arXiv subjects

Li Lu

Publications and source records attributed to Li Lu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Observation of sequential quantum oscillations induced by mini-Landau bands in a three-dimensional Dirac semiconductor

Quantum oscillations, the oscillatory behavior of electrical and thermodynamic properties, are typically observed in metals and vanish in the quantum limit under strong magnetic fields1. Phenomena such as the fractional quantum Hall effect2, the Hofstadter butterfly3,4, and recent observations of quantum oscillations in exotic insulators are notable exceptions5-12. The narrow-gap Dirac semiconductor ZrTe5, a less exotic material without strong correlations or artificially engineered superlattices, nevertheless exhibits resistance oscillations in the quantum limit13 but can be interpreted within a simple Zeeman-effect-based picture14,15, which remains conventional quantum oscillations without exotic properties. Here, we report the observation of unexpected mini-oscillations superimposed on Zeeman-effect-induced main oscillations in the quantum limit. The subtracted mini-oscillations are periodic in 1/B with the highest frequency equal to 2.1% of the first Brillouin zone and have extremely heavy effective mass ~ 2me, which is unexpected in ZrTe5 given its ultralow carrier density. Additionally, the mini-oscillations exhibit sequential features that are synchronized with the main oscillations, suggesting an internal structure of the Landau bands. However, they appear incompatible with the Hofstadter butterfly due to the highly anisotropic/three-dimensional crystal structure. These sequential mini-oscillations correlate with the commensurability resonance effect with subunity fractions observed in angular magnetoresistance, relating to the formation of mini-Landau bands. Our results present solid experimental evidence of exotic quantum oscillations in the quantum limit beyond currently available mechanisms, and establish ZrTe5, a prototypical Dirac semiconductor, as a simple platform parallel to correlated insulators for exploring exotic oscillations.

cond-mat.mes-hall↗

arXiv2026

Emergence of a symmetry-broken Chern insulator near a moiré Kondo breakdown

Moiré semiconductors built on angle-aligned transition metal dichalcogenide (TMD) heterobilayers provide a physical realization of the Kondo lattice model, in which one TMD layer is prepared in a Mott insulating state supporting a lattice of local magnetic moments and the other layer in a metallic state supporting itinerant carriers. The artificial Kondo lattice enables the exploration of exotic states of matter near a continuously tunable Kondo breakdown. Here we report the emergence of a symmetry-broken Chern insulator at a moiré hole filling factor 4/3 in angle-aligned MoTe2/WSe2 moiré bilayers, which realize a chiral Kondo lattice. The symmetry-broken Chern insulator, which exhibits integer quantized Hall conductance at a fractional moiré filling, breaks the translational symmetry of the lattice spontaneously; it also appears only near a magnetic field-induced Kondo breakdown in the mixed-valence regime of the material. We further demonstrate that the magnetic field required to induce the Kondo breakdown and to stabilize the symmetry-broken Chern insulator is twist angle dependent. The results present new opportunities for exploring the subtle interplay between topology and Kondo interactions in moiré semiconductors.

cond-mat.mes-hall↗

arXiv2026

Evaluating and Enhancing the Vulnerability Reasoning Capabilities of Large Language Models

Large Language Models (LLMs) have demonstrated remarkable proficiency in vulnerability detection. However, a critical reliability gap persists: models frequently yield correct detection verdicts based on hallucinated logic or superficial patterns that deviate from the actual root cause. This misalignment remains largely obscured because contemporary benchmarks predominantly prioritize coarse-grained classification metrics, lacking the granular ground truth required to evaluate the underlying reasoning process. To bridge this gap, we first construct a benchmark consisting of two datasets: (1) real-world vulnerabilities with expert-curated causal reasoning as ground truth, and (2) semantically equivalent code perturbations for assessing reasoning robustness. Our large-scale empirical study reveals that even state-of-the-art models struggle to maintain logical consistency during semantic code comprehension, exhibiting 12 systematic failure patterns. Addressing these limitations, we propose DAGVul, a novel framework that models vulnerability reasoning as a Directed Acyclic Graph (DAG) generation task. Unlike linear chain-of-thought (CoT), our approach explicitly maps causal dependencies to enforce structural consistency. By further introducing Reinforcement Learning with Verifiable Rewards (RLVR), we align model reasoning trace with program-intrinsic logic. Experimental results demonstrate that our framework improves the reasoning F1-score by an average of 18.9% over all the baselines. Remarkably, our 8B-parameter implementation not only outperforms existing models of comparable scale but also surpasses specialized large-scale reasoning models, including Qwen3-30B-Reasoning and GPT-OSS-20B-High. It is even competitive with state-of-the-art models like Claude-Sonnet-4.5 (75.47% vs. 76.11%), establishing new efficiency in vulnerability reasoning across model scales.

cs.CR↗

arXiv2026

Divide and Conquer: Multimodal Video Deepfake Detection via Cross-Modal Fusion and Localization

This paper presents a system for detecting fake audio-visual content (i.e., video deepfake), developed for Track 2 of the DDL Challenge. The proposed system employs a two-stage framework, comprising unimodal detection and multimodal score fusion. Specifically, it incorporates an audio deepfake detection module and an audio localization module to analyze and pinpoint manipulated segments in the audio stream. In parallel, an image-based deepfake detection and localization module is employed to process the visual modality. To effectively leverage complementary information across different modalities, we further propose a multimodal score fusion strategy that integrates the outputs from both audio and visual modules. Guided by a detailed analysis of the training and evaluation dataset, we explore and evaluate several score calculation and fusion strategies to improve system robustness. Overall, the final fusion-based system achieves an AUC of 0.87, an AP of 0.55, and an AR of 0.23 on the challenge test set, resulting in a final score of 0.5528.

cs.MM↗

arXiv2026

Deterministic non-local parity control and supercurrent-based detection in an Andreev molecule

The ability to manipulate and detect the parity of quantum states in superconductor-semiconductor hybrid systems is pivotal to realizing the promise of topological quantum computation. However, as these architectures scale toward artificial Kitaev chains with phase-control loops, local accessibility becomes restricted, constraining conventional local parity control and detection. While Andreev molecules offer a platform for non-local intervention, deterministic protocols for parity manipulation have yet to be experimentally established. Here, we demonstrate deterministic non-local control over the parity configuration of a quantum dot (QD) by electrically modulating the coherent hybridization with a spatially adjacent QD within an Andreev molecule. By systematically investigating three distinct joint parity configuration regimes in the elastic co-tunneling limit, we experimentally uncover the operational conditions for this non-local control. In conjunction with theoretical simulations establishing a global phase diagram, we identify a set of universal selection rules governing parity transitions, dictated by the symmetry-imposed interplay between the joint parity configuration and the dominant inter-dot coupling mechanism (elastic co-tunneling vs. crossed Andreev reflection). Furthermore, we establish the supercurrent, directly signaled by zero-bias conductance peaks, as an intrinsic, sensor-free probe of the parity configuration, obviating the need for auxiliary charge sensors. Our results provide a validated physical framework for parity engineering, offering a key building block for scalable, multi-QD superconducting architectures.

cond-mat.mes-hall↗

arXiv2026

Additive problems on $\lfloor p^c \rfloor$

The sequence $$ \mathbb{P}^{(c)}=(\lfloor p^c \rfloor)_{p\in \mathbb{P}}\quad (c>0,c\notin \mathbb{N}), $$ is an important subsequence of the well-known Piatetski-Shapiro sequence, where $\mathbb{P}$ is the set of prime numbers and $\lfloor \cdot \rfloor$ is the floor function. We prove that for all $c \in (0, 13/15)$, any large enough integer $N$ can be represented as $$ N=\lfloor p^c\rfloor+q, $$ where $p$ and $q$ are primes. We also prove the result holds for almost all fixed positive $c \in \mathbb{R}\setminus\mathbb{Z}$. Moreover, we investigate shifted primes in this sequence, obtaining an asymptotic formula for all $c \in (0, 13/15)$ and an almost-all result for fixed positive $c \in \mathbb{R}\setminus\mathbb{Z}$.

math.NT↗

arXiv2025

Improvements on exponential sums related to Piatetski-Shapiro primes

We prove a new bound to the exponential sum of the form $$ \sum_{h \sim H}δ_h \mathop{\sum_{m\sim M}\sum_{n\sim N}}_{mn\sim x}a_{m}b_{n}\e\big(αmn + h(mn + u)^γ\big), $$ by a new approach to the Type I sum. The sum can be applied to many problems related to Piatetski-Shapiro primes, which are primes of the form $\lfloor n^c \rfloor$. In this paper, we improve the admissible range of the Balog-Friedlander condition, which leads to an improvement to the ternary Goldbach problem with Piatetski-Shapiro primes. We also investigate the distribution of Piatetski-Shapiro primes in arithmetic progressions, Piatetski-Shapiro primes in the intersection of multiple Beatty sequences and so on.

math.NT↗

arXiv2025

The Piatetski-Shapiro prime number theorem

The Piatetski-Shapiro sequences are of the form $\mathcal{N}_{c} := (\lfloor n^{c} \rfloor)_{n=1}^\infty$, where $\lfloor \cdot \rfloor$ is the integer part. It is expected that there are infinitely many primes in a Piatetski-Shapiro sequence for $c \in (1,2)$. In this article, we prove there are infinitely many Piatetski-Shapiro prime numbers for $1 < c < 1.1612\dots$ with an asymptotic formula. As a key idea, we prove a new bound for related type $I$ sum.

math.NT↗

arXiv2025

BlinkBud: Detecting Hazards from Behind via Sampled Monocular 3D Detection on a Single Earbud

Failing to be aware of speeding vehicles approaching from behind poses a huge threat to the road safety of pedestrians and cyclists. In this paper, we propose BlinkBud, which utilizes a single earbud and a paired phone to online detect hazardous objects approaching from behind of a user. The core idea is to accurately track visually identified objects utilizing a small number of sampled camera images taken from the earbud. To minimize the power consumption of the earbud and the phone while guaranteeing the best tracking accuracy, a novel 3D object tracking algorithm is devised, integrating both a Kalman filter based trajectory estimation scheme and an optimal image sampling strategy based on reinforcement learning. Moreover, the impact of constant user head movements on the tracking accuracy is significantly eliminated by leveraging the estimated pitch and yaw angles to correct the object depth estimation and align the camera coordinate system to the user's body coordinate system, respectively. We implement a prototype BlinkBud system and conduct extensive real-world experiments. Results show that BlinkBud is lightweight with ultra-low mean power consumptions of 29.8 mW and 702.6 mW on the earbud and smartphone, respectively, and can accurately detect hazards with a low average false positive ratio (FPR) and false negative ratio (FNR) of 4.90% and 1.47%, respectively.

cs.CV↗

arXiv2025

REACT-LLM: A Benchmark for Evaluating LLM Integration with Causal Features in Clinical Prognostic Tasks

Large Language Models (LLMs) and causal learning each hold strong potential for clinical decision making (CDM). However, their synergy remains poorly understood, largely due to the lack of systematic benchmarks evaluating their integration in clinical risk prediction. In real-world healthcare, identifying features with causal influence on outcomes is crucial for actionable and trustworthy predictions. While recent work highlights LLMs' emerging causal reasoning abilities, there lacks comprehensive benchmarks to assess their causal learning and performance informed by causal features in clinical risk prediction. To address this, we introduce REACT-LLM, a benchmark designed to evaluate whether combining LLMs with causal features can enhance clinical prognostic performance and potentially outperform traditional machine learning (ML) methods. Unlike existing LLM-clinical benchmarks that often focus on a limited set of outcomes, REACT-LLM evaluates 7 clinical outcomes across 2 real-world datasets, comparing 15 prominent LLMs, 6 traditional ML models, and 3 causal discovery (CD) algorithms. Our findings indicate that while LLMs perform reasonably in clinical prognostics, they have not yet outperformed traditional ML models. Integrating causal features derived from CD algorithms into LLMs offers limited performance gains, primarily due to the strict assumptions of many CD methods, which are often violated in complex clinical data. While the direct integration yields limited improvement, our benchmark reveals a more promising synergy.

cs.LG↗

arXiv2025

FsimNNs: An Open-Source Graph Neural Network Platform for SEU Simulation-based Fault Injection

Simulation-based fault injection is a widely adopted methodology for assessing circuit vulnerability to Single Event Upsets (SEUs); however, its computational cost grows significantly with circuit complexity. To address this limitation, this work introduces an open-source platform that exploits Spatio-Temporal Graph Neural Networks (STGNNs) to accelerate SEU fault simulation. The platform includes three STGNN architectures incorporating advanced components such as Atrous Spatial Pyramid Pooling (ASPP) and attention mechanisms, thereby improving spatio-temporal feature extraction. In addition, SEU fault simulation datasets are constructed from six open-source circuits with varying levels of complexity, providing a comprehensive benchmark for performance evaluation. The predictive capability of the STGNN models is analyzed and compared on these datasets. Moreover, to further investigate the efficiency of the approach, we evaluate the predictive capability of STGNNs across multiple test cases and discuss their generalization capability. The developed platform and datasets are released as open-source to support reproducibility and further research on https://github.com/luli2021/FsimNNs.

cs.AR↗

arXiv2025

Stacking-orientation and twist-angle control on integer and fractional Chern insulators in moiré rhombohedral graphene

Rhombohedral-stacked multilayer graphene aligned with hexagonal boron nitride has emerged as an excellent platform for investigating exotic quantum phenomena arising from the interplay between electron correlations and nontrivial topology. However, the microscopic mechanism governing the emergence of both the integer and fractional Chern insulator states in this system remains an open question. In this work, we systematically investigate the electrical transport properties of RMG/hBN moiré devices with controlled alignment orientations and twist angles. We demonstrate that alignment orientation strongly modulates correlated phenomena in the moiré-proximal regime, while having negligible influence on the formation of integer and fractional Chern insulators in the moiré-distant regime. Instead, the moiré periodicity, tuned by the twist angle, serves as the key parameter controlling the stability of these correlated topological states in the moiré-distant regime. Furthermore, in the moiré-proximal regime of one specific alignment, we observe anomalous Hall effect and a variety of competing phases near ν = 1, including integer Chern insulator states, extended Chern insulator states, and trivial insulators, whose stability is highly sensitive to both the applied displacement electric field and magnetic field. Our results underscore the critical role of stacking-alignment and twist-angle engineering in exploring novel quantum states based on rhombohedral-stacked multilayer graphene moiré systems.

cond-mat.mes-hall↗

arXiv2025

Evidence of anisotropic three-dimensional weak-localization in TiSe$_{2}$ nanoflakes

TiSe$_2$ is a typical transition-metal dichalcogenide known for its charge-density wave order. In this study, we report the observation of an unusual anisotropic negative magnetoresistance in exfoliated TiSe$_2$ nanoflakes at low temperatures. Unlike the negative magnetoresistance reported in most other transition-metal dichalcogenides, our results cannot be explained by either the conventional two-dimensional weak localization effect or the Kondo effect. A comprehensive analysis of the data suggests that the observed anisotropic negative magnetoresistance in TiSe$_2$ flakes is most likely caused by the three-dimensional weak localization effect. Our findings contribute to a deeper understanding of the phase-coherent transport processes in TiSe$_2$.

cond-mat.mes-hall↗

arXiv2025

Exchange operation of Majorana zero modes in topological insulator-based Josephson trijunctions

Majorana zero modes are anyons obeying non-Abelian exchange statistics distinct from fermions or bosons. While significant progresses have been achieved in the past two decades in searching for these exotic excitations in solid-state systems, their non-Abelian nature remains unverified, as definitive proof requires braiding operations. Here, we report preliminarily experimental advances in creating, manipulating, and exchanging the presumed Majorana zero modes in an envelope-shaped Josephson device composed of multiple trijunctions on a topological insulator surface. We observed the signatures of in-gap states migration consistent with the expectations of the Fu-Kane model, supporting the realization of an exchange operation. This work would establish a critical pathway toward ultimately braiding Majorana zero modes in the Fu-Kane scheme of topological quantum computation.

cond-mat.mes-hall↗

arXiv2025

Intrinsic Non-linearity of Josephson Junctions as an Alternative Origin of the Missing First Shapiro Step

The missing first Shapiro step in microwave-irradiated Josephson junctions has been widely interpreted as a hallmark of Majorana bound states. However, conventional mechanisms like junction underdamping or Joule heating can produce similar signatures. Here, we demonstrate that the intrinsic non-linear current-voltage characteristic of low-to-moderate transparency junctions can also suppress the first step, accompanied by distinctive zigzag boundaries between the zeroth and first step at intermediate driving frequencies. Microwave measurements on Al/WTe2 junctions and numerical simulations of a non-linear resistively and capacitively shunted junction model reveal the first step collapse induced by switching jumps of current, together with zigzag features absent in scenarios solely driven by finite \b{eta} or Joule heating. This zigzag signature therefore provides a crucial diagnostic tool, emphasizing the necessity of comprehensive analysis of microwave spectra before attributing the absence of the first Shapiro step to Majorana physics.

cond-mat.mes-hall↗

arXiv2025

Broad nonlocal spectrum in the Pb-InSb hybrid three terminals for potential realization of Kitaev chains

Hybrid superconductor-semiconductor(SC-SM) nanowires remain one of the foremost platforms for engineering topological superconductivity and Majorana zero modes(MZMs) towards fault-tolerant topological qubits, especially with the rapid development of artificial Kitaev chains. In contrast to the widely used aluminum(Al)-based hybrids, lead(Pb) offers a bulk superconducting gap of ~1.4meV and a critical temperature of ~7.2K, giving rise to a proximity-induced gap that is roughly five times larger than that obtained with Al. Here we present the first three-terminal Pb-hybrid devices and perform nonlocal differential-conductance spectroscopy on this platform. The nonlocal measurement simultaneously resolves a dual-gap feature of the parent Pb gap and the large, hard, gate-tunable induced superconducting gap, distinguished by a switch between electron- and hole-like dissipation processes. Within the induced gap we observe several types of Andreev bound states(ABSs) that undergo singlet-doublet transitions. Moreover, by tuning gate voltages we achieve gate-controlled resonating sign reversals of the nonlocal conductance, identifying three distinct regimes that correspond to different configurations of quantum-dot(QD) resonances(single-resonance, double-resonance, and series-resonance). Finally, the coupling between ABSs and QDs also present and can be modulated from the weak- to strong-coupling limit, indicating the feasibility of realizing the artificial Kitaev chains. Crucially, the robust nonlocal signatures persist up to temperatures(~1K) far above the operating temperature of Al-based devices thanks to the unusually large induced gap, thereby widening the accessible parameter space greatly and underscoring the suitability of Pb-based hybrids for implementing warm temperature artificial Kitaev chains and the topological quantum devices protected by a substantially larger topological gap.

quant-ph↗

arXiv2025

Magnetic-Field Control of Tomonaga-Luttinger Liquids in Ta2Pd3Te5 Edge States

Ta2Pd3Te5 is a quasi-one-dimensional transition-metal telluride whose heavy atoms endow the material with strong spin-orbit coupling, while the Fermi level inside the bulk gap makes the low-energy electronic structure highly tunable.Theory and early experiments have already identified a wealth of emergent phases in this platform: an excitonic insulator driven by electron-hole binding, a second-order topological insulator protected by crystalline symmetry, a potential topological-protected quantum-spin-Hall edge, and proximity-induced edge supercurrents when coupled to a conventional s-wave superconductor. These properties make it a promising platform for hosting Majorana zero modes and quantum computation, provided that time-reversal symmetry can be broken by a Zeeman gap. In this work, we demonstrate that the one-dimensional edge channels of exfoliated Ta2Pd3Te5 host a robust and tunable Tomonaga-Luttinger liquid by electrostatic gating because it shifts the chemical potential across the bulk gap without changing the gap size. More importantly, the application of a magnetic field introduces a Zeeman gap that systematically increases the TLL power-law exponent alpha. Furthermore, rotating the field reveals a pronounced twofold anisotropy--alpha is maximal for a field parallel to the edge and minimal for a perpendicular orientation--originating from an orientation-dependent edge g-factor that is likely amplified by quantum-confinement-induced orbital-angular-moment quenching. The existence of gate-tunable edge supercurrents together with the field-controlled Zeeman gap provides a direct route to break time-reversal symmetry in a particle-hole-symmetric superconducting gap and thus to engineer a topological superconducting phase, paving the way towards Majorana-based quantum devices.

cond-mat.mes-hall↗

arXiv2025

Instant4D: 4D Gaussian Splatting in Minutes

Dynamic view synthesis has seen significant advances, yet reconstructing scenes from uncalibrated, casual video remains challenging due to slow optimization and complex parameter estimation. In this work, we present Instant4D, a monocular reconstruction system that leverages native 4D representation to efficiently process casual video sequences within minutes, without calibrated cameras or depth sensors. Our method begins with geometric recovery through deep visual SLAM, followed by grid pruning to optimize scene representation. Our design significantly reduces redundancy while maintaining geometric integrity, cutting model size to under 10% of its original footprint. To handle temporal dynamics efficiently, we introduce a streamlined 4D Gaussian representation, achieving a 30x speed-up and reducing training time to within two minutes, while maintaining competitive performance across several benchmarks. Our method reconstruct a single video within 10 minutes on the Dycheck dataset or for a typical 200-frame video. We further apply our model to in-the-wild videos, showcasing its generalizability. Our project website is published at https://instant4d.github.io/.

cs.CV↗