arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 217 records · Page 12Linked to original sources

arXiv2025

Energy Extraction via Magnetic Reconnection in Kerr-Sen-AdS$_{4}$ Black Hole: Circular Plasma and Plunging Plasma

In this paper, we investigate the power and efficiency of energy extraction through magnetic reconnection in the Kerr-Sen-AdS$_{4}$ black hole, with a primary focus on both the circular orbit and plunging region. We plot the allowed regions for energy extraction and present corresponding power and efficiency values, comparing them with those of the Blandford-Znajek (BZ) mechanism. Our analysis reveals that energy extraction remains feasible even at a spin parameter as low as 0.5, significantly below previously reported thresholds, and the extracted power can exceed that of the BZ process under certain conditions. Furthermore, the dilatonic scalar charge $b$ and the AdS radius $l$ collectively contribute to lowering the spin threshold for energy extraction. The energy extraction process in the plunging region was further examined by analyzing the permissible energy extraction region, as well as the corresponding power output and efficiency. One can find that the energy extraction is possible even at a spin as low as 0.25. Crucially, parameter $b$ actively lowers the energy extraction spin threshold, while parameter $l$ exerts a counteracting effect, impeding such reduction. This trend shows a marked contrast with circular orbit behavior. Additionally, both power and efficiency in the plunging region consistently surpass those in the circular orbit region, indicating superior energy extraction capability in plunging region.

gr-qc↗

arXiv2025

Asymptotically Optimal Repair of Reed-Solomon Codes with Small Sub-Packetization under Rack-Aware Model

This paper presents a comprehensive study on the asymptotically optimal repair of Reed-Solomon (RS) codes with small sub-packetization, specifically tailored for rack-aware distributed storage systems. Through the utilization of multi-base expansion, we introduce a novel approach that leverages monomials to construct linear repair schemes for RS codes. Our repair schemes which adapt to all admissible parameters achieve asymptotically optimal repair bandwidth while significantly reducing the sub-packetization compared with existing schemes. Furthermore, our approach is capable of repairing RS codes with asymptotically optimal repair bandwidth under the homogeneous storage model, achieving smaller sub-packetization than existing methods.

cs.IT↗

arXiv2025

Enhancing Satellite Quantum Key Distribution with Dual Band Reconfigurable Intelligent Surfaces

This paper presents a novel system architecture for hybrid satellite communications, integrating quantum key distribution (QKD) and classical radio frequency (RF) data transmission using a dual-band reconfigurable intelligent surface (RIS). The motivation is to address the growing need for global, secure, and reliable communications by leveraging the security of quantum optical links and the robustness of classical RF channels within a unified framework. By employing a frequency-selective RIS, the system independently optimizes both quantum (850 nm) and classical (S-band) channels in real time, dynamically adapting to environmental fluctuations such as atmospheric turbulence and rain attenuation. The joint optimization of the quantum bit error rate (QBER) and the classical signal-to noise ratio (SNR) is formulated as a quadratic unconstrained binary optimization (QUBO) problem, enabling efficient adaptive phase control utilizing both quantum and classical computational methods. Comprehensive theoretical modeling and simulations, benchmarked against experimental data from the Micius satellite, demonstrate substantial performance gains. Notably, the RIS assisted system reduces QBER from approximately 2.5% to 0.7%, increases the secure key rate (SKR) to over 30,000 bits per second, and enhances classical RF SNR by about 3 dB at high elevation angles. These results illustrate the practical potential of hybrid RIS-assisted satellite links to deliver robust, efficient, and secure global communications.

eess.SP↗

arXiv2025

Universal approach to light driven "superconductivity" via preformed pairs

While there are many different mechanisms which have been proposed to understand the physics behind light induced ``superconductivity", what seems to be common to the class of materials in which this is observed are strong pairing correlations, which are present in the normal state. Here we argue, that the original ideas of Eliashberg are applicable to such a pseudogap phase and that with exposure to radiation the fermions are redistributed to higher energies where they are less deleterious to pairing. What results then is a photo-induced state with dramatically enhanced number of nearly condensed fermion pairs. In this phase, because the a.c. conductivity, $σ(ω) = σ_1(ω) + i σ_2(ω)$, is dominated by the bosonic contribution, it can be computed using conventional (Aslamazov Larkin) fluctuation theory. We, thereby, observe the expected fingerprint of this photoinduced ``superconducting" state which is a $1/ω$ dependence in $σ_2$ with fits to the data of the same quality as found for the so-called photo-enhanced (Drude) conductivity scenario. Here, however, we have a microscopic understanding of the characteristic low energy scale which appears in transport and which is necessarily temperature dependent. This approach also provides insight into recent observations of concomitant diamagnetic fluctuations. Our calculations suggest that the observed light-induced phase in these strongly paired superconductors has only short range phase coherence without long range superconducting order.

cond-mat.supr-con↗

arXiv2025

FSDA-DG: Improving Cross-Domain Generalizability of Medical Image Segmentation with Few Source Domain Annotations

Deep learning-based medical image segmentation faces significant challenges arising from limited labeled data and domain shifts. While prior approaches have primarily addressed these issues independently, their simultaneous occurrence is common in medical imaging. A method that generalizes to unseen domains using only minimal annotations offers significant practical value due to reduced data annotation and development costs. In pursuit of this goal, we propose FSDA-DG, a novel solution to improve cross-domain generalizability of medical image segmentation with few single-source domain annotations. Specifically, our approach introduces semantics-guided semi-supervised data augmentation. This method divides images into global broad regions and semantics-guided local regions, and applies distinct augmentation strategies to enrich data distribution. Within this framework, both labeled and unlabeled data are transformed into extensive domain knowledge while preserving domain-invariant semantic information. Additionally, FSDA-DG employs a multi-decoder U-Net pipeline semi-supervised learning (SSL) network to improve domain-invariant representation learning through consistent prior assumption across multiple perturbations. By integrating data-level and model-level designs, FSDA-DG achieves superior performance compared to state-of-the-art methods in two challenging single domain generalization (SDG) tasks with limited annotations. The code is publicly available at https://github.com/yezanting/FSDA-DG.

cs.CV↗

arXiv2025

More Vulnerable than You Think: On the Stability of Tool-Integrated LLM Agents

Current evaluations of tool-integrated LLM agents typically focus on end-to-end tool-usage evaluation while neglecting their stability. This limits their real-world applicability, as various internal or external factors can cause agents to crash or behave abnormally. Our research addresses this by investigating whether agents are vulnerable to errors throughout the entire tool invocation process, including reading tool documentation, selecting tools and generating parameters, and processing the tool's response. Through extensive experiments, we observe that agents are highly susceptible to errors at each stage and agents based on open-source models are more vulnerable than those based on proprietary models. We also find that increasing the model size does not significantly improve tool invocation reasoning and may make agents more vulnerable to attacks resembling normal user instructions. This highlights the importance of evaluating agent stability and offers valuable insights for future LLM development and evaluation.

cs.CL↗

arXiv2025

Properties of the cores and filaments in the Ophiuchus molecular cloud and its L1688 hub-filament system

Analyzing filaments and cores in molecular clouds is key to understanding galactic star formation and its environmental dependence. This paper studies the properties and distribution of dense cores and filaments in the Ophiuchus molecular cloud, with a focus on the L1688 hub-filament system (HFS) and its star formation potential. We extracted sources and filaments from Herschel images and a 13.5 arcsec resolution surface density map using the getsf method, identified prestellar cores among the extracted sources, evaluated core mass segregation, and constructed the core mass function (CMF). We derived properties of the filaments from their radial surface density profiles, constructed the filament linear density function (FLDF), and assessed the mass distribution in the L1688 HFS to estimate the core and filament formation efficiencies (CFE, FFE). The results suggest that the filament-dominated core formation is a key mechanism in star formation within the system.

astro-ph.GA↗

arXiv2025

Universal coherent atom-molecule oscillations in the dynamics of the unitary Bose gas near a narrow Feshbach resonance

Quench experiments on a unitary Bose gas around a broad Feshbach resonance have led to the discovery of universal dynamics. This universality is manifested in the measured atomic momentum distributions where, asymptotically, a quasi-equilibrated metastable state is found in which both the momentum distribution and the time scales are determined by the particle density. In this paper we present counterpart studies but for the case of a very narrow Feshbach resonance of $^{133}$Cs atoms having a width of 8.3 mG. In dramatic contrast to the behavior reported earlier, a rapid quench of an atomic condensate to unitarity is observed to ultimately lead to coherent oscillations involving dynamically produced condensed and non-condensed molecules and atoms. The same characteristic frequency, determined by the Feshbach coupling, is observed in all types of particles. To understand these quench dynamics and how these different particle species are created, we develop a beyond Hartree-Fock-Bogoliubov dynamical framework including a new type of cross correlation between atoms and molecules. This leads to a quantitative consistency with the measured frequency. Our results, which can be applied to the general class of bosonic superfluids associated with narrow Feshbach resonances, establish a new paradigm for universal dynamics dominated by quantum many-body interactions.

cond-mat.quant-gas↗

arXiv2025

Droplets of Bosons at a Narrow Resonance

We consider bosons interacting through a narrow $s$-wave resonance. Such a resonance is characterized by an infinite scattering length and a large and negative effective range $r_0$. We argue that any number $N\ge3$ of bosons can form a self-bound cluster with the binding energy per particle increasing as $N^2$ for $1\ll N\ll (-r_0/a_\text{bg})^{1/2}$, where $a_\text{bg}$ is the background scattering length (between atoms and molecules). In the opposite limit $N\gg (-r_0/a_\text{bg})^{1/2}$, bosons form droplets with binding energy per particle saturating to a constant value independent of the particle number. The stability of clusters and droplets when the interaction is detuned from the resonance is also studied.

cond-mat.quant-gas↗

arXiv2025

Experimental realization of the bucket-brigade quantum random access memory

Quantum random access memory (QRAM) enables efficient classical data access for quantum computers -- a prerequisite for many quantum algorithms to achieve quantum speedup. Despite various proposals, the experimental realization of QRAM remains largely unexplored. Here, we experimentally investigate the circuit-based bucket-brigade QRAM with a superconducting quantum processor. To facilitate the experimental implementation, we introduce a hardware-efficient gate decomposition scheme for quantum routers, which effectively reduces the depth of the QRAM circuit by more than 30% compared to the conventional controlled-SWAP-based implementation. We further propose an error mitigation method to boost the QRAM query fidelity. With these techniques, we are able to experimentally implement the QRAM architectures with two and three layers, achieving query fidelities up to 0.800 $\pm$ 0.026 and 0.604$\pm$0.005, respectively. Additionally, we study the error propagation mechanism and the scalability of our QRAM implementation, providing experimental evidence for the noise resilience nature of the bucket-brigade QRAM architecture. Our results highlight the potential of superconducting quantum processors for realizing a scalable QRAM architecture.

quant-ph↗

arXiv2025

COME: Adding Scene-Centric Forecasting Control to Occupancy World Model

World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspective shifts) from scene evolvement (agent interactions), leading to suboptimal predictions. Instead, we propose to separate environmental changes from ego-motion by leveraging the scene-centric coordinate systems. In this paper, we introduce COME: a framework that integrates scene-centric forecasting Control into the Occupancy world ModEl. Specifically, COME first generates ego-irrelevant, spatially consistent future features through a scene-centric prediction branch, which are then converted into scene condition using a tailored ControlNet. These condition features are subsequently injected into the occupancy world model, enabling more accurate and controllable future occupancy predictions. Experimental results on the nuScenes-Occ3D dataset show that COME achieves consistent and significant improvements over state-of-the-art (SOTA) methods across diverse configurations, including different input sources (ground-truth, camera-based, fusion-based occupancy) and prediction horizons (3s and 8s). For example, under the same settings, COME achieves 26.3% better mIoU metric than DOME and 23.7% better mIoU metric than UniScene. These results highlight the efficacy of disentangled representation learning in enhancing spatio-temporal prediction fidelity for world models. Code and videos will be available at https://github.com/synsin0/COME.

cs.CV↗

arXiv2025

VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation

Data visualization generation using Large Language Models (LLMs) has shown promising results but often produces suboptimal visualizations that require human intervention for improvement. In this work, we introduce VIS-Shepherd, a specialized Multimodal Large Language Model (MLLM)-based critic to evaluate and provide feedback for LLM-generated data visualizations. At the core of our approach is a framework to construct a high-quality visualization critique dataset, where we collect human-created visualization instances, synthesize corresponding LLM-generated instances, and construct high-quality critiques. We conduct both model-based automatic evaluation and human preference studies to evaluate the effectiveness of our approach. Our experiments show that even small (7B parameters) open-source MLLM models achieve substantial performance gains by leveraging our high-quality visualization critique dataset, reaching levels comparable to much larger open-source or even proprietary models. Our work demonstrates significant potential for MLLM-based automated visualization critique and indicates promising directions for enhancing LLM-based data visualization generation. Our project page: https://github.com/bopan3/VIS-Shepherd.

cs.CV↗

arXiv2025

Extended Hybrid Zero Dynamics for Bipedal Walking of the Knee-less Robot SLIDER

Knee-less bipedal robots like SLIDER have the advantage of ultra-lightweight legs and improved walking energy efficiency compared to traditional humanoid robots. In this paper, we firstly introduce an improved hardware design of the SLIDER bipedal robot with new line-feet and more optimized mass distribution that enables higher locomotion speeds. Secondly, we propose an extended Hybrid Zero Dynamics (eHZD) method, which can be applied to prismatic joint robots like SLIDER. The eHZD method is then used to generate a library of gaits with varying reference velocities in an offline way. Thirdly, a Guided Deep Reinforcement Learning (DRL) algorithm is proposed to use the pre-generated library to create walking control policies in real-time. This approach allows us to combine the advantages of both HZD (for generating stable gaits with a full-dynamics model) and DRL (for real-time adaptive gait generation). The experimental results show that this approach achieves 150% higher walking velocity than the previous MPC-based approach.

cs.RO↗

arXiv2025

XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation

Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing Large Language Model (LLM)'s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an intermediate representation to capture better structured relational knowledge in the corpus, elevating the precision and comprehensiveness of generation results. However, developers usually face challenges in analyzing the effectiveness of GraphRAG on their dataset due to GraphRAG's complex information processing pipeline and the overwhelming amount of LLM invocations involved during graph construction and query, which limits GraphRAG interpretability and accessibility. This research proposes a visual analysis framework that helps RAG developers identify critical recalls of GraphRAG and trace these recalls through the GraphRAG pipeline. Based on this framework, we develop XGraphRAG, a prototype system incorporating a set of interactive visualizations to facilitate users' analysis process, boosting failure cases collection and improvement opportunities identification. Our evaluation demonstrates the effectiveness and usability of our approach. Our work is open-sourced and available at https://github.com/Gk0Wk/XGraphRAG.

cs.IR↗

arXiv2025

Simulating fluid vortex interactions on a superconducting quantum processor

Vortex interactions are commonly observed in atmospheric turbulence, plasma dynamics, and collective behaviors in biological systems. However, accurately simulating these complex interactions is highly challenging due to the need to capture fine-scale details over extended timescales, which places computational burdens on traditional methods. In this study, we introduce a quantum vortex method, reformulating the Navier--Stokes (NS) equations within a quantum mechanical framework to enable the simulation of multi-vortex interactions on a quantum computer. We construct the effective Hamiltonian for the vortex system and implement a spatiotemporal evolution circuit to simulate its dynamics over prolonged periods. By leveraging eight qubits on a superconducting quantum processor with gate fidelities of 99.97\% for single-qubit gates and 99.76\% for two-qubit gates, we successfully reproduce natural vortex interactions. This method bridges classical fluid dynamics and quantum computing, offering a novel computational platform for studying vortex dynamics. Our results demonstrate the potential of quantum computing to tackle longstanding challenges in fluid dynamics and broaden applications across both natural and engineering systems.

quant-ph↗

arXiv2025

Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces

Modern scientific discovery increasingly relies on high-performance computing for complex modeling and simulation. A key challenge in improving parallel program performance is efficiently mapping tasks to processors and data to memory, a process dictated by intricate, low-level system code known as mappers. Developing high-performance mappers demands days of manual tuning, posing a significant barrier for domain scientists without systems expertise. We introduce a framework that automates mapper development with generative optimization, leveraging richer feedback beyond scalar performance metrics. Our approach features the Agent-System Interface, which includes a Domain-Specific Language (DSL) to abstract away the low-level complexity of system code and define a structured search space, as well as AutoGuide, a mechanism that interprets raw execution output into actionable feedback. Unlike traditional reinforcement learning methods such as OpenTuner, which rely solely on scalar feedback, our method finds superior mappers in far fewer iterations. With just 10 iterations, it outperforms OpenTuner even after 1000 iterations, achieving 3.8X faster performance. Our approach finds mappers that surpass expert-written mappers by up to 1.34X speedup across nine benchmarks while reducing tuning time from days to minutes.

cs.LG↗

arXiv2025

Probability-Consistent Preference Optimization for Enhanced LLM Reasoning

Recent advances in preference optimization have demonstrated significant potential for improving mathematical reasoning capabilities in large language models (LLMs). While current approaches leverage high-quality pairwise preference data through outcome-based criteria like answer correctness or consistency, they fundamentally neglect the internal logical coherence of responses. To overcome this, we propose Probability-Consistent Preference Optimization (PCPO), a novel framework that establishes dual quantitative metrics for preference selection: (1) surface-level answer correctness and (2) intrinsic token-level probability consistency across responses. Extensive experiments show that our PCPO consistently outperforms existing outcome-only criterion approaches across a diverse range of LLMs and benchmarks. Our code is publicly available at https://github.com/YunqiaoYang/PCPO.

cs.CL↗

arXiv2025

EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning

Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggle with complex real-world scenarios like business negotiations, which require strategic reasoning-an ability to navigate dynamic environments and align long-term goals amidst uncertainty. Existing methods for strategic reasoning face challenges in adaptability, scalability, and transferring strategies to new contexts. To address these issues, we propose explicit policy optimization (EPO) for strategic reasoning, featuring an LLM that provides strategies in open-ended action space and can be plugged into arbitrary LLM agents to motivate goal-directed behavior. To improve adaptability and policy transferability, we train the strategic reasoning model via multi-turn reinforcement learning (RL),utilizing process rewards and iterative self-play. Experiments across social and physical domains demonstrate EPO's ability of long-term goal alignment through enhanced strategic reasoning, achieving state-of-the-art performance on social dialogue and web navigation tasks. Our findings reveal various collaborative reasoning mechanisms emergent in EPO and its effectiveness in generating novel strategies, underscoring its potential for strategic reasoning in real-world applications. Code and data are available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/EPO.

cs.CL↗