arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 541 records · Page 30Linked to original sources

arXiv2025

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association

Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-commerce, customers increasingly seek LLM-based assistants to generate scripts and recommend products at each step, thereby facilitating convenient and efficient shopping experiences. However, this capability remains underexplored due to several challenges, including the inability of LLMs to simultaneously conduct script planning and product retrieval, difficulties in matching products caused by semantic discrepancies between planned actions and search queries, and a lack of methods and benchmark data for evaluation. In this paper, we step forward by formally defining the task of E-commerce Script Planning (EcomScript) as three sequential subtasks. We propose a novel framework that enables the scalable generation of product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. By applying our framework to real-world e-commerce data, we construct the very first large-scale EcomScript dataset, EcomScriptBench, which includes 605,229 scripts sourced from 2.4 million products. Human annotations are then conducted to provide gold labels for a sampled subset, forming an evaluation benchmark. Extensive experiments reveal that current (L)LMs face significant challenges with EcomScript tasks, even after fine-tuning, while injecting product purchase intentions improves their performance.

cs.CL↗

arXiv2025

Numerical simulations on First-order phase transition through thermal fluctuation

In this Letter, we numerically present the possibility of the first-order phase transition occurring through the thermal fluctuation in the early universe. We find that when the temperature is slightly higher than the mass scale of the background field, the bubble-like field configurations appear proceeded by oscillons, which expand and collide to finish the phase transition. We provide the false vacuum decay rate and the accompanied gravitational waves. We also present the vacuum phase transition comparison of the quantum tunneling case and thermal fluctuation case.

hep-ph↗

arXiv2025

LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer

In recent years, large-scale pre-trained diffusion transformer models have made significant progress in video generation. While current DiT models can produce high-definition, high-frame-rate, and highly diverse videos, there is a lack of fine-grained control over the video content. Controlling the motion of subjects in videos using only prompts is challenging, especially when it comes to describing complex movements. Further, existing methods fail to control the motion in image-to-video generation, as the subject in the reference image often differs from the subject in the reference video in terms of initial position, size, and shape. To address this, we propose the Leveraging Motion Prior (LMP) framework for zero-shot video generation. Our framework harnesses the powerful generative capabilities of pre-trained diffusion transformers to enable motion in the generated videos to reference user-provided motion videos in both text-to-video and image-to-video generation. To this end, we first introduce a foreground-background disentangle module to distinguish between moving subjects and backgrounds in the reference video, preventing interference in the target video generation. A reweighted motion transfer module is designed to allow the target video to reference the motion from the reference video. To avoid interference from the subject in the reference video, we propose an appearance separation module to suppress the appearance of the reference subject in the target video. We annotate the DAVIS dataset with detailed prompts for our experiments and design evaluation metrics to validate the effectiveness of our method. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in generation quality, prompt-video consistency, and control capability. Our homepage is available at https://vpx-ecnu.github.io/LMP-Website/

cs.CV↗

arXiv2025

Covariant analysis of electromagnetic current on the light cone: exposition with scalar Yukawa theory

We present the first systematic investigation of the Lorentz covariance of the charge form factor for a strongly coupled scalar theory in (3+1)-dimensions. Our results are based on the first non-perturbative solution of the scalar Yukawa theory with a Fock sector expansion including up to thee-particles (one mock nucleon plus two mock pions or two mock nucleons plus one mock anti-nucleon). The light-front Hamiltonian is constructed and renormalized using a Fock sector dependent scheme. The derived eigenvalue equation is then solved non-perturbatively to obtain the wave functions, which are then used to compute the current matrix element. We perform a covariant analysis of the current matrix element taking into account possible violation of the Poincaré symmetry due to the Fock sector truncation. The physical form factor depends on two boost invariants $ζ, Δ^2_\perp$, instead of the single Lorentz invariant $Q^2$. Instead of adopting the conventional Drell-Yan frame $ζ= 0$, we evaluate the form factor in general frames, and use the frame dependence to quantitatively gauge the loss of the Lorentz covariance. Our numerical result shows that as more Fock sectors are included, the frame dependence reduces dramatically. In particular, the anti-nucleon degree of freedom plays an important role in the reduction of the frame dependence, even though it only takes a small portion within the state vector. We also find that there is no zero-mode contribution to the current for the scalar Yukawa theory.

hep-ph↗

arXiv2025

The GenUI Study: Exploring the Design of Generative UI Tools to Support UX Practitioners and Beyond

AI can now generate high-fidelity UI mock-up screens from a high-level textual description, promising to support UX practitioners' work. However, it remains unclear how UX practitioners would adopt such Generative UI (GenUI) models in a way that is integral and beneficial to their work. To answer this question, we conducted a formative study with 37 UX-related professionals that consisted of four roles: UX designers, UX researchers, software engineers, and product managers. Using a state-of-the-art GenUI tool, each participant went through a week-long, individual mini-project exercise with role-specific tasks, keeping a daily journal of their usage and experiences with GenUI, followed by a semi-structured interview. We report findings on participants' workflow using the GenUI tool, how GenUI can support all and each specific roles, and existing gaps between GenUI and users' needs and expectations, which lead to design implications to inform future work on GenUI development.

cs.HC↗

arXiv2025

MPS-Prover: Advancing Stepwise Theorem Proving by Multi-Perspective Search and Data Curation

Automated Theorem Proving (ATP) in formal languages remains a formidable challenge in AI, demanding rigorous logical deduction and navigating vast search spaces. While large language models (LLMs) have shown promising performance, existing stepwise provers often suffer from biased search guidance, leading to inefficiencies and suboptimal proof strategies. This paper introduces the Multi-Perspective Search Prover (MPS-Prover), a novel stepwise ATP system designed to overcome these limitations. MPS-Prover incorporates two key innovations: a highly effective post-training data curation strategy that prunes approximately 40% of redundant training data without sacrificing performance, and a multi-perspective tree search mechanism. This search integrates a learned critic model with strategically designed heuristic rules to diversify tactic selection, prevent getting trapped in unproductive states, and enhance search robustness. Extensive evaluations demonstrate that MPS-Prover achieves state-of-the-art performance on multiple challenging benchmarks, including miniF2F and ProofNet, outperforming prior 7B parameter models. Furthermore, our analyses reveal that MPS-Prover generates significantly shorter and more diverse proofs compared to existing stepwise and whole-proof methods, highlighting its efficiency and efficacy. Our work advances the capabilities of LLM-based formal reasoning and offers a robust framework and a comprehensive analysis for developing more powerful theorem provers.

cs.AI↗

arXiv2025

Degeneration of Calabi-Yau metrics and canonical basis

For polarised degenerations of Calabi-Yau manifolds whose essential skeleton has dimension $1\leq m\leq n$, we show that the $C^0$ potential theoretic limit of the Calabi-Yau metrics agrees with the non-archimedean Calabi-Yau metric on the Berkovich analytification. Moreover, this limit data can be encoded into the unique minimiser of the Kontorovich functional of an optimal transport problem, under some algebro-geometric assumptions on the existence of a canonical basis of sections for tensor powers of the polarisation line bundle.

math.DG↗

arXiv2025

Neutron Spin Resonance Near a Lifshitz Transition in Overdoped Ba$_{0.4}$K$_{0.6}$Fe$_2$As$_2$

Elucidating the relationship between spin excitations and fermiology is essential for clarifying the pairing mechanism in iron-based superconductors (FeSCs). Here, we report inelastic neutron scattering results on the hole overdoped Ba$_{0.4}$K$_{0.6}$Fe$_2$As$_2$ near a Lifshitz transition, where the electron pocket at $M$ point is nearly replace by four hole pockets. In the normal state, the spin excitations are observed at incommensurate wave vectors with chimney-like dispersions. By cooling down to the superconducting state, a neutron spin resonance mode emerges with a peak energy of $E_r=$ 14-15 meV weakly modulated along $L$-direction. The incommensurability notably increases at low energies, giving rise to downward dispersions of the resonance mode. This behavior contrasts sharply with the upward dispersions of resonance observed in optimally doped Ba$_{0.67}$K$_{0.33}$Fe$_2$As$_2$ contributed by the hole to electron scattering, but resembles with the cases in KFe$_2$As$_2$ and KCa$_2$Fe$_4$As$_4$F$_2$ where the fermiology are dominated by hole pockets. These results highlight the critical role of electronic structure modifications near the Fermi level, especially in governing interband scattering under imperfect nesting conditions, which fundamentally shape the spin dynamics of FeSCs.

cond-mat.supr-con↗

arXiv2025

Time-dependent Hole States in Multiconfigurational Time-Dependent Hartree-Fock Approaches: Applications in Photoionization of Water Molecule

By simulating the real-time multielectron wavefunction with the multi-configurational time-dependent Hartree-Fock (MCTDHF) approach, we conduct an \textit{ab initio} study of the single-photon ionization process of a body-fixed water molecule ($\mathrm{H_2O}$) driven by attosecond pulses. To this end, we present a full-dimensional implementation of the MCTDHF method based on one-center expansions, allowing for the simulation of arbitrarily polarized lasers and multi-center polyatomic potentials. With a rigorous definition of the time-dependent hole state (TDHS) using the time-domain generalization of extended Koopmans' theorem (TD-EKT), we derive the reduced ion density matrix within the MCTDHF framework, which inherently encodes the total and channel-resolved photoionization cross sections of $\mathrm{H_2O}$. The cross sections obtained are benchmarked against existing experimental and theoretical results, validating the TDHS formalism. Furthermore, by adjusting the phase delay and intensity ratio of a pair of orthogonally polarized attosecond pulses, we explore the ultrafast control of attosecond coherence between electronic states of $\mathrm{H_2O^+}$.

physics.atom-ph↗

arXiv2025

Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligence

Multimodal learning enhances the perceptual capabilities of cognitive systems by integrating information from different sensory modalities. However, existing multimodal fusion research typically assumes static integration, not fully incorporating key dynamic mechanisms found in the brain. Specifically, the brain exhibits an inverse effectiveness phenomenon, wherein weaker unimodal cues yield stronger multisensory integration benefits; conversely, when individual modal cues are stronger, the effect of fusion is diminished. This mechanism enables biological systems to achieve robust cognition even with scarce or noisy perceptual cues. Inspired by this biological mechanism, we explore the relationship between multimodal output and information from individual modalities, proposing an inverse effectiveness driven multimodal fusion (IEMF) strategy. By incorporating this strategy into neural networks, we achieve more efficient integration with improved model performance and computational efficiency, demonstrating up to 50% reduction in computational cost across diverse fusion methods. We conduct experiments on audio-visual classification, continual learning, and question answering tasks to validate our method. Results consistently demonstrate that our method performs excellently in these tasks. To verify universality and generalization, we also conduct experiments on Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN), with results showing good adaptability to both network types. Our research emphasizes the potential of incorporating biologically inspired mechanisms into multimodal networks and provides promising directions for the future development of multimodal artificial intelligence. The code is available at https://github.com/Brain-Cog-Lab/IEMF.

cs.NE↗

arXiv2025

Private Transformer Inference in MLaaS: A Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service (MLaaS) raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private Transformer Inference (PTI) offers a solution by utilizing cryptographic techniques such as secure multi-party computation and homomorphic encryption, enabling inference while preserving both user data and model privacy. This paper reviews recent PTI advancements, highlighting state-of-the-art solutions and challenges. We also introduce a structured taxonomy and evaluation framework for PTI, focusing on balancing resource efficiency with privacy and bridging the gap between high-performance inference and data privacy.

cs.CR↗

arXiv2025

An effcient variational quantum Korkin-Zolotarev algorithm for solving shortest vector problems

Noisy intermediate-scale quantum cryptanalysis focuses on the capability of near-term quantum devices to solve the mathematical problems underlying cryptography, and serves as a cornerstone for the design of post-quantum cryptographic algorithms. For the shortest vector problem (SVP), which is one of the computationally hard problems in lattice-based cryptography, existing near-term quantum cryptanalysis algorithms map the problem onto a fully-connected quantum Ising Hamiltonian, and obtain the solution by optimizing for the first excited state. However, as the quantum system scales with the problem size, determining the first excited state becomes intractable due to the exponentially increased complexity for large-scale SVP instances. In this paper, we propose a variational quantum Korkin-Zolotarev (VQKZ) algorithm, which significantly reduces the qubit requirement for solving the SVP. Specifically, by transforming the original SVP into a series of subproblems on projected sublattices, the proposed VQKZ algorithm enables near-term quantum devices to solve SVP instances with lattice dimensions 61.39% larger than those solvable by previous methods. Furthermore, numerical simulations demonstrate that the proposed VQKZ algorithm can significantly outperform existing methods in terms of the length of solution vectors.

quant-ph↗

arXiv2025

A Comprehensive Survey of Electrical Stimulation Haptic Feedback in Human-Computer Interaction

Haptic perception and feedback play a pivotal role in interactive experiences, forming an essential component of human-computer interaction (HCI). In recent years, the field of haptic interaction has witnessed significant advancements, particularly in the area of electrical haptic feedback, driving innovation across various domains. To gain a comprehensive understanding of the current state of research and the latest developments in electrical haptic interaction, this study systematically reviews the literature in this area. Our investigation covers key aspects including haptic devices, haptic perception mechanisms, the comparison and integration of electrical haptic feedback with other feedback modalities, and their diverse applications. Specifically, we conduct a systematic analysis of 110 research papers to explore the forefront of electrical haptic feedback, providing insights into its latest trends, challenges, and future directions.

cs.HC↗

arXiv2025

The origin of strings and rings in the atomic dynamics of disordered systems

It has long been believed that the atomic dynamics in disordered structures, such as undercooled liquids and pre-melted interfaces, are characterized by collective atomic rearrangements in the form of quasi-one-dimensional chains of atomic displacements (strings) and their closed forms (rings). Here, we show by molecular dynamics (MD) simulations that strings involving more than a few atoms do not form by a single collective event. Instead, they represent trajectories of propagating local density perturbations, which we call densitons. The atoms on this trajectory are almost indistinguishable from their environments except for the moving head of the string (densiton). A densiton migrates by either single-atom jumps or a concerted rearrangement of 2-3 atoms. The simulations reveal a remarkable similarity between the strings in disordered and crystalline structures, in which the densitons localize into point defects. This work calls for a significant reinterpretation of the string concept and instead proposes a densiton model of the atomic dynamics.

cond-mat.soft↗

arXiv2025

Attosecond-Resolved Photoionization Dynamics and Interference-Enhanced Photoelectron Circular Dichroism in Chiral Molecules

Chiral molecules exhibit enantiosensitive light-matter interactions, with photoelectron circular dichroism (PECD) serving as a sensitive probe of molecular chirality through the asymmetry in the photoelectron wavepacket amplitude. Here, we demonstrate a photoelectron interferometric approach to access the phase of the photoelectron wavepacket and uncover attosecond dynamics in chiral molecule photoionization. Using circularly polarized attosecond XUV pulse trains synchronized with IR fields, we reveal distinct time delays between forward- and backward-ejected photoelectrons in a randomly oriented ensemble of chiral molecules. Moreover, we predict a pronounced enhancement of PECD due to the interference of the two photoionization pathways. The forward-backward time delay difference and the PECD are more prominent when the IR field counter-rotates with the XUV field. These results imply the counter-rotating IR field is more efficient in generating odd-parity photoelectron wavepackets in continuum-continuum transitions, highlighting the critical role of long-range chiral potential. Our work demonstrates a way of coherent control over the chiral photoelectron wavepackets, providing a route to enhance chiral signals and manipulate ultrafast chiral dynamics on attosecond time scales.

physics.atom-ph↗

arXiv2025

Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture

Multimodal sentiment analysis, a pivotal task in affective computing, seeks to understand human emotions by integrating cues from language, audio, and visual signals. While many recent approaches leverage complex attention mechanisms and hierarchical architectures, we propose a lightweight, yet effective fusion-based deep learning model tailored for utterance-level emotion classification. Using the benchmark IEMOCAP dataset, which includes aligned text, audio-derived numeric features, and visual descriptors, we design a modality-specific encoder using fully connected layers followed by dropout regularization. The modality-specific representations are then fused using simple concatenation and passed through a dense fusion layer to capture cross-modal interactions. This streamlined architecture avoids computational overhead while preserving performance, achieving a classification accuracy of 92% across six emotion categories. Our approach demonstrates that with careful feature engineering and modular design, simpler fusion strategies can outperform or match more complex models, particularly in resource-constrained environments.

cs.CL↗

arXiv2025

Spatiotemporal Non-Uniformity-Aware Online Task Scheduling in Collaborative Edge Computing for Industrial Internet of Things

Mobile edge computing mitigates the shortcomings of cloud computing caused by unpredictable wide-area network latency and serves as a critical enabling technology for the Industrial Internet of Things (IIoT). Unlike cloud computing, mobile edge networks offer limited and distributed computing resources. As a result, collaborative edge computing emerges as a promising technology that enhances edge networks' service capabilities by integrating computational resources across edge nodes. This paper investigates the task scheduling problem in collaborative edge computing for IIoT, aiming to optimize task processing performance under long-term cost constraints. We propose an online task scheduling algorithm to cope with the spatiotemporal non-uniformity of user request distribution in distributed edge networks. For the spatial non-uniformity of user requests across different factories, we introduce a graph model to guide optimal task scheduling decisions. For the time-varying nature of user request distribution and long-term cost constraints, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time subproblems that do not require prior knowledge of future system states. Given the NP-hard nature of the subproblems, we design a heuristic-based hierarchical optimization approach incorporating enhanced discrete particle swarm and harmonic search algorithms. Finally, an imitation learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Comprehensive theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed schemes.

cs.PF↗

arXiv2025

AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit

Adaptive teaming-the capability of agents to effectively collaborate with unfamiliar teammates without prior coordination-is widely explored in virtual video games but overlooked in real-world multi-robot contexts. Yet, such adaptive collaboration is crucial for real-world applications, including border surveillance, search-and-rescue, and counter-terrorism operations. To address this gap, we introduce AT-Drone, the first dedicated benchmark explicitly designed to facilitate comprehensive training and evaluation of adaptive teaming strategies in multi-drone pursuit scenarios. AT-Drone makes the following key contributions: (1) An adaptable simulation environment configurator that enables intuitive and rapid setup of adaptive teaming multi-drone pursuit tasks, including four predefined pursuit environments. (2) A streamlined real-world deployment pipeline that seamlessly translates simulation insights into practical drone evaluations using edge devices and Crazyflie drones. (3) A novel algorithm zoo integrated with a distributed training framework, featuring diverse algorithms explicitly tailored, for the first time, to multi-pursuer and multi-evader settings. (4) Standardized evaluation protocols with newly designed unseen drone zoos, explicitly designed to rigorously assess the performance of adaptive teaming. Comprehensive experimental evaluations across four progressively challenging multi-drone pursuit scenarios confirm AT-Drone's effectiveness in advancing adaptive teaming research. Real-world drone experiments further validate its practical feasibility and utility for realistic robotic operations. Videos, code and weights are available at \url{https://sites.google.com/view/at-drone}.

cs.RO↗