arXiv subjects

Yong Hu

Publications and source records attributed to Yong Hu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Anomalous Nodal Gap in a Doped Spin-1/2 Antiferromagnetic Mott Insulator

Many emergent phenomena appear in doped Mott insulators near the insulator-to-metal transition. In high-temperature cuprate superconductors, superconductivity arises when antiferromagnetic (AFM) order is gradually suppressed by carrier doping, and a $\textit{d}$-wave superconducting gap forms when an enigmatic nodal gap evolves into a point node. Here, we examine electron-doped Sr$_{2}$IrO$_{4}$, the 5$\textit{d}$-electron counterpart of cuprates, using angle-resolved photoemission spectroscopy. At low doping levels, we observe the formation of electronic states near the Fermi level, accompanied by a gap at the AFM zone boundary, mimicking the AFM gap in electron-doped cuprates. With increasing doping, a distinct gap emerges along the (0,0)-($π$,$π$) nodal direction, paralleling that observed in hole-doped cuprates. This anomalous nodal gap persists after the collapse of the AFM gap and gradually decreases with further doping. It eventually vanishes into a point node of the reported $\textit{d}$-wave gap. These observations replicate the characteristic features in both electron- and hole-doped cuprates, indicating a unified route toward nodal metallicity in doped spin-1/2 AFM Mott insulators.

cond-mat.supr-con↗

arXiv2025

Moduli spaces of threefolds on the Noether line

In this paper, we study the moduli spaces of canonical threefolds with any prescribed geometric genus $p_g \ge 5$ which have the smallest possible canonical volume. This minimal volume is equal to the smallest half-integer that is larger than or equal to $\frac43 p_g -\frac{10}3$, and the threefolds in question are said to lie on the (refined) Noether line. For every such moduli space, we establish an explicit stratification, compute the dimension of all strata, and estimate the number of its irreducible components. Thus it yields a complete classification of threefolds on the (refined) Noether line. A new and unexpected phenomenon is that the number of irreducible components of the moduli space grows linearly with $p_g$, while the moduli space of canonical surfaces on the Noether line with any prescribed geometric genus has at most two irreducible components. The key idea in the proof is to relate these canonical threefolds $X$ to simple fibrations in $(1, 2)$-surfaces. In turn, this depends on the observation that a general member in $|K_X|$ is a canonical surface on the Noether line.

math.AG↗

arXiv2025

RAGRouter: Learning to Route Queries to Multiple Retrieval-Augmented Language Models

Retrieval-Augmented Generation (RAG) significantly improves the performance of Large Language Models (LLMs) on knowledge-intensive tasks. However, varying response quality across LLMs under RAG necessitates intelligent routing mechanisms, which select the most suitable model for each query from multiple retrieval-augmented LLMs via a dedicated router model. We observe that external documents dynamically affect LLMs' ability to answer queries, while existing routing methods, which rely on static parametric knowledge representations, exhibit suboptimal performance in RAG scenarios. To address this, we formally define the new retrieval-augmented LLM routing problem, incorporating the influence of retrieved documents into the routing framework. We propose RAGRouter, a RAG-aware routing design, which leverages document embeddings and RAG capability embeddings with contrastive learning to capture knowledge representation shifts and enable informed routing decisions. Extensive experiments on diverse knowledge-intensive tasks and retrieval settings, covering open and closed-source LLMs, show that RAGRouter outperforms the best individual LLM and existing routing methods. With an extended score-threshold-based mechanism, it also achieves strong performance-efficiency trade-offs under low-latency constraints. The code and data are available at https://github.com/OwwO99/RAGRouter.

cs.CL↗

arXiv2025

Single crystal growth, structural and physical properties, and absence of a charge density wave in Ti_{0.85}Fe6Ge6

Kagome materials with charge density waves (CDWs) are fascinating quantum systems, offering an ideal platform to explore intertwined orders and to uncover novel mechanisms behind CDW formation. Chemical models have been developed and applied to predict CDW in $AM_6X_6$-type kagome materials, such as the rattling chain model based on ScV6Sn6 and the magnetic energy-saving model based on FeGe. In this study, we successfully synthesized Ti_{0.85}Fe6Ge6 single crystals using the vapor transport method. As predicted by the rattling chain model, these crystals are expected to exhibit kagome CDW behavior. Magnetization measurements indicate that Ti_{0.85}Fe6Ge6 is an easy-axis antiferromagnet with T_N = 488 K and transport measurements reveal metallic behavior primarily driven by electron-type carriers. However, no clear signatures of a CDW were observed in Ti_{0.85}Fe6Ge6. Density functional theory calculations demonstrate a markedly distinct electronic structure compared to related compounds: instead of a carrier-doping-induced rigid shift, the density of states shifted away from the Fermi level. Consistent with our structural investigations, the absence of a CDW and the unusual band structure can be attributed to the bonding characteristic within Ti_{0.85}Fe6Ge6. The strong covalent bonds of Ti-Ge1b, along with the solid Ge1b-Ge1b dimers, prevent the Ti-Ge1b-Ge1b-Ti chain from rattling. The presence of Fe-Fe antibonding state at the Fermi level enhances the spin polarization and depletes the electronic density around the Fermi level. Our results suggest that both the ionic radius and the bonding characteristics of the filler atom are crucial for the formation of CDWs in kagome materials. These factors can serve as supplementary terms to the rattling chain model, providing new insights for the discovery of novel kagome CDW materials.

cond-mat.mtrl-sci↗

arXiv2025

Selecting Optimal Camera Views for Gait Analysis: A Multi-Metric Assessment of 2D Projections

Objective: To systematically quantify the effect of the camera view (frontal vs. lateral) on the accuracy of 2D markerless gait analysis relative to 3D motion capture ground truth. Methods: Gait data from 18 subjects were recorded simultaneously using frontal, lateral and 3D motion capture systems. Pose estimation used YOLOv8. Four metrics were assessed to evaluate agreement: Dynamic Time Warping (DTW) for temporal alignment, Maximum Cross-Correlation (MCC) for signal similarity, Kullback-Leibler Divergence (KLD) for distribution differences, and Information Entropy (IE) for complexity. Wilcoxon signed-rank tests (significance: $p < 0.05$) and Cliff's delta ($δ$) were used to measure statistical differences and effect sizes. Results: Lateral views significantly outperformed frontal views for sagittal plane kinematics: step length (DTW: $53.08 \pm 24.50$ vs. $69.87 \pm 25.36$, $p = 0.005$) and knee rotation (DTW: $106.46 \pm 38.57$ vs. $155.41 \pm 41.77$, $p = 0.004$). Frontal views were superior for symmetry parameters: trunk rotation (KLD: $0.09 \pm 0.06$ vs. $0.30 \pm 0.19$, $p < 0.001$) and wrist-to-hipmid distance (MCC: $105.77 \pm 29.72$ vs. $75.20 \pm 20.38$, $p = 0.003$). Effect sizes were medium-to-large ($δ: 0.34$--$0.76$). Conclusion: Camera view critically impacts gait parameter accuracy. Lateral views are optimal for sagittal kinematics; frontal views excel for trunk symmetry. Significance: This first systematic evidence enables data-driven camera deployment in 2D gait analysis, enhancing clinical utility. Future implementations should leverage both views via disease-oriented setups.

cs.CV↗

arXiv2025

CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking

The effectiveness of large language models (LLMs) to fact-check misinformation remains uncertain, despite their growing use. To this end, we present CANDY, a benchmark designed to systematically evaluate the capabilities and limitations of LLMs in fact-checking Chinese misinformation. Specifically, we curate a carefully annotated dataset of ~20k instances. Our analysis shows that current LLMs exhibit limitations in generating accurate fact-checking conclusions, even when enhanced with chain-of-thought reasoning and few-shot prompting. To understand these limitations, we develop a taxonomy to categorize flawed LLM-generated explanations for their conclusions and identify factual fabrication as the most common failure mode. Although LLMs alone are unreliable for fact-checking, our findings indicate their considerable potential to augment human performance when deployed as assistive tools in scenarios. Our dataset and code can be accessed at https://github.com/SCUNLP/CANDY

cs.CL↗

arXiv2025

Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning

The functionality of Large Language Model (LLM) agents is primarily determined by two capabilities: action planning and answer summarization. The former, action planning, is the core capability that dictates an agent's performance. However, prevailing training paradigms employ end-to-end, multi-objective optimization that jointly trains both capabilities. This paradigm faces two critical challenges: imbalanced optimization objective allocation and scarcity of verifiable data, making it difficult to enhance the agent's planning capability. To address these challenges, we propose Reinforcement Learning with Tool-use Rewards (RLTR), a novel framework that decouples the training process to enable a focused, single-objective optimization of the planning module. Crucially, RLTR introduces a reward signal based on tool-use completeness to directly evaluate the quality of tool invocation sequences. This method offers a more direct and reliable training signal than assessing the final response content, thereby obviating the need for verifiable data. Our experiments demonstrate that RLTR achieves an 8%-12% improvement in planning performance compared to end-to-end baselines. Moreover, this enhanced planning capability, in turn, translates to a 5%-6% increase in the final response quality of the overall agent system.

cs.LG↗

arXiv2025

Automated Privacy Information Annotation in Large Language Model Interactions

Users interacting with large language models (LLMs) under their real identifiers often unknowingly risk disclosing private information. Automatically notifying users whether their queries leak privacy and which phrases leak what private information has therefore become a practical need. Existing privacy detection methods, however, were designed for different objectives and application domains, typically tagging personally identifiable information (PII) in anonymous content, which is insufficient in real-name interaction scenarios with LLMs. In this work, to support the development and evaluation of privacy detection models for LLM interactions that are deployable on local user devices, we construct a large-scale multilingual dataset with 249K user queries and 154K annotated privacy phrases. In particular, we build an automated privacy annotation pipeline with strong LLMs to automatically extract privacy phrases from dialogue datasets and annotate leaked information. We also design evaluation metrics at the levels of privacy leakage, extracted privacy phrase, and privacy information. We further establish baseline methods using light-weight LLMs with both tuning-free and tuning-based methods, and report a comprehensive evaluation of their performance. Evaluation results reveal a gap between current performance and the requirements of real-world LLM applications, motivating future research into more effective local privacy detection methods grounded in our dataset.

cs.CL↗

arXiv2025

A Bayesian Approach towards Atomically-precise Localization in Fluorescence Microscopy

Super-resolution microscopy has revolutionized the imaging of complex physical and biological systems by surpassing the Abbe diffraction limit. Recent advancements, particularly in single-molecule localization microscopy (SMLM), have pushed localization below nanometer precision, by applying prior knowledge of correlated fluorescence emission from single emitters. However, achieving a refinement from 1 nm to 1 Angstrom demands a hundred-fold increase in collected photon signal. This quadratic resource scaling imposes a fundamental barrier in SMLM, where the intense photon collection is challenged by photo-bleaching, prolonged integration times, and inherent practical constraints. Here, we break this limit by harnessing the periodic nature of the atomic lattice structure. Applying this discrete grid imaging technique (DIGIT) in a quantum emitter system, we observe an exponential collapse of localization uncertainty once surpassing the host crystal's atomic lattice constant. We further applied DIGIT to a large-scale quantum emitter array, enabling parallel positioning of each emitter through wide-field imaging. Collectively, these advancements establish DIGIT as a competitive tool for achieving unprecedented, precise measurements, ultimately paving the way to direct optical resolution of crystal and atomic features within quantum and biological systems.

physics.optics↗

arXiv2025

Pythagoras number of quartic orders containing $\sqrt{2}$

Let $K$ be a quartic number field containing $\sqrt{2}$ and let $\mathcal{O}\subseteq K$ be an order such that $\sqrt{2}\in \mathcal{O}$. We prove that the Pythagoras number of $\mathcal{O}$ is at most 5. This confirms a conjecture of Krásenský, Raška and Sgallová. The proof makes use of Beli's theory of bases of norm generators for quadratic lattices over dyadic local fields.

math.NT↗

arXiv2025

The Noether inequality for threefolds and three moduli spaces with minimal volumes

We establish the Noether inequality \[\textrm{Vol}(X)\geq \frac{4}{3}p_g(X)-\frac{10}{3}\] for all projective $3$-folds $X$ of general type with geometric genus $5\leq p_g(X)\leq 10$ where $\textrm{Vol}(X)$ is the canonical volume. This result resolves all remaining cases of the Noether inequality for $3$-folds. We further investigate the moduli spaces of canonical $3$-folds with small genera and minimal volumes. For a $3$-fold of general type with geometric genus $2$ and with minimal canonical volume $\frac{1}{3}$, we prove that its canonical model is a hypersurface of degree $16$ in $\mathbb{P}(1,1,2,3,8)$, which gives an explicit description of its canonical ring. This implies that the coarse moduli space $\mathcal{M}_{\frac{1}{3}, 2}$, parametrizing all canonical $3$-folds with canonical volume $\frac{1}{3}$ and geometric genus $2$, is an irreducible unirational variety of dimension $189$. Parallel studies show that $\mathcal{M}_{1, 3}$ is irreducible, unirational, and $236$-dimensional, and that $\mathcal{M}_{2, 4}$ is irreducible, unirational, and $270$-dimensional. As being conceived, every member in these 3 families is simply-connected.

math.AG↗

arXiv2025

Exceptional sensitivity near the bistable transition point of a hybrid quantum system

Phase transitions can dramatically alter system dynamics, unlocking new behavior and improving performance. Exceptional points (EPs), where the eigenvalues and corresponding eigenvectors of a coupled linear system coalesce, are particularly relevant for sensing applications as they can increase sensor response to external perturbations to a range of phenomena from optical phase shifts to gravitational waves. However, the coalescence of eigenstates at linear EPs amplifies noise, negating the signal-to-noise ratio (SNR) enhancement. Here, we overcome this limitation using nonlinearity, which exhibits exceptional SNR around a bistable transition point (BP). We couple a state-of-the-art diamond quantum sensor to a nonlinear Van der Pol oscillator, forming a self-oscillating hybrid system that exhibits both a single-valued and bistable phase. The boundaries between these phases are marked by both adiabatic and deterministic non-adiabatic transitions that enable chiral state switching and state coalescence at the BP. Crucially, NV magnetometry performed near the BP exhibits a 17x enhancement in SNR, achieving a record sensitivity of 170 fT/\sqrt{Hz}. This result surpasses the sensitivity limit of an ideal, thermally-limited electron magnetometer and resolves a long-standing debate regarding EP-like physics in advanced quantum sensing.

quant-ph↗

arXiv2025

Multi-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines

We present a systematic investigation of Multi-modal Retrieval Augmented Multi-modal Generation (M$^2$RAG), a novel task that enables foundation models to process multi-modal web content and generate multi-modal responses, which exhibits better information density and readability. Despite its potential impact, M$^2$RAG remains understudied, lacking comprehensive analysis and high-quality data resources. To address this gap, we establish a comprehensive benchmark through a rigorous data curation pipeline, and employ text-modal metrics and multi-modal metrics based on foundation models for evaluation. We further propose several strategies for foundation models to process M$^2$RAG task effectively and construct a training set by filtering high-quality samples using our designed metrics. Our extensive experiments demonstrate the reliability of our proposed metrics, a landscape of model performance within our designed strategies, and show that our fine-tuned 7B-8B models outperform the GPT-4o model and approach the state-of-the-art OpenAI o3-mini. Additionally, we perform fine-grained analyses across diverse domains and validate the effectiveness of our designs in data curation pipeline. All resources, including codes, datasets, and model weights, will be publicly released.

cs.CL↗

arXiv2025

Text-driven 3D Human Generation via Contrastive Preference Optimization

Recent advances in Score Distillation Sampling (SDS) have improved 3D human generation from textual descriptions. However, existing methods still face challenges in accurately aligning 3D models with long and complex textual inputs. To address this challenge, we propose a novel framework that introduces contrastive preferences, where human-level preference models, guided by both positive and negative prompts, assist SDS for improved alignment. Specifically, we design a preference optimization module that integrates multiple models to comprehensively capture the full range of textual features. Furthermore, we introduce a negation preference module to mitigate over-optimization of irrelevant details by leveraging static-dynamic negation prompts, effectively preventing ``reward hacking". Extensive experiments demonstrate that our method achieves state-of-the-art results, significantly enhancing texture realism and visual alignment with textual descriptions, particularly for long and complex inputs.

cs.CV↗

arXiv2025

Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning

Electroencephalogram (EEG) technology, particularly high-density EEG (HD EEG) devices, is widely used in fields such as neuroscience. HD EEG devices improve the spatial resolution of EEG by placing more electrodes on the scalp, which meet the requirements of clinical diagnostic applications such as epilepsy focus localization. However, this technique faces challenges, such as high acquisition costs and limited usage scenarios. In this paper, spatio-temporal adaptive diffusion models (STAD) are proposed to pioneer the use of diffusion models for achieving spatial SR reconstruction from low-resolution (LR, 64 channels or fewer) EEG to high-resolution (HR, 256 channels) EEG. Specifically, a spatio-temporal condition module is designed to extract the spatio-temporal features of LR EEG, which are then used as conditional inputs to direct the reverse denoising process. Additionally, a multi-scale Transformer denoising module is constructed to leverage multi-scale convolution blocks and cross-attention-based diffusion Transformer blocks for conditional guidance to generate subject-adaptive SR EEG. Experimental results demonstrate that the STAD significantly enhances the spatial resolution of LR EEG and quantitatively outperforms existing methods. Furthermore, STAD demonstrate their value by applying synthetic SR EEG to classification and source localization tasks, indicating their potential to substantially boost the spatial resolution of EEG.

eess.SP↗

arXiv2025

SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding

Brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is a popular paradigm for its simplicity and high information transfer rate (ITR). Accurate and fast SSVEP decoding is crucial for reliable BCI performance. However, conventional decoding methods demand longer time windows, and deep learning models typically require subject-specific fine-tuning, leaving challenges in achieving optimal performance in cross-subject settings. This paper proposed a biofocal masking attention-based method (SSVEP-BiMA) that synergistically leverages the native and symmetric-antisymmetric components for decoding SSVEP. By utilizing multiple signal representations, the network is able to integrate features from a wider range of sample perspectives, leading to more generalized and comprehensive feature learning, which enhances both prediction accuracy and robustness. We performed experiments on two public datasets, and the results demonstrate that our proposed method surpasses baseline approaches in both accuracy and ITR. We believe that this work will contribute to the development of more efficient SSVEP-based BCI systems.

cs.LG↗

arXiv2025

JGHand: Joint-Driven Animatable Hand Avater via 3D Gaussian Splatting

Since hands are the primary interface in daily interactions, modeling high-quality digital human hands and rendering realistic images is a critical research problem. Furthermore, considering the requirements of interactive and rendering applications, it is essential to achieve real-time rendering and driveability of the digital model without compromising rendering quality. Thus, we propose Jointly 3D Gaussian Hand (JGHand), a novel joint-driven 3D Gaussian Splatting (3DGS)-based hand representation that renders high-fidelity hand images in real-time for various poses and characters. Distinct from existing articulated neural rendering techniques, we introduce a differentiable process for spatial transformations based on 3D key points. This process supports deformations from the canonical template to a mesh with arbitrary bone lengths and poses. Additionally, we propose a real-time shadow simulation method based on per-pixel depth to simulate self-occlusion shadows caused by finger movements. Finally, we embed the hand prior and propose an animatable 3DGS representation of the hand driven solely by 3D key points. We validate the effectiveness of each component of our approach through comprehensive ablation studies. Experimental results on public datasets demonstrate that JGHand achieves real-time rendering speeds with enhanced quality, surpassing state-of-the-art methods.

cs.CV↗

arXiv2025

Inverse Design of Winding Tuple for Non-Hermitian Topological Edge Modes

The interplay between topological localization and non-Hermiticity localization in non-Hermitian crystal systems results in a diversity of shapes of topological edge modes (EMs), offering opportunities to manipulate these modes for potential topological applications. The characterization of the domain of EMs and the engineering of these EMs require detailed information about their wave functions, which conventional calculation of topological invariants cannot provide. In this Letter, by recognizing EMs as specified solutions of eigenequation, we derive their wave functions in an extended non-Hermitian Su-Schrieffer-Heeger model. We then inversely construct a winding tuple $\left \{ w_{\scriptscriptstyle GBZ},w_{\scriptscriptstyle BZ}\right \} $ that characterizes the existence of EMs and their spatial distribution. Moreover, we define a novel spectral winding number equivalent to $w_{\scriptscriptstyle BZ}$, which is determined by the product of energies of different bands. The inverse design of topological invariants allows us to categorize the localized nature of EMs even in systems lacking sublattice symmetry, which can facilitate the manipulation and utilization of EMs in the development of novel quantum materials and devices.

cond-mat.mes-hall↗