arXiv subjects

Mengqi Wang

Publications and source records attributed to Mengqi Wang.

At least 19 recordsLinked to original sources

arXiv2026

Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection

Large language models (LLMs) are trained on massive and largely undisclosed corpora that may contain copyrighted or privacy-sensitive content. Data contamination detection (DCD) therefore aims to determine whether a given text is a member of the pre-training corpus of a target LLM. Recent state-of-the-art DCD methods follow a feature-based paradigm that derives membership features from the input text and the corresponding model output. However, most modern LLMs undergo post-training, such as instruction tuning, preference optimization, and reasoning-oriented training, which can alter model outputs and shift the corresponding membership features, thereby reducing the separability between members and non-members. To address this problem, we propose CalibDCD, a broadly applicable calibration framework for feature-based DCD methods, comprising (1) Multi-View Shift Detection, which identifies recurring feature shifts associated with post-training, and (2) Bounded Feature Correction, which selectively mitigates their influence on membership prediction. Specifically, Multi-View Shift Detection evaluates controlled prompt variants on known non-member texts and consolidates the most informative views to identify recurring feature shifts. Bounded Feature Correction selectively adjusts feature components aligned with the detected shifts and controls the correction extent to preserve useful detection information. Experiments show that CalibDCD consistently improves existing feature-based detectors, with gains of up to 7.0% in AUC and 15.0% in TPR@5%FPR.

cs.CL↗

arXiv2026

Interpretable Column Annotation with LLM-Symbolized Decision Process Materialization

Column annotation (CA), including column type annotation (CTA) and column property annotation (CPA), aims to identify the meanings of table columns and the semantic relationships among them. Recent CA methods usually use various neural models to learn column representations and directly map them to label categories, thereby (1) sacrificing model interpretability and adaptivity, and (2) overlooking rich label semantics and ultimately limiting accuracy. To address these limitations, we propose SymCA, an LLM-empowered interpretable CA framework that materializes column annotation as a global-to-local symbolic decision process. SymCA consists of two components: (1) global skeleton induction, which constructs a semantic skeleton over the label space, and (2) local substrate evolution, which evolves predictive substrates within the skeleton. Specifically, to exploit label semantics while preserving an interpretable decision process, the global skeleton induction module leverages LLMs to generate candidate hypernym-inspired tree-structured semantic skeletons and employs a Minimum Bayes Risk (MBR)-based consensus strategy to select a robust skeleton against generation variance. Since different internal nodes require different evidence to distinguish among their child nodes, the local substrate evolution module materializes each internal node as an executable and evolvable predictive substrate. Over multiple evolution rounds, each substrate trains an interpretable random forest classifier with the current operator set, leverages the LLM to propose node-specific operator modifications, and uses an exploration-exploitation strategy to prioritize promising substrates. Extensive experiments demonstrate that SymCA is accurate, robust, and interpretable, outperforming the strongest baselines by an average of 6.42% in Micro-F1 and 11.03% in Macro-F1.

cs.CL↗

arXiv2026

Molecular chiral discrimination through symmetry-breaking spin dynamics

Molecular chirality plays a crucial role in physics, chemistry, life sciences and pharmacology. Nowadays, the chiral discrimination and control at the single-molecule level is urgently needed to reveal the origin of the chirality-relevant phenomena by recovering the information disturbed by the ensemble averaging. The method of magnetic resonance (MR), as one of powerful tools for structure analysis, is blind to the molecular chirality in the absence of a chiral reagent. Here we propose and experimentally demonstrate a direct MR-based method for determining the chirality at the single-molecule level through constructing the symmetry-breaking dynamics of nearby nuclear spins. In principle, the mirror asymmetry of two enantiomers in real space is manifested by breaking the joint symmetry of the mirror reflection and time reversal in spin space under spin dynamics. Experimentally, two enantiomers are indistinguishable from the dynamics of strongly-coupled but unpolarized nuclear spins, but diverge evidently in the dynamical results that break the field-inversion symmetry after spins are polarized. Our method and results will benefit the study of chirality-induced properties in the fields of chemistry and biology.

quant-ph↗

arXiv2026

Spin-based magnetic detection of optically trapped single cell in microfluidic channel

Combining optical tweezers with fluorescence microscopy is a powerful tool for single-cell analysis, playing a pivotal role in disease diagnosis, cell sorting, and the investigation of cellular dynamics. However, fluorescence detection faces challenges such as blinking, photobleaching and autofluorescence in biotissues. To address these limitations, we developed a magnetic detection strategy by integrating quantum magnetometry using nitrogen-vacancy centers into optical tweezers, demonstrating precise trapping and manipulation of individual cells in microfluidic environment. We detected a magnetic signal of 89 {\mu}T from a single cell labeled with magnetic nanoparticles, compared to a noise floor of 3.9 {\mu}T observed in unlabeled cells. This platform provides a promising approach for high-precision single-cell analysis and holds significant potential for probing cellular activities within biological microenvironments.

physics.optics↗

arXiv2026

Asymptotic Expansion and Bounds for the Bias of Empirical Tail Value-at-Risk

Tail Value-at-Risk (TVaR) is a widely adopted risk measure playing a critically important role in both academic research and industry practice in insurance. In data applications, TVaR is often estimated using the empirical method, owing to its simplicity and nonparametric nature. The empirical TVaR has been explicitly advocated by regulatory authorities as a standard approach for computing TVaR. However, prior literature has pointed out that the empirical TVaR estimator is negatively biased, which can lead to a systemic underestimation of risk in finite-sample applications. This paper aims to deepen the understanding of the bias of the empirical TVaR estimator in two dimensions: its magnitude as well as the key distributional and structural determinants driving the severity of the bias. To this end, we derive a leading-term approximation for the bias based on its asymptotic expansion. The closed-form expression associated with the leading-term approximation enables us to obtain analytical insights into the structural properties governing the bias of the empirical TVaR estimator. To account for the discrepancy between the leading-term approximation and the true bias, we further derive an explicit upper bound for the bias. We validate the proposed bias analysis framework via simulations and demonstrate its practical relevance using real data.

math.ST↗

arXiv2025

Quantum relaxometry for detecting biomolecular interactions with single NV centers

The investigation of biomolecular interactions at the single-molecule level has emerged as a pivotal research area in life science, particularly through optical, mechanical, and electrochemical approaches. Spins existing widely in biological systems, offer a unique degree of freedom for detecting such interactions. However, most previous studies have been largely confined to ensemble-level detection in the spin degree. Here, we developed a molecular interaction analysis method approaching single-molecule level based on relaxometry using the quantum sensor, nitrogen-vacancy (NV) center in diamond. Experiments utilized an optimized diamond surface functionalized with a polyethylenimine nanogel layer, achieving $\sim$10 nm average protein distance and mitigating interfacial steric hindrance. Then we measured the strong interaction between streptavidin and spin-labeled biotin complexes, as well as the weak interaction between bovine serum albumin and biotin complexes, at both the micrometer scale and nanoscale. For the micrometer-scale measurements using ensemble NV centers, we re-examined the often-neglected fast relaxation component and proposed a relaxation rate evaluation method, substantially enhancing the measurement sensitivity. Furthermore, we achieved nanoscale detection approaching single-molecule level using single NV centers. This methodology holds promise for applications in molecular screening, identification and kinetic studies at the single-molecule level, offering critical insights into molecular function and activity mechanisms.

quant-ph↗

arXiv2025

Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection

Error detection (ED), which aims to identify incorrect or inconsistent cell values in tabular data, is important for ensuring data quality. Recent state-of-the-art ED methods leverage the pre-trained knowledge and semantic capability of large language models (LLMs) to directly label whether a cell is erroneous. However, this LLM-as-a-labeler pipeline produces predictions through an implicit black-box process with limited traceability and explicit justification, and relies on stochastic single-pass inference, resulting in inconsistent and insufficiently robust detections across contexts. To address these limitations, we propose an LLM-as-an-inducer framework that uses an LLM to induce a decision tree for ED, termed TreeED, and ensembles multiple such trees for consensus detection, termed ForestED. Based on prompts derived from data context, decision tree specifications, and output requirements, TreeED queries the LLM to induce a decision tree skeleton whose root-to-leaf paths specify the stepwise procedure for evaluating a sample. Each tree contains three types of nodes: (1) rule nodes that perform simple validation checks, such as format or range constraints; (2) Graph Neural Network (GNN) nodes that capture complex patterns, such as functional dependencies; and (3) leaf nodes that output the final decision as error or clean. ForestED employs uncertainty sampling to obtain multiple informative row subsets and constructs a decision tree for each subset using TreeED. It then applies an Expectation-Maximization-based algorithm to jointly estimate tree reliability and optimize the consensus ED prediction. Experiments demonstrate that our methods are accurate, explainable, and robust, achieving an average F1-score improvement of 16.1% over the best baseline.

cs.CL↗

arXiv2025

SAM Guided Semantic and Motion Changed Region Mining for Remote Sensing Change Captioning

Remote sensing change captioning is an emerging and popular research task that aims to describe, in natural language, the content of interest that has changed between two remote sensing images captured at different times. Existing methods typically employ CNNs/Transformers to extract visual representations from the given images or incorporate auxiliary tasks to enhance the final results, with weak region awareness and limited temporal alignment. To address these issues, this paper explores the use of the SAM (Segment Anything Model) foundation model to extract region-level representations and inject region-of-interest knowledge into the captioning framework. Specifically, we employ a CNN/Transformer model to extract global-level vision features, leverage the SAM foundation model to delineate semantic- and motion-level change regions, and utilize a specially constructed knowledge graph to provide information about objects of interest. These heterogeneous sources of information are then fused via cross-attention, and a Transformer decoder is used to generate the final natural language description of the observed changes. Extensive experimental results demonstrate that our method achieves state-of-the-art performance across multiple widely used benchmark datasets. The source code of this paper will be released on https://github.com/Event-AHU/SAM_ChangeCaptioning

cs.CV↗

arXiv2025

EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregation

Event cameras produce asynchronous event streams that are spatially sparse yet temporally dense. Mainstream event representation learning algorithms typically use event frames, voxels, or tensors as input. Although these approaches have achieved notable progress, they struggle to address the undersampling problem caused by spatial sparsity. In this paper, we propose a novel hypergraph-guided spatio-temporal event stream completion mechanism, which connects event tokens across different times and spatial locations via hypergraphs and leverages contextual information message passing to complete these sparse events. The proposed method can flexibly incorporate RGB tokens as nodes in the hypergraph within this completion framework, enabling multi-modal hypergraph-based information completion. Subsequently, we aggregate hypergraph node information across different time steps through self-attention, enabling effective learning and fusion of multi-modal features. Extensive experiments on both single- and multi-label event classification tasks fully validated the effectiveness of our proposed framework. The source code of this paper will be released on https://github.com/Event-AHU/EvRainDrop.

cs.CV↗

arXiv2025

Strain-engineered nanoscale spin polarization reversal in diamond nitrogen-vacancy centers

The ability to control solid-state quantum emitters is fundamental to advancing quantum technologies. The performance of these systems is fundamentally governed by their spin-dependent photodynamics, yet conventional control methods using cavities offer limited access to key non-radiative processes. Here we demonstrate that anisotropic lattice strain serves as a powerful tool for manipulating spin dynamics in solid-state systems. Under high pressure, giant shear strain gradients trigger a complete reversal of the intrinsic spin polarization, redirecting ground-state population from $|0\rangle$ to $|\pm 1\rangle$ manifold. We show that this reprogramming arises from strain-induced mixing of the NV center's excited states and dramatic alteration of intersystem crossing, which we quantify through a combination of opto-magnetic spectroscopy and a theoretical model that disentangles symmetry-preserving and symmetry-breaking strain contributions. Furthermore, the polarization reversal is spatially mapped with a transition region below 120 nm, illustrating sub-diffraction-limit control. Our work establishes strain engineering as a powerful tool for tailoring quantum emitter properties, opening avenues for programmable quantum light sources, high-density spin-based memory, and hybrid quantum photonic devices.

quant-ph↗

arXiv2025

Audio-Conditioned Diffusion LLMs for ASR and Deliberation Processing

Diffusion-based large language models (DLLMs) have recently attracted growing interest as an alternative to autoregressive decoders. In this work, we present an empirical study on using the diffusion-based large language model LLaDA for automatic speech recognition (ASR). We first investigate its use as an external deliberation-based processing module for Whisper-LLaMA transcripts. By leveraging the bidirectional attention and denoising capabilities of LLaDA, we explore random masking, low-confidence masking, and semi-autoregressive strategies, showing that Whisper-LLaDA substantially reduces WER compared with the baseline. On LibriSpeech, the best cascade system achieves 2.25%/4.94% WER on test-clean/test-other, representing a 12.3% relative improvement over the Whisper-LLaMA baseline on the test-other split. In contrast, a plain-text LLaDA without acoustic features fails to improve accuracy, highlighting the importance of audio-conditioned embeddings. We further evaluate Whisper-LLaDA as a standalone decoder for ASR with diffusion-based and semi-autoregressive decoding. Most experimental configurations achieve faster inference than the Whisper-LLaMA baseline, although recognition accuracy is slightly lower. These findings offer an empirical view of diffusion-based LLMs for ASR and point to promising directions for improvements. Code and model are open-sourced at https://github.com/liuzhan22/Diffusion-ASR.

eess.AS↗

arXiv2025

High-resolution electric field imaging based on intermittent-contact mode scanning NV center electrometry

Scanning nitrogen-vacancy (NV) center electrometry has shown potential for quantitative quantum imaging of electric fields at the nanoscale. However, achieving nanoscale spatial resolution remains a challenge since employing gradiometry to overcome electrostatic screening causes resolution-limiting trade-offs including the averaging effect and the sensor-sample proximity. Here, we demonstrate a scanning NV center protocol that achieves an enhanced spatial resolution of approximately 10 nm. We develop an axially symmetric probe with a sub-nanometer oscillating amplitude, which simultaneously provides robust intermittent-contact mode feedback and ensures close engagement between the diamond tip and the sample. As an example, we experimentally demonstrate a 10 nm spatial resolution on ferroelectric lithium niobate. Scanning NV center electrometry with this resolution can directly resolve the nanoscale polar textures and dynamics of emerging ferroelectrics, which commonly arise on the scale of tens of nanometers.

quant-ph↗

arXiv2025

General ab initio framework for electronic-order-induced lattice-dynamics symmetry breaking

Conventional \textit{ab initio} approaches are unable to describe phonon time-reversal symmetry ($\mathcal{T}$) breaking. Here, we develop an \textit{ab initio} framework, grounded in molecular Berry curvature (MBC) theory, that captures electronic-order-driven symmetry breaking in lattice dynamics. Using Co$_3$Sn$_2$S$_2$ as a model system, our \textit{ab initio} framework yields phonon spectra that break both $\mathcal{T}$ and mirror symmetries, quantitatively reproduce the observed phonon splittings observed in experiments, and reveal distinct microscopic origins for the $E_g$ and $E_u$ modes: $E_g$ splitting is governed by MBC and is accurately captured by our algorithm, whereas $E_u$ splitting is enhanced by the Fano resonance and matches the experimental data once the Fano-factor correction is included. Leveraging this algorithm, we predict several candidate materials with nonzero electronic-order-driven symmetry breaking in lattice dynamics, establishing a first-principles route to understand electron-phonon coupling, phonon magnetism, and related Hall-type lattice responses.

cond-mat.mtrl-sci↗

arXiv2025

How to Retrieve Examples in In-context Learning to Improve Conversational Emotion Recognition using Large Language Models?

Large language models (LLMs) have enabled a wide variety of real-world applications in various domains. However, creating a high-performing application with high accuracy remains challenging, particularly for subjective tasks like emotion recognition. Inspired by the SLT 2024 GenSER Challenge, this study investigates approaches to improving conversational emotion recognition (CER) by LLMs. Specifically, we explore how to retrieve high-quality examples in in-context learning (ICL) to enhance CER. We propose various strategies based on random and augmented example retrieval and also analyze the impact of conversational context on CER accuracy. Experiments were conducted on the three datasets including IEMOCAP, MELD and EmoryNLP. The results show that augmented example retrieval consistently outperforms other techniques under investigation across all datasets, highlighting the importance of retrieving coherent targeted examples and enhancing them through paraphrasing.

cs.CL↗

arXiv2025

LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements

Health, Safety, and Environment (HSE) compliance assessment demands dynamic real-time decision-making under complicated regulations and complex human-machine-environment interactions. While large language models (LLMs) hold significant potential for decision intelligence and contextual dialogue, their capacity for domain-specific knowledge in HSE and structured legal reasoning remains underexplored. We introduce HSE-Bench, the first benchmark dataset designed to evaluate the HSE compliance assessment capabilities of LLM. HSE-Bench comprises over 1,000 manually curated questions drawn from regulations, court cases, safety exams, and fieldwork videos, and integrates a reasoning flow based on Issue spotting, rule Recall, rule Application, and rule Conclusion (IRAC) to assess the holistic reasoning pipeline. We conduct extensive evaluations on different prompting strategies and more than 10 LLMs, including foundation models, reasoning models and multimodal vision models. The results show that, although current LLMs achieve good performance, their capabilities largely rely on semantic matching rather than principled reasoning grounded in the underlying HSE compliance context. Moreover, their native reasoning trace lacks the systematic legal reasoning required for rigorous HSE compliance assessment. To alleviate these, we propose a new prompting technique, Reasoning of Expert (RoE), which guides LLMs to simulate the reasoning process of different experts for compliance assessment and reach a more accurate unified decision. We hope our study highlights reasoning gaps in LLMs for HSE compliance and inspires further research on related tasks.

cs.CL↗

arXiv2025

Entanglement-Enhanced Nanoscale Single-Spin Sensing

Detecting individual spins--including stable and metastable states--represents a fundamental challenge in quantum sensing with broad applications across condensed matter physics, quantum chemistry, and single-molecule magnetic resonance imaging. While nitrogen-vacancy (NV) centers in diamond have emerged as powerful nanoscale sensors, their performance for single-spin detection remains constrained by substantial environmental noise and restricted sensing volume. Here, we propose and demonstrate an entanglement-enhanced sensing protocol that overcomes these limitations through the strategic use of entangled NV pairs. Our approach achieves a 3.4-fold enhancement in sensitivity and a 1.6-fold reduction in spatial resolution relative to single NV centers under ambient conditions. The protocol employs carefully engineered entangled states that amplify target spin signals through quantum interference while suppressing environmental noise. Crucially, we extend these capabilities to resolve metastable single-spin dynamics, directly observing stochastic transitions between different spin states by identifying state-dependent coupling strengths. This dual functionality enables simultaneous detection of static and dynamic spin species for studying complex quantum systems. The achieved performance establishes entanglement-enhanced sensing as a viable pathway toward atomic-scale characterization of quantum materials and interface.

quant-ph↗

arXiv2025

Room-temperature hybrid 2D-3D quantum spin system for enhanced magnetic sensing and many-body dynamics

Advances in hybrid quantum systems and their precise control are pivotal for developing advanced quantum technologies. Two-dimensional (2D) materials with optically accessible spin defects have emerged as a promising platform for building integrated quantum spin systems due to their exceptional flexibility and scalability. However, experimentally realizing such systems and demonstrating their superiority remains challenging. Here, we present a hybrid spin system operating under ambient conditions, integrating boron vacancy (V_B^-) spins in 2D hexagonal boron nitride flakes with a single nitrogen vacancy (NV) center in 3D single-crystal diamonds. This combined system achieves full controllability and exhibits enhanced performance for nanoscale magnetic sensing, including an improved dynamic range. Moreover, we investigate the rich many-body spin dynamics within the hybrid system, which enables us to estimate the concentration of V_B^- spins. This work provides a critical foundation for advancing the development of 2D-3D integrated quantum spin systems.

quant-ph↗

arXiv2025

Fundamentals of non-parametric statistical inference for integrated quantiles

We present a general non-parametric statistical inference theory for integrals of quantiles without assuming any specific sampling design or dependence structure. Technical considerations are accompanied by examples and discussions, including those pertaining to the bias of empirical estimators. To illustrate how the general results can be adapted to specific situations, we derive - at a stroke and under minimal conditions - consistency and asymptotic normality of the empirical tail-value-at-risk, Lorenz and Gini curves at any probability level in the case of the simple random sampling, thus facilitating a comparison of our results with what is already known in the literature. Results, notes and references concerning dependent (i.e., time series) data are also offered. As a by-product, our general results provide new and unified proofs of large-sample properties of a number of classical statistical estimators, such as trimmed means, and give additional insights into the origins of, and the reasons for, various necessary and sufficient conditions.

math.ST↗