arXiv subjects

Kun Jiang

Publications and source records attributed to Kun Jiang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Interband pairing as the origin of the sublattice dichotomy in monolayer FeSe/SrTiO_3

Sublattice dichotomy in monolayer FeSe/SrTiO$_3$, signaling the breaking of symmetries exchanging the two Fe sublattices, has recently been reported. We propose that interband pairing serves as the origin of this dichotomy, regardless of whether the symmetry is broken in the normal state or in the pairing state. If symmetry breaking occurs in the normal state, the Fermi surfaces are sublattice-polarized, and the intersublattice d-wave pairing naturally acts as interband pairing, reproducing the observed dichotomy in the spectra. Alternatively, if symmetry breaking takes place in the pairing state, it manifests as the coexistence of intraband and interband pairing, with the constraint that interband pairings share the same sign while intraband pairings carry opposite signs. In both cases, interband pairing is indispensable, establishing it as a key ingredient for understanding superconductivity in monolayer FeSe/SrTiO$_3$.

cond-mat.supr-con↗

arXiv2025

Sublattice Dichotomy in Monolayer FeSe Superconductor

The pairing mechanism behind the monolayer FeSe is one essential question for iron-based superconductors. In this work, we show the sublattice degree of freedoms of monolayer FeSe plays a special role in its pairing properties, namely the sublattice dichotomy. The high-quality monolayer FeSe samples with atomic flat $1\times1$ topography on the SrTiO$_3$(001) substrates are grown by molecular beam epitaxy. By comparing the tunneling spectra at $α$ and $β$ Fe sublattices, we find the coherence peak of $α$-Fe at the inner gap $+V_i$ is higher than $β$-Fe while the coherence peak of $β$-Fe at $-V_i$ is higher than $α$-Fe with a similar amount. We also observed a reversed effect at the outer gap $\pm V_o$. We propose the $η$-pairing mechanism between $k$ and $-k+Q$ is the key mechanism for this unconventional sublattice dichotomy effect.

cond-mat.supr-con↗

arXiv2025

Electronic structure of $A$V$_3$Sb$_5$ kagome metals

The kagome metals $A$V$_3$Sb$_5$ ($A=$ K, Cs, Rb) have become a fascinating materials platform following the discovery of many novel quantum states due to the interplay between electronic correlation, topology, and geometry. Understanding their physical origin requires constructing effective theories that capture the low-energy electronic structure and electronic interactions. While the band structure calculated by density functional theory (DFT) broadly agrees with experiments in the unbroken symmetry phase, the multiorbital nature challenges a proper understanding of the band structure and its description by tight-binding models. Here, we point out the unusual and puzzling properties of the DFT electronic structure, including the sublattice type of the van Hove singularities, the geometric shape of the Fermi surface, and the orbital content of the low-energy band dispersion, which cannot be described by the commonly used one-orbital or multiorbital kagome tight-binding models. We address these fundamental puzzles and develop an extended Slater-Koster formalism that can successfully resolve these issues. We discover the important role of site-symmetry and interorbital hopping structure and provide a concrete multiorbital tight-binding model description of the electronic structure for $A$V$_3$Sb$_5$ and the family of ``135'' compounds with other transition metals. This is a crucial step toward studying the effects of electron-electron interactions for the correlated and topological states in kagome metals and superconductors.

cond-mat.str-el↗

arXiv2025

The Mottness and the Anderson localization in bilayer nickelate La$_3$Ni$_2$O$_{7-δ}$

The oxygen content plays a pivotal role in determining the electronic and superconducting properties of the recently discovered La$_3$Ni$_2$O$_{7-δ}$ superconductors. In this work, we investigate the impact of oxygen vacancies on the insulating behavior of La$_3$Ni$_2$O$_{7-δ}$ across the doping range $δ= 0$ to $0.5$. At $δ= 0.5$, we construct a bilayer two-orbital Hubbard model to describe the system. Using dynamical mean-field theory, we demonstrate that the model captures the characteristics of a bilayer Mott insulator. To explore the effects of disorder within the range $δ= 0$ to $0.5$, we treat the system as a mixture of metallic and Mott insulating phases. By applying the dynamical cluster approximation and the typical medium dynamical cluster approximation, we identify an Anderson localization transition at a critical doping of $δ\sim 0.2$ through the geometric average of the local density of states. This Anderson localization transition is the key reason for the suppression of superconductivity in La$_3$Ni$_2$O$_{7-δ}$. These results provide a quantitative explanation of recent experimental observations and highlight the critical influence of oxygen content on the physical properties of La$_3$Ni$_2$O$_{7-δ}$.

cond-mat.str-el↗

arXiv2025

Intelligent Prediction and Optimization of Open-Hole Wellbore Multiphysics Stability: A Synergistic PINN-DRL Approach

To address the dual challenge of predicting multiphysics-induced instability and optimizing drilling fluid parameters for open-hole wellbores under long-term exposure, a high-fidelity system of coupled governing equations was developed. This system integrates seepage, hydration-induced softening, thermal diffusion, and elasto-plastic response to capture the nonlinear dynamics of wellbore stability evolution. A two-dimensional numerical model in a polar coordinate system was established using COMSOL Multiphysics to simulate multi-lithology and multi-parameter perturbations. This process generated a high-dimensional dataset characterizing the evolution of Von Mises stress, plastic strain, pore pressure, temperature, and water content, and its physical consistency was examined. Subsequently, the Seepage-Thermal-Water-Mechanical Physics-Informed Neural Network (STWM-PINN) is proposed. This model embeds governing equation residuals and initial-boundary constraints to achieve high-precision, physically consistent predictions of the wellbore's spatio-temporal evolution under the supervision of finite observational data, laying a foundation for parameter control. Building on this, a Double-Noise Soft Actor-Critic (DN-SAC) algorithm is integrated. A reward function was designed to minimize the probability of instability while considering control smoothness and physical boundary constraints, enabling continuous-space optimization of drilling fluid parameters. A case study demonstrates that the proposed method delays the onset of instability by an average of 32.33% and a maximum of 53.35%, significantly reducing instability risk. This study provides a decision-support framework with engineering application potential for intelligent wellbore instability prediction and drilling fluid control.

physics.geo-ph↗

arXiv2025

Anderson localization: a density matrix approach

Anderson localization is a quantum phenomenon in which disorder localizes electronic wavefunctions. In this work, we propose a new approach to study Anderson localization based on the density matrix formalism. Drawing an analogy to the standard transfer matrix method, we extract the localization length from the modular density matrix in quasi-one-dimensional systems. This approach successfully captures the metal-insulator transition in the three-dimensional Anderson model and in the two-dimensional Anderson model with spin-orbit coupling. It can be also readily extended to multiorbital systems. We further generalize the formalism to interacting systems, showing that the one-dimensional spinless attractive model exhibits the expected metallic phase, consistent with previous studies. More importantly, we demonstrate the existence of a two-dimensional metallic phase in the presence of Hubbard interactions and disorder. This method offers a new perspective on Anderson localization and its interplay with interactions.

cond-mat.dis-nn↗

arXiv2025

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving

Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce \textbf{AgentThink}, a pioneering unified framework that integrates Chain-of-Thought (CoT) reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. AgentThink's core innovations include: \textbf{(i) Structured Data Generation}, which establishes an autonomous driving tool library to automatically construct structured, self-verified reasoning data explicitly incorporating tool usage for diverse driving scenarios; \textbf{(ii) A Two-stage Training Pipeline}, employing Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to equip VLMs with the capability for autonomous tool invocation; and \textbf{(iii) Agent-style Tool-Usage Evaluation}, introducing a novel multi-tool assessment protocol to rigorously evaluate the model's tool invocation and utilization. Experiments on the DriveLMM-o1 benchmark demonstrate that AgentThink significantly boosts overall reasoning scores by \textbf{53.91%} and enhances answer accuracy by \textbf{33.54%}, while markedly improving reasoning quality and consistency. Furthermore, ablation studies and robust zero-shot/few-shot generalization experiments across various benchmarks underscore its powerful capabilities. These findings highlight a promising trajectory for developing trustworthy and tool-aware autonomous driving models. Code is available at https://github.com/curryqka/AgentThink.

cs.RO↗

arXiv2025

MTRDrive: Memory-Tool Synergistic Reasoning for Robust Autonomous Driving in Corner Cases

Vision-Language Models(VLMs) have demonstrated significant potential for end-to-end autonomous driving, yet a substantial gap remains between their current capabilities and the reliability necessary for real-world deployment. A critical challenge is their fragility, characterized by hallucinations and poor generalization in out-of-distribution (OOD) scenarios. To bridge this gap, we introduce MTRDrive, a novel framework that integrates procedural driving experiences with a dynamic toolkit to enhance generalization and proactive decision-making. MTRDrive addresses these limitations through a closed-loop system that combines a memory-based experience retrieval mechanism with dynamic toolkits. This synergy enables the model to interact more effectively with its environment, improving both reasoning and decision-making capabilities with the help of our memory-tool synergistic reasoning. Additionally, we introduce a new benchmark based on complex Roadwork construction scenarios to rigorously evaluate zero-shot generalization. Extensive experiments demonstrate the superior effectiveness of our approach. On the public NAVSIM benchmark, our 3B-parameter MTRDrive model achieves an exceptional PDMS of 88.3 without chain-of-thought and sets a state-of-the-art performance bar on high-level planning, with a driving metric score of 79.8\% and a planning accuracy of 82.6\%. Rigorous zero-shot evaluation on the new Roadwork-VLM benchmark shows a strong ability to reason robustly in unseen scenarios, achieving a driving metric score of 80.2\%. These results highlight MTRDrive's potential to advance autonomous driving toward safer and more reliable systems.

cs.RO↗

arXiv2025

RAVE: End-to-end Hierarchical Visual Localization with Rasterized and Vectorized HD map

Accurate localization serves as an important component in autonomous driving systems. Traditional rule-based localization involves many standalone modules, which is theoretically fragile and requires costly hyperparameter tuning, therefore sacrificing the accuracy and generalization. In this paper, we propose an end-to-end visual localization system, RAVE, in which the surrounding images are associated with the HD map data to estimate pose. To ensure high-quality observations for localization, a low-rank flow-based prior fusion module (FLORA) is developed to incorporate misaligned map prior into the perceived BEV features. Pursuing a balance among efficiency, interpretability, and accuracy, a hierarchical localization module is proposed, which efficiently estimates poses through a decoupled BEV neural matching-based pose solver (DEMA) using rasterized HD map, and then refines the estimation through a Transformer-based pose regressor (POET) using vectorized HD map. The experimental results demonstrate that our method can perform robust and accurate localization under varying environmental conditions while running efficiently.

cs.RO↗

arXiv2025

Recent progress in nickelate superconductors

The discovery of superconductivity in nickelate compounds has opened new avenues in the study of high-temperature superconductors. Here we provide a comprehensive overview of recent progress in the field, including all different nickelate systems, reduced-Ruddlesden-Popper-type infinite layer LaNiO$_2$, Ruddlesden-Popper-type bilayer La$_3$Ni$_2$O$_7$ and trilayer La$_4$Ni$_3$O$_{10}$. We begin by introducing the superconducting properties of the hole-doped LaNiO$_2$ system, which marked the starting point for nickelate superconductivity. We then turn to the bilayer La$_3$Ni$_2$O$_7$ system, discussing both its high-pressure and thin-film superconducting phases. This is followed by an examination of the trilayer La$_4$Ni$_3$O$_{10}$ system and other related multilayer nickelates. Throughout the review, we highlight emerging trends, key challenges, and open questions. We conclude by addressing current limitations in materials synthesis and characterization, and future directions that may help uncover the mechanisms driving superconductivity in these complex oxide systems.

cond-mat.supr-con↗

arXiv2025

Realistic and Controllable 3D Gaussian-Guided Object Editing for Driving Video Generation

Corner cases are crucial for training and validating autonomous driving systems, yet collecting them from the real world is often costly and hazardous. Editing objects within captured sensor data offers an effective alternative for generating diverse scenarios, commonly achieved through 3D Gaussian Splatting or image generative models. However, these approaches often suffer from limited visual fidelity or imprecise pose control. To address these issues, we propose G^2Editor, a framework designed for photorealistic and precise object editing in driving videos. Our method leverages a 3D Gaussian representation of the edited object as a dense prior, injected into the denoising process to ensure accurate pose control and spatial consistency. A scene-level 3D bounding box layout is employed to reconstruct occluded areas of non-target objects. Furthermore, to guide the appearance details of the edited object, we incorporate hierarchical fine-grained features as additional conditions during generation. Experiments on the Waymo Open Dataset demonstrate that G^2Editor effectively supports object repositioning, insertion, and deletion within a unified framework, outperforming existing methods in both pose controllability and visual quality, while also benefiting downstream data-driven tasks.

cs.CV↗

arXiv2025

Unveiling the landscape of Mottness and its proximity to superconductivity in 4Hb-TaS$_2$

Mott physics is at the root of a plethora of many-body quantum phenomena in quantum materials. Recently, the stacked or twisted structures of van der Waals (vdW) materials have emerged as a unique platform for realizing exotic correlated states in the vicinity of the Mott transition. However, the definitive feature of Mottness and how it rules the low-energy electronic state remain elusive and experimentally inaccessible in many interesting regimes. Here, we quantitatively describe a filling-controlled Mott state and its interplay with superconductivity by scanning tunnelling spectroscopy in a vdW bulk heterostructure, 4Hb-TaS$_2$, that interleaves strongly correlated 1T-TaS$_2$ layers with superconducting 1H-Ta$_2$ layers. The fine tunability of electron doping induced by interlayer charge transfer allows us to continuously track the spectral function with unsurpassed energy resolution from a depleted narrow band (0.2 electrons per site) toward a Mott transition at half filling. The gradually emerging Mott-Hubbard bands, followed by the sharpening and vanishing of the central quasiparticle peak as predicted in the Brinkman-Rice scenario, unambiguously demonstrate the Mott physics at play. Importantly, the renormalization of the low-energy electrons acts destructively on the superconducting pairing potential, leaving behind nonsuperconducting, paramagnetic puddles at the nanoscale. Our results reveal a seminal system near the border of the Mott criterion that enables us to illustrate the predictive power of the Hubbard model, and set such heterostructures as promising ground for realizing new correlated states in the heavily doped Mott regime.

cond-mat.supr-con↗

arXiv2025

EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving

Autonomous driving faces significant challenges in achieving human-like iterative decision-making, which continuously generates, evaluates, and refines trajectory proposals. Current generation-evaluation frameworks isolate trajectory generation from quality assessment, preventing iterative refinement essential for planning, while reinforcement learning methods collapse multi-dimensional preferences into scalar rewards, obscuring critical trade-offs and yielding scalarization bias.To overcome these issues, we present EvaDrive, a novel multi-objective reinforcement learning framework that establishes genuine closed-loop co-evolution between trajectory generation and evaluation via adversarial optimization. EvaDrive frames trajectory planning as a multi-round adversarial game. In this game, a hierarchical generator continuously proposes candidate paths by combining autoregressive intent modeling for temporal causality with diffusion-based refinement for spatial flexibility. These proposals are then rigorously assessed by a trainable multi-objective critic that explicitly preserves diverse preference structures without collapsing them into a single scalarization bias.This adversarial interplay, guided by a Pareto frontier selection mechanism, enables iterative multi-round refinement, effectively escaping local optima while preserving trajectory diversity.Extensive experiments on NAVSIM and Bench2Drive benchmarks demonstrate SOTA performance, achieving 94.9 PDMS on NAVSIM v1 (surpassing DiffusionDrive by 6.8, DriveSuprim by 5.0, and TrajHF by 0.9) and 64.96 Driving Score on Bench2Drive. EvaDrive generates diverse driving styles via dynamic weighting without external preference data, introducing a closed-loop adversarial framework for human-like iterative decision-making, offering a novel scalarization-free trajectory optimization approach.

cs.LG↗

arXiv2025

PriorFusion: Unified Integration of Priors for Robust Road Perception in Autonomous Driving

With the growing interest in autonomous driving, there is an increasing demand for accurate and reliable road perception technologies. In complex environments without high-definition map support, autonomous vehicles must independently interpret their surroundings to ensure safe and robust decision-making. However, these scenarios pose significant challenges due to the large number, complex geometries, and frequent occlusions of road elements. A key limitation of existing approaches lies in their insufficient exploitation of the structured priors inherently present in road elements, resulting in irregular, inaccurate predictions. To address this, we propose PriorFusion, a unified framework that effectively integrates semantic, geometric, and generative priors to enhance road element perception. We introduce an instance-aware attention mechanism guided by shape-prior features, then construct a data-driven shape template space that encodes low-dimensional representations of road elements, enabling clustering to generate anchor points as reference priors. We design a diffusion-based framework that leverages these prior anchors to generate accurate and complete predictions. Experiments on large-scale autonomous driving datasets demonstrate that our method significantly improves perception accuracy, particularly under challenging conditions. Visualization results further confirm that our approach produces more accurate, regular, and coherent predictions of road elements.

cs.CV↗

arXiv2025

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic

Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analyzing traffic patterns have gained traction due to their applicability to city-scale policy development. However, conventional methodologies often rely on fragmented grid-based techniques, which may overlook critical interdependencies among spatial elements and temporal continuity. These limitations can compromise analytical effectiveness in complex urban environments. To address these challenges, we propose PriorMotion, a data integration framework designed to systematically uncover movement patterns through driving dynamics analysis. Our approach combines multi-scale empirical observations with customized analytical tools to capture evolving spatial-temporal trends in urban traffic. Comprehensive evaluations demonstrate that PriorMotion significantly enhances analytical outcomes, including increased accuracy in traffic pattern analysis, improved adaptability to heterogeneous data environments, and reduced long-term projection errors. Validation confirms its effectiveness for urban infrastructure management applications requiring precise characterization of complex spatial-temporal interactions.

cs.CV↗

arXiv2025

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems

In our previous work, we proposed a novel neuron model based on symmetric differential equations and demonstrated its potential as an efficient signal propagator. Building upon that foundation, the present study delves deeper into the intrinsic dynamics and functional diversity of this model. By systematically exploring the parameter space and employing a range of mathematical analysis tools, we theoretically reveal the system 's core property of functional duality. Specifically, the model exhibits two distinct trajectory behaviors: one is asymptotically stable, corresponding to a reliable signal propagator; the other is Lyapunov stable, characterized by sustained self-excited oscillations, functioning as a signal generator. To enable effective monitoring and prediction of system states during simulations, we introduce a novel intermediate-state metric termed on-road energy. Simulation results confirm that transitions between the two functional modes can be induced through parameter adjustments or modifications to the connection structure. Moreover, we show that oscillations can be effectively suppressed by introducing external signals. These findings draw a compelling parallel to the dual roles of biological neurons in both information transmission and rhythm generation, thereby establishing a solid theoretical basis and a clear functional roadmap for the broader application of this model in neuromorphic engineering.

cs.NE↗

arXiv2025

Multimodal HD Mapping for Intersections by Intelligent Roadside Units

High-definition (HD) semantic mapping of complex intersections poses significant challenges for traditional vehicle-based approaches due to occlusions and limited perspectives. This paper introduces a novel camera-LiDAR fusion framework that leverages elevated intelligent roadside units (IRUs). Additionally, we present RS-seq, a comprehensive dataset developed through the systematic enhancement and annotation of the V2X-Seq dataset. RS-seq includes precisely labelled camera imagery and LiDAR point clouds collected from roadside installations, along with vectorized maps for seven intersections annotated with detailed features such as lane dividers, pedestrian crossings, and stop lines. This dataset facilitates the systematic investigation of cross-modal complementarity for HD map generation using IRU data. The proposed fusion framework employs a two-stage process that integrates modality-specific feature extraction and cross-modal semantic integration, capitalizing on camera high-resolution texture and precise geometric data from LiDAR. Quantitative evaluations using the RS-seq dataset demonstrate that our multimodal approach consistently surpasses unimodal methods. Specifically, compared to unimodal baselines evaluated on the RS-seq dataset, the multimodal approach improves the mean Intersection-over-Union (mIoU) for semantic segmentation by 4\% over the image-only results and 18\% over the point cloud-only results. This study establishes a baseline methodology for IRU-based HD semantic mapping and provides a valuable dataset for future research in infrastructure-assisted autonomous driving systems.

cs.RO↗

arXiv2025

A Survey on Vision-Language-Action Models for Autonomous Driving

The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language understanding, and control within a single policy. Researchers in autonomous driving are actively adapting these methods to the vehicle domain. Such models promise autonomous vehicles that can interpret high-level instructions, reason about complex traffic scenes, and make their own decisions. However, the literature remains fragmented and is rapidly expanding. This survey offers the first comprehensive overview of VLA for Autonomous Driving (VLA4AD). We (i) formalize the architectural building blocks shared across recent work, (ii) trace the evolution from early explainer to reasoning-centric VLA models, and (iii) compare over 20 representative models according to VLA's progress in the autonomous driving domain. We also consolidate existing datasets and benchmarks, highlighting protocols that jointly measure driving safety, accuracy, and explanation quality. Finally, we detail open challenges - robustness, real-time efficiency, and formal verification - and outline future directions of VLA4AD. This survey provides a concise yet complete reference for advancing interpretable socially aligned autonomous vehicles. Github repo is available at \href{https://github.com/JohnsonJiang1996/Awesome-VLA4AD}{SicongJiang/Awesome-VLA4AD}.

cs.CV↗