arXiv subjects

Qiang Gao

Publications and source records attributed to Qiang Gao.

At least 55 records · Page 3Linked to original sources

arXiv2025

CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision

Tool invocation significantly enhances the capabilities of Large Language Models (LLMs), yet challenges persist, particularly in complex task scenarios. Current methods, such as instruction-enhanced reasoning and supervised fine-tuning, often result in unnecessarily long reasoning paths and face difficulties in verifying the correctness of intermediate steps. In this paper, we propose CodeTool, a novel framework for stepwise code generation that improves LLM tool invocation by leveraging the concise and easily verifiable nature of code. CodeTool incorporates two distinct process rewards: the On-the-spot Reward, which provides immediate feedback on the accuracy of each tool invocation, and the Latent Reward, which assesses the contribution of each step toward overall task completion. By maximizing the cumulative reward of the On-the-spot and Latend Rewards at each step, LLMs are guided to follow efficient and accurate reasoning paths. Extensive experiments on StableToolBench and RestBench-TMDB demonstrate the superiority of CodeTool over existing approaches.

cs.AI↗

arXiv2025

Superconducting phase interference effect in momentum space

Detecting the phases of the superconducting order parameter is pivotal for unraveling the pairing symmetry of superconducting electrons. Conventional methods for probing these phases have focused on macroscopic interference effects, such as the Josephson effect. However, at the microscopic level, phase interference effects within momentum space have remained elusive due to the inherent difficulty of extracting phase information from individual momentum points. By introducing the hybridization effect between a primary band and its replica bands arising from density wave orders or other interactions, we uncover a novel superconducting phase interference effect at the intersection points on the Fermi surfaces of these bands. This effect elucidates the remarkable anomalies recently observed in the single-particle spectral function through angle-resolved photoemission spectroscopy (ARPES) in $Bi_2Sr_2CaCu_2O_{8+δ}$ (Bi2212) superconductors. It can also emerge in twisted junctions of superconductors with coherent tunneling, offering an alternative framework for probing the relative superconducting phase through twisted superstructures.

cond-mat.supr-con↗

arXiv2025

DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor

Video Quality Assessment (VQA) aims to evaluate video quality based on perceptual distortions and human preferences. Despite the promising performance of existing methods using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), they often struggle to align closely with human perceptions, particularly in diverse real-world scenarios. This challenge is exacerbated by the limited scale and diversity of available datasets. To address this limitation, we introduce a novel VQA framework, DiffVQA, which harnesses the robust generalization capabilities of diffusion models pre-trained on extensive datasets. Our framework adapts these models to reconstruct identical input frames through a control module. The adapted diffusion model is then used to extract semantic and distortion features from a resizing branch and a cropping branch, respectively. To enhance the model's ability to handle long-term temporal dynamics, a parallel Mamba module is introduced, which extracts temporal coherence augmented features that are merged with the diffusion features to predict the final score. Experiments across multiple datasets demonstrate DiffVQA's superior performance on intra-dataset evaluations and its exceptional generalization across datasets. These results confirm that leveraging a diffusion model as a feature extractor can offer enhanced VQA performance compared to CNN and ViT backbones.

cs.CV↗

arXiv2025

Spectroscopic evidence of intra-unit-cell charge redistribution in charge-neutral magnetic topological insulator Sb-doped MnBi6Te10

The magnetic topological insulator MnBi$_{6}$Te$_{10}$ has emerged as a promising candidate for realizing the quantum anomalous Hall effect (QAHE), owing to its ability to retain ferromagnetism through precise control of anti-site defects. The next important task for realizing the QAHE is to tune the chemical potential into the energy gap formed by the broken time-reversal symmetry. Here we reveal an intra-unit-cell charge redistribution even when the overall doping suggests a near-charge-neutral condition. By performing time- and angle-resolved photoemission spectroscopy (trARPES) on the optimally 18% Sb-doped MnBi$_{6}$Te$_{10}$, we observe transient surface photovoltage (SPV) effects on both the MnBi$_{2}$Te$_{4}$ and single-Bi$_{2}$Te$_{3}$ terminations. Furthermore, we observe a time-dependent splitting of the band structure indicating multiple SPV shifts with different magnitudes. This observation suggests that adjacent plateaus with nominally the same terminating layer exhibit a strong intra-unit-cell charge redistribution, resulting in spontaneous electrical polarization. This is consistent with static micro-ARPES measurements revealing significant doping deviations from the charge-neutral configuration. Our findings underscore the challenges of engineering the family of Mn-Bi-Te materials to realize QAHE purely through chemical doping. Achieving the desired topological quantum phase requires both a uniform carrier doping and a ferromagnetic ground state. Furthermore, the light-induced polarization within each unit cell of ferromagnetic Mn(Bi$_{0.82}$Sb$_{0.18}$)$_{6}$Te$_{10}$ may open new possibilities for optoelectronic and spintronics.

cond-mat.mes-hall↗

arXiv2025

Orbital textures and evolution of correlated insulating state in monolayer 1T phase transition metal dichalcogenides

Strong electron-electron interaction can induce Mott insulating state, which is believed to host unusual correlated phenomena such as quantum spin liquid when quantum fluctuation dominates and unconventional superconductivity through doping. Transition metal compounds as correlated materials provide a versatile platform to engineer the Mott insulating state. Previous studies mostly focused on the controlling of the repulsive interaction and bandwidth of the electrons by gating or doping. Here, we performed angle-resolved photoemission spectroscopy (ARPES) on monolayer 1T phase NbSe2, TaSe2, and TaS2 and directly observed their band structures with characteristic lower Hubbard bands. By systematically investigating the orbital textures and temperature dependence of the energy gap of the materials in this family, we discovered that hybridization of the chalcogen p states with lower Hubbard band stabilizes the Mott phase via tuning of the bandwidth, as shown by a significant increase of the transition temperature (TC) at a stronger hybridization strength. Our findings reveal a mechanism for realizing a robust Mott insulating phase and establish monolayer 1T phase transition metal dichalcogenide family as a promising platform for exploring correlated electron problems.

cond-mat.str-el↗

arXiv2025

Bootstrapping the Quantum Hall problem

The bootstrap method aims to solve problems by imposing constraints on the space of physical observables, which often follow from physical assumptions such as positivity and symmetry. Here, we employ a bootstrap approach to study interacting electrons in the lowest Landau level by minimizing the energy as a function of the static structure factor subject to a set of constraints, bypassing the need to construct the full many-body wavefunction. This approach rigorously lower bounds the ground state energy, making it complementary to conventional variational upper bounds. We show that the lower bound we obtain is relatively tight, within at most 5\% from the ground state energy computed with exact diagonalization (ED) at small system sizes, and generally gets tighter as we include more constraints. In addition to energetics, our results reproduce the correct power law dependence of the pair correlation function at short distances and the existence of a large entanglement gap in the two-particle entanglement spectra for the Laughlin states at $ν= 1/3$. We further identify signatures of the composite Fermi liquid state close to half-filling. This shows that the bootstrap approach is capable, in principle, of describing non-trivial gapped topologically ordered, as well as gapless, phases. At the end, we will discuss possible extensions and limitations of this approach. Our work establishes numerical bootstrap as a promising method to study many-body phases in topological bands, paving the way to its application in moiré platforms where the energetic competition between fractional quantum anomalous Hall, symmetry broken, and gapless states remains poorly understood.

cond-mat.str-el↗

arXiv2025

Ground state and multiple normalized solutions of quasilinear Schrödinger equations in the $L^2$-supercritical case and the Sobolev critical case

This paper is devoted to studying the existence of normalized solutions for the following quasilinear Schrödinger equation \begin{equation*} \begin{aligned} -Δu-uΔu^2 +λu=|u|^{p-2}u \quad\mathrm{in}\ \mathbb{R}^{N}, \end{aligned} \end{equation*} where $N=3,4$, $λ$ appears as a Lagrange multiplier and $p \in (4+\frac{4}{N},2\cdot2^*]$. The solutions correspond to critical points of the energy functional subject to the $L^2$-norm constraint $\int_{\mathbb{R}^N}|u|^2dx=a^2>0$. In the Sobolev critical case $p=2\cdot 2^*$, the energy functional has no critical point. As for $L^2$-supercritical case $p \in (4+\frac{4}{N},2\cdot2^*)$: on the one hand, taking into account Pohozaev manifold and perturbation method, we obtain the existence of ground state normalized solutions for the non-radial case; on the other hand, we get the existence of infinitely many normalized solutions in $H^1_r(\mathbb{R}^N)$. Moreover, our results cover several relevant existing results. And in the end, we get the asymptotic properties of energy as $a$ tends to $+\infty$ and $a$ tends to $0^+$.

math.AP↗

arXiv2025

Spectroscopic evidence for possible quantum spin liquid behavior in a two-dimensional Mott insulator

Mott insulators with localized magnetic moments will exhibit a quantum spin liquid (QSL) state when the quantum fluctuations are strong enough to suppress the ordering of the spins. Such an entangled state will give rise to collective excitations, in which spin and charge information are carried separately. Our angle-resolved photoemission spectroscopy (ARPES) measurements on single-layer 1T-TaS2 show a flat band around the zone center and a gap opening of about 200 meV in the low temperature, indicating 2D Mott insulating nature in the system. This flat band is dispersionless in momentum space but shows anomalously broad width around the zone center and the spectral weight decays rapidly as momentum increases. The observation is described as a spectral continuum from electron fractionalization, corroborated by a low energy effective model.The intensity of the flat band is reduced by surface doping with magnetic adatoms and the gap is closing, a result from the interaction between spin impurities coupled with spinons and the chargons, which gives rise to a charge redistribution. Doping with nonmagnetic impurities behaves differently as the chemical potential shift dominates. These findings provide insight into the QSL states of strongly correlated electrons on 2D triangular lattices.

cond-mat.str-el↗

arXiv2025

A Topological Superconductor Tuned by Electronic Correlations

A topological superconductor, characterized by either a chiral order parameter or a chiral topological surface state in proximity to bulk superconductivity, is foundational to topological quantum computing. As in other topological phases of matter, electronic correlations can tune topological superconductivity via modifications of the low-energy Fermiology. Such tuning has not been realized so far. Here we uncover a unique topological superconducting phase in competition with electronic correlations in 10-unit-cell thick FeTe$_{x}$Se$_{1-x}$ films grown on SrTiO$_{3}$ substrates. When the Te content $x$ exceeds $0.7$, we observe a rapid increase of the effective mass for the Fe $d_{xy}$ band, with the emergence of a superconducting topological surface state confirmed by high-resolution angle-resolved photoemission spectroscopy; however, near the FeTe limit, the system enters an incoherent regime where the topological surface state becomes unidentifiable and superconductivity is suppressed. Theory suggests that the electron-electron interactions in the odd-parity $xy^-$ band with a strong $d_{xy}$ character lead to an orbital-selective correlated phase. Our work establishes FeTe$_{x}$Se$_{1-x}$ thin films as a unique platform where electronic correlations sensitively modulate topological superconductivity, suggesting opportunities to use tunable electron-electron interactions to engineer new topological phases in a broad class of materials.

cond-mat.supr-con↗

arXiv2025

Invertible Koopman neural operator for data-driven modeling of partial differential equations

Koopman operator theory is a popular candidate for data-driven modeling because it provides a global linearization representation for nonlinear dynamical systems. However, existing Koopman operator-based methods suffer from shortcomings in constructing the well-behaved observable function and its inverse and are inefficient enough when dealing with partial differential equations (PDEs). To address these issues, this paper proposes the Invertible Koopman Neural Operator (IKNO), a novel data-driven modeling approach inspired by the Koopman operator theory and neural operator. IKNO leverages an Invertible Neural Network to parameterize observable function and its inverse simultaneously under the same learnable parameters, explicitly guaranteeing the reconstruction relation, thus eliminating the dependency on the reconstruction loss, which is an essential improvement over the original Koopman Neural Operator (KNO). The structured linear matrix inspired by the Koopman operator theory is parameterized to learn the evolution of observables' low-frequency modes in the frequency space rather than directly in the observable space, sustaining IKNO is resolution-invariant like other neural operators. Moreover, with preprocessing such as interpolation and dimension expansion, IKNO can be extended to operator learning tasks defined on non-Cartesian domains. We fully support the above claims based on rich numerical and real-world examples and demonstrate the effectiveness of IKNO and superiority over other neural operators.

cs.LG↗

arXiv2025

Robust Super-Moiré in Large Angle Single-Twist Bilayers

Forming long wavelength moiré superlattices (MSL) at small-angle twist van der Waals (vdW) bilayers has been a key approach to creating moiré flat bands. The small-angle twist, however, leads to strong lattice reconstruction, causing domain walls and moiré disorders, which pose considerable challenges in engineering such platforms. At large twist angles, the rigid lattices render a more robust, but shorter wavelength MSL, making it difficult to engineer flat bands. Here, we depict a novel approach to tailoring robust super-moiré (SM) structures that combines the advantages of both small-twist and large-twist transition metal dichalcogenides (TMDs) bilayers using only a single twist angle near a commensurate angle. Structurally, we unveil the spontaneous formation of a periodic arrangement of three inequivalent commensurate moiré (CM) stacking, where the angle deviation from the commensurate angle can tune the periodicity. Electronically, we reveal a large set of van Hove singularities (VHSs) that indicate strong band hybridization, leading to flat bands near the valence band maximum. Our study paves the way for a new platform of robust SM bilayers with structural rigidity and controllable wavelength, extending the investigation of the interplay among band topology, quantum geometry, and moiré superconductivity to the large twist angle regime.

cond-mat.mtrl-sci↗

arXiv2025

Secure Resource Allocation via Constrained Deep Reinforcement Learning

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios, including different task loads, data sizes, and MEC capacities, show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications.

cs.LG↗

arXiv2025

Unveiling potential candidates for rare-earth-free permanent magnet and magnetocaloric effect applications: a high throughput screening in Fe-N alloys

Based on high-throughput density functional theory calculations, we have found 49 ferromag-netic cases in FexN1-x (0<x<1) compounds, focusing especially on permanent magnet and giant magnetocaloric effect applications. It is found that 15 compounds are potential permanent mag-nets with a magneto-crystalline anisotropy energy more than 1 MJ/m3, filling in the gap of appli-cation spectrum between high-performance and widely used permanents. Among the potential permanent magnets, Fe2N can be classified as a hard magnet while the other 14 compounds can be classified as semi-hard magnets. According to the calculations of magnetic deformation proxy, 40 compounds are identified as potential giant magnetocaloric effect candidates. We suspect that Fe-N compounds provide fine opportunities for applications in both rare-earth free permanent magnets and magnetocaloric effect.

cond-mat.mtrl-sci↗

arXiv2025

MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity

Retrieval Augmented Generation (RAG) has proven to be highly effective in boosting the generative performance of language model in knowledge-intensive tasks. However, existing RAG framework either indiscriminately perform retrieval or rely on rigid single-class classifiers to select retrieval methods, leading to inefficiencies and suboptimal performance across queries of varying complexity. To address these challenges, we propose a reinforcement learning-based framework that dynamically selects the most suitable retrieval strategy based on query complexity. % our solution Our approach leverages a multi-armed bandit algorithm, which treats each retrieval method as a distinct ``arm'' and adapts the selection process by balancing exploration and exploitation. Additionally, we introduce a dynamic reward function that balances accuracy and efficiency, penalizing methods that require more retrieval steps, even if they lead to a correct result. Our method achieves new state of the art results on multiple single-hop and multi-hop datasets while reducing retrieval costs. Our code are available at https://github.com/FUTUREEEEEE/MBA .

cs.AI↗

arXiv2024

The Fe-N system: crystal structure prediction, phase stability, and mechanical properties

Nitriding introduces nitrides into the surface of steels, significantly enhancing the surface me-chanical properties. By combining the variable composition evolutionary algorithm and first-principles calculations based on density functional theory, 50 thermodynamically stable or metastable Fe-N compounds with various stoichiometric ratios were identified, exhibiting also dynamic and mechanical stability. The mechanical properties of these structures were systemati-cally studied, including the bulk modulus, shear modulus, Young's modulus, Poisson's ratio, Pugh's ratio, Cauchy pressure, Klemen parameters, universal elastic anisotropy, Debye tempera-ture, and Vickers hardness. All identified stable and metastable Fe-N compounds were found in the ductile region, with most exhibiting homogeneous elastic properties and isotropic metallic bonding. As the nitrogen concentration increases, their bulk moduli generally increase as well. The Vickers hardness values of Fe-N compounds range from 3.5 to 10.5 GPa, which are signifi-cantly higher than that of pure Fe (2.0 GPa), due to the stronger Fe-N bonds strength. This study provides insights into optimizing and designing Fe-N alloys with tailored mechanical properties.

cond-mat.mtrl-sci↗

arXiv2024

A Magnetic Compression method for sub-THz electron beam generation from RF freqencies

Current THz electron sources struggle with low energy gain and device miniaturization. We propose a magnetic compression method designed for relativistic electrons to perform post-compression on the beam from radiofrequency accelerators, to produce sub-THz electron beam with exceptionally high energy ($>1$ J). Through simulation studies, we longitudinally compress a relativistic electron beam with energy of 60 MeV and frequency of 3 GHz across a time span of 24 ns, yielding an electron pulse train at a 0.1 THz. The compressed beam exhibits a pulse width of 0.8 ns, a total charge of 24 nC, and an energy of 1.4 J, providing a new potential for ultra-high-energy THz electron beams generation.

physics.acc-ph↗

arXiv2024

Recent Advances in Graphene-Based Humidity Sensors with the Focus of Structural Design: A Review

The advent of the 5G era means that the concepts of robot, VR/AR, UAV, smart home, smart healthcare based on IoT (Internet of Things) have gradually entered human life. Since then, intelligent life has become the dominant direction of social development. Humidity sensors, as humidity detection tools, not only convey the comfort of human living environment, but also display great significance in the fields of meteorology, medicine, agriculture and industry. Graphene-based materials exhibit tremendous potential in humidity sensing owing to their ultra-high specific surface area and excellent electron mobility under room temperature for application in humidity sensing. This review begins with the introduction of examples of various synthesis strategies of graphene, followed by the device structure and working mechanism of graphene-based humidity sensor. In addition, several different structural design methods of graphene are summarized, demonstrating the structural design of graphene can not only optimize the performance of graphene, but also bring significant advantages in humidity sensing. Finally, key challenges hindering the further development and practical application of high-performance graphene-based humidity sensors are discussed, followed by presenting the future perspectives.

cond-mat.mes-hall↗

arXiv2024

Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network

Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI.

cs.CL↗