Tootfinder

Quantum Zeno Effect in the Spatial Evolution of a Single Atom
Zheng-Yuan Zhang, Han-Chao Chen, Xin Liu, Li-Hua Zhang, Bang Liu, Shi-Yao Shao, Jun Zhang, Qi-Feng Wang, Qing Li, Yu Ma, Tian-Yu Han, Ya-Jun Wang, Dong-Yang Zhu, Jia-Dou Nan, Yi-Ming Yin, Qiao-Qiao Fang, Dong-Sheng Ding, Bao-Sen Shi
https://arxiv.org/abs/2509.24438

Quantum Zeno Effect in the Spatial Evolution of a Single Atom
The quantum Zeno effect (QZE) reveals that frequent measurements can suppress quantum evolution, but the detailed dynamics of the system under finite-duration measurements in experiments remain insufficiently explored. Here, we employ an optical dipole trap as a projective measurement to study the motion of a single cold atom in free space. By monitoring atomic loss, we directly observe the QZE in single-atom motion in free space and find that the effect of dipole measurements on the atom compr…

Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning
Yulei Qin, Xiaoyu Tan, Zhengbao He, Gang Li, Haojia Lin, Zongyi Li, Zihan Xu, Yuchen Shi, Siqi Cai, Renting Rui, Shaofei Cai, Yuzheng Cai, Xuan Zhang, Sheng Ye, Ke Li, Xing Sun
https://arxiv.org/abs/2509.22601

Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning
Reinforcement learning (RL) is the dominant paradigm for sharpening strategic tool use capabilities of LLMs on long-horizon, sparsely-rewarded agent tasks, yet it faces a fundamental challenge of exploration-exploitation trade-off. Existing studies stimulate exploration through the lens of policy entropy, but such mechanical entropy maximization is prone to RL training instability due to the multi-turn distribution shifting. In this paper, we target the progressive exploration-exploitation bala…

Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective
Siwei Wang, Yifei Shen, Haoran Sun, Shi Feng, Shang-Hua Teng, Li Dong, Yaru Hao, Wei Chen
https://arxiv.org/abs/2509.22613

Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective
Recent reinforcement learning (RL) methods have substantially enhanced the planning capabilities of Large Language Models (LLMs), yet the theoretical basis for their effectiveness remains elusive. In this work, we investigate RL's benefits and limitations through a tractable graph-based abstraction, focusing on policy gradient (PG) and Q-learning methods. Our theoretical analyses reveal that supervised fine-tuning (SFT) may introduce co-occurrence-based spurious solutions, whereas RL achieves c…

MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes
Changsheng Zhao, Ernie Chang, Zechun Liu, Chia-Jung Chang, Wei Wen, Chen Lai, Rick Cao, Yuandong Tian, Raghuraman Krishnamoorthi, Yangyang Shi, Vikas Chandra
https://arxiv.org/abs/2509.24945

MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes
The paradigm shift in large language models (LLMs) from instinctive responses to chain-of-thought (CoT) reasoning has fueled two prevailing assumptions: (1) reasoning capabilities only emerge in sufficiently large models, and (2) such capabilities require training on massive datasets. While the first assumption has already been challenged by recent sub-billion-parameter reasoning models such as Qwen3-0.6B and DeepSeek distilled variants, the second remains largely unquestioned. In this work, we…

AutoML in Cybersecurity: An Empirical Study
Sherif Saad, Kevin Shi, Mohammed Mamun, Hythem Elmiligi
https://arxiv.org/abs/2509.23621 https://arxiv.org/pdf/…

AutoML in Cybersecurity: An Empirical Study
Automated machine learning (AutoML) has emerged as a promising paradigm for automating machine learning (ML) pipeline design, broadening AI adoption. Yet its reliability in complex domains such as cybersecurity remains underexplored. This paper systematically evaluates eight open-source AutoML frameworks across 11 publicly available cybersecurity datasets, spanning intrusion detection, malware classification, phishing, fraud detection, and spam filtering. Results show substantial performance va…

Robot Learning from Any Images
Siheng Zhao, Jiageng Mao, Wei Chow, Zeyu Shangguan, Tianheng Shi, Rong Xue, Yuxi Zheng, Yijia Weng, Yang You, Daniel Seita, Leonidas Guibas, Sergey Zakharov, Vitor Guizilini, Yue Wang
https://arxiv.org/abs/2509.22970

Robot Learning from Any Images
We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a single image without requiring additional hardware or digital assets. Our framework democratizes robotic data generation by producing massive visuomotor robotic demonstrations within minutes from a wide range of image sources, including camera captures, robotic datasets, and Internet images. At its core, our approach …

Preference-Based Dynamic Ranking Structure Recognition
Nan Lu, Jian Shi, Xin-Yu Tian
https://arxiv.org/abs/2509.24493 https://arxiv.org/pdf/2509.24493

Preference-Based Dynamic Ranking Structure Recognition
Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic…

Boosting Pointer Analysis With Large Language Model-Enhanced Allocation Function Detection
Baijun Cheng, Kailong Wang, Ling Shi, Haoyu Wang, Peng Di, Yao Guo, Ding Li, Xiangqun Chen
https://arxiv.org/abs/2509.22530

Boosting Pointer Analysis With Large Language Model-Enhanced Allocation Function Detection
Pointer analysis is foundational for many static analysis tasks, yet its effectiveness is often hindered by imprecise modeling of heap allocations, particularly in C/C++ programs where user-defined allocation functions (AFs) are pervasive. Existing approaches largely overlook these custom allocators, leading to coarse aliasing and reduced analysis precision. In this paper, we present AFD, a novel technique that enhances pointer analysis by automatically identifying and modeling custom allocatio…

"In my defense, only three hours on Instagram": Designing Toward Digital Self-Awareness and Wellbeing
Karthik S. Bhat, Jiayue Melissa Shi, Wenxuan Song, Dong Whi Yoo, Koustuv Saha
https://arxiv.org/abs/2509.21860

"In my defense, only three hours on Instagram": Designing Toward Digital Self-Awareness and Wellbeing
Screen use pervades daily life, shaping work, leisure, and social connections while raising concerns for digital wellbeing. Yet, reducing screen time alone risks oversimplifying technology's role and neglecting its potential for meaningful engagement. We posit self-awareness -- reflecting on one's digital behavior -- as a critical pathway to digital wellbeing. We developed WellScreen, a lightweight probe that scaffolds daily reflection by asking people to estimate and report smartphone use. In …

HAPT: Heterogeneity-Aware Automated Parallel Training on Heterogeneous Clusters
Antian Liang, Zhigang Zhao, Kai Zhang, Xuri Shi, Chuantao Li, Chunxiao Wang, Zhenying He, Yinan Jing, X. Sean Wang
https://arxiv.org/abs/2509.24859

HAPT: Heterogeneity-Aware Automated Parallel Training on Heterogeneous Clusters
With the rapid evolution of GPU architectures, the heterogeneity of model training infrastructures is steadily increasing. In such environments, effectively utilizing all available heterogeneous accelerators becomes critical for distributed model training. However, existing frameworks, which are primarily designed for homogeneous clusters, often exhibit significant resource underutilization when deployed on heterogeneous accelerators and networks. In this paper, we present Hapt, an automated pa…

Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments
Fangyu Liu, Peiwen Jiang, Wenjin Wang, Chao-Kai Wen, Shi Jin, Jun Zhang
https://arxiv.org/abs/2509.23590

Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments
Foundation model-based semantic transmission has recently shown great potential in wireless image communication. However, existing methods exhibit two major limitations: (i) they overlook the varying importance of semantic components for specific downstream tasks, and (ii) they insufficiently exploit wireless domain knowledge, resulting in limited robustness under dynamic channel conditions. To overcome these challenges, this paper proposes a foundation model-based adaptive semantic image trans…

A Nonparametric Bayesian Solution of the Empirical Stochastic Inverse Problem
Haiyi Shi, Lei Yang, Jiarui Chi, Troy Butler, Haonan Wang, Derek Bingham, Don Estep
https://arxiv.org/abs/2509.22597

A Nonparametric Bayesian Solution of the Empirical Stochastic Inverse Problem
The stochastic inverse problem is a key ingredient in making inferences, predictions, and decisions for complex science and engineering systems. We formulate and analyze a nonparametric Bayesian solution for the stochastic inverse problem. Key properties of the solution are proved and the convergence and error of a computational solution obtained by random sampling is analyzed. Several applications illustrate the results.

AssertGen: Enhancement of LLM-aided Assertion Generation through Cross-Layer Signal Bridging
Hongqin Lyu, Yonghao Wang, Yunlin Du, Mingyu Shi, Zhiteng Chao, Wenxing Li, Tiancheng Wang, Huawei Li
https://arxiv.org/abs/2509.23674

AssertGen: Enhancement of LLM-aided Assertion Generation through Cross-Layer Signal Bridging
Assertion-based verification (ABV) serves as a crucial technique for ensuring that register-transfer level (RTL) designs adhere to their specifications. While Large Language Model (LLM) aided assertion generation approaches have recently achieved remarkable progress, existing methods are still unable to effectively identify the relationship between design specifications and RTL designs, which leads to the insufficiency of the generated assertions. To address this issue, we propose AssertGen, an…

Energy loss of heavy-flavor quarks in color string medium
Daria Prokhorova, Shuzhe Shi, Evgeny Andronov
https://arxiv.org/abs/2509.24316 https://arxiv.org/…

Energy loss of heavy-flavor quarks in color string medium
The paper presents preliminary estimates of heavy-flavor (HF) quark energy loss during its propagation through the non-equilibrated medium formed in minimum bias proton-proton (p+p) collisions at LHC energies. The study is inspired by the ongoing hot debates on whether tiny droplets of Quark-Gluon Plasma can be created in collisions of small systems. In this work, we model a p+p event with a fluctuating number of color strings originated from multi-pomeron exchanges. Considered longitudinal osc…

The dual foliation of polar actions on nonnegatively curved manifolds
Yi Shi
https://arxiv.org/abs/2509.24155 https://arxiv.org/pdf/2509.24155

The dual foliation of polar actions on nonnegatively curved manifolds
We prove that any dual leaf $L^{\#}$ of a simply connected, complete nonnegatively curved polar manifold $M$ is totally geodesic and closed in $M$, and $L^{\#}$ is itself a complete nonnegatively curved polar manifold. Furthermore, the dual foliation on $M$ induces a Riemannian submersion with totally geodesic fibers from $M$ to a homogeneous space.

Hybrid Method of Moments and Generalized Scattering Matrix: Applications to Antennas in Radomes, Reflectors, and Implantable Media
Chenbo Shi, Shichen Liang, Xin Gu, Jin Pan, Le Zuo
https://arxiv.org/abs/2509.22000

Hybrid Method of Moments and Generalized Scattering Matrix: Applications to Antennas in Radomes, Reflectors, and Implantable Media
Electromagnetic analysis of antennas embedded in or interacting with large surrounding structures poses inherent multiscale challenges: the antenna is electrically small yet geometrically detailed, while the environment is electrically large but comparatively smooth. To address this, we present a hybrid method of moments (MoM) and generalized scattering matrix (GSM) framework that achieves a clean separation between fine-scale and large-scale complexities while preserving their full mutual coup…

CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Zijie Xu, Xinyu Shi, Yiting Dong, Zihan Huang, Zhaofei Yu
https://arxiv.org/abs/2509.23791

CaRe-BN: Precise Moving Statistics for Stabilizing Spiking Neural Networks in Reinforcement Learning
Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision-making on neuromorphic hardware by mimicking the event-driven dynamics of biological neurons. However, due to the discrete and non-differentiable nature of spikes, directly trained SNNs rely heavily on Batch Normalization (BN) to stabilize gradient updates. In online Reinforcement Learning (RL), imprecise BN statistics hinder exploitation, resulting in slower convergence and suboptimal policies. This challenge limits…

Advancing Quantum Many-Body GW Calculations on Exascale Supercomputing Platforms
Benran Zhang, Daniel Weinberg, Chih-En Hsu, Aaron R. Altman, Yuming Shi, James B. White III, Derek Vigil-Fowler, Steven G. Louie, Jack R. Deslippe, Felipe H. da Jornada, Zhenglu Li, Mauro Del Ben
https://arxiv.org/abs/2509.23018

Advancing Quantum Many-Body GW Calculations on Exascale Supercomputing Platforms
Advanced ab initio materials simulations face growing challenges as increasing systems and phenomena complexity requires higher accuracy, driving up computational demands. Quantum many-body GW methods are state-of-the-art for treating electronic excited states and couplings but often hindered due to the costly numerical complexity. Here, we present innovative implementations of advanced GW methods within the BerkeleyGW package, enabling large-scale simulations on Frontier and Aurora exascale pl…

Robust Transitivity of Partially Hyperbolic Diffeomorphisms with Interval Central Leaves
Wenchao Li, Yi Shi, Mingyang Xia
https://arxiv.org/abs/2509.21954 https://

Robust Transitivity of Partially Hyperbolic Diffeomorphisms with Interval Central Leaves
For a boundary-preserving partially hyperbolic diffeomorphism with interval central leaves, we completely characterize the $C^k$-robust transitivity $(k\geq 2)$ by boundary interconnection. As an application, if the boundary SRB measures admit negative central Lyapunov exponents, then boundary interconnection also completely characterizes the phenomenon of robustly intermingled basins for boundary SRB measures.

Wafer-Level Prototyping Tools for CMOS Bioelectronic Sensors
Advait Madhavan, Ruohong Shi, Alokik Kanwal, Glenn Holland, Jacob M. Majikes, Paul N. Patrone, Anthony J. Kearsley, Arvind Balijepalli
https://arxiv.org/abs/2509.24075

Wafer-Level Prototyping Tools for CMOS Bioelectronic Sensors
Integrating biology with complementary metal-oxide-semiconductor (CMOS) sensors can enable highly parallel measurements with minimal parasitic effects, significantly enhancing sensitivity. However, realizing this potential often requires overcoming substantial barriers related to design, fabrication, and heterogeneous integration. In this context, we present a comprehensive suite of tools and methods designed for wafer-scale biosensor prototyping that is sensitive, highly parallelizable, and ma…

Exploring Magnetic Phases in Dual-Species Mott insulating Spinor Lattice Gases
Rui-Shan Li, Zong-Zhen Pan, Shi-Jie Yang, Yi Zheng
https://arxiv.org/abs/2509.23142 https://

Exploring Magnetic Phases in Dual-Species Mott insulating Spinor Lattice Gases
We explore the Mott insulating phases of dual-species bosonic spinor lattice gases, emphasizing the intriguing interplay between synthetic flux and inter-species spin exchange interaction. One of the species is subjected to Raman assisted tunneling, which leads to a synthetic flux within the framework of synthetic dimensions. In the deep Mott regime, the low energy physics is governed by an unconventional and highly tunable spin model, which is characterized by two distinct spin chains. The syn…

Critical dynamics and superconducting state preparation in the quenched Kitaev chain with pairing imbalance
Y. B. Shi, Y. X. Zhang, S. W. Liu, Z. Song
https://arxiv.org/abs/2509.21540

Critical dynamics and superconducting state preparation in the quenched Kitaev chain with pairing imbalance
The dynamical balance of the pairing term plays a crucial role in the emergence of topological superconductivity in the p-wave spinless Kitaev chain, particularly in the non-Hermitian regime. In this work, we systematically investigate the effects of non-Hermitian pairing terms on both equilibrium and nonequilibrium phenomena in the Kitaev chain. Our analysis focuses on two representative forms of pairing imbalance: uniform and staggered. We demonstrate that a uniform imbalance induces only min…

Nonclassical phonon pair
Yu Wang, Zhen Shen, Mai Zhang, Zhi-Peng Shi, Hong-Yi Kuang, Shuai Wan, Fang-Wen Sun, Guang-Can Guo, Chun-Hua Dong
https://arxiv.org/abs/2509.24429 https…

Nonclassical phonon pair
Quantum-correlated photon pairs are crucial resources for modern quantum information science. Similarly, the reliable generation of nonclassical phonon pairs is vital for advancing engineerable solid-state quantum devices and hybrid quantum networks based on phonons. Here, we present a novel approach to generate quantum-correlated phonon pairs in a suspended silicon microstructure initialized in its motional ground state. By simultaneously implementing red- and blue-detuned laser pulses, equiva…

Action-aware Dynamic Pruning for Efficient Vision-Language-Action Manipulation
Xiaohuan Pei, Yuxing Chen, Siyu Xu, Yunke Wang, Yuheng Shi, Chang Xu
https://arxiv.org/abs/2509.22093

Action-aware Dynamic Pruning for Efficient Vision-Language-Action Manipulation
Robotic manipulation with Vision-Language-Action models requires efficient inference over long-horizon multi-modal context, where attention to dense visual tokens dominates computational cost. Existing methods optimize inference speed by reducing visual redundancy within VLA models, but they overlook the varying redundancy across robotic manipulation stages. We observe that the visual token redundancy is higher in coarse manipulation phase than in fine-grained operations, and is strongly correl…

SecureAgentBench: Benchmarking Secure Code Generation under Realistic Vulnerability Scenarios
Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo
https://arxiv.org/abs/2509.22097

SecureAgentBench: Benchmarking Secure Code Generation under Realistic Vulnerability Scenarios
Large language model (LLM) powered code agents are rapidly transforming software engineering by automating tasks such as testing, debugging, and repairing, yet the security risks of their generated code have become a critical concern. Existing benchmarks have offered valuable insights but remain insufficient: they often overlook the genuine context in which vulnerabilities were introduced or adopt narrow evaluation protocols that fail to capture either functional correctness or newly introduced…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[12/21]:
- Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation
Li, Hu, Shang, Wu, Liu, Liu, Gao, Shi, Zhang, Zhang, Shi, YU, Wu, Wu, Jia, Xiang, He, Li

Crosslisted article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- PerformSinger: Multimodal Singing Voice Synthesis Leveraging Synchronized Lip Cues from Singing P...
Ke Gu, Zhicong Wu, Peng Bai, Sitong Qiao, Zhiqi Jiang, Junchen Lu, Xiaodong Shi, Xinyuan Qian

Unsupervised Single-Channel Speech Separation with a Diffusion Prior under Speaker-Embedding Guidance
Runwu Shi, Kai Li, Chang Li, Jiang Wang, Sihan Tan, Kazuhiro Nakadai
https://arxiv.org/abs/2509.24395

Unsupervised Single-Channel Speech Separation with a Diffusion Prior under Speaker-Embedding Guidance
Speech separation is a fundamental task in audio processing, typically addressed with fully supervised systems trained on paired mixtures. While effective, such systems typically rely on synthetic data pipelines, which may not reflect real-world conditions. Instead, we revisit the source-model paradigm, training a diffusion generative model solely on anechoic speech and formulating separation as a diffusion inverse problem. However, unconditional diffusion models lack speaker-level conditioning…

On a class of third order differential equations describing pseudospherical or spherical surfaces
Mingyue Guo, Jing Kang, Zhenhua Shi, Zhiwei Wu
https://arxiv.org/abs/2508.20515

On a class of third order differential equations describing pseudospherical or spherical surfaces
In this paper, we study third order nonlinear partial differential equations which describe surfaces of constant curvature. From the flatness of connection 1-forms, we present a classification of equations with the type $u_t - u_{xxt} = λu^2 u_{xxx} + G(u, u_x, u_{xx}) (λ\in\mathbb{R})$, which describe pseudospherical or spherical surfaces. We show that series of typical soliton equations belong to certain subclass, such as the generalized Camassa-Holm equation, which gives a geometric explan…

Decoupling hydrodynamization from thermalization via nonlinear Boltzmann equation
Xingjian Lu, Shuzhe Shi
https://arxiv.org/abs/2509.23978 https://arxiv.or…

Decoupling hydrodynamization from thermalization via nonlinear Boltzmann equation
The early thermalization puzzle arises from the unexpectedly early applicability of hydrodynamics in heavy-ion collisions. While hydrodynamics has traditionally been associated with the onset of local thermal equilibrium, its derivations -- whether microscopic or macroscopic -- rely instead on linearization around equilibrium. However, the linearization timescale -- the time at which a system's evolution begins to follow a linearized equation -- has not been systematically investigated. In this…

Numerical computation of electromagnetically sourced nonlinear tails
Zhen-Tao He, Jia Du, Jiageng Jiao, Caiying Shao, Junxi Shi, Yu Tian, Hongbao Zhang
https://arxiv.org/abs/2508.20499

Numerical computation of electromagnetically sourced nonlinear tails
Amazingly, recent studies indicate that nonlinear effects are of great significance for modelling black hole ringdown. Transient electromagnetic events in the astrophysical environment are typically high-energetic, potentially responsible for some nonlinearities in ringdown. Motivated by the desire to understand these nonlinearities, we solve the inhomogeneous Bardeen-Press-Teukolsky equation numerically, and find second-order gravitational tails induced by an electromagnetic source. Our result…

MegaCacheX: Towards Cost-Effective Hierarchical Collaborative Content Caching in Emerging Mega-Constellations
Haoyang Shi, Xing Zhang, Sitong Li, Minghang Li, Xinming Lu, Shaoxiang Xu, Guoquan Wang
https://arxiv.org/abs/2508.20433

MegaCacheX: Towards Cost-Effective Hierarchical Collaborative Content Caching in Emerging Mega-Constellations
Significant latency in global content delivery primarily arises from insufficient terrestrial infrastructure. Deploying space-based content delivery networks within emerging mega-constellations provides an effective means to bridge the digital divide. However, space-based caching faces constraints from physical-layer dynamics, including dynamic topologies, time-varying inter-satellite link conditions, and limited onboard energy. In addition, existing mechanisms often lack fine-grained content c…

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- Voyager: Real-Time Splatting City-Scale Gaussians on Resource-Constrained Devices
Liu, Zhu, Li, Wang, Shi, Gan, Li, Leng, Guo, Feng

Prediction of Distant Metastasis for Head and Neck Cancer Patients Using Multi-Modal Tumor and Peritumoral Feature Fusion Network
Zizhao Tang (School of Artificial Intelligence, Xidian University), Changhao Liu (Department of Radiotherapy, Xijing Hospital, Air Force Medical University of PLA), Nuo Tong (School of Artificial Intelligence, Xidian University), Shuiping Gou (School of Artificial Intelligence, Xidian University), Mei Shi (Department of Radiotherapy, Xijing Hospital, Air For…

Prediction of Distant Metastasis for Head and Neck Cancer Patients Using Multi-Modal Tumor and Peritumoral Feature Fusion Network
Metastasis remains the major challenge in the clinical management of head and neck squamous cell carcinoma (HNSCC). Reliable pre-treatment prediction of metastatic risk is crucial for optimizing treatment strategies and prognosis. This study develops a deep learning-based multimodal framework to predict metastasis risk in HNSCC patients by integrating computed tomography (CT) images, radiomics, and clinical data. 1497 HNSCC patients were included. Tumor and organ masks were derived from pretrea…

Near-Optimal Simultaneous Estimation of Quantum State Moments
Xiao Shi, Jiyu Jiang, Xian Wu, Jingu Xie, Hongshun Yao, Xin Wang
https://arxiv.org/abs/2509.24842 https://

Near-Optimal Simultaneous Estimation of Quantum State Moments
Estimating nonlinear properties such as Rényi entropies and observable-weighted moments serves as a central strategy for spectrum spectroscopy, which is fundamental to property prediction and analysis in quantum information science, statistical mechanics, and many-body physics. However, existing approaches are susceptible to noise and require significant resources, making them challenging for near-term quantum hardware. In this work, we introduce a framework for resource-efficient simultaneous…

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training
Junjin Xiao, Yandan Yang, Xinyuan Chang, Ronghan Chen, Feng Xiong, Mu Xu, Wei-Shi Zheng, Qing Zhang
https://arxiv.org/abs/2509.24948

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training
Vision-Language-Action (VLA) models trained via imitation learning suffer from significant performance degradation in data-scarce scenarios due to their reliance on large-scale demonstration datasets. Although reinforcement learning (RL)-based post-training has proven effective in addressing data scarcity, its application to VLA models is hindered by the non-resettable nature of real-world environments. This limitation is particularly critical in high-risk domains such as industrial automation,…

Crosslisted article(s) found for physics.chem-ph. https://arxiv.org/list/physics.chem-ph/new
[1/1]:
- Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials
Shi Yin, Zujian Dai, Xinyang Pan, Lixin He

No More Manual Guides: Automatic and Scalable Generation of High-Quality Excel Tutorials
Yuhang Xie (Peking University), Jian Mu (Nanjing University), Xiaojun Ma (Microsoft), Chaoyun Zhang (Microsoft), Lu Wang (Microsoft), Mengyu Zhou (Microsoft), Mugeng Liu (Peking University), Si Qin (Microsoft), Qingwei Lin (Microsoft), Saravan Rajmohan (Microsoft), Shi Han (Microsoft), Dongmei Zhang (Microsoft)

No More Manual Guides: Automatic and Scalable Generation of High-Quality Excel Tutorials
Excel is one of the most widely used productivity tools across domains, offering rich functionality but also overwhelming users with its complexity. This creates a persistent demand for tutorials to support effective usage. However, existing tutorials are manually authored by experts, require frequent updates after each software release, and incur substantial labor costs. Prior work has not achieved fully automated tutorial generation, since existing methods still depend on handcrafted operatio…

Automated simulation-based design via multi-fidelity active learning and optimisation for laser direct drive implosions
A. J. Crilly, P. W. Moloney, D. Shi, E. A. Ferdinandi
https://arxiv.org/abs/2508.20878

Automated simulation-based design via multi-fidelity active learning and optimisation for laser direct drive implosions
The design of inertial fusion experiments is a complex task as driver energy must be delivered in a precise manner to a structured target to achieve a fast, but hydrodynamically stable, implosion. Radiation-hydrodynamics simulation codes are an essential tool in this design process. However, multi-dimensional simulations that capture hydrodynamic instabilities are more computationally expensive than optimistic, 1D, spherically symmetric simulations which are often the primary design tool. In th…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
Haizhou Shi, Yibin Wang, Ligong Han, Huan Zhang, Hao Wang

Background Prompt for Few-Shot Out-of-Distribution Detection
Songyue Cai, Zongqian Wu, Yujie Mo, Liang Peng, Ping Hu, Xiaoshuang Shi, Xiaofeng Zhu
https://arxiv.org/abs/2509.21055

Background Prompt for Few-Shot Out-of-Distribution Detection
Existing foreground-background (FG-BG) decomposition methods for the few-shot out-of-distribution (FS-OOD) detection often suffer from low robustness due to over-reliance on the local class similarity and a fixed background patch extraction strategy. To address these challenges, we propose a new FG-BG decomposition framework, namely Mambo, for FS-OOD detection. Specifically, we propose to first learn a background prompt to obtain the local background similarity containing both the background an…

Crosslisted article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Toward a Realistic Encoding Model of Auditory Affective Understanding in the Brain
Guandong Pan, Yaqian Yang, Shi Chen, Xin Wang, Longzhao Liu, Hongwei Zheng, Shaoting Tang

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Rohan Phanse, Yijie Zhou, Kejian Shi, Wencai Zhang, Yixin Liu, Yilun Zhao, Arman Cohan
https://arxiv.org/abs/2508.20867

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Retrieval-augmented systems are typically evaluated in settings where information required to answer the query can be found within a single source or the answer is short-form or factoid-based. However, many real-world applications demand the ability to integrate and summarize information scattered across multiple sources, where no single source is sufficient to respond to the user's question. In such settings, the retrieval component of a RAG pipeline must recognize a variety of relevance signa…

Tetratomic states of microwave dressed and associated ultracold 23Na40K molecules
Zhengyu Gu, Xuansheng Zhou, Wei Chen, Wei Han, Fulin Deng, Tao Shi, Pengjun Wang, Jing Zhang
https://arxiv.org/abs/2509.23634

Tetratomic states of microwave dressed and associated ultracold 23Na40K molecules
Ultracold diatomic molecules have achieved significant breakthroughs in recent years, enabling the exploration of quantum chemistry, precision measurements, and strongly correlated many-body physics. Extending ultracold molecular complexity to polyatomic molecules, such as triatomic and tetratomic molecules, has attracted considerable interest. However, the realization of ultracold polyatomic molecules remains technically challenging due to their complex energy-level structures. While only a fe…

Meridional Circulation II: A Unified Mechanism for Lithium Depletion in Solar Analogs and the Lithium Dip in Mid-F Cluster Stars
Xuefeng Li, Jianrong Shi, Yan Li, Hongliang Yan, Jinghua Zhang
https://arxiv.org/abs/2508.19715

Meridional Circulation II: A Unified Mechanism for Lithium Depletion in Solar Analogs and the Lithium Dip in Mid-F Cluster Stars
The behavior of lithium (Li) in Population I main sequence stars challenges standard stellar theory. Two phenomena stand out: the solar Li problem which extends to Li depletion in solar analogs and the Li dip observed in mid-F stars within open clusters. Building on the meridional circulation-driven radial mixing framework previously developed to explain Li-enriched red clump stars, we explore its relevance to Li depletion on the main sequence. First, our models reproduce the observed $A(\text{…

On Minimization/Maximization of the Generalized Multi-Order Complex Quadratic Form With Constant-Modulus Constraints
Chunxuan Shi, Yongzhe Li, Ran Tao
https://arxiv.org/abs/2508.19822

On Minimization/Maximization of the Generalized Multi-Order Complex Quadratic Form With Constant-Modulus Constraints
In this paper, we study the generalized problem that minimizes or maximizes a multi-order complex quadratic form with constant-modulus constraints on all elements of its optimization variable. Such a mathematical problem is commonly encountered in various applications of signal processing. We term it as the constant-modulus multi-order complex quadratic programming (CMCQP) in this paper. In general, the CMCQP is non-convex and difficult to solve. Its objective function typically relates to metr…

MindGuard: Tracking, Detecting, and Attributing MCP Tool Poisoning Attack via Decision Dependence Graph
Zhiqiang Wang, Junyang Zhang, Guanquan Shi, HaoRan Cheng, Yunhao Yao, Kaiwen Guo, Haohua Du, Xiang-Yang Li
https://arxiv.org/abs/2508.20412

MindGuard: Tracking, Detecting, and Attributing MCP Tool Poisoning Attack via Decision Dependence Graph
The Model Context Protocol (MCP) is increasingly adopted to standardize the interaction between LLM agents and external tools. However, this trend introduces a new threat: Tool Poisoning Attacks (TPA), where tool metadata is poisoned to induce the agent to perform unauthorized operations. Existing defenses that primarily focus on behavior-level analysis are fundamentally ineffective against TPA, as poisoned tools need not be executed, leaving no behavioral trace to monitor. Thus, we propose M…

Demonstration of quantum error detection in a silicon quantum processor
Chunhui Zhang, Chunhui Li, Zhen Tian, Yan Jiang, Feng Xu, Shihang Zhang, Hao Wang, Yu-Ning Zhang, Xuesong Bai, Baolong Zhao, Yi-Fei Zhang, Huan Shu, Jiaze Liu, Kunrong Wu, Chao Huang, Keji Shi, Mingchao Duan, Tao Xin, Peihao Huang, Tianluo Pan, Song Liu, Guanyong Wang, Guangchong Hu, Yu He, Dapeng Yu

Demonstration of quantum error detection in a silicon quantum processor
Quantum error detection is essential in realizing large-scale universal quantum computation, especially for quantum error correction (QEC). However, key elements for FTQC have yet to be realized in silicon qubits. Here, we demonstrate quantum error detection on a donor-based silicon quantum processor comprising four-nuclear spin qubits and one electron spin as an auxiliary qubit. The entanglement capability of this system is validated through the establishment of two-qubit Bell state entangleme…

Sensitive Constraints on Coherent Radio Emission from Five Isolated White Dwarfs
Lei Zhang, Alexander Wolszczan, Joshua Pritchard, Ryan S. Lynch, Di Li, Erbil Gugercinoglu, Pei Wang, Andrew Zic, Yuanming Wang, Pavan A. Uttarkar, Shi Dai
https://arxiv.org/abs/2508.19520

Sensitive Constraints on Coherent Radio Emission from Five Isolated White Dwarfs
Coherent, periodic radio emission from pulsars has been widely interpreted as evidence of neutron stars as strongly magnetized compact objects. In recent years, radio pulses have also been detected from white dwarfs (WDs) in tight binary systems, raising the question of whether isolated WDs could similarly host pulsar-like emission. We conducted the most sensitive search to date for coherent radio signals from five isolated, rapidly rotating, and magnetized WDs, using the Five-hundred-meter Ape…

A non-invasive dry-transfer method for fabricating mesoscopic devices on sensitive materials
Zhongmou Jia, Yiwen Ma, Zhongchen Xu, Xue Yang, Jianfei Xiao, Jiezhong He, Yunteng Shi, Zhiyuan Zhang, Duolin Wang, Sicheng Zhou, Bingbing Tong, Peiling Li, Ziwei Dou, Xiaohui Song, Guangtong Liu, Jie Shen, Zhaozheng Lyu, Youguo Shi, Jiangping Hu, Li Lu, Fanming Qu
http…

A non-invasive dry-transfer method for fabricating mesoscopic devices on sensitive materials
Many materials with novel or exotic properties are highly sensitive to environmental factors such as air, solvents, and heat, which complicates device fabrication and limits their potential applications. Here, we present a universal submicron fabrication method for mesoscopic devices using a dry-transfer technique, tailored specifically for sensitive materials. This approach utilizes PMMA masks, combined with a water-dissoluble coating as a sacrificial layer, to ensure that sensitive materials …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[8/21]:
- Factor Decorrelation Enhanced Data Removal from Deep Predictive Models
Wenhao Yang, Lin Li, Xiaohui Tao, Kaize Shi

Local Statistics of the $M_n$-Dimer Model
Nickolas Anderson, Moriah Elkin, Elizabeth Kelley, Nicholas Ovenhouse, Kayla Wright
https://arxiv.org/abs/2508.19224 https://

Local Statistics of the $M_n$-Dimer Model
The classical dimer model is concerned with the (weighted) enumeration of perfect matchings of a graph. An $n$-dimer cover is a multiset of edges that can be realized as the disjoint union of $n$ individual matchings. For a probability measure recently defined by Douglas, Kenyon, and Shi, which we call the $M_n$-dimer model, we study random $n$-dimer covers on bipartite graphs with matrix edge weights and produce formulas for local edge statistics and correlations. We also classify local moves …

Toward a Realistic Encoding Model of Auditory Affective Understanding in the Brain
Guandong Pan, Yaqian Yang, Shi Chen, Xin Wang, Longzhao Liu, Hongwei Zheng, Shaoting Tang
https://arxiv.org/abs/2509.21381

Toward a Realistic Encoding Model of Auditory Affective Understanding in the Brain
In affective neuroscience and emotion-aware AI, understanding how complex auditory stimuli drive emotion arousal dynamics remains unresolved. This study introduces a computational framework to model the brain's encoding of naturalistic auditory inputs into dynamic behavioral/neural responses across three datasets (SEED, LIRIS, self-collected BAVE). Guided by neurobiological principles of parallel auditory hierarchy, we decompose audio into multilevel auditory features (through classical algorit…

Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads
Lei Xie, Fan Liu, Shenghui Song, Shi Jin
https://arxiv.org/abs/2508.18728 http…

Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads
Integrated sensing and communication (ISAC) plays a crucial role in 6G, to enable innovative applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilot and random data payload components, poses challenges for target detection due to two reasons: 1) these two components cause coupled shifts in both the mean and variance of the received signal, and 2) the random data payloads are typically unknown t…

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search
Fei Liu, Qingfu Zhang, Jialong Shi, Xialiang Tong, Kun Mao, Mingxuan Yuan

ARCADE: A Real-Time Data System for Hybrid and Continuous Query Processing across Diverse Data Modalities
Jingyi Yang, Songsong Mo, Jiachen Shi, Zihao Yu, Kunhao Shi, Xuchen Ding, Gao Cong
https://arxiv.org/abs/2509.19757

ARCADE: A Real-Time Data System for Hybrid and Continuous Query Processing across Diverse Data Modalities
The explosive growth of multimodal data - spanning text, image, video, spatial, and relational modalities, coupled with the need for real-time semantic search and retrieval over these data - has outpaced the capabilities of existing multimodal and real-time database systems, which either lack efficient ingestion and continuous query capability, or fall short in supporting expressive hybrid analytics. We introduce ARCADE, a real-time data system that efficiently supports high-throughput ingestio…

Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
Luke Bhan, Miroslav Krstic, Yuanyuan Shi
https://arxiv.org/abs/2508.20367 https://…

Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
In this work, we propose a rigorous method for implementing predictor feedback controllers in nonlinear systems with unknown and arbitrarily long actuator delays. To address the analytically intractable nature of the predictor, we approximate it using a learned neural operator mapping. This mapping is trained once, offline, and then deployed online, leveraging the fast inference capabilities of neural networks. We provide a theoretical stability analysis based on the universal approximation the…

The SymTFT for $N$-ality defects: Part I
Justin Kaidi, Xiaoyi Shi, Soichiro Shimamori, Zhengdi Sun
https://arxiv.org/abs/2509.19429 https://arxiv.org/pdf/2…

The SymTFT for $N$-ality defects: Part I
In order to obtain the SymTFT for a theory with an $N$-ality extension of a discrete, Abelian group $G$, one begins by considering a bulk $G$-gauge theory, and then gauges an appropriate $\mathbb{Z}_N$ symmetry. This procedure involves three choices: the choice of a suitable bulk $\mathbb{Z}_N$ symmetry, of a fractionalization class, and of a discrete torsion. The first choice is, somewhat surprisingly, the most involved, and in this paper we discuss it in detail. In particular, we show that th…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/9]:
- SEDM: Scalable Self-Evolving Distributed Memory for Agents
Haoran Xu, Jiacong Hu, Ke Zhang, Lei Yu, Yuxin Tang, Xinyuan Song, Yiqun Duan, Lynn Ai, Bill Shi

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
Wei Ma, Yixiao Yang, Qiang Hu, Shi Ying, Zhi Jin, Bo Du, Zhenchang Xing, Tianlin Li, Junjie Shi, Yang Liu, Linxiao Jiang
https://arxiv.org/abs/2508.20737

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
Applications of Large Language Models~(LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions. Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance. This paper decomposes LLM applications into a three-layer architecture: \textbf{\textit{System Shell Layer}}, \textbf{\textit{Prompt Orchestration Layer}}, and \textbf{\textit{LLM In…

Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference
Rongzhi Li, Ruogu Du, Zefang Chu, Sida Zhao, Chunlei Han, Zuocheng Shi, Yiwen Shao, Huanle Han, Long Huang, Zherui Liu, Shufan Liu
https://arxiv.org/abs/2508.19559

Taming the Chaos: Coordinated Autoscaling for Heterogeneous and Disaggregated LLM Inference
Serving Large Language Models (LLMs) is a GPU-intensive task where traditional autoscalers fall short, particularly for modern Prefill-Decode (P/D) disaggregated architectures. This architectural shift, while powerful, introduces significant operational challenges, including inefficient use of heterogeneous hardware, network bottlenecks, and critical imbalances between prefill and decode stages. We introduce HeteroScale, a coordinated autoscaling framework that addresses the core challenges of …

Reasoning Beyond Majority Vote: An Explainable SpeechLM Framework for Speech Emotion Recognition
Bo-Hao Su, Hui-Ying Shih, Jinchuan Tian, Jiatong Shi, Chi-Chun Lee, Carlos Busso, Shinji Watanabe
https://arxiv.org/abs/2509.24187

Reasoning Beyond Majority Vote: An Explainable SpeechLM Framework for Speech Emotion Recognition
Speech Emotion Recognition (SER) is typically trained and evaluated on majority-voted labels, which simplifies benchmarking but masks subjectivity and provides little transparency into why predictions are made. This neglects valid minority annotations and limits interpretability. We propose an explainable Speech Language Model (SpeechLM) framework that frames SER as a generative reasoning task. Given an utterance, the model first produces a transcript, then outputs both an emotion label and a c…

Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets
Team Hunyuan3D, :, Bowen Zhang, Chunchao Guo, Haolin Liu, Hongyu Yan, Huiwen Shi, Jingwei Huang, Junlin Yu, Kunhong Li, Linus, Penghao Wang, Qingxiang Lin, Sicong Liu, Xianghui Yang, Yixuan Tang, Yunfei Zhao, Zeqiang Lai, Zhihao Liang, Zibo Zhao
https://arxiv.or…

Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets
Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits controllability and practical adoption. To address this gap, we present Hunyuan3D-Omni, a unified framework for fine-grained, controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images, Hunyuan3D-Omni accepts point clouds, voxels, bounding …

AT-CXR: Uncertainty-Aware Agentic Triage for Chest X-rays
Xueyang Li, Mingze Jiang, Gelei Xu, Jun Xia, Mengzhao Jia, Danny Chen, Yiyu Shi
https://arxiv.org/abs/2508.19322 https:…

AT-CXR: Uncertainty-Aware Agentic Triage for Chest X-rays
Agentic AI is advancing rapidly, yet truly autonomous medical-imaging triage, where a system decides when to stop, escalate, or defer under real constraints, remains relatively underexplored. To address this gap, we introduce AT-CXR, an uncertainty-aware agent for chest X-rays. The system estimates per-case confidence and distributional fit, then follows a stepwise policy to issue an automated decision or abstain with a suggested label for human intervention. We evaluate two router designs that…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[20/21]:
- MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Rec...
Zhao, Chang, Liu, Chang, Wen, Lai, Cao, Tian, Krishnamoorthi, Shi, Chandra

RARO: Reliability-aware Conversion with Enhanced Read Performance for QLC SSDs
Yanyun Wang, Dingcui Yu, Yina Lv, Yunpeng Song, Yumiao Zhao, Liang Shi
https://arxiv.org/abs/2508.19530

RARO: Reliability-aware Conversion with Enhanced Read Performance for QLC SSDs
Quad-level cell (QLC) flash offers significant benefits in cost and capacity, but its limited reliability leads to frequent read retries, which severely degrade read performance. A common strategy in high-density flash storage is to program selected blocks in a low-density mode (SLC), sacrificing some capacity to achieve higher I/O performance. This hybrid storage architecture has been widely adopted in consumer-grade storage systems. However, existing hybrid storage schemes typically focus on …

Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval
Yixuan Tang, Yuanyuan Shi, Yiqun Sun, Anthony Kum Hoe Tung
https://arxiv.org/abs/2508.19758

Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval
Access to diverse perspectives is essential for understanding real-world events, yet most news retrieval systems prioritize textual relevance, leading to redundant results and limited viewpoint exposure. We propose NEWSCOPE, a two-stage framework for diverse news retrieval that enhances event coverage by explicitly modeling semantic variation at the sentence level. The first stage retrieves topically relevant content using dense retrieval, while the second stage applies sentence-level clusterin…

A Multi-Messenger Search for the Supermassive Black Hole Binary in 3C 66B with the Parkes Pulsar Timing Array
Jacob Cardinal Tremblay, Boris Goncharov, Rutger van Haasteren, N. D. Ramesh Bhat, Zu-Cheng Chen, Valentina Di Marco, Satoru Iguchi, Agastya Kapur, Wenhua Ling, Rami Mandow, Saurav Mishra, Daniel J. Reardon, Ryan M. Shannon, Hiroshi Sudou, Jingbo Wang, Shi-Yi Zhao, Xing-Jiang Zhu, Andrew Zic

A Multi-Messenger Search for the Supermassive Black Hole Binary in 3C 66B with the Parkes Pulsar Timing Array
A sub-parsec supermassive black hole binary at the center of the galaxy 3C 66B is a promising candidate for continuous gravitational wave searches with Pulsar Timing Arrays. In this work, we search for such a signal in the third data release of the Parkes Pulsar Timing Array. Matching our priors to estimates of binary parameters from electromagnetic observations, we find log Bayes factor of 0.22, highlighting that the source can be neither confirmed nor ruled out. We place upper limits at 95% c…

Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization
Boyu Ma, Jiaxiao Shi, Yiming Ji, Zhengpu Wang
https://arxiv.org/abs/2508.20458

Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization
This article proposes the Ecological Cycle Optimizer (ECO), a novel metaheuristic algorithm inspired by energy flow and material cycling in ecosystems. ECO draws an analogy between the dynamic process of solving optimization problems and ecological cycling. Unique update strategies are designed for the producer, consumer and decomposer, aiming to enhance the balance between exploration and exploitation processes. Through these strategies, ECO is able to achieve the global optimum, simulating th…

RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting
Zhan Shi, Yefeng Yuan, Yuhong Liu, Liang Cheng, Yi Fang
https://arxiv.org/abs/2508.19286 https://

RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting
The performance of modern machine learning systems depends on access to large, high-quality datasets, often sourced from user-generated content or proprietary, domain-specific corpora. However, these rich datasets inherently contain sensitive personal information, raising significant concerns about privacy, data security, and compliance with regulatory frameworks. While conventional anonymization techniques can remove explicit identifiers, such removal may result in performance drop in downstre…

Giant Anomalous Hall Conductivity and Gilbert Damping in Room-temperature Ferromagnetic Half-Heusler Alloys PtMnBi
Hong-Xue Jiang, Jia-wan Li, Shi-Bo Zhao, Jie Wang, Yusheng Hou
https://arxiv.org/abs/2508.19551

Giant Anomalous Hall Conductivity and Gilbert Damping in Room-temperature Ferromagnetic Half-Heusler Alloys PtMnBi
Half-Heusler alloys have emerged as promising candidates for novel spintronic applications due to their exceptional properties including the high Curie temperature (TC) above room temperature and large anomalous Hall conductivity (AHC). In this work, we systematically study the magnetic and electronic properties of PtMnBi in α-, \{beta}-, and γ-phase using first-principles calculations and Monte Carlo simulations. The three phases are found to be ferromagnetic metals. In particular, the α-ph…

Bose-Einstein condensate of ultracold sodium-rubidium molecules with tunable dipolar interactions
Zhaopeng Shi, Zerong Huang, Fulin Deng, Wei-Jian Jin, Su Yi, Tao Shi, Dajun Wang
https://arxiv.org/abs/2508.20518

Bose-Einstein condensate of ultracold sodium-rubidium molecules with tunable dipolar interactions
Realizing Bose-Einstein condensation of polar molecules is a long-standing challenge in ultracold physics and quantum science due to near-universal two-body collisional losses. Here, we report the production of a Bose-Einstein condensate of ground-state sodium-rubidium molecules via high efficiency evaporative cooling, with losses suppressed using the dual microwave shielding technique. The ability to tune the dipolar interaction between these ultracold polar molecules is crucial for producing …

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Hexin Liu, Haoyang Zhang, Qiquan Zhang, Xiangyu Zhang, Dongyuan Shi, Eng Siong Chng, Haizhou Li
https://arxiv.org/abs/2509.24310

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Code-switching automatic speech recognition (CS-ASR) presents unique challenges due to language confusion introduced by spontaneous intra-sentence switching and accent bias that blurs the phonetic boundaries. Although the constituent languages may be individually high-resource, the scarcity of annotated code-switching data further compounds these challenges. In this paper, we systematically analyze CS-ASR from both model-centric and data-centric perspectives. By comparing state-of-the-art algor…

Observation of Inelastic Meson Scattering in a Floquet System using a Digital Quantum Simulator
Ziting Wang, Zi-Yong Ge, Yun-Hao Shi, Zheng-An Wang, Si-Yun Zhou, Hao Li, Kui Zhao, Yue-Shan Xu, Wei-Guo Ma, Hao-Tian Liu, Cai-Ping Fang, Jia-Cheng Song, Tian-Ming Li, Jia-Chi Zhang, Yu Liu, Cheng-Lin Deng, Guangming Xue, Haifeng Yu, Kai Xu, Kaixuan Huang, Franco Nori, Heng Fan

Observation of Inelastic Meson Scattering in a Floquet System using a Digital Quantum Simulator
Lattice gauge theories provide a non-perturbative framework for understanding confinement and hadronic physics, but their real-time dynamics remain challenging for classical computations. However, quantum simulators offer a promising alternative for exploring such dynamics beyond classical capabilities. Here, we experimentally investigate meson scattering using a superconducting quantum processor. Employing a digital protocol, we realize a Floquet spin chain equivalent to a one-dimensional Floq…

Crosslisted article(s) found for math.DS. https://arxiv.org/list/math.DS/new
[1/1]:
- Delay-adaptive Control of Nonlinear Systems with Approximate Neural Operator Predictors
Luke Bhan, Miroslav Krstic, Yuanyuan Shi

Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Zhijian Zhou, Junyi An, Zongkai Liu, Yunfei Shi, Xuan Zhang, Fenglei Cao, Chao Qu, Yuan Qi
https://arxiv.org/abs/2508.16521

Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
Generating physically realistic 3D molecular structures remains a core challenge in molecular generative modeling. While diffusion models equipped with equivariant neural networks have made progress in capturing molecular geometries, they often struggle to produce equilibrium structures that adhere to physical principles such as force field consistency. To bridge this gap, we propose Reinforcement Learning with Physical Feedback (RLPF), a novel framework that extends Denoising Diffusion Policy …

The Barrow entropies in the thermodynamics of high-dimensional Gauss-Bonnet black holes
Yuxuan Shi, Hongbo Cheng
https://arxiv.org/abs/2508.18926 https://a…

The Barrow entropies in the thermodynamics of high-dimensional Gauss-Bonnet black holes
We study the thermodynamics of $D$-dimensional Gauss-Bonnet black holes with Barrow entropy. It is found that the Gauss-Bonnet coupling and the Barrow factor revise the thermodynamic variables such as event horizon, Hawking temperature, entropy and heat capacity. It is interesting that the larger five-dimensional black holes exist stably and the smaller ones evaporate to disappear owing to the nature of heat capacities amended by the coupling and factor. The discussions exhibit that the $D$-dim…

PerformSinger: Multimodal Singing Voice Synthesis Leveraging Synchronized Lip Cues from Singing Performance Videos
Ke Gu, Zhicong Wu, Peng Bai, Sitong Qiao, Zhiqi Jiang, Junchen Lu, Xiaodong Shi, Xinyuan Qian
https://arxiv.org/abs/2509.22718

PerformSinger: Multimodal Singing Voice Synthesis Leveraging Synchronized Lip Cues from Singing Performance Videos
Existing singing voice synthesis (SVS) models largely rely on fine-grained, phoneme-level durations, which limits their practical application. These methods overlook the complementary role of visual information in duration prediction.To address these issues, we propose PerformSinger, a pioneering multimodal SVS framework, which incorporates lip cues from video as a visual modality, enabling high-quality "duration-free" singing voice synthesis. PerformSinger comprises parallel multi-branch multi…

Scalar leptoquark contributions to the $gg\rightarrow Zh$ process
Shi-Ping He
https://arxiv.org/abs/2508.19642 https://arxiv.org/pdf/2508.19642

Scalar leptoquark contributions to the $gg\rightarrow Zh$ process
In this manuscript, we study the general scalar leptoquark contributions to the $gg\rightarrow Zh$ process at one-loop level. We find that the contributions only show up for the off-diagonal Higgs and $Z$ boson interactions with scalar leptoquarks, which can lead to a new tensor structure different from the standard model. In the one-field and two-field models, the contributions vanish exactly; thus, the contributions are possible at least in the three-field models. We exemplify the contributio…

Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents
Zhixin Lin, Jungang Li, Shidong Pan, Yibo Shi, Yue Yao, Dongliang Xu
https://arxiv.org/abs/2508.19493

Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents
Smartphones bring significant convenience to users but also enable devices to extensively record various types of personal information. Existing smartphone agents powered by Multimodal Large Language Models (MLLMs) have achieved remarkable performance in automating different tasks. However, as the cost, these agents are granted substantial access to sensitive users' personal information during this operation. To gain a thorough understanding of the privacy awareness of these agents, we present …

PolGS: Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction
Yufei Han, Bowen Tie, Heng Guo, Youwei Lyu, Si Li, Boxin Shi, Yunpeng Jia, Zhanyu Ma
https://arxiv.org/abs/2509.19726

PolGS: Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction
Efficient shape reconstruction for surfaces with complex reflectance properties is crucial for real-time virtual reality. While 3D Gaussian Splatting (3DGS)-based methods offer fast novel view rendering by leveraging their explicit surface representation, their reconstruction quality lags behind that of implicit neural representations, particularly in the case of recovering surfaces with complex reflective reflectance. To address these problems, we propose PolGS, a Polarimetric Gaussian Splatti…

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Anyu Ying, Natarajan Balaji Shankar, Chyi-Jiunn Lin, Mohan Shi, Pu Wang, Hye-jin Shim, Siddhant Arora, Hugo Van hamme, Abeer Alwan, Shinji Watanabe
https://arxiv.org/abs/2508.16576

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Despite advancements in ASR, child speech recognition remains challenging due to acoustic variability and limited annotated data. While fine-tuning adult ASR models on child speech is common, comparisons with flat-start training remain underexplored. We compare flat-start training across multiple datasets, SSL representations (WavLM, XEUS), and decoder architectures. Our results show that SSL representations are biased toward adult speech, with flat-start training on child speech mitigating the…

TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones
Minghao Tu, Chun Yu, Xiyuan Shen, Zhi Zheng, Li Chen, Yuanchun Shi
https://arxiv.org/abs/2508.16926

TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones
Text boxes serve as portals to diverse functionalities in today's smartphone applications. However, when it comes to specific functionalities, users always need to navigate through multiple steps to access particular text boxes for input. We propose TextOnly, a unified function portal that enables users to access text-related functions from various applications by simply inputting text into a sole text box. For instance, entering a restaurant name could trigger a Google Maps search, while a gre…

MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
Hao Shi, Bin Xie, Yingfei Liu, Lin Sun, Fengrong Liu, Tiancai Wang, Erjin Zhou, Haoqiang Fan, Xiangyu Zhang, Gao Huang
https://arxiv.org/abs/2508.19236

MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it and struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived representations for immediate control, while the hippocampal system preserves verbatim episodic details and semantic gist of past experience for long-term memory. Inspired by these mechanisms, we propose…

A Two-Stage Kick Scenario for the Peculiar LMXB GX 1 4
Xiangyu Ivy Wang, Shi-Jie Gao, Xiang-Dong Li
https://arxiv.org/abs/2509.21177 https://arxiv.org/pdf/…

A Two-Stage Kick Scenario for the Peculiar LMXB GX 1+4
The low-mass X-ray binary (LMXB) GX 1+4 stands out with its unique properties. Despite being an old system, it hosts a strongly magnetized neutron star (NS), a trait usually linked to younger systems. Its exceptionally long orbital period (1160 days) and low eccentricity (0.101) imply that the NS formed with minimal mass loss and a weak natal kick. These features collectively point towards the NS having formed through the accretion induced collapse (AIC) of a white dwarf (WD). However, GX 1+4's…

ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale
Ge Shi, Hanieh Sadri, Qian Wang, Yu Zhang, Ying Xiong, Yong Zhang, Zhenan Fan
https://arxiv.org/abs/2508.17624

ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale
Expert-Specialized Fine-Tuning (ESFT) adapts Mixture-of-Experts (MoE) large language models to enhance their task-specific performance by selectively tuning the top-activated experts for the task. Serving these fine-tuned models at scale is challenging: deploying merged models in isolation is prohibitively resource-hungry, while existing multi-adapter serving systems with LoRA-style additive updates are incompatible with ESFT's expert-oriented paradigm. We present ExpertWeave, a system that ser…

Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation
Yaqi Li, Peng Chen, Mingyang Han, Bu Pi, Haoxiang Shi, Runzhou Zhao, Yang Yao, Xuan Zhang, Jun Song
https://arxiv.org/abs/2508.18032

Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation
Despite the promising progress of recent autoregressive models in text-to-image (T2I) generation, their ability to handle multi-attribute and ambiguous prompts remains limited. To address these limitations, existing works have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and employed reinforcement learning (RL) to improve reasoning capabilities. However, most models provide reward signals only at the end of the generation stage. This monolithic final-only guidance makes…

DRQA: Dynamic Reasoning Quota Allocation for Controlling Overthinking in Reasoning Large Language Models
Kaiwen Yan, Xuanqing Shi, Hongcheng Guo, Wenxuan Wang, Zhuosheng Zhang, Chengwei Qin
https://arxiv.org/abs/2508.17803

DRQA: Dynamic Reasoning Quota Allocation for Controlling Overthinking in Reasoning Large Language Models
Reasoning large language models (RLLMs), such as OpenAI-O3 and DeepSeek-R1, have recently demonstrated remarkable capabilities by performing structured and multi-step reasoning. However, recent studies reveal that RLLMs often suffer from overthinking, i.e., producing unnecessarily lengthy reasoning chains even for simple questions, leading to excessive token consumption and computational inefficiency. Interestingly, we observe that when processing multiple questions in batch mode, RLLMs exhibit…

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Tong Nie, Yuewen Mei, Yihong Tang, Junlin He, Jie Sun, Haotian Shi, Wei Ma, Jian Sun
https://arxiv.org/abs/2509.20102

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often constrained to a single, fixed trade-off between competing objectives such as adversariality and realism. This yields behavior-specific models that cannot be steered at inference time, lacking the efficiency and flexibility to generate tailored scenarios for diverse training and testing requirements. In view of this, we reframe the task of adversa…

PSR J1838-0655: X-Ray Observations with NICER and NuSTAR
Xian-Ao Wang, Han-Long Peng, Jin-Tao Zheng, Shi-Qi Zhou, Wen-Tao Ye, Ming-Yu Ge, Xiang-Hua Li, Shi-Jie Zheng
https://arxiv.org/abs/2508.17678

PSR J1838-0655: X-Ray Observations with NICER and NuSTAR
We report on the timing and spectral properties of PSR J1838-0655 using joint observations from the Neutron Star Interior Composition Explorer (NICER) and the Nuclear Spectroscopic Telescope Array (NuSTAR). By disentangling the pulsar's emission from its surrounding wind nebula across joint Chandra, NuSTAR, and NICER observations, we find the pulsar's broad-band X-ray spectrum (1.3--79\,keV) is best-described by a broken power-law model. The model features photon indices of $Γ_1 = 1.19 \pm 0.0…

DRES: Benchmarking LLMs for Disfluency Removal
Maria Teleki, Sai Janjur, Haoran Liu, Oliver Grabner, Ketan Verma, Thomas Docog, Xiangjue Dong, Lingfeng Shi, Cong Wang, Stephanie Birkelbach, Jason Kim, Yin Zhang, James Caverlee
https://arxiv.org/abs/2509.20321

DRES: Benchmarking LLMs for Disfluency Removal
Disfluencies -- such as "um," "uh," interjections, parentheticals, and edited statements -- remain a persistent challenge for speech-driven systems, degrading accuracy in command interpretation, summarization, and conversational agents. We introduce DRES (Disfluency Removal Evaluation Suite), a controlled text-level benchmark that establishes a reproducible semantic upper bound for this task. DRES builds on human-annotated Switchboard transcripts, isolating disfluency removal from ASR errors an…

BurstEngine: an Efficient Distributed Framework for Training Transformers on Extremely Long Sequences of over 1M Tokens
Ao Sun, Weilin Zhao, Xu Han, Cheng Yang, Zhiyuan Liu, Chuan Shi, Maosong sun
https://arxiv.org/abs/2509.19836

BurstEngine: an Efficient Distributed Framework for Training Transformers on Extremely Long Sequences of over 1M Tokens
Existing methods for training LLMs on long-sequence data, such as Tensor Parallelism and Context Parallelism, exhibit low Model FLOPs Utilization as sequence lengths and number of GPUs increase, especially when sequence lengths exceed 1M tokens. To address these challenges, we propose BurstEngine, an efficient framework designed to train LLMs on long-sequence data. BurstEngine introduces BurstAttention, an optimized distributed attention with lower communication cost than RingAttention. BurstAt…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/8]:
- One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo
Hao-Nan Shi, Ting-Ji Huang, Lu Han, De-Chuan Zhan, Han-Jia Ye

On-Demand Zeeman Nuclear Frequency Comb Quantum Memory
Yanli Shi, Xiwen Zhang, Yuri Shvyd'ko, Olga Kocharovskaya
https://arxiv.org/abs/2508.18645 https://

On-Demand Zeeman Nuclear Frequency Comb Quantum Memory
The emerging hard X-ray - nuclear interfaces offer unique potential advantages over traditional optical-atomic interfaces for room-temperature, solid-state quantum information processing, including lower background noise, tighter focusing, and exceptionally high resonance quality. Leveraging such interfaces, a major milestone was recently achieved with the first implementation of nuclear quantum memory in the hard X-ray range [S. Velten et al., Nuclear quantum memory for hard X-ray photon wave …

UniEM-3M: A Universal Electron Micrograph Dataset for Microstructural Segmentation and Generation
Nan wang, Zhiyi Xia, Yiming Li, Shi Tang, Zuxin Fan, Xi Fang, Haoyi Tao, Xiaochen Cai, Guolin Ke, Linfeng Zhang, Yanhui Hong
https://arxiv.org/abs/2508.16239

UniEM-3M: A Universal Electron Micrograph Dataset for Microstructural Segmentation and Generation
Quantitative microstructural characterization is fundamental to materials science, where electron micrograph (EM) provides indispensable high-resolution insights. However, progress in deep learning-based EM characterization has been hampered by the scarcity of large-scale, diverse, and expert-annotated datasets, due to acquisition costs, privacy concerns, and annotation complexity. To address this issue, we introduce UniEM-3M, the first large-scale and multimodal EM dataset for instance-level u…

Z-Scores: A Metric for Linguistically Assessing Disfluency Removal
Maria Teleki, Sai Janjur, Haoran Liu, Oliver Grabner, Ketan Verma, Thomas Docog, Xiangjue Dong, Lingfeng Shi, Cong Wang, Stephanie Birkelbach, Jason Kim, Yin Zhang, James Caverlee
https://arxiv.org/abs/2509.20319

Z-Scores: A Metric for Linguistically Assessing Disfluency Removal
Evaluating disfluency removal in speech requires more than aggregate token-level scores. Traditional word-based metrics such as precision, recall, and F1 (E-Scores) capture overall performance but cannot reveal why models succeed or fail. We introduce Z-Scores, a span-level linguistically-grounded evaluation metric that categorizes system behavior across distinct disfluency types (EDITED, INTJ, PRN). Our deterministic alignment module enables robust mapping between generated text and disfluent …

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Tong Nie, Yuewen Mei, Yihong Tang, Junlin He, Jie Sun, Haotian Shi, Wei Ma, Jian Sun
https://arxiv.org/abs/2509.20102

Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often constrained to a single, fixed trade-off between competing objectives such as adversariality and realism. This yields behavior-specific models that cannot be steered at inference time, lacking the efficiency and flexibility to generate tailored scenarios for diverse training and testing requirements. In view of this, we reframe the task of adversa…

4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration
Hao Tang, Rongxi Yi, Lei Li, Kaiyi Cao, Jiapeng Zhao, Yihan Xiao, Minghai Shi, Peng Yuan, Yan Xi, Hui Tang, Wei Li, Zhan Wu, Yixin Zhou
https://arxiv.org/abs/2508.16138

4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration
Conventional computed tomography (CT) lacks the ability to capture dynamic, weight-bearing joint motion. Functional evaluation, particularly after surgical intervention, requires four-dimensional (4D) imaging, but current methods are limited by excessive radiation exposure or incomplete spatial information from 2D techniques. We propose an integrated 4D joint analysis platform that combines: (1) a dual robotic arm cone-beam CT (CBCT) system with a programmable, gantry-free trajectory optimized …

Weak anharmonicity sensing by single- and two-photon drives
Shi-Yu Zeng, Chao-Qun Ai, Xi Cheng, Ming-Wei Yan, Xun-Wei Xu
https://arxiv.org/abs/2509.19891 https://

Weak anharmonicity sensing by single- and two-photon drives
The optical cavity undergoes a quantum phase transition when the strength of a two-photon drive exceeds a critical point (CP), and the great sensitivity of CP in sensing has been recognized. However, these methodologies are customized to sense linear perturbations, and quantum noise is divergent at the CP. Here, we propose a scheme for sensing the weak Kerr nonlinearity in an optical cavity by both single- and two-photon drives, based on the CP for phase transition. We show that the mean photon…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/7]:
- AT-CXR: Uncertainty-Aware Agentic Triage for Chest X-rays
Xueyang Li, Mingze Jiang, Gelei Xu, Jun Xia, Mengzhao Jia, Danny Chen, Yiyu Shi