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Feng Wang

Publications and source records attributed to Feng Wang.

At least 19 recordsLinked to original sources
arXiv2026

Information-theoretic entropy imaging of quantum matter

Phase transitions can generate intricate spatial organization invisible to spatially averaged probes, yet quantitative measures of this organization remain limited. Here we develop "entropy imaging," an information-theoretic technique that harnesses lossless compression to quantify spatial complexity, predictability, and order in quantum materials. Using this approach, emergent spatial textures can be imaged and converted into an experimentally-accessible observable: the computable information density (CID), a compression-based measure of information complexity that approaches the Shannon entropy rate in the large-system limit. Applied to millikelvin microwave impedance microscopy of graphene, the method reveals a pronounced maximum in the CID near quantum Hall transitions, a quantitative measure of the information-rich spatial organization of the critical landscape. A disordered Hofstadter model reproduces this behavior and shows that the CID can identify the quantum critical point from a sequence of images, without ensemble averaging over disorder realizations. The same framework tracks fractional quantum Hall transitions and resolves isolated fractional puddles that coexist with a disordered background. By contrast, the trivial band-edge crossover in a MoSe2/WS2 moiré heterobilayer exhibits qualitatively different behavior, suggesting that the pronounced CID enhancement observed at the quantum Hall critical point is not simply a generic feature of a conductive-insulating crossover. We further show that the geometric information encoded in CID is not reducible to a correlation length, and it can even distinguish spatial textures with identical multifractal statistics. Together, these results establish lossless compression as a framework for quantifying emergent spatial organization in quantum materials, with portability across imaging modalities and physical systems.

cond-mat.mes-hall↗
arXiv2026

Cola2: Co-Adaptive Hierarchical Language Modeling with Continuous Semantic Compression and Balanced Discrete Generation

Hierarchical language generation separates continuous semantic modeling from discrete text realization, but its effectiveness depends on what the latent space compresses and how the discrete generator uses it. We propose Cola2, a framework that jointly studies continuous semantic compression, the balance between reconstruction and generation, and continuous-discrete co-adaptation. Cola2 dynamically segments text into semantic spans, controls the compression rate through auxiliary-loss-free negative feedback, and combines reconstruction and generation supervision to learn compact, predictive latent representations. A block-causal masked diffusion decoder is trained with clean, noisy, and dropped latent conditions, while classifier-free guidance adjusts the balance between semantic reconstruction and autonomous generation. Token-prediction gradients update the encoder through the latents, whereas Flow Matching prior learning is decoupled from representation learning by stop-gradient. During inference, generated text is re-encoded under a fixed span layout to keep the latent history consistent with the actual text context. Experiments on five downstream tasks with about 210 billion training tokens validate the core designs and show competitive, unsaturated scaling. With 346.03 billion source tokens, an unconverged checkpoint reaches a 60.10 average across eight tasks and surpasses Llama-3-8B on HellaSwag and StoryCloze despite its reported pretraining budget being over 43 times larger. These results demonstrate the potential of continuous-latent hierarchical language generation.

cs.CL↗
arXiv2026

Workflow-Aware Communication and Computation Scheduling for Mobile Edge AI Agents

AI agents are increasingly deployed in mobile edge environments to autonomously handle diverse tasks through multi-step workflows. Unlike conventional dependency-aware tasks with predefined dependency structures, agent workflows are dynamically generated based on intermediate execution results. To minimize the weighted sum of job completion latencies, this paper models partially revealed agent workflows using dynamic workflow graphs and formulates an online scheduling problem that jointly optimizes computation placement, wireless resource block allocation, and edge computing resource allocation. To address the uncertainty of future workflow evolution, we first transform the weighted completion time objective into an equivalent per-slot holding cost and characterize the optimal policy through a Bellman equation. We then construct a tractable look-ahead cost-to-go surrogate based on the revealed workflow and the estimated remaining latencies of active jobs. The proposed scheduling algorithm leverages the surrogate to determine computation placement and uses the resulting operation criticality to allocate wireless and edge computing resources online. Simulation results show that the proposed algorithm outperforms the benchmark schemes across diverse scenarios.

cs.NI↗
arXiv2026

Imaging Gate-Tunable Single-Layer Molecular Kagome Lattices

Kagome lattices provide a simple geometry for engineering electronic states that exhibit novel quantum behavior. In conventional kagome materials, however, the relevant states are often embedded in chemically complex three-dimensional crystals and are difficult to isolate or tune. Molecule-based kagome networks offer structural precision and tunability, but have largely been confined to metallic substrates incompatible with 2D devices. Here we report growth of single-layer Ni$_3$(HITP)$_2$ metal-organic frameworks (MOFs) on van der Waals substrates including graphene-based field-effect devices using metal-salt molecular beam epitaxy. Low-temperature scanning tunneling spectroscopy resolves kagome-derived flat-band and van Hove singularity-based spectral features consistent with first-principles calculations. Gating hybrid 2D MOF devices allows us to energetically shift kagome spectral features, thus establishing electrostatic control of chemically programmable 2D kagome electronic lattices.

cond-mat.mtrl-sci↗
arXiv2026

Re-emergence of fingering in solidifying liquid-metal drops

Solidification is expected to suppress fingering by removing mobile liquid. Here we report an unexpected re-emergence of fingering in impacting molten-tin drops: cooling first weakens peripheral fingers, but stronger undercooling restores them. At weak undercooling, fingers originate from a decelerating liquid rim and solidification reduces the effective acceleration that drives Rayleigh--Taylor-like amplification. At strong undercooling, solidification structures develop while liquid is still spreading, creating lateral mobility contrasts that redirect the residual flow. Comparison between the kinematically estimated solidification time and the experimentally measured fingering-onset and growth-termination times reveals a reversal in temporal ordering across the temperature range. A physics-based reduced-order model combines solidification-modified rim deceleration with mobility-gradient growth, captures the characteristic onset dynamics and accounts for the final finger length within the same physical framework.These findings establish solidification as an active means of regulating interfacial flow and morphology, with broad implications spanning metal-based additive manufacturing and debris mitigation in semiconducting industry.

physics.flu-dyn↗
arXiv2026

Transforming Image Editors into Video Editors

Recent image editing systems have achieved impressive semantic understanding, visual fidelity, and instruction-following ability, while video editing remains substantially more difficult and costly. In this paper, we present a simple alternative to end-to-end video editing: instead of training a monolithic video editor, we transform a strong image editor into a video editor through anchor-based generation. Our key insight is that video editing can be decomposed into two subproblems: editing a sparse set of keyframes and propagating those edits across time. Based on this observation, we propose Anchor-based Video Editing (AVE), a two-stage framework in which a powerful image editor first performs composed editing on selected keyframes, and a motion-guided image-to-video diffusion model then generates the final video by treating the edited keyframes as fixed anchors. This design directly inherits the strengths of modern image editors while avoiding expensive end-to-end video editing training. Experiments on IVEBench and VIE-Bench show that AVE achieves strong performance in instruction following, temporal consistency, and content fidelity. Further ablations reveal that final video editing quality is strongly correlated with the quality of the image editor, suggesting that future progress in video editing may come from stronger image editing foundations and lightweight transfer to video. Code is available at https://github.com/wangf3014/AVE.

cs.CV↗
arXiv2026

Ambipolar metal-insulator transition in bilayer MoSe2

When Coulomb interactions dominate kinetic energy, dilute two-dimensional carriers form a Wigner solid. Both its melting and disorder-driven localization can drive density-tuned metal-insulator transitions, but their relative roles and interplay have been debated for decades. Atomically thin semiconductors offer a new test because transport and scanning tunneling microscopy can probe the same material, yet poor contacts impede transport where correlations are strongest. Here we introduce contact-free radio-frequency reflectometry that senses sheet resistance through capacitive gate coupling. It resolves the electron and previously inaccessible hole transitions in the same bilayer MoSe2 device, whose carriers share disorder but not mass. Their critical densities differ fourfold yet correspond to comparable Coulomb-to-kinetic-energy ratios, where tunneling microscopy finds solid-liquid coexistence. Magnetoresistance peaks sharply at each transition, reflecting a Pomeranchuk-like, field-driven expansion of the Wigner solid against the correlated liquid. Our results demonstrate interaction-driven transitions in high-quality two-dimensional semiconductors, opening quantitative transport in contact-limited systems.

cond-mat.str-el↗
arXiv2026

Generalised Mixing-Plane Method for Compressible Reacting-Mixture Flows in Steady Multiphysics Turbomachinery Simulations

The mixing-plane method plays a pivotal role in steady simulations of multiple turbomachinery components. With advances in computational power, a whole-engine gas turbine simulation is no longer an elusive approach. However, whole-engine simulations require simultaneous coupling of the compressor, turbine and combustor. A key challenge is that the working fluid after the combustor can no longer be treated as a perfect gas, since the combustion introduces composition variations and combustion products. Conventional mixing-plane methods based on a fixed-composition gas cannot guarantee a thermodynamically consistent mixed-out state. To overcome the difficulty, this paper extends the classic mixing-plane approach to handle compressible reacting flows. The nonlinearity in the thermodynamic closure is addressed using a novel nested algorithm combining an outer pressure-root search with an inner enthalpy inversion. Physical state checks and power-law pressure sampling are incorporated to improve numerical robustness at low normal velocities where the pressure root approaches its upper physical bound. The performance of the method is demonstrated in three configurations: a quasi-1D interface test elucidating the thermodynamic stiffness of the proposed mixing-plane formulation for different fuel types and flow conditions; the Darmstadt transonic compressor case that verifies the proposed method reduces to the classic mixing-plane formulation when the mixture fraction reduces to zero; and a whole-engine simulation of the KJ66 micro-turbojet is used to demonstrate the performance of the method for multiphysics turbomachinery simulations. Good agreement with experimental data is observed, and the maximum relative mass-flow error across the mixing planes remains below 0.11%, demonstrating conservative and thermodynamically consistent interface coupling for compressible reacting turbomachinery flows.

physics.flu-dyn↗
arXiv2026

From Noisy Telemetry to Actionable Warnings: GPU Failure Prediction in Industrial Clusters

GPU clusters are critical infrastructure for AI services, but accurate and actionable GPU failure prediction remains a problem in production settings. We study ticket-linked telemetry from a ByteDance GPU cluster and identify three obstacles: workload-confounded telemetry, heterogeneous fault precursors, and the gap between window-level predictions and actionable alerts. These findings motivate Falcon, a fault-specific warning framework combining missingness-aware temporal and peer-relative features, fault-specific learner selection, and an event policy based on thresholding, persistence, and cooldown. On the test set, Falcon achieves the highest F1 among four baselines and reaches 70.6% F1 on the best-performing fault type. Detected cases provide median lead times of 17.34-35.57 hours. We further report a production deployment, where Falcon is calibrated toward high-precision alerts to reflect false-positive costs. Together, these results show that fault-specific modeling improves early warning from noisy production GPU telemetry.

cs.SE↗
arXiv2026

CoRe: Co-Evolving Reward Models for Mitigating Latent Reward Hacking in Video Diffusion Models

Latent reward models (LRMs) enable efficient alignment of video diffusion models by scoring intermediate states directly in latent space. However, we find that optimizing against a fixed latent reward rapidly leads to latent reward hacking: the predicted reward stays high while perceptual and motion quality deteriorate. Our analysis identifies distributional escape as the central cause: within a few hundred updates, the generator moves beyond the reward model's training support, where its scores no longer reflect video quality. Based on this insight, we introduce CoRe, a co-evolving reward framework that treats latent-space alignment as a dynamic interaction between the generator and the reward model. Rather than optimizing against a stationary proxy, CoRe continually refits the reward model on the generator's current samples while anchoring it to real-video preferences, so the generator cannot gain reward by drifting away from the data. On Wan2.1-T2V-1.3B, experiments show that CoRe consistently improves generation quality over both the pretrained model and prior alignment methods, while avoiding the quality collapse of fixed-reward optimization.

cs.AI↗
arXiv2026

A Rapid Integrated Tokamak Modelling Approach for Coupled Profile and Equilibrium Evolution

During plasma heating and current ramps the thermal and magnetic states of a tokamak evolve on different timescales. We present a rapid integrated tokamak modelling approach, which couples a variational fixed-boundary Grad--Shafranov equilibrium to sparse particle and energy flux matching and to weak-form current diffusion. An ITER-like pure-deuterium scenario is examined: a current ramp from 10 to 15~MA over 20~s, a 20~s flat-top and a 20~s ramp-down to 14~MA, under 80~MW of electron-cyclotron heating applied at the start. The toroidal current density at the pedestal top more than doubles during the ramp-up and then decreases during the flat-top as current redistributes inward. Thermal stored energy rises from 185.52 to 252.70~MJ and approaches its final value within the first few seconds. An independent calculation with the Fusion Synthesis Engine (FUSE) gives a stored-energy difference of 0.02\% at 60~s, whereas the largest profile differences occur in the core current density. Halving the coupling interval changes stored energy by at most 0.006\% at the selected radial resolution. The 60~s pulse, advanced through 600 coupling intervals of 0.1~s, takes 8.62~s on an Apple M4 Pro, which is at least two orders of magnitude faster than conventional integrated tokamak modelling solvers.

physics.plasm-ph↗
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

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

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↗