arXiv subjects

Tao Wang

Publications and source records attributed to Tao Wang.

At least 127 records · Page 7Linked to original sources

arXiv2026

Machine-learning-identified two-dimensional van der Waals multiferroics for four-state nonvolatile memory

Two-dimensional (2D) van der Waals (vdW) multiferroics offer an attractive platform for four-state nonvolatile memory by combining switchable ferroelectric polarization and magnetization within a single material system. However, their development is hindered by the scarcity of synthesizable candidates and the lack of non-destructive readout schemes. Here, we combine machine-learning screening with first-principles calculations to explore the 2D vdW ABC$_2$X$_6$ family and identify a set of high-confidence multiferroic candidates. Among them, AuCrP$_2$S$_6$ monolayer emerges as a representative system with a ferromagnetic ground state, a sizable out-of-plane polarization of 7.46 pC/m, and a moderate ferroelectric switching barrier of $\sim$130 meV/f.u. Moreover, the nonlinear optical response mediated by the bulk photovoltaic effect (BPVE) in AuCrP$_2$S$_6$ provides a dual-channel probe of the ferroic orders, in which the polarization direction governs the photocurrent sign while the magnetic order selects the spin channel via robust exchange splitting. This intrinsic coupling enables the non-destructive readout of four logic states within a single atomic layer, thereby providing a practical blueprint for next-generation multistate optoelectronic memory.

cond-mat.mtrl-sci↗

arXiv2026

Where to Spend Rollouts: Hit-Utility Optimal Rollout Allocation for Group-Based RLVR

Reinforcement learning with verifiable rewards (RLVR) has emerged as a central paradigm for improving the reasoning capabilities of large language models. Group-based policy optimization methods, such as GRPO, typically allocate a fixed number of rollouts to every prompt. This uniform allocation can be inefficient: it over-allocates compute to prompts whose sampled groups are already saturated while under-exploring prompts for which additional samples may reveal useful correct trajectories. To address this limitation, we introduce hit utility, the posterior probability that at least one rollout in a proposed additional allocation for a prompt will be correct. Building on this notion, we propose Hit-Utility Optimal Rollout Allocation (HORA), a learning-free rollout allocation policy that maximizes total posterior hit utility within each allocation batch. HORA adaptively reallocates rollout budgets while leaving the downstream reward evaluation and group-based advantage estimator unchanged. Across four mathematical reasoning benchmarks and three model scales, HORA preserves comparable Pass@1 and improves Pass@K over compute-matched GRPO in ten of twelve model--benchmark configurations, with one tie and one saturated exception. It is also drop-in compatible with other group-based estimators such as RLOO. Ablation studies indicate that the uniform prior used by HORA is competitive with five prompt-conditioned learned-prior alternatives.

cs.LG↗

arXiv2026

MUltiplexed Survey Telescope (MUST) Science White Paper I: Overview of Large-Scale Structure Cosmology in the Era of Stage-V Spectroscopic Surveys

The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg$^2$ field of view. MUST aims to conduct the first Stage-V spectroscopic survey in the 2030s, mapping the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to a redshift of z ~ 5.5, corresponding to approximately 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, ranging from local bright galaxies and luminous red galaxies to emission-line galaxies, and high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, tests of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe.

astro-ph.CO↗

arXiv2026

A ferroelectric junction transistor memory made from switchable van der Waals p-n heterojunctions

Van der Waals (vdW) p-n heterojunctions are important building blocks for advanced electronics and optoelectronics, in which high-quality heterojunctions essentially determine device performances or functionalities. Creating tunable depletion regions with substantially suppressed leakage currents presents huge challenges, but is crucial for heterojunction applications. Here, by using band-aligned p-type SnSe and n-type ferroelectric α-In2Se3 as a model, we report near-ideal multifunctional vdW p-n heterojunctions with small reverse leakage currents (0.1 pA) and a desired diode ideality factor (1.95). We realize ferroelectric-tuned band alignment with a giant barrier modulation of 900 meV. Based on such tunable heterojunctions, we propose and demonstrate a fundamental different memory device termed ferroelectric junction field-effect transistor memory, which shows large memory windows (1.8 V), ultrafast speed (100 ns), high operation temperature (393 K), and low cycle-to-cycle variation (2%). Additionally, the reliable synaptic characteristics of these memory devices promise low-power neuromorphic computing. Our work provides a new device platform with switchable memory heterojunctions, applicable to high performance brain-inspired electronics and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2026

Risk-Controlled Post-Processing of Decision Policies

Predictive models are often deployed through existing decision policies that stakeholders are reluctant to change unless a risk constraint requires intervention. We study risk-controlled post-processing: given a deterministic baseline policy, choose a new policy that maximizes agreement with the baseline subject to a chance constraint on a user-specified loss. At the population level, we show that the optimal policy has a threshold structure: it follows the baseline except on contexts where switching to the oracle fallback policy yields a large reduction in conditional violation risk. At the finite-sample level, given a fitted fallback policy and score, we develop a post-processing algorithm that uses calibration data to select a threshold. Leveraging tools from algorithmic stability and stochastic processes, we show that under regularity conditions, in the i.i.d. setting, the expected excess risk of the post-processed policy is $O(\log n/n)$. In the special case when an exact-safe fallback policy is available, the algorithm achieves precise expected risk control under exchangeability. In this setting, we also give high-probability near-optimality guarantees on the post-processed policy. Experiments on a COVID-19 radiograph diagnosis task, an LLM routing problem, and a synthetic multiclass decision task show that targeted post-processing can meet or nearly meet risk budgets while preserving substantially more agreement with the baseline than score-blind random mixing.

stat.ML↗

arXiv2026

Spin Correlations in Recirculating Multipass Alkali Cells for Advancing Quantum Magnetometry

Multipass cells enable long optical path lengths in compact volumes and are central to quantum technologies such as atomic magnetometers and optical quantum memories. In optical magnetometry, multipass geometries enhance sensitivity by increasing optical depth, reducing photon shot noise, and enabling quantum non-demolition detection. However, in conventional cylindrical multipass cells, Lissajous beam trajectories lead to repeated revisiting and incomplete mirror coverage, limiting effective volume utilization. Here we present a recirculating multipass alkali cell that overcomes these limitations by increasing the active-to-cell volume ratio and minimizing beam spot overlap. We develop an analytical ABCD-matrix model to predict beam trajectories, spot sizes, and astigmatism, validated by Zemax simulations. We further introduce a general analytical framework for spin correlation noise that incorporates astigmatism and spatial intensity distributions. By deriving the spin-noise time-correlation function and spectrum, we show how beam intensity profiles influence spin diffusion noise. Our results demonstrate improved beam coverage, reduced spot overlap, and enhanced spin correlation, particularly for concave mirrors with long focal lengths, while showing that avoiding tightly-focused regions significantly suppresses spin diffusion noise. These findings establish recirculating multipass cells as a practical, high-performance platform for precision atomic sensing and other multipass-cavity-based quantum devices.

quant-ph↗

arXiv2026

CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training

As training scales grow, collective communication libraries (CCL) increasingly face anomalies arising from complex interactions among hardware, software, and environmental factors. These anomalies typically manifest as slow/hang communication, the most frequent and time-consuming category to diagnose. However, traditional diagnostic methods remain inaccurate and inefficient, frequently requiring hours or even days for root cause analysis. To address this, we propose CCL-D, a high-precision diagnostic system designed to detect and locate slow/hang anomalies in large-scale distributed training. CCL-D integrates a rank-level real-time probe with an intelligent decision analyzer. The probe measures cross-layer anomaly metrics using a lightweight distributed tracing framework to monitor communication traffic. The analyzer performs automated anomaly detection and root-cause location, precisely identifying the faulty GPU rank. Deployed on a 4,000-GPU cluster over one year, CCL-D achieved near-complete coverage of known slow/hang anomalies and pinpointed affected ranks within 6 minutes-substantially outperforming existing solutions.

cs.DC↗

arXiv2026

The Intermediate-Mass Black Hole Reverberation Mapping Project: Stable Optical Continuum Lags of an IMBH in the Dwarf Galaxy NGC 4395 Over Years

NGC 4395 is a nearby dwarf spiral galaxy hosting an active galactic nucleus (AGN) powered by an intermediate-mass black hole (IMBH, $M_{\rm BH} \sim 10^{4}$--$10^{5}\,M_\odot$). Recent optical continuum reverberation mapping studies have suggested potential lag variations between different epochs, offering important clues to the physical mechanisms governing variability in the vicinity of the central black hole. We present continuous intranight multi-band photometric monitoring of NGC 4395 based on five nights of observations, including three nights from the Faulkes Telescope North (two of which are archival) and two new nights from Mephisto. This represents the first systematic investigation of optical continuum lag stability in a galaxy hosting a robustly confirmed IMBH. By applying difference-imaging techniques to both the new observations and the reprocessed archival data, we detect statistically significant optical inter-band lags of $\sim 5$--15 minutes, which increase monotonically with increasing wavelength. No obvious $u$-band lag excess is observed, implying a negligible fractional contribution from diffuse continuum (DC) emission to the optical continuum, in agreement with our spectral decomposition results. The inter-band lags remain stable over multi-year baselines. We suggest that this long-term lag stability may be related to the minor DC contribution, a relatively steady disk-corona structure, and the unusually high X-ray-to-optical luminosity ratio characteristic of low-luminosity AGNs, which likely allows X-ray reprocessing to dominate over other potential variability mechanisms. Future facilities like Gemini/SCORPIO, with its simultaneous optical-to-near-infrared coverage, will be ideally suited to play an important role in advancing this field.

astro-ph.GA↗

arXiv2026

Sapphire Photonic Crystal Fiber Sensor

Sapphire optical fiber shows great promise for remote sensing in extreme environments approaching 2000 degC, by using laser-processing to form a single-mode waveguide within it. However, for practical application, longer devices with high manufacturability and reliability are required. We report the design, modeling, fabrication, and optimization of an index-guiding sapphire photonic crystal fiber Bragg grating temperature sensor. The device is fabricated using femtosecond laser direct writing to inscribe both the photonic crystal waveguide and the Bragg grating. A spatial light modulator was used to compensate for the mismatch between the immersion objective and the high-index oil used. This improved the aspect ratio and suppressed cracking during fabrication, for higher reliability. The design results in a 6-fold reduction in fabrication time over an equivalent depressed cladding waveguide, significantly reducing the cost of manufacture. Devices up to 7 cm long were fabricated and spliced to standard single-mode fiber. The propagation loss was estimated to be 0.7 dB/cm and the Bragg gratings had a bandwidth of approximately 0.12 nm. Devices were tested in a furnace showing a temperature sensitivity of between 19.0-32.3 pm/degC over a range 25-1200 degC. These longer devices have the potential to enable practical high precision extreme temperature monitoring in many applications, with lower manufacturing cost and higher reliability.

physics.optics↗

arXiv2026

Q-ARE: An Evaluation Dataset for Query Based API Recommendation

As software systems grow in scale, developers face increasing difficulty in selecting appropriate Application Programming Interfaces (APIs) from numerous options. Efficiently identifying APIs that satisfy functional requirements has become a key challenge. To evaluate the semantic understanding of existing query-based API recommendation methods, this paper constructs Q-ARE (Query-based API Recommendation Evaluation), a dataset based on open-source Java projects from GitHub. Methods and their invocation chains are analyzed to identify third-party APIs directly or indirectly invoked by target methods, recursively expanding multi-level invocations to unify hierarchical call structures into API recommendation target sets. Furthermore, we introduce two metrics: API Call Depth, measuring the invocation distance between a query method and a target API, and Invocation Density, quantifying the proportion of code lines associated with the target API in the invocation chain. Based on Q-ARE, we systematically evaluate several query-based API recommendation methods and general Large Language Models (LLMs). Results show that performance drops significantly as API Call Depth increases and invocation density decreases, indicating that existing methods still struggle with multi-level method invocation structures. Q-ARE and its metrics provide a new benchmark for assessing semantic understanding in API recommendation and offer insights for improving future algorithms.

cs.SE↗

arXiv2026

Stability and existence of relativistic plasma--vacuum interfaces

We consider the free boundary problem for relativistic plasma--vacuum interfaces in two and three spatial dimensions. The plasma flow is governed by the equations of ideal relativistic magnetohydrodynamics, while the vacuum magnetic and electric fields satisfy Maxwell's equations. The plasma and vacuum magnetic fields are tangential to the interface, which moves with the plasma flow. This yields a nonlinear, multidimensional hyperbolic problem with a free boundary that is characteristic of variable multiplicity. We identify a quantitative stability condition and establish the linear stability of three-dimensional relativistic plasma--vacuum interfaces in the sense that the variable-coefficient linearized problem satisfies energy estimates in anisotropic Sobolev spaces. In estimating tangential derivatives, we exploit an intrinsic cancellation effect to convert the boundary term into an instant integral. We then separate the estimate involving spatial derivatives from that involving time derivatives, so that the instant integral can be mainly absorbed by the instant energy under the stability condition. Moreover, we prove the local-in-time existence and uniqueness of solutions to the nonlinear problem in two-dimensional space, provided that the plasma and vacuum magnetic fields do not vanish simultaneously at any point of the initial interface. The proof combines the solvability and tame estimate of the linearized problem with a suitable modified Nash--Moser iteration. In particular, to establish its solvability, the two-dimensional linearized problem is reduced to a transport equation for the interface function and a hyperbolic boundary problem with maximally nonnegative boundary conditions.

math.AP↗

arXiv2026

Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC)

The task of text-to-image generation has achieved tremendous success in practice, with emerging concept generation models capable of producing highly personalized and customized content. Fervor for concept generation is increasing rapidly among users, and platforms for concept sharing have sprung up. The concept owners may upload malicious concepts and disguise them with non-malicious text descriptions and example images to deceive users into downloading and generating malicious content. The platform needs a quick method to determine whether a concept is malicious to prevent the spread of malicious concepts. However, simply relying on concept image generation to judge whether a concept is malicious requires time and computational resources. Especially, as the number of concepts uploaded and downloaded on the platform continues to increase, this approach becomes impractical and poses a risk of generating malicious content. In this paper, we propose Concept QuickLook, the first systematic work to incorporate malicious concept detection into research, which performs detection based solely on concept files without generating any images. We define malicious concepts and design two operational modes for detection: concept matching and fuzzy detection. Extensive experiments demonstrate that the proposed Concept QuickLook can detect malicious concepts and demonstrate practicality in concept sharing platforms. We also design robustness experiments to further validate the effectiveness of the solution. We hope this work can initiate malicious concept detection tasks and provide some inspiration.

cs.CR↗

arXiv2026

SVOM/VT: Flight Model Verification and Pre-launch Testing

This paper presents pre-launch testing and calibration results for the SVOM/VT (Space-based Variable Objects Monitor, Visible Telescope) Flight Model (FM), validating its performance under simulated space conditions through thermal vacuum cycling, energy concentration analysis, stray light suppression, and CCD/electronics calibrations (gain, noise, quantum efficiency). The results confirm full compliance with design requirements: stray light suppression achieves point-source transmittance $<10^{-7}$ at $30^\circ$ off-axis, thermal control maintains stable CCD temperatures ($-75^\circ$C for the red channel, $-65^\circ$C for the blue channel), and detection sensitivity meets the limiting magnitude of 22.50 (SNR $>$ 3 with 300 seconds exposure). Early in-orbit tests further validate performance, yielding limiting magnitudes of 22.70 (V-band, red) and 22.78 (blue), consistent with pre-launch specifications.

astro-ph.IM↗

arXiv2026

Quiescent fractions in high-redshift galaxy groups reflect their hot-or-cold state of gas accretion

Cold accretion and quenching are closely related aspects of galaxy evolution, as sustained gas supply is required to maintain star formation. High-redshift galaxy groups therefore provide a valuable laboratory for testing how the thermal state of accreting gas relates to the emergence of quiescence. We measure quiescent fractions in a sample of 16 spectroscopically confirmed galaxy groups at $1.6<z<3.6$, spanning halo masses from $10^{12.8},{\rm M_\odot}$ to $10^{13.9},{\rm M_\odot}$, by fitting the SEDs of candidate member galaxies selected from the COSMOS2020 catalog and using a membership-probability approach to estimate group quiescent fractions. We compare these quiescent fractions to the expected cold or hot accretion state of each halo and find evidence for a correlation: quiescent fractions reach about 50 percent in groups in the hot-accretion regime and are consistent with zero in groups in the cold-accretion regime. In mature hot-accreting groups, massive quiescent galaxies are preferentially found in the inner regions ($R<0.5R_{\rm vir}$), with a 4.4-sigma excess relative to the outskirts. Most groups lack a clearly established brightest group galaxy and instead show small stellar-mass gaps, typically $M_{*,1}/M_{*,2}<3$, indicating that they remain in an active assembly phase rather than being dynamically evolved systems. Consistently, the stellar-mass excess of the dominant galaxy, measured relative to the SHMR expectation, does not predict the group quiescent fraction. Taken together, our results support a picture in which the cold-to-hot transition in gas accretion contributes to the onset of quiescence, possibly through inside-out starvation associated with filament disruption in shock-heated intra-group gas, and suggest that environment plays a greater role than internal processes in shaping the quiescent galaxy population in these structures.

astro-ph.GA↗

arXiv2026

SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation

Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and the inherent subjectivity of assessment. To address these challenges, we introduce SpeechParaling-Bench, a comprehensive benchmark for paralinguistic-aware speech generation. It expands existing coverage from fewer than 50 to over 100 fine-grained features, supported by more than 1,000 English-Chinese parallel speech queries, and is organized into three progressively challenging tasks: fine-grained control, intra-utterance variation, and context-aware adaptation. To enable reliable evaluation, we further develop a pairwise comparison pipeline, in which candidate responses are evaluated against a fixed baseline by an LALM-based judge. By framing evaluation as relative preference rather than absolute scoring, this approach mitigates subjectivity and yields more stable and scalable assessments without costly human annotation. Extensive experiments reveal substantial limitations in current LALMs. Even leading proprietary models struggle with comprehensive static control and dynamic modulation of paralinguistic features, while failure to correctly interpret paralinguistic cues accounts for 43.3% of errors in situational dialogue. These findings underscore the need for more robust paralinguistic modeling toward human-aligned voice assistants.

cs.CL↗

arXiv2026

ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving

Vision-Language Models (VLMs) have become central to autonomous driving systems, yet their deployment is severely bottlenecked by the massive computational overhead of multi-view camera and multi-frame video input. Existing token pruning methods, primarily designed for single-image inputs, treat each frame or view in isolation and thus fail to exploit the inherent spatio-temporal redundancies in driving scenarios. To bridge this gap, we propose ST-Prune, a training-free, plug-and-play framework comprising two complementary modules: Motion-aware Temporal Pruning (MTP) and Ring-view Spatial Pruning (RSP). MTP addresses temporal redundancy by encoding motion volatility and temporal recency as soft constraints within the diversity selection objective, prioritizing dynamic trajectories and current-frame content over static historical background. RSP further resolves spatial redundancy by exploiting the ring-view camera geometry to penalize bilateral cross-view similarity, eliminating duplicate projections and residual background that temporal pruning alone cannot suppress. These two modules together constitute a complete spatio-temporal pruning process, preserving key scene information under strict compression. Validated across four benchmarks spanning perception, prediction, and planning, ST-Prune establishes new state-of-the-art for training-free token pruning. Notably, even at 90\% token reduction, ST-Prune achieves near-lossless performance with certain metrics surpassing the full-model baseline, while maintaining inference speeds comparable to existing pruning approaches.

cs.CV↗

arXiv2026

NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation

Recent advances in 3D Gaussian Splatting (3DGS) have enabled highly efficient and photorealistic novel view synthesis. However, segmenting objects accurately in 3DGS remains challenging due to the discrete nature of Gaussian representations, which often leads to aliasing and artifacts at object boundaries. In this paper, we introduce NG-GS, a novel framework for high-quality object segmentation in 3DGS that explicitly addresses boundary discretization. Our approach begins by automatically identifying ambiguous Gaussians at object boundaries using mask variance analysis. We then apply radial basis function (RBF) interpolation to construct a spatially continuous feature field, enhanced by multi-resolution hash encoding for efficient multi-scale representation. A joint optimization strategy aligns 3DGS with a lightweight NeRF module through alignment and spatial continuity losses, ensuring smooth and consistent segmentation boundaries. Extensive experiments on NVOS, LERF-OVS, and ScanNet benchmarks demonstrate that our method achieves state-of-the-art performance, with significant gains in boundary mIoU. Code is available at https://github.com/BJTU-KD3D/NG-GS.

cs.CV↗

arXiv2026

Photometric Metallicities for 367,324 stars of Omega Centauri

Omega Centauri is the most massive and chemically complex multi-population globular cluster with a wide metallicity range that has been extensively studied photometrically and spectroscopically. Using the wide metallicity range of omega Cen, HST photometry (F275W, F336W, F435W, F625W), and MUSE spectroscopy ([M/H]), we derive [M/H]- and M_{F625W}-dependent stellar loci to estimate photometric metallicities from HST colors. Our tests yield metallicity precisions of 0.10\,dex for giants and 0.22\,dex for fainter dwarfs. We construct a photometric metallicity catalog from simultaneous F336W, F435W, and F625W observations (plus F275W where available), containing 20,778 giants and 346,546 dwarfs. A subsample of 20,533 giants is used to study the spatial metallicity distribution and gradient. We find no significant metallicity gradient within the half-light radius, consistent with previous work. Moreover, the previously reported ring-like structure is less pronounced in our data, and no physically significant, irregular two-dimensional metallicity pattern is detected, indicating that the stellar subpopulations are well mixed within the half-light radius. Our catalog significantly extends the metallicity sample of omega Cen, and this approach can be applied to other HST data to estimate photometric metallicities.

astro-ph.SR↗