arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 325 records · Page 18Linked to original sources

arXiv2025

LLM Encoder vs. Decoder: Robust Detection of Chinese AI-Generated Text with LoRA

The rapid growth of large language models (LLMs) has heightened the demand for accurate detection of AI-generated text, particularly in languages like Chinese, where subtle linguistic nuances pose significant challenges to current methods. In this study, we conduct a systematic comparison of encoder-based Transformers (Chinese BERT-large and RoBERTa-wwm-ext-large), a decoder-only LLM (Alibaba's Qwen2.5-7B/DeepSeek-R1-Distill-Qwen-7B fine-tuned via Low-Rank Adaptation, LoRA), and a FastText baseline using the publicly available dataset from the NLPCC 2025 Chinese AI-Generated Text Detection Task. Encoder models were fine-tuned using a novel prompt-based masked language modeling approach, while Qwen2.5-7B was adapted for classification with an instruction-format input and a lightweight classification head trained via LoRA. Experiments reveal that although encoder models nearly memorize training data, they suffer significant performance degradation under distribution shifts (RoBERTa: 76.3% test accuracy; BERT: 79.3%). FastText demonstrates surprising lexical robustness (83.5% accuracy) yet lacks deeper semantic understanding. In contrast, the LoRA-adapted Qwen2.5-7B achieves 95.94% test accuracy with balanced precision-recall metrics, indicating superior generalization and resilience to dataset-specific artifacts. These findings underscore the efficacy of decoder-based LLMs with parameter-efficient fine-tuning for robust Chinese AI-generated text detection. Future work will explore next-generation Qwen3 models, distilled variants, and ensemble strategies to enhance cross-domain robustness further.

cs.CL↗

arXiv2025

Unraveling the Redox Mechanisms Underlying FLASH Radiotherapy: Critical Dose Thresholds and NRF2-Driven Tissue Sparing

FLASH radiotherapy (FLASH-RT) achieves tumor control comparable to conventional dose-rate irradiation (CONV-RT) while significantly reducing radiation damage to normal tissues. However, the physical conditions triggering the FLASH sparing effect remain unclear, and mechanisms related to oxidative stress and redox regulation are poorly understood. This study utilizes a murine acute intestinal toxicity model to investigate how beam parameters influence the FLASH sparing effect and tumor control using innovative FLASH-RT and CONV-RT combined irradiation. Results demonstrate for the first time that a substantially reduced FLASH dose can still elicit sparing effect, provided a total dose threshold is met. Kinetic simulation and experimental validation demonstrate that FLASH-RT enhances peroxyl radical recombination, reducing reactive oxygen species (ROS) and malondialdehyde levels. Antioxidant interventions further confirm the essential role of free radicals. RNA sequencing and molecular analyses reveal that FLASH-RT activates the nuclear factor E2-related factor 2 (NRF2) antioxidant pathway while suppressing extracellular signal-regulated kinases (ERK) signaling, thereby enhancing cellular redox defenses, reducing apoptosis, and mitigating ROS-mediated tissue injury. These findings highlight the feasibility of optimizing the FLASH-RT therapeutic window through redox modulation and provide a foundation for developing free radical-targeted strategies to improve its therapeutic efficacy.

physics.med-ph↗

arXiv2025

Analytical two-loop amplitudes of $e^{+} e^{-} \longrightarrow \boldsymbol{J} / \boldsymbolψ+\boldsymbolη_c$ at $B$ factories

In double charmonium production, a long-standing challenge is that the theoretical predictions are not consistent with the measurements at B factories. Within the NRQCD framework, the next-to-leading order (NLO) calculation has proved its power to cut down the discrepancy between theory and experiments. To further clarify this puzzle, we have performed the next-to-next-to-leading order (NNLO) calculation. The amplitude is obtained as an analytical asymptotic expansion in the ratio of the squared charm-quark mass over the squared center-of-mass energy, $m_c^2/s$. We investigate the origin of the leading logarithms by performing a region analysis, revealing the intricate factorization structure in this process. We provide numerical predictions on the total cross sections of $J/ψ+η_c$ production, which agree with the experimental results. Extension of our computation to $Υ+η_b$ production is also discussed.

hep-ph↗

arXiv2025

Self-Organizing Agent Network for LLM-based Workflow Automation

Recent multi-agent frameworks built upon large language models (LLMs) have demonstrated remarkable capabilities in complex task planning. However, in real-world enterprise environments, business workflows are typically composed through modularization and reuse of numerous subprocesses, resulting in intricate workflows characterized by lengthy and deeply nested execution paths. Such complexity poses significant challenges for LLM-driven orchestration, as extended reasoning chains and state-space explosions severely impact planning effectiveness and the proper sequencing of tool invocations. Therefore, developing an orchestration method with controllable structures capable of handling multi-layer nesting becomes a critical issue. To address this, we propose a novel structure-driven orchestration framework Self-Organizing Agent Network (SOAN). SOAN incrementally builds a formalized agent network by identifying and encapsulating structural units as independent agents, enhancing modularity and clarity in orchestration. Extensive evaluations were performed using multiple benchmarks as well as a real-world enterprise workflow dataset. Experimental results demonstrate that SOAN significantly outperforms state-of-the-art methods in terms of adaptability, fault tolerance, and execution efficiency.

cs.MA↗

arXiv2025

Phonon-scattering-induced quantum linear magnetoresistance up to room temperature

The realization of quantum transport effects at elevated temperatures has long intrigued researchers due to the implications for unveiling novel physics and developing quantum devices. In this work, we report remarkable quantum linear magnetoresistance (LMR) in the Weyl semiconductor tellurium at high temperatures of 40-300 K under strong magnetic fields up to 60 T. At high fields, the Weyl band features a large energy gap between the lowest and first Landau levels, which suppresses thermal excitation and preserves Landau quantization at high temperatures. The LMR is observed as long as majority carriers remain in the lowest Landau level without requiring monochromaticity, allowing it to persist up to room temperature. The inverse relationship between the LMR slope and temperature provides clear evidence that quantum LMR originates from high-temperature phonon scattering in the quantum limit, firstly demonstrating a theoretical prediction made nearly fifty years ago. This study highlights the key role of electron-phonon interaction and reveals an innovative quantum mechanism for achieving high-temperature LMR, fundamentally distinct from previous findings. Our results bridge a gap in the understanding of phonon-mediated quantum-limit physics and establish strong magnetic fields at high temperatures as a promising platform for exploring novel quantum phenomena.

cond-mat.mes-hall↗

arXiv2025

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

Face images play a crucial role in numerous applications; however, real-world conditions frequently introduce degradations such as noise, blur, and compression artifacts, affecting overall image quality and hindering subsequent tasks. To address this challenge, we organized the VQualA 2025 Challenge on Face Image Quality Assessment (FIQA) as part of the ICCV 2025 Workshops. Participants created lightweight and efficient models (limited to 0.5 GFLOPs and 5 million parameters) for the prediction of Mean Opinion Scores (MOS) on face images with arbitrary resolutions and realistic degradations. Submissions underwent comprehensive evaluations through correlation metrics on a dataset of in-the-wild face images. This challenge attracted 127 participants, with 1519 final submissions. This report summarizes the methodologies and findings for advancing the development of practical FIQA approaches.

cs.CV↗

arXiv2025

Bring Your Rear Cameras for Egocentric 3D Human Pose Estimation

Egocentric 3D human pose estimation has been actively studied using cameras installed in front of a head-mounted device (HMD). While frontal placement is the optimal and the only option for some tasks, such as hand tracking, it remains unclear if the same holds for full-body tracking due to self-occlusion and limited field-of-view coverage. Notably, even the state-of-the-art methods often fail to estimate accurate 3D poses in many scenarios, such as when HMD users tilt their heads upward -- a common motion in human activities. A key limitation of existing HMD designs is their neglect of the back of the body, despite its potential to provide crucial 3D reconstruction cues. Hence, this paper investigates the usefulness of rear cameras for full-body tracking. We also show that simply adding rear views to the frontal inputs is not optimal for existing methods due to their dependence on individual 2D joint detectors without effective multi-view integration. To address this issue, we propose a new transformer-based method that refines 2D joint heatmap estimation with multi-view information and heatmap uncertainty, thereby improving 3D pose tracking. Also, we introduce two new large-scale datasets, Ego4View-Syn and Ego4View-RW, for a rear-view evaluation. Our experiments show that the new camera configurations with back views provide superior support for 3D pose tracking compared to only frontal placements. The proposed method achieves significant improvement over the current state of the art (>10% on MPJPE). The source code, trained models, and datasets are available on our project page at https://4dqv.mpi-inf.mpg.de/EgoRear/.

cs.CV↗

arXiv2025

Bioinspired underwater soft robots: from biology to robotics and back

The ocean vast unexplored regions and diverse soft-bodied marine organisms have spurred interest in bio-inspired underwater soft robotics. Recent advances have enabled new capabilities in underwater movement, sensing, and interaction. However, these efforts are largely unidirectional, with biology guiding robotics while insights from robotics rarely feed back into biology. Here we propose a holistic, bidirectional framework that integrates biological principles, robotic implementation, and biological validation. We show that soft robots can serve as experimental tools to probe biological functions and even test evolutionary hypotheses. Their inherent compliance also allows them to outperform rigid systems in unstructured environments, supporting applications in marine exploration, manipulation, and medicine. Looking forward, we introduce bio-universal-inspired robotics, a paradigm that transcends species-specific mimicry by identifying convergent principles across species to inspire more adaptable designs. Despite rapid progress, challenges persist in material robustness, actuation efficiency, autonomy, and intelligence. By uniting biology and engineering, soft robots can advance ocean exploration and deepen scientific discovery.

cs.RO↗

arXiv2025

QCD corrections to Higgs boson pair production and decay to the $b\bar{b}τ^+τ^-$ final state

We present a comprehensive investigation of next-to-leading order (NLO) quantum chromodynamics (QCD) corrections to Higgs boson pair production and the subsequent decay into $b\bar{b}τ^+ τ^-$. We adopt the narrow-width approximation to separate the production and decay contributions and employ the dipole subtraction method to address infrared divergences inherent in perturbative QCD corrections. After applying a set of typical experimental cuts, we show that NLO QCD corrections to the decay process induce a significant reduction of the fiducial cross section and reshape important kinematic distributions at the 13.6 TeV LHC, such as the invariant mass of the Higgs boson pair and the transverse momentum of the leading $b$-jet. We also investigate the dependence of the kinematic distributions on the Higgs self-coupling and provide the signal acceptance as a function of the Higgs self-coupling modifier with full QCD corrections.

hep-ph↗

arXiv2025

GMF-Drive: Gated Mamba Fusion with Spatial-Aware BEV Representation for End-to-End Autonomous Driving

Diffusion-based models are redefining the state-of-the-art in end-to-end autonomous driving, yet their performance is increasingly hampered by a reliance on transformer-based fusion. These architectures face fundamental limitations: quadratic computational complexity restricts the use of high-resolution features, and a lack of spatial priors prevents them from effectively modeling the inherent structure of Bird's Eye View (BEV) representations. This paper introduces GMF-Drive (Gated Mamba Fusion for Driving), an end-to-end framework that overcomes these challenges through two principled innovations. First, we supersede the information-limited histogram-based LiDAR representation with a geometrically-augmented pillar format encoding shape descriptors and statistical features, preserving critical 3D geometric details. Second, we propose a novel hierarchical gated mamba fusion (GM-Fusion) architecture that substitutes an expensive transformer with a highly efficient, spatially-aware state-space model (SSM). Our core BEV-SSM leverages directional sequencing and adaptive fusion mechanisms to capture long-range dependencies with linear complexity, while explicitly respecting the unique spatial properties of the driving scene. Extensive experiments on the challenging NAVSIM benchmark demonstrate that GMF-Drive achieves a new state-of-the-art performance, significantly outperforming DiffusionDrive. Comprehensive ablation studies validate the efficacy of each component, demonstrating that task-specific SSMs can surpass a general-purpose transformer in both performance and efficiency for autonomous driving.

cs.CV↗

arXiv2025

Learning to Align, Aligning to Learn: A Unified Approach for Self-Optimized Alignment

Alignment methodologies have emerged as a critical pathway for enhancing language model alignment capabilities. While SFT (supervised fine-tuning) accelerates convergence through direct token-level loss intervention, its efficacy is constrained by offline policy trajectory. In contrast, RL(reinforcement learning) facilitates exploratory policy optimization, but suffers from low sample efficiency and stringent dependency on high-quality base models. To address these dual challenges, we propose GRAO (Group Relative Alignment Optimization), a unified framework that synergizes the respective strengths of SFT and RL through three key innovations: 1) A multi-sample generation strategy enabling comparative quality assessment via reward feedback; 2) A novel Group Direct Alignment Loss formulation leveraging intra-group relative advantage weighting; 3) Reference-aware parameter updates guided by pairwise preference dynamics. Our theoretical analysis establishes GRAO's convergence guarantees and sample efficiency advantages over conventional approaches. Comprehensive evaluations across complex human alignment tasks demonstrate GRAO's superior performance, achieving 57.70\%,17.65\% 7.95\% and 5.18\% relative improvements over SFT, DPO, PPO and GRPO baselines respectively. This work provides both a theoretically grounded alignment framework and empirical evidence for efficient capability evolution in language models.

cs.LG↗

arXiv2025

Spin transport revealed by the spin quantum geometry

We present the framework of \textit{spin quantum geometry}, which is fundamentally linked to the spin degree of freedom of Bloch electrons and incorporates both the spin quantum geometric tensor (QGT) and the recently introduced Zeeman QGT, to elucidate the spin transport. We show that the spin and Zeeman QGTs, respectively, provide a unified framework for revealing known spin currents, such as the intrinsic spin Hall effect, and spin magnetization, such as the Edelstein effect, of Bloch electrons under an electric field. In addition, we predict the linear displacement spin Hall effect, wherein an AC electric field induces a transverse spin current in insulating systems. Furthermore, we propose two novel nonlinear spin responses: the nonlinear Drude spin current (NDSC) and the nonlinear Drude spin magnetization (NDSM), both of which exhibit a quadratic dependence on the relaxation time, like the nonlinear Drude charge current, and are governed by the \textit{spin quantum geometry}. Finally, we evaluate the NDSC and NDSM with Dirac models of topological insulators and find that, in the moderately dirty regime, the NDSC and NDSM can exceed their respective nonlinear intrinsic counterparts, which have recently garnered significant interest in spintronics.

cond-mat.mtrl-sci↗

arXiv2025

TextMaster: A Unified Framework for Realistic Text Editing via Glyph-Style Dual-Control

In image editing tasks, high-quality text editing capabilities can significantly reduce both human and material resource costs. Existing methods, however, face significant limitations in terms of stroke accuracy for complex text and controllability of generated text styles. To address these challenges, we propose TextMaster, a solution capable of accurately editing text across various scenarios and image regions, while ensuring proper layout and controllable text style. Our method enhances the accuracy and fidelity of text rendering by incorporating high-resolution standard glyph information and applying perceptual loss within the text editing region. Additionally, we leverage an attention mechanism to compute intermediate layer bounding box regression loss for each character, enabling the model to learn text layout across varying contexts. Furthermore, we propose a novel style injection technique that enables controllable style transfer for the injected text. Through comprehensive experiments, we demonstrate the state-of-the-art performance of our method.

cs.CV↗

arXiv2025

Improved constraints on Higgs boson self-couplings with quartic and cubic power dependencies of the cross section

Precise determination of the Higgs boson self-couplings is essential for understanding the mechanism underlying electroweak symmetry breaking. However, owing to the limited number of Higgs boson pair events at the LHC, only loose constraints have been established to date. Current constraints are based on the assumption that the cross section is a quadratic function of the trilinear Higgs self-coupling within the $κ$ framework. Incorporating higher-order quantum corrections from virtual Higgs bosons would significantly alter this functional form, introducing new quartic and cubic power dependencies on the trilinear Higgs self-coupling. To derive this new functional form, we propose a specialized renormalization procedure that tracks all Higgs self-couplings at each calculation step. Additionally, we introduce renormalization constants for coupling modifiers within the $κ$ framework to ensure the cancellation of all ultraviolet divergences. With new functional forms of the cross sections in both the gluon-gluon fusion and vector boson fusion channels, the upper limit of $κ_{λ_{\rm 3H}}=λ_{\rm 3H}/λ_{\rm 3H}^{\rm SM}$ set by the ATLAS (CMS) collaboration is reduced from 6.6 (6.49) to 5.5 (5.39). However, extracting a meaningful constraint on the quartic Higgs self-coupling $λ_{\rm 4H}$ from Higgs boson pair production data remains challenging. We also present the invariant mass distributions of the Higgs boson pair at different values of $κ_λ$, which could assist in setting optimal cuts for experimental analysis.

hep-ph↗

arXiv2025

Nonlinear Magnetoelectric Edelstein Effect

The linear Edelstein effect is a cornerstone phenomenon in spintronics that describes the generation of spin magnetization in response to an applied electric field. Recent theoretical advances have reignited interest in its nonlinear counterpart, the nonlinear Edelstein effect, in which spin magnetization is induced by a second-order electric field. However, the intrinsic contribution to both effects is generally forbidden in systems preserving time-reversal symmetry ($\mathcal{T}$) or composite symmetries such as $\mathcal{T}τ_{1/2}$, where $τ_{1/2}$ denotes a half-lattice translation. In such systems, spin magnetization typically emerges either from extrinsic mechanisms but limited to metals due to their Fermi-surface property, or from dynamical electric fields with a terahertz driving frequency. Here, we propose a new mechanism for spin magnetization, arising from the interplay of magnetic and electric fields, termed the nonlinear magnetoelectric Edelstein effect. Remarkably, its intrinsic component, determined purely by the material's band structure, can appear even in $\mathcal{T}$-invariant materials, but lacking inversion symmetry ($\mathcal{P}$), including insulators. On the other hand, we illustrate that its extrinsic component can serve as a sensitive indicator of the Néel vector reversal in $\mathcal{P}\mathcal{T}$-symmetric antiferromagnetic materials, offering a novel route for antiferromagnetic order detection. To validate our theory, we perform explicit calculations using a two-band Dirac model and a tight-binding model on a honeycomb lattice, finding that both effects yield sizable spin magnetization. Our findings establish the nonlinear magnetoelectric Edelstein effect as a versatile platform for both exploring nonlinear spin physics and enabling symmetry-based detection of antiferromagnetic order.

cond-mat.mes-hall↗

arXiv2025

SceneMI: Motion In-betweening for Modeling Human-Scene Interactions

Modeling human-scene interactions (HSI) is essential for understanding and simulating everyday human behaviors. Recent approaches utilizing generative modeling have made progress in this domain; however, they are limited in controllability and flexibility for real-world applications. To address these challenges, we propose reformulating the HSI modeling problem as Scene-aware Motion In-betweening - a more tractable and practical task. We introduce SceneMI, a framework that supports several practical applications, including keyframe-guided character animation in 3D scenes and enhancing the motion quality of imperfect HSI data. SceneMI employs dual scene descriptors to comprehensively encode global and local scene context. Furthermore, our framework leverages the inherent denoising nature of diffusion models to generalize on noisy keyframes. Experimental results demonstrate SceneMI's effectiveness in scene-aware keyframe in-betweening and generalization to the real-world GIMO dataset, where motions and scenes are acquired by noisy IMU sensors and smartphones. We further showcase SceneMI's applicability in HSI reconstruction from monocular videos.

cs.CV↗

arXiv2025

Hypergraph Turán problem of the generalized triangle with bounded matching number

Let $\mathcal{H}$ be a 3-graph on $n$ vertices. The matching number $ν(\mathcal{H})$ is defined as the maximum number of disjoint edges in $\mathcal{H}$. The generalized triangle $F_5$ is a 3-graph on the vertex set $\{a,b,c,d,e\}$ with the edge set $\{abc, abd,cde\}$. In this paper, we showed that an $F_5$-free 3-graph $\mathcal{H}$ with matching number at most $s$ has at most $s\lfloor (n-s)^2/4\rfloor$ edges for $n\geq 30(s+1)$ and $s\geq 3$. For the proof, we establish a 2-colored version of Mantel's theorem, which may be of independent interests.

math.CO↗

arXiv2025

PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, where the LLM's ability to generate responses based on the combination of a given query and retrieved documents is crucial. However, most benchmarks focus on overall RAG system performance, rarely assessing LLM-specific capabilities. Current benchmarks emphasize broad aspects such as noise robustness, but lack a systematic and granular evaluation framework on document utilization. To this end, we introduce \textit{Placeholder-RAG-Benchmark}, a multi-level fine-grained benchmark, emphasizing the following progressive dimensions: (1) multi-level filtering abilities, (2) combination abilities, and (3) reference reasoning. To provide a more nuanced understanding of LLMs' roles in RAG systems, we formulate an innovative placeholder-based approach to decouple the contributions of the LLM's parametric knowledge and the external knowledge. Experiments demonstrate the limitations of representative LLMs in the RAG system's generation capabilities, particularly in error resilience and context faithfulness. Our benchmark provides a reproducible framework for developing more reliable and efficient RAG systems. Our code is available in https://github.com/Alipay-Med/PRGB.

cs.CL↗