arXiv subjects

Yu Wang

Publications and source records attributed to Yu Wang.

At least 487 records · Page 27Linked to original sources

arXiv2025

Shadows of three black holes in static equilibrium configuration

In this paper, we employ a ray-tracing algorithm to simulate the shadows of three equal-mass black holes in static equilibrium across a wide parameter space. We find that the shadows consist of a larger primary shadow and several distorted, eyebrow-like secondary shadows. The boundaries of these profiles exhibit self-similar fractal structures, which can be attributed to the photon chaotic scattering. In certain parameter spaces, we also observe the ring-like shadows, with the ring diameter associated with the spacing of black holes. Furthermore, when the black holes approach each other sufficiently, their shadows can merge into a standard disk, suggesting a shadow degeneracy between closely arranged triple black holes and a single massive, spherically symmetric black hole. The shadow features of the triple black holes revealed in this study have potential implications for analyzing the shadow formation mechanisms, as well as the gravitational lensing during the merger and inspiral of black holes.

gr-qc↗

arXiv2025

Enhanced Krylov Methods for Molecular Hamiltonians: Reduced Memory Cost and Complexity Scaling via Tensor Hypercontraction

We introduce an algorithm that is simultaneously memory-efficient and low-scaling for applying ab initio molecular Hamiltonians to matrix-product states (MPS) via the tensor-hypercontraction (THC) format. These gains carry over to Krylov subspace methods, which can find low-lying eigenstates and simulate quantum time evolution while avoiding local minima and maintaining high accuracy. In our approach, the molecular Hamiltonian is represented as a sum of products of four MPOs, each with a bond dimension of only 2. Iteratively applying the MPOs to the current quantum state in MPS form, summing and re-compressing the MPS leads to a scheme with the same asymptotic memory cost as the bare MPS and reduces the computational cost scaling compared to the Krylov method using a conventional MPO construction. We provide a detailed theoretical derivation of these statements and conduct supporting numerical experiments to demonstrate the advantage. Our algorithm is highly parallelizable and thus lends itself to large-scale HPC simulations.

cond-mat.str-el↗

arXiv2025

Multi-Sequence Parotid Gland Lesion Segmentation via Expert Text-Guided Segment Anything Model

Parotid gland lesion segmentation is essential for the treatment of parotid gland diseases. However, due to the variable size and complex lesion boundaries, accurate parotid gland lesion segmentation remains challenging. Recently, the Segment Anything Model (SAM) fine-tuning has shown remarkable performance in the field of medical image segmentation. Nevertheless, SAM's interaction segmentation model relies heavily on precise lesion prompts (points, boxes, masks, etc.), which are very difficult to obtain in real-world applications. Besides, current medical image segmentation methods are automatically generated, ignoring the domain knowledge of medical experts when performing segmentation. To address these limitations, we propose the parotid gland segment anything model (PG-SAM), an expert diagnosis text-guided SAM incorporating expert domain knowledge for cross-sequence parotid gland lesion segmentation. Specifically, we first propose an expert diagnosis report guided prompt generation module that can automatically generate prompt information containing the prior domain knowledge to guide the subsequent lesion segmentation process. Then, we introduce a cross-sequence attention module, which integrates the complementary information of different modalities to enhance the segmentation effect. Finally, the multi-sequence image features and generated prompts are feed into the decoder to get segmentation result. Experimental results demonstrate that PG-SAM achieves state-of-the-art performance in parotid gland lesion segmentation across three independent clinical centers, validating its clinical applicability and the effectiveness of diagnostic text for enhancing image segmentation in real-world clinical settings.

cs.CV↗

arXiv2025

D3P: Dynamic Denoising Diffusion Policy via Reinforcement Learning

Diffusion policies excel at learning complex action distributions for robotic visuomotor tasks, yet their iterative denoising process poses a major bottleneck for real-time deployment. Existing acceleration methods apply a fixed number of denoising steps per action, implicitly treating all actions as equally important. However, our experiments reveal that robotic tasks often contain a mix of \emph{crucial} and \emph{routine} actions, which differ in their impact on task success. Motivated by this finding, we propose \textbf{D}ynamic \textbf{D}enoising \textbf{D}iffusion \textbf{P}olicy \textbf{(D3P)}, a diffusion-based policy that adaptively allocates denoising steps across actions at test time. D3P uses a lightweight, state-aware adaptor to allocate the optimal number of denoising steps for each action. We jointly optimize the adaptor and base diffusion policy via reinforcement learning to balance task performance and inference efficiency. On simulated tasks, D3P achieves an averaged 2.2$\times$ inference speed-up over baselines without degrading success. Furthermore, we demonstrate D3P's effectiveness on a physical robot, achieving a 1.9$\times$ acceleration over the baseline.

cs.RO↗

arXiv2025

CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control

City-scale traffic signal control (TSC) involves thousands of heterogeneous intersections with varying topologies, making cooperative decision-making across intersections particularly challenging. Given the prohibitive computational cost of learning individual policies for each intersection, some researchers explore learning a universal policy to control each intersection in a decentralized manner, where the key challenge is to construct a universal representation method for heterogeneous intersections. However, existing methods are limited to universally representing information of heterogeneous ego intersections, neglecting the essential representation of influence from their heterogeneous neighbors. Universally incorporating neighborhood information is nontrivial due to the intrinsic complexity of traffic flow interactions, as well as the challenge of modeling collective influences from neighbor intersections. To address these challenges, we propose CityLight, which learns a universal policy based on representations obtained with two major modules: a Neighbor Influence Encoder to explicitly model neighbor's influence with specified traffic flow relation and connectivity to the ego intersection; a Neighbor Influence Aggregator to attentively aggregate the influence of neighbors based on their mutual competitive relations. Extensive experiments on five city-scale datasets, ranging from 97 to 13,952 intersections, confirm the efficacy of CityLight, with an average throughput improvement of 11.68% and a lift of 22.59% for generalization.

eess.SY↗

arXiv2025

Multiscale Coupled Polarization and BKT Transitions in Tow-Dimensional Hybrid Organic-Inorganic Perovskites

We present an extended two-dimensional XY rotor model specifically designed to capture the polarization dynamics of hybrid organic-inorganic perovskite monolayers. This framework integrates nearest and next-nearest neighbor couplings, crystalline anisotropy inherent to perovskite lattice symmetries, external bias fields, and long-range dipolar interactions that are prominent in layered perovskite architectures. Through a combination of analytical coarse-graining and large-scale Monte Carlo simulations on 64*64 lattices, we identify two distinct thermodynamic regimes: a low-temperature quasi-ferroelectric state characterized by finite polarization and domain wall formation, and a higher-temperature Berezinskii-Kosterlitz-Thouless (BKT) crossover associated with vortex-antivortex unbinding and the suppression of long-range order. Our results reproduce key experimental signatures observed in quasi-two-dimensional perovskites, including dual peaks in dielectric susceptibility, enhanced vortex density near the transition, multistable polarization hysteresis under applied fields, and the scaling behavior of domain wall widths. This minimal yet realistic model provides a unifying perspective on how topological transitions and ferroelectric ordering coexist in layered perovskite systems, offering quantitative guidance for interpreting the emergent polar vortex lattices and complex phase behavior recently reported in hybrid perovskite thin films.

cond-mat.mtrl-sci↗

arXiv2025

PLGS: Robust Panoptic Lifting with 3D Gaussian Splatting

Previous methods utilize the Neural Radiance Field (NeRF) for panoptic lifting, while their training and rendering speed are unsatisfactory. In contrast, 3D Gaussian Splatting (3DGS) has emerged as a prominent technique due to its rapid training and rendering speed. However, unlike NeRF, the conventional 3DGS may not satisfy the basic smoothness assumption as it does not rely on any parameterized structures to render (e.g., MLPs). Consequently, the conventional 3DGS is, in nature, more susceptible to noisy 2D mask supervision. In this paper, we propose a new method called PLGS that enables 3DGS to generate consistent panoptic segmentation masks from noisy 2D segmentation masks while maintaining superior efficiency compared to NeRF-based methods. Specifically, we build a panoptic-aware structured 3D Gaussian model to introduce smoothness and design effective noise reduction strategies. For the semantic field, instead of initialization with structure from motion, we construct reliable semantic anchor points to initialize the 3D Gaussians. We then use these anchor points as smooth regularization during training. Additionally, we present a self-training approach using pseudo labels generated by merging the rendered masks with the noisy masks to enhance the robustness of PLGS. For the instance field, we project the 2D instance masks into 3D space and match them with oriented bounding boxes to generate cross-view consistent instance masks for supervision. Experiments on various benchmarks demonstrate that our method outperforms previous state-of-the-art methods in terms of both segmentation quality and speed.

cs.CV↗

arXiv2025

Doubling property of self-similar measures with overlaps

Recently, Yang, Yuan and Zhang [Doubling properties of self-similar measures and Bernoulli measures on self-affine Sierpinski sponges, Indiana Univ. Math. J., 73 (2024), 475-492] characterized when a self-similar measure satisfying the open set condition is doubling. In this paper, we study when a self-similar measure with overlaps is doubling. Let $m\geq 2$ and let $β>1$ be the Pisot number satisfying $β^m=\sum_{j=0}^{m-1}β^j$. Let $\mathbf{p}=(p_1,p_2)$ be a probability weight and let $μ_{\mathbf{p}}$ be the self-similar measure associated to the IFS $\{ S_1(x)={x}/β, S_2(x)={x}/β+(1-{1}/β),\}.$ Yung [...,Indiana Univ. Math. J., ] proved that when $m=2$, $μ_{\mathbf{p}}$ is doubling if and only if $\mathbf{p}=(1/2,1/2)$. We show that for $m\geq 3$, $μ_{\mathbf{p}}$ is always non-doubling.

math.MG↗

arXiv2025

DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models

Large vision-language models (LVLMs) have demonstrated exceptional performance on complex multimodal tasks. However, they continue to suffer from significant hallucination issues, including object, attribute, and relational hallucinations. To accurately detect these hallucinations, we investigated the variations in cross-modal attention patterns between hallucination and non-hallucination states. Leveraging these distinctions, we developed a lightweight detector capable of identifying hallucinations. Our proposed method, Detecting Hallucinations by Cross-modal Attention Patterns (DHCP), is straightforward and does not require additional LVLM training or extra LVLM inference steps. Experimental results show that DHCP achieves remarkable performance in hallucination detection. By offering novel insights into the identification and analysis of hallucinations in LVLMs, DHCP contributes to advancing the reliability and trustworthiness of these models. The code is available at https://github.com/btzyd/DHCP.

cs.CV↗

arXiv2025

DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image

Depth enhancement, which uses RGB images as guidance to convert raw signals from dToF into high-precision, dense depth maps, is a critical task in computer vision. Although existing super-resolution-based methods show promising results on public datasets, they often rely on idealized assumptions like accurate region correspondences and reliable dToF inputs, overlooking calibration errors that cause misalignment and anomaly signals inherent to dToF imaging, limiting real-world applicability. To address these challenges, we propose a novel completion-based method, named DEPTHOR, featuring advances in both the training strategy and model architecture. First, we propose a method to simulate real-world dToF data from the accurate ground truth in synthetic datasets to enable noise-robust training. Second, we design a novel network that incorporates monocular depth estimation (MDE), leveraging global depth relationships and contextual information to improve prediction in challenging regions. On the ZJU-L5 dataset, our training strategy significantly enhances depth completion models, achieving results comparable to depth super-resolution methods, while our model achieves state-of-the-art results, improving Rel and RMSE by 27% and 18%, respectively. On a more challenging set of dToF samples we collected, our method outperforms SOTA methods on preliminary stereo-based GT, improving Rel and RMSE by 23% and 22%, respectively. Our Code is available at https://github.com/ShadowBbBb/Depthor

cs.CV↗

arXiv2025

FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models

The increasing demand to process long and high-resolution videos significantly burdens Large Vision-Language Models (LVLMs) due to the enormous number of visual tokens. Existing token reduction methods primarily prune tokens based on importance metrics, such as cumulative attention scores. However, even important tokens may exhibit high redundancy caused by similarity among adjacent video frames and repetitive visual elements. To address this limitation, we propose FrameFusion, a novel token reduction approach integrating similarity-based merging with importance-based pruning. We conduct a thorough study on token similarity characteristics, revealing three key insights: (1) spatially corresponding visual tokens between adjacent frames have higher cosine similarities compared to other token pairs; (2) high token similarities prominently decrease in deeper model layers; and (3) token similarity rankings are highly consistent across different layers. Guided by these observations, FrameFusion computes token similarities exclusively between corresponding visual tokens from adjacent frames, applies token merging at initial successive layers followed by pruning in deeper layers, and adopts a cascaded merging strategy to further enhance efficiency. We evaluate FrameFusion comprehensively across six diverse LVLMs, ranging from 2B to 72B parameters, using five video benchmarks encompassing video retrieval, question-answering, and spatial-temporal understanding tasks. Experiments show that FrameFusion reduces visual tokens by 70%, achieving 1.6-3.6x end-to-end speedups, with an average performance impact of less than 3%. Our code is available at: https://github.com/thu-nics/FrameFusion.

cs.CV↗

arXiv2025

Megrez2 Technical Report

We present Megrez2, a novel lightweight and high-performance language model architecture optimized for device native deployment. Megrez2 introduces a novel cross-layer expert sharing mechanism, which significantly reduces total parameter count by reusing expert modules across adjacent transformer layers while maintaining most of the model's capacity. It also incorporates pre-gated routing, enabling memory-efficient expert loading and faster inference. As the first instantiation of the Megrez2 architecture, we introduce the Megrez2-Preview model, which is pre-trained on a 5-trillion-token corpus and further enhanced through supervised fine-tuning and reinforcement learning with verifiable rewards. With only 3B activated and 7.5B stored parameters, Megrez2-Preview demonstrates competitive or superior performance compared to larger models on a wide range of tasks, including language understanding, instruction following, mathematical reasoning, and code generation. These results highlight the effectiveness of the Megrez2 architecture to achieve a balance between accuracy, efficiency, and deployability, making it a strong candidate for real-world, resource-constrained applications.

cs.CL↗

arXiv2025

HoliTracer: Holistic Vectorization of Geographic Objects from Large-Size Remote Sensing Imagery

With the increasing resolution of remote sensing imagery (RSI), large-size RSI has emerged as a vital data source for high-precision vector mapping of geographic objects. Existing methods are typically constrained to processing small image patches, which often leads to the loss of contextual information and produces fragmented vector outputs. To address these, this paper introduces HoliTracer, the first framework designed to holistically extract vectorized geographic objects from large-size RSI. In HoliTracer, we enhance segmentation of large-size RSI using the Context Attention Net (CAN), which employs a local-to-global attention mechanism to capture contextual dependencies. Furthermore, we achieve holistic vectorization through a robust pipeline that leverages the Mask Contour Reformer (MCR) to reconstruct polygons and the Polygon Sequence Tracer (PST) to trace vertices. Extensive experiments on large-size RSI datasets, including buildings, water bodies, and roads, demonstrate that HoliTracer outperforms state-of-the-art methods. Our code and data are available in https://github.com/vvangfaye/HoliTracer.

cs.CV↗

arXiv2025

Aime: Towards Fully-Autonomous Multi-Agent Framework

Multi-Agent Systems (MAS) powered by Large Language Models (LLMs) are emerging as a powerful paradigm for solving complex, multifaceted problems. However, the potential of these systems is often constrained by the prevalent plan-and-execute framework, which suffers from critical limitations: rigid plan execution, static agent capabilities, and inefficient communication. These weaknesses hinder their adaptability and robustness in dynamic environments. This paper introduces Aime, a novel multi-agent framework designed to overcome these challenges through dynamic, reactive planning and execution. Aime replaces the conventional static workflow with a fluid and adaptive architecture. Its core innovations include: (1) a Dynamic Planner that continuously refines the overall strategy based on real-time execution feedback; (2) an Actor Factory that implements Dynamic Actor instantiation, assembling specialized agents on-demand with tailored tools and knowledge; and (3) a centralized Progress Management Module that serves as a single source of truth for coherent, system-wide state awareness. We empirically evaluated Aime on a diverse suite of benchmarks spanning general reasoning (GAIA), software engineering (SWE-bench Verified), and live web navigation (WebVoyager). The results demonstrate that Aime consistently outperforms even highly specialized state-of-the-art agents in their respective domains. Its superior adaptability and task success rate establish Aime as a more resilient and effective foundation for multi-agent collaboration.

cs.AI↗

arXiv2025

SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users

Social simulation is transforming traditional social science research by modeling human behavior through interactions between virtual individuals and their environments. With recent advances in large language models (LLMs), this approach has shown growing potential in capturing individual differences and predicting group behaviors. However, existing methods face alignment challenges related to the environment, target users, interaction mechanisms, and behavioral patterns. To this end, we introduce SocioVerse, an LLM-agent-driven world model for social simulation. Our framework features four powerful alignment components and a user pool of 10 million real individuals. To validate its effectiveness, we conducted large-scale simulation experiments across three distinct domains: politics, news, and economics. Results demonstrate that SocioVerse can reflect large-scale population dynamics while ensuring diversity, credibility, and representativeness through standardized procedures and minimal manual adjustments.

cs.CL↗

arXiv2025

Third-order exceptional surface in a pseudo-Hermitian superconducting circuit

Compared with an isolated exceptional point, exceptional surfaces in non-Hermitian systems are more robust against environment noises, fabrication errors, and experimental uncertainties. Thanks to this, exceptional surfaces can be applied to enhance the sensitivity of sensors and develop new quantum techniques. Over the past few years, several works have been devoted to studying high-order exceptional surfaces. However, they are restricted to non-Hermitian systems without pseudo-Hermiticity. To date, research on high-order exceptional surfaces in pseudo-Hermitian systems still remains an untouched area. In this work, we propose a pseudo-Hermitian superconducting circuit, which consists of three circularly-coupled superconducting cavities with the balanced gain and loss. We then study the third-order exceptional surface in the proposed circuit. By investigating the eigenvalues, we find that in the parameter space, all third-order exceptional points of the circuit form a third-order exceptional line in the parity-time-symmetric case. When the parity-time-symmetric condition is extended to pseudo-Hermitian conditions, we find more third-order exceptional points, which constitute a third-order exceptional surface in the parameter space. The proposed scheme is universal and can be applied to explore third-order exceptional surfaces in other physical systems, such as optomechanical systems, cavity-magnon systems, and photonic micro-ring systems. This work is of fundamental interest in quantum mechanics and opens a way for studying high-order exceptional surfaces in pseudo-Hermitian systems.

quant-ph↗

arXiv2025

Personalization of Large Language Models: A Survey

Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as recommendation systems. In this work, we bridge the gap between these two separate main directions for the first time by introducing a taxonomy for personalized LLM usage and summarizing the key differences and challenges. We provide a formalization of the foundations of personalized LLMs that consolidates and expands notions of personalization of LLMs, defining and discussing novel facets of personalization, usage, and desiderata of personalized LLMs. We then unify the literature across these diverse fields and usage scenarios by proposing systematic taxonomies for the granularity of personalization, personalization techniques, datasets, evaluation methods, and applications of personalized LLMs. Finally, we highlight challenges and important open problems that remain to be addressed. By unifying and surveying recent research using the proposed taxonomies, we aim to provide a clear guide to the existing literature and different facets of personalization in LLMs, empowering both researchers and practitioners.

cs.CL↗

arXiv2025

Investigating the Dainotti Relation in Gamma-Ray Bursts through Multipolar Electromagnetic Radiation

The Dainotti relation empirically connects the isotropic plateau luminosity ($L_X$) in gamma-ray bursts (GRBs) X-ray afterglows to the rest-frame time at which the plateau ends ($T_a^*$), enabling both the standardization of GRBs and their use as cosmological probes. However, the precise physical mechanisms underlying this correlation remain an active area of research. Although magnetars, highly magnetized neutron stars, have been proposed as central engines powering GRB afterglows, traditional dipole spin-down radiation models fail to account for the full diversity of observed behaviors. This limitation necessitates a more comprehensive framework. We propose that multipolar magnetic field emissions from magnetars offer a plausible explanation for the Dainotti relation. Unlike simple dipole fields, higher-order multipolar configurations enable more complex energy dissipation processes. The coexistence of multiple components can plausibly explain the range of afterglow decay indices found from a sample of 238 GRBs with plateau features from the Swift-XRT database up to the end of December 2024, the majority of which deviate from the dipolar prediction of $α= -2$, and more crucially, the spin-down physics yields a link between $L_X$ and $T_a^*$ in a way that preserves the Dainotti correlation with a slope of $b = - 1$, independent of the specific multipole order. Moreover, we find that the inclusion of higher order multipoles can explain the range of plateau energies found in the Dainotti relations. Thus, a unified picture emerges in which multipolar fields are able to reproduce both the slope and the normalization of the correlation.

astro-ph.HE↗