arXiv subjects

Ying Li

Publications and source records attributed to Ying Li.

At least 145 records · Page 8Linked to original sources

arXiv2025

Cabibbo-suppressed charged-current semileptonic decays of $Ξ_b$ baryons

We present the first perturbative QCD calculations of the $Ξ_b \to (Λ, Σ)$ transition form factors at leading order in $α_s$, which govern the Cabibbo-suppressed semileptonic decays $Ξ_b \to (Λ, Σ)\ell ν_\ell$ with $\ell = e, μ, τ$. Using these form factors, we evaluate differential and integrated branching fractions and angular observables within the helicity formalism. The branching ratios are predicted to be of order $10^{-4}$ for $Σ$ final states and $10^{-5}$ for $Λ$ final states, making them accessible to ongoing experiments such as LHCb. Ratios of decay rates between $τ$ and $e$ channels are also provided, offering new probes of lepton-flavor universality. Lepton-mass effects are found to significantly impact the integrated angular observables. Furthermore, a combined analysis of $b \to u$ and $b \to c$ transitions in $Ξ_b$ decays yields sub-percent precision for the ratios $\mathcal{R}_\ell(Σ/Ξ_c)$, enabling an independent determination of $|V_{ub}/V_{cb}|$ once the relevant decay-rate measurements become available.

hep-ph↗

arXiv2025

Breaking the illusion: Automated Reasoning of GDPR Consent Violations

Recent privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have established legal requirements for obtaining user consent regarding the collection, use, and sharing of personal data. These regulations emphasize that consent must be informed, freely given, specific, and unambiguous. However, there are still many violations, which highlight a gap between legal expectations and actual implementation. Consent mechanisms embedded in functional web forms across websites play a critical role in ensuring compliance with data protection regulations such as the GDPR and CCPA, as well as in upholding user autonomy and trust. However, current research has primarily focused on cookie banners and mobile app dialogs. These forms are diverse in structure, vary in legal basis, and are often difficult to locate or evaluate, creating a significant challenge for automated consent compliance auditing. In this work, we present Cosmic, a novel automated framework for detecting consent-related privacy violations in web forms. We evaluate our developed tool for auditing consent compliance in web forms, across 5,823 websites and 3,598 forms. Cosmic detects 3,384 violations on 94.1% of consent forms, covering key GDPR principles such as freely given consent, purpose disclosure, and withdrawal options. It achieves 98.6% and 99.1% TPR for consent and violation detection, respectively, demonstrating high accuracy and real-world applicability.

cs.CR↗

arXiv2025

Socio-technical aspects of Agentic AI

Agentic Artificial Intelligence (AI) represents a fundamental shift in the design of intelligent systems, characterized by interconnected components that collectively enable autonomous perception, reasoning, planning, action, and learning. Recent research on agentic AI has largely focused on technical foundations, including system architectures, reasoning and planning mechanisms, coordination strategies, and application-level performance across domains. However, the societal, ethical, economic, environmental, and governance implications of agentic AI remain weakly integrated into these technical treatments. This paper addresses this gap by presenting a socio-technical analysis of agentic AI that explicitly connects core technical components with societal context. We examine how architectural choices in perception, cognition, planning, execution, and memory introduce dependencies related to data governance, accountability, transparency, safety, and sustainability. To structure this analysis, we adopt the MAD-BAD-SAD construct as an analytical lens, capturing motivations, applications, and moral dilemmas (MAD); biases, accountability, and dangers (BAD); and societal impact, adoption, and design considerations (SAD). Using this lens, we analyze ethical considerations, implications, and challenges arising from contemporary agentic AI systems and assess their manifestation across emerging applications, including healthcare, education, industry, smart and sustainable cities, social services, communications and networking, and earth observation and satellite communications. The paper further identifies open challenges and suggests future research directions, framing agentic AI as an integrated socio-technical system whose behavior and impact are co-produced by algorithms, data, organizational practices, regulatory frameworks, and social norms.

cs.CY↗

arXiv2025

Resonant ALP-Portal Dark Matter Annihilation as a Solution to the $B^{\pm} \to K^{\pm} ν\barν$ Excess

The Belle II collaboration recently reported a $2.7σ$ excess in the rare decay $B^\pm \to K^\pm ν\barν$, potentially signaling new physics. We propose an axion-like particle (ALP)-portal dark matter (DM) framework to explain this anomaly while satisfying the observed DM relic abundance. By invoking a resonant annihilation mechanism ($m_a \sim 2m_χ$), we demonstrate that the ALP-mediated interactions between the Standard Model and DM sectors simultaneously account for the $B^\pm \to K^\pm ν\barν$ anomaly and thermal freeze-out dynamics. Two distinct scenarios-long-lived ALPs decaying outside detectors (displaced diphotons) and ALPs decaying invisibly to DM pairs (missing energy)-are examined. While the displaced diphotons scenario is excluded by kaon decay bounds ($K^\pm \to π^\pm + \text{inv.}$), the invisible decay channel remains unconstrained and aligns with Belle II's missing energy signature. Using the coupled Boltzmann equation formalism, we rigorously incorporate early kinetic decoupling effects, revealing deviations up to a factor of 20 from traditional relic density predictions in resonance regions. For the missing energy scenario, the viable parameter space features ALP-SM and ALP-DM couplings: $g_{aWW}(g_{aγγ}) \in (7.13 \times 10^{-5} - 9.60 \times 10^{-5})\, \text{GeV}^{-1}$ (from $B^\pm \to K^\pm a$) and $g_{aχχ} \in (7.12\times10^{-5} - 7.73\times 10^{-3})\, \text{GeV}^{-1}$ (for resonant annihilation), accommodating ALP masses $m_a \in (0.6, 4.8)\, \text{GeV}$. Therefore, this work establishes the ALP portal as a viable bridge between the $B^\pm \to K^\pm ν\barν$ anomaly and thermal DM production, emphasizing precision calculations of thermal decoupling in resonance regimes.

hep-ph↗

arXiv2025

Symbol Detection in Ambient Backscatter Communications Under Residual Time Synchronization Errors

Ambient backscatter communications (AmBC), where a backscatter transmitter (BT) modulates and reflects ambient signals to a backscatter receiver (BR), have been deemed a low-power communication technology for the Internet of Things. Previous work on symbol detection in AmBC assumed perfect time synchronization (TS), which is unrealistic in practice. The residual TS errors (RTSE) cause \emph{partial sample mismatch}, degrading symbol detection performance. To address this, we propose a new AmBC symbol detection framework that incorporates the BT's current and adjacent symbols, as well as channel coefficients. Using energy detector (ED) as a case study, we derive both exact and approximate bit error rate (BER) expressions. Our results show that the ED's BER performance degrades significantly under RTSE, with the symbol detection threshold optimized under the assumption of perfect TS. We then derive a closed-form expression for a near-optimal symbol detection threshold that minimizes BER under RTSE. To estimate the required parameters for the detection threshold, we propose a novel method exploiting the attributes of the BR's received signal samples. The analytical results are verified by simulation results.

cs.IT↗

arXiv2025

Multi-View Oriented GPLVM: Expressiveness and Efficiency

The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the spectral density and the kernel function. By modeling the spectral density with a bivariate Gaussian mixture, we then derive a generic and expressive kernel termed Next-Gen Spectral Mixture (NG-SM) for MV-GPLVMs. To address the inherent computational inefficiency of the NG-SM kernel, we design a new form of random Fourier feature approximation. Combined with a tailored reparameterization trick, this approximation enables scalable variational inference for both the model and the unified latent representations. Numerical evaluations across a diverse range of multi-view datasets demonstrate that our proposed method consistently outperforms state-of-the-art models in learning meaningful latent representations.

stat.ML↗

arXiv2025

Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems

Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs are limited by the use of multiple independent stationary Gaussian processes (GPs), leading to prohibitive computational and parametric complexity in high-dimensional settings and restricted modeling capacity for non-stationary dynamics. To address these challenges, we propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems. Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive non-stationary implicit process prior that can capture complex transition dynamics while significantly reducing model complexity. For the inference of the implicit process, we develop a variational inference algorithm that jointly approximates the posterior over the underlying GP and the neural network parameters defining the normalizing flows. To avoid explicit variational parameterization of the latent states, we further incorporate the ensemble Kalman filter (EnKF) into the variational framework, enabling accurate and efficient state estimation. Extensive empirical evaluations on synthetic and real-world datasets demonstrate the superior performance of our ETGPSSM in system dynamics learning, high-dimensional state estimation, and time-series forecasting, outperforming existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.

stat.ML↗

arXiv2025

Open Polymer Challenge: Post-Competition Report

Machine learning (ML) offers a powerful path toward discovering sustainable polymer materials, but progress has been limited by the lack of large, high-quality, and openly accessible polymer datasets. The Open Polymer Challenge (OPC) addresses this gap by releasing the first community-developed benchmark for polymer informatics, featuring a dataset with 10K polymers and 5 properties: thermal conductivity, radius of gyration, density, fractional free volume, and glass transition temperature. The challenge centers on multi-task polymer property prediction, a core step in virtual screening pipelines for materials discovery. Participants developed models under realistic constraints that include small data, label imbalance, and heterogeneous simulation sources, using techniques such as feature-based augmentation, transfer learning, self-supervised pretraining, and targeted ensemble strategies. The competition also revealed important lessons about data preparation, distribution shifts, and cross-group simulation consistency, informing best practices for future large-scale polymer datasets. The resulting models, analysis, and released data create a new foundation for molecular AI in polymer science and are expected to accelerate the development of sustainable and energy-efficient materials. Along with the competition, we release the test dataset at https://www.kaggle.com/datasets/alexliu99/neurips-open-polymer-prediction-2025-test-data. We also release the data generation pipeline at https://github.com/sobinalosious/ADEPT, which simulates more than 25 properties, including thermal conductivity, radius of gyration, and density.

cs.LG↗

arXiv2025

ManualVLA: A Unified VLA Model for Chain-of-Thought Manual Generation and Robotic Manipulation

Vision-Language-Action (VLA) models have recently emerged, demonstrating strong generalization in robotic scene understanding and manipulation. However, when confronted with long-horizon tasks that require defined goal states, such as LEGO assembly or object rearrangement, existing VLA models still face challenges in coordinating high-level planning with precise manipulation. Therefore, we aim to endow a VLA model with the capability to infer the "how" process from the "what" outcomes, transforming goal states into executable procedures. In this paper, we introduce ManualVLA, a unified VLA framework built upon a Mixture-of-Transformers (MoT) architecture, enabling coherent collaboration between multimodal manual generation and action execution. Unlike prior VLA models that directly map sensory inputs to actions, we first equip ManualVLA with a planning expert that generates intermediate manuals consisting of images, position prompts, and textual instructions. Building upon these multimodal manuals, we design a Manual Chain-of-Thought (ManualCoT) reasoning process that feeds them into the action expert, where each manual step provides explicit control conditions, while its latent representation offers implicit guidance for accurate manipulation. To alleviate the burden of data collection, we develop a high-fidelity digital-twin toolkit based on 3D Gaussian Splatting, which automatically generates manual data for planning expert training. ManualVLA demonstrates strong real-world performance, achieving an average success rate 32% higher than the previous hierarchical SOTA baseline on LEGO assembly and object rearrangement tasks.

cs.RO↗

arXiv2025

AI Agent for Source Finding by SoFiA-2 for SKA-SDC2

Source extraction is crucial in analyzing data from next-generation, large-scale sky surveys in radio bands, such as the Square Kilometre Array (SKA). Several source extraction programs, including SoFiA and Aegean, have been developed to address this challenge. However, finding optimal parameter configurations when applying these programs to real observations is non-trivial. For example, the outcomes of SoFiA intensely depend on several key parameters across its preconditioning, source-finding, and reliability-filtering modules. To address this issue, we propose a framework to automatically optimize these parameters using an AI agent based on a state-of-the-art reinforcement learning (RL) algorithm, i.e., Soft Actor-Critic (SAC). The SKA Science Data Challenge 2 (SDC2) dataset is utilized to assess the feasibility and reliability of this framework. The AI agent interacts with the environment by adjusting parameters based on the feedback from the SDC2 score defined by the SDC2 Team, progressively learning to select parameter sets that yield improved performance. After sufficient training, the AI agent can automatically identify an optimal parameter configuration that outperform the benchmark set by Team SoFiA within only 100 evaluation steps and with reduced time consumption. Our approach could address similar problems requiring complex parameter tuning, beyond radio band surveys and source extraction. Yet, high-quality training sets containing representative observations and catalogs of ground truth are essential.

cs.LG↗

arXiv2025

KSHSeek: Data-Driven Approaches to Mitigating and Detecting Knowledge-Shortcut Hallucinations in Generative Models

The emergence of large language models (LLMs) has significantly advanced the development of natural language processing (NLP), especially in text generation tasks like question answering. However, model hallucinations remain a major challenge in natural language generation (NLG) tasks due to their complex causes. We systematically expand on the causes of factual hallucinations from the perspective of knowledge shortcuts, analyzing hallucinations arising from correct and defect-free data and demonstrating that knowledge-shortcut hallucinations are prevalent in generative models. To mitigate this issue, we propose a high similarity pruning algorithm at the data preprocessing level to reduce spurious correlations in the data. Additionally, we design a specific detection method for knowledge-shortcut hallucinations to evaluate the effectiveness of our mitigation strategy. Experimental results show that our approach effectively reduces knowledge-shortcut hallucinations, particularly in fine-tuning tasks, without negatively impacting model performance in question answering. This work introduces a new paradigm for mitigating specific hallucination issues in generative models, enhancing their robustness and reliability in real-world applications.

cs.CL↗

arXiv2025

ParticleGS: Learning Neural Gaussian Particle Dynamics from Videos for Prior-free Physical Motion Extrapolation

The ability to extrapolate dynamic 3D scenes beyond the observed timeframe is fundamental to advancing physical world understanding and predictive modeling. Existing dynamic 3D reconstruction methods have achieved high-fidelity rendering of temporal interpolation, but typically lack physical consistency in predicting the future. To overcome this issue, we propose ParticleGS, a physics-based framework that reformulates dynamic 3D scenes as physically grounded systems. ParticleGS comprises three key components: 1) an encoder that decomposes the scene into static properties and initial dynamic physical fields; 2) an evolver based on Neural Ordinary Differential Equations (Neural ODEs) that learns continuous-time dynamics for motion extrapolation; and 3) a decoder that reconstructs 3D Gaussians from evolved particle states for rendering. Through this design, ParticleGS integrates physical reasoning into dynamic 3D representations, enabling accurate and consistent prediction of the future. Experiments show that ParticleGS achieves state-of-the-art performance in extrapolation while maintaining rendering quality comparable to leading dynamic 3D reconstruction methods.

cs.CV↗

arXiv2025

UVLM: Benchmarking Video Language Model for Underwater World Understanding

Recently, the remarkable success of large language models (LLMs) has achieved a profound impact on the field of artificial intelligence. Numerous advanced works based on LLMs have been proposed and applied in various scenarios. Among them, video language models (VidLMs) are particularly widely used. However, existing works primarily focus on terrestrial scenarios, overlooking the highly demanding application needs of underwater observation. To overcome this gap, we introduce UVLM, an under water observation benchmark which is build through a collaborative approach combining human expertise and AI models. To ensure data quality, we have conducted in-depth considerations from multiple perspectives. First, to address the unique challenges of underwater environments, we selected videos that represent typical underwater challenges including light variations, water turbidity, and diverse viewing angles to construct the dataset. Second, to ensure data diversity, the dataset covers a wide range of frame rates, resolutions, 419 classes of marine animals, and various static plants and terrains. Next, for task diversity, we adopted a structured design where observation targets are categorized into two major classes: biological and environmental. Each category includes content observation and change/action observation, totaling 20 distinct task types. Finally, we designed several challenging evaluation metrics to enable quantitative comparison and analysis of different methods. Experiments on two representative VidLMs demonstrate that fine-tuning VidLMs on UVLM significantly improves underwater world understanding while also showing potential for slight improvements on existing in-air VidLM benchmarks, such as VideoMME and Perception text. The dataset and prompt engineering will be released publicly.

cs.CV↗

arXiv2025

ManipDreamer3D : Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectory

Data scarcity continues to be a major challenge in the field of robotic manipulation. Although diffusion models provide a promising solution for generating robotic manipulation videos, existing methods largely depend on 2D trajectories, which inherently face issues with 3D spatial ambiguity. In this work, we present a novel framework named ManipDreamer3D for generating plausible 3D-aware robotic manipulation videos from the input image and the text instruction. Our method combines 3D trajectory planning with a reconstructed 3D occupancy map created from a third-person perspective, along with a novel trajectory-to-video diffusion model. Specifically, ManipDreamer3D first reconstructs the 3D occupancy representation from the input image and then computes an optimized 3D end-effector trajectory, minimizing path length while avoiding collisions. Next, we employ a latent editing technique to create video sequences from the initial image latent and the optimized 3D trajectory. This process conditions our specially trained trajectory-to-video diffusion model to produce robotic pick-and-place videos. Our method generates robotic videos with autonomously planned plausible 3D trajectories, significantly reducing human intervention requirements. Experimental results demonstrate superior visual quality compared to existing methods.

cs.RO↗

arXiv2025

Branching ratios and CP asymmetries of $B^0 \to η_c f_0$ in the improved perturbative QCD formalism

Motivated by the idea of fragmented scalar glueball, we investigate the decays $B^0 \to η_c f_0$ within the improved perturbative QCD (iPQCD) framework by including the known next-to-leading order corrections. Here, $B^0$ and $f_0$ denote the neutral $B_{d,s}^0$ mesons and the light scalar mesons $f_0(500, 980, 1370, 1500)$ under the $q\bar q$ assignment. The CP-averaged branching ratios (BRs) and the CP asymmetries of $B^0 \to η_c f_0$ are evaluated with the $f_0(500)[f_0(1370)]-f_0(980)[f_0(1500)]$ mixing in quark-flavor basis. For effective comparisons with the near-future measurements, we further derive the $B^0 \to η_c f_0 (\to π^+ π^-/K^+ K^-)$ BRs under the narrow-width approximation. ${\rm BR}(B_s^0 \to η_c f_0(980) (\to π^+ π^-))= (2.87^{+1.38}_{-1.29}) \times 10^{-4}$ and ${\rm BR}(B_d^0 \to η_c f_0(500)(\to π^+π^-))/{\rm BR}(B_s^0 \to η_c f_0(980)(\to π^+π^-)) = (12^{+8}_{-7})\%$ obtained in the iPQCD formalism agree with the available measurements and/or predictions within uncertainties. Large BRs of $B_s^0 \to η_c f_0(1500) (\to π^+ π^-/K^+ K^-)$ and large direct CP asymmetries of $B^0 \to η_c f_0(1370, 1500)$ are accessible in the LHCb and Belle-II experiments. The experimental tests of these iPQCD predictions would help us to understand the nature of these light scalars more deeply and provide evidences to decipher $f_0(1500)$ as a primary or fragmented scalar glueball potentially.

hep-ph↗

arXiv2025

ZeroLog: Zero-Label Generalizable Cross-System Log-based Anomaly Detection

Log-based anomaly detection is an important task in ensuring the stability and reliability of software systems. One of the key problems in this task is the lack of labeled logs. Existing works usually leverage large-scale labeled logs from mature systems to train an anomaly detection model of a target system based on the idea of transfer learning. However, these works still require a certain number of labeled logs from the target system. In this paper, we take a step forward and study a valuable yet underexplored setting: zero-label cross-system log-based anomaly detection, that is, no labeled logs are available in the target system. Specifically, we propose ZeroLog, a system-agnostic representation meta-learning method that enables cross-system log-based anomaly detection under zero-label conditions. To achieve this, we leverage unsupervised domain adaptation to perform adversarial training between the source and target domains, aiming to learn system-agnostic general feature representations. By employing meta-learning, the learned representations are further generalized to the target system without any target labels. Experimental results on three public log datasets from different systems show that ZeroLog reaches over 80% F1-score without labels, comparable to state-of-the-art cross-system methods trained with labeled logs, and outperforms existing methods under zero-label conditions.

cs.SE↗

arXiv2025

Generality Is Not Enough: Zero-Label Cross-System Log-Based Anomaly Detection via Knowledge-Level Collaboration

Log-based anomaly detection is crucial for ensuring software system stability. However, the scarcity of labeled logs limits rapid deployment to new systems. Cross-system transfer has become an important research direction. State-of-the-art approaches perform well with a few labeled target logs, but limitations remain: small-model methods transfer general knowledge but overlook mismatches with the target system's proprietary knowledge; LLM-based methods can capture proprietary patterns but rely on a few positive examples and incur high inference cost. Existing LLM-small model collaborations route 'simple logs' to the small model and 'complex logs' to the LLM based on output uncertainty. In zero-label cross-system settings, supervised sample complexity is unavailable, and such routing does not consider knowledge separation. To address this, we propose GeneralLog, a novel LLM-small model collaborative method for zero-label cross-system log anomaly detection. GeneralLog dynamically routes unlabeled logs, letting the LLM handle 'proprietary logs' and the small model 'general logs,' enabling cross-system generalization without labeled target logs. Experiments on three public log datasets show that GeneralLog achieves over 90% F1-score under a fully zero-label setting, significantly outperforming existing methods.

cs.SE↗

arXiv2025

Correlated PQCD Analysis of the Semileptonic Decays $\overline{B}^0 \to D^{()+}\ell^-\barν_\ell$ and the Nonleptonic Decays $ \overline{B}^0 \to D^{()+}π^-$

We present a unified analysis of $\overline{B}^0 \to D^{(*)+}\ell^-\barν_\ell$ and $\overline{B}^0 \to D^{(*)+}π^-$ decays using the perturbative QCD (PQCD) approach. The $B \to D^{(*)}$ transition form factors are calculated at low $q^2$ and extrapolated to the high-$q^2$ region using the latest lattice QCD results via a model-independent $z$-expansion. This hybrid method provides a precise form factor description across the full kinematic range. We then predict the branching fractions and the lepton flavor universality ratios $R(D) = 0.336^{+0.014}_{-0.013}$ and $R(D^*) = 0.271^{+0.010}_{-0.010}$, which are consistent with the latest experimental averages. Furthermore, we perform a correlated study of the nonleptonic $ \overline{B}^0 \to D^{(*)+}π^-$ decays, calculating both factorizable and nonfactorizable amplitudes. To reduce hadronic uncertainties, we introduce and calculate the differential ratio $R^{(*)}_{π/\ell}(q^2)$, defined between nonleptonic and semileptonic decay rates, providing a sensitive test of factorization and possible new physics effects. The predictions presented here can be directly tested in ongoing Belle II and LHCb experiments.

hep-ph↗