arXiv subjects

Xin Wang

Publications and source records attributed to Xin Wang.

At least 487 records · Page 27Linked to original sources

arXiv2025

Towards Multimodal Graph Large Language Model

Multi-modal graphs, which integrate diverse multi-modal features and relations, are ubiquitous in real-world applications. However, existing multi-modal graph learning methods are typically trained from scratch for specific graph data and tasks, failing to generalize across various multi-modal graph data and tasks. To bridge this gap, we explore the potential of Multi-modal Graph Large Language Models (MG-LLM) to unify and generalize across diverse multi-modal graph data and tasks. We propose a unified framework of multi-modal graph data, task, and model, discovering the inherent multi-granularity and multi-scale characteristics in multi-modal graphs. Specifically, we present five key desired characteristics for MG-LLM: 1) unified space for multi-modal structures and attributes, 2) capability of handling diverse multi-modal graph tasks, 3) multi-modal graph in-context learning, 4) multi-modal graph interaction with natural language, and 5) multi-modal graph reasoning. We then elaborate on the key challenges, review related works, and highlight promising future research directions towards realizing these ambitious characteristics. Finally, we summarize existing multi-modal graph datasets pertinent for model training. We believe this paper can contribute to the ongoing advancement of the research towards MG-LLM for generalization across multi-modal graph data and tasks.

cs.LG↗

arXiv2025

Multi-modal Generative AI: Multi-modal LLMs, Diffusions, and the Unification

Multi-modal generative AI (Artificial Intelligence) has attracted increasing attention from both academia and industry. Particularly, two dominant families of techniques have emerged: i) Multi-modal large language models (LLMs) demonstrate impressive ability for multi-modal understanding; and ii) Diffusion models exhibit remarkable multi-modal powers in terms of multi-modal generation. Therefore, this paper provides a comprehensive overview of multi-modal generative AI, including multi-modal LLMs, diffusions, and the unification for understanding and generation. To lay a solid foundation for unified models, we first provide a detailed review of both multi-modal LLMs and diffusion models respectively, including their probabilistic modeling procedure, multi-modal architecture design, and advanced applications to image/video LLMs as well as text-to-image/video generation. Furthermore, we explore the emerging efforts toward unified models for understanding and generation. To achieve the unification of understanding and generation, we investigate key designs including autoregressive-based and diffusion-based modeling, as well as dense and Mixture-of-Experts (MoE) architectures. We then introduce several strategies for unified models, analyzing their potential advantages and disadvantages. In addition, we summarize the common datasets widely used for multi-modal generative AI pretraining. Last but not least, we present several challenging future research directions which may contribute to the ongoing advancement of multi-modal generative AI.

cs.AI↗

arXiv2025

LumiTex: Towards High-Fidelity PBR Texture Generation with Illumination Context

Physically-based rendering (PBR) provides a principled standard for realistic material-lighting interactions in computer graphics. Despite recent advances in generating PBR textures, existing methods fail to address two fundamental challenges: 1) materials decomposition from image prompts under limited illumination cues, and 2) seamless and view-consistent texture completion. To this end, we propose LumiTex, an end-to-end framework that comprises three key components: (1) a multi-branch generation scheme that disentangles albedo and metallic-roughness under shared illumination priors for robust material understanding, (2) a lighting-aware material attention mechanism that injects illumination context into the decoding process for physically grounded generation of albedo, metallic, and roughness maps, and (3) a geometry-guided inpainting module based on a large view synthesis model that enriches texture coverage and ensures seamless, view-consistent UV completion. Extensive experiments demonstrate that LumiTex achieves state-of-the-art performance in texture quality, surpassing both existing open-source and commercial methods.

cs.CV↗

arXiv2025

Beyond the Monsters: A More Complete Census of Black Hole Activity at Cosmic Dawn

JWST has revealed an abundance of low-luminosity active galactic nuclei (AGN) at high redshifts ($z > 3$), pushing the limits of black hole (BH) science in the early Universe. Results have claimed that these BHs are significantly more massive than expected from the BH mass-host galaxy stellar mass relation derived from the local Universe. We present a comprehensive census of the BH populations in the early Universe through a detailed stacking analysis of galaxy populations, binned by luminosity and redshift, using JWST spectroscopy from the CEERS, JADES, RUBIES, and GLASS extragalactic deep field surveys. Broad H$α$ detections in $31\%$ of the stacked spectra (5/16 bins) imply median BH masses of $10^{5.21} - 10^{6.13}~ \rm{M_{\odot}}$ and the stacked SEDs of these bins indicate median stellar masses of $10^{7.84} - 10^{8.56} ~\rm{M_{\odot}}$. This suggests that the median galaxy hosts a BH that is at most a factor of 10 times over-massive compared to its host galaxy and lies closer to the locally derived $M_{BH}-M_*$ relation. We investigate the seeding properties of the inferred BHs and find that they can be well-explained by a light stellar remnant seed undergoing moderate Eddington accretion. Our results indicate that individual detections of AGN are more likely to sample the upper envelope of the $M_{BH}-M_*$ distribution, while stacking on ``normal" galaxies and searching for AGN signatures can overcome the selection bias of individual detections.

astro-ph.GA↗

arXiv2025

Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection

Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem. Recent studies have demonstrated that the student-teacher (S-T) framework effectively addresses this challenge. However, most S-T frameworks rely solely on pre-trained teacher networks to guide student networks in learning multi-scale similar features, overlooking the potential of the student networks to enhance learning through multi-scale feature fusion. In this study, we propose a novel model named PFADSeg, which integrates a pre-trained teacher network, a denoising student network with multi-scale feature fusion, and a guided anomaly segmentation network into a unified framework. By adopting a unique teacher-encoder and student-decoder denoising mode, the model improves the student network's ability to learn from teacher network features. Furthermore, an adaptive feature fusion mechanism is introduced to train a self-supervised segmentation network that synthesizes anomaly masks autonomously, significantly increasing detection performance. Rigorous evaluations on the widely-used MVTec AD dataset demonstrate that PFADSeg exhibits excellent performance, achieving an image-level AUC of 98.9%, a pixel-level mean precision of 76.4%, and an instance-level mean precision of 78.7%.

cs.CV↗

arXiv2025

SF-Recon: Simplification-Free Lightweight Building Reconstruction via 3D Gaussian Splatting

Lightweight building surface models are crucial for digital city, navigation, and fast geospatial analytics, yet conventional multi-view geometry pipelines remain cumbersome and quality-sensitive due to their reliance on dense reconstruction, meshing, and subsequent simplification. This work presents SF-Recon, a method that directly reconstructs lightweight building surfaces from multi-view images without post-hoc mesh simplification. We first train an initial 3D Gaussian Splatting (3DGS) field to obtain a view-consistent representation. Building structure is then distilled by a normal-gradient-guided Gaussian optimization that selects primitives aligned with roof and wall boundaries, followed by multi-view edge-consistency pruning to enhance structural sharpness and suppress non-structural artifacts without external supervision. Finally, a multi-view depth-constrained Delaunay triangulation converts the structured Gaussian field into a lightweight, structurally faithful building mesh. Based on a proposed SF dataset, the experimental results demonstrate that our SF-Recon can directly reconstruct lightweight building models from multi-view imagery, achieving substantially fewer faces and vertices while maintaining computational efficiency. Website:https://lzh282140127-cell.github.io/SF-Recon-project/

cs.CV↗

arXiv2025

V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models

Recent progress in generative video models, such as Veo-3, has shown surprising zero-shot reasoning abilities, creating a growing need for systematic and reliable evaluation. We introduce V-ReasonBench, a benchmark designed to assess video reasoning across four key dimensions: structured problem-solving, spatial cognition, pattern-based inference, and physical dynamics. The benchmark is built from both synthetic and real-world image sequences and provides a diverse set of answer-verifiable tasks that are reproducible, scalable, and unambiguous. Evaluations of six state-of-the-art video models reveal clear dimension-wise differences, with strong variation in structured, spatial, pattern-based, and physical reasoning. We further compare video models with strong image models, analyze common hallucination behaviors, and study how video duration affects Chain-of-Frames reasoning. Overall, V-ReasonBench offers a unified and reproducible framework for measuring video reasoning and aims to support the development of models with more reliable, human-aligned reasoning skills.

cs.CV↗

arXiv2025

MAMMOTH-Grism: Revisiting the Mass-Metallicity Relation in Protocluster Environments at Cosmic Noon

We present one of the first measurements of the mass-metallicity relation (MZR) in multiple massive protoclusters at cosmic noon, using Hubble Space Telescope (HST) G141 slitless spectroscopy from the MAMMOTH-Grism survey. We identify 63 protocluster member galaxies across three overdense structures at $z = 2\text{-}3$ with robust detections of [OIII], H$β$, and [OII] emission. The sample spans gas-phase metallicities of $12 + \log(\text{O/H}) = 8.2\text{-}8.6$, dust-corrected H$β$-based star formation rates (SFRs) of $10$-$250\,M_\odot\,\text{yr}^{-1}$, and stellar masses of $M_\ast \sim 10^{9.4}$-$10^{10.5}\,M_\odot$, derived via spectral energy distribution fitting using deep HST and ground-based photometry. We stack spectra in five $M_\ast$ bins to obtain average metallicities and SFRs. Relative to field galaxies at similar redshifts, protocluster members show elevated SFRs at $M_\ast < 10^{10.25}\,M_\odot$ and a systematically shallower MZR: $12 + \log(\text{O/H}) = (6.96 \pm 0.13) + (0.143 \pm 0.017) \times \log(M_{\ast}/M_{\odot})$. We detect a mass-dependent environmental offset: massive protocluster galaxies are metal-poor compared to field counterparts of similar mass, whereas lower-mass systems exhibit comparable or mildly enhanced metallicities. This trend is consistent with a scenario where cold-mode accretion dilutes the interstellar medium (ISM) across the full mass range, while efficient recycling of feedback-driven outflows preferentially enriches the ISM in low-mass galaxies. Finally, we assess the dependence of metallicity offsets on local overdensity and find no significant trend, likely reflecting the survey's bias toward protocluster cores.

astro-ph.GA↗

arXiv2025

U2UData+: A Scalable Swarm UAVs Autonomous Flight Dataset for Embodied Long-horizon Tasks

Swarm UAV autonomous flight for Embodied Long-Horizon (ELH) tasks is crucial for advancing the low-altitude economy. However, existing methods focus only on specific basic tasks due to dataset limitations, failing in real-world deployment for ELH tasks. ELH tasks are not mere concatenations of basic tasks, requiring handling long-term dependencies, maintaining embodied persistent states, and adapting to dynamic goal shifts. This paper presents U2UData+, the first large-scale swarm UAV autonomous flight dataset for ELH tasks and the first scalable swarm UAV data online collection and algorithm closed-loop verification platform. The dataset is captured by 15 UAVs in autonomous collaborative flights for ELH tasks, comprising 12 scenes, 720 traces, 120 hours, 600 seconds per trajectory, 4.32M LiDAR frames, and 12.96M RGB frames. This dataset also includes brightness, temperature, humidity, smoke, and airflow values covering all flight routes. The platform supports the customization of simulators, UAVs, sensors, flight algorithms, formation modes, and ELH tasks. Through a visual control window, this platform allows users to collect customized datasets through one-click deployment online and to verify algorithms by closed-loop simulation. U2UData+ also introduces an ELH task for wildlife conservation and provides comprehensive benchmarks with 9 SOTA models. U2UData+ can be found at https://fengtt42.github.io/U2UData-2/.

cs.RO↗

arXiv2025

Data-driven control of network systems: Accounting for communication adaptivity and security

Over the past decades, network systems have surged in significance, driven by merging technological advancements. These systems play pivotal roles in diverse applications ranging from autonomous driving to smart grids, yet they confront complexities arising from network imperfections and intricate interconnections, which challenge system identification, controller design, as well as stability and performance analysis. This survey provides an in-depth exploration of network systems from most recent data-driven perspective, across four key issues: communication delay, aperiodic sampling, network security, and distributed configurations. By doing so, this survey enhances our comprehension of the challenges and theoretical innovations within the realm of network systems.

eess.SY↗

arXiv2025

The Impact of Quantization on Large Reasoning Model Reinforcement Learning

Strong reasoning capabilities can now be achieved by large-scale reinforcement learning (RL) without any supervised fine-tuning. Although post-training quantization (PTQ) and quantization-aware training (QAT) are well studied in the context of fine-tuning, how quantization impacts RL in large reasoning models (LRMs) remains an open question. To answer this question, we conducted systematic experiments and discovered a significant gap in reasoning performance on mathematical benchmarks between post-RL quantized models and their quantization-aware RL optimized counterparts. Our findings suggest that quantization-aware RL training negatively impacted the learning process, whereas PTQ and QLoRA led to greater performance.

cs.LG↗

arXiv2025

Generating spatially separated correlated multiphoton states in nonlinear waveguide quantum electrodynamics

Strongly correlated multi-photon states are indispensable resources for advanced quantum technologies, yet their deterministic generation remains challenging due to the inherent weak nonlinearity in most optical systems. Here, we propose a scalable architecture for producing correlated few-photon entangled states via cascaded inelastic scattering in a nonlinear waveguide. When a single photon scatters off a far detuned excited two-level emitter, it coherently converts into a propagating doublon, a bound photon pair with anomalous dispersion. This doublon can subsequently scatter off a downstream excited emitter to further convert into a triplon, and so on, thereby establishing a photon-number amplification cascade $|\cdot \rangle \!\! \rightarrow \!\! |\!\!: \rangle \!\! \rightarrow \! \! |\!\!\therefore \rangle \!\! \to \!\! ...$ Central to this process is the concept of a pseudo-giant atom, which we introduce here to capture the non-local scattering potential emergent from the wave functions of bound states. By implementing this scheme using a real giant atom with multiple engineered coupling points, we achieve unidirectional and full controllable photon conversion without backscattering. The resulting output state forms a programmable superposition of spatially and temporally isolated photon-number components, automatically sorted by their distinct group velocities. This work opens a new paradigm in quantum state engineering, enabling on-demand generation of complex multi-photon resources for quantum simulation, metrology, and scalable quantum networks.

quant-ph↗

arXiv2025

Probing the influence of the protocluster environment on galaxy morphology at z = 2.23

As galaxies evolve in dense cluster and protocluster environments, they interact and quench their star formation, which gradually transforms the galaxy population from star-forming galaxies to quiescent galaxies. This transformation is identifiable by observing galaxy colors and can be seen in the morphological transformation of late-type galaxies into early-type galaxies, which creates the morphology-density relation seen when comparing populations in clusters to co-eval field galaxies. However, high-z (z > 2) galaxy morphology studies are hindered by the high angular resolution necessary to characterize morphology. We present a study of HST WFC3 F160W observations of protoclusters from the MAMMOTH survey (BOSS1244 and BOSS1542) at z ~ 2.23 with populations of previously identified HAEs. By measuring the Sersic index of 151 HAEs, we look for the early morphological transformation of star-forming galaxies in these well-studied, large, non-virialized protoclusters, which we believe are precursors of present-day clusters. We find the morphology of the populations of star-forming protocluster galaxies does not differ from the co-eval field. However, we identify a population of clumpy, potentially merging galaxies, which could lead to an increase in the population of early-type galaxies in these structures. Additionally, in BOSS1244, which has two previously identified massive quiescent galaxies including a BCG, we find an abundance of early-type galaxies near both the BCG and two co-eval high-z quasars. Although we find a strong similarity between the morphology of field and protocluster galaxies, the population of early-type star-forming galaxies surrounding the spectroscopically confirmed quiescent BCG in BOSS1244, something not seen in BOSS1542, may point to differences in the evolutionary state of these co-eval protoclusters and be a sign of an early forming cluster core in BOSS1244.

astro-ph.GA↗

arXiv2025

HyperD: Hybrid Periodicity Decoupling Framework for Traffic Forecasting

Accurate traffic forecasting plays a vital role in intelligent transportation systems, enabling applications such as congestion control, route planning, and urban mobility optimization. However, traffic forecasting remains challenging due to two key factors: (1) complex spatial dependencies arising from dynamic interactions between road segments and traffic sensors across the network, and (2) the coexistence of multi-scale periodic patterns (e.g., daily and weekly periodic patterns driven by human routines) with irregular fluctuations caused by unpredictable events (e.g., accidents, weather, or construction). To tackle these challenges, we propose HyperD (Hybrid Periodic Decoupling), a novel framework that decouples traffic data into periodic and residual components. The periodic component is handled by the Hybrid Periodic Representation Module, which extracts fine-grained daily and weekly patterns using learnable periodic embeddings and spatial-temporal attention. The residual component, which captures non-periodic, high-frequency fluctuations, is modeled by the Frequency-Aware Residual Representation Module, leveraging complex-valued MLP in frequency domain. To enforce semantic separation between the two components, we further introduce a Dual-View Alignment Loss, which aligns low-frequency information with the periodic branch and high-frequency information with the residual branch. Extensive experiments on four real-world traffic datasets demonstrate that HyperD achieves state-of-the-art prediction accuracy, while offering superior robustness under disturbances and improved computational efficiency compared to existing methods.

cs.AI↗

arXiv2025

SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMs

Artificial Intelligence (AI) has made it possible for anyone to create images, audio, and video with unprecedented ease, enriching education, communication, and creative expression. At the same time, the rapid rise of AI-generated media has introduced serious risks, including misinformation, identity misuse, and the erosion of public trust as synthetic content becomes increasingly indistinguishable from real media. Although deepfake detection has advanced, many existing tools remain closed-source, limited in modality, or lacking transparency and educational value, making it difficult for users to understand how detection decisions are made. To address these gaps, we introduce SynthGuard, an open, user-friendly platform for detecting and analyzing AI-generated multimedia using both traditional detectors and multimodal large language models (MLLMs). SynthGuard provides explainable inference, unified image and audio support, and an interactive interface designed to make forensic analysis accessible to researchers, educators, and the public. The SynthGuard platform is available at: https://in-engr-nova.it.purdue.edu/

cs.MM↗

arXiv2025

MPCGNet: A Multiscale Feature Extraction and Progressive Feature Aggregation Network Using Coupling Gates for Polyp Segmentation

Automatic segmentation methods of polyps is crucial for assisting doctors in colorectal polyp screening and cancer diagnosis. Despite the progress made by existing methods, polyp segmentation faces several challenges: (1) small-sized polyps are prone to being missed during identification, (2) the boundaries between polyps and the surrounding environment are often ambiguous, (3) noise in colonoscopy images, caused by uneven lighting and other factors, affects segmentation results. To address these challenges, this paper introduces coupling gates as components in specific modules to filter noise and perform feature importance selection. Three modules are proposed: the coupling gates multiscale feature extraction (CGMFE) module, which effectively extracts local features and suppresses noise; the windows cross attention (WCAD) decoder module, which restores details after capturing the precise location of polyps; and the decoder feature aggregation (DFA) module, which progressively aggregates features, further extracts them, and performs feature importance selection to reduce the loss of small-sized polyps. Experimental results demonstrate that MPCGNet outperforms recent networks, with mDice scores 2.20% and 0.68% higher than the second-best network on the ETIS-LaribPolypDB and CVC-ColonDB datasets, respectively.

cs.CV↗

arXiv2025

SMART: A Surrogate Model for Predicting Application Runtime in Dragonfly Systems

The Dragonfly network, with its high-radix and low-diameter structure, is a leading interconnect in high-performance computing. A major challenge is workload interference on shared network links. Parallel discrete event simulation (PDES) is commonly used to analyze workload interference. However, high-fidelity PDES is computationally expensive, making it impractical for large-scale or real-time scenarios. Hybrid simulation that incorporates data-driven surrogate models offers a promising alternative, especially for forecasting application runtime, a task complicated by the dynamic behavior of network traffic. We present \ourmodel, a surrogate model that combines graph neural networks (GNNs) and large language models (LLMs) to capture both spatial and temporal patterns from port level router data. \ourmodel outperforms existing statistical and machine learning baselines, enabling accurate runtime prediction and supporting efficient hybrid simulation of Dragonfly networks.

cs.LG↗

arXiv2025

Interspecific information use facilitates species coexistence in ecosystems

Explaining how competing species coexist remains a central question in ecology. The well-known competitive exclusion principle (CEP) states that two species competing for the same resource cannot stably coexist, and more generally, that the number of consumer species is bounded by the number of resource species at steady state. However, the remarkable species diversity observed in natural ecosystems, exemplified by the paradox of the plankton, challenges this principle. Here, we show that interspecific social information use among predators provides a mechanism that fundamentally relaxes the constraints of competitive exclusion. A model of predation dynamics that incorporates interspecific information use naturally explains coexistence beyond the limits imposed by CEP. Our model quantitatively reproduces two classical experiments that contradicts the CEP and captures coexistence patterns documented in natural ecosystems, offering a general mechanism for the maintenance of biodiversity in ecological communities.

physics.bio-ph↗