arXiv subjects

Qing Wang

Publications and source records attributed to Qing Wang.

At least 73 records · Page 4Linked to original sources

arXiv2026

MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan

Industrial recommendation systems typically involve multiple scenarios, yet existing cross-domain (CDR) and multi-scenario (MSR) methods often require prohibitive resources and strict input alignment, limiting their extensibility. We propose MTFM (Meituan Foundation Model for Recommendation), a transformer-based framework that addresses these challenges. Instead of pre-aligning inputs, MTFM transforms cross-domain data into heterogeneous tokens, capturing multi-scenario knowledge in an alignment-free manner. To enhance efficiency, we first introduce a multi-scenario user-level sample aggregation that significantly enhances training throughput by reducing the total number of instances. We further integrate Grouped-Query Attention and a customized Hybrid Target Attention to minimize memory usage and computational complexity. Furthermore, we implement various system-level optimizations, such as kernel fusion and the elimination of CPU-GPU blocking, to further enhance both training and inference throughput. Offline and online experiments validate the effectiveness of MTFM, demonstrating that significant performance gains are achieved by scaling both model capacity and multi-scenario training data.

cs.IR↗

arXiv2026

SynAT: Enhancing Security Knowledge Bases via Automatic Synthesizing Attack Tree from Crowd Discussions

Cyber attacks have become a serious threat to the security of software systems. Many organizations have built their security knowledge bases to safeguard against attacks and vulnerabilities. However, due to the time lag in the official release of security information, these security knowledge bases may not be well maintained, and using them to protect software systems against emergent security risks can be challenging. On the other hand, the security posts on online knowledge-sharing platforms contain many crowd security discussions and the knowledge in those posts can be used to enhance the security knowledge bases. This paper proposes SynAT, an automatic approach to synthesize attack trees from crowd security posts. Given a security post, SynAT first utilize the Large Language Model (LLM) and prompt learning to restrict the scope of sentences that may contain attack information; then it utilizes a transition-based event and relation extraction model to extract the events and relations simultaneously from the scope; finally, it applies heuristic rules to synthesize the attack trees with the extracted events and relations. An experimental evaluation is conducted on 5,070 Stack Overflow security posts, and the results show that SynAT outperforms all baselines in both event and relation extraction, and achieves the highest tree similarity in attack tree synthesis. Furthermore, SynAT has been applied to enhance HUAWEI's security knowledge base as well as public security knowledge bases CVE and CAPEC, which demonstrates SynAT's practicality.

cs.CR↗

arXiv2026

Higher-Order Topological Superconductivity and Electrically Tunable Majorana Corner Modes in Monolayer MnXPb$_2$ (X=Se, Te)-Pb Heterostructure

Higher-order topological superconductors host Majorana zero modes localized at corners or hinges, providing a promising route toward scalable and controllable Majorana networks without vortices or magnetic flux. Here we propose a symmetry-enforced higher-order topological superconductivity based on antiferromagnetic topological insulators, specifically realized in MnXPb$_2$ (X = Se, Te)-Pb heterostructure. We show that the intrinsic boundary dichotomy-gapless Dirac states protected by an effective time-reversal symmetry on antiferromagnetic edges and magnetic gaps on ferromagnetic edges-naturally generates Majorana corner modes as mass domain walls. Superconducting proximity converts the antiferromagnetic edges into one-dimensional topological superconductors, and the intersections between superconducting and magnetic edges bind Majorana zero modes as mass domain walls. Combining first-principles calculations with a calibrated effective boundary theory, we demonstrate robust corner localization and purely electrical control of Majorana fusion and braiding in a triangular geometry. Our results establish MnXPb$_2$ as experimentally promising platform for electrically programmable Majorana networks in two dimensions.

cond-mat.supr-con↗

arXiv2026

GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control

Training reinforcement learning (RL) policies for legged robots remains challenging due to high-dimensional continuous actions, hardware constraints, and limited exploration. Existing methods for locomotion and whole-body control work well for position-based control with environment-specific heuristics (e.g., reward shaping, curriculum design, and manual initialization), but are less effective for torque-based control, where sufficiently exploring the action space and obtaining informative gradient signals for training is significantly more difficult. We introduce Growing Policy Optimization (GPO), a training framework that applies a time-varying action transformation to restrict the effective action space in the early stage, thereby encouraging more effective data collection and policy learning, and then progressively expands it to enhance exploration and achieve higher expected return. We prove that this transformation preserves the PPO update rule and introduces only bounded, vanishing gradient distortion, thereby ensuring stable training. We evaluate GPO on both quadruped and hexapod robots, including zero-shot deployment of simulation-trained policies on hardware. Policies trained with GPO consistently achieve better performance. These results suggest that GPO provides a general, environment-agnostic optimization framework for learning legged locomotion.

cs.RO↗

arXiv2026

Frame-Guided Synthetic Claim Generation for Automatic Fact-Checking Using High-Volume Tabular Data

Automated fact-checking benchmarks have largely ignored the challenge of verifying claims against real-world, high-volume structured data, instead focusing on small, curated tables. We introduce a new large-scale, multilingual dataset to address this critical gap. It contains 78,503 synthetic claims grounded in 434 complex OECD tables, which average over 500K rows each. We propose a novel, frame-guided methodology where algorithms programmatically select significant data points based on six semantic frames to generate realistic claims in English, Chinese, Spanish, and Hindi. Crucially, we demonstrate through knowledge-probing experiments that LLMs have not memorized these facts, forcing systems to perform genuine retrieval and reasoning rather than relying on parameterized knowledge. We provide a baseline SQL-generation system and show that our benchmark is highly challenging. Our analysis identifies evidence retrieval as the primary bottleneck, with models struggling to find the correct data in massive tables. This dataset provides a critical new resource for advancing research on this unsolved, real-world problem.

cs.CL↗

arXiv2026

Lithium and sodium decorated PHE-graphene for high capacity hydrogen storage: A DFT and GCMC study

Porous nanocarbon materials are seen as potential excellent materials for hydrogen storage due to their high surface area, excellent cycling stability and favorable kinetics. This study employs Density Functional Theory (DFT) simulations to investigate key property of Li$^-$ and Na$^-$ modified PHE-graphene, including structural stability, electronic properties, and hydrogen storage capabilities. The results show that when each Li atom adsorbs six hydrogen molecules, the material reaches the maximum hydrogen adsorption gravimetric density of 15.20 wt%. Additionally, through Grand Canonical Monte Carlo (GCMC) simulations, we obtained the hydrogen weight ratios and adsorption enthalpy curves for Li- and Na-modified PHE under varying temperature and pressure conditions. These findings indicate that both Li- and Na-modified PHE-graphene are exceptional candidates for hydrogen storage materials, particularly in mobile applications.

cond-mat.mtrl-sci↗

arXiv2026

Impact of Nuclear Reaction Rates on Calcium Production in Population III Stars: A Global Analysis

We investigate the sensitivity of calcium production to nuclear reaction rates of a 40 solar-mass Population III star using 1D multi-zone stellar models. A comprehensive nuclear reaction network was constructed, and all $(p,γ)$ and $(p,α)$ reaction rates were individually varied by a factor of 10 up and down, identifying 13 preliminary key reactions for calcium production. To propagate the reaction rate uncertainties on calcium production, two sets of Monte Carlo simulations were performed for these key reactions: one adopting STARLIB reaction rates and the other incorporating updated rates from recent experimental data and evaluations. Our results show that Monte Carlo simulations using the updated rates show good agreement with the observed calcium abundance of the extremely iron-poor star SMSS J031300.36-670839.3 within the 68% confidence interval predicted by the models. In contrast, the observed calcium abundance lies marginally outside the 68% C.I. when using the STARLIB rates. Spearman rank-order correlation analysis and SHAP values show that the $(p,γ)$ and $(p,α)$ reactions of F18 and F19 exhibit strong coupled effects on calcium production. These reaction-rate uncertainties need to be reduced to constrain the stellar model predictions. Our study provides insights for future nuclear physics experiments aimed at reducing reaction rate uncertainties in the nucleosynthesis of Population III Stars. Additionally, comparisons between 20 solar-mass and 40 solar-mass Population III stellar models confirm that the latter, with updated reaction rates, is more capable of reproducing the observed Ca abundance and [Ca/Mg] ratio.

astro-ph.SR↗

arXiv2026

A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction

This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT). IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision. The model is trained with a two-stage progressive strategy. The first stage performs speech-text alignment and emotional attribute modeling via self-distillation, while the second stage conducts end-to-end cross-modal joint optimization to ensure consistency between textual and spoken emotional expressions. Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation under both LLM-based and human evaluations.

cs.CL↗

arXiv2026

Towards Unbiased Cross-Modal Representation Learning for Food Image-to-Recipe Retrieval

This paper addresses the challenges of learning representations for recipes and food images in the cross-modal retrieval problem. As the relationship between a recipe and its cooked dish is cause-and-effect, treating a recipe as a text source describing the visual appearance of a dish for learning representation, as the existing approaches, will create bias misleading image-and-recipe similarity judgment. Specifically, a food image may not equally capture every detail in a recipe, due to factors such as the cooking process, dish presentation, and image-capturing conditions. The current representation learning tends to capture dominant visual-text alignment while overlooking subtle variations that determine retrieval relevance. In this paper, we model such bias in cross-modal representation learning using causal theory. The causal view of this problem suggests ingredients as one of the confounder sources and a simple backdoor adjustment can alleviate the bias. By causal intervention, we reformulate the conventional model for food-to-recipe retrieval with an additional term to remove the potential bias in similarity judgment. Based on this theory-informed formulation, we empirically prove the oracle performance of retrieval on the Recipe1M dataset to be MedR=1 across the testing data sizes of 1K, 10K, and even 50K. We also propose a plug-and-play neural module, which is essentially a multi-label ingredient classifier for debiasing. New state-of-the-art search performances are reported on the Recipe1M dataset.

cs.CV↗

arXiv2025

A Wave-Based Simulation Model for Cross-Beam Energy Transfer and Stimulated Brillouin Scattering in Laser-Plasma Systems

We present WEBS (WavE-Based Simulations), an efficient wave-based simulation model designed to investigate the dynamic interplay between cross-beam energy transfer (CBET) and stimulated Brillouin scattering (SBS) in laser-plasma systems. By employing a unified Schrodinger-type envelope formulation for the laser and ion-acoustic waves, our model enables the use of a single, unconditionally stable Du Fort-Frankel numerical scheme, which maintains excellent long-term energy conservation even with coarse spatial grids. This approach not only achieves high computational efficiency validated against particle-in-cell simulations but also allows the selective activation or suppression of CBET and SBS processes, offering a clear diagnostic of their mutual coupling. Our simulations reveal that at high laser intensities, CBET and SBS reach a coupled steady state, leading to significant deviations from classical fluid theory predictions. Specifically, CBET gain is suppressed due to enhanced SBS reflectivity, while strong asymmetry in SBS reflectivity emerges between the interacting beams. These findings highlight regimes where the two instabilities strongly influence each other, providing critical insights for inertial confinement fusion research and offering a practical numerical tool for instability control and scenario design.

physics.plasm-ph↗

arXiv2025

DINO-BOLDNet: A DINOv3-Guided Multi-Slice Attention Network for T1-to-BOLD Generation

Generating BOLD images from T1w images offers a promising solution for recovering missing BOLD information and enabling downstream tasks when BOLD images are corrupted or unavailable. Motivated by this, we propose DINO-BOLDNet, a DINOv3-guided multi-slice attention framework that integrates a frozen self-supervised DINOv3 encoder with a lightweight trainable decoder. The model uses DINOv3 to extract within-slice structural representations, and a separate slice-attention module to fuse contextual information across neighboring slices. A multi-scale generation decoder then restores fine-grained functional contrast, while a DINO-based perceptual loss encourages structural and textural consistency between predictions and ground-truth BOLD in the transformer feature space. Experiments on a clinical dataset of 248 subjects show that DINO-BOLDNet surpasses a conditional GAN baseline in both PSNR and MS-SSIM. To our knowledge, this is the first framework capable of generating mean BOLD images directly from T1w images, highlighting the potential of self-supervised transformer guidance for structural-to-functional mapping.

cs.CV↗

arXiv2025

SS4Rec: Continuous-Time Sequential Recommendation with State Space Models

Sequential recommendation is a key area in the field of recommendation systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec.

cs.IR↗

arXiv2025

SwitchDelta: Asynchronous Metadata Updating for Distributed Storage with In-Network Data Visibility

Distributed storage systems typically maintain strong consistency between data nodes and metadata nodes by adopting ordered writes: 1) first installing data; 2) then updating metadata to make data visible.We propose SwitchDelta to accelerate ordered writes by moving metadata updates out of the critical path. It buffers in-flight metadata updates in programmable switches to enable data visibility in the network and retain strong consistency. SwitchDelta uses a best-effort data plane design to overcome the resource limitation of switches and designs a novel metadata update protocol to exploit the benefits of in-network data visibility. We evaluate SwitchDelta in three distributed in-memory storage systems: log-structured key-value stores, file systems, and secondary indexes. The evaluation shows that SwitchDelta reduces the latency of write operations by up to 52.4% and boosts the throughput by up to 126.9% under write-heavy workloads.

cs.DC↗

arXiv2025

LLMs-based Augmentation for Domain Adaptation in Long-tailed Food Datasets

Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured by users in the free-living environment. In addition to this domain-shift problem, the real-world food datasets tend to be long-tailed distributed and some dishes of different categories exhibit subtle variations that are difficult to distinguish visually. In this paper, we present a framework empowered with large language models (LLMs) to address these challenges in food recognition. We first leverage LLMs to parse food images to generate food titles and ingredients. Then, we project the generated texts and food images from different domains to a shared embedding space to maximize the pair similarities. Finally, we take the aligned features of both modalities for recognition. With this simple framework, we show that our proposed approach can outperform the existing approaches tailored for long-tailed data distribution, domain adaptation, and fine-grained classification, respectively, on two food datasets.

cs.CV↗

arXiv2025

Interplay of spin-orbit coupling and trigonal crystal field enhances superconductivity in $LaAlO_3/KTaO_3$ (111)

In conventional superconductors, bulk physical properties typically degrade as the film thickness approaches the two-dimensional (2D) limit. Here in the (111) oriented LaAlO3/KTaO3 (LAO/KTO) heterostructure, we demonstrate experimental evidence that reducing the conducting layer thickness at the interface significantly enhances superconducting transition temperature Tc, in direct contrast to conventional wisdom. From the sum frequency generation (SFG) spectroscopy and superconducting upper-critical field measurements, both the trigonal symmetry and spin orbit scattering are enhanced with the increased Tc. We attribute the enhanced superconductivity (SC) to the synergic interplay between spin-orbit coupling (SOC) and trigonal crystal field, resulting in an enhanced electron-phonon coupling. Furthermore, we show the existence of unconventional SC: the approaching linear temperature dependence of normal state resistance with increasing Tc and the existence of a quantum critical point (QCP) near the superconducting phase. Our findings provide important insight into the underlying mechanism of the strong orientation-dependent KTO interface SC.

cond-mat.supr-con↗

arXiv2025

Adversarial Attack on Black-Box Multi-Agent by Adaptive Perturbation

Evaluating security and reliability for multi-agent systems (MAS) is urgent as they become increasingly prevalent in various applications. As an evaluation technique, existing adversarial attack frameworks face certain limitations, e.g., impracticality due to the requirement of white-box information or high control authority, and a lack of stealthiness or effectiveness as they often target all agents or specific fixed agents. To address these issues, we propose AdapAM, a novel framework for adversarial attacks on black-box MAS. AdapAM incorporates two key components: (1) Adaptive Selection Policy simultaneously selects the victim and determines the anticipated malicious action (the action would lead to the worst impact on MAS), balancing effectiveness and stealthiness. (2) Proxy-based Perturbation to Induce Malicious Action utilizes generative adversarial imitation learning to approximate the target MAS, allowing AdapAM to generate perturbed observations using white-box information and thus induce victims to execute malicious action in black-box settings. We evaluate AdapAM across eight multi-agent environments and compare it with four state-of-the-art and commonly-used baselines. Results demonstrate that AdapAM achieves the best attack performance in different perturbation rates. Besides, AdapAM-generated perturbations are the least noisy and hardest to detect, emphasizing the stealthiness.

cs.MA↗

arXiv2025

SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data

Split Federated Learning is a system-efficient federated learning paradigm that leverages the rich computing resources at a central server to train model partitions. Data heterogeneity across silos, however, presents a major challenge undermining the convergence speed and accuracy of the global model. This paper introduces Step-wise Momentum Fusion (SMoFi), an effective and lightweight framework that counteracts gradient divergence arising from data heterogeneity by synchronizing the momentum buffers across server-side optimizers. To control gradient divergence over the training process, we design a staleness-aware alignment mechanism that imposes constraints on gradient updates of the server-side submodel at each optimization step. Extensive validations on multiple real-world datasets show that SMoFi consistently improves global model accuracy (up to 7.1%) and convergence speed (up to 10.25$\times$). Furthermore, SMoFi has a greater impact with more clients involved and deeper learning models, making it particularly suitable for model training in resource-constrained contexts.

cs.LG↗

arXiv2025

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on external knowledge makes RAG systems vulnerable to corpus poisoning attacks that manipulate generated outputs via poisoned document injection. Existing poisoning attack strategies typically treat the retrieval and generation stages as disjointed, limiting their effectiveness. We propose Joint-GCG, the first framework to unify gradient-based attacks across both retriever and generator models through three innovations: (1) Cross-Vocabulary Projection for aligning embedding spaces, (2) Gradient Tokenization Alignment for synchronizing token-level gradient signals, and (3) Adaptive Weighted Fusion for dynamically balancing attacking objectives. Evaluations demonstrate that Joint-GCG achieves at most 25% and an average of 5% higher attack success rate than previous methods across multiple retrievers and generators. While optimized under a white-box assumption, the generated poisons show unprecedented transferability to unseen models. Joint-GCG's innovative unification of gradient-based attacks across retrieval and generation stages fundamentally reshapes our understanding of vulnerabilities within RAG systems. Our code is available at https://github.com/NicerWang/Joint-GCG.

cs.CR↗