arXiv subjects

Xinyue Huang

Publications and source records attributed to Xinyue Huang.

14 recordsLinked to original sources

arXiv2026

Excitons probe intrinsic flat band Mottness in a van der Waals heterostructure

Excitons provide a sensitive optical probe of electronic correlations in nearby two-dimensional materials, yet their coupling to intrinsic flat-band Mott systems remains largely unexplored. Here we combine gate-tunable optical spectroscopy with first-principles calculations to study monolayer WSe$_2$ in direct contact with the van der Waals Mott insulator Nb$_3$Cl$_8$. The gate evolution of WSe$_2$ excitonic resonances reveals signatures of a correlation-reconstructed Mott gap in Nb$_3$Cl$_8$ that is absent from the single-particle band picture. In the electron-doped regime, the WSe$_2$ 2s Rydberg exciton undergoes a multistage evolution and develops into interlayer attractive and repulsive polaron branches, showing that a Rydberg exciton can be dressed by strongly correlated flat-band electrons in an adjacent Mott layer. Under an out-of-plane magnetic field, spin-polarized Nb$_3$Cl$_8$ states further induce valley-selective exciton coupling, producing a strongly enhanced circular polarization of the WSe$_2$ exciton emission. These results extend exciton-based sensing and exciton-polaron physics to intrinsic flat-band Mott materials, providing an optical route to probe and engineer correlation-driven interfacial quasiparticles.

cond-mat.mtrl-sci↗

arXiv2026

Anderson Lattice in Incommensurate $\bf{Nb_3Cl_8}$/Graphene van der Waals Heterostructures

The periodic Anderson model}, traditionally realized in rare-earth compounds with limited tunability, have hindered systematic exploration of correlated quantum phenomena. Here, we introduce a strategy for {realizing and }engineering {this model} in incommensurate van der Waals heterostructures by coupling a Mott insulator (Nb$_3$Cl$_8$) with itinerant electrons (from monolayer graphene), circumventing strict lattice-matching requirements. Through magnetotransport and slave spin mean-field calculations, we demonstrate the hybridization gap ($Δ\approx30$ meV), gate-tunable metal-insulator transition, and band-selective electron effective mass enhancement, hallmarks of Kondo coherence. The heterostructure exhibits a nearly order-of-magnitude enhancement in the effective electron mass between hybridized and conventional graphene-like regimes, alongside in-plane magnetic field-induced metal-insulator transitions. Top gate-temperature phase mapping reveals competing correlated states, including insulating and hidden-order phases. This work establishes an electrically tunable van der Waals platform for studying correlated states generated by coupling a Mott-insulating layer to an itinerant-electron system, providing a materials route for exploring low-dimensional correlated quantum phases.

cond-mat.str-el↗

arXiv2026

Layer-parity-dependent interfacial coupling in Nb$_3$Cl$_8$/graphene van der Waals heterostructures

Strongly correlated two-dimensional systems provide compelling platforms for investigating exotic quantum phenomena. Niobium chloride (Nb$_3$Cl$_8$), a single-band Mott insulator, exhibits a remarkable out-of-plane polarization in its topmost layer that oscillates with layer parity, manifesting as an odd-even effect. Using atomic force microscopy (AFM) and Kelvin probe force microscopy (KPFM), this layer-parity-dependent polarization can be effectively characterized through surface morphology and potential mapping, enabling the unambiguous identification of different surface phases. We then fabricated dual-gate Hall devices by coupling different surface phases of Nb$_3$Cl$_8$ with monolayer graphene to investigate how the topmost-layer out-of-plane polarization influences interfacial coupling and the resulting transport behavior. Our results reveal significant phase-dependent variations in charge transfer, carrier densities, and hybridization gaps (25.2 meV for Phase 1 and 30.0 meV for Phase 2). Density functional theory calculations corroborate these experimental findings, showing that distinct out-of-plane polarizations in the topmost layer lead to different orbital overlaps and interfacial coupling strengths. These findings highlight the critical importance of surface polarization and orbital orientation in engineering the properties of strongly correlated van der Waals heterostructures.

cond-mat.mtrl-sci↗

arXiv2026

Layer-parity-defined surface polarization in Nb$_3$Cl$_8$ for excitonic modulation at van der Waals interfaces

The intrinsic symmetry breaking in the breathing kagome lattice of layered Nb$_3$Cl$_8$ provides a unique mechanism for realizing electrically polar surfaces. In each monolayer, the trimerization of Nb atoms breaks inversion and mirror symmetries, generating an out-of-plane electric dipole. The AB-stacked $α$ phase arranges adjacent layer dipoles antiferroelectrically, leaving the uncompensated surface polarization strictly governed by layer parity. Here, using atomic force microscopy operated in Kelvin probe force microscopy mode, we directly visualize layer-dependent polarization states in exfoliated Nb$_3$Cl$_8$ flakes and resolve a pronounced odd-even oscillation of the surface electrostatic potential. Beyond this parity-locked antiferroelectric order, we further identify intralayer polar domains in which local atomic reconstructions of the breathing kagome network reverse the out-of-plane dipole of the surface layer, producing ferroelectric-like stacking configurations. By interfacing monolayer MoSe$_2$ with Nb$_3$Cl$_8$, we demonstrate that these surface-polarization textures effectively modulate adjacent excitonic emission through domain-dependent interfacial band alignment and charge transfer. Our findings establish Nb$_3$Cl$_8$ as an intrinsic layer-polarized van der Waals platform and show that layer parity provides powerful structural degree of freedom for programming excitonic and optoelectronic responses at van der Waals interfaces.

cond-mat.mtrl-sci↗

arXiv2026

Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation

As LLMs become increasingly woven into everyday workflows, user queries sent to cloud hosted LLMs routinely mix task-essential content with task non-essential sensitive disclosures, yet type based PII redaction is context agnostic and may raise two issues: over disclosing untyped sensitive context and over removing answer bearing spans. We recast privacy preserving query rewriting under Contextual Integrity: a span should be forwarded only if it is necessary for the task. We introduce DelegateCI-Bench, the first task based Contextual Integrity benchmark for privacy-conscious delegation, comprising 3,167 samples that combine high quality synthetic data spanning 11 tasks and 20 task types, WildChat based real user queries, and a medical challenge set with dense sensitive information. Building on this benchmark, we propose a CI-guided reinforcement learning framework that converts essential and non-essential sensitive spans into verifiable optimization signals, and train a query rewriter to preserve task critical information while suppressing unnecessary sensitive disclosure. Experiments show that our learned rewriter achieves the best privacy-utility tradeoff, achieving up to +10.1 average utility over on-device baselines.

cs.CR↗

arXiv2026

Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR

Optical Character Recognition (OCR) is fundamental to Vision-Language Models (VLMs) and high-quality data generation for LLM training. Yet, despite progress in average OCR accuracy, state-of-the-art VLMs still struggle with detecting sample-level errors and lack effective unsupervised quality control. We introduce Consensus Entropy (CE), a training-free, model-agnostic metric that estimates output reliability by measuring inter-model agreement entropy. The core insight is that correct predictions converge in output space, while errors diverge. Based on CE, we develop CE-OCR, a lightweight multi-model framework that verifies outputs by ensemble agreement, selects the best outputs, and further improves efficiency through adaptive routing. Experiments demonstrate that CE is robust for quality verification, improving F1 scores by 42.1% over VLM-as-Judge. CE-OCR achieves consistent OCR gains, outperforming self-consistency and single-model baselines at the same cost. Notably, CE requires no training or supervision, enabling plug-and-play integration. Code: https://github.com/Aslan-yulong/consensus-entropy.

cs.CV↗

arXiv2026

Compositionally tuned phase transformations enhance pyroelectric energy harvesting from low-grade heat

Phase-transforming pyroelectric materials have emerged as promising candidates for low-grade thermal energy harvesting. However, whether first-order transformations with large pyroelectric coefficient or second-order transformations with better reversibility are preferable remains unclear. Here we report compositionally tunable phase transformations in Ba$_{1-x}$Sr$_x$TiO$_3$ ($x \in [0, 0.3]$), revealing evolution from first-order to second-order character. We identify a transitional regime between Sr$_{0.15}$ and Sr$_{0.22}$ where transformation mechanism fundamentally changes. Within this regime, Sr$_{0.19}$ achieves optimal lattice compatibility, exhibiting electrical leakage suppressed by over two orders of magnitude while retaining substantial polarization response. Energy conversion demonstrations show the multilayer Sr$_{0.19}$ device delivers pyroelectric current of $\sim$1.6 $μ$A at 64$~^\circ$C with an energy density of 1.6 mJ/cm$^3$ per cycle and 5.5\% conversion efficiency. Remarkably, this composition operates stably over 10,000 full energy conversion cycles without external bias field or recharging, demonstrating that transitional regime compositions provide the optimal balance between energy density and operational durability for practical low-grade heat harvesting.

cond-mat.mtrl-sci↗

arXiv2026

Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents

Optimizing large language models for industrial sales requires balancing long-term commercial objectives (e.g., conversion rate) with immediate linguistic constraints such as fluency and compliance. Conventional reinforcement learning often merges these heterogeneous goals into a single reward, causing high-magnitude session-level rewards to overwhelm subtler turn-level signals, which leads to unstable training or reward hacking. To address this issue, we propose Dual-Horizon Credit Assignment (DuCA), a framework that disentangles optimization across time scales. Its core, Horizon-Independent Advantage Normalization (HIAN), separately normalizes advantages from turn-level and session-level rewards before fusion, ensuring balanced gradient contributions from both immediate and long-term objectives to the policy update. Extensive experiments with a high-fidelity user simulator show DuCA outperforms the state-of-the-art GRPO baseline, achieving a 6.82% relative improvement in conversion rate, reducing inter-sentence repetition by 82.28%, and lowering identity detection rate by 27.35%, indicating a substantial improvement for an industrial sales scenario that effectively balances the dual demands of strategic performance and naturalistic language generation.

cs.AI↗

arXiv2026

Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks

Accurate estimation of the relative concentrations of chromophores in a spectroscopic photoacoustic (sPA) image can reveal immense structural, functional, and molecular information about physiological processes. However, due to nonlinearities and ill-posedness inherent to sPA imaging, concentration estimation is intractable. The Spectroscopic Photoacoustic Optical Inversion Autoencoder (SPOI-AE) aims to address the sPA optical inversion and spectral unmixing problems without assuming linearity. Herein, SPOI-AE was trained and tested on \textit{in vivo} mouse lymph node sPA images with unknown ground truth chromophore concentrations. SPOI-AE better reconstructs input sPA pixels than conventional algorithms while providing biologically coherent estimates for optical parameters, chromophore concentrations, and the percent oxygen saturation of tissue. SPOI-AE's unmixing accuracy was validated using a simulated mouse lymph node phantom ground truth.

cs.LG↗

arXiv2025

Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3

This paper presents a novel Retrieval-Augmented Generation (RAG) framework tailored for complex question answering tasks, addressing challenges in multi-hop reasoning and contextual understanding across lengthy documents. Built upon LLaMA 3, the framework integrates a dense retrieval module with advanced context fusion and multi-hop reasoning mechanisms, enabling more accurate and coherent response generation. A joint optimization strategy combining retrieval likelihood and generation cross-entropy improves the model's robustness and adaptability. Experimental results show that the proposed system outperforms existing retrieval-augmented and generative baselines, confirming its effectiveness in delivering precise, contextually grounded answers.

cs.CL↗

arXiv2025

Physics-informed Machine Learning Analysis for Nanoscale Grain Mapping by Synchrotron Laue Microdiffraction

Understanding the grain morphology, orientation distribution, and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a CNN feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution, and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micron-sized probes.

cond-mat.mtrl-sci↗

arXiv2025

Polarized electroluminescence with magnetic spectral tuning in van der Waals magnet CrSBr

Polarized wavelength-tunable electroluminescence (EL) represents a critical on-demand functionality for next-generation optoelectronics. While conventional van der Waals (vdW) EL devices offer discrete wavelength switching constrained by fixed emission states, we report a novel platform enabling continuous spectral tuning combined with intrinsically polarized emission. By leveraging exciton-assisted inelastic tunneling in the anisotropic magnet CrSBr, our devices achieve uniform EL with a near unity degree of linear polarization ($\approx$ 94.3$\%$). The strong magneto-electronic coupling in CrSBr facilitates continuous magnetic-field-controlled spectral tuning through spin canting-induced band renormalization. This work establishes vdW magnets as a versatile platform for developing reconfigurable polarized light sources with simultaneous spectral and polarization control.

cond-mat.mtrl-sci↗

arXiv2024

Intrinsic localized excitons in MoSe$_2$/CrSBr heterostructures

We present a comprehensive investigation of optical properties in MoSe$_2$/CrSBr heterostructures, unveiling the presence of localized excitons represented by a new emission feature, X$^*$. We demonstrate through temperature- and power-dependent photoluminescence spectroscopy that X$^*$ originates from excitons confined by intrinsic defects within the CrSBr layer. The valley polarization of X$^*$ and trion peaks displays opposite polarity under a magnetic field, which closely correlates with the magnetic order of CrSBr. This is attributed to spin-dependent charge transfer mechanisms across the heterointerface, supported by density functional theory calculations revealing a type-II band alignment and spin-polarized band structures. Furthermore, the strong in-plane anisotropy of CrSBr induces unique polarization-dependent responses in MoSe$_2$ emissions. Our study highlights the crucial role of defects in shaping excitonic properties. It offers valuable insights into spectral-resolved proximity effects in van der Waals heterostructures between semiconductor and magnet, contributing to advancing spintronic and valleytronic devices.

cond-mat.mes-hall↗

arXiv2023

Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG

Due to the increasing challenges posed by the relentless rise in the design complexity of integrated circuits, Boolean Satisfiability (SAT) has emerged as a robust alternative to structural APTG techniques. However, the high cost of transforming a circuit testing problem to a Conjunctive Normal Form (CNF) limits the application of SAT in industrial ATPG scenarios, resulting in a loss of test coverage. In Order to address this problem, this paper proposes a conflict-driven structural learning (CDSL) ATPG algorithm firstly, in which the conflict-driven heuristic methods in modern SAT solver are implemented on the logic cone of fault propagation and activation directly. The proposed CDSL algorithm is composed of three parts: (1) According to the implication graph, various conflict constraints have been learned to prune search space. (2) Conflict-driven implication and justification have been applied to increase decision accuracy and solving efficiency. (3) A conflict-based diagnosis method is further proposed in the case of low coverage debug, leading to making the aborted faults testable by relaxing or modifying some constraints on primary inputs. Extensive experimental results on industrial circuits demonstrate the effectiveness and efficiency of the proposed CDSL algorithm. It is shown that compared with the SAT-based ATPG, the proposed CDSL can on average decrease $25.6\%$ aborted faults with $94.51\%$ less run time. With a two-stage computational flow, it has shown that the proposed CDSL can lead to $46.37\%$ less aborted faults than a one-stage structural algorithm, further with the $3.19\%$ improvement on fault coverage. In addition, the conflict diagnosis can lead to $8.89\%$ less aborted faults on average, and $0.271\%$ improvement in fault coverage rate.

cs.AI↗