics.flu-dyn↗

arXiv2026

Valerant: An Automatic Navigable Game Map Generator via Action-Conditioned World Model Exploration

World Action Models (WAMs) couple predictive world modeling with action generation, allowing anticipated future states to guide agent behavior. Although WAMs are rapidly advancing embodied AI, general-purpose counterparts remain largely unexplored in games. Existing game-oriented approaches often combine action-conditioned world models with external policies and reward functions to realize WAM-like decision-making, yet they operate mainly in 2D visual observation space and do not instantiate persistent 3D geometry. Extending this paradigm to 3D games introduces a distinct challenge. In autonomous driving and robotics, the physical environment exists independently of the model, providing a persistent 3D world in which selected actions can be executed. Games have no such external substrate; the virtual world itself must be instantiated. Most playable games require a persistent and navigable space, while 3D games additionally require explicit geometry that supports movement and interaction. Action-conditioned video rollouts provide visual observations but not this spatial representation. We present \textsc{Valerant}, a training-free framework that transforms a pretrained action-conditioned world model into a WAM for exploring and constructing 3D game maps. By coupling predictive visual rollouts with SLAM-based spatial reconstruction and exploration-driven action selection, \textsc{Valerant} progressively transforms a single image into a persistent 3D game map. This framework extends WAM-based interaction beyond 2D visual simulation and offers a new approach to reducing manual effort in 3D game-map creation.

cs.AI↗

arXiv2026

SRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognition

Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the widespread adoption of deep neural network (DNN)-based SAR automatic target recognition (SAR-ATR) systems, they remain vulnerable to adversarial examples and tend to over-rely on background regions, leading to degraded adversarial robustness. Existing adversarial attacks for SAR-ATR often require visually perceptible distortions to achieve effective performance, thereby necessitating an attack method that balances effectiveness and stealthiness. In this paper, a novel attack method termed Space-Reweighted Adversarial Warping (SRAW) is proposed, which generates adversarial examples through optimized spatial deformation with reweighted budgets across foreground and background regions. Extensive experiments demonstrate that SRAW significantly degrades the performance of state-of-the-art SAR-ATR models and consistently outperforms existing methods in terms of imperceptibility and adversarial transferability. Code is made available at https://github.com/boremycin/SAR-ATR-TransAttack.

cs.CV↗

arXiv2026

Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards

We present Jina-OCR-v1, an end-to-end document parsing model built to serve on low-budget GPUs. It combines the compressed-vision encoder and the 3B mixture-of-experts decoder of DeepSeek-OCR, which activates about 570M parameters per token, with a FastMTP speculative decoding head that shares a single draft block recursively across K=3 prediction steps. Greedy verification makes decoding lossless. Post-training combines instruction alignment, robustness fine-tuning on difficult documents, and GRPO under dense verifiable rewards: deterministic formula, table, and structural checks that award partial credit. The training data mixes cleaned public corpora with targeted synthetic pages. At the default dynamic-resolution setting, Jina-OCR-v1 scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, and reaches the highest page throughput in our comparison at 2.57 pages per second. On a low-budget GPU such as the NVIDIA L4, FastMTP doubles decoding speed over greedy autoregressive decoding. The model is publicly available at https://huggingface.co/jinaai/jina-ocr-v1.

cs.CL↗

arXiv2026

Optical Voltage Profiling of 2D Semiconductors via Proximal Exciton Sensing

High contact resistances in atomically thin semiconductors often mask intrinsic electrical transport properties, particularly at low carrier densities where exotic correlated states emerge. We introduce optical voltage profiling, a noninvasive wide-field technique that replaces local voltage probes with a proximal monolayer MoSe$_2$ exciton sensor. Isolated by thin hexagonal boron nitride, this sensor converts the target's local electrostatic potential into spatially resolved modulations of exciton reflectance. Through pixel-wise in situ calibration, these signals yield quantitative two-dimensional voltage maps of an actively biased semiconductor device. Using this method, we demonstrate the carrier-density-driven metal-insulator transition in bilayer MoSe$_2$ and obtain channel resistances below 1 k$Ω$ despite M$Ω$-scale two-terminal resistances in the metallic region. The optically derived resistance exhibits a metal-insulator crossover near the resistance quantum $h/e^2$, and the voltage maps and reconstructed local conductivity reveal pronounced spatial heterogeneity in both insulating and metallic regimes. Beyond resolving channel resistance under high contact-resistance conditions, the technique provides spatially resolved access to microscopic transport heterogeneity in functional van der Waals devices.

cond-mat.mes-hall↗

arXiv2026

Best Reaction Target To Determine Proton Distribution Radii of Atomic Nuclei

We found that a heavy target such as Pb is most suitable for determining the proton distribution radii of unstable nuclei through charge-changing cross-section ($σ_\text{cc}$) measurements. As a heavy ion probe, low-$Z$ targets are routinely used to determine nucleon distribution radii of unstable isotopes. This approach has recently been extended to study proton distribution radii from $σ_\text{cc}$ measurements. However, empirical scaling factors have to be introduced to apply the Glauber models. In the present work, we systematically investigated the scaling factor using 39 new $σ_\text{cc}$ data of 18 $p$-shell nuclei on hydrogen, carbon, silver, and lead targets at around 240 MeV/nucleon. Together with the existing data, we reveal a universal dependence of the scaling factor on both the masses of target nuclei and the separation energies of projectile nuclei. The scaling factors decrease with increasing target-nucleus mass and converge to 1 for the highest-$Z$ target, making the scaling unnecessary. We conclude that instead of a low-$Z$ target, employing a heavy target such as Pb in $σ_\text{cc}$ measurements is the best option to determine the proton distribution radii of unstable nuclei.

nucl-ex↗

arXiv2026

Evidence for Dynamical Filtering: High Binary Fraction, Hard-binary Excess, and Unresolved Triples in the Surviving Core of NGC 6791

We present a deep photometric analysis of the main-sequence (MS) population in the old, metal-rich open cluster (OC) NGC 6791 using Gaia Data Release 3 data. After correcting for differential reddening, we use the Bayesian model comparison to test whether stellar rotation can account for the observed MS broadening and find that a rotation-dominated interpretation is strongly disfavored. We therefore infer that unresolved multiplicity is the primary contributor to the photometric offsets. We derive a high-q companion fraction of $54.3\% \pm 2.8\%$ for systems with $q \gtrsim 0.5$, significantly higher than typical values reported for most OCs and the field. The inferred offset distribution is not consistent with a flat mass-ratio distribution but instead shows an excess toward high mass ratios ($q \sim 0.8$--$1.0$), suggestive of preferential survival of hard binaries in a dynamically evolved environment. We also identify a population of stars lying above the equal-mass binary limit ($ΔG > 0.75$ mag), which is difficult to explain with ordinary MS binaries alone and is plausibly interpreted as candidate unresolved triple or higher-order multiple systems. A Kolmogorov--Smirnov test, together with Monte Carlo label-shuffling experiments, shows no statistically significant difference between the projected radial distributions of the single-star and binary/multiple populations within the observed field. Taken together, these results are consistent with the picture that NGC 6791 is the dynamically processed inner remnant of a once more massive cluster.

astro-ph.SR↗

arXiv2026

Neutral Atom Quantum Computing: Principles, Routes, Progress, and Challenges

Neutral atom quantum computing utilizes laser-trapped neutral atoms as qubits and realizes quantum logic gate operations through Rydberg-state interactions. In recent years, it has become one of the most vibrant directions in quantum computing hardware. This paper systematically reviews the working principles of neutral-atom quantum computers, including qubit encoding, atom trapping and manipulation, Rydberg states and interactions, the Rydberg blockade quantum gate mechanism, and atom rearrangement with reconfigurable architectures. The mainstream technical routes are surveyed, represented by optical tweezer arrays combined with Rydberg interactions, optical lattice schemes, and dipole trap arrays. A panoramic review is provided of domestic and international research progress from theoretical foundations in 2000 to the latest achievements in 2026, including thousand-qubit-scale systems, logical qubits, and quantum error correction experiments. Key breakthroughs are highlighted, such as the 6100-atom qubit array, continuous operation of a 3000-qubit system, quantum simulation of the Kitaev honeycomb model, toric code error correction demonstrations, encoding rates exceeding 1/2, and fault-tolerant architectures. The core bottlenecks are analyzed in depth, including the scalability--fidelity trade-off, engineering implementation of quantum error correction, atom loss and mid-circuit replenishment, laser system industrialization, control electronics scalability, and long-distance quantum interconnection. This paper aims to provide a systematic reference for academic research and technological development in this field.

quant-ph↗

arXiv2026

A Pressure-Robust Nonconforming Immersed Finite Element Method for Stokes Interface Problems

It is well established that an appropriate modification of test functions may lead to pressure-robust mixed methods for Stokes problems. However, for immersed finite element approximations of Stokes interface problems on unfitted meshes, it remains unclear whether the velocity error is independent of the pressure, since the velocity and the pressure are coupled in one of the interface conditions. In this paper, we provide a positive answer through a novel decomposition of the discontinuous pressure into a continuous component and a velocity-dependent discontinuous component. We demonstrate that the immersed Crouzeix--Raviart/$P_0$ element method achieves pressure robustness via an $H(\operatorname{div})$-conforming reconstruction of the test functions on the right-hand side. The stability and optimal error estimates of the proposed method are established with constants independent of the interface position relative to the mesh. Numerical experiments are presented to validate the theoretical findings.

math.NA↗

arXiv2026

HI-HCQC: A Tightly-Coupled Hardware Interface with High-Efficiency Communication for Hybrid Classical-Quantum Computing

Hybrid classical-quantum computing requires frequent data exchange between classical processors and quantum control hardware. However, existing superconducting quantum control systems are commonly connected through loosely coupled interfaces such as Ethernet, resulting in high communication latency and limited task throughput. To address this issue, we present HI-HCQC, an RFSoC-based hardware interface for tightly coupled hybrid classical-quantum computing. HI-HCQC integrates high-speed RF-DACs, RF-ADCs, programmable logic, embedded processors, clock synchronization circuits, and a PCIe Gen3 x8 interface, enabling direct microwave pulse synthesis, qubit readout, and high-throughput data transfer between host servers and quantum measurement-control units. Experimental results show that HI-HCQC supports six control channels and one multiplexed readout channel, achieves stable microwave generation and acquisition, and successfully performs qubit spectroscopy, Rabi oscillation, T1 measurement, single-shot readout, randomized benchmarking, and CZ-gate characterization. Compared with a conventional control system, HI-HCQC reduces end-to-end execution latency for representative quantum gate and circuit tasks and significantly improves task throughput. These results demonstrate that PCIe-coupled RFSoC control hardware provides a practical foundation for scalable and efficient hybrid classical-quantum computing systems.

cs.DC↗

arXiv2026

From Cloud to Crowd: Democratizing LLM Service with Decentralized Edge Collaboration for RAG

The rapid advancement of large language models (LLMs) has increased demand for scalable and cost-effective deployment, especially for mobile and edge devices. Cloud-hosted LLMs are powerful but expensive and difficult to scale due to vendor lock-in and high resource needs, resulting in high expenses and unstable performance under load. Recent efforts focus on deploying small language models (SLMs), distilled or pruned from LLMs, on resource-constrained edge devices to reduce costs and improve scalability. However, edge-based SLMs face limited knowledge coverage and notable accuracy gap compared to cloud-based LLMs. To address this, we present DEFRAG, a decentralized edge collaboration system for retrieval-augmented generation (RAG) that optimizes both retrieval and generation across heterogeneous edge devices. For retrieval, DEFRAG compresses and shares knowledge graphs, using hybrid retrieval to expand knowledge coverage. For generation, DEFRAG introduces an optimizer that adaptively selects SLMs and RAG parameters per query, balancing accuracy and cost. We implement DEFRAG on a heterogeneous edge testbed and evaluate it on benchmark QA datasets. We also test it under mobile route stress, non-uniform data placement, and a domain-specific QA workload. The results show that DEFRAG maintains stable service quality and cost efficiency under these broader settings. Results show that DEFRAG narrows the SLM-LLM accuracy gap, while reducing cost by up to 98.4% and increasing peak throughput by up to 97.8% over centralized services. These findings demonstrate the potential of DEFRAG for democratized LLM services at the edge.

cs.DC↗

arXiv2026

Genie Sim PanoWorld: An Infinite Indoor 3D World Generation Pipeline via Panoramic Scene Modeling and Simulation

We address the problem of reconstructing a high-fidelity, freely navigable 3D scene from a single $360^\circ$ panorama, without per-scene optimization or multi-view capture. Existing methods either lack metric trajectory control, which hinders reliable downstream 3D reconstruction, or struggle with large disocclusions under long-range camera motion while requiring high-end multi-GPU servers.We present Genie Sim PanoWorld, a two-stage feed-forward pipeline that bridges generation and reconstruction via an explicit, trajectory-controllable panoramic video. A NavMesh-planned $\mathrm{SE}(3)$ roaming trajectory is injected into a latent video diffusion model through dense geometry-warped conditioning; long--short trajectory mixed training and a self-consistency objective based on shortcut models together yield high-fidelity video in four CFG-free denoising steps. A feed-forward panoramic reconstructor then lifts the generated video into a high-fidelity 3D Gaussian scene that supports real-time, free-viewpoint roaming and can be directly used as a simulation-ready asset for embodied AI applications. Experiments show that Genie Sim PanoWorld outperforms geometry-conditioned baselines in both panoramic video generation and downstream 3D reconstruction, while generalizing zero-shot to unseen indoor scenes.

cs.CV↗

arXiv2026

Visualizing the Impact of Quenched Disorder on 2D Electron Wigner Solids

Electron Wigner solids (WSs)1-12 provide an ideal system for understanding the competing effects of electron-electron and electron-disorder interactions, a central unsolved problem in condensed matter physics. Progress in this topic has been limited by a lack of single-defect-resolved experimental measurements as well as accurate theoretical tools to enable realistic experiment/theory comparison. Here we overcome these limitations by combining atomically-resolved scanning tunneling microscopy (STM) with neural-quantum-state quantum Monte Carlo (NQS-QMC) simulation of disordered 2D electron WSs to discover new disorder-induced physical regimes of correlated electron behavior. STM was used to image the electron density ($n_e$) dependent evolution of electron WSs in gate-tunable bilayer MoSe2 devices with varying long-range ($n_\mathrm{LR}$) and short-range ($n_\mathrm{SR}$) disorder densities. These images were compared to NQS-QMC simulations using realistic disorder maps extracted from experiment, thus allowing the roles of different disorder types to be disentangled. We identify two distinct physical regimes for disordered electron WSs that depend on $n_\mathrm{SR}$. For $n_\mathrm{SR} \lesssim n_e$ the WS behavior is dominated by long-range disorder and features extensive mixed solid-liquid phases, a new type of local re-entrant melting/crystallization, and prominent Friedel oscillations. In contrast, when $n_\mathrm{SR} \gg n_e$ these features are suppressed and a more robust amorphous WS phase emerges that persists to higher ne, highlighting the importance of short-range disorder in this regime. Our work establishes a powerful framework for studying disordered quantum solids via a combined experimental-theoretical approach.

cond-mat.str-el↗

arXiv2026

Terahertz electrodynamics in a zero-field Wigner crystal

In clean two-dimensional (2D) systems, electrons are expected to self-organize into a regular lattice, a Wigner crystal, when their mutual Coulomb repulsion overwhelms kinetic energy. Understanding the Wigner crystal at zero magnetic field is a long-sought goal in physics, thanks to its fundamental simplicity and possible connection to the density-driven metal-insulator transition. To date, evidence for such a crystal has been reported across various platforms. However, the AC conductivity of a zero-field Wigner crystal, a key observable characterizing its electrodynamics, has never been measured. Here, we develop an ultrasensitive on-chip terahertz (THz) spectroscopy technique to probe the AC conductivity in electrostatically gated monolayer MoSe2 encapsulated in hexagonal boron nitride. We observe a sub-THz resonance corresponding to the pinning mode of a zero-field Wigner crystal, whose frequency is orders of magnitude higher than those under high magnetic fields. Using the pinning mode as an indicator, we reveal that moderate disorder notably stabilizes the Wigner crystal. With increasing density towards melting, we find that the pinning mode of the Wigner crystal coexists with a growing Drude component characteristic of an electron liquid, and the competition between these two components in the conductivity spectra leads to the insulator-metal transition of the 2D electron system. Our findings not only elucidate the low-energy electrodynamics of a zero-field Wigner crystal, but also establish on-chip THz spectroscopy as a powerful probe for correlated quantum phases in two-dimensional materials.

cond-mat.mes-hall↗

arXiv2026

AgentOmnia: Scaling Agentic Models for Full-Scenario Applications

Large language model agents have advanced rapidly, yet progress remains fragmented across domains, capabilities, task difficulty, and interaction settings. We frame this as full-scenario agentic scaling and present AgentOmnia, a framework coordinating task-space definition, data synthesis, post-training, evaluation, and improvement across To-Consumer (ToC), To-Business (ToB), and To-Employee (ToE) applications. An extensible Domain x Capability x Atomic Difficulty taxonomy aligns these stages and enables fine-grained diagnosis with OmniaBench. AgentOmnia combines bidirectional environment-task synthesis with tool-dependency, program-structured, and solver-based pipelines, constructing 5,018 stateful environments with 255,375 tools and 52,361 tasks. Programs, solvers, and verifiers provide correctness signals, while supervised fine-tuning, online agentic reinforcement learning, and a rollback curriculum support post-training. Evaluation failures translate into Product Requirement Documents (PRDs) for targeted self-evolution. Starting from Qwen3-30B-A3B-Thinking-2507, AgentOmnia raises the pass rate on the OmniaBench challenging subset from 9.16% to 37.11% and the macro-average across OmniaBench, $τ^2$-Bench, DeepPlanning, and VitaBench from 22.86% to 41.69%. Under a unified protocol,it leads the evaluated agentic post-trained baselines on OmniaBench and retains the highest four-benchmark macro-average. It also surpasses Qwen3-235B-A22B-Thinking-2507 on all four benchmarks and exceeds Qwen3.5-35B-A3B on the macro-average. Gains span three application splits, ten capability dimensions, eight atomic-difficulty factors, and 76 of 90 level-1 domains, indicating broad rather than category-specific improvement. A one-round study provides initial evidence for PRD-guided self-evolution, motivating validation at larger scales and in industrial settings.

cs.AI↗

arXiv2026

jina-reranker-v3.5: An Efficient Listwise Reranker with Hybrid Attention and Self-Distillation

Listwise rerankers are the discriminative core of agentic retrieval pipelines, yet production deployment demands efficiency, domain robustness, and fluency on semi-structured data at the same time. We present jina-reranker-v3.5, a 0.6B-parameter listwise reranker that meets these demands together without sacrificing the cross-document comparison that makes its predecessor jina-reranker-v3 effective. jina-reranker-v3.5 keeps the last-but-not-late (LBNL) interaction of jina-reranker-v3 and reworks it along three axes. It replaces uniform global attention with a hybrid schedule of three sliding-window layers followed by two global layers, pinning the terminal layer to global as LBNL readout requires. It trains on a curated multi-domain mixture that spans legal, medical, financial, multilingual, and structured retrieval. It transfers quality through a three-stage self-distillation recipe in which a full-attention teacher sets an upper bound that a sparse-attention student then recovers under a staged adaptation protocol. jina-reranker-v3.5 reaches 63.20 nDCG@10 on BEIR, matching a 4B model at roughly 7x fewer parameters, and improves over jina-reranker-v3 on MIRACL and RTEB as well. Its largest gains come on semi-structured retrieval, where it lifts nDCG@10 by 9.6 points over jina-reranker-v3 and leads all rerankers of comparable size. The hybrid schedule further cuts listwise inference latency by up to 1.56x. We release the model weights on Hugging Face under a non-commercial license.

cs.IR↗

arXiv2026

Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems

Molecular simulations of complex chemical systems, such as catalysis, electrochemistry, and energy storage, often need to capture the interplay of effects such as electronic structure, finite-temperature fluctuations, and electric-field response. Such complexity is difficult to address with traditional ab initio calculations, which are limited by the time and length scales they can reach. AI-accelerated ab initio (AI2) methods use machine learning potentials trained on first-principles data to replace expensive electronic-structure calculations, extending ab initio accuracy to these regimes, but their routine application requires reliable workflows that connect first-principles calculations, model training, molecular dynamics, enhanced sampling, trajectory analysis, and HPC orchestration. Here we present ai2-kit, a software toolkit for developing accessible, reproducible, and extensible AI2 workflows. ai2-kit provides high-semantic-density command-line interfaces and Python APIs for structure and dataset conversion, batch task generation, active-learning screening, job orchestration, and workflow recovery. We demonstrate ai2-kit in four representative applications: active-learning-based machine learning potential construction, free-energy perturbation for redox and acid-base processes, electrochemical machine learning potentials for electrified interfaces, and spectroscopies from machine learning molecular dynamics. ai2-kit also provides AI-agent skills that help users adapt these use cases into customized workflows for their own chemical systems and computational software stacks. Together, ai2-kit helps turn AI2 methods from bespoke computational protocols into reusable and extensible workflows for complex chemical systems, from model construction to property prediction.

physics.chem-ph↗