arXiv subjects

Li Yang

Publications and source records attributed to Li Yang.

At least 91 records · Page 5Linked to original sources

arXiv2025

Research on Comprehensive Classroom Evaluation System Based on Multiple AI Models

The promotion of the national education digitalization strategy has facilitated the development of teaching quality evaluation towards all-round, process-oriented, precise, and intelligent directions, inspiring explorations into new methods and technologies for educational quality assurance. Classroom teaching evaluation methods dominated by teaching supervision and student teaching evaluation suffer from issues such as low efficiency, strong subjectivity, and limited evaluation dimensions. How to further advance intelligent and objective evaluation remains a topic to be explored. This paper, based on image recognition technology, speech recognition technology, and AI large language models, develops a comprehensive evaluation system that automatically generates evaluation reports and optimization suggestions from two dimensions: teacher teaching ability and classroom teaching effectiveness. This study establishes a closed-loop classroom evaluation model that comprehensively evaluates student and teaching conditions based on multi-dimensional data throughout the classroom teaching process, and further analyzes the data to guide teaching improvement. It meets the requirements of all-round and process-oriented classroom evaluation in the era of digital education, effectively solves the main problems of manual evaluation methods, and provides data collection and analysis methods as well as technologies for relevant research on educational teaching evaluation.

cs.CY↗

arXiv2025

Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey

Traffic Surveillance Systems (TSS) have become increasingly crucial in modern intelligent transportation systems, with vision technologies playing a central role for scene perception and understanding. While existing surveys typically focus on isolated aspects of TSS, a comprehensive analytical framework bridging low-level and high-level perception tasks, particularly considering emerging technologies, remains lacking. This paper presents a systematic review of vision technologies in TSS, examining both low-level perception tasks (object detection, classification, and tracking) and high-level perception tasks (parameter estimation, anomaly detection, and behavior understanding). Specifically, we first provide a detailed methodological categorization and comprehensive performance evaluation for each task. Our investigation reveals five fundamental limitations in current TSS: perceptual data degradation in complex scenarios, data-driven learning constraints, semantic understanding gaps, sensing coverage limitations and computational resource demands. To address these challenges, we systematically analyze five categories of current approaches and potential trends: advanced perception enhancement, efficient learning paradigms, knowledge-enhanced understanding, cooperative sensing frameworks and efficient computing frameworks, critically assessing their real-world applicability. Furthermore, we evaluate the transformative potential of foundation models in TSS, which exhibit remarkable zero-shot learning abilities, strong generalization, and sophisticated reasoning capabilities across diverse tasks. This review provides a unified analytical framework bridging low-level and high-level perception tasks, systematically analyzes current limitations and solutions, and presents a structured roadmap for integrating emerging technologies, particularly foundation models, to enhance TSS capabilities.

cs.CV↗

arXiv2025

Alter-Piezoresponse in Two-Dimensional Lieb-Lattice Altermagnets

Altermagnetism, featuring alternating spin structures in reciprocal space, has sparked growing interest. Here, we predict novel real-space alternative piezomagnetic and piezoelectric responses in an emerging altermagnetic family of Lieb lattices, specifically transition-metal chalcogenides M2WS4 (M = Mn, Fe, Co). The unique S4T crystal-spin symmetry leads to distinct magnetic and electric responses depending on the direction of applied stress. When subjected to axial stress, they exhibit a giant piezomagnetic response, which is about one to two orders of magnitude larger than that of most piezomagnetic materials, while the residual C2 symmetry suppresses the piezoelectric effect. In contrast, diagonal stress induces an imbalance of oppositely aligned electric dipole moments and a significant piezoelectric response, while in-plane mirror symmetry inhibits the piezomagnetic effect. This alternative piezoresponse offers an unprecedented opportunity to precisely control electric and magnetic properties independently, opening new avenues for altermagnetic materials in high-fidelity multifunctional memory and sensor applications.

cond-mat.mtrl-sci↗

arXiv2025

Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection

Smart contract vulnerability detection remains a major challenge in blockchain security. Existing vulnerability detection methods face two main issues: (1) Existing datasets lack comprehensive coverage and high-quality explanations for preference learning. (2) Large language models (LLMs) often struggle with accurately interpreting specific concepts in smart contract security. Empirical analysis shows that even after continual pre-training (CPT) and supervised fine-tuning (SFT), LLMs may misinterpret the execution order of state changes, resulting in incorrect explanations despite making correct detection decisions. To address these challenges, we propose Smart-LLaMA-DPO based on LLaMA-3.1-8B. We construct a comprehensive dataset covering four major vulnerability types and machine-unauditable vulnerabilities, including precise labels, explanations, and locations for SFT, as well as high-quality and low-quality output pairs for Direct Preference Optimization (DPO). Second, we perform CPT using large-scale smart contract to enhance the LLM's understanding of specific security practices in smart contracts. Futhermore, we conduct SFT with our comprehensive dataset. Finally, we apply DPO, leveraging human feedback and a specially designed loss function that increases the probability of preferred explanations while reducing the likelihood of non-preferred outputs. We evaluate Smart-LLaMA-DPO on four major vulnerability types: reentrancy, timestamp dependence, integer overflow/underflow, and delegatecall, as well as machine-unauditable vulnerabilities. Our method significantly outperforms state-of-the-art baselines, with average improvements of 10.43% in F1 score and 7.87% in accuracy. Moreover, both LLM evaluation and human evaluation confirm that our method generates more correct, thorough, and clear explanations.

cs.CR↗

arXiv2025

ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding

Continual Knowledge Graph Embedding (CKGE) seeks to integrate new knowledge while preserving past information. However, existing methods struggle with efficiency and scalability due to two key limitations: (1) suboptimal knowledge preservation between snapshots caused by manually designed node/relation importance scores that ignore graph dependencies relevant to the downstream task, and (2) computationally expensive graph traversal for node/relation importance calculation, leading to slow training and high memory overhead. To address these limitations, we introduce ETT-CKGE (Efficient, Task-driven, Tokens for Continual Knowledge Graph Embedding), a novel task-guided CKGE method that leverages efficient task-driven tokens for efficient and effective knowledge transfer between snapshots. Our method introduces a set of learnable tokens that directly capture task-relevant signals, eliminating the need for explicit node scoring or traversal. These tokens serve as consistent and reusable guidance across snapshots, enabling efficient token-masked embedding alignment between snapshots. Importantly, knowledge transfer is achieved through simple matrix operations, significantly reducing training time and memory usage. Extensive experiments across six benchmark datasets demonstrate that ETT-CKGE consistently achieves superior or competitive predictive performance, while substantially improving training efficiency and scalability compared to state-of-the-art CKGE methods. The code is available at: https://github.com/lijingzhu1/ETT-CKGE/tree/main

cs.CL↗

arXiv2025

Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics

In computational biophysics, where molecular data is expanding rapidly and system complexity is increasing exponentially, large language models (LLMs) and agent-based systems are fundamentally reshaping the field. This perspective article examines the recent advances at the intersection of LLMs, intelligent agents, and scientific computation, with a focus on biophysical computation. Building on these advancements, we introduce ADAM (Agent for Digital Atoms and Molecules), an innovative multi-agent LLM-based framework. ADAM employs cutting-edge AI architectures to reshape scientific workflows through a modular design. It adopts a hybrid neural-symbolic architecture that combines LLM-driven semantic tools with deterministic symbolic computations. Moreover, its ADAM Tool Protocol (ATP) enables asynchronous, database-centric tool orchestration, fostering community-driven extensibility. Despite the significant progress made, ongoing challenges call for further efforts in establishing benchmarking standards, optimizing foundational models and agents, building an open collaborative ecosystem and developing personalized memory modules. ADAM is accessible at https://sidereus-ai.com.

physics.comp-ph↗

arXiv2025

Towards Practical Defect-Focused Automated Code Review

The complexity of code reviews has driven efforts to automate review comments, but prior approaches oversimplify this task by treating it as snippet-level code-to-text generation and relying on text similarity metrics like BLEU for evaluation. These methods overlook repository context, real-world merge request evaluation, and defect detection, limiting their practicality. To address these issues, we explore the full automation pipeline within the online recommendation service of a company with nearly 400 million daily active users, analyzing industry-grade C++ codebases comprising hundreds of thousands of lines of code. We identify four key challenges: 1) capturing relevant context, 2) improving key bug inclusion (KBI), 3) reducing false alarm rates (FAR), and 4) integrating human workflows. To tackle these, we propose 1) code slicing algorithms for context extraction, 2) a multi-role LLM framework for KBI, 3) a filtering mechanism for FAR reduction, and 4) a novel prompt design for better human interaction. Our approach, validated on real-world merge requests from historical fault reports, achieves a 2x improvement over standard LLMs and a 10x gain over previous baselines. While the presented results focus on C++, the underlying framework design leverages language-agnostic principles (e.g., AST-based analysis), suggesting potential for broader applicability.

cs.SE↗

arXiv2025

Item-Language Model for Conversational Recommendation

Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, and video capabilities. Recommender systems, on the other hand, have been critical for information seeking and item discovery needs. Recently, there have been attempts to apply LLMs for recommendations. One difficulty of current attempts is that the underlying LLM is usually not trained on the recommender system data, which largely contains user interaction signals and is often not publicly available. Another difficulty is user interaction signals often have a different pattern from natural language text, and it is currently unclear if the LLM training setup can learn more non-trivial knowledge from interaction signals compared with traditional recommender system methods. Finally, it is difficult to train multiple LLMs for different use-cases, and to retain the original language and reasoning abilities when learning from recommender system data. To address these three limitations, we propose an Item-Language Model (ILM), which is composed of an item encoder to produce text-aligned item representations that encode user interaction signals, and a frozen LLM that can understand those item representations with preserved pretrained knowledge. We conduct extensive experiments which demonstrate both the importance of the language-alignment and of user interaction knowledge in the item encoder.

cs.IR↗

arXiv2025

Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation

Tensor Robust Principal Component Analysis (TRPCA) is a fundamental technique for decomposing multi-dimensional data into a low-rank tensor and an outlier tensor, yet existing methods relying on sparse outlier assumptions often fail under structured corruptions. In this paper, we propose a self-guided data augmentation approach that employs adaptive weighting to suppress outlier influence, reformulating the original TRPCA problem into a standard Tensor Principal Component Analysis (TPCA) problem. The proposed model involves an optimization-driven weighting scheme that dynamically identifies and downweights outlier contributions during tensor augmentation. We develop an efficient proximal block coordinate descent algorithm with closed-form updates to solve the resulting optimization problem, ensuring computational efficiency. Theoretical convergence is guaranteed through a framework combining block coordinate descent with majorization-minimization principles. Numerical experiments on synthetic and real-world datasets, including face recovery, background subtraction, and hyperspectral denoising, demonstrate that our method effectively handles various corruption patterns. The results show the improvements in both accuracy and computational efficiency compared to state-of-the-art methods.

math.NA↗

arXiv2025

MOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models

Smart contract vulnerabilities pose significant security risks to blockchain systems, potentially leading to severe financial losses. Existing methods face several limitations: (1) Program analysis-based approaches rely on predefined patterns, lacking flexibility for new vulnerability types; (2) Deep learning-based methods lack explanations; (3) Large language model-based approaches suffer from high false positives. We propose MOS, a smart contract vulnerability detection framework based on mixture-of-experts tuning (MOE-Tuning) of large language models. First, we conduct continual pre-training on a large-scale smart contract dataset to provide domain-enhanced initialization. Second, we construct a high-quality MOE-Tuning dataset through a multi-stage pipeline combining LLM generation and expert verification for reliable explanations. Third, we design a vulnerability-aware routing mechanism that activates the most relevant expert networks by analyzing code features and their matching degree with experts. Finally, we extend the feed-forward layers into multiple parallel expert networks, each specializing in specific vulnerability patterns. We employ a dual-objective loss function: one for optimizing detection and explanation performance, and another for ensuring reasonable distribution of vulnerability types to experts through entropy calculation. Experiments show that MOS significantly outperforms existing methods with average improvements of 6.32% in F1 score and 4.80% in accuracy. The vulnerability explanations achieve positive ratings (scores of 3-4 on a 4-point scale) of 82.96%, 85.21% and 94.58% for correctness, completeness, and conciseness through human and LLM evaluation.

cs.SE↗

arXiv2025

Discovery of a Robust Non-Janus Hybrid MoSH Monolayer as a Two-Gap Superconductor via High-Throughput Computational Screening

The atomic-scale determination of hydrogen positions in MoSH monolayers remains experimentally challenging, and existing studies are confined to Janus-type configurations. Here, we combine high-throughput structural screening with first-principles calculations to predict a novel non-Janus Hybrid 1T$^{'}$-MoSH monolayer, which energetically surpasses all previously reported MoSH phases with a binding energy of -3.02 eV. This structure emerges as a hybrid of MoS$_2$ and MoH$_2$, featuring alternating S and H atoms on both sides of the Mo layer. Comprehensive stability analyses confirm its robustness in energy, mechanics, dynamics, and thermodynamics (stable up to 1600 K). Remarkably, anisotropic Migdal-Eliashberg theory predicts Hybrid 1T$^{'}$-MoSH as a two-gap superconductor with a critical temperature T$_c$ of 16.34 K, driven by strong electron-phonon coupling ($λ$$=$1.39). Substituting Mo with Hf, Ta, or Ti drastically suppresses T$_c$ $\sim$ (0.53-2.42 K), highlighting Mo$^{'}$s unique role in enhancing superconductivity. Our work not only expands the family of 2D transition metal chalcogenides but also proposes a promising candidate for quantum technologies, bridging theoretical design to functional material discovery.

cond-mat.mtrl-sci↗

arXiv2025

ANNs-SaDE: A Machine-Learning-Based Design Automation Framework for Microwave Branch-Line Couplers

The traditional method for designing branch-line couplers involves a trial-and-error optimization process that requires multiple design iterations through electromagnetic (EM) simulations. Thus, it is extremely time consuming and labor intensive. In this paper, a novel machine-learning-based framework is proposed to tackle this issue. It integrates artificial neural networks with a self-adaptive differential evolution algorithm (ANNs-SaDE). This framework enables the self-adaptive design of various types of microwave branch-line couplers by precisely optimizing essential electrical properties, such as coupling factor, isolation, and phase difference between output ports. The effectiveness of the ANNs-SaDE framework is demonstrated by the designs of folded single-stage branch-line couplers and multi-stage wideband branch-line couplers.

eess.SP↗

arXiv2025

Language-Inspired Modeling Reveals Redundant Encoding of N4-acetylcytidine(ac4C) Modifications in mRNA

The ac4C modification on mRNA has been demonstrated to be associated with various diseases; however, its molecular mechanism remains unclear. The wet lab experiments produced relatively rough data, which lack precise ac4C modification sites, and extracting valuable information from such data remains a challenge. In this study, we integrate linguistics, traditional machine learning, and deep learning, establishing a link between the understanding of mRNA data and natural language processing (NLP). Through our analysis, we successfully revealed key information about ac4C in mRNA and uncovered the information storage mechanism of ac4C redundancy on a single sequence. This redundant information storage method in mRNA facilitates the transmission of ac4C information and promotes the enrichment of ac4C.

q-bio.OT↗

arXiv2025

Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks

The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for CICIDS2017 datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

cs.CR↗

arXiv2025

Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis

Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security approaches.

cs.CR↗

arXiv2025

Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing

We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight that not all samples and tokens contribute equally to the model's output, and probing a small portion of each batch effectively identifies crucial weights, enabling tailored dynamic pruning for different batches. It comprises three main stages: probing, history-informed pruning, and full inference. In the probing stage, PP selects a small yet crucial set of hidden states, based on residual importance, to run a few model layers ahead. During the history-informed pruning stage, PP strategically integrates the probing states with historical states. Subsequently, it structurally prunes weights based on the integrated states and the PP importance score, a metric developed specifically to assess the importance of each weight channel in maintaining performance. In the final stage, full inference is conducted on the remaining weights. A major advantage of PP is its compatibility with existing models, as it operates without requiring additional neural network modules or fine-tuning. Comprehensive evaluations of PP on LLaMA-2/3 and OPT models reveal that even minimal probing-using just 1.5% of FLOPs-can substantially enhance the efficiency of structured pruning of LLMs. For instance, when evaluated on LLaMA-2-7B with WikiText2, PP achieves a 2.56 times lower ratio of performance degradation per unit of runtime reduction compared to the state-of-the-art method at a 40% pruning ratio. Our code is available at https://github.com/Qi-Le1/Probe_Pruning.

cs.CL↗

arXiv2025

DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation

Code review is a vital but demanding aspect of software development, generating significant interest in automating review comments. Traditional evaluation methods for these comments, primarily based on text similarity, face two major challenges: inconsistent reliability of human-authored comments in open-source projects and the weak correlation of text similarity with objectives like enhancing code quality and detecting defects. This study empirically analyzes benchmark comments using a novel set of criteria informed by prior research and developer interviews. We then similarly revisit the evaluation of existing methodologies. Our evaluation framework, DeepCRCEval, integrates human evaluators and Large Language Models (LLMs) for a comprehensive reassessment of current techniques based on the criteria set. Besides, we also introduce an innovative and efficient baseline, LLM-Reviewer, leveraging the few-shot learning capabilities of LLMs for a target-oriented comparison. Our research highlights the limitations of text similarity metrics, finding that less than 10% of benchmark comments are high quality for automation. In contrast, DeepCRCEval effectively distinguishes between high and low-quality comments, proving to be a more reliable evaluation mechanism. Incorporating LLM evaluators into DeepCRCEval significantly boosts efficiency, reducing time and cost by 88.78% and 90.32%, respectively. Furthermore, LLM-Reviewer demonstrates significant potential of focusing task real targets in comment generation.

cs.SE↗

arXiv2024

Temperature dependent spin-phonon coupling of boron-vacancy centers in hexagonal boron nitride

The negatively charged boron-vacancy center ($\mathrm{V}_{\mathrm{B}}^-$) in hexagonal boron nitride (hBN) has recently emerged as a highly promising quantum sensor. Compared to the nitrogen-vacancy (NV) center in diamond, the change with temperature of the spin transition energy of $\mathrm{V}_{\mathrm{B}}^-$ is more than an order of magnitude larger, making it a potential nanoscale thermometer with superior sensitivity. However, the underlying mechanism of the observed large temperature dependence remains an open question. In this work, using isotopically purified $\mathrm{h}{}^{10}\mathrm{B}{}^{15}\mathrm{N}$, we systematically characterize the zero-field splitting, hyperfine interaction, and spin relaxation time of $\mathrm{V}_{\mathrm{B}}^-$ from 10 to 350$~$K. We carry out first-principle calculations of the $\mathrm{V}_{\mathrm{B}}^-$ spin-phonon interaction and show that a second-order effect from finite-temperature phonon excitations is responsible for the observed changes in experiments. By fitting our experimental results to a physically motivated model, we extract the dominant phonon mode which agrees well with our simulations. Finally, we investigate the dynamic nuclear spin polarization process at cryogenic temperatures. Our results provide key insights in $\mathrm{V}_{\mathrm{B}}^-$ centers and their utilization as nanoscale thermometers and phonon sensors.

quant-ph↗