arXiv subjects

Meng Li

Publications and source records attributed to Meng Li.

At least 127 records · Page 7Linked to original sources

arXiv2026

Experimental Realization of All-Optical Terahertz Attoclock

The attoclock is a powerful tool for probing ultrafast electron dynamics with attosecond precision.Here, we demonstrate an all-optical terahertz (THz) attoclock that reconstructs photoionization dynamics by detecting the THz radiation emitted from Ar atoms ionized by two-color (800 nm/400 nm) laser fields. In this approach, the polarization direction of the emitted THz field reflects the direction of the photoelectron drift velocity and thus serves as a direct observable that encodes the effective ionization delay, analogous to the angular deflection of photoelectrons in conventional attoclocks. By precisely tailoring the relative phase and ellipticity of the driving fields, we observe intensity-dependent rotations of the THz polarization. These rotations, which reveal changes of the effective delay, are consistent with both conventional attoclock measurements and time-dependent Schrödinger equation simulations. Our experiment establishes the feasibility of the THz attoclock as a vacuum-free and contactless probe of tunneling dynamics, offering a transformative alternative for investigating condensed-matter systems where photoelectron detection is challenging.

physics.optics↗

arXiv2026

Beyond Physical Labels: Redefining Domains for Robust WiFi-based Gesture Recognition

In this paper, we propose GesFi, a novel WiFi-based gesture recognition system that introduces WiFi latent domain mining to redefine domains directly from the data itself. GesFi first processes raw sensing data collected from WiFi receivers using CSI-ratio denoising, Short-Time Fast Fourier Transform, and visualization techniques to generate standardized input representations. It then employs class-wise adversarial learning to suppress gesture semantic and leverages unsupervised clustering to automatically uncover latent domain factors responsible for distributional shifts. These latent domains are then aligned through adversarial learning to support robust cross-domain generalization. Finally, the system is applied to the target environment for robust gesture inference. We deployed GesFi under both single-pair and multi-pair settings using commodity WiFi transceivers, and evaluated it across multiple public datasets and real-world environments. Compared to state-of-the-art baselines, GesFi achieves up to 78% and 50% performance improvements over existing adversarial methods, and consistently outperforms prior generalization approaches across most cross-domain tasks.

cs.HC↗

arXiv2026

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

General reasoning represents a long-standing and formidable challenge in artificial intelligence. Recent breakthroughs, exemplified by large language models (LLMs) and chain-of-thought prompting, have achieved considerable success on foundational reasoning tasks. However, this success is heavily contingent upon extensive human-annotated demonstrations, and models' capabilities are still insufficient for more complex problems. Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labeled reasoning trajectories. The proposed RL framework facilitates the emergent development of advanced reasoning patterns, such as self-reflection, verification, and dynamic strategy adaptation. Consequently, the trained model achieves superior performance on verifiable tasks such as mathematics, coding competitions, and STEM fields, surpassing its counterparts trained via conventional supervised learning on human demonstrations. Moreover, the emergent reasoning patterns exhibited by these large-scale models can be systematically harnessed to guide and enhance the reasoning capabilities of smaller models.

cs.CL↗

arXiv2026

Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture

Large Language Models (LLMs) utilize extensive knowledge databases and show powerful text generation ability. However, their reliance on high-quality copyrighted datasets raises concerns about copyright infringements in generated texts. Current research often employs prompt engineering or semantic classifiers to identify copyrighted content, but these approaches have two significant limitations: (1) Challenging to identify which specific subdataset (e.g., works from particular authors) influences an LLM's output. (2) Treating the entire training database as copyrighted, hence overlooking the inclusion of non-copyrighted training data. We propose Inner-Probe, a lightweight framework designed to evaluate the influence of copyrighted sub-datasets on LLM-generated texts. Unlike traditional methods relying solely on text, we discover that the results of multi-head attention (MHA) during LLM output generation provide more effective information. Thus, Inner-Probe performs sub-dataset contribution analysis using a lightweight LSTM based network trained on MHA results in a supervised manner. Harnessing such a prior, Inner-Probe enables non-copyrighted text detection through a concatenated global projector trained with unsupervised contrastive learning. Inner-Probe demonstrates 3x improved efficiency compared to semantic model training in sub-dataset contribution analysis on Books3, achieves 15.04% - 58.7% higher accuracy over baselines on the Pile, and delivers a 0.104 increase in AUC for non-copyrighted data filtering.

cs.CL↗

arXiv2025

LacAIDes: Generative AI-Supported Creative Interactive Circuits Crafting to Enliven Traditional Lacquerware

Lacquerware, a representative craft of Chinese intangible cultural heritage, is renowned for its layered aesthetics and durability but faces declining engagement. While prior human-computer interaction research has explored embedding interactive circuits to transform lacquerware into responsive artifacts, most studies have focused on fabrication techniques rather than supporting makers in creatively designing such interactions at a low threshold. To address this gap, we present LacAIDes, a Generative AI powered creativity-support tool built on a multi-agent workflow aligned with the double diamond model of design thinking. LacAIDes enables exploration and creation of culturally grounded interactive circuits without requiring prior technical expertise. We evaluated LacAIDes in a longitudinal workshop with 34 participants using a mixed-method approach. Results show that LacAIDes demonstrated high usability, enhanced creative engagement in craft making, and encouraged critical reflection on the role of Generative AI in digital craft practices. This work contributes to human-computer interaction by introducing a novel creativity-support tool and providing empirical insights into revitalizing traditional craft making through Generative AI.

cs.HC↗

arXiv2025

Bend to Mend: Toward Trustworthy Variational Bayes with Valid Uncertainty Quantification

Variational Bayes (VB) is a popular and computationally efficient method to approximate the posterior distribution in Bayesian inference, especially when the exact posterior is analytically intractable and sampling-based approaches are computationally prohibitive. While VB often yields accurate point estimates, its uncertainty quantification (UQ) is known to be unreliable. For example, credible intervals derived from VB posteriors tend to exhibit undercoverage, failing to achieve nominal frequentist coverage probabilities. In this article, we address this challenge by proposing Trustworthy Variational Bayes (TVB), a method to recalibrate the UQ of broad classes of VB procedures. Our approach follows a bend-to-mend strategy: we intentionally misspecify the likelihood to correct VB's flawed UQ. In particular, we first relax VB by building on a recent fractional VB method, and then identify the optimal fraction parameter using conformal techniques such as sample splitting and bootstrapping. This yields recalibrated UQ for any given parameter of interest. On the theoretical front, we establish that the calibrated credible intervals achieve asymptotically correct frequentist coverage for a given parameter of interest; this, to the best of our knowledge, is the first such theoretical guarantee for VB. On the practical front, we introduce the "TVB table", which enables (1) massive parallelization and remains agnostic to the parameter of interest during its construction, and (2) efficient identification of the optimal fraction parameter for any specified parameter of interest. The proposed method is illustrated via Gaussian mixture models and Bayesian mixture linear regression models, and numerical experiments demonstrate that the TVB method outperforms standard VB and achieves normal frequentist coverage in finite samples. A real data application is also discussed.

stat.ME↗

arXiv2025

Optimal plug-in Gaussian processes for modeling derivatives

Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy by differentiating the posterior distribution with GP priors for derivatives of any order. This practically appealing plug-in GP method has been previously perceived as suboptimal and degraded, but this is not necessarily the case. We provide posterior contraction rates for plug-in GPs and establish that they achieve optimal rates simultaneously for all derivative orders. We show that the posterior measure of the regression function and its derivatives, with the same choice of hyperparameter that does not depend on the order of derivatives, converges at the minimax optimal rate up to a logarithmic factor for functions in certain classes. We analyze a data-driven hyperparameter tuning method based on empirical Bayes, and show that it satisfies the optimal rate condition while maintaining computational efficiency. This article to the best of our knowledge provides the first positive result for plug-in GPs in the context of inferring derivative functionals, and leads to a practically simple nonparametric Bayesian method with optimal and adaptive hyperparameter tuning for simultaneously estimating the regression function and its derivatives. Simulations show competitive finite sample performance of the plug-in GP method. A climate change application for analyzing the global sea-level rise is discussed.

math.ST↗

arXiv2025

MCGA: Mixture of Codebooks Hyperspectral Reconstruction via Grayscale-Aware Attention

Reconstructing hyperspectral images (HSIs) from RGB inputs provides a cost-effective alternative to hyperspectral cameras, but reconstructing high-dimensional spectra from three channels is inherently ill-posed. Existing methods typically directly regress RGB-to-HSI mappings using large attention networks, which are computationally expensive and handle ill-posedness only implicitly. We propose MCGA, a Mixture-of-Codebooks with Grayscale-aware Attention framework that explicitly addresses these challenges using spectral priors and photometric consistency. MCGA first learns transferable spectral priors via a mixture-of-codebooks (MoC) from heterogeneous HSI datasets, then aligns RGB features with these priors through grayscale-aware photometric attention (GANet). Efficiency and robustness are further improved via top-K attention design and test-time adaptation (TTA). Experiments on multiple real-world benchmarks demonstrate the state-of-the-art accuracy, strong cross-dataset generalization, and 4-5x faster inference. Codes will be available once acceptance at https://github.com/Fibonaccirabbit/MCGA.

cs.CV↗

arXiv2025

Mapis: A Knowledge-Graph Grounded Multi-Agent Framework for Evidence-Based PCOS Diagnosis

Polycystic Ovary Syndrome (PCOS) constitutes a significant public health issue affecting 10% of reproductive-aged women, highlighting the critical importance of developing effective diagnostic tools. Previous machine learning and deep learning detection tools are constrained by their reliance on large-scale labeled data and an lack of interpretability. Although multi-agent systems have demonstrated robust capabilities, the potential of such systems for PCOS detection remains largely unexplored. Existing medical multi-agent frameworks are predominantly designed for general medical tasks, suffering from insufficient domain integration and a lack of specific domain knowledge. To address these challenges, we propose Mapis, the first knowledge-grounded multi-agent framework explicitly designed for guideline-based PCOS diagnosis. Specifically, it built upon the 2023 International Guideline into a structured collaborative workflow that simulates the clinical diagnostic process. It decouples complex diagnostic tasks across specialized agents: a gynecological endocrine agent and a radiology agent collaborative to verify inclusion criteria, while an exclusion agent strictly rules out other causes. Furthermore, we construct a comprehensive PCOS knowledge graph to ensure verifiable, evidence-based decision-making. Extensive experiments on public benchmarks and specialized clinical datasets, benchmarking against nine diverse baselines, demonstrate that Mapis significantly outperforms competitive methods. On the clinical dataset, it surpasses traditional machine learning models by 13.56%, single-agent by 6.55%, and previous medical multi-agent systems by 7.05% in Accuracy.

cs.MA↗

arXiv2025

Visualizing metal-mediated nucleation and growth of GaN

Understanding the atomic-scale mechanisms governing metal-mediated nucleation and growth of gallium nitride (GaN) and related alloys is critical for tailoring their structural and functional properties in advanced electronic, optoelectronic, and quantum devices. Using real-time environmental transmission electron microscopy (E-TEM) in conjunction with Gibbs free energy calculations, we elucidate the distinct processes of GaN nucleation and growth from Ga droplet arrays with and without GaN pre-nuclei. For the lowest temperatures, although GaN nucleation at Ga droplet arrays is not observed, GaN growth occurs preferentially at pre-existing GaN nuclei, presumably due to the reduced Gibbs free energy for NH3 decomposition at Ga/GaN interfaces. For intermediate to high temperatures, E-TEM reveals nucleation and growth of GaN from Ga droplets with and without GaN nuclei, with enhanced crystallinity for the GaN nuclei, due to epitaxial templating. These results highlight the critical role of the Ga/GaN interface in facilitating NH3 decomposition and GaN growth, offering fundamental insights into metal-mediated nucleation and growth of GaN and related materials.

cond-mat.mtrl-sci↗

arXiv2025

CDKFormer: Contextual Deviation Knowledge-Based Transformer for Long-Tail Trajectory Prediction

Predicting the future movements of surrounding vehicles is essential for ensuring the safe operation and efficient navigation of autonomous vehicles (AVs) in urban traffic environments. Existing vehicle trajectory prediction methods primarily focus on improving overall performance, yet they struggle to address long-tail scenarios effectively. This limitation often leads to poor predictions in rare cases, significantly increasing the risk of safety incidents. Taking Argoverse 2 motion forecasting dataset as an example, we first investigate the long-tail characteristics in trajectory samples from two perspectives, individual motion and group interaction, and deriving deviation features to distinguish abnormal from regular scenarios. On this basis, we propose CDKFormer, a Contextual Deviation Knowledge-based Transformer model for long-tail trajectory prediction. CDKFormer integrates an attention-based scene context fusion module to encode spatiotemporal interaction and road topology. An additional deviation feature fusion module is proposed to capture the dynamic deviations in the target vehicle status. We further introduce a dual query-based decoder, supported by a multi-stream decoder block, to sequentially decode heterogeneous scene deviation features and generate multimodal trajectory predictions. Extensive experiments demonstrate that CDKFormer achieves state-of-the-art performance, significantly enhancing prediction accuracy and robustness for long-tailed trajectories compared to existing methods, thus advancing the reliability of AVs in complex real-world environments.

cs.RO↗

arXiv2025

H2EAL: Hybrid-Bonding Architecture with Hybrid Sparse Attention for Efficient Long-Context LLM Inference

Large language models (LLMs) have demonstrated remarkable proficiency in a wide range of natural language processing applications. However, the high energy and latency overhead induced by the KV cache limits the edge deployment, especially for long contexts. Emerging hybrid bonding (HB) technology has been proposed as a promising alternative to conventional near-memory processing (NMP) architectures, offering improved bandwidth efficiency and lower power consumption while exhibiting characteristics of distributed memory. In this paper, we propose H2EAL, a hybrid bonding-based accelerator with sparse attention algorithm-hardware co-design for efficient LLM inference at the edge. At the algorithm level, we propose a hybrid sparse attention scheme with static and dynamic sparsity for different heads to fully leverage the sparsity with high accuracy. At the hardware level, we co-design the hardware to support hybrid sparse attention and propose memory-compute co-placement to address the distributed memory bottleneck. Since different attention heads exhibit different sparse patterns and the attention structure often mismatches the HB architecture, we further develop a load-balancing scheduler with parallel tiled attention to address workload imbalance and optimize the mapping strategy. Extensive experiments demonstrate H2EAL achieves 5.20~48.21x speedup and 6.22~73.48x energy efficiency improvement over baseline HB implementation, with a negligible average accuracy drop of 0.87% on multiple benchmarks.

cs.PF↗

arXiv2025

Machine Learning Pipeline for Denoising Low Signal-To-Noise Ratio and Out-of-Distribution Transmission Electron Microscopy Datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material's structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets have not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model's computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

cond-mat.mtrl-sci↗

arXiv2025

Photovoltaic potential of tin perovskites revealed through layer-by-layer investigation of optoelectronic and charge transport properties

Tin perovskites are the most promising environmentally friendly alternative to lead perovskites. Among tin perovskites, FASnI3 (CH4N2SnI3) shows optimum band gap, and easy processability. However, the performance of FASnI3 based solar cells is incomparable to lead perovskites for several reasons, including energy band mismatch between the perovskite absorber film and the charge transporting layers (CTLs) for both types of carriers, i.e., for electrons (ETLs) and holes (HTLs). However, the band diagrams in the literature are inconsistent, and the charge extraction dynamics are poorly understood. In this paper, we study the energy band positions of FASnI3 based perovskites using Kelvin probe (KP) and photoelectron yield spectroscopy (PYS) to provide a precise band diagram of the most used device stack. In addition, we analyze the defects within the current energetic landscape of tin perovskites. We uncover the role of bathocuproine (BCP) in enhancing the electron extraction at the fullerene C60/BCP interface. Furthermore, we used transient surface photovoltage (tr-SPV) for the first time for tin perovskites to understand the charge extraction dynamics of the most reported HTLs such as NiOx and PEDOT, and ETLs such as C60, ICBA, and PCBM. Finally, we used Hall effect, KP, and time-resolved photoluminescence (TRPL) to estimate an accurate value of the p-doping concentration in FASnI3 and showed a consistent result of 1.5 * 1017 cm-3. Our findings prove that the energetic system of tin halide perovskites is deformed and should be redesigned independently from lead perovskites to unlock the full potential of tin perovskites.

cond-mat.mtrl-sci↗

arXiv2025

The Professional Challenges of Industrial Designer in Industry 4.0

The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.

cs.HC↗

arXiv2025

ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning

The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Concurrently, existing high-difficulty benchmarks often suffer from narrow disciplinary focus, oversimplified answer formats, and vulnerability to data contamination, creating a fidelity gap with real-world scientific inquiry. To address these challenges, we introduce ATLAS (AGI-Oriented Testbed for Logical Application in Science), a large-scale, high-difficulty, and cross-disciplinary evaluation suite composed of approximately 800 original problems. Developed by domain experts (PhD-level and above), ATLAS spans seven core scientific fields: mathematics, physics, chemistry, biology, computer science, earth science, and materials science. Its key features include: (1) High Originality and Contamination Resistance, with all questions newly created or substantially adapted to prevent test data leakage; (2) Cross-Disciplinary Focus, designed to assess models' ability to integrate knowledge and reason across scientific domains; (3) High-Fidelity Answers, prioritizing complex, open-ended answers involving multi-step reasoning and LaTeX-formatted expressions over simple multiple-choice questions; and (4) Rigorous Quality Control, employing a multi-stage process of expert peer review and adversarial testing to ensure question difficulty, scientific value, and correctness. We also propose a robust evaluation paradigm using a panel of LLM judges for automated, nuanced assessment of complex answers. Preliminary results on leading models demonstrate ATLAS's effectiveness in differentiating their advanced scientific reasoning capabilities. We plan to develop ATLAS into a long-term, open, community-driven platform to provide a reliable "ruler" for progress toward Artificial General Intelligence.

cs.CL↗

arXiv2025

Compactness of products of block Hankel and Toeplitz operators

Motivated by the Sarason problem on the products of Hankel and Toeplitz operators on analytic function spaces, we characterize the compactness of products of block Hankel and Toeplitz operators on the vector-valued Hardy space of the unit disk via harmonic extension of the symbols and Douglas algebras generated by the symbols. Additionally, we provide a complete answer to the question of when the product of a block Hankel operator and a block Toeplitz operator is a block Hankel operator.

math.FA↗

arXiv2025

LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine Components

The health condition of wind turbine (WT) components is crucial for ensuring stable and reliable operation. However, existing fault detection methods are largely limited to visual recognition, producing structured outputs that lack semantic interpretability and fail to support maintenance decision-making. To address these limitations, this study proposes an integrated framework that combines YOLOMS with a large language model (LLM) for intelligent fault analysis and diagnosis. Specifically, YOLOMS employs multi-scale detection and sliding-window cropping to enhance fault feature extraction, while a lightweight key-value (KV) mapping module bridges the gap between visual outputs and textual inputs. This module converts YOLOMS detection results into structured textual representations enriched with both qualitative and quantitative attributes. A domain-tuned LLM then performs semantic reasoning to generate interpretable fault analyses and maintenance recommendations. Experiments on real-world datasets demonstrate that the proposed framework achieves a fault detection accuracy of 90.6\% and generates maintenance reports with an average accuracy of 89\%, thereby improving the interpretability of diagnostic results and providing practical decision support for the operation and maintenance of wind turbines.

cs.CV↗