arXiv subjects

Kun Jiang

Publications and source records attributed to Kun Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Intra-unit-cell Josephson scanning tunneling spectroscopy of the cuprate Bi$_{1.2}$Pb$_{0.8}$Sr$_2$CaCu$_2$O$_{8+δ}$

The predominantly $d_{x^2-y^2}$-wave pairing symmetry of cuprate superconductors is well established, yet its microscopic manifestation in local Cooper-pair tunneling remains less explored. Here, we investigate Bi$_{1.2}$Pb$_{0.8}$Sr$_2$CaCu$_2$O$_{8+δ}$, using Josephson scanning tunneling spectroscopy with a conventional $s$-wave superconducting Pb tip. We observe a zero-bias conductance enhancement whose approximately quadratic dependence on junction conductance is consistent with phase-diffusive Josephson tunneling. Atomic-resolution conductance maps and site-resolved spectra reveal a pronounced intra-unit-cell modulation, with enhanced Josephson response near oxygen sites and strong suppression near copper sites and along diagonal directions. Microscopic calculations incorporating the bilayer electronic structure and realistic Wannier-based tunneling matrix elements reproduce the observed spatial pattern through interference between pairing contributions of opposite signs. Our results reveal how the sign-changing $d$-wave superconducting order parameter manifests itself in atomic-scale Josephson tunneling, providing complementary evidence for unconventional pairing symmetry in cuprates.

cond-mat.supr-con↗

arXiv2026

RSKD: Grounding Semantic Consistency in Driving VLMs via Distillation

Knowledge distillation can transfer large multimodal models to compact models for autonomous driving, but uniform token-level matching can dilute planning-relevant semantics and amplify teacher errors. We propose RSKD, a semantic-consistency-guided distillation framework that routes supervision according to both the task importance of an answer phrase and its semantic consistency with the teacher prediction. Task-critical semantic phrases are localized in the ground-truth answer, and a phrase-level consistency score controls the balance between Jensen--Shannon divergence (JSD) distillation and ground-truth supervision. Teacher information is emphasized only where it is semantically consistent, while semantically inconsistent phrases receive stronger ground-truth supervision. We evaluate RSKD on DrivingVQA, LingoQA, and MapLMv2. RSKD improves over the compared student-based baselines on DrivingVQA and LingoQA and remains competitive on MapLMv2.

cs.CV↗

arXiv2026

Nodeless d-wave pairing in monolayer FeSe revealed by intra-unit-cell Josephson interferometry

The Josephson effect provides a direct phase-sensitive probe of superconducting order and has played a central role in identifying unconventional pairing symmetries1-6. Conventional Josephson interferometry, however, extracts phase information through engineered junction geometries and crystallographic orientations, rather than by resolving the internal phase structure of the pair state within a single unit cell. Here, we use a superconducting Nb tip to realize atomic-scale Josephson interferometry in monolayer FeSe/SrTiO3. Despite a fully gapped quasiparticle spectrum, the local Josephson response exhibits a striking intra-unit-cell pattern, with maxima centred on Fe-Fe bonds and robust nodal lines along the Fe-Se directions. We show that these nodal lines arise from destructive interference between Josephson tunnelling amplitudes associated with opposite-sign pairing components. Microscopic calculations reproduce the observed spatial pattern and identify it as a fingerprint of nodeless d-wave pairing. Our results establish intra-unit-cell Josephson interferometry as a local phase-sensitive probe of pairing symmetry in unconventional superconductors.

cond-mat.supr-con↗

arXiv2026

Coulomb-Driven Electronic Textures in Lightly Hole-Doped Cuprates

Motivated by recent experiments on Ca$_2$CuO$_2$Cl$_2$, we present a comprehensive study of the interplay between dopants and localized Zhang-Rice singlets in lightly hole-doped cuprates. Owing to the poorly screened Coulomb potential, the interactions between dopants and localized Zhang-Rice singlets favor asymmetric electronic configurations characterized by effective electric "dipoles". Using the MPS-based Lanczos method, we show that these localized holes give rise to in-gap bound states within the charge-transfer gap. When tunneling matrix-element effects are included, the bound states exhibit a characteristic $4a_0 \times 2a_0$ internal structure, consistent with experimental observations. Our results suggest that Coulomb interactions between dopants and localized Zhang-Rice singlets, together with tunneling matrix-element effects, provide a microscopic mechanism for the observed $4a_0$ electronic structures.

cond-mat.supr-con↗

arXiv2026

CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations

While autonomous agents show promise for network operations, existing telecom troubleshooting evaluations fail to capture the partial observability and heterogeneity of real-world telecom environments. We introduce CTBench, a public benchmark evaluating agent troubleshooting capabilities in root cause analysis (RCA) and path restoration. Constructed by domain experts, CTBench features rich metadata and golden evidence trajectories, utilizing metrics that assess both final accuracy and diagnostic reasoning. Our evaluation of state-of-the-art agents reveals that while they excel at path endpoint identification, they severely underperform in RCA. Crucially, even when agents produce correct answers, they frequently fail to gather the requisite diagnostic evidence demanded in operational practice. Finally, we demonstrate that increased computational expenditure does not guarantee improved diagnostic competence, highlighting critical gaps in current agent capabilities.

cs.AI↗

arXiv2026

Test-time Adaptation for Point Cloud Denoising under Unseen Noise

Although deep learning-based point cloud denoising methods have demonstrated impressive results in recent years, their effectiveness usually degrades substantially in real-world scenarios where the noise distribution of test point clouds deviates from that of the training set. To address this challenge, we propose a test-time adaptive denoising (TTAD) framework that enables existing denoising approaches to adapt to unknown noise patterns at test time without relying on additional supervision or retraining from source data. Taking a pre-trained denoising model and a test noisy point cloud as input, TTAD first employs a residual-driven noise modeling (RDNM) module to estimate the sample-specific noise distribution through analyzing the deviation between the noisy input and its pseudo-clean version denoised by the initial model. Then, a noise-aware contrastive adaptation (NACA) module is designed to build sample-specific noisy-noisier test-time training pair from the denoised point cloud, and update the model parameters over a few adaptation steps per sample at test time through contrastive learning. Furthermore, TTAD naturally extends to a continual TTAD (CTTAD) setting, facilitating efficient progressive refinement across sequential test samples with consistent noise distributions, while reducing the computational cost per sample. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach significantly improves the generalization ability of existing denoising models under unseen noise, achieving superior performance across multiple evaluation metrics.

cs.CV↗

arXiv2026

$d$-wave superconductivity in quintuple-layer bulk superconductor La$_6$Ni$_5$O$_{12}$

The recent realization of bulk superconductivity in the quintuple-layer nickelate La$_6$Ni$_5$O$_{12}$ opens new opportunities to probe the pairing symmetry of the reduced Ruddlesden--Popper (RP) nickelate family. Using an effective five-layer Hubbard model and variational Monte Carlo calculations, we find that a spin-singlet $d_{x^2-y^2}$-wave state is energetically favored, while extended-$s$-wave pairing is strongly suppressed. Based on the optimized superconducting state, we further calculate several experimentally accessible observables. The tunneling density of states exhibits a characteristic V-shaped low-energy spectrum, the superfluid density shows a linear low-temperature depletion, the Knight shift decreases below $T_c$, and the spin-lattice relaxation rate follows $1/T_1T\propto T^2$, together providing consistent signatures of a clean nodal superconductor. Extending the same framework to related reduced RP nickelates, we find a robust $d$-wave tendency in infinite-layer LaNiO$_{2}$, while trilayer La$_4$Ni$_3$O$_{8}$ at its nominal filling lies close to the boundary between $d$-wave superconductivity and a non-superconducting state. These results establish bulk La$_6$Ni$_5$O$_{12}$ as a promising platform for determining the pairing symmetry of nickelate superconductors and suggest a common $d_{x^2-y^2}$-wave pairing tendency across the reduced RP nickelate family.

cond-mat.supr-con↗

arXiv2026

Frustration from Localized Zhang-Rice States: A Unified Theory of Doping-Driven Magnetic Transitions in Cuprates

The microscopic mechanism by which doped holes disrupt the antiferromagnetic (AFM) order is one of the fundamental questions in cuprates. In this work, we propose a unified microscopic theory in which doped holes form spatially localized Zhang-Rice singlets which actively mediate emergent spin exchange. Rather than acting as simple non-magnetic vacancies, these localized states introduce emergent next-nearest $J_2$ and third-nearest $J_3$ neighbor superexchanges. This dopant-induced exchange pathway generates significant magnetic frustration, naturally explaining the rapid collapse of the Néel AFM order and the emergence of a spin-glass phase on the hole-doped side. In contrast, electron doping primarily leads to spin dilution, allowing AFM order to persist to substantially higher doping levels, consistent with the experimentally observed electron-hole asymmetry. Our findings provide a unified microscopic framework for understanding doping-driven magnetic transitions in lightly doped cuprates.

cond-mat.str-el↗

arXiv2026

VGGTWorld-VLA: Intent-Conditioned 3D World Evolution for Autonomous Driving

VGGT provides a strong foundation for geometry-centric world models by recovering unified 3D scene geometry from visual observations. Although recent extensions enable temporal 3D prediction, their future evolution remains weakly conditioned on driving intentions and actions, limiting their ability to model alternative action-dependent futures. We propose VGGTWorld-VLA, an intention-conditioned extension of VGGT-World for controllable 3D world evolution in autonomous driving. First, we introduce an action--semantic conditioning mechanism that injects complementary driving semantics and ego-motion representations into the future-token stream, enabling different future geometry predictions for the same observed scene under alternative ego actions. Second, we develop a geometry--language--action bridge that adapts historical geometry, VLA semantic features, and maneuver and trajectory representations for joint conditioning of future geometry prediction. We evaluate future geometry prediction on NAVSIM, while conditioning ablations further examine the contributions of semantic and action information. Compared with the baseline, our method demonstrates competitive geometry prediction performance. Ablation studies further support the effectiveness of semantic and action conditioning. These results demonstrate the potential of semantic and action conditioning for controllable VGGT-based world prediction in autonomous driving.

cs.CV↗

arXiv2026

MASC: A Multi-Agent Self-Calibration Framework with Latent Construct Alignment for Consistent Client Role-Playing in Psychological Counseling

Large language models are increasingly used to simulate clients for counselor training and psychological counseling research, but reliable simulation requires clients to remain psychologically coherent across extended interactions. Existing role-playing methods largely rely on static profile prompts and may exhibit persona drift, unrealistic cooperativeness, or inconsistent psychological states, communicative actions, and emotions. Existing evaluations also lack a unified testbed for both stable client characteristics and evolving psychological dynamics. We propose MASC, a Multi-Agent Self-Calibration framework with latent construct alignment for consistent client role-playing in psychological counseling. MASC combines construct-guided generation, collaborative refinement, consistency verification, and memory-based revision in a closed calibration loop that detects and corrects inconsistencies as dialogue unfolds. We further introduce CRPC-Bench, a benchmark covering session-level profile information and Big-Five personality traits, as well as turn-level psychological state, communicative action, and emotion expression. CRPC-Bench contains 38 motivational interviewing client profiles augmented with personality and emotion annotations. Experiments show that MASC outperforms existing methods across profile, personality, receptivity, and turn-level consistency, with the heterogeneous configuration achieving the strongest overall performance. MASC and CRPC-Bench provide a unified foundation for developing and evaluating psychologically coherent client simulations for AI-assisted counseling research and training.

cs.AI↗

arXiv2026

XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments

Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex environments. Current cloud pipelines rely on generic vision-language models (VLMs) that lack geometric reasoning and domain semantics due to their 2D image-text pretraining. To address this mismatch, we propose XEmbodied, a cloud-side foundation model that endows VLMs with intrinsic 3D geometric awareness and interaction with physical cues (e.g., occupancy grids, 3D boxes). Instead of treating geometry as auxiliary input, XEmbodied integrates geometric representations via a structured 3D Adapter and distills physical signals into context tokens using an Efficient Image-Embodied Adapter. Through progressive domain curriculum and reinforcement learning post-training, XEmbodied preserves general capabilities while demonstrating robust performance across 18 public benchmarks. It significantly improves spatial reasoning, traffic semantics, embodied affordance, and out-of-distribution generalization for large-scale scenario mining and embodied VQA.

cs.CV↗

arXiv2026

Contribution of remote bands to orbital magnetization in twisted bilayer graphene

Motivated by recent theoretical and experimental works on orbital magnetization $M_{\mathrm{orb}}$ for the interacting system, we develop a gauge-invariant framework to compute $M_{\mathrm{orb}}$ for correlated phases of magic-angle twisted bilayer graphene within self-consistent Hartree-Fock approximation. Based on the projector formulation of the theory of orbital magnetization, we evaluate both $M_{\mathrm{orb}}$ and the self-rotation contribution $m_{\mathrm{SR}}$ directly from the Hartree-Fock Hamiltonian. We demonstrate that, in contrast to topological invariants such as the Chern number, both $M_{\mathrm{orb}}$ and $m_{\mathrm{SR}}$ obtain substantial contributions from remote bands and thus require careful convergence with respect to the number of included remote bands. Applying this approach to correlated phases at integer fillings, we obtain converged $M_{\mathrm{orb}}$ and $m_{\mathrm{SR}}$ for time reversal symmetry broken Chern insulating states at $ν=\pm3$ and for competing correlated phases at other integer fillings. Our results establish a systematic and controlled approach for evaluating orbital magnetization in correlated moiré systems and clarify the crucial role of remote bands in determining their magnetic response.

cond-mat.str-el↗

arXiv2026

Leveraging Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Views

High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail scenarios. However, reconstructing a reusable vehicle representation from in-the-wild onboard images remains challenging for two reasons. First, image-to-3D generation methods generally produce models without reliable metric scale. Second, onboard cameras usually observe only one side of a target vehicle, making conventional multi-view reconstruction incomplete on unobserved regions. To solve these problems, we propose a feed-forward vehicle asset reconstruction method, which leverages two complementary priors to reconstruct 3D Gaussian representations for vehicles using sparse one-sided observations. To achieve metric-scale reconstruction, a visual foundation model is first utilized to serve as a visual prior for Gaussian initialization. The Gaussian attributes are then estimated by a learnable encoder-decoder module. A symmetry-aware cloning strategy is presented to complete the unobserved side directly in Gaussian space, which exploits the bilateral structure of vehicles as a geometric prior. Experiments on the public dataset demonstrate that the proposed method significantly outperforms existing approaches in both vehicle asset completeness and geometric accuracy.

cs.CV↗

arXiv2026

Bound states in doped charge transfer insulators

Understanding the physics of doped charge transfer insulators is the most important problem in high-temperature superconductivity. In this work, we show that an in-gap bound state emerges from the localized hole of the doped charge transfer insulator. We propose an approximate ground state wavefunction based on one localized Zhang-Rice singlet and the Neel state. By calculating the excitation states with one hole added and removed from this ground state, we successfully identify the existence of bound states inside the charge transfer gap. This feature is further confirmed by a Lanczos calculation based on matrix product states (MPS) for a system of $4\times4$ CuO$_2$ unit cells. How these bound states evolve into metallic states is further discussed. Our findings identify the key component of recent STM results on lightly doped Ca$_2$CuO$_2$Cl$_2$ and provide a new understanding of hole-doped charge transfer insulators.

cond-mat.supr-con↗

arXiv2026

Electronically Inactive Intercalated La$_2$NiO$_4$ Layer in Superconducting La$_5$Ni$_3$O$_{11}$

The recent discovery of superconductivity in La$_5$Ni$_3$O$_{11}$ extends the family of superconducting Ruddlesden--Popper nickelates beyond La$_3$Ni$_2$O$_7$. Unlike conventional members of a single Ruddlesden--Popper series, La$_5$Ni$_3$O$_{11}$ contains an intercalated La$_2$NiO$_4$ layer between La$_3$Ni$_2$O$_7$ blocks, raising the question of whether this additional layer participates in the low-energy electronic structure. Here, we combine density functional theory, Wannier-based tight-binding modeling, and rotationally invariant slave-boson calculations to investigate the electronic role of the intercalated layer. We find that realistic electronic parameters place the La$_2$NiO$_4$ layer in gapped insulating regimes rather than a paramagnetic metallic state. Furthermore, realistic interlayer hybridization fails to generate any appreciable La$_2$NiO$_4$-derived spectral weight at the Fermi level. Our results demonstrate that the low-energy electronic structure of La$_5$Ni$_3$O$_{11}$ is governed primarily by the La$_3$Ni$_2$O$_7$ block, with the intercalated La$_2$NiO$_4$ layer remaining electronically inactive. This establishes a minimal low-energy description of La$_5$Ni$_3$O$_{11}$ and provides a unified framework for understanding superconductivity in intercalated Ruddlesden--Popper nickelates.

cond-mat.supr-con↗

arXiv2026

nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks

The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches. While learning-based planners have achieved considerable success in passenger vehicles, their performance on heavy-duty trucks, particularly modern distributed electric-drive trucks (DETs), remains largely unexplored. To facilitate research and application of learning-based planners in DETs, this letter presents the first high-fidelity benchmark, called nuTruck, designed to support large-scale neural network training and closed-loop evaluation. Given the complex dynamics and high rollover susceptibility of DETs, we first incorporate a highly accurate nonlinear truck dynamical model into the simulation, which enables independent driving and steering of all wheels and captures dynamic load transfer caused by acceleration, deceleration, and cornering, thereby allowing quantitative assessment of rollover risk in closed-loop simulation. Second, we adapt several rule-based and learning-based planners as baselines for DETs and evaluate their performance in closed-loop simulation. Finally, using real-world driving scenarios from the nuPlan dataset, we conduct extensive closed-loop evaluations, analyzing not only conventional collision-free planning performance, but also the dynamical safety of the planned trajectories. The proposed nuTruck benchmark is expected to serve as a new standard for fair and realistic evaluation of autonomous driving planners on DETs.

cs.RO↗

arXiv2026

Anisotropic vortex motion and two-dimensional superconducting transition

Vortex motion plays a central role in determining the resistance of two-dimensional superconductors, both in the context of the Berezinskii-Kosterlitz-Thouless (BKT) transition and in the mixed state of type-II superconductors under magnetic fields. In this study, we introduce an anisotropic pinning potential to investigate vortex-induced resistance across the BKT transition and the upper critical field $H_{c2}$ transition. Our results demonstrate that the anisotropic pinning potential gives rise to distinct critical temperatures and upper critical fields along two orthogonal directions of current transport. These findings provide a general route toward the realization of multiple "critical temperatures" in two-dimensional superconductors.

cond-mat.supr-con↗

arXiv2026

Dual-Flow Reinforcement Learning with State-Aware Exploration

In complex continuous-control reinforcement learning tasks, multimodal optimal actions often coincide with uncertain, multimodal return distributions, making reliable value estimation and multimodal exploration challenging. Existing value estimation methods using unimodal Gaussians restrict expressiveness and yield biased estimates. Recent generative policies can represent multimodal actions but often collapse to a few modes and under-explore high-value areas of the action space. Motivated by these challenges, we propose Dual-Flow RL, a unified actor-critic framework that jointly models a continuous return distribution and a multimodal policy distribution using conditional flow matching (CFM). This design supports reliable value estimation and sustained multimodal exploration. To further enhance exploration, we introduce an Entropy-Covariance Exploration Regulator (ECER) that enables state-aware exploration regulation leveraging policy entropy and action-uncertainty covariance. Experiments on DeepMind Control Suite and Humanoid-Bench show that Dual-Flow RL achieves state-of-the-art performance on most tasks, significantly outperforming prior diffusion-based and flow-based methods.

cs.LG↗