arXiv subjects

Yang Gao

Publications and source records attributed to Yang Gao.

At least 163 records · Page 9Linked to original sources

arXiv2026

Mitigating Measurement Crosstalk via Pulse Shaping

Quantum error correction protocols require rapid and repeated qubit measurements. While multiplexed readout in superconducting quantum systems improves efficiency, fast probe pulses introduce spectral broadening, leading to signal leakage into neighboring readout resonators. This crosstalk results in qubit dephasing and degraded readout fidelity. Here, we introduce a pulse shaping technique inspired by the derivative removal by adiabatic gate (DRAG) protocol to suppress measurement crosstalk during fast readout. By engineering a spectral notch at neighboring resonator frequencies, the method effectively mitigates spurious signal interference. Our approach integrates seamlessly with existing readout architectures, enabling fast, low-crosstalk multiplexed measurements without additional hardware overhead - a critical advancement for scalable quantum computing.

quant-ph↗

arXiv2026

EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios

As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing the first diverse benchmark tailored for educational scenarios, incorporating synthetic data containing 9 major scenarios and over 4,000 distinct educational contexts. To enable comprehensive assessment, we propose a set of multi-dimensional evaluation metrics that cover 12 critical aspects relevant to both teachers and students. We further apply human annotation to ensure the effectiveness of the model-generated evaluation responses. Additionally, we succeed to train a relatively small-scale model on our constructed dataset and demonstrate that it can achieve performance comparable to state-of-the-art large models (e.g., Deepseek V3, Qwen Max) on the test set. Overall, this work provides a practical foundation for the development and evaluation of education-oriented language models. Code and data are released at https://github.com/ybai-nlp/EduBench.

cs.CL↗

arXiv2026

Training-Free Video Editing via Optical Flow-Enhanced Score Distillation

The rapid advancement in visual generation, particularly the emergence of pre-trained text-to-image and text-to-video models, has catalyzed growing interest in training-free video editing research. Mirroring training-free image editing techniques, current approaches preserve original video information through video input inversion and manipulating intermediate features and attention during the inference process to achieve content editing. Although they have demonstrated promising results, the lossy nature of the inversion process poses significant challenges in maintaining unedited regions of the video. Furthermore, feature and attention manipulation during inference can lead to unintended over-editing and face challenges in both local temporal continuity and global content consistency. To address these challenges, this study proposes a score distillation paradigm based on pre-trained text-to-video models, where the original video is iteratively optimized through multiple steps guided by editing gradients provided by score distillation to ultimately obtain the target video. The iterative optimization starting from the original video, combined with content preservation loss, ensures the maintenance of unedited regions in the original video and suppresses over-editing. To further guarantee video content consistency and temporal continuity, we additionally introduce a global consistency auxiliary loss and optical flow prediction-based local editing gradient smoothing. Experiments demonstrate that these strategies effectively address the aforementioned challenges, achieving comparable or superior performance across multiple dimensions including preservation of unedited regions, local temporal continuity, and global content consistency of editing results, compared to state-of-the-art methods.

cs.CV↗

arXiv2026

Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD

Information-theoretic (IT) generalization bounds have been used to study the generalization of learning algorithms. These bounds are intrinsically data- and algorithm-dependent so that one can exploit the properties of data and algorithm to derive tighter bounds. However, we observe that although the flatness bias is crucial for SGD's generalization, these bounds fail to capture the improved generalization under better flatness and are also numerically loose. This is caused by the inadequate leverage of SGD's flatness bias in existing IT bounds. This paper derives a more flatness-leveraging IT bound for the flatness-favoring SGD. The bound indicates the learned models generalize better if the large-variance directions of the final weight covariance have small local curvatures in the loss landscape. Experiments on deep neural networks show our bound not only correctly reflects the better generalization when flatness is improved, but is also numerically much tighter. This is achieved by a flexible technique called "omniscient trajectory". When applied to Gradient Descent's minimax excess risk on convex-Lipschitz-Bounded problems, it improves representative IT bounds' $Ω(1)$ rates to $O(1/\sqrt{n})$. It also implies a by-pass of memorization-generalization trade-offs.

cs.LG↗

arXiv2025

Adapting In-Domain Few-Shot Segmentation to New Domains without Source Domain Retraining

Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes in new domains, which is often challenging due to the diverse characteristics of target domains and the limited availability of support data. Most CD-FSS methods redesign and retrain in-domain FSS models using abundant base data from the source domain, which are effective but costly to train. To address these issues, we propose adapting informative model structures of the well-trained FSS model for target domains by learning domain characteristics from few-shot labeled support samples during inference, thereby eliminating the need for source domain retraining. Specifically, we first adaptively identify domain-specific model structures by measuring parameter importance using a novel structure Fisher score in a data-dependent manner. Then, we progressively train the selected informative model structures with hierarchically constructed training samples, progressing from fewer to more support shots. The resulting Informative Structure Adaptation (ISA) method effectively addresses domain shifts and equips existing well-trained in-domain FSS models with flexible adaptation capabilities for new domains, eliminating the need to redesign or retrain CD-FSS models on base data. Extensive experiments validate the effectiveness of our method, demonstrating superior performance across multiple CD-FSS benchmarks. Codes are at https://github.com/fanq15/ISA.

cs.CV↗

arXiv2025

FUSE-RSVLM: Feature Fusion Vision-Language Model for Remote Sensing

Large vision-language models (VLMs) exhibit strong performance across various tasks. However, these VLMs encounter significant challenges when applied to the remote sensing domain due to the inherent differences between remote sensing images and natural images. Existing remote sensing VLMs often fail to extract fine-grained visual features and suffer from visual forgetting during deep language processing. To address this, we introduce MF-RSVLM, a Multi-Feature Fusion Remote Sensing Vision--Language Model that effectively extracts and fuses visual features for RS understanding. MF-RSVLM learns multi-scale visual representations and combines global context with local details, improving the capture of small and complex structures in RS scenes. A recurrent visual feature injection scheme ensures the language model remains grounded in visual evidence and reduces visual forgetting during generation. Extensive experiments on diverse RS benchmarks show that MF-RSVLM achieves state-of-the-art or highly competitive performance across remote sensing classification, image captioning, and VQA tasks. Our code is publicly available at https://github.com/Yunkaidang/RSVLM.

cs.CV↗

arXiv2025

Two-stage Respiratory Motion-resolved Radial MR Image Reconstruction Using an Interpretable Deep Unrolled Network

Due to the prolonged MRI encoding process, respiratory motion can cause undesired artifacts and image blurring, degrading image quality and limiting clinical applications in abdominal and pulmonary imaging. In this work, we develop a two-stage respiratory motion-resolved radial MR image reconstruction pipeline using an interpretable deep unrolled network (MoraNet), enabling high-quality imaging under free-breathing conditions. Firstly, low-resolution images are reconstructed from the central region of successive golden-angle radial k-space to extract respiratory motion signals. The binned k-space data based on the respiratory signal are then used to reconstruct the motion-resolved high-resolution image for each motion state. The MoraNet applies nonuniform fast Fourier transform (NUFFT) to operate radial encoding and convolutional neural network (CNN) modules to conduct image regularizations. The MoraNet was trained on retrospectively acquired lung MRI images for both fully sampled and undersampled acquisitions. The performance of the proposed method was evaluated on digital CT/MRI breathing XCAT (CoMBAT) phantom data, QUASAR motion phantom data acquired from a 1.0T MRI scanner and volunteer chest data acquired from a 1.5T MRI scanner. The MoraNet pipeline was compared with motion-averaged reconstruction and a conventional compressed sensing (CS)-based method in terms of SSIM, RMSE and computation time. Simulation and experimental results demonstrated that the proposed network could provide accurate respiratory signal estimation and enable effective motion correction. Compared with the CS method, the MoraNet preserved better structural details with lower RMSE and higher SSIM values at acceleration factor of 4, and meanwhile took ten-fold faster inference time.

physics.med-ph↗

arXiv2025

Atomic-scale spin sensing of a 2D $d$-wave altermagnet via helical tunneling

Altermagnetism simultaneously possesses nonrelativistic spin responses and zero net magnetization, thus combining advantages of ferromagnetism and antiferromagnetism. This superiority originates from its unique dual feature, i.e., opposite-magnetic sublattices in real space and alternating spin polarization in momentum space enforced by the same crystal symmetry. Therefore, the determination of an altermagnetic order and its unique spin response inherently necessitates atomic-scale spin-resolved measurements in real and momentum spaces, an experimental milestone yet to be achieved. Here, via utilizing the helical edge (hinge) modes of a higher order topological insulator as the spin sensor, we realize spin-resolved scanning tunneling microscopy which enables us to pin down the dual-space feature of a layered $d$-wave altermagnet, KV$_2$Se$_2$O. In real space, atomic-registered mapping demonstrates the checkerboard antiferromagnetic order together with density-wave lattice modulation, and in momentum space, spin-resolved spectroscopic imaging provides a direct visualization of d-wave spin splitting of the band structure. Critically, using this new topology-guaranteed spin filter we directly reveal the unidirectional, spin-polarized quasiparticle excitations originating from the crystal symmetry-paired X and Y valleys around opposite magnetic sublattices simultaneously --the unique spin response for $d$-wave altermagnetism. Our experiments establish a solid basis for the exploration and utilization of altermagnetism in layered materials and further facilitate access to atomic-scale spin sensing and manipulating of 2D quantum materials.

cond-mat.mes-hall↗

arXiv2025

LibContinual: A Comprehensive Library towards Realistic Continual Learning

A fundamental challenge in Continual Learning (CL) is catastrophic forgetting, where adapting to new tasks degrades the performance on previous ones. While the field has evolved with diverse methods, this rapid surge in diverse methodologies has culminated in a fragmented research landscape. The lack of a unified framework, including inconsistent implementations, conflicting dependencies, and varying evaluation protocols, makes fair comparison and reproducible research increasingly difficult. To address this challenge, we propose LibContinual, a comprehensive and reproducible library designed to serve as a foundational platform for realistic CL. Built upon a high-cohesion, low-coupling modular architecture, LibContinual integrates 19 representative algorithms across five major methodological categories, providing a standardized execution environment. Meanwhile, leveraging this unified framework, we systematically identify and investigate three implicit assumptions prevalent in mainstream evaluation: (1) offline data accessibility, (2) unregulated memory resources, and (3) intra-task semantic homogeneity. We argue that these assumptions often overestimate the real-world applicability of CL methods. Through our comprehensive analysis using strict online CL settings, a novel unified memory budget protocol, and a proposed category-randomized setting, we reveal significant performance drops in many representative CL methods when subjected to these real-world constraints. Our study underscores the necessity of resource-aware and semantically robust CL strategies, and offers LibContinual as a foundational toolkit for future research in realistic continual learning. The source code is available from \href{https://github.com/RL-VIG/LibContinual}{https://github.com/RL-VIG/LibContinual}.

cs.LG↗

arXiv2025

Frequency Domain Berry Curvature Effect on Time Refraction

We demonstrate that there exist frequency domain Berry curvature in the wave function of photons in dispersive optical systems. This property arises from the frequency dispersion of its dielectric function, which makes Maxwell equations a non-standard eigenvalue equation, with the eigenvalue (frequency) appearing inside the operator itself. We study this new Berry curvature effect on time refraction of magnetoplasmon-polariton as an example. It can induce deflection in the trajectory of a photon and make the ray swing.

cond-mat.mes-hall↗

arXiv2025

Experimental Evidence of Néel-order-driven Magneto-optical Kerr Effect in an Altermagnetic Insulator

The magneto-optical Kerr effect (MOKE) is investigated in hematite, a collinear antiferromagnetic insulator, across a broad wavelength spectrum. By combining the optical measurements with magnetometry results, we unambiguously demonstrate that the Néel-order contribution dominates the MOKE signal, while contributions from net magnetization and external magnetic fields are negligible. This conclusion is quantitatively supported by first-principles calculations, and qualitatively by a symmetry analysis that the Néel contribution appears at the first order in spin-orbit coupling while the magnetization contribution starts only at the third order. This study clarifies the altermagnetic origin of the pronounced MOKE in hematite, underscoring the potential of altermagnets as a promising new class of magneto-optical materials.

cond-mat.other↗

arXiv2025

MAGIC: Achieving Superior Model Merging via Magnitude Calibration

The proliferation of pre-trained models has given rise to a wide array of specialised, fine-tuned models. Model merging aims to merge the distinct capabilities of these specialised models into a unified model, requiring minimal or even no additional training. A core objective of model merging is to ensure the merged model retains the behavioural characteristics of the specialised models, typically achieved through feature alignment. We identify that features consist of two critical components: direction and magnitude. Prior research has predominantly focused on directional alignment, while the influence of magnitude remains largely neglected, despite its pronounced vulnerability to perturbations introduced by common merging operations (e.g., parameter fusion and sparsification). Such perturbations to magnitude inevitably lead to feature deviations in the merged model from the specialised models, resulting in subsequent performance degradation. To address this, we propose MAGnItude Calibration (MAGIC), a plug-and-play framework that rectifies layer-wise magnitudes in feature and weight spaces, with three variants. Specifically, our Feature Space Calibration (FSC) realigns the merged model's features using a small set of unlabelled data, while Weight Space Calibration (WSC) extends this calibration to the weight space without requiring additional data. Combining these yields Dual Space Calibration (DSC). Comprehensive experiments demonstrate that MAGIC consistently boosts performance across diverse Computer Vision tasks (+4.3% on eight datasets) and NLP tasks (+8.0% on Llama) without additional training. Our code is available at: https://github.com/lyymuwu/MAGIC

cs.LG↗

arXiv2025

A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs

Multimodal large language models (MLLMs) demonstrate strong perception and reasoning performance on existing remote sensing (RS) benchmarks. However, most prior benchmarks rely on low-resolution imagery, and some high-resolution benchmarks suffer from flawed reasoning-task designs. We show that text-only LLMs can perform competitively with multimodal vision-language models on RS reasoning tasks without access to images, revealing a critical mismatch between current benchmarks and the intended evaluation of visual understanding. To enable faithful assessment, we introduce RSHR-Bench, a super-high-resolution benchmark for RS visual understanding and reasoning. RSHR-Bench contains 5,329 full-scene images with a long side of at least 4,000 pixels, with up to about 3 x 10^8 pixels per image, sourced from widely used RS corpora and UAV collections. We design four task families: multiple-choice VQA, open-ended VQA, image captioning, and single-image evaluation. These tasks cover nine perception categories and four reasoning types, supporting multi-turn and multi-image dialog. To reduce reliance on language priors, we apply adversarial filtering with strong LLMs followed by rigorous human verification. Overall, we construct 3,864 VQA tasks, 3,913 image captioning tasks, and 500 fully human-written or verified single-image evaluation VQA pairs. Evaluations across open-source, closed-source, and RS-specific VLMs reveal persistent performance gaps in super-high-resolution scenarios. Code: https://github.com/Yunkaidang/RSHR

cs.CV↗

arXiv2025

Magnon Thermal Hall Effect Induced By Symmetric Exchange Interaction

By analyzing the spin-group symmetry of magnons, we establish two generalized Onsager's relations in the magnon thermal Hall effect, which reveals the rich and complicated structures of the magnon Berry curvature in the parameter space of different types of the exchange coupling. As an important consequence, we find that the diagonal and off-diagonal part of the symmetric anisotropic exchange coupling together can support the planar magnon thermal Hall effect without the DM interaction. This removes the local inversion-symmetry-breaking condition for generating the magnon thermal Hall effect. Moreover, we predict an exotic phenomenon that the thermal conductivity exhibits angular dependence with respect to the in-plane magnetization. Our work lays the ground for decoding the coupling between magnon transport and different types of exchange interactions.

cond-mat.mes-hall↗

arXiv2025

ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation

One-shot imitation learning (OSIL) offers a promising way to teach robots new skills without large-scale data collection. However, current OSIL methods are primarily limited to short-horizon tasks, thus limiting their applicability to complex, long-horizon manipulations. To address this limitation, we propose ManiLong-Shot, a novel framework that enables effective OSIL for long-horizon prehensile manipulation tasks. ManiLong-Shot structures long-horizon tasks around physical interaction events, reframing the problem as sequencing interaction-aware primitives instead of directly imitating continuous trajectories. This primitive decomposition can be driven by high-level reasoning from a vision-language model (VLM) or by rule-based heuristics derived from robot state changes. For each primitive, ManiLong-Shot predicts invariant regions critical to the interaction, establishes correspondences between the demonstration and the current observation, and computes the target end-effector pose, enabling effective task execution. Extensive simulation experiments show that ManiLong-Shot, trained on only 10 short-horizon tasks, generalizes to 20 unseen long-horizon tasks across three difficulty levels via one-shot imitation, achieving a 22.8% relative improvement over the SOTA. Additionally, real-robot experiments validate ManiLong-Shot's ability to robustly execute three long-horizon manipulation tasks via OSIL, confirming its practical applicability.

cs.RO↗

arXiv2025

SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas

In this paper, we propose SEA, a novel approach for active robot exploration through semantic map prediction and a reinforcement learning-based hierarchical exploration policy. Unlike existing learning-based methods that rely on one-step waypoint prediction, our approach enhances the agent's long-term environmental understanding to facilitate more efficient exploration. We propose an iterative prediction-exploration framework that explicitly predicts the missing areas of the map based on current observations. The difference between the actual accumulated map and the predicted global map is then used to guide exploration. Additionally, we design a novel reward mechanism that leverages reinforcement learning to update the long-term exploration strategies, enabling us to construct an accurate semantic map within limited steps. Experimental results demonstrate that our method significantly outperforms state-of-the-art exploration strategies, achieving superior coverage ares of the global map within the same time constraints.

cs.RO↗

arXiv2025

Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents

Large Language Models (LLMs) are increasingly applied to software engineering (SWE), with SWE-bench as a key benchmark. Solutions are split into SWE-Agent frameworks with multi-turn interactions and workflow-based Agentless methods with single-turn verifiable steps. We argue these paradigms are not mutually exclusive: reasoning-intensive Agentless training induces skill priors, including localization, code edit, and self-reflection that enable efficient and effective SWE-Agent adaptation. In this work, we first curate the Agentless training recipe and present Kimi-Dev, an open-source SWE LLM achieving 60.4\% on SWE-bench Verified, the best among workflow approaches. With additional SFT adaptation on 5k publicly-available trajectories, Kimi-Dev powers SWE-Agents to 48.6\% pass@1, on par with that of Claude 3.5 Sonnet (241022 version). These results show that structured skill priors from Agentless training can bridge workflow and agentic frameworks for transferable coding agents.

cs.AI↗

arXiv2025

The MALATANG survey: star formation, dense gas, and AGN feedback in NGC 1068

We aim to investigate the interplay between dense molecular gas, star formation, and active galactic nucleus (AGN) feedback in the luminous infrared galaxy (LIRG) NGC 1068 at sub-kiloparsec scales. We present the HCN (4-3) and HCO$^+$ (4-3) maps of NGC 1068, obtained with JCMT as part of the Mapping the dense molecular gas in the strongest star-forming galaxies (MALATANG) project, and perform spatially resolved analyses of their correlations with infrared luminosity and soft X-ray emission. Spatially resolved relations between the luminosities of infrared dust emission and dense molecular gas tracers ($L_{\rm IR}-L'_{\rm dense}$) are found to be nearly linear, without clear evidence of excess contributions from AGN activity. The spatially resolved X-ray emission ($L^{\rm gas}_{0.5-2\,\mathrm{keV}}$) displays a radially-dependent twofold correlation with the star formation rate (SFR), suggesting distinct gas-heating mechanisms between the galaxy center and the outer regions. A super-linear scaling is obtained in galactic center regions with SFR surface density ($Σ_{\rm SFR}$) $>$ 8.2 $\times$ 10$^{-6}$ $M_\odot$ yr$^{-1}$ kpc$^{-2}$: log($L^{\rm gas}_{0.5-2\,\mathrm{keV}}$/erg s$^{-1}$) = 2.2 log(SFR/$M_\odot$ yr$^{-1}$) + 39.1. We further found a statistically significant super-linear correlation ($β= 1.34$ $\pm$ 0.86) between $L^{\rm gas}_{0.5-2\,\mathrm{keV}}$/SFR and HCN(4-3)/CO(1-0) intensity ratio, whereas no such trend is seen for HCO$^+$(4-3)/CO(1-0) or CO(3-2)/CO(1-0). These findings indicate that AGN feedback does not dominate star formation regulation on sub-kiloparsec scales, and that the excitation of dense gas traced by HCN (4-3) may be more directly influenced by high-energy feedback processes compared to HCO$^+$ (4-3) and CO (3-2).

astro-ph.GA↗