arXiv subjects

Mengwei Xie

Publications and source records attributed to Mengwei Xie.

8 recordsLinked to original sources

arXiv2026

Infinite Magnetoresistance and Vortex Coupling in the Pb/BSCCO Heterostructure

Combining superconductivity with spintronics provides exciting opportunities to realize low-dissipation quantum devices. Here we report the synthesis, characterization and magnetotransport measurements of the Pb/Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (BSCCO) superconducting heterostructures, where an insulating PbO$_{x}$ layer spontaneously forms at the interface. Non-volatile switching between superconducting (logical "0") and normal ("1") states in Pb films by an external field, i.e., infinite magnetoresistance (IMR), can be realized and are attributed to the strong trapping and pinning of vortices in BSCCO. Furthermore, butterfly-shaped hysteresis loops in magnetoresistance, pronounced resistance dips/jumps and thermal reset to superconducting states can be observed and are direct manifestations of the peculiar vortex dynamics in BSCCO and vortex coupling across the Pb/BSCCO interface. Our work demonstrates a simple and effective way to realize IMR through superconducting vortices and opens up new opportunities to study the vortex interactions across the superconducting interfaces.

cond-mat.supr-con↗

arXiv2026

Multigap nodeless superconductivity in Dirac semimetal PdTe

PdTe has recently been reported to be a type-II Dirac semimetal while a bulk nodal and surface nodeless superconductivity (SC) has been claimed to coexist. In this work, we applied point-contact spectroscopy (PCS) method to systematically study the superconducting gap in PdTe single crystals with a SC transition temperature $T_{c}=4.3$ K. The obtained differential conductance curves show a common deviation from a single-gap superconducting behavior and can be better fitted by a two-gap Blonder-Tinkham-Klapwijk model, suggesting the larger gap $\Delta_{L}$ with $2\Delta_{L}$=3.7 $k_{B}T_{c}$ and the smaller gap $\Delta_S$ yielding $2\Delta_{S}$=1.1-2.2 $k_{B}T_{c}$ with a weak interband scattering. The variations of conductance spectra among different contacts are proposed to be caused by the anisotropy of Fermi surface topology associated with different gaps.

cond-mat.supr-con↗

arXiv2025

AMap: Distilling Future Priors for Ahead-Aware Online HD Map Construction

Online High-Definition (HD) map construction is pivotal for autonomous driving. While recent approaches leverage historical temporal fusion to improve performance, we identify a critical safety flaw in this paradigm: it is inherently ``spatially backward-looking." These methods predominantly enhance map reconstruction in traversed areas, offering minimal improvement for the unseen road ahead. Crucially, our analysis of downstream planning tasks reveals a severe asymmetry: while rearward perception errors are often tolerable, inaccuracies in the forward region directly precipitate hazardous driving maneuvers. To bridge this safety gap, we propose AMap, a novel framework for Ahead-aware online HD Mapping. We pioneer a ``distill-from-future" paradigm, where a teacher model with privileged access to future temporal contexts guides a lightweight student model restricted to the current frame. This process implicitly compresses prospective knowledge into the student model, endowing it with ``look-ahead" capabilities at zero inference-time cost. Technically, we introduce a Multi-Level BEV Distillation strategy with spatial masking and an Asymmetric Query Adaptation module to effectively transfer future-aware representations to the student's static queries. Extensive experiments on the nuScenes and Argoverse 2 benchmark demonstrate that AMap significantly enhances current-frame perception. Most notably, it outperforms state-of-the-art temporal models in critical forward regions while maintaining the efficiency of single current frame inference.

cs.CV↗

arXiv2025

EagleVision: A Dual-Stage Framework with BEV-grounding-based Chain-of-Thought for Spatial Intelligence

Video-based spatial reasoning -- such as estimating distances, judging directions, or understanding layouts from multiple views -- requires selecting informative frames and, when needed, actively seeking additional viewpoints during inference. Existing multimodal large language models (MLLMs) consume a fixed set of uniformly sampled frames and cannot request new views once reasoning begins, often missing the geometric cues necessary for reliable spatial judgments. We present EagleVision, a dual-stage framework that combines geometry-aware frame selection with active, Bird's-Eye-View (BEV)-grounded reasoning. In the first stage (macro perception), a semantics-perspective-fusion determinantal point process (SPF-DPP) selects a compact set of keyframes that jointly maximize semantic relevance and viewpoint diversity under a fixed token budget. In the second stage (micro verification), the model performs iterative spatial Chain-of-Thought: at each step it can either reason in text or predict a pose on the BEV plane to retrieve the nearest real frame, forming a closed-loop hypothesize-look-verify cycle. The querying policy is trained purely via reinforcement learning with a spatial grounding reward, requiring no human-annotated reasoning traces. On VSI-Bench and SQA3D, EagleVision achieves state-of-the-art performance among open-source vision-language models.

cs.CV↗

arXiv2025

Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Maps

Lane-level navigation is critical for geographic information systems and navigation-based tasks, offering finer-grained guidance than road-level navigation by standard definition (SD) maps. However, it currently relies on expansive global HD maps that cannot adapt to dynamic road conditions. Recently, online perception (OP) maps have become research hotspots, providing real-time geometry as an alternative, but lack the global topology needed for navigation. To address these issues, Online Navigation Refinement (ONR), a new mission is introduced that refines SD-map-based road-level routes into accurate lane-level navigation by associating SD maps with OP maps. The map-to-map association to handle many-to-one lane-to-road mappings under two key challenges: (1) no public dataset provides lane-to-road correspondences; (2) severe misalignment from spatial fluctuations, semantic disparities, and OP map noise invalidates traditional map matching. For these challenges, We contribute: (1) Online map association dataset (OMA), the first ONR benchmark with 30K scenarios and 2.6M annotated lane vectors; (2) MAT, a transformer with path-aware attention to aligns topology despite spatial fluctuations and semantic disparities and spatial attention for integrates noisy OP features via global context; and (3) NR P-R, a metric evaluating geometric and semantic alignment. Experiments show that MAT outperforms existing methods at 34 ms latency, enabling low-cost and up-to-date lane-level navigation.

cs.CV↗

arXiv2025

SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions

Accurate lane topology is essential for autonomous driving, yet traditional methods struggle to model the complex, non-linear structures-such as loops and bidirectional lanes-prevalent in real-world road structure. We present SeqGrowGraph, a novel framework that learns lane topology as a chain of graph expansions, inspired by human map-drawing processes. Representing the lane graph as a directed graph $G=(V,E)$, with intersections ($V$) and centerlines ($E$), SeqGrowGraph incrementally constructs this graph by introducing one vertex at a time. At each step, an adjacency matrix ($A$) expands from $n \times n$ to $(n+1) \times (n+1)$ to encode connectivity, while a geometric matrix ($M$) captures centerline shapes as quadratic B\'ezier curves. The graph is serialized into sequences, enabling a transformer model to autoregressively predict the chain of expansions, guided by a depth-first search ordering. Evaluated on nuScenes and Argoverse 2 datasets, SeqGrowGraph achieves state-of-the-art performance.

cs.CV↗

arXiv2025

FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving

Vision-Language-Action (VLA) models offer significant potential for end-to-end driving, yet their reasoning is often constrained by textual Chains-of-Thought (CoT). This symbolic compression of visual information creates a modality gap between perception and planning by blurring spatio-temporal relations and discarding fine-grained cues. We introduce FSDrive, a framework that empowers VLAs to "think visually" using a novel visual spatio-temporal CoT. FSDrive first operates as a world model, generating a unified future frame that combines a predicted background with explicit, physically-plausible priors like future lane dividers and 3D object boxes. This imagined scene serves as the visual spatio-temporal CoT, capturing both spatial structure and temporal evolution in a single representation. The same VLA then functions as an inverse-dynamics model to plan trajectories conditioned on current observations and this visual CoT. We enable this with a unified pre-training paradigm that expands the model's vocabulary with visual tokens and jointly optimizes for semantic understanding (VQA) and future-frame prediction. A progressive curriculum first generates structural priors to enforce physical laws before rendering the full scene. Evaluations on nuScenes and NAVSIM show FSDrive improves trajectory accuracy and reduces collisions, while also achieving competitive FID for video generation with a lightweight autoregressive model and advancing scene understanding on DriveLM. These results confirm that our visual spatio-temporal CoT bridges the perception-planning gap, enabling safer, more anticipatory autonomous driving. Code is available at https://github.com/MIV-XJTU/FSDrive.

cs.CV↗

arXiv2024

Distinct pressure evolution of superconductivity and charge-density-wave in kagome superconductor CsV$_3$Sb$_5$ thin flakes

It is intriguing to explore the coexistence and (or) competition between charge-density-wave (CDW) and superconductivity (SC) in many correlated electron systems, such as cuprates, organic superconductors and dichacolgenides. Among them, the recently discovered $\mathbb{Z} _2$ topological kagome metals AV$_3$Sb$_5$ (A=K, Rb, Cs) serve as an ideal platform to study the intricate relation between them. Here, we report the electrical resistance measurements on CsV$_3$Sb$_5$ thin flakes ($\approx$ 60 nm) under hydrostatic pressure up to 2.12 GPa to compare its pressure phase diagram of CDW and SC with its bulk form. Even though the CDW transition temperature (T$_{CDW}$) in CsV$_3$Sb$_5$ thin flakes is still monotonically suppressed under pressure and totally vanishes at P$_2$=1.83 GPa similar to the bulk, the superconducting transition temperature (T$_c$) shows an initial decrease and consequent increase up to its maximum $\sim$ 8.03 K at P$_2$, in sharp contrast with the M-shaped double domes in the bulk CsV$_3$Sb$_5$. Our results suggest the important role of reduced dimensionality on the CDW state and its interplay with the SC, offering a new perspective to explore the exotic nature of CsV$_3$Sb$_5$.

cond-mat.supr-con↗