arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 253 records · Page 14Linked to original sources

arXiv2026

A Robust Geometric Distortion Solution for Main Survey Camera of CSST

The advancement in sensitivity and field of view of next-generation wide-field survey telescopes requires astrometric measurements with high precision, even in the presence of significant geometric distortions. To address this challenge, we develop a Weighted Polynomial Distortion Correction in 2-Phase (WPDC-2P) method. This approach enhances stellar cross-matching, incorporates distance-based weighting into the traditional polynomial fitting, and employs a look-up table to absorb the remaining distortion residuals. Validated on simulated data from the Main Survey Camera of the \emph{Chinese Space Station Survey Telescope} (CSST), incorporating geometric distortions up to approximately $200$ pixels, the method achieves astrometric standard deviation ranging from 0.013 to 0.107 pixels (0.03 pixels for the $g$-1 detector) across all 18 detectors. Under extreme crowding conditions (e.g., globular cluster NGC 2298), the astrometric precision for the $g$-1 detector reaches 0.05-pixel level within the central region ($r_d < 4000$), despite a centroiding precision of $\sim$0.04 pixels. When applied to the Beijing-Arizona Sky Survey data, for which the standard pipeline delivers an astrometric uncertainty of $\sim$20 mas, our method reduces the positional scatter to $ σ_{Δα}=5.494$ mas (0.01 pixels) and $ σ_{Δδ}=9.981$ mas (0.02 pixels) using only a weighted 3rd-order polynomial correction. The method has been integrated into the CSST data processing pipeline and is prepared for further refinement using on-orbit calibration data.

astro-ph.IM↗

arXiv2026

CrystaL: Spontaneous Emergence of Visual Latents in MLLMs

Multimodal Large Language Models (MLLMs) have achieved remarkable performance by integrating powerful language backbones with large-scale visual encoders. Among these, latent Chain-of-Thought (CoT) methods enable implicit reasoning in continuous hidden states, facilitating seamless vision-language integration and faster inference. However, existing heuristically predefined supervision signals in latent CoT provide limited guidance for preserving critical visual information in intermediate latent states. To address this limitation, we propose CrystaL (Crystallized Latent Reasoning), a single-stage framework with two paths to process intact and corrupted images, respectively. By explicitly aligning the attention patterns and prediction distributions across the two paths, CrystaL crystallizes latent representations into task-relevant visual semantics, without relying on auxiliary annotations or external modules. Extensive experiments on perception-intensive benchmarks demonstrate that CrystaL consistently outperforms state-of-the-art baselines, achieving substantial gains in fine-grained visual understanding while maintaining robust reasoning capabilities.

cs.CV↗

arXiv2026

Collinear spin density wave state in distorted square-lattice GdNiSn$_4$

We characterize the magnetic ground state of the newly synthesized lanthanide intermetallic GdNiSn$_4$ via resonant elastic x-ray scattering measurements. This compound forms distorted square nets of Gd that initially order magnetically below 23 K followed by a lower temperature transition at 16 K. Our scattering data identify the ground state order as a single-$q$ incommensurate, collinear order that slides towards a commensurate wave vector above the 16 K transition. Magnetic symmetry analysis combined with azimuthal dependence resolves the ground state magnetic structure as a moment-modulated spin density wave state with Gd moments oriented parallel to the in-plane a-axis. We discuss connections between the observed magnetic order and electronic properties in this square-net compound.

cond-mat.str-el↗

arXiv2026

Chart-RL: Generalized Chart Comprehension via Reinforcement Learning with Verifiable Rewards

Accurate chart comprehension represents a critical challenge in advancing multimodal learning systems, as extensive information is compressed into structured visual representations. However, existing vision-language models (VLMs) frequently struggle to generalize on unseen charts because it requires abstract, symbolic, and quantitative reasoning over structured visual representations. In this work, we introduce Chart-RL, an effective reinforcement learning (RL) method that employs mathematically verifiable rewards to enhance chart question answering in VLMs. Our experiments demonstrate that Chart-RL consistently outperforms supervised fine-tuning (SFT) across different chart understanding benchmarks, achieving relative improvements of 16.7% on MutlChartQA, and 11.5% on ChartInsights. We conduct robustness analysis, where Chart-RL achieves enhanced performance in 18 of 25 perturbed chart categories, demonstrating strong consistency and reasoning capability across visual variations. Furthermore, we demonstrate that task difficulty and inherent complexity are more critical than data quantity in RL training. For instance, Chart-RL trained on merely 10 complex chart-query examples significantly outperforms models trained on over 6,000 simple examples. Additionally, training on challenging reasoning tasks not only improves in-domain generalization relative to simpler tasks, but also facilitate strong transfer to out-of-domain visual mathematical problems.

cs.LG↗

arXiv2026

Bounding the number of spacetime dimensions from precessing black hole binaries with the third-generation gravitational-wave detectors

In the theories with extra dimensions, gravitational waves can leak into extra dimensions, resulting in a reduction in the amplitude of the observed gravitational waves. Such an effect modifies the standard luminosity distance of gravitational wave sources, which enables constraining extra dimensions by measuring both the luminosity distance and redshift from gravitational wave events. The main purpose of this paper is to assess the capacity of the third-generation gravitational wave detector network, Einstein Telescope and Cosmic Explorer, to constrain the theory of extra dimensions through observations of precessing binary black hole mergers. To this end, we generate a dataset of signals of precessing binary black hole mergers detectable by the detector network with one year of observations. We then employ the Fisher information matrix method for parameter estimation, together with a dark siren approach that obtains the redshift information from the galaxy catalog within a hierarchical Bayesian framework. Our results show that the precession in the binary system can significantly improve the precision of the luminosity distances and narrow their redshift ranges, thus leading to a tighter constraint on the extra dimensions. Based on this approach, we constrain the number of spacetime dimensions $D$ and the screening scale $R_{\rm c}$ of the extra dimensions, obtaining $D = 3.99^{+0.07}_{-0.06}$ and $\log_{10}(R_c/\mathrm{Mpc}) > 3.76$ at $68\%$ credible level. This result improves previous ones from an analysis with GWTC-3 by about one order of magnitude. This work provides theoretical foundations and projected sensitivities for future third-generation gravitational wave observations in exploring the effects of extra dimensions.

gr-qc↗

arXiv2026

Pessimistic Auxiliary Policy for Offline Reinforcement Learning

Offline reinforcement learning aims to learn an agent from pre-collected datasets, avoiding unsafe and inefficient real-time interaction. However, inevitable access to out-ofdistribution actions during the learning process introduces approximation errors, causing the error accumulation and considerable overestimation. In this paper, we construct a new pessimistic auxiliary policy for sampling reliable actions. Specifically, we develop a pessimistic auxiliary strategy by maximizing the lower confidence bound of the Q-function. The pessimistic auxiliary strategy exhibits a relatively high value and low uncertainty in the vicinity of the learned policy, avoiding the learned policy sampling high-value actions with potentially high errors during the learning process. Less approximation error introduced by sampled action from pessimistic auxiliary strategy leads to the alleviation of error accumulation. Extensive experiments on offline reinforcement learning benchmarks reveal that utilizing the pessimistic auxiliary strategy can effectively improve the efficacy of other offline RL approaches.

cs.AI↗

arXiv2026

HyperParallel: A Supernode-Affinity AI Framework

The emergence of large-scale, sparse, multimodal, and agentic AI models has coincided with a shift in hardware toward supernode architectures that integrate hundreds to thousands of accelerators with ultra-low-latency interconnects and unified memory pools. However, existing AI frameworks are not designed to exploit these architectures efficiently, leading to high programming complexity, load imbalance, and poor memory utilization. In this paper, we propose a supernode-affinity AI framework that treats the supernode as a single logical computer and embeds hardware-aware orchestration into the framework. Implemented in MindSpore, our HyperParallel architecture comprises HyperOffload for automated hierarchical memory management, HyperMPMD for fine-grained MPMD parallelism across heterogeneous workloads, and HyperShard for declarative parallel strategy specification. Together, these techniques significantly improve training and inference efficiency while reducing parallel programming and system tuning overhead, demonstrating the necessity of supernode affinity for next-generation AI frameworks.

cs.DC↗

arXiv2026

Towards Realistic Detection Pipelines of Taiji: New Challenges in Data Analysis and High-Fidelity Simulations of Space-Based Gravitational Wave Antenna

Taiji, a Chinese space-based gravitational wave (GW) detection project, aims to explore the millihertz GW universe with unprecedented sensitivity. By observing astrophysical and cosmological sources, including Galactic binaries, massive black hole binaries, extreme mass-ratio inspirals, and stochastic gravitational wave backgrounds, etc., Taiji is expected to deliver transformative insights into astrophysics, cosmology, and fundamental physics. However, Taiji's data analysis faces unique challenges compared to ground-based detectors like LIGO-Virgo-KAGRA, such as the overlap of numerous signals, extended data durations, more rigorous accuracy requirements for the waveform templates, incompletely characterized noise spectra, non-stationary noises, and various data anomalies. Taking Taiji as a representative example, this paper reviews the data characteristics and data analysis challenges of space-based GW detection, and introduces the second round of Taiji Data Challenge, a collection of simulation datasets designed as a shared platform for resolving these critical issues. This platform distinguishes itself from previous works by the systematic integration of orbital dynamics based on a full drag-free and attitude control simulation, extended noise sources, more complicated and overlapping GW signals, second-generation time-delay interferometry, and the coupling effect of time-varying arm-lengths, etc. Concurrently released is the open-source toolkit Triangle, which offers the capabilities for customized simulation of signals, noises, and other instrumental effects. By taking a step further towards realistic detection, Taiji Data Challenge II and Triangle altogether serve as a new testbed, supporting the development of Taiji's global analysis and end-to-end pipelines, and ultimately bridging the gaps between observation and scientific objectives.

gr-qc↗

arXiv2026

CityLens: Evaluating Large Vision-Language Models for Urban Socioeconomic Sensing

Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce \textit{CityLens}, a comprehensive benchmark designed to evaluate the capabilities of Large Vision-Language Models (LVLMs) in predicting socioeconomic indicators from satellite and street view imagery. We construct a multi-modal dataset covering a total of 17 globally distributed cities, spanning 6 key domains: economy, education, crime, transport, health, and environment, reflecting the multifaceted nature of urban life. Based on this dataset, we define 11 prediction tasks and utilize 3 evaluation paradigms: Direct Metric Prediction, Normalized Metric Estimation, and Feature-Based Regression. We benchmark 17 state-of-the-art LVLMs across these tasks. These make CityLens the most extensive socioeconomic benchmark to date in terms of geographic coverage, indicator diversity, and model scale. Our results reveal that while LVLMs demonstrate promising perceptual and reasoning capabilities, they still exhibit limitations in predicting urban socioeconomic indicators. CityLens provides a unified framework for diagnosing these limitations and guiding future efforts in using LVLMs to understand and predict urban socioeconomic patterns. The code and data are available at https://github.com/tsinghua-fib-lab/CityLens.

cs.AI↗

arXiv2026

SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation

The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to constrain the divergence between the generator and the fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep denoising importance from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Together with a scaled VFM-based discriminator, our final model, dubbed \textbf{SenseFlow}, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX.1 dev. The source code is available at \href{https://github.com/XingtongGe/SenseFlow}{https://github.com/XingtongGe/SenseFlow}

cs.CV↗

arXiv2026

Gravitational wave standard sirens from GWTC-3 combined with DESI DR2 and DESY5: A late-universe probe of the Hubble constant and dark energy

Recently, the combination of the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) baryon acoustic oscillation (BAO) data and the Planck cosmic microwave background (CMB) measurements has shown a $\sim$3$σ$ preference for a dynamical dark energy model with a phantom-crossing behavior. However, such a phantom-crossing dark energy evolution further exacerbates the already severe Hubble tension in the $Λ$CDM model. Moreover, there exists a $\sim2σ$ tension between the DESI DR2 BAO and CMB datasets. Therefore, it is essential to measure the Hubble constant and dark-energy equation-of-state (EoS) parameters using only late-universe observations. In this work, we investigate a novel late-universe data combination: gravitational-wave (GW) standard sirens, BAO, and Type Ia supernovae (SNe Ia). This combination provides a fully distance-ladder- and CMB-independent determination of the Hubble constant and the dark-energy EoS. Using 47 GW standard sirens from the third Gravitational-Wave Transient Catalog, the DESI DR2 BAO data, and DESY5 SNe Ia data, in the $w_0w_a$CDM model, we obtain $H_0=74.8^{+6.3}_{-8.9}$ km s$^{-1}$ Mpc$^{-1}$, $Ω_{\rm m}=0.320^{+0.015}_{-0.012}$, $w_0=-0.775^{+0.072}_{-0.074}$, and $w_a=-0.80\pm0.47$, indicating a mild phantom-crossing behavior within the $1σ$ credible interval with an $H_0$ value consistent with the distance ladder measurements. Our analysis demonstrates the power of GW standard sirens in breaking parameter degeneracies, and this novel data combination provides joint constraints on the Hubble constant and the dark-energy EoS parameters.

astro-ph.CO↗

arXiv2026

Robust evidence for dynamical dark energy in light of DESI DR2 and joint ACT, SPT, and Planck data

Recent baryon acoustic oscillation (BAO) measurements released by DESI, when combined with cosmic microwave background (CMB) data and type Ia supernova (SN) data, suggest a significant preference for dynamical dark energy (DDE) that exhibits the phantom-like behavior in the past and has transitioned into quintessence-like behavior today. In this work, we conduct a comprehensive analysis of six representative DDE parametrization models by utilizing the latest and most precise CMB data jointly from ACT, SPT, and Planck, in conjunction with BAO data from DESI DR2 and SN data from DESY5, PantheonPlus, and Union3. Our overall analysis indicates that the preference for DDE in the Quintom-B regime remains robust, regardless of the DDE parameterization model and the data combination employed. The trend of this preference is significantly strengthened with the support of DESY5 SN data. Specifically, when using the CMB+DESI+DESY5 data, for the Barboza-Alcaniz (BA) model, we obtain $w_0 = -0.785 \pm 0.047$ and $w_a = -0.43^{+0.10}_{-0.09}$, which significantly deviate from the $Λ$CDM values and provide evidence for DDE at the $4.2σ$ level. By the reconstruction of the dark energy equation of state $w(z)$, normalized dark energy density $f_{\mathrm{DE}}(z)$, and the deceleration parameter $q(z)$, we also observe clear departures from $Λ$CDM, further reinforcing the case for DDE. Furthermore, the Bayesian evidence analysis indicates that the Chevallier-Polarski-Linder, BA and Exponential models are moderately favored relative to $Λ$CDM based on the CMB+DESI+DESY5 data.

astro-ph.CO↗

arXiv2026

RFAConv: Receptive-Field Attention Convolution for Improving Convolutional Neural Networks

In the realm of deep learning, spatial attention mechanisms have emerged as a vital method for enhancing the performance of convolutional neural networks. However, these mechanisms possess inherent limitations that cannot be overlooked. This work delves into the mechanism of spatial attention and reveals a new insight. It is that the mechanism essentially addresses the issue of convolutional parameter sharing. By addressing this issue, the convolutional kernel can efficiently extract features by employing varying weights at distinct locations. However, current spatial attention mechanisms focus on shallow attention to spatial features, which is insufficient to address the fundamental challenge of parameter sharing in convolutions involving larger kernels. In response to this challenge, we introduce a novel attention mechanism known as Receptive-Field Attention (RFA). Compared to existing spatial attention methods, RFA not only concentrates on the receptive-field spatial features but also offers effective attention weights for large convolutional kernels. Building upon the RFA concept, a Receptive-Field Attention Convolution (RFAConv) is proposed to supplant the conventional standard convolution. Notably, it offers nearly negligible increment of computational overhead and parameters, while significantly improving network performance. Furthermore, this work reveals that current spatial attention mechanisms require enhanced prioritization of receptive-field spatial features to optimize network performance. To validate the advantages of the proposed methods, we conduct many experiments across several authoritative datasets, including ImageNet, COCO, VOC, and Roboflow...

cs.CV↗

arXiv2026

Observational challenges to holographic and Ricci dark energy paradigms: Insights from ACT DR6 and DESI DR2

Recent studies suggest that dark energy may be dynamical rather than being a mere cosmological constant $Λ$. In this work, we examine the viability of two physically well-motivated dynamical dark energy models -- holographic dark energy (HDE) and Ricci dark energy (RDE) -- by confronting them with the latest observational data, including ACT cosmic microwave background anisotropies, DESI baryon acoustic oscillations, and DESY5 supernovae. Our analysis reveals a fundamental tension between early- and late-universe constraints within both frameworks: ACT favors a quintom scenario where the dark energy equation of state evolves from $w>-1$ at early times to $w<-1$ at late times, while DESI+DESY5 exhibits a distinct preference for quintessence where $w>-1$ across cosmic evolution. Critically, the RDE model fails to provide a coherent description of cosmic evolution, as it manifests severe tensions (exceeding $10σ$ significance) between early- and late-universe parameter reconstructions. In addition, Bayesian evidence disfavors both models relative to the $Λ$CDM model. Our findings statistically exclude the original HDE and RDE models and uncover a severe discrepancy between early- and late-universe observations described by them, leading to the conclusion that the HDE and RDE models can be ruled out by current observational data.

astro-ph.CO↗

arXiv2026

Beyond Attribution: Unified Concept-Level Explanations

There is an increasing need to integrate model-agnostic explanation techniques with concept-based approaches, as the former can explain models across different architectures while the latter makes explanations more faithful and understandable to end-users. However, existing concept-based model-agnostic explanation methods are limited in scope, mainly focusing on attribution-based explanations while neglecting diverse forms like sufficient conditions and counterfactuals, thus narrowing their utility. To bridge this gap, we propose a general framework UnCLE to elevate existing local model-agnostic techniques to provide concept-based explanations. Our key insight is that we can uniformly extend existing local model-agnostic methods to provide unified concept-based explanations with large pre-trained model perturbation. We have instantiated UnCLE to provide concept-based explanations in three forms: attributions, sufficient conditions, and counterfactuals, and applied it to popular text, image, and multimodal models. Our evaluation results demonstrate that UnCLE provides explanations more faithful than state-of-the-art concept-based explanation methods, and provides richer explanation forms that satisfy various user needs.

cs.LG↗

arXiv2026

MUSE: Harnessing Precise and Diverse Semantics for Few-Shot Whole Slide Image Classification

In computational pathology, few-shot whole slide image classification is primarily driven by the extreme scarcity of expert-labeled slides. Recent vision-language methods incorporate textual semantics generated by large language models, but treat these descriptions as static class-level priors that are shared across all samples and lack sample-wise refinement. This limits both the diversity and precision of visual-semantic alignment, hindering generalization under limited supervision. To overcome this, we propose the stochastic MUlti-view Semantic Enhancement (MUSE), a framework that first refines semantic precision via sample-wise adaptation and then enhances semantic richness through retrieval-augmented multi-view generation. Specifically, MUSE introduces Sample-wise Fine-grained Semantic Enhancement (SFSE), which yields a fine-grained semantic prior for each sample through MoE-based adaptive visual-semantic interaction. Guided by this prior, Stochastic Multi-view Model Optimization (SMMO) constructs an LLM-generated knowledge base of diverse pathological descriptions per class, then retrieves and stochastically integrates multiple matched textual views during training. These dynamically selected texts serve as enriched semantic supervisions to stochastically optimize the vision-language model, promoting robustness and mitigating overfitting. Experiments on three benchmark WSI datasets show that MUSE consistently outperforms existing vision-language baselines in few-shot settings, demonstrating that effective few-shot pathology learning requires not only richer semantic sources but also their active and sample-aware semantic optimization. Our code is available at: https://github.com/JiahaoXu-god/CVPR2026_MUSE.

cs.CV↗

arXiv2026

TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation

Given that Large Language Models (LLMs) are increasingly applied to automate software development, comprehensive software assurance spans three distinct goals: regression prevention, reactive reproduction, and proactive discovery. Current evaluations systematically overlook the third goal. Specifically, they either treat existing code as ground truth (a compliance trap) for regression prevention, or depend on post-failure artifacts (e.g., issue reports) for bug reproduction-so they rarely surface defects before failures. To bridge this gap, we present TestExplora, a benchmark designed to evaluate LLMs as proactive testers within full-scale, realistic repository environments. TestExplora contains 2,389 tasks from 482 repositories and hides all defect-related signals. Models must proactively find bugs by comparing implementations against documentation-derived intent, using documentation as the oracle. Furthermore, to keep evaluation sustainable and reduce leakage, we propose continuous, time-aware data collection. Our evaluation reveals a significant capability gap: state-of-the-art models achieve a maximum Fail-to-Pass (F2P) rate of only 16.06%. Further analysis indicates that navigating complex cross-module interactions and leveraging agentic exploration are critical to advancing LLMs toward autonomous software quality assurance. Consistent with this, SWEAgent instantiated with GPT-5-mini achieves an F2P of 17.27% and an F2P@5 of 29.7%, highlighting the effectiveness and promise of agentic exploration in proactive bug discovery tasks.

cs.SE↗

arXiv2026

Reciprocal Specific Relative Entropy between Continuous Martingales

We introduce a novel notion of divergence between continuous martingales; the reciprocal specific relative entropy. First, we motivate this definition from multiple perspectives. Thereafter, we solve the reciprocal specific relative entropy minimization problem over the set of win-martingales (used as models for prediction markets Aldous (2013)). Surprisingly, we show that the optimizer is the renowned neutral Wright-Fisher diffusion. We also justify that this diffusion is in a sense the most salient win-martingale, since it is uniquely selected when we suitably perturb the degenerate martingale optimal transport problem of variance minimization.

math.OC↗