arXiv subjects

Xingyu Wang

Publications and source records attributed to Xingyu Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

DocMMIR: A Framework for Document Multi-modal Information Retrieval

The rapid advancement of unsupervised representation learning and large-scale pre-trained vision-language models has significantly improved cross-modal retrieval tasks. However, existing multi-modal information retrieval (MMIR) studies lack a comprehensive exploration of document-level retrieval and suffer from the absence of cross-domain datasets at this granularity. To address this limitation, we introduce DocMMIR, a novel multi-modal document retrieval framework designed explicitly to unify diverse document formats and domains, including Wikipedia articles, scientific papers (arXiv), and presentation slides, within a comprehensive retrieval scenario. We construct a large-scale cross-domain multimodal benchmark, comprising 450K samples, which systematically integrates textual and visual information. Our comprehensive experimental analysis reveals substantial limitations in current state-of-the-art MLLMs (CLIP, BLIP2, SigLIP-2, ALIGN) when applied to our tasks, with only CLIP demonstrating reasonable zero-shot performance. Furthermore, we conduct a systematic investigation of training strategies, including cross-modal fusion methods and loss functions, and develop a tailored approach to train CLIP on our benchmark. This results in a +31% improvement in MRR@10 compared to the zero-shot baseline. All our data and code are released in https://github.com/J1mL1/DocMMIR.

cs.IR↗

arXiv2025

Affine web of type Q

We introduce a new diagrammatic $\Bbbk$-linear monoidal supercategory $QWeb^\bullet$, the affine web supercategory of type $Q$, where $\Bbbk$ is a commutative ring of characteristic not two. This category is the affinization of the web category of type $Q$, originally introduced by Brown and Kujawa. It serves as the type $Q$ analog of the affine web category introduced by Davidson, Kujawa, Muth and Zhu, and independently by Wang and one of the authors. We obtain diagrammatic integral bases for the Hom-spaces of this category. We show that $QWeb^\bullet$ provides a combinatorial model for a natural monoidal supercategory of endosuperfunctors for Lie superalgebras of type $Q$. .

math.RT↗

arXiv2025

Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions

While densely annotated image captions significantly facilitate the learning of robust vision-language alignment, methodologies for systematically optimizing human annotation efforts remain underexplored. We introduce Chain-of-Talkers (CoTalk), an AI-in-the-loop methodology designed to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints (e.g., total human annotation time). The framework is built upon two key insights. First, sequential annotation reduces redundant workload compared to conventional parallel annotation, as subsequent annotators only need to annotate the ``residual'' -- the missing visual information that previous annotations have not covered. Second, humans process textual input faster by reading while outputting annotations with much higher throughput via talking; thus a multimodal interface enables optimized efficiency. We evaluate our framework from two aspects: intrinsic evaluations that assess the comprehensiveness of semantic units, obtained by parsing detailed captions into object-attribute trees and analyzing their effective connections; extrinsic evaluation measures the practical usage of the annotated captions in facilitating vision-language alignment. Experiments with eight participants show our Chain-of-Talkers (CoTalk) improves annotation speed (0.42 vs. 0.30 units/sec) and retrieval performance (41.13% vs. 40.52%) over the parallel method.

cs.CL↗

arXiv2025

The vector-valued Allen-Cahn equation with potentials of high-dimensional double-wells under Robin boundary conditions

This work investigates the vector-valued Allen-Cahn equation with potentials of high-dimensional double-wells under Robin boundary conditions. We establish local-in-time convergence of solutions to mean curvature flow with a fixed contact angle $0<α\leq 90^\circ$, for a broad class of boundary energy densities and well-prepared initial data. The limiting sharp-interface system is derived, comprising harmonic heat flows in the bulk and minimal pair conditions at phase boundaries. The analysis combines the relative entropy method with gradient flow calibrations and weak convergence techniques. These results extend prior works on the analysis of the vector-valued case without boundary effects (Comm. Pure Appl. Math., 78:1199-1247, 2025) and the scalar-valued case with boundary contact energy (Calc. Var. Partial Differ. Equ., 61:201, 2022).

math.AP↗

arXiv2025

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse tasks involving 96 object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related information, including multi-view observations, proprioceptive robot state information, and linguistic task descriptions. To ensure data consistency and reliability for imitation learning, RoboMIND is built on a unified data collection platform and a standardized protocol, covering four distinct robotic embodiments: the Franka Emika Panda, the UR5e, the AgileX dual-arm robot, and a humanoid robot with dual dexterous hands. Our dataset also includes 5k real-world failure demonstrations, each accompanied by detailed causes, enabling failure reflection and correction during policy learning. Additionally, we created a digital twin environment in the Isaac Sim simulator, replicating the real-world tasks and assets, which facilitates the low-cost collection of additional training data and enables efficient evaluation. To demonstrate the quality and diversity of our dataset, we conducted extensive experiments using various imitation learning methods for single-task settings and state-of-the-art Vision-Language-Action (VLA) models for multi-task scenarios. By leveraging RoboMIND, the VLA models achieved high manipulation success rates and demonstrated strong generalization capabilities. To the best of our knowledge, RoboMIND is the largest multi-embodiment teleoperation dataset collected on a unified platform, providing large-scale and high-quality robotic training data. Our project is at https://x-humanoid-robomind.github.io/.

cs.RO↗

arXiv2025

Understanding and Detecting Peer Dependency Resolving Loop in npm Ecosystem

As the default package manager for Node.js, npm has become one of the largest package management systems in the world. To facilitate dependency management for developers, npm supports a special type of dependency, Peer Dependency, whose installation and usage differ from regular dependencies. However, conflicts between peer dependencies can trap the npm client into infinite loops, leading to resource exhaustion and system crashes. We name this problem PeerSpin. Although PeerSpin poses a severe risk to ecosystems, it was overlooked by previous studies, and its impacts have not been explored. To bridge this gap, this paper conducts the first in-depth study to understand and detect PeerSpin in the npm ecosystem. First, by systematically analyzing the npm dependency resolution, we identify the root cause of PeerSpin and characterize two peer dependency patterns to guide detection. Second, we propose a novel technique called Node-Replacement-Conflict based PeerSpin Detection, which leverages the state of the directory tree during dependency resolution to achieve accurate and efficient PeerSpin detection. Based on this technique, we developed a tool called PeerChecker to detect PeerSpin. Finally, we apply PeerChecker to the entire NPM ecosystem and find that 5,662 packages, totaling 72,968 versions, suffer from PeerSpin. Up until now, we confirmed 28 real PeerSpin problems by reporting them to the package maintainer. We also open source all PeerSpin analysis implementations, tools, and data sets to the public to help the community detect PeerSpin issues and enhance the reliability of the npm ecosystem.

cs.SE↗

arXiv2025

Sample Path Large Deviations for Multivariate Heavy-Tailed Hawkes Processes and Related Lévy Processes

In this paper, we develop sample path large deviations for multivariate Hawkes processes with heavy-tailed mutual excitation rates. Our results address a broad class of rare events in Hawkes processes at the sample path level and, via the cluster representation of Hawkes processes and a recent result on the tail asymptotics of the cluster sizes, unravel the most likely configurations of (multiple) large clusters that could trigger the target events. Our proof hinges on establishing the asymptotic equivalence, in terms of M-convergence, between a suitably scaled multivariate Hawkes process and a coupled Lévy process with multivariate hidden regular variation. Hence, along the way, we derive a sample path large deviations principle for a class of Lévy processes with multivariate hidden regular variation, which not only plays an auxiliary role in our analysis but is also of independent interest.

math.PR↗

arXiv2025

Tail Asymptotics of Cluster Sizes in Multivariate Heavy-Tailed Hawkes Processes

We examine a distributional fixed-point equation related to a multi-type branching process that is key in the cluster sizes analysis of multivariate heavy-tailed Hawkes processes. Specifically, we explore the tail behavior of its solution and demonstrate the emergence of a form of multivariate hidden regular variation. Large values of the cluster size vector result from one or several significant jumps. A discrete optimization problem involving any given rare event set of interest determines the exact configuration of these large jumps and the degree of hidden regular variation. Our proofs rely on a detailed probabilistic analysis of the spatiotemporal structure of multiple large jumps in multi-type branching processes.

math.PR↗

arXiv2025

Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Although some prompting methods, such as Chain-of-Thought, can improve the reasoning ability of LLMs to some extent, they suffer from an unfaithful issue where derived conclusions may not align with the generated reasoning chain. To address this issue, some studies employ the approach of propositional logic to further enhance logical reasoning abilities of LLMs. However, the potential omissions in the extraction of logical expressions in these methods can cause information loss in the logical reasoning process, thereby generating incorrect results. To this end, we propose Logic-of-Thought (LoT) prompting which employs propositional logic to generate expanded logical information descriptions and utilizes them as an additional augmentation to original contexts, thereby ensuring information completeness and enhancing logical reasoning ability. LoT is orthogonal to existing prompting methods and can be seamlessly integrated with them. Extensive experiments demonstrate that LoT boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks. In particular, LoT enhances Chain-of-Thought's performance on the ReClor dataset by +4.35%, improves Chain-of-Thought with Self-Consistency's performance on the RuleTaker dataset by +3.52%, and boosts performance of Tree-of-Thoughts on the ProofWriter dataset by +8%.

cs.CL↗

arXiv2025

Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics

We study decentralized multi-agent multi-armed bandits in fully heavy-tailed settings, where clients communicate over sparse random graphs with heavy-tailed degree distributions and observe heavy-tailed (homogeneous or heterogeneous) reward distributions with potentially infinite variance. The objective is to maximize system performance by pulling the globally optimal arm with the highest global reward mean across all clients. We are the first to address such fully heavy-tailed scenarios, which capture the dynamics and challenges in communication and inference among multiple clients in real-world systems. In homogeneous settings, our algorithmic framework exploits hub-like structures unique to heavy-tailed graphs, allowing clients to aggregate rewards and reduce noises via hub estimators when constructing UCB indices; under $M$ clients and degree distributions with power-law index $α> 1$, our algorithm attains a regret bound (almost) of order $O(M^{1 -\frac{1}α} \log{T})$. Under heterogeneous rewards, clients synchronize by communicating with neighbors, aggregating exchanged estimators in UCB indices; With our newly established information delay bounds on sparse random graphs, we prove a regret bound of $O(M \log{T})$. Our results improve upon existing work, which only address time-invariant connected graphs, or light-tailed dynamics in dense graphs and rewards.

cs.LG↗

arXiv2025

Phase Transition for Potentials of High-Dimensional Wells with a Mass-Type Constraint

Inspired by Lin-Pan-Wang (Comm. Pure Appl. Math., 65(6): 833-888, 2012), we continue to study the corresponding time-independent case of the Keller-Rubinstein-Sternberg problem. To be precise, we explore the asymptotic behavior of minimizers as $\varepsilon\to0$, for the functional $$\mathbf{E}_\varepsilon(u)= \int_Ω\left(|\nabla u|^{2}+\frac{1}{\varepsilon^{2}} F(u)\right) d x$$under a mass-type constraint $\int_Ωρ(u)\, dx=m$, where $ρ:\mathbb{R}^k \to \mathbb{R}\in Lip(\mathbb{R}^k)$ is specialized as a density function with $m$ representing a fixed total mass. The potential function $F$ vanishes on two disjoint, compact, connected, smooth Riemannian submanifolds $N^{\pm}\subset\mathbb{R}^k$. We analyze the expansion of $\mathbf{E}_\varepsilon(u_\varepsilon)$ for various density functions $ρ$, identifying the leading-order term in the asymptotic expansion, which depends on the geometry of the domain and the energy of minimal connecting orbits between $N^+$ and $N^-$. Furthermore, we estimate the higher-order term under different geometric assumptions and characterize the convergence $u_{\varepsilon_i}\to v $ in the ${L}^1$ sense.

math.AP↗

arXiv2024

Large Deviations and Metastability Analysis for Heavy-Tailed Dynamical Systems

This paper introduces novel frameworks for large deviations and metastability analysis in heavy-tailed stochastic dynamical systems. We develop and apply these frameworks within the context of stochastic difference equation $X^η_{j+1}(x) = X^η_{j}(x) + ηa\big( X^η_{j}(x)\big) + ησ\big( X^η_{j}(x)\big)Z_{j+1}$ and its variation with truncated dynamics $X^{η|b}_{j+1}(x) = X^{η|b}_{j}( x) + φ_b\big(ηa\big( X^{η|b}_{j}( x)\big) + ησ\big( X^{η|b}_{j}( x)\big) Z_{j+1}\big)$, where $φ_b(x) = (x/\|x\|)\max\{\|x\|, b\}$. The truncation operator $φ_b(\cdot)$ is often introduced as a modulation mechanism in heavy-tailed systems, such as stochastic gradient descent algorithms in deep learning. Thus, it is crucial to successfully analyze both $X^η_{j}(x)$ and $X^{η|b}_{j}(x)$. We establish locally uniform sample-path large deviations for both processes and translate these asymptotics into precise characterizations of the joint distributions of the first exit times and exit locations. Our large deviations asymptotics are sharp enough to rigorously characterize \emph{the catastrophe principle} by establishing the distributional limit of the sample paths conditional on the rare events of interest, thereby revealing the most likely paths through which rare events arise in heavy-tailed dynamical systems. Moreover the resulting limit theorem unveils a discrete hierarchy of phase transitions (i.e., exit times) as the truncation threshold $b$ varies. Together, these developments serve as a heavy-tailed counterpart of the classical Freidlin-Wentzell theory. We also present the corresponding results for continuous-time processes in the appendix.

math.PR↗

arXiv2024

VersiCode: Towards Version-controllable Code Generation

Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development, marked by frequent library updates. This gap significantly limits LLMs' deployment in realistic settings. In this paper, we propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM). In conjunction, we introduce VersiCode, a comprehensive Python dataset specifically designed to evaluate LLMs on these two tasks, together with a novel evaluation metric, Critical Diff Check (CDC@1), which assesses code generation against evolving API requirements. We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge, even for GPT-4o and other strong frontier models. We believe the novel tasks, dataset, and metric open up a new, important research direction that will further enhance LLMs' real-world applicability. The code and resources can be found at https://github.com/wutong8023/VersiCode.

cs.SE↗

arXiv2024

Strongly Efficient Rare-Event Simulation for Regularly Varying Lévy Processes with Infinite Activities

In this paper, we address rare-event simulation for heavy-tailed Lévy processes with infinite activities. The presence of infinite activities poses a critical challenge, making it impractical to simulate or store the precise sample path of the Lévy process. We present a rare-event simulation algorithm that incorporates an importance sampling strategy based on heavy-tailed large deviations, the stick-breaking approximation for the extrema of Lévy processes, the Asmussen-Rosiński approximation, and the randomized debiasing technique. By establishing a novel characterization for the Lipschitz continuity of the law of Lévy processes, we show that the proposed algorithm is unbiased and strongly efficient under mild conditions, and hence applicable to a broad class of Lévy processes. In numerical experiments, our algorithm demonstrates significant improvements in efficiency compared to the crude Monte-Carlo approach.

math.PR↗

arXiv2024

Electronic Phase Propagation Speed in BaFe$_2$As$_2$ Revealed by Dilatometry

Thermal expansion offers deep insights into phase transitions in condensed matter physics. Utilizing an advanced AC-temperature dilatometer with picometer resolution, this study clearly resolves the antiferromagnetic and structural transition in BaFe$_2$As$_2$. The implementation of temperature oscillation reveals a hysteresis near the transition temperature $T_\mathrm{N}$ with unprecedented resolution. Unexpectedly, we find that the hysteretic width exhibits a universal dependence on the parameters of temperature oscillation and the sample's longidutinal dimension, which in turn reveals a finite transition speed. Our quantitative analysis shows that this phase boundary propagates at a mere 188 $μ$m/s - a speed seven orders of magnitude slower than acoustic waves. It suggests a hidden thermodynamic constraint imposed by the electronic degrees of freedom. Our research not only sheds light on the dynamics of phase transitions between different correlated phases, but also establishes high precision dilatometry as a powerful tool for material studies. This measurement technique, when properly modified, can be extended to studies of other material properties such as piezoelectric, magneto-restriction, elastic modulus, etc.

cond-mat.supr-con↗

arXiv2024

ModuleGuard:Understanding and Detecting Module Conflicts in Python Ecosystem

Python has become one of the most popular programming languages for software development due to its simplicity, readability, and versatility. As the Python ecosystem grows, developers face increasing challenges in avoiding module conflicts, which occur when different packages have the same namespace modules. Unfortunately, existing work has neither investigated the module conflict comprehensively nor provided tools to detect the conflict. Therefore, this paper systematically investigates the module conflict problem and its impact on the Python ecosystem. We propose a novel technique called InstSimulator, which leverages semantics and installation simulation to achieve accurate and efficient module extraction. Based on this, we implement a tool called ModuleGuard to detect module conflicts for the Python ecosystem. For the study, we first collect 97 MC issues, classify the characteristics and causes of these MC issues, summarize three different conflict patterns, and analyze their potential threats. Then, we conducted a large-scale analysis of the whole PyPI ecosystem (4.2 million packages) and GitHub popular projects (3,711 projects) to detect each MC pattern and analyze their potential impact. We discovered that module conflicts still impact numerous TPLs and GitHub projects. This is primarily due to developers' lack of understanding of the modules within their direct dependencies, not to mention the modules of the transitive dependencies. Our work reveals Python's shortcomings in handling naming conflicts and provides a tool and guidelines for developers to detect conflicts.

cs.SE↗

arXiv2023

Computational prediction of ideal strength for a material

The ideal strength is crucial for predicting material behavior under extreme conditions, which can provide insights into material limits, guide design and engineer for enhanced performance and durability. In this work, we present a method within an allows for the estimation of tensile, shear, and indentation strengths in any crystallographic direction or plane. We have examined the strain-stress relationships of several well-known structures and compared our findings with previous work, demonstrating the effectiveness of our approach. Moreover, we performed extensive investigations into the indentation strength of hexagonal WC, \b{eta}-SiC, and MgA$l_2O_4$. The current study uncovers the modes of structural deformation and the underlying atomistic mechanisms. The insights gained from this study have significant implications for the further exploration and design of superhard materials.

cond-mat.mtrl-sci↗

arXiv2022

Exploiting potentialities for space-based quantum communication network: downlink quantum key distribution modelling and scheduling analysis

With the goal of a space-based quantum network is to have satellites distribute keys between any nodes on the ground, we consider an evolved quantum network from a near-term form, in which a space-based relay, satellite executes a sequence of satellite-based quantum key distribution (SatQKD) missions, allowing any two ground nodes to have a shared secure key. Accordingly, we develop a comprehensive framework for the dynamic simulation of SatQKD and consider scheduling QKD downlink in future space-based quantum communication network. The embedding of precise orbital model including beam diffractions and atmosphere effects makes our framework more realistic. Incorporated with the local meteorological data to channel loss model, the cloud cover contribution to transmission disturbance is quantified to realize a quasi-experimental scenario. We provide a trade-off consideration between the duration available for communications and its corresponding average link loss during one orbit, which could be used to support decisions involving the locations of ground stations and the selection of the orbital parameters for a quantum satellite. Our work also shows that satellite downlink schedule could allow for the possibility to consider strategies for different SatQKD missions such as extending connection for distant ground nodes, prioritized delivery, or promoting keys utilization, which can be used as a guideline to support decisions for future satellite application.

quant-ph↗