arXiv subjects

Xun Liu

Publications and source records attributed to Xun Liu.

At least 19 recordsLinked to original sources

arXiv2026

Phase stability and mechanical response of Ag-interlayered Al/Cu resistance spot-welded joints

Dissimilar Al/Cu joints are essential to battery-pack assemblies; however, their mechanical strength is limited by brittle Al-Cu intermetallic compounds (IMCs) such as Al2Cu and Al4Cu9. Interlayer strategies to suppress these phases remain largely empirical, lacking a predictive framework linking interlayer chemistry to the phases that form and to their intrinsic mechanical character. Here an Ag interlayer is introduced and combines computational thermodynamics, first-principles calculations, microstructural characterization, and mechanical testing into a single self-consistent description of the joint. CALculation of PHAse Diagrams (CALPHAD) equilibrium and Scheil simulations predict the solidification path of the Al-rich Al-Ag fusion zone and explain why Cu incorporation is limited when Ag is present; energy-dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD) confirm an FCC Al-Ag solid solution as the dominant constituent. First-principles phonon calculations within the quasiharmonic approximation yield finite-temperature entropy and Gibbs energy, benchmarked against CALPHAD, while elastic constants assess ductility via the Pugh criterion (i.e., the bulk/shear (B/G) modulus ratio). All Al-Ag phases, including the observed solid solution, exceed the Pugh threshold of 1.75, whereas the targeted Al-Cu IMCs do not, giving a mechanistic basis for the interlayer's effectiveness. This microstructural change translates into improved performance: nominal strength rises from 47.9 to 67.4 MPa. Nanoindentation gives a fusion-zone reduced modulus of 82.8 GPa (Young's modulus 82.0 GPa), versus a calculated 0 K Voigt-Reuss-Hill value of 71.4 GPa. The present work establishes a transferable CALPHAD, first-principles, and experiment workflow for rational interlayer selection in dissimilar-metal joining.

cond-mat.mtrl-sci↗

arXiv2026

AgentAbstain: Do LLM Agents Know When Not to Act?

Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain. This gap poses real risks: under ambiguity, conflicting constraints, or tool failures, agents may execute unintended and irreversible actions. To close this gap, we present the first systematic evaluation framework for agentic abstention: the calibrated ability of tool-using LLM agents to recognize when not to act. At its core, AgentAbstain is a paired-task benchmark built on an agent-native taxonomy of 8 abstention scenarios across pre-execution reasoning and runtime discovery. It contains 263 paired tasks across 42 executable sandbox environments, where each pair consists of a should-act task and a should-abstain variant produced through a controlled perturbation to the instruction, tool, or environment state. To scale this paired design and resist data contamination, we propose AbstainGen, a fully automated pipeline that synthesizes sandbox environments and generates paired tasks end-to-end, validated by deterministic replay and semantic LLM judges; fresh task instances can be regenerated on demand, and three independent annotators rate 94-98% of sampled tasks as well-designed. Across 17 frontier LLMs in 4 agent harnesses, the best agent (Gemini 3.1 Pro) achieves only 59.5% paired accuracy (correct on both the act and abstain sides of each paired task). More importantly, abstention capability is largely independent of general task-solving capability, indicating that scaling task-solving alone will not close this gap. We further identify failure modes such as post-hoc abstention, in which agents execute irreversible actions before recognizing abstention triggers. Our code and dataset are open-sourced at agentabstain.github.io.

cs.AI↗

arXiv2026

D$^2$Turb: Depth-Aware Simulation and Decoupled Learning for Single-Frame Atmospheric Turbulence Mitigation

Single-frame atmospheric turbulence mitigation is inherently ill-posed due to spatially varying blur coupled with non-rigid geometric distortion. Existing end-to-end approaches trained on flat-field simulations often struggle to balance texture recovery with geometric rectification. To overcome this limitation, we propose D$^2$Turb, a unified framework that bridges physics-grounded simulation with explicitly decoupled restoration. First, we introduce a Depth-Aware Turbulence Synthesis protocol that incorporates scene depth into the phase-to-space formulation. This generates physically consistent, depth-dependent degradations and provides a crucial intermediate tilt supervision signal for disentangled learning. Building upon this simulation engine, D$^2$Turb decomposes restoration into two interactive stages: texture deblurring and geometric rectification. The texture deblurring stage employs a deblurring backbone to recover fine-grained details while preserving geometric distortion for the subsequent rectification stage. To mitigate the information fragmentation commonly observed in cascaded designs, we further propose an Adaptive Structural Prior Injection (ASPI) mechanism that dynamically transfers deep structural representations from the deblurring module to guide dense flow prediction for spatial unwarping. Extensive experiments demonstrate that D$^2$Turb achieves state-of-the-art performance on both synthetic and real-world datasets, with consistent improvements in both texture recovery and geometric fidelity. Our code and pre-trained models are publicly available at https://github.com/HertzDot222/D2Turb.

cs.CV↗

arXiv2026

Enhanced $s^\pm$-wave superconductivity in electron-doped La$_3$Ni$_2$O$_7$

In cuprates, electron doping yields a much lower superconducting $T_c$ than hole doping. For recently discovered nickelate superconductors, the analogous doping strategies become more challenging. Consequently, while hole-doped Ruddlesden-Popper (RP) nickelates have been extensively studied, electron-doped RP nickelates remain rarely explored both experimentally and theoretically. Here we fill this gap by systematically investigating the two-orbital bilayer model for three representative systems: bulk La$_3$Ni$_2$O$_7$ at ambient pressure and 15\,GPa, and a heterostructure La$_3$Ni$_2$O$_7$:La$_3$Al$_2$O$_7$ that provides a feasible experimental route to electron doping. Using first-principle calculations and large-scale dynamical cluster quantum Monte Carlo simulations, we find that electron doping generically enhances $s^\pm$-wave pairing superconductivity (SC) in all three cases, with the heterostructure showing the highest $T_c$ in the underdoped regime. Furthermore, our results suggest an inter-orbital cooperative mechanism that the pairing on the $d_{x^2-y^2}$ orbital, induced by that on the $d_{z^2}$ orbital, plays a vital role in the SC. This work provides the theoretical prediction of enhanced SC in electron-doped RP nickelates and calls for future experimental verification.

cond-mat.supr-con↗

arXiv2026

DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents

AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and flexibility, such agents raise significant security and safety concerns. A growing number of real-world incidents have shown that adversaries can easily manipulate agents into performing harmful actions, such as leaking API keys, deleting user data, or initiating unauthorized transactions. Evaluating agent security is inherently challenging, as agents operate in dynamic, untrusted environments involving external tools, heterogeneous data sources, and frequent user interactions. However, realistic, controllable, and reproducible environments for large-scale risk assessment remain largely underexplored. To address this gap, we introduce the DecodingTrust-Agent Platform (DTap), the first controllable and interactive red-teaming platform for AI agents, spanning 14 real-world domains and over 50 simulation environments that replicate widely used systems such as Google Workspace, Paypal, and Slack. To scale the risk assessment of agents in DTap, we further propose DTap-Red, the first autonomous red-teaming agent that systematically explores diverse injection vectors (e.g., prompt, tool, skill, environment, combinations) and autonomously discovers effective attack strategies tailored to varying malicious goals. Using DTap-Red, we curate DTap-Bench, a large-scale red-teaming dataset comprising high-quality instances across domains, each paired with a verifiable judge to automatically validate attack outcomes. Through DTap, we conduct large-scale evaluations of popular AI agents built on various backbone models, spanning security policies, risk categories, and attack strategies, revealing systematic vulnerability patterns and providing valuable insights for developing secure next-generation agents.

cs.AI↗

arXiv2026

Perpendicular electric field induced competing $s^\pm$- and $d$-wave pairings in La$_3$Ni$_2$O$_7$ thin film

Inspired by the possibility that superconducting properties may be altered by applying a perpendicular electric field in the Ruddlesden-Popper (RP) bilayer nickelate La$_3$Ni$_2$O$_7$ thin film, we investigated the imbalanced two-orbital bilayer Hubbard model with a layer potential bias using dynamical cluster quantum Monte Carlo calculations. Focusing on the pairing symmetry evolution with the potential bias between layers for the undoped, hole-doped, and electron-doped regimes, we found that the $s^\pm$-wave pairing, originating from the $d_{z^2}$ orbital at high temperature regime as well as $d_{x^2-y^2}$ orbital at lower temperatures, is suppressed by the potential bias; while a possible pairing symmetry transition from $s^\pm$-wave to $d$-wave pairing occurs, driven by the interlayer orbital mismatch and the transfer of electrons into the $d_{x^2-y^2}$ orbitals. Our large-scale many-body calculations align with the previous expectation from weak-coupling methods and provide further insight into the superconducting mechanism in RP nickelates.

cond-mat.supr-con↗

arXiv2026

OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation

The end-to-end generative paradigm is revolutionizing advertising recommendation systems, driving a shift from traditional cascaded architectures towards unified modeling. However, practical deployment faces three core challenges: the misalignment between interest objectives and business value, the target-agnostic limitation of generative processes, and the disconnection between generation and ranking stages. Existing solutions often fall into a dilemma where single-stage fusion induces optimization tension, while stage decoupling causes irreversible information loss. To address this, we propose OneRanker, achieving architectural-level deep integration of generation and ranking. First, we design a value-aware multi-task decoupling architecture. By leveraging task token sequences and causal mask, we separate interest coverage and value optimization spaces within shared representations, effectively alleviating target conflicts. Second, we construct a coarse-to-fine collaborative target awareness mechanism, utilizing Fake Item Tokens for implicit awareness during generation and a ranking decoder for explicit value alignment at the candidate level. Finally, we propose input-output dual-side consistency guarantees. Through Key/Value pass-through mechanisms and Distribution Consistency (DC) Constraint Loss, we achieve end-to-end collaborative optimization between generation and ranking. The full deployment on Tencent's WeiXin channels advertising system has shown a significant improvement in key business metrics (GMV - Normal +1.34\%), providing a new paradigm with industrial feasibility for generative advertising recommendations.

cs.IR↗

arXiv2026

Overcoming sensitivity-bandwidth trade-off in mid-infrared spectroscopy by a microresonator-anchored swept laser

Optical frequency combs have revolutionized high-precision spectroscopy, yet an intrinsic trade-off between spectroscopic signal-to-noise ratio (sSNR) and measurement bandwidth ($B$) fundamentally constrains sensitive, broadband measurements. While broadband swept lasers offer a potential solution, generating broadband, ultrafast and linearly sweeping lasers with a narrow linewidth remains a significant challenge, particularly in the fingerprint mid-infrared (mid-IR) band. Here we overcome this limitation by using a microresonator-anchored ultrafast sweeping Fourier domain mode-locked (FDML) laser for mid-IR spectroscopy. We introduce a dual-microresonator-anchor approach: a microcomb provides frequency calibration and a high-Q microresonator resolves the instantaneous FDML lasing lineshape. The strategy enables accurate correction of the FDML laser's sweep nonlinearity and broad linewidth in the near-IR, allowing the FDML laser to function as a high-fidelity mid-IR light via difference frequency generation. The system achieves a record sSNR$\times$$B$ of 1.3$\times$10$^5$ THz$\cdot \sqrt{\rm Hz}$ and methane sensing precision of 9 ppb$\cdot$m$\cdot$$\sqrt{\rm s}$, while retaining GHz resolution to distinguish methane isotope. We further demonstrate broadband, coherent swept laser phase spectroscopy in the mid-IR, tolerating losses up to 78 dB. This work leverages advances in integrated photonics to overcome the fundamental limitations of precision spectroscopy, paving the way for next-generation, broadband, and ultra-sensitive mid-IR spectroscopic sensing systems.

physics.optics↗

arXiv2026

HyCARD-Net: A Synergistic Hybrid Intelligence Framework for Cardiovascular Disease Diagnosis

Cardiovascular disease (CVD) remains the foremost cause of mortality worldwide, underscoring the urgent need for intelligent and data-driven diagnostic tools. Traditional predictive models often struggle to generalize across heterogeneous datasets and complex physiological patterns. To address this, we propose a hybrid ensemble framework that integrates deep learning architectures, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), with classical machine learning algorithms, including K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGB), using an ensemble voting mechanism. This approach combines the representational power of deep networks with the interpretability and efficiency of traditional models. Experiments on two publicly available Kaggle datasets demonstrate that the proposed model achieves superior performance, reaching 82.30 percent accuracy on Dataset I and 97.10 percent on Dataset II, with consistent gains in precision, recall, and F1-score. These findings underscore the robustness and clinical potential of hybrid AI frameworks for predicting cardiovascular disease and facilitating early intervention. Furthermore, this study directly supports the United Nations Sustainable Development Goal 3 (Good Health and Well-being) by promoting early diagnosis, prevention, and management of non-communicable diseases through innovative, data-driven healthcare solutions.

cs.AI↗

arXiv2025

Interfacial Strain Modulated Correlated Plasmons in La1.85Sr0.15CuO4 and Their Role in High-temperature Superconductivity

High-temperature superconductivity in cuprate materials remains a major challenge in physics due to the complexity of their strongly correlated electronic states. Interfacial strain is a powerful lever for tuning electronic correlations in complex oxides, offering new pathways to control emergent quantum phases. Here, we report the discovery of interfacial strain modulated correlated plasmons observed exclusively in superconducting La1.85Sr0.15CuO4 (LSCO) through spectroscopic ellipsometry. This form of plasmons is absent in the non-superconducting LSCO counterparts. Detailed analysis reveals that these correlated plasmons, arising from the collective excitations within Mott-correlated bands, are driven by long-range electronic correlations in the Cu-O planes. Furthermore, long-range electronic correlations, intricately modulated by interfacial strain, may play a crucial role in the emergence of superconductivity and in tuning the transition temperature. Dynamical cluster approximation (DCA) with quantum Monte Carlo (QMC) calculations of the extended Hubbard model suggest that long-range Coulomb interactions play an important role in LSCO, showing good agreement with our experimental findings. The collective evidence from both the experimental results and theoretical findings provides new insights into the nature of collective excitations and their pivotal role in the emergence of high-temperature superconductivity.

cond-mat.str-el↗

arXiv2025

SCoTER: Structured Chain-of-Thought Transfer for Enhanced Recommendation

Harnessing the reasoning power of Large Language Models (LLMs) for recommender systems is hindered by two fundamental challenges. First, current approaches lack a mechanism for automated, data-driven discovery of effective reasoning patterns, relying instead on brittle manual templates or unstable zero-shot prompting. Second, they employ structure-collapsing integration: direct prompting incurs prohibitive online inference costs, while feature extraction collapses reasoning chains into single vectors, discarding stepwise logic. To address these challenges, we propose SCoTER (Structured Chain-of-Thought Transfer for Enhanced Recommendation), a unified framework that treats pattern discovery and structure-aware transfer as a jointly optimized problem. Specifically, SCoTER operationalizes this through two synergistic components: a Generate-Validate-Mine (GVM) pipeline for automated pattern discovery and a structure-preserving integration architecture that transfers stepwise logic to efficient models. Empirically, experiments on four benchmarks demonstrate consistent improvements across diverse backbones. Moreover, in production deployment on the Tencent Advertising Platform, SCoTER achieved a 2.14\% lift in Gross Merchandise Value (GMV) while eliminating online LLM inference costs. Overall, SCoTER presents a practical and unified framework for integrating structured LLM reasoning into recommender systems, validated by consistent improvements in both offline benchmarks and online production environments.

cs.IR↗

arXiv2025

GPR: Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation

As an intelligent infrastructure connecting users with commercial content, advertising recommendation systems play a central role in information flow and value creation within the digital economy. However, existing multi-stage advertising recommendation systems suffer from objective misalignment and error propagation, making it difficult to achieve global optimality, while unified generative recommendation models still struggle to meet the demands of practical industrial applications. To address these issues, we propose GPR (Generative Pre-trained Recommender), the first one-model framework that redefines advertising recommendation as an end-to-end generative task, replacing the traditional cascading paradigm with a unified generative approach. To realize GPR, we introduce three key innovations spanning unified representation, network architecture, and training strategy. First, we design a unified input schema and tokenization method tailored to advertising scenarios, mapping both ads and organic content into a shared multi-level semantic ID space, thereby enhancing semantic alignment and modeling consistency across heterogeneous data. Second, we develop the Heterogeneous Hierarchical Decoder (HHD), a dual-decoder architecture that decouples user intent modeling from ad generation, achieving a balance between training efficiency and inference flexibility while maintaining strong modeling capacity. Finally, we propose a multi-stage joint training strategy that integrates Multi-Token Prediction (MTP), Value-Aware Fine-Tuning and the Hierarchy Enhanced Policy Optimization (HEPO) algorithm, forming a complete generative recommendation pipeline that unifies interest modeling, value alignment, and policy optimization. GPR has been fully deployed in the Tencent Weixin Channels advertising system, delivering significant improvements in key business metrics including GMV and CTCVR.

cs.IR↗

arXiv2025

ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks

As vision-language models (VLMs) gain prominence, their multimodal interfaces also introduce new safety vulnerabilities, making the safety evaluation challenging and critical. Existing red-teaming efforts are either restricted to a narrow set of adversarial patterns or depend heavily on manual engineering, lacking scalable exploration of emerging real-world VLM vulnerabilities. To bridge this gap, we propose ARMs, an adaptive red-teaming agent that systematically conducts comprehensive risk assessments for VLMs. Given a target harmful behavior or risk definition, ARMs automatically optimizes diverse red-teaming strategies with reasoning-enhanced multi-step orchestration, to effectively elicit harmful outputs from target VLMs. We propose 11 novel multimodal attack strategies, covering diverse adversarial patterns of VLMs (e.g., reasoning hijacking, contextual cloaking), and integrate 17 red-teaming algorithms into ARMs via model context protocol (MCP). To balance the diversity and effectiveness of the attack, we design a layered memory with an epsilon-greedy attack exploration algorithm. Extensive experiments on instance- and policy-based benchmarks show that ARMs achieves SOTA attack success rates, exceeding baselines by an average of 52.1% and surpassing 90% on Claude-4-Sonnet. We show that the diversity of red-teaming instances generated by ARMs is significantly higher, revealing emerging vulnerabilities in VLMs. Leveraging ARMs, we construct ARMs-Bench, a large-scale multimodal safety dataset comprising over 30K red-teaming instances spanning 51 diverse risk categories, grounded in both real-world multimodal threats and regulatory risks. Safety fine-tuning with ARMs-Bench substantially improves the robustness of VLMs while preserving their general utility, providing actionable guidance to improve multimodal safety alignment against emerging threats.

cs.AI↗

arXiv2025

Spatially Parallel All-optical Neural Networks

All-optical neural networks (AONNs) have emerged as a promising paradigm for ultrafast and energy-efficient computation. These networks typically consist of multiple serially connected layers between input and output layers--a configuration we term spatially series AONNs, with deep neural networks (DNNs) being the most prominent examples. However, such series architectures suffer from progressive signal degradation during information propagation and critically require additional nonlinearity designs to model complex relationships effectively. Here we propose a spatially parallel architecture for all-optical neural networks (SP-AONNs). Unlike series architecture that sequentially processes information through consecutively connected optical layers, SP-AONNs divide the input signal into identical copies fed simultaneously into separate optical layers. Through coherent interference between these parallel linear sub-networks, SP-AONNs inherently enable nonlinear computation without relying on active nonlinear components or iterative updates. We implemented a modular 4F optical system for SP-AONNs and evaluated its performance across multiple image classification benchmarks. Experimental results demonstrate that increasing the number of parallel sub-networks consistently enhances accuracy, improves noise robustness, and expands model expressivity. Our findings highlight spatial parallelism as a practical and scalable strategy for advancing the capabilities of optical neural computing.

physics.optics↗

arXiv2025

Practice on Long Behavior Sequence Modeling in Tencent Advertising

Long-sequence modeling has become an indispensable frontier in recommendation systems for capturing users' long-term preferences. However, user behaviors within advertising domains are inherently sparse, posing a significant barrier to constructing long behavioral sequences using data from a single advertising domain alone. This motivates us to collect users' behaviors not only across diverse advertising scenarios, but also beyond the boundaries of the advertising domain into content domains-thereby constructing unified commercial behavior trajectories. This cross-domain or cross-scenario integration gives rise to the following challenges: (1) feature taxonomy gaps between distinct scenarios and domains, (2) inter-field interference arising from irrelevant feature field pairs, and (3) target-wise interference in temporal and semantic patterns when optimizing for different advertising targets. To address these challenges, we propose several practical approaches within the two-stage framework for long-sequence modeling. In the first (search) stage, we design a hierarchical hard search method for handling complex feature taxonomy hierarchies, alongside a decoupled embedding-based soft search to alleviate conflicts between attention mechanisms and feature representation. In the second (sequence modeling) stage, we introduce: (a) Decoupled Side Information Temporal Interest Networks (TIN) to mitigate inter-field conflicts; (b) Target-Decoupled Positional Encoding and Target-Decoupled SASRec to address target-wise interference; and (c) Stacked TIN to model high-order behavioral correlations. Deployed in production on Tencent's large-scale advertising platforms, our innovations delivered significant performance gains: an overall 4.22% GMV lift in WeChat Channels and an overall 1.96% GMV increase in WeChat Moments.

cs.IR↗

arXiv2025

Free-field approaches to boundary $\mathcal{W} \big[ \widehat{g} \big] (p,p') $ minimal models

We apply the background charged bosonic free-field approach to the rational principal quantum Drinfeld-Sokolov (QDS) $\mathcal{W} \big[ \widehat{g} \big](p,p')$ minimal models with boundaries, where $g$ is a finite bosonic simple Lie algebra. Their Ishibashi states are expressed using the free bosonic Ishibashi states, by applying the Fock space resolutions. The Coulomb-gas formalism is applied to the calculations of the disk two-point correlation functions in some well-studied and lesser familiar rational QDS $\mathcal{W} \big[ \widehat{g} \big](p,p')$ minimal models. The analytical expressions can be obtained by the repeated applications of the Pochhammer contour integral expression and the Taylor expansions of Lauricella's hypergeometric functions $F_{D}^{(n)}$.

hep-th↗

arXiv2025

Superconductivity in imbalanced bilayer Hubbard model: enhanced $d$-wave and weakened $s^\pm$-wave pairing

We investigate the bilayer model with two layers of imbalanced densities coupled by the interlayer hybridization. Using the large-scale dynamical cluster quantum Monte Carlo simulation, we discovered that increased hybridization induces a transition in the superconducting pairing from $d$-wave to $s^{\pm}$-wave and the superconducting $T_c$ of $d$-wave pairing exhibits a non-monotonic dependence on the density imbalance. Remarkably, the optimal superconductivity(SC) occurs at a moderate imbalance. Our results support the possibility of $T_c$ enhancement in composite picture where the underdoped layer provides the pairing strength while the overdoped layer promotes the phase coherence. In addition, the SC can be possibly hosted by a single layer, which is reminiscent of our recent exploration on the trilayer Hubbard model. Our present study thus provides new insight that the SC can be enhanced via the layer differentiation.

cond-mat.supr-con↗

arXiv2025

Hybrid Boundary Physics-Informed Neural Networks for Solving Navier-Stokes Equations with Complex Boundary

Physics-informed neural networks (PINN) have achieved notable success in solving partial differential equations (PDE), yet solving the Navier-Stokes equations (NSE) with complex boundary conditions remains a challenging task. In this paper, we introduce a novel Hybrid Boundary PINN (HB-PINN) method that combines a pretrained network for efficient initialization with a boundary-constrained mechanism. The HB-PINN method features a primary network focused on inner domain points and a distance metric network that enhances predictions at the boundaries, ensuring accurate solutions for both boundary and interior regions. Comprehensive experiments have been conducted on the NSE under complex boundary conditions, including the 2D cylinder wake flow and the 2D blocked cavity flow with a segmented inlet. The proposed method achieves state-of-the-art (SOTA) performance on these benchmark scenarios, demonstrating significantly improved accuracy over existing PINN-based approaches.

physics.comp-ph↗