arXiv subjects

Lingxiao Zhao

Publications and source records attributed to Lingxiao Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Pressure induced magnetic-field-free superconducting diode effect in NbSe2 flake

The superconducting diode effect (SDE) is a fascinating nonreciprocal phenomenon where the critical current is different for opposite current directions. It is widely believed that realizing SDE requires breaking both inversion symmetry (IS) and time-reversal symmetry (TRS), which are usually achieved via heterostructure engineering and applying external magnetic fields. Here, we report a pressure-induced magnetic-field-free SDE in NbSe2 flakes without any heterostructures. We show that pressure alone breaks the IS, as confirmed by the second harmonic generation. Crucially, upon applying an out-of-plane magnetic field (B), the SDE exhibits even-in-B behavior, implying the absence of explicit TRS breaking. This finding challenges the prevailing theoretical paradigm and demonstrates that a magnetic-field-free SDE can emerge without explicitly breaking TRS. Thereby, our work establishes pressure engineering as a powerful tool for inducing nonreciprocal superconductivity and designing versatile, magnetic-field-free superconducting devices.

cond-mat.supr-con↗

arXiv2026

Pressure-induced concurrent amorphization and superconductivity in topological material NbNiTe5

We have systematically studied the structural and electronic properties of a topological material NbNiTe5 under high pressure. The evolution of the normal state resistance shows a non-monotonic trend from 0.7 GPa to 5.1 GPa, in accordance with the second-order transition along the inter-layer direction observed in X-ray diffraction and Raman spectra. At around 10 GPa, the sample starts amorphization, which is concurrent with the emergence of superconductivity. Upon further compression, the structural disorder enhances and the superconducting transition becomes clearer, suggesting that the superconductivity is modulated by the degree of disorder in NbNiTe5 under high pressure. Within 45.7 GPa, the superconducting transition temperature (Tc) slowly rises from 0.6 K at 9.5 GPa to 1.4 K at 45.7 GPa. Our findings extend the family of transition metal chalcogenide superconductors and shed new light on understanding superconductivity in disordered systems.

cond-mat.supr-con↗

arXiv2026

Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis

Developing robust artificial intelligence models for 4D (3D + time) medical imaging is constrained by limited annotated data, inter-device domain shifts, and privacy restrictions. To address this, we propose a 4D controllable generative framework for anatomically consistent data augmentation. A semi-supervised variational autoencoder learns a compact latent representation of anatomical volumes while jointly predicting aligned segmentation masks in a unified framework. Anatomical structure is then disentangled from temporal dynamics through a cascaded latent diffusion model (LDM). A static LDM generates subject-specific anatomy conditioned on clinical priors (diagnosis and volumes measures) and a subsequent motion LDM estimates residual latent motions, ensuring strict temporal coherence across the 4D sequence. The proposed approach was evaluated on cine cardiac MRI as a representative 4D imaging application. Experiments across multiple datasets demonstrate high controllability of static anatomy (Pearson r > 0.8) and strong temporal coherence (FVD = 288.08). In cross-vendor generalization experiments, augmenting training sets with synthetic 4D sequences significantly improves downstream segmentation performance. Using nnU-Net, the proposed augmentation strategy improves the average Dice score by 1.4% and reduces the Hausdorff Distance by 3.0mm compared to training on real data alone, for the left ventricle, Dice improves by 2.8% with a 5.4mm reduction in boundary error. Overall, this framework provides a scalable and controllable solution for 4D medical image synthesis, supporting the development of more robust models with limited annotations and cross-vendor variability. Code available on https://github.com/cyiheng/4DCardiacMRISynthesis.

cs.CV↗

arXiv2026

Multiband transport hierarchy and large Nernst effect in EuAuBi: Establishing a Nernst scaling for asymmetric multiband systems

In correlated materials, coexisting pockets of vastly different carrier densities raise two fundamental questions: which pocket governs the various transport coefficients, and does the conventional Nernst scaling $ν/T \propto μ/E_F$, originally derived for single-band systems, still hold? We address both questions in the polar semimetal EuAuBi, where a dilute electron pocket ($n_e \sim 10^{16}~\mathrm{cm}^{-3}$) coexists with a dense hole pocket ($n_h \sim 10^{21}~\mathrm{cm}^{-3}$). We find a clear hierarchy: the hole pocket dominates the longitudinal resistivity; the Hall effect crosses from electron- to hole-dominance with increasing field; the Seebeck coefficient is dominated by the electron pocket at low temperature and by both pockets at high temperature. Remarkably, the Nernst effect is governed entirely by the ultrahigh-mobility electron pocket, yielding a large low-field signal of $\sim 5~μ\mathrm{V/K}$ near 1~T at 202~K, comparable to anomalous Nernst signals in magnetic Weyl semimetals. By analyzing the two-band thermoelectric conductivity, we show that the Nernst coefficient follows a scaling $ν/T \propto μ_e/{E_{F, tot}}$. This scaling originates from a compensation between the electron-to-hole conductivity ratio and the Fermi-energy ratio, establishing that the large Nernst effect is a semiclassical multiband phenomenon rather than a topological Berry-curvature contribution. This understanding advances the thermoelectric transport physics of multiband electronic systems and offers a guiding principle for low-field transverse thermoelectric design.

cond-mat.str-el↗

arXiv2026

StreamAgent: Towards Anticipatory Agents for Streaming Video Understanding

Real-time streaming video understanding in domains such as autonomous driving and intelligent surveillance poses challenges beyond conventional offline video processing, requiring continuous perception, proactive decision making, and responsive interaction based on dynamically evolving visual content. However, existing methods rely on alternating perception-reaction or asynchronous triggers, lacking task-driven planning and future anticipation, which limits their real-time responsiveness and proactive decision making in evolving video streams. To this end, we propose a StreamAgent that anticipates the temporal intervals and spatial regions expected to contain future task-relevant information to enable proactive and goal-driven responses. Specifically, we integrate question semantics and historical observations through prompting the anticipatory agent to anticipate the temporal progression of key events, align current observations with the expected future evidence, and subsequently adjust the perception action (e.g., attending to task-relevant regions or continuously tracking in subsequent frames). To enable efficient inference, we design a streaming KV-cache memory mechanism that constructs a hierarchical memory structure for selective recall of relevant tokens, enabling efficient semantic retrieval while reducing the overhead of storing all tokens in the traditional KV-cache. Extensive experiments on streaming and long video understanding tasks demonstrate that our method outperforms existing methods in response accuracy and real-time efficiency, highlighting its practical value for real-world streaming scenarios.

cs.CV↗

arXiv2026

Hydrodynamics of the viscous electron fluid in cadmium

Thanks to electron-electron ($e$-$e$) collisions conserving momentum, metallic electron fluids are viscous. Yet, this viscosity is rarely detectable in bulk transport. Here, we report on the canonical realization of the Gurzhi effect in an elemental three-dimensional metal: cadmium. Using focused ion beam microstructuring to tune the effective thickness, we detected a low-temperature size-dependent resistivity upturn in a finite window sandwiched between ballistic and diffusive regimes. Within this window, the electrical conductivity displays a simultaneous quadratic dependence on both sample size and temperature -- fingerprint of a hydrodynamic flow. This leads us to quantify the amplitude and the temperature dependence of kinematic and dynamic viscosity of the electron fluid. In cadmium, in contrast with graphene and $^3$He, the rate of momentum-conserving $e$-$e$ collisions is not set by the main Fermi energy, but by Lilliputian energy scales and inter-valley bottlenecks.

cond-mat.str-el↗

arXiv2026

Voxtral Realtime

We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adapt offline models through chunking or sliding windows, Voxtral Realtime is trained end-to-end for streaming, with explicit alignment between audio and text streams. Our architecture builds on the Delayed Streams Modeling framework, introducing a new causal audio encoder and Ada RMS-Norm for improved delay conditioning. We scale pretraining to a large-scale dataset spanning 13 languages. At a delay of 480ms, Voxtral Realtime achieves performance on par with Whisper, the most widely deployed offline transcription system. We release the model weights under the Apache 2.0 license.

cs.AI↗

arXiv2026

Voxtral TTS

We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens. These tokens are encoded and decoded with Voxtral Codec, a speech tokenizer trained from scratch with a hybrid VQ-FSQ quantization scheme. In human evaluations conducted by native speakers, Voxtral TTS is preferred for multilingual voice cloning due to its naturalness and expressivity, achieving a 68.4\% win rate over ElevenLabs Flash v2.5. We release the model weights under a CC BY-NC license.

cs.AI↗

arXiv2026

Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning

LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify such issues, yet fail to reflect this in standard generative responses. This reveals a fundamental know-act gap between discriminative recognition and generative behavior. Prior work largely characterizes this issue in narrow settings, such as math word problems or question answering, with limited focus on how to integrate these two modes. In this work, we present a comprehensive analysis using FaultyScience, a newly constructed large-scale, cross-disciplinary benchmark of faulty scientific questions. We show that the gap is pervasive and stems from token-level autoregression, which entangles task selection (validate vs. answer) with content generation, preventing discriminative knowledge from being utilized. To address this, we propose DeIllusionLLM, a task-level autoregressive framework that explicitly models this decision. Through self-distillation, the model unifies discriminative judgment and generative reasoning within a single backbone. Empirically, DeIllusionLLM substantially reduces answer-despite-error failures under natural prompting while maintaining general reasoning performance, demonstrating that self-distillation is an effective and scalable solution for bridging the discriminative-generative know-act gap

cs.AI↗

arXiv2026

Spin-density-wave transition in monolayer-trilayer La3Ni2O7 single crystals

The recent discovery of high-temperature superconductivity in pressurized Ruddlesden-Popper nickelates stimulated intense research into their correlated electron physics. Establishing the diversity of ground states across different Ruddlesden-Popper phases is crucial for elucidating the superconducting mechanisms in these nickelates. Motivated by the recent report of superconductivity in hybrid 1212-type La5Ni3O11, we synthesized and investigated the long-range-ordered hybrid 1313-type La3Ni2O7. In contrast to its bilayer counterpart, the 1313-type La3Ni2O7 exhibits characteristic semiconducting behavior at ambient pressure, displaying a distinct anomaly at 170 K. This behavior is consistently evidenced by measurements of both magnetic susceptibility and specific heat. Nuclear magnetic resonance spectroscopy unambiguously indicates a spin-density-wave transition occurring at 170 K. High-pressure electrical transport measurements demonstrate the induction of metallization under pressure, yet reveal no discernible traces of superconductivity up to 65 GPa. Our findings establish hybrid 1313-type La3Ni2O7 as a new member of the Ruddlesden-Popper nickelate family exhibiting a distinct spin-density-wave transition, and offers a new platform for investigating the interplay among crystal structure, electronic orders, and superconductivity in hybrid nickelates.

cond-mat.supr-con↗

arXiv2026

Weakly anisotropic superconductivity of Pr4Ni3O10 single crystals

Since the discovery of high-temperature superconductivity, studying the upper critical field and its anisotropy has been crucial for understanding superconducting mechanism and guiding applications. Here we perform in situ high-pressure angular-dependent electrical transport measurements on Pr4Ni3O10 single crystals using a custom diamond anvil cell (DAC) rotator and confirming its anisotropic superconductivity. The anisotropy parameter is approximately 1.6, decreasing with increasing temperature and approaches 1 near Tc. Comparing effective mass anisotropy and inter-block distance in cuprates and iron-based superconductors (FeSCs) reveals that Pr4Ni3O10 single crystals superconductors are consistent with a two-band model, where intralayer quantum confinement within the unit cell induces interlayer coherence, thereby leading to three-dimensional (3D) superconductivity. This study not only establishes the existence of anisotropic superconductivity in bulk Ruddlesden-Popper nickelates, but also provide critical insight into the role of dimensionality in high-temperature superconductivity.

cond-mat.supr-con↗

arXiv2026

Ministral 3

We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes: 3B, 8B, and 14B parameters. For each model size, we release three variants: a pretrained base model for general-purpose use, an instruction finetuned, and a reasoning model for complex problem-solving. In addition, we present our recipe to derive the Ministral 3 models through Cascade Distillation, an iterative pruning and continued training with distillation technique. Each model comes with image understanding capabilities, all under the Apache 2.0 license.

cs.CL↗

arXiv2025

Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing

Multimodal large language models (MLLMs) extend LLMs with visual understanding through a three-stage pipeline: multimodal preprocessing, vision encoding, and LLM inference. While these stages enhance capability, they introduce significant system bottlenecks. First, multimodal preprocessing-especially video decoding-often dominates Time-to-First-Token (TTFT). Most systems rely on CPU-based decoding, which severely limits throughput, while existing GPU-based approaches prioritize throughput-oriented parallelism and fail to meet the latency-sensitive requirements of MLLM inference. Second, the vision encoder is a standalone, compute-intensive stage that produces visual embeddings and cannot be co-batched with LLM prefill or decoding. This heterogeneity forces inter-stage blocking and increases token-generation latency. Even when deployed on separate GPUs, these stages underutilize available compute and memory resources, reducing overall utilization and constraining system throughput. To address these challenges, we present FlashCodec and UnifiedServe, two complementary designs that jointly optimize the end-to-end MLLM pipeline. FlashCodec accelerates the multimodal preprocessing stage through collaborative multi-GPU video decoding, reducing decoding latency while preserving high throughput. UnifiedServe optimizes the vision-to-text and inference stages using a logically decoupled their execution to eliminate inter-stage blocking, yet physically sharing GPU resources to maximize GPU system utilization. By carefully orchestrating execution across stages and minimizing interference, UnifiedServe Together, our proposed framework forms an end-to-end optimized stack that can serve up to 3.0$\times$ more requests or enforce 1.5$\times$ tighter SLOs, while achieving up to 4.4$\times$ higher throughput compared to state-of-the-art systems.

cs.DC↗

arXiv2025

Scalable Sondheimer oscillations driven by commensurability between two quantizations

The electrical conductivity of metallic crystals exhibits size effects when the electron mean free path exceeds the sample thickness. One such phenomenon, known as Sondheimer oscillations, was discovered decades ago. These oscillations, periodic in magnetic field, have been hitherto treated with no reference to Landau quantization. Here, we present a study of longitudinal and transverse conductivity in cadmium single crystals with thicknesses ranging from 12.6 to 475 $μ$m, and demonstrate that the amplitude of the first ten oscillations is determined by the quantum of conductance and a length scale that depends on the sample thickness, the magnetic length and the Fermi surface geometry. We argue that this scaling is unexpected in semiclassical scenarios and it arises from the degeneracy of the momentum derivative of the cross-sectional area $A$ along the orientation of the magnetic field $\frac{\partial A}{\partial k_z}$ in cadmium, which couples Landau quantization to the discretization of $k_z$ imposed by the finite sample thickness. We show that the oscillating component of the conductivity is uniquely governed by fundamental constants and the ratio of two degeneracies, which acts as an inverted filling factor. Our conjecture is supported by the absence of such scaling in thin copper crystals.

cond-mat.mes-hall↗

arXiv2025

Experimental Realization of the Topologically Nontrivial Phase in Monolayer Si$_2$Te$_2$

The free-standing monolayer Si$_2$Te$_2$ (ML-Si$_2$Te$_2$) has been theoretically predicted to host a room-temperature quantum spin Hall phase. However, its experimental realization remains challenge due to the absence of a three-dimensional counterpart. Here, we demonstrate that HfTe$_2$ serves as an ideal substrate for the epitaxial growth of ML-Si$_2$Te$_2$, preserving its topological phase. Scanning tunneling microscopy and spectroscopy confirm a strain-free ${(1 \times 1)}$ lattice of ML-Si$_2$Te$_2$, along with a sizable band gap, which is well captured by first-principles calculations. Moreover, distinct edge states, independent of step geometry and exhibiting a broad spatial distribution, are observed at ML-Si$_2$Te$_2$ step edges, underscoring its topological nature.

cond-mat.mtrl-sci↗

arXiv2025

Devstral: Fine-tuning Language Models for Coding Agent Applications

We introduce Devstral-Small, a lightweight open source model for code agents with the best performance among models below 100B size. In this technical report, we give an overview of how we design and develop a model and craft specializations in agentic software development. The resulting model, Devstral-Small is a small 24B model, fast and easy to serve. Despite its size, Devstral-Small still attains competitive performance compared to models more than an order of magnitude larger.

cs.SE↗

arXiv2025

Self-Tuning Self-Supervised Image Anomaly Detection

Self-supervised learning (SSL) has emerged as a promising paradigm that presents supervisory signals to real-world problems, bypassing the extensive cost of manual labeling. Consequently, self-supervised anomaly detection (SSAD) has seen a recent surge of interest, since SSL is especially attractive for unsupervised tasks. However, recent works have reported that the choice of a data augmentation function has significant impact on the accuracy of SSAD, posing augmentation search as an essential but nontrivial problem due to lack of labeled validation data. In this paper, we introduce ST-SSAD, the first unsupervised approach to end-to-end augmentation tuning for SSAD. To this end, our work presents two key contributions. The first is a new unsupervised validation loss that quantifies the alignment between augmented training data and unlabeled validation data. The second is new differentiable augmentation functions, allowing data augmentation hyperparameter(s) to be tuned in an end-to-end manner. Experiments on two testbeds with semantic class anomalies and subtle industrial defects show that ST-SSAD gives significant performance gains over existing works. All our code and testbeds are available at https://github.com/jaeminyoo/ST-SSAD.

cs.LG↗

arXiv2025

Sizable superconducting gap and anisotropic chiral topological superconductivity in the Weyl semimetal PtBi$_2$

Topological superconductors offer a fertile ground for realizing Majorana zero modes -- topologically protected, zero-energy quasiparticles that are resilient to local perturbations and hold great promise for fault-tolerant quantum computing. Recent studies have presented encouraging evidence for intrinsic topological superconductivity in the Weyl semimetal trigonal PtBi$_2$, hinting at a robust surface phase potentially stable beyond the McMillan limit. However, due to substantial spatial variations in the observed superconducting (SC) gap $Δ$ the nature of the underlying order parameter $Δ$($k$) remained under debate. Here we report the realization of sizable surface SC gaps ($Δ> 10\,\mathrm{meV}$) in PtBi$_2$, exhibiting remarkable spatial uniformity from hundreds of nanometers down to the atomic level, as revealed by scanning tunneling microscopy and spectroscopy. Building on this spatial homogeneity -- indicative of long-range phase coherence -- we uncover previously unobserved low-energy Andreev bound states (ABSs) that ubiquitously emerge within the SC gap across the surface. Theoretical simulations that closely reproduce the experimental spectra, reveal an anisotropic chiral pairing symmetry of $Δ$($k$), and further suggest that the observed ABSs are of topological origin. The combination of a large, nontrivial pairing gap and accessible surface states establishes PtBi$_2$ as a compelling platform for investigating topological superconductivity and its associated Majorana modes.

cond-mat.supr-con↗