arXiv subjects

Li Huang

Publications and source records attributed to Li Huang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Lessons from Formally Verified Deployed Software Systems (Extended version)

The technology of formal software verification has made spectacular advances, but how much does it actually benefit the development of practical software? Considerable disagreement remains about the practicality of building systems with mechanically-checked proofs of correctness. Is this prospect confined to a few expensive, life-critical projects, or can the idea be applied to a wide segment of the software industry? To help answer this question, the present survey examines a range of projects, in various application areas, that have produced formally verified systems and deployed them for actual use. It considers the technologies used, the form of verification applied, the results obtained, and the lessons that the software industry should draw regarding its ability to benefit from formal verification techniques and tools. Note: this version is the extended article, covering all the systems identified as relevant. A shorter version, covering only a selection, is also available (see https://doi.org/10.1145/3785652).

cs.SE↗

arXiv2026

Intertwined atomic-nanoscale-microscale structures via intralayer anisotropic Fe-chains in the layered ferromagnet FePd2Te2

Controlling mesoscale and nanoscale material structures and properties through self-organized atomic behavior is essential for atomic-scale manufacturing. However, direct and visual studies on the cross-scale effects of such atomic self-organization on mesoscopic structures remain scarce. Here, we report the intertwined atomic-nanoscale-mesoscale structures via the intralayer Fe-chains in the sandwich-like layered FePd2Te2 crystal by scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The hierarchical orthogonal corrugated morphologies are directly revealed and attributed to its chain-orientation-determined twinning-domain effect. Both Fe-chains of middle-sublayer and two kinds of Te atoms of top-sublayer are further atomically resolved at the sub-Å level, indicating the critical effects of Pd-atoms/voids on the intra-layer anisotropic Fe-chains and the interlayer structural alignment. The thermal-induced and strain-related structural transitions of surface layer are further investigated and discussed based on the proposed filling model of Pd-voids by the intralayer Pd-atoms. Our work not only provides deep understanding of this exotic layered magnetic material, and will inspire more perspectives for tailoring its anisotropic atomic-to-mesoscale structures and properties.

cond-mat.mtrl-sci↗

arXiv2025

Intention Chain-of-Thought Prompting with Dynamic Routing for Code Generation

Large language models (LLMs) exhibit strong generative capabilities and have shown great potential in code generation. Existing chain-of-thought (CoT) prompting methods enhance model reasoning by eliciting intermediate steps, but suffer from two major limitations: First, their uniform application tends to induce overthinking on simple tasks. Second, they lack intention abstraction in code generation, such as explicitly modeling core algorithmic design and efficiency, leading models to focus on surface-level structures while neglecting the global problem objective. Inspired by the cognitive economy principle of engaging structured reasoning only when necessary to conserve cognitive resources, we propose RoutingGen, a novel difficulty-aware routing framework that dynamically adapts prompting strategies for code generation. For simple tasks, it adopts few-shot prompting; for more complex ones, it invokes a structured reasoning strategy, termed Intention Chain-of-Thought (ICoT), which we introduce to guide the model in capturing task intention, such as the core algorithmic logic and its time complexity. Experiments across three models and six standard code generation benchmarks show that RoutingGen achieves state-of-the-art performance in most settings, while reducing total token usage by 46.37% on average across settings. Furthermore, ICoT outperforms six existing prompting baselines on challenging benchmarks.

cs.AI↗

arXiv2025

Ultrafast μeV-Precision Bandgap Engineering in Low-Dimensional Topological Insulators

Precise and ultrafast control of electronic band structures is a central challenge for advancing quantum functional materials and devices. Conventional approaches--such as chemical doping, lattice strain, or external gating--offer robust stability but remain confined to the quasi-static regime, far from the intrinsic femto- to picosecond dynamics that govern many-body interactions. Here, using cryogenic transient reflectance spectroscopy, we realize dynamic bandgap engineering in the anisotropic topological insulator $α$-Bi$_4$Br$_4$ with unprecedented micro-electron-volt ($μ$eV) precision. The exceptional sensitivity arises from the cooperative action of long-lived topological carriers, stabilized by restricted bulk-to-edge scattering phase space, together with symmetry-resolved coherent phonons that modulate inter-chain hopping. These channels jointly modify Coulomb screening and interband transitions, enabling both gradual and oscillatory control of the electronic structure. Supported by first-principles and tight-binding theory, we further demonstrate a dual-pump coherent control strategy for continuous, mode-selective tuning of electronic energies with $μ$eV accuracy. This framework paves the way for ultrafast on-demand band-structure engineering, pointing toward new frontiers in quantum optoelectronics, precision measurement in molecular and biological systems, and attosecond control of matter.

cond-mat.mes-hall↗

arXiv2025

Intrinsic Moiré Higher-Order Topology Beyond Effective Moiré Lattice Models

Moiré superlattices provide a compelling platform for exploring exotic correlated physics. Electronic interference within these systems often results in flat bands with localized electrons, which are typically described by effective moiré lattice models. While conventional models treat moiré sites as indivisible, analogous to atoms in a crystal, this picture overlooks a crucial distinction: unlike a true atom, a moiré site is composed of tens to thousands of atoms and is therefore spatially divisible. Here, we introduce a universal mechanism rooted in this spatial divisibility to create topological boundary states in moiré materials. Through tight-binding and density functional theory calculations, we demonstrate that cutting a moiré site with a physical boundary induces bulk topological polarization, generating robust boundary states with fractional charges. We further show that when the net edge polarization is canceled, this mechanism drives the system into an intrinsic moiré higher-order topological insulator (mHOTI) phase. As a concrete realization, we predict that twisted bilayer tungsten disulfide ($WS_2$) is a robust mHOTI with experimentally detectable corner states when its boundaries cut through moiré hole sites. Our findings generalize the theoretical framework of moiré higher-order topology, highlight the critical role of edge terminations, and suggest new opportunities for realizing correlated HOTIs and higher-order superconductivity in moiré platforms.

cond-mat.mes-hall↗

arXiv2025

MoRA: On-the-fly Molecule-aware Low-Rank Adaptation Framework for LLM-based Multi-Modal Molecular Assistant

Effectively integrating molecular graph structures with Large Language Models (LLMs) is a key challenge in drug discovery. Most existing multi-modal alignment methods typically process these structures by fine-tuning the LLM or adding a static adapter simultaneously. However, these approaches have two main limitations: (1) it optimizes a shared parameter space across all molecular inputs, limiting the model's ability to capture instance-specific structural features; and (2) fine-tuning the LLM for molecular tasks can lead to catastrophic forgetting, undermining its general reasoning capabilities. In this paper, instead of static task-oriented adaptation, we propose an instance-specific parameter space alignment approach for each molecule on-the-fly. To this end, we introduce Molecule-aware Low-Rank Adaptation (MoRA) that produces a unique set of low-rank adaptation weights for each input molecular graph. These weights are then dynamically injected into a frozen LLM, allowing the model to adapt its reasoning to the structure of each molecular input, while preserving the LLM's core knowledge. Extensive experiments demonstrate that on key molecular tasks, such as chemical reaction prediction and molecular captioning, MoRA's instance-specific dynamic adaptation outperforms statically adapted baselines, including a 14.1% relative improvement in reaction prediction exact match and a 22% reduction in error for quantum property prediction. The code is available at https://github.com/jk-sounds/MoRA.

cs.LG↗

arXiv2025

ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection

One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, traditional anomaly detection methods focus on modeling spatial or temporal dependencies independently, resulting in suboptimal representation learning and limited sensitivity to anomalous dispersion in high-dimensional spaces. In this work, we conduct an empirical analysis showing that both normal and anomalous samples tend to scatter in high-dimensional space, especially anomalous samples are markedly more dispersed. We formalize this dispersion phenomenon as scattering, quantified by the mean pairwise distance among sample representations, and leverage it as an inductive signal to enhance spatio-temporal anomaly detection. Technically, we propose ScatterAD to model representation scattering across temporal and topological dimensions. ScatterAD incorporates a topological encoder for capturing graph-structured scattering and a temporal encoder for constraining over-scattering through mean squared error minimization between neighboring time steps. We introduce a contrastive fusion mechanism to ensure the complementarity of the learned temporal and topological representations. Additionally, we theoretically show that maximizing the conditional mutual information between temporal and topological views improves cross-view consistency and enhances more discriminative representations. Extensive experiments on multiple public benchmarks show that ScatterAD achieves state-of-the-art performance on multivariate time series anomaly detection. Code is available at this repository: https://github.com/jk-sounds/ScatterAD.

cs.LG↗

arXiv2025

AI Pangaea: Unifying Intelligence Islands for Adapting Myriad Tasks

The pursuit of artificial general intelligence continuously demands generalization in one model across myriad tasks, even those not seen before. However, current AI models are isolated from each other for being limited to specific tasks, now first defined as Intelligence Islands. To unify Intelligence Islands into one, we propose Pangaea, the first AI supercontinent akin to the geological Pangaea. Pangaea encodes any data into a unified format and accumulates universal knowledge through pre-training on 296 datasets across diverse modalities. Eventually, it demonstrates remarkable generalization across 45 general tasks and 15 scientific tasks encompassing a wide range of scientific subjects. By investigating Pangaea deeper, the scaling effect of modality is revealed, quantifying the universal knowledge accumulation across modalities as the cumulative distribution function of a geometric distribution. On the whole, Pangaea shows strong potential to handle myriad tasks, indicating a new direction toward artificial general intelligence.

cs.AI↗

arXiv2025

Intrinsic Quantum Clusters in Kagome Weyl Semimetal Co3Sn2S2

Impurities and intrinsic point defects, which profoundly influence spin, charge, and topological degrees of freedom, are crucial parameters for tuning quantum states in quantum materials. The magnetic Weyl semimetal Co3Sn2S2 with its strong spin-orbit coupling, intrinsic ferromagnetism, and kagome lattice of correlated electrons, provides a compelling platform for studying impurity excited states. Yet, the role of intrinsic impurities in shaping its quantum states remains elusive. Here, we uncover intrinsic quantum clusters-localized intrinsic point defects that act as tunable quantum perturbations capable of reshaping electronic states and order parameters, on the surface of Co3Sn2S2 via scanning tunneling microscopy/spectroscopy and non contact atomic force microscopy, combined with scanning transmission electron microscopy/electron energy loss spectroscopy. These clusters are identified as native oxygen defects that dominate the intrinsic defect landscape on both cleaved surface terminations. On the Sn-terminated surface, oxygen impurities occupy hollow sites between three Sn atoms, and tune the flat band near the Fermi level, which exhibits orbital magnetism induced unconventional Zeeman effect under an applied magnetic field. On the S-terminated surface, oxygen interstitials reside slightly off center relative to the S lattice and generate occupied impurity states that retain sixfold symmetry at higher energies but reduce to C2 symmetry at lower energies. In contrast, these impurity states show no measurable magnetic response. Our findings establish that intrinsic oxygen-related quantum clusters act as tunable local perturbations in a topological kagome magnet, offering a versatile platform to probe and engineer impurity-driven phenomena in correlated and topological systems.

cond-mat.mtrl-sci↗

arXiv2025

E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model

Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved text-based ERG, challenges remain in handling multimodal emotional content and maintaining identity consistency. Thus, we propose E3RG, an Explicit Emotion-driven Empathetic Response Generation System based on multimodal LLMs which decomposes MERG task into three parts: multimodal empathy understanding, empathy memory retrieval, and multimodal response generation. By integrating advanced expressive speech and video generative models, E3RG delivers natural, emotionally rich, and identity-consistent responses without extra training. Experiments validate the superiority of our system on both zero-shot and few-shot settings, securing Top-1 position in the Avatar-based Multimodal Empathy Challenge on ACM MM 25. Our code is available at https://github.com/RH-Lin/E3RG.

cs.AI↗

arXiv2025

Sliding Engineering Spin-Valley-Layer Coupling and Altermagnetism in Bilayer Antiferromagnetic Honeycomb Lattices

Valley polarization and altermagnetism are two emerging fundamental phenomena in condensed matter physics, offering unprecedented opportunites for information encoding and processing in novel energy-efficient devices. By coupling valley and spin degrees of freedom with ferroic orders such as ferroelectricity, nonvolatile memory functionalities can be achieved. Here, we propose a way to realize ferroelectric-valley (FE-valley) and FE-altermagnetic coupling in a bilayer antiferromagnetic (AFM) honeycomb lattices based on an effective four-band spin-full $k\cdot p$ model. Our proposal is validated in bilayer MnPTe$_3$ through first-principles calculations. A spontaneous out-of-plane electric polarization occurs in AB- (BA-) stacking configuration, which is reversibly switchable via interlayer sliding. Remarkably, polarization reversal simultaneously inverts both layer-resolved valley polarization and altermagnetic spin splitting. This dual control enables tunable layer-spin-locked anomalous valley Hall effects and an unprecedented magnetoelectric response in 2D antiferromagnets. Our work establishes a general paradigm for electrically programmable valleytronic and spintronic functionalities of 2D AFM materials.

cond-mat.mtrl-sci↗

arXiv2025

Do AI models help produce verified bug fixes?

Among areas of software engineering where AI techniques -- particularly, Large Language Models -- seem poised to yield dramatic improvements, an attractive candidate is Automatic Program Repair (APR), the production of satisfactory corrections to software bugs. Does this expectation materialize in practice? How do we find out, making sure that proposed corrections actually work? If programmers have access to LLMs, how do they actually use them to complement their own skills? To answer these questions, we took advantage of the availability of a program-proving environment, which formally determines the correctness of proposed fixes, to conduct a study of program debugging with two randomly assigned groups of programmers, one with access to LLMs and the other without, both validating their answers through the proof tools. The methodology relied on a division into general research questions (Goals in the Goal-Query-Metric approach), specific elements admitting specific answers (Queries), and measurements supporting these answers (Metrics). While applied so far to a limited sample size, the results are a first step towards delineating a proper role for AI and LLMs in providing guaranteed-correct fixes to program bugs. These results caused surprise as compared to what one might expect from the use of AI for debugging and APR. The contributions also include: a detailed methodology for experiments in the use of LLMs for debugging, which other projects can reuse; a fine-grain analysis of programmer behavior, made possible by the use of full-session recording; a definition of patterns of use of LLMs, with 7 distinct categories; and validated advice for getting the best of LLMs for debugging and Automatic Program Repair.

cs.SE↗

arXiv2025

Real-space titration and manipulation of particle-like correlated electrons in doped Mott insulator

The localized (particle-like) correlated electrons deserve particular attention as they govern various exotic quantum phenomena, such as quantum spin liquids, Wigner crystals, and Mott insulators in correlated systems. However, direct observation and manipulation of these particle-like electrons at the atomic or single-electron scale remain highly challenging. Here, we successfully realize and directly visualize particle-like correlated electrons in 1T-TaS2 through hole doping. The potential-dependent local electronic structure of single particle-like electron is revealed by angle-resolved photoemission spectroscopy (ARPES), scanning tunneling spectroscopy (STS) combined with theoretical calculations. The complex correlated interactions including nearest-neighbor attractive interactions and many-body repulsive interactions are further demonstrated and discussed based on the spatial distribution of particle-like electrons. Furthermore, the tentative manipulation of the particle-like electrons is successfully achieved by the energy-excitation mechanism. Our results not only provide profound insights into particle-like electrons in correlated systems, but also establish a versatile platform for designing and controlling quantum states at the atomic scale.

cond-mat.str-el↗

arXiv2025

Distinguishing scale-dependent Planck stars from renormalization group improved Schwarzschild black holes by Gravitational waves

Extreme mass-ratio inspirals (EMRIs), consisting of a stellar-mass black hole orbiting a supermassive black hole, are among the primary targets for future space-based gravitational wave detectors. By analyzing the emitted gravitational wave signals, we can probe the nature of compact objects in the strong-field region. To achieve this, we examine the effects of gravitational radiation. In this work, we base our calculations on the general relativistic Schwarzschild background and calculate the energy and angular momentum fluxes of gravitational waves. We perform a theoretical analysis of the equations of motion and the orbital evolution equations for EMRIs. The gravitational waveforms generated by the different periodic orbits of timelike test particles around scale-dependent Planck stars or renormalization group improved Schwarzschild black holes are investigated using both time-domain and frequency-domain methods. The time-domain method employs the ``analytic kludge" (AK) approach, while the frequency-domain method utilizes the discrete Fourier transform. We calculate the characteristic strain of the corresponding gravitational waves and compare them with the sensitivity curves of both ground-based and space-based detectors. These gravitational wave sensitivity curves can be experimentally tested for both spacetimes considered. Additionally, we use two approximate methods--the large eccentricity (EL) method and the small eccentricity (ES) method--to study the orbital evolution of EMRIs and compare the results with equatorial orbits derived from geodesic equations. Our findings will contribute to a deeper understanding of the nature of spacetime.

gr-qc↗

arXiv2025

Strong Field Optical Hall Effect in 2D Weyl Semimetal

The study of interplay between the geometric nature of Bloch electrons and transverse responses under strong field offers new opportunities for optoelectronic applications. Here, we present a comprehensive study of the strong-field response of Weyl Dirac nodes in bilayer T'-WTe2 using time-dependent first-principles formalism. The electron dynamics is explored focusing on the mid-infrared frequency, ranging from the perturbative to nonperturbative regime. In the nonperturbative regime, the high-harmonic generation (HHG) spectra under a strong field clearly exhibit a plateau and energy cutoffs for both longitudinal and anomalous Hall (transverse) currents, with the latter being due to the large interband Berry curvature of the Weyl-Dirac semimetal. For the longitudinal harmonics, the intraband contributions increase with intensity, resulting in a complex interplay between interband polarization and intraband motions. Remarkably, if we take a comprehensive all-band perspective enabled by time-dependent density functional calculations, the anomalous Hall responses are purely attributed to the interband processes, even in the nonperturbative regime, thus Hall HHG can be crucial to understand the carrier dynamics. Our findings suggest that HHG associated with the ultrafast strong-field driven electron dynamics holds immense potential for exploring the nonlinear high Hall responses in Weyl semimetal.

cond-mat.mes-hall↗

arXiv2025

Atomic to mesoscale hierarchical structures and magnetic states in an anisotropic layered ferromagnet FePd2Te2

Two-dimensional (2D) magnetic materials have predominantly exhibited easy-axis or easy-plane anisotropy and display a high sensitivity to the underlying crystal structure and lattice symmetry. Recently, an in-plane anisotropic 2D ferromagnet of FePd2Te2 has been discovered with intriguing structure and quasi-one-dimensional spin system. Here, we report a real-space investigation of its twinning structure and magnetic states using atomic/magnetic force microscopy (AFM/MFM) combined with scanning tunneling microscopy (STM). The atomic to mesoscale hierarchical structures with the orthogonal and corrugated compressive /tensile(C/T) regions are directly observed due to the intrinsic twinning-domain characteristic. The structure-related intact ferromagnetic (FM), field-induced polarized-FM states and their transitions are comparatively discussed at the mesoscale with the corresponding macroscopic magnetic measurements. Temperature- and field-dependent evolution of magnetic phase are further investigated at the FM and PM states, and summarized to obtain a unique H-T phase diagram of FePd2Te2. Our work provides key results for understanding the complicated magnetic properties of FePd2Te2, and suggests new directions for manipulating magnetic states through the atomic and mesoscale structure engineering.

cond-mat.mtrl-sci↗

arXiv2025

AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection

Current multimodal large language models (MLLMs) struggle to understand circuit schematics due to their limited recognition capabilities. This could be attributed to the lack of high-quality schematic-netlist training data. Existing work such as AMSnet applies schematic parsing to generate netlists. However, these methods rely on hard-coded heuristics and are difficult to apply to complex or noisy schematics in this paper. We therefore propose a novel net detection mechanism based on segmentation with high robustness. The proposed method also recovers positional information, allowing digital reconstruction of schematics. We then expand AMSnet dataset with schematic images from various sources and create AMSnet 2.0. AMSnet 2.0 contains 2,686 circuits with schematic images, Spectre-formatted netlists, OpenAccess digital schematics, and positional information for circuit components and nets, whereas AMSnet only includes 792 circuits with SPICE netlists but no digital schematics.

cs.CV↗

arXiv2025

Loop unrolling: formal definition and application to testing

Testing processes usually aim at high coverage, but loops severely limit coverage ambitions since the number of iterations is generally not predictable. Most testing teams address this issue by adopting the extreme solution of limiting themselves to branch coverage, which only considers loop executions that iterate the body either once or not at all. This approach misses any bug that only arises after two or more iterations. To achieve more meaningful coverage, testing strategies may unroll loops, in the sense of using executions that iterate loops up to n times for some n greater than one, chosen pragmatically in consideration of the available computational power. While loop unrolling is a standard part of compiler optimization techniques, its use in testing is far less common. Part of the reason is that the concept, while seemingly intuitive, lacks a generally accepted and precise specification. The present article provides a formal definition and a set of formal properties of unrolling. All the properties have mechanically been proved correct (through the Isabelle proof assistant). Using this definition as the conceptual basis, we have applied an unrolling strategy to an existing automated testing framework and report the results: how many more bugs get detected once we unroll loops more than once? These results provide a first assessment of whether unrolling should become a standard part of test generation and test coverage measurement.

cs.SE↗