arXiv subjects

Wei Tian

Publications and source records attributed to Wei Tian.

At least 19 recordsLinked to original sources

arXiv2026

Experimental investigation of altermagnetic order in Cr-doped FeSb2

Altermagnets are a class of materials with compensated magnetic moments, in which spin sublattices are related by specific rotational symmetries other than inversion or translation. This allows time-reversal symmetry to be broken without a net magnetization. Cr-doped FeSb2 has been theoretically proposed as a candidate d-wave altermagnetic system, yet its magnetic ground state has remained unresolved. Here, we synthesize single crystals of Fe1-xCrxSb2 and investigate their electrical transport and magnetic properties, with a focus on Fe0.85Cr0.15Sb2. Magnetization measurements suggest spin-compensated ordering below ~3.5 K, where magnetic moments align along the crystallographic b-direction. Transport measurements reveal a crossover from large positive to negative magnetoresistance, while an anomalous Hall response emerges below 5 K, indicating time-reversal symmetry breaking. Muon spin relaxation measurements confirm that the magnetic ordering below 3.5 K is bulk in nature. The absence of coherent oscillations in zero-field {\mu}SR spectra and of magnetic Bragg intensity in single-crystal neutron diffraction establishes that the magnetically ordered state is short-range or disordered, rather than collinear altermagnetic order. These results demonstrate that Cr-doping alone breaks time-reversal symmetry without stabilizing long-range altermagnetic order in FeSb2.

cond-mat.mtrl-sci↗

arXiv2026

CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction. We propose CSRP, a three-stage framework that progressively builds correction capability through Continual Pre-training (CPT) on 5.9M balanced samples to internalize domain knowledge, Chain-of-Thought SFT with explicit error reasoning for diagnostic transparency, and Group Relative Policy Optimization with a novel Efficiency-Aware Reward that explicitly penalizes unnecessary edits. On the NACGEC benchmark, CSRP achieves state-of-the-art performance with 50.99 $F_{0.5}$ and 57.17 precision, substantially outperforming previous best results while effectively mitigating the over-correction bias inherent in MLE-trained models. Our method also advances CSCD spelling correction to 59.61 F1, surpassing GPT-4 by 5.20 points. Comprehensive ablation studies demonstrate that the RL alignment stage contributes a 8\% relative gain over the SFT baseline, and that this gain is orthogonal to the contribution of large-scale CPT, validating that explicit optimization for edit efficiency is essential for high-quality grammatical error correction. Our code is available at https://github.com/TW-NLP/ChineseErrorCorrector.

cs.CL↗

arXiv2026

Coexisting Paramagnetic Spins and Long-Range Magnetic Order in Ba$_4$(Ru$_{0.92}$Ir$_{0.08}$)$_3$O$_{10}$

We investigate the effect of dilute Ir substitution on the magnetism of the trimer-based ruthenate Ba$_4$Ru$_3$O$_{10}$ using neutron diffraction, magnetic susceptibility measurements, atomistic simulations, and first-principles calculations. Neutron diffraction shows that Ir doping preserves the zigzag antiferromagnetic structure and the ordered-moment magnitude of the parent compound, in which the moments reside exclusively on the two outer Ru(2) sites of each $\rm Ru_3O_{12}$ trimer, while the central Ru(1) site remains nonmagnetic. The N\'eel temperature is reduced from $\approx\!105$ K to 84.0(1) K upon 8% Ir substitution, while magnetic susceptibility reveals a pronounced low-temperature Curie-like upturn, indicating the coexistence of paramagnetic spins with long-range antiferromagnetic order. Density-functional calculations shows that Ir preferentially occupies the central Ru(1) site, where its extended $5d$ orbitals disrupt the Ru-Ru molecular-orbital network and intra/inter-trimer exchange pathways. Atomistic simulations incorporating this paramagnetic dilution reproduce the suppressed ordering temperature and the coexistence of ordered and paramagnetic components.

cond-mat.str-el↗

arXiv2026

Magnetic order and excitations in Ce$_3$TiBi$_5$ and Ce$_3$ZrBi$_5$

The R3MBi5 rare-earth intermetallics (R = rare earth, M = Ti, Zr, Sc) provide a versatile platform to explore how Kondo hybridization, RKKY exchange, magnetic frustration, and broken inversion symmetry may cooperate to generate unusual magnetic behavior. We present a comprehensive neutron scattering investigation of the magnetic structure, crystal electric field (CEF), and low-energy excitations in the locally noncentrosymmetric Kondo-lattice compounds Ce3TiBi5 and Ce3ZrBi5. Powder and single-crystal neutron diffraction reveals incommensurate cycloidal antiferromagnetic order in Ce3TiBi5 with propagation vector k = (0, 0, 0.388) and a reduced ordered moment of m = 0.53(3)$\mu_{B}$. Ce3ZrBi5 exhibits a qualitatively similar magnetic diffraction profile, with k $\simeq$ (0, 0, 0.37). Inelastic neutron scattering measurements resolve two clear, well-separated CEF excitations in both compounds with nearly the same profile, confirming a well-isolated Kramers doublet ground state. At low energies, a broad, quasi-elastic magnetic response is observed at T $\simeq$ TN, whose momentum-dependence is inconsistent with that expected from conventional collective excitations of localized moments. This discrepancy, along with a Kondo temperature estimate TK ~ 3--5 K -- comparable to TN -- indicates sizable Kondo hybridization, which accounts for the moment reduction and the spiral magnetic order that appears to involve the magnetic hard direction. Our results place these compounds in a regime where local inversion symmetry breaking, anisotropic CEF effects, and competing Kondo and RKKY interactions collectively give rise to unconventional magnetic order.

cond-mat.str-el↗

arXiv2025

Competing magnetic phases in Cr$_{3+\delta}$Te$_4$ are spatially segregated

Cr$_{1+x}$Te$_2$ is a self-intercalated vdW system that is of current interest for its room-temperature FM phases and tunable topological properties. Early NPD measurements on the monoclinic phase Cr$_3$Te$_4$ ($x=0.5$) presented evidence for competing FM and AFM phases. Here we apply neutron diffraction to a single crystal of Cr$_{3+\delta}$Te$_4$ with $\delta=-0.10$ and discover that it consists of two distinct monoclinic phases, one with FM order below $T_{\rm C} \approx 321$ K and another that develops AFM order below $T_{\rm N} \approx 86$ K. In contrast, we find that a crystal with $\delta=-0.26$ exhibits only FM order. The single-crystal analysis is complemented by results obtained with NPD, XPD, and TEM measurements on the $\delta=-0.10$ composition. From observations of spontaneous magnetostriction of opposite sign at $T_{\rm C}$ and $T_{\rm N}$, along with the TEM evidence for both monoclinic phases in a single thin ($\approx$ 100 nm) grain, we conclude that the two phases must have a fine-grained ($\lesssim$ 100 nm) intergrowth character, as might occur from high-temperature spinodal decomposition during the growth process. Calculations of the relaxed lattice structures for the FM and AFM phases with DFT provide a rationalization of the observed spontaneous magnetostrictions. Correlations between the magnitude and orientation of the magnetic moments with lattice parameter variation demonstrate that the magnetic orders are sensitive to strain, thus explaining why magnetic ordering temperatures and anisotropies can be different between bulk and thin-film samples, when the latter are subject to epitaxial strain. Our results point to the need to investigate the supposed coexistence FM and AFM phases reported elsewhere in the Cr$_{1+x}$Te$_2$ system, such as in the Cr$_5$Te$_8$ phase ($x=0.25$).

cond-mat.str-el↗

arXiv2025

AgentBay: A Hybrid Interaction Sandbox for Seamless Human-AI Intervention in Agentic Systems

The rapid advancement of Large Language Models (LLMs) is catalyzing a shift towards autonomous AI Agents capable of executing complex, multi-step tasks. However, these agents remain brittle when faced with real-world exceptions, making Human-in-the-Loop (HITL) supervision essential for mission-critical applications. In this paper, we present AgentBay, a novel sandbox service designed from the ground up for hybrid interaction. AgentBay provides secure, isolated execution environments spanning Windows, Linux, Android, Web Browsers, and Code interpreters. Its core contribution is a unified session accessible via a hybrid control interface: An AI agent can interact programmatically via mainstream interfaces (MCP, Open Source SDK), while a human operator can, at any moment, seamlessly take over full manual control. This seamless intervention is enabled by Adaptive Streaming Protocol (ASP). Unlike traditional VNC/RDP, ASP is specifically engineered for this hybrid use case, delivering an ultra-low-latency, smoother user experience that remains resilient even in weak network environments. It achieves this by dynamically blending command-based and video-based streaming, adapting its encoding strategy based on network conditions and the current controller (AI or human). Our evaluation demonstrates strong results in security, performance, and task completion rates. In a benchmark of complex tasks, the AgentBay (Agent + Human) model achieved more than 48% success rate improvement. Furthermore, our ASP protocol reduces bandwidth consumption by up to 50% compared to standard RDP, and in end-to-end latency with around 5% reduction, especially under poor network conditions. We posit that AgentBay provides a foundational primitive for building the next generation of reliable, human-supervised autonomous systems.

cs.AI↗

arXiv2025

ChineseErrorCorrector3-4B: State-of-the-Art Chinese Spelling and Grammar Corrector

This paper introduces ChineseErrorCorrector3-4B, a unified model for Chinese spelling and grammatical error correction based on Qwen3-4B. The model demonstrates outstanding performance in general text correction tasks and achieves state-of-the-art results in both spelling correction (CSC) and grammatical correction (CGC). On several authoritative benchmark datasets -- including SIGHAN-2015, EC-LAW, MCSC, and NaCGEC -- the model's F1 and F0.5 scores significantly surpass existing publicly available models, ranking first in both spelling and grammatical error correction tasks.

cs.CL↗

arXiv2025

Optimising Underwater Neutrino Telescopes for All-Flavour Point Source Sensitivity

High-energy neutrino astronomy has advanced rapidly in recent years, with IceCube, KM3NeT, and Baikal-GVD establishing a diffuse astrophysical flux and pointing to promising source candidates. These achievements mark the transition from first detections to detailed source studies, motivating next-generation detectors with larger volumes, improved angular resolution, and full neutrino-flavour sensitivity. We present a performance study of large underwater neutrino telescopes, taking the proposed TRIDENT array in the South China Sea as a case study, with a focus on comparing the performance of various detector configurations against the TRIDENT baseline design. Both track-like events primarily from muon neutrinos, which provide precise directional information, and cascade events from all flavours, which offer superior energy resolution, diffuse-source sensitivity, and all-sky flavour coverage, are included to achieve a balanced performance across source types. The time to discover potential astrophysical sources with both track- and cascade-like events is used as the figure of merit to compare a variety of detector design choices. Our results show that, for a fixed number of optical modules, simply enlarging the instrumented volume does not inherently lead to improved performance, while taller strings can provide modest gains across all detector channels, within engineering constraints. Distributing dense clusters of strings over a large volume is found to generally worsen discovery potential compared to the baseline layout. Finally, the optical properties of the sea-water emerge as the key factor dictating the optimisation of detector layout, highlighting the need for in-situ measurements and early deployment of optical modules to guide the final array configuration.

astro-ph.IM↗

arXiv2025

MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation

Large language models (LLMs) are now used worldwide, yet their multimodal understanding and reasoning often degrade outside Western, high-resource settings. We propose MMA-ASIA, a comprehensive framework to evaluate LLMs' cultural awareness with a focus on Asian contexts. MMA-ASIA centers on a human-curated, multilingual, and multimodally aligned multiple-choice benchmark covering 8 Asian countries and 10 languages, comprising 27,000 questions; over 79 percent require multi-step reasoning grounded in cultural context, moving beyond simple memorization. To our knowledge, this is the first dataset aligned at the input level across three modalities: text, image (visual question answering), and speech. This enables direct tests of cross-modal transfer. Building on this benchmark, we propose a five-dimensional evaluation protocol that measures: (i) cultural-awareness disparities across countries, (ii) cross-lingual consistency, (iii) cross-modal consistency, (iv) cultural knowledge generalization, and (v) grounding validity. To ensure rigorous assessment, a Cultural Awareness Grounding Validation Module detects "shortcut learning" by checking whether the requisite cultural knowledge supports correct answers. Finally, through comparative model analysis, attention tracing, and an innovative Vision-ablated Prefix Replay (VPR) method, we probe why models diverge across languages and modalities, offering actionable insights for building culturally reliable multimodal LLMs.

cs.CL↗

arXiv2025

Strain-tunable anomalous Hall effect in hexagonal MnTe

The ability to control and manipulate time-reversal ($T$) symmetry-breaking phases with near-zero net magnetization is a sought-after goal in spintronic devices. The recently discovered hexagonal altermagnet manganese telluride ($\alpha$-MnTe) is a prime example. It has a compensated altermagnetic ground state where the magnetic moments are aligned in each layer and stacked antiparallel along the $c$ axis, yet it exhibits a spontaneous anomalous Hall effect (AHE) that breaks the $T$-symmetry with a vanishingly small $c$-axis ferromagnetic (FM) moment. However, the presence of three 120$^\circ$ separated in-plane magnetic domains presents a challenge in understanding the origin of the AHE and the effective control of the altermagnetic state. Here we use neutron scattering to show that symmetry breaking anisotropic strain, induced by compressive uniaxial pressure along the nearest-neighbor (NN) Mn-Mn bond directions, detwins $\alpha$-MnTe into a single in-plane magnetic domain. This control over in-plane domains allows us to unambiguously establish that the in-plane moments are aligned along the NNN Mn-Mn bond direction, irrespective of the applied strain directions. Mounting the sample on a piezoelectric strain cell along both NN and NNN directions can drive the sample into a single-domain state that significantly sharpens the AHE hysteresis loop and extends the AHE to lower temperatures. Furthermore, tuning the uniaxial strain reverses the sign of the AHE near room temperature. Remarkably, this is achieved without altering the altermagnetic phase-transition temperature or substantially changing the small $c$-axis FM moment. Combined with our phenomenological model, we argue that these effects result from the modification of the electronic Berry curvature by a combination of both spin-orbit coupling and strain. (See the full abstract in the PDF.)

cond-mat.str-el↗

arXiv2025

EuAuSb: An odd-parity helical variation on altermagnetism

EuAuSb is a triangular-lattice Dirac semimetal in which a topological Hall effect has been observed to develop in association with a magnetically-ordered phase. Our single-crystal neutron diffraction measurements have identified an incommensurate helical order in which individual ferromagnetic Eu$^{2+}$ layers rotate in-plane by $\sim$120$^{\circ}$ from one layer to the next. An in-plane magnetic field distorts the incommensurate order, eventually leading to a first order transition to a state that is approximately commensurate and that is continuously polarized as the bulk magnetization approaches saturation. From an analysis of the magnetic diffraction intensities versus field, we find evidence for a dip in the ordered in-plane moment at the same field where the topological Hall effect is a maximum, and we propose that this is due to field-induced quantum spin fluctuations. Our electronic structure calculations yield exchange constants compatible with the helical order and show that the bands near the Fermi level lose their spin degeneracy via a mechanism similar to that in the collinear altermagnets. We find that, unlike the even symmetry seen in the altermagnets, the spin-splitting in EuAuSb has odd-wave symmetry similar to that recently found in a number of coplanar magnetic materials.

cond-mat.str-el↗

arXiv2025

DriveSplat: Unified Neural Gaussian Reconstruction for Dynamic Driving Scenes

Reconstructing large-scale dynamic driving scenes remains challenging due to the coexistence of static environments with extreme depth variation and diverse dynamic actors exhibiting complex motions. Existing Gaussian Splatting based methods have primarily focused on limited-scale or object-centric settings, and their applicability to large-scale dynamic driving scenes remains underexplored, particularly in the presence of extreme scale variation and non-rigid motions. In this work, we propose DriveSplat, a unified neural Gaussian framework for reconstructing dynamic driving scenes within a unified Gaussian-based representation. For static backgrounds, we introduce a scene-aware learnable level-of-detail (LOD) modeling strategy that explicitly accounts for near, intermediate, and far depth ranges in driving environments, enabling adaptive multi-scale Gaussian allocation. For dynamic actors, we use an object-centric formulation with neural Gaussian primitives, modeling motion through a global rigid transformation and handling non-rigid dynamics via a two-stage deformation that first adjusts anchors and subsequently updates the Gaussians. To further regularize the optimization, we incorporate dense depth and surface normal priors from pre-trained models as auxiliary supervision. Extensive experiments on the Waymo and KITTI benchmarks demonstrate that DriveSplat achieves state-of-the-art performance in novel-view synthesis while producing temporally stable and geometrically consistent reconstructions of dynamic driving scenes. Project page: https://physwm.github.io/drivesplat.

cs.CV↗

arXiv2025

Dipolar and quadrupolar correlations in the $5d^2$ Re-based double perovskites Ba$_2$YReO$_6$ and Ba$_2$ScReO$_6$

Double perovskites containing heavy transition metal ions are an important family of compounds for the study of the interplay between electron correlation and spin-orbit coupling. Here, by combining magnetic susceptibility, heat capacity, and neutron scattering measurements, we investigate the dipolar and quadrupolar correlations in two prototype rhenium-based double perovskite compounds, Ba$_2$YReO$_6$ and Ba$_2$ScReO$_6$. A type-I dipolar antiferromagnetic ground state with a propagation vector $\mathbf{q} = (0, 0, 1)$ is observed in both compounds. At temperatures above the magnetic transitions, a quadrupolar ordered phase is identified. Weak spin excitations, which are gapped at low temperatures and softened in the correlated paramagnetic phase, are explained using a minimal model that considers both the dipolar and quadrupolar interactions. At larger wavevectors, we observe dominant phonon excitations that are well described by density functional calculations.

cond-mat.str-el↗

arXiv2025

Efficient Methods for Accurate Sparse Trajectory Recovery and Map Matching

Real-world trajectories are often sparse with low-sampling rates (i.e., long intervals between consecutive GPS points) and misaligned with road networks, yet many applications demand high-quality data for optimal performance. To improve data quality with sparse trajectories as input, we systematically study two related research problems: trajectory recovery on road network, which aims to infer missing points to recover high-sampling trajectories, and map matching, which aims to map GPS points to road segments to determine underlying routes. In this paper, we present efficient methods TRMMA and MMA for accurate trajectory recovery and map matching, respectively, where MMA serves as the first step of TRMMA. In MMA, we carefully formulate a classification task to map a GPS point from sparse trajectories to a road segment over a small candidate segment set, rather than the entire road network. We develop techniques in MMA to generate effective embeddings that capture the patterns of GPS data, directional information, and road segments, to accurately align sparse trajectories to routes. For trajectory recovery, TRMMA focuses on the segments in the route returned by MMA to infer missing points with position ratios on road segments, producing high-sampling trajectories efficiently by avoiding evaluation of all road segments. Specifically, in TRMMA, we design a dual-transformer encoding process to cohesively capture latent patterns in trajectories and routes, and an effective decoding technique to sequentially predict the position ratios and road segments of missing points. We conduct extensive experiments to compare TRMMA and MMA with numerous existing methods for trajectory recovery and map matching, respectively, on 4 large real-world datasets. TRMMA and MMA consistently achieve the best result quality, often by a significant margin.

cs.DB↗

arXiv2025

DiffPose-Animal: A Language-Conditioned Diffusion Framework for Animal Pose Estimation

Animal pose estimation is a fundamental task in computer vision, with growing importance in ecological monitoring, behavioral analysis, and intelligent livestock management. Compared to human pose estimation, animal pose estimation is more challenging due to high interspecies morphological diversity, complex body structures, and limited annotated data. In this work, we introduce DiffPose-Animal, a novel diffusion-based framework for top-down animal pose estimation. Unlike traditional heatmap regression methods, DiffPose-Animal reformulates pose estimation as a denoising process under the generative framework of diffusion models. To enhance semantic guidance during keypoint generation, we leverage large language models (LLMs) to extract both global anatomical priors and local keypoint-wise semantics based on species-specific prompts. These textual priors are encoded and fused with image features via cross-attention modules to provide biologically meaningful constraints throughout the denoising process. Additionally, a diffusion-based keypoint decoder is designed to progressively refine pose predictions, improving robustness to occlusion and annotation sparsity. Extensive experiments on public animal pose datasets demonstrate the effectiveness and generalization capability of our method, especially under challenging scenarios with diverse species, cluttered backgrounds, and incomplete keypoints.

cs.CV↗

arXiv2025

A Cost Effective Optimization of the hybrid-DOM Design for TRIDENT

TRIDENT is a planned multi-cubic-kilometer deep-sea neutrino telescope to be built in the South China Sea, designed to rapidly discover high-energy astrophysical neutrino sources with sensitivity to all neutrino flavors. Achieving this at scale requires a detector design that balances performance with power, cost, and mechanical simplicity. This study presents a cost-effective optimization of TRIDENT's hybrid Digital Optical Module (hDOM) design, comparing configurations using high-quantum-efficiency (QE) 3-inch PMTs and larger 4-inch PMTs, the latter evaluated with both baseline and enhanced QE assumptions. Using full-chain detector simulations incorporating site-specific seawater optical properties and realistic backgrounds, we assess performance in all-flavor neutrino detection efficiency, directional reconstruction, and tau neutrino flavor identification from 1 TeV to 10 PeV. We find that if 4-inch PMTs can achieve QE comparable to 3-inch PMTs, their performance matches or improves upon that of the 3-inch design, while significantly reducing channel count, power consumption, and cost. These findings support the 4-inch PMT hDOM as a promising and scalable choice for TRIDENT's future instrumentation.

hep-ex↗

arXiv2025

R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model

We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or bird's eye view (BEV) images, we represent both LiDAR and 4D radar point clouds using voxel features, which more effectively capture 3D shape information. Subsequently, we propose the Latent Voxel Diffusion Model (LVDM), which performs the diffusion process in the latent space. Additionally, a novel Latent Point Cloud Reconstruction (LPCR) module is utilized to reconstruct point clouds from high-dimensional latent voxel features. As a result, R2LDM effectively generates LiDAR-like point clouds from paired raw radar data. We evaluate our approach on two different datasets, and the experimental results demonstrate that our model achieves 6- to 10-fold densification of radar point clouds, outperforming state-of-the-art baselines in 4D radar point cloud super-resolution. Furthermore, the enhanced radar point clouds generated by our method significantly improve downstream tasks, achieving up to 31.7% improvement in point cloud registration recall rate and 24.9% improvement in object detection accuracy.

cs.CV↗

arXiv2025

Competing Pair Density Wave and Uniform $d$-wave Superconductivity in Phase Separated 214 Cuprates at the 1/8 Anomaly

Compelling evidence exists for electronic phase separation in cuprate high-$T_c$ superconductors, emerging near 1/8 hole doping. At these dopings and low temperatures, intertwined charge and spin stripes coexist with more uniformly doped regions in the two-dimensional (2$D$) copper-oxide planes. Each region is capable of developing superconducting pairing, either as a pair density wave (PDW) within the stripes or as a uniform $d$-wave condensate ($d$-SC) in the more homogeneous regions. Using neutron scattering on single crystals of La$_{1.875-y}$Nd$_{y}$Sr$_{0.125}$CuO$_4$, we demonstrate that the onset temperatures for spin stripe order ($T_N$) and superconductivity ($T_c$) merge as the average ordered moment vanishes in LSCO ($y = 0$), whereas Nd doping stabilizes static stripe order and suppresses $T_c$. Because the spin stripes possess the same in-plane periodicity (8$a$) as the PDW and establish the framework in which the PDW resides, the stabilization of spin stripe order enhances PDW correlations. Thus, the competition between $d$-wave pairing in the uniform regions and PDW pairing in the stripe-ordered regions can be controlled by the Nd concentration in La$_{1.875-y}$Nd$_{y}$Sr$_{0.125}$CuO$_4$, allowing the superconducting $T_c$ to vary by nearly an order of magnitude at a fixed 1/8 hole doping.

cond-mat.str-el↗