Tootfinder

Sola, which wants to use AI agents to automate businesses' repetitive, manual tasks, raised $21M, including a $3.5M seed and a $17.5M Series A led by a16z (Emma Hinchliffe/Fortune)
https://fortune.com/2025/08/14/sola-solutions-ai-startup-r…

Exclusive: Two MIT dropouts raised $21 million for their AI startup that does legacy businesses’ grunt work
Sola Solutions' technology, backed by Andreessen Horowitz and Sarah Guo, automates grunt work for businesses, replacing RPA.

UI-Venus Technical Report: Building High-performance UI Agents with RFT
Zhangxuan Gu, Zhengwen Zeng, Zhenyu Xu, Xingran Zhou, Shuheng Shen, Yunfei Liu, Beitong Zhou, Changhua Meng, Tianyu Xia, Weizhi Chen, Yue Wen, Jingya Dou, Fei Tang, Jinzhen Lin, Yulin Liu, Zhenlin Guo, Yichen Gong, Heng Jia, Changlong Gao, Yuan Guo, Yong Deng, Zhenyu Guo, Liang Chen, Weiqiang Wang

UI-Venus Technical Report: Building High-performance UI Agents with RFT
We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and navigation tasks using only several hundred thousand high-quality training samples through reinforcement finetune (RFT) based on Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% / 50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e., Screenspot-V2 / Pro, surpassing the previous …

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
Changxin Ke, Rui Zhang, Shuo Wang, Li Ding, Guangli Li, Yuanbo Wen, Shuoming Zhang, Ruiyuan Xu, Jin Qin, Jiaming Guo, Chenxi Wang, Ling Li, Qi Guo, Yunji Chen
https://arxiv.org/abs/2506.11153

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation
The rise of GPU-based high-performance computing (HPC) has driven the widespread adoption of parallel programming models such as CUDA. Yet, the inherent complexity of parallel programming creates a demand for the automated sequential-to-parallel approaches. However, data scarcity poses a significant challenge for machine learning-based sequential-to-parallel code translation. Although recent back-translation methods show promise, they still fail to ensure functional equivalence in the translate…

FuXi-\beta: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model
Yufei Ye, Wei Guo, Hao Wang, Hong Zhu, Yuyang Ye, Yong Liu, Huifeng Guo, Ruiming Tang, Defu Lian, Enhong Chen
https://arxiv.org/abs/2508.10615

FuXi-β: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model
Scaling laws for autoregressive generative recommenders reveal potential for larger, more versatile systems but mean greater latency and training costs. To accelerate training and inference, we investigated the recent generative recommendation models HSTU and FuXi-$α$, identifying two efficiency bottlenecks: the indexing operations in relative temporal attention bias and the computation of the query-key attention map. Additionally, we observed that relative attention bias in self-attention mec…

ADHO (adho.org) updates at the opening of #DH2025 from Diane and Michael - the Italian Digital Humanities association officially joins ADHO, and announces awards including the Zampoli prize to Stylo software, the conference bursaries winners and Fortier prize nominees. Also note the Code of Conduct!

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
Yuchen Zhu, Wei Guo, Jaemoo Choi, Guan-Horng Liu, Yongxin Chen, Molei Tao
https://arxiv.org/abs/2508.10684 h…

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function $π\propto\mathrm{e}^{-U}$ is known up to a normalizing constant, which is an important task in fields such as statistical physics, machine learning, combinatorial optimization, etc. To better address this challenging task when the state space has a large cardinality and the distribution is multi-modal, we propose $\textbf{M}$asked $\textbf{D}$iffusion $\te…

Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Liangxuan Guo, Bin Zhu, Qingqian Tao, Kangning Liu, Xun Zhao, Xianzhe Qin, Jin Gao, Guangfu Hao
https://arxiv.org/abs/2509.11067

Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce \textsc{Agentic Lybic}, a novel multi-agent system where the entire architecture operates as a finite-state machine (FSM). This core innovation enables dynamic orchestration. Our system comprises four components: a Controller, a Manager, three Workers (Technician for code-based operations, Operator for GUI interactions, and Analyst for decision su…

A Survey on Diffusion Language Models
Tianyi Li, Mingda Chen, Bowei Guo, Zhiqiang Shen
https://arxiv.org/abs/2508.10875 https://arxiv.org/pdf/2508.10875

A Survey on Diffusion Language Models
Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm. By generating tokens in parallel through an iterative denoising process, DLMs possess inherent advantages in reducing inference latency and capturing bidirectional context, thereby enabling fine-grained control over the generation process. While achieving a several-fold speed-up, recent advancements have allowed DLMs to show performance comparable to their …

Finite-correlation-secure quantum key distribution
Yang-Guang Shan, Jia-Xuan Li, Zhen-Qiang Yin, Shuang Wang, Wei Chen, De-Yong He, Guang-Can Guo, Zheng-Fu Han
https://arxiv.org/abs/2507.11243

Finite-correlation-secure quantum key distribution
Correlation between different pulses is a nettlesome problem in quantum key distribution (QKD). All existing solutions for this problem need to characterize the strength of the correlation, which may reduce the security of QKD to an accurate characterization. In this article, we propose a new protocol immune to all correlations of all dimensions, with the only requirements of non-entangled and finite-ranged correlation, and bounded vacuum probability. Additionally, the new protocol is side-chan…

GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation
Hang Yin, Haoyu Wei, Xiuwei Xu, Wenxuan Guo, Jie Zhou, Jiwen Lu
https://arxiv.org/abs/2509.10454

GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation
In this paper, we propose a training-free framework for vision-and-language navigation (VLN). Existing zero-shot VLN methods are mainly designed for discrete environments or involve unsupervised training in continuous simulator environments, which makes it challenging to generalize and deploy them in real-world scenarios. To achieve a training-free framework in continuous environments, our framework formulates navigation guidance as graph constraint optimization by decomposing instructions into…

BandFuzz: An ML-powered Collaborative Fuzzing Framework
Wenxuan Shi, Hongwei Li, Jiahao Yu, Xinqian Sun, Wenbo Guo, Xinyu Xing
https://arxiv.org/abs/2507.10845

BandFuzz: An ML-powered Collaborative Fuzzing Framework
Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative fuzzing relaxes the assumptions on target programs, providing constant and robust performance across various programs. Ideally, collaborative fuzzing should be a more promising direction toward generic fuzz…

Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
Jingyu Tang, Chaoran Chen, Jiawen Li, Zhiping Zhang, Bingcan Guo, Ibrahim Khalilov, Simret Araya Gebreegziabher, Bingsheng Yao, Dakuo Wang, Yanfang Ye, Tianshi Li, Ziang Xiao, Yaxing Yao, Toby Jia-Jun Li
https://arxiv.org/abs/…

Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
The dark patterns, deceptive interface designs manipulating user behaviors, have been extensively studied for their effects on human decision-making and autonomy. Yet, with the rising prominence of LLM-powered GUI agents that automate tasks from high-level intents, understanding how dark patterns affect agents is increasingly important. We present a two-phase empirical study examining how agents, human participants, and human-AI teams respond to 16 types of dark patterns across diverse scenario…

Duality and Infinite Distance Limits in Asymmetric Freely Acting Orbifolds
George Gkountoumis, Chris Hull, Guo-En Nian, Stefan Vandoren
https://arxiv.org/abs/2506.11699

Duality and Infinite Distance Limits in Asymmetric Freely Acting Orbifolds
We use freely acting asymmetric orbifolds of type IIB string theory to construct a class of theories in four dimensions with eight supercharges. Their low energy effective field theories resemble $STU$ models, but have different duality groups: the orbifold's free action reduce the duality groups to congruence subgroups of the modular group. The fundamental domain is consequently larger and contains new interesting points at infinite distance on the real axis bounding the upper half plane. We v…

A continuous-time model to interpolate between speed and function value restart in accelerated first order methods
Juan Jos\'e Maul\'en, Huiyuan Guo, Juan Peypouquet
https://arxiv.org/abs/2506.11267

A continuous-time model to interpolate between speed and function value restart in accelerated first order methods
We introduce a new restarting scheme for a continuous inertial dynamics with Hessian driven-damping, and establish a linear convergence rate for the function values along the restarted trajectories. The proposed routine is a generalization of existing speed restart schemes, and interpolates between speed and function value restarts, considerably delaying the restarting time, while preserving the function value decrease. Numerical experiments show an improvement in the convergence rates for both…

Advances in Speech Separation: Techniques, Challenges, and Future Trends
Kai Li, Guo Chen, Wendi Sang, Yi Luo, Zhuo Chen, Shuai Wang, Shulin He, Zhong-Qiu Wang, Andong Li, Zhiyong Wu, Xiaolin Hu
https://arxiv.org/abs/2508.10830

Advances in Speech Separation: Techniques, Challenges, and Future Trends
The field of speech separation, addressing the "cocktail party problem", has seen revolutionary advances with DNNs. Speech separation enhances clarity in complex acoustic environments and serves as crucial pre-processing for speech recognition and speaker recognition. However, current literature focuses narrowly on specific architectures or isolated approaches, creating fragmented understanding. This survey addresses this gap by providing systematic examination of DNN-based speech separation te…

Probing Cosmic Neutrino Background through Parametric Fluorescence
Guo-yuan Huang, Shun Zhou
https://arxiv.org/abs/2507.10868 https://

Probing Cosmic Neutrino Background through Parametric Fluorescence
We point out that relic neutrinos from the Big Bang may induce the parametric fluorescence in molecular systems, which offers a novel way to discover cosmic neutrino background. By coherently scattering with molecular energy levels, a massive neutrino can spontaneously ``decay" into a lighter neutrino and an infrared signal photon, i.e., $ν^{}_{i} + M \to ν^{}_{j} + γ^{}_{\rm S} + M$, where the molecular state $M$ remains unchanged after the scattering. Because the amplitudes of different ra…

Large language model-empowered next-generation computer-aided engineering
Jiachen Guo, Chanwook Park, Dong Qian, Thomas J. R. Hughes, Wing Kam Liu
https://arxiv.org/abs/2509.11447

Large language model-empowered next-generation computer-aided engineering
Software development has entered a new era where large language models (LLMs) now serve as general-purpose reasoning engines, enabling natural language interaction and transformative applications across diverse domains. This paradigm is now extending into computer-aided engineering (CAE). Recent applications of LLMs in CAE have successfully automated routine tasks, including CAD model generation and FEM simulations. Nevertheless, these contributions, which primarily serve to reduce manual labor…

The Future is Fluid: Revolutionizing DOA Estimation with Sparse Fluid Antennas
He Xu, Tuo Wu, Ye Tian, Ming Jin, Wei Liu, Qinghua Guo, Maged Elkashlan, Matthew C. Valenti, Chan-Byoung Chae, Kin-Fai Tong, Kai-Kit Wong
https://arxiv.org/abs/2508.10826

The Future is Fluid: Revolutionizing DOA Estimation with Sparse Fluid Antennas
This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoF) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application o…

Replaced article(s) found for math-ph. https://arxiv.org/list/math-ph/new
[1/1]:
- A generalization of the Witten conjecture through spectral curve
Shuai Guo, Ce Ji, Qingsheng Zhang

Tracing the Physical Lineage of GRB 211211A: Population Constraints on NS-WD Merger Gamma-Ray Bursts
Xiao-Tian Xu, Bin-Bin Zhang, Yun-Lang Guo, Xiang-Dong Li
https://arxiv.org/abs/2508.10551

Tracing the Physical Lineage of GRB 211211A: Population Constraints on NS-WD Merger Gamma-Ray Bursts
The peculiar long gamma-ray burst (GRB) event, GRB 211211A, is known for it is association with a kilonova feature. Whereas most long GRBs are thought to originate in the core collapse of massive stars, the presence of kilonova suggests GRB 211211A was instead produced by a merger of a compact object binary. Building on the interpretation put forward by \citet{Yang2022Natur.612..232Y}--who argue that GRB 211211A was powered by a massive white-dwarf + neutron-star (WD-NS) merger--we adopt this W…

Optimal Inference of the Mean Outcome under Optimal Treatment Regime
Shuoxun Xu (Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China, Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA), Xinzhou Guo (Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China)

Optimal Inference of the Mean Outcome under Optimal Treatment Regime
When an optimal treatment regime (OTR) is considered, we need to evaluate the OTR in a valid and efficient way. The classical inference applied to the mean outcome under OTR, assuming the OTR is the same as the estimated OTR, might be biased when the regularity assumption that OTR is unique is violated. Although several methods have been proposed to allow nonregularity in such inference, its optimality is unclear due to challenges in deriving semiparametric efficiency bounds under potential non…

Hidden fully-compensated ferrimagnetism
San-Dong Guo
https://arxiv.org/abs/2507.11118 https://arxiv.org/pdf/2507.11118

Hidden fully-compensated ferrimagnetism
Incorporating zero-net-magnetization magnets that exhibit spin-splitting into spintronics delivers key advantages: faster switching dynamics, greater immunity to destabilizing fields, lower power consumption, and markedly improved overall efficiency. The collinear magnets with net-zero magnetization and spin-splitting mainly include altermagnet and fully-compensated ferrimagnet, which provide possibility to achieve hidden spin polarization (HSP) with net-zero spin polarization in total but non-…

Phase-Field Modeling and Energy-Stable Schemes for Osmotic Flow through Semi-Permeable
Ruihan Guo, Jie Shen, Shixin Xu, Xianmin Xu
https://arxiv.org/abs/2506.11374

Phase-Field Modeling and Energy-Stable Schemes for Osmotic Flow through Semi-Permeable
We present a thermodynamically consistent phase-field model for simulating fluid transport across semi-permeable membranes, with a particular focus on osmotic pressure effects. The model extends the classical Navier-Stokes-Cahn-Hilliard (NSCH) system by introducing an Allen-Cahn-type transmembrane flux governed by chemical potential imbalances, resulting in a strongly coupled system involving fluid motion, solute transport, and interface dynamics. To solve this system efficiently and accurately…

Arcturus: A Cloud Overlay Network for Global Accelerator with Enhanced Performance and Stability
Matthew Yang Liu, Chuang Chen, Pengcheng Lv, Hui Guo, Yanan Zhang, Cong Wang, Yusen Li, Zhenyu Li, Yu-Chu Tian
https://arxiv.org/abs/2507.10928

Arcturus: A Cloud Overlay Network for Global Accelerator with Enhanced Performance and Stability
Global Accelerator (GA) services play a vital role in ensuring low-latency, high-reliability communication for real-time interactive applications. However, existing GA offerings are tightly bound to specific cloud providers, resulting in high costs, rigid deployment, and limited flexibility, especially for large-scale or budget-sensitive deployments. Arcturus is a cloud-native GA framework that revisits the design of GA systems by leveraging low-cost, heterogeneous cloud resources across multip…

From Kac particles to the Landau equation with hard potentials: BBGKY hierarchy method
Shuchen Guo
https://arxiv.org/abs/2508.10697 https://arxiv.org/pdf/2…

From Kac particles to the Landau equation with hard potentials: BBGKY hierarchy method
We study the Kac particle model for the space-homogenous Landau equation with hard potentials. By showing a sharper Povzner-type inequality, we obtain the uniform-in-time and uniform-in-N propagation of exponential moment for the first marginal of the solution of the many-particle Liouville equation. This key property enables us to show the uniqueness of weak solutions of the corresponding infinite Landau hierarchy by coupling method. As a result, we prove the propagation of chaos for the Landa…

Crosslisted article(s) found for math.QA. https://arxiv.org/list/math.QA/new
[1/1]:
- Induced structures of averaging commutative and cocommutative infinitesimal bialgebras via a new ...
Chengming Bai, Li Guo, Guilai Liu, Quan Zhao

An Interpretable AI framework Quantifying Traditional Chinese Medicine Principles Towards Enhancing and Integrating with Modern Biomedicine
Haoran Li, Xingye Cheng, Ziyang Huang, Jingyuan Luo, Qianqian Xu, Qiguang Zhao, Tianchen Guo, Yumeng Zhang, Linda Lidan Zhong, Zhaoxiang Bian, Leihan Tang, Aiping Lyu, Liang Tian
https://arx…

An Interpretable AI framework Quantifying Traditional Chinese Medicine Principles Towards Enhancing and Integrating with Modern Biomedicine
Traditional Chinese Medicine diagnosis and treatment principles, established through centuries of trial-and-error clinical practice, directly maps patient-specific symptom patterns to personalised herbal therapies. These empirical holistic mapping principles offer valuable strategies to address remaining challenges of reductionism methodologies in modern biomedicine. However, the lack of a quantitative framework and molecular-level evidence has limited their interpretability and reliability. He…

On the universal calibration of Pareto-type linear combination tests
Parijat Chakraborty, F. Richard Guo, Kerby Shedden, Stilian Stoev
https://arxiv.org/abs/2509.12066 https://

On the universal calibration of Pareto-type linear combination tests
It is often of interest to test a global null hypothesis using multiple, possibly dependent, $p$-values by combining their strengths while controlling the Type I error. Recently, several heavy-tailed combinations tests, such as the harmonic mean test and the Cauchy combination test, have been proposed: they map $p$-values into heavy-tailed random variables before combining them in some fashion into a single test statistic. The resulting tests, which are calibrated under the assumption of indepe…

Gromov hyperbolicity III: improved geometric characterization in Euclidean spaces and beyond
Chang-Yu Guo, Manzi Huang, Xiantao Wang
https://arxiv.org/abs/2509.10403 https://

Gromov hyperbolicity III: improved geometric characterization in Euclidean spaces and beyond
This is the third article of a series of our recent works, addressing an open question of Bonk-Heinonen-Koskela [3], to study the relationship between (inner) uniformality and Gromov hyperbolicity in infinite dimensional spaces. Our main focus of this paper is to establish improved geometric characterization of Gromov hyperbolicity. More precisely, we develop an elementary measure-independent approach to establish the geometric characterization of Gromov hyperbolicity for general proper Eucli…

SPHENIC: Topology-Informed Multi-View Clustering for Spatial Transcriptomics
Chenkai Guo, Yikai Zhu, Jing Yangum, Renxiang Guan, Por Lip Yee, Guangdun Peng, Dayu Hu
https://arxiv.org/abs/2508.10646

SPHENIC: Topology-Informed Multi-View Clustering for Spatial Transcriptomics
By incorporating spatial location information, spatial-transcriptomics clustering yields more comprehensive insights into cell subpopulation identification. Despite recent progress, existing methods have at least two limitations: (i) topological learning typically considers only representations of individual cells or their interaction graphs; however, spatial transcriptomic profiles are often noisy, making these approaches vulnerable to low-quality topological signals, and (ii) insufficient mod…

zERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literature
Rui Zhou, Haohui Ma, Tianle Xin, Lixin Zou, Qiuyue Hu, Hongxi Cheng, Mingzhi Lin, Jingjing Guo, Sheng Wang, Guoqing Zhang, Yanjie Wei, Liangzhen Zheng
https://arxiv.org/abs/2508.09995

zERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literature
The rapid expansion of enzyme kinetics literature has outpaced the curation capabilities of major biochemical databases, creating a substantial barrier to AI-driven modeling and knowledge discovery. We present zERExtractor, an automated and extensible platform for comprehensive extraction of enzyme-catalyzed reaction and activity data from scientific literature. zERExtractor features a unified, modular architecture that supports plug-and-play integration of state-of-the-art models, including la…

Streaming 4D Visual Geometry Transformer
Dong Zhuo, Wenzhao Zheng, Jiahe Guo, Yuqi Wu, Jie Zhou, Jiwen Lu
https://arxiv.org/abs/2507.11539 https://

Streaming 4D Visual Geometry Transformer
Perceiving and reconstructing 4D spatial-temporal geometry from videos is a fundamental yet challenging computer vision task. To facilitate interactive and real-time applications, we propose a streaming 4D visual geometry transformer that shares a similar philosophy with autoregressive large language models. We explore a simple and efficient design and employ a causal transformer architecture to process the input sequence in an online manner. We use temporal causal attention and cache the histo…

Crosslisted article(s) found for math.RT. https://arxiv.org/list/math.RT/new
[1/1]:
- Induced structures of averaging commutative and cocommutative infinitesimal bialgebras via a new ...
Chengming Bai, Li Guo, Guilai Liu, Quan Zhao

Conceptual Design Report of Super Tau-Charm Facility: The Accelerator
Jiancong Bao, Anton Bogomyagkov, Zexin Cao, Mingxuan Chang, Fangzhou Chen, Guanghua Chen, Qi Chen, Qushan Chen, Zhi Chen, Kuanjun Fan, Hailiang Gong, Duan Gu, Hao Guo, Tengjun Guo, Chongchao He, Tianlong He, Kaiwen Hou, Hao Hu, Tongning Hu, Xiaocheng Hu, Dazhang Huang, Pengwei Huang, Ruixuan Huang, Zhicheng Huang, Hangzhou Li, Renkai Li, Sangya Li, Weiwei Li, Xuan Li, Xunfeng Li, Yu Liang, Chao Liu, Tao Liu, Xiaoyu L…

Conceptual Design Report of Super Tau-Charm Facility: The Accelerator
Electron-positron colliders operating in the GeV region of center-of-mass energies or the Tau-Charm energy region, have been proven to enable competitive frontier research, due to its several unique features. With the progress of high energy physics in the last two decades, a new-generation Tau-Charm factory, Super Tau Charm Facility (STCF) has been actively promoting by the particle physics community in China. STCF holds great potential to address fundamental questions such as the essence of c…

Multi-Channel Microwave-to-Optics Conversion Utilizing a Hybrid Photonic-Phononic Waveguide
Yuan-Hao Yang, Jia-Qi Wang, Zheng-Xu Zhu, Yu Zeng, Ming Li, Yan-Lei Zhang, Juanjuan Lu, Qiang Zhang, Weiting Wang, Chun-Hua Dong, Xin-Biao Xu, Guang-Can Guo, Luyan Sun, Chang-Ling Zou
https://arxiv.org/abs/2509.10052…

Multi-Channel Microwave-to-Optics Conversion Utilizing a Hybrid Photonic-Phononic Waveguide
Efficient and coherent conversion between microwave and optical signals is crucial for a wide range of applications, from quantum information processing to microwave photonics and radar systems. However, existing conversion techniques rely on cavity-enhanced interactions, which limit the bandwidth and calability. Here, we demonstrate the first multi-channel microwave-to-optics conversion by introducing a traveling-wave architecture that leverages a hybrid photonic-phononic waveguide on thin-fil…

Induced structures of averaging commutative and cocommutative infinitesimal bialgebras via a new splitting of perm algebras
Chengming Bai, Li Guo, Guilai Liu, Quan Zhao
https://arxiv.org/abs/2509.09884

Induced structures of averaging commutative and cocommutative infinitesimal bialgebras via a new splitting of perm algebras
It is well-known that an averaging operator on a commutative associative algebra gives rise to a perm algebra. This paper lifts this process to the level of bialgebras. For this purpose, we first give an infinitesimal bialgebra structure for averaging commutative associative algebras and characterize it by double constructions of averaging Frobenius commutative algebras. To find the bialgebra counterpart of perm algebras that is induced by such averaging bialgebras, we need a new two-part split…

Energy Bunching from Sub-Cycle Ionization Injection in Laser Wakefield Acceleration
A. Angella, E. L\"ofquist, C. Gustafsson, V. Poulain, F. D'Souza, C. Guo, A. Persson, P. Eng-Johnsson, C. -G. Wahlstr\"om, O. Lundh
https://arxiv.org/abs/2508.10829

Energy Bunching from Sub-Cycle Ionization Injection in Laser Wakefield Acceleration
We report the first experimental observation of carrier-envelope phase-driven energy bunching in laser wakefield acceleration. Using a few-cycle (~9 fs), multi-terawatt laser pulse and ionization injection in a helium-nitrogen gas mixture, we observe electron spectra composed of multiple quasi-monoenergetic peaks with regular narrow energy spacing. This comb structure arises from intermittent injection from successive half-cycles of the laser field, enabled by the evolving carrier-envelope phas…

Antiferromagnetic ordering and critical behavior induced giant magnetocaloric effect in distorted kagome lattice Gd$_3$BWO$_9$
Zhuoqun Wang, Xueling Cui, Tim Treu, Jiesen Guo, Xinyang Liu, Marvin Klinger, Christian Heil, Nvsen Ma, Xianlei Sheng, Zheng Deng, Xingye Lu, Xiancheng Wang, Wei Li, Philipp Gegenwart, Changqing Jin, Kan Zhao
https://

Antiferromagnetic ordering and critical behavior induced giant magnetocaloric effect in distorted kagome lattice Gd$_3$BWO$_9$
We synthesize the high-quality Gd$_3$BWO$_9$ single crystal and investigate its lowtemperature magnetic and thermodynamic properties. Below $T\rm_{N}$ = 1.08 K, the anisotropic behavior of magnetic susceptibilities reveals that the Gd$^{3+}$ moments exhibit the dominant antiferromagnetic coupling along the $c$-axis, while displaying a ferromagnetic arrangement in kagome plane. With pronounced magnetic frustration, in adiabatic demagnetization refrigeration experiments starting from initial cond…

MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools
Zikang Guo, Benfeng Xu, Chiwei Zhu, Wentao Hong, Xiaorui Wang, Zhendong Mao
https://arxiv.org/abs/2509.09734

MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools
The Model Context Protocol (MCP) is rapidly emerging as a pivotal open standard, designed to enhance agent-tool integration and interoperability, and is positioned to unlock a new era of powerful, interconnected, and genuinely utilitarian agentic AI. However, despite MCP's growing adoption, existing benchmarks often fail to capture real-world agent performance within this new paradigm, leading to a distorted perception of their true operational value and an inability to reliably differentiate p…

Two-Instrument Screening under Soft Budget Constraints
Xinli Guo
https://arxiv.org/abs/2508.10724 https://arxiv.org/pdf/2508.10724

Two-Instrument Screening under Soft Budget Constraints
We study soft budget constraints in multi-tier public finance when an upper-tier government uses two instruments: an ex-ante grant schedule and an ex-post rescue. Under convex rescue costs and standard primitives, the three-stage leader-follower problem collapses to one dimensional screening with a single allocation index: the cap on realized rescue. A hazard-based characterization delivers a unified rule that nests (i) no rescue, (ii) a threshold-cap with commitment, and (iii) a threshold--lin…

Marginal Metals and Kosterlitz-Thouless Type Phase Transition in Disordered Altermagnets
Chang-An Li, Bo Fu, Huaiming Guo, Bj\"orn Trauzettel, Song-Bo Zhang
https://arxiv.org/abs/2507.10762

Marginal Metals and Kosterlitz-Thouless Type Phase Transition in Disordered Altermagnets
Altermagnetism, a recently discovered magnetic phase characterized by spin-split bands without net magnetization, has emerged as promising platform for novel physics and potential applications. However, its stability against disorder-ubiquitous in real materials-remains poorly understood. Here, we study the electron localization properties of two-dimensional altermagnets subject to disorder. Remarkably, we discover a disorder-driven phase transition from a marginal metallic phase to an insulato…

eHashPipe: Lightweight Top-K and Per-PID Resource Monitoring with eBPF
Yuanjun Dai (Case Western Reserve University, Cleveland, USA), Qingzhe Guo (Case Western Reserve University, Cleveland, USA), Xiangren Wang (University of Florida, Gainesville, USA)
https://arxiv.org/abs/2509.09879

eHashPipe: Lightweight Top-K and Per-PID Resource Monitoring with eBPF
System-level resource monitoring with both precision and efficiency is a continuous challenge. We introduce eHashPipe, a lightweight, real-time resource observability system utilizing eBPF and the HashPipe sketching algorithm. eHashPipe supports two tracking modes: Top-k monitoring to identify the most resource-demanding processes and specific PID tracking to detail the behavior of selected processes. We implement two in-kernel eBPF pipelines for on-CPU time and memory usage. Unlike traditional…

Measurement of Beam-Recoil Observables $C_x$ and $C_z$ for $K^ \Lambda$ Photoproduction
CLAS Collaboration, S. Adhikari, B. A. Raue, D. S. Carman, L. Guo, T. Chetry, P. Achenbach, J. S. Alvarado, M. J. Amaryan, W. R. Armstrong, H. Atac, H. Avakian, L. Baashen, N. A. Baltzell, L. Barion, M. Bashkanov, M. Battaglieri, F. Benmokhtar, A. Bianconi, A. S. Biselli, S. Boiarinov, M. Bondi, F. Bossu, K. -Th. Brinkmann, W. J. Briscoe, W. K. Brooks, S. Bueltmann, V. D. Burkert, T. Cao, R. Capobia…

Measurement of Beam-Recoil Observables $C_x$ and $C_z$ for $K^+Λ$ Photoproduction
Exclusive photoproduction of $K^+ Λ$ final states off a proton target has been an important component in the search for missing nucleon resonances and our understanding of the production of final states containing strange quarks. Polarization observables have been instrumental in this effort. The current work is an extension of previously published CLAS results on the beam-recoil transferred polarization observables $C_x$ and $C_z$. We extend the kinematic range up to invariant mass $W=3.33$~G…

Crosslisted article(s) found for math.MG. https://arxiv.org/list/math.MG/new
[1/1]:
- Gromov hyperbolicity III: improved geometric characterization in Euclidean spaces and beyond
Chang-Yu Guo, Manzi Huang, Xiantao Wang

Crosslisted article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
- zERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific ...
Zhou, Ma, Xin, Zou, Hu, Cheng, Lin, Guo, Wang, Zhang, Wei, Zheng

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
Shunpeng Yang, Zhen Fu, Zhefeng Cao, Guo Junde, Patrick Wensing, Wei Zhang, Hua Chen
https://arxiv.org/abs/2506.11470

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
Generalizing locomotion policies across diverse legged robots with varying morphologies is a key challenge due to differences in observation/action dimensions and system dynamics. In this work, we propose Multi-Loco, a novel unified framework combining a morphology-agnostic generative diffusion model with a lightweight residual policy optimized via reinforcement learning (RL). The diffusion model captures morphology-invariant locomotion patterns from diverse cross-embodiment datasets, improving…

Secure quantum key distribution against correlated leakage source
Jia-Xuan Li, Yang-Guang Shan, Rong Wang, Feng-Yu Lu, Zhen-Qiang Yin, Shuang Wang, Wei Chen, De-Yong He, Guang-Can Guo, Zheng-Fu Han
https://arxiv.org/abs/2507.11251

Secure quantum key distribution against correlated leakage source
Quantum key distribution (QKD) provides information theoretic security based on quantum mechanics, however, its practical deployment is challenged by imperfections of source devices. Among various source loopholes, correlations between transmitted pulses pose a significant yet underexplored security risk, potentially compromising QKD's theoretical guarantees. In this work, we propose a security analysis framework for QKD under correlations, enabling finite-key analysis for the first time by ext…

CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs
Hanxi Guo, Siyuan Cheng, Kaiyuan Zhang, Guangyu Shen, Xiangyu Zhang
https://arxiv.org/abs/2506.11059

CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs
Large language models (LLMs) have become integral to modern software development, producing vast amounts of AI-generated source code. While these models boost programming productivity, their misuse introduces critical risks, including code plagiarism, license violations, and the propagation of insecure programs. As a result, robust detection of AI-generated code is essential. To support the development of such detectors, a comprehensive benchmark that reflects real-world conditions is crucial. …

MetaH2: A Snapshot Metasurface HDR Hyperspectral Camera
Yuxuan Liu, Qi Guo
https://arxiv.org/abs/2507.08282 https://arxiv.org/pdf/250…

MetaH2: A Snapshot Metasurface HDR Hyperspectral Camera
We present a metasurface camera that jointly performs high-dynamic range (HDR) and hyperspectral imaging in a snapshot. The system integrates exposure bracketing and computed tomography imaging spectrometry (CTIS) by simultaneously forming multiple spatially multiplexed projections with unique power ratios and chromatic aberrations on a photosensor. The measurements are subsequently processed through a deep reconstruction model to generate an HDR image and a hyperspectral datacube. Our simulati…

Rotational Properties of Superradiant Condensates
Yin-da Guo, Kai-Dong Zhou, Shou-Shan Bao, Hong Zhang
https://arxiv.org/abs/2508.09567 https://arxiv.org/p…

Rotational Properties of Superradiant Condensates
Superradiance enables scalar fields to extract energy and angular momentum from a rotating black hole (BH), leading to the formation of a BH-condensate system. The condensate appears to exhibit global rotation as a rigid body, with phase propagation occurring only in the azimuthal direction, such a picture leads to unphysical superluminal velocities at large distances. In this work, we demonstrate that the apparent rotation is a collective effect arising from local radial, polar, and azimuthal …

InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis
Tao Han, Wanghan Xu, Junchao Gong, Xiaoyu Yue, Song Guo, Luping Zhou, Lei Bai
https://arxiv.org/abs/2509.10441

InfGen: A Resolution-Agnostic Paradigm for Scalable Image Synthesis
Arbitrary resolution image generation provides a consistent visual experience across devices, having extensive applications for producers and consumers. Current diffusion models increase computational demand quadratically with resolution, causing 4K image generation delays over 100 seconds. To solve this, we explore the second generation upon the latent diffusion models, where the fixed latent generated by diffusion models is regarded as the content representation and we propose to decode arbit…

Fast and Efficient Implementation of the Maximum Likelihood Estimation for the Linear Regression with Gaussian Model Uncertainty
Ruohai Guo, Jiang Zhu, Xing Jiang, Fengzhong Qu
https://arxiv.org/abs/2507.11249

Fast and Efficient Implementation of the Maximum Likelihood Estimation for the Linear Regression with Gaussian Model Uncertainty
The linear regression model with a random variable (RV) measurement matrix, where the mean of the random measurement matrix has full column rank, has been extensively studied. In particular, the quasiconvexity of the maximum likelihood estimation (MLE) problem was established, and the corresponding Cramer-Rao bound (CRB) was derived, leading to the development of an efficient bisection-based algorithm known as RV-ML. In contrast, this work extends the analysis to both overdetermined and underde…

Optimizing Freight Rail Electrification: A Framework for Charge Station Selection and Battery Charge/Swap Scheduling
Jia Guo, Elnaz Irannezhad
https://arxiv.org/abs/2509.10157 h…

Optimizing Freight Rail Electrification: A Framework for Charge Station Selection and Battery Charge/Swap Scheduling
Battery electric freight trains are crucial for decarbonization by providing zero-emission transportation alternatives. The proper adoption of battery electric freight trains depends on an efficient battery electrification strategy, involving both infrastructure setup and charge scheduling. The study presents a comprehensive model for the optimal design of charging infrastructure and charge scheduling for each train. To provide more refueling flexibility, we allow batteries to be either charged…

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Repeated Padding : Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Jianzhe Zhao, Xingwei Wang

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong
Sirui Han, Junqi Zhu, Ruiyuan Zhang, Yike Guo
https://arxiv.org/abs/2507.11502 http…

HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong
This paper presents the development of HKGAI-V1, a foundational sovereign large language model (LLM), developed as part of an initiative to establish value-aligned AI infrastructure specifically tailored for Hong Kong. Addressing the region's unique multilingual environment (Cantonese, Mandarin, and English), its distinct socio-legal context under the "one country, two systems" framework, and specific local cultural and value considerations, the model is built upon the DeepSeek architecture and…

Efficient Integration of Multi-View Attributed Graphs for Clustering and Embedding
Yiran Li, Gongyao Guo, Jieming Shi, Sibo Wang, Qing Li
https://arxiv.org/abs/2508.09452 https:…

Efficient Integration of Multi-View Attributed Graphs for Clustering and Embedding
A multi-view attributed graph (MVAG) G captures the diverse relationships and properties of real-world entities through multiple graph views and attribute views. Effectively utilizing all views in G is essential for MVAG clustering and embedding, which are important for applications like recommendation systems, anomaly detection, social network analysis, etc. Existing methods either achieve inferior result quality or incur significant computational costs to handle large-scale MVAGs. In this p…

Dual Reinforcement Learning Synergy in Resource Allocation: Emergence of Self-Organized Momentum Strategy
Zhen-Na Zhang, Guo-Zhong Zhen, Sheng-Feng Deng, Li Chen, Chao-Ran Cai, Ji-Qiang Zhang
https://arxiv.org/abs/2509.11161

Dual Reinforcement Learning Synergy in Resource Allocation: Emergence of Self-Organized Momentum Strategy
In natural ecosystems and human societies, self-organized resource allocation and policy synergy are ubiquitous and significant. This work focuses on the synergy between Dual Reinforcement Learning Policies in the Minority Game (DRLP-MG) to optimize resource allocation. Our study examines a mixed-structured population with two sub-populations: a Q-subpopulation using Q-learning policy and a C-subpopulation adopting the classical policy. We first identify a synergy effect between these subpopula…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- QUEST: Query Optimization in Unstructured Document Analysis
Sun, Deng, Chai, Jin, Guo, Han, Yuan, Wang, Cao

Spin ordering-induced fully-compensated ferrimagnetism
San-Dong Guo, Shaobo Chen, Guangzhao Wang
https://arxiv.org/abs/2507.10848 https://

Spin ordering-induced fully-compensated ferrimagnetism
Fully-compensated ferrimagnets exhibit zero net magnetic moment yet display non-relativistic global spin splitting, making them highly advantageous for constructing high-performance spintronic devices. The general strategy is to break the inversion symmetry of conventional antiferromagnets or the rotational/mirror symmetry of altermagnets to achieve fully-compensated ferrimagnets. Here, we propose to induce fully-compensated ferrimagnetism by engineering the spin ordering rather than modifying …

CAKL: Commutative algebra k-mer learning of genomics
Faisal Suwayyid, Yuta Hozumi, Hongsong Feng, Mushal Zia, JunJie Wee, Guo-Wei Wei
https://arxiv.org/abs/2508.09406 https://…

CAKL: Commutative algebra k-mer learning of genomics
Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fundamental tool in algebraic geometry and number theory, has rarely been used in data and biological sciences. In this study, we introduce commutative algebra k-mer learning (CAKL) as the first-ever nonlinear algebraic framework for analyzing genomic sequences. CAKL bridges between commutative algebra, algebraic topology…

Pressure in active matter
Guo Yu, Ruiyao Li, Fukang Li, Jiayu Zhang, Xiyue Li, Zequ Chen, Joscha Mecke, Yongxiang Gao
https://arxiv.org/abs/2508.09496 https://

Pressure in active matter
In the last decade, the study of pressure in active matter has attracted growing attention due to its fundamental relevance to nonequilibrium statistical physics. Active matter systems are composed of particles that consume energy to sustain persistent motion, which are inherently far from equilibrium. These particles can exhibit complex behaviors, including motility-induced phase separation, density-dependent clustering, and anomalous stress distributions, motivating the introduction of active…

From the Klein-Gordon Equation to the Relativistic Quantum Hydrodynamic System: Local Well-posedness
Ben Duan, Jun Li, Bin Guo, Rongrong Yan
https://arxiv.org/abs/2509.10084 htt…

From the Klein-Gordon Equation to the Relativistic Quantum Hydrodynamic System: Local Well-posedness
In the Klein-Gordon equation, the quantum and relativistic parameters are intricately coupled, which complicates the direct consideration of quantum fluctuations. In this paper, the so-called Relativistic Quantum Hydrodynamics System is derived from the Klein-Gordon equation with Poisson effects via the Madelung transformation, providing a fresh perspective for analyzing the singular limits, such as the semi-classical limits and non-relativistic limits. The Relativistic Quantum Hydrodynamics Sy…

A candidate field for deep imaging of the Epoch of Reionization observed with MWA
Xueying Zhang, Qian Zheng, Linhui Wu, Quan Guo, Stefan W. Duchesne, Mengfan He, Huanyuan Shan, Xiang-ping Wu, Melanie Johnston-Hollitt, Feiyu Zhao, Qingyuan Ma
https://arxiv.org/abs/2507.08048

A candidate field for deep imaging of the Epoch of Reionization observed with MWA
Deep imaging of structures from the Cosmic Dawn (CD) and the Epoch of Reionization (EoR) in five targeted fields is one of the highest priority scientific objectives for the Square Kilometre Array (SKA). Selecting 'quiet' fields, which allow deep imaging, is critical for future SKA CD/EoR observations. Pre-observations using existing radio facilities will help estimate the computational capabilities required for optimal data quality and refine data reduction techniques. In this study, we utiliz…

TFZ: Topology-Preserving Compression of 2D Symmetric and Asymmetric Second-Order Tensor Fields
Nathaniel Gorski, Xin Liang, Hanqi Guo, Bei Wang
https://arxiv.org/abs/2508.09235 …

TFZ: Topology-Preserving Compression of 2D Symmetric and Asymmetric Second-Order Tensor Fields
In this paper, we present a novel compression framework, TFZ, that preserves the topology of 2D symmetric and asymmetric second-order tensor fields defined on flat triangular meshes. A tensor field assigns a tensor - a multi-dimensional array of numbers - to each point in space. Tensor fields, such as the stress and strain tensors, and the Riemann curvature tensor, are essential to both science and engineering. The topology of tensor fields captures the core structure of data, and is useful in …

Urban-STA4CLC: Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change
Ziyi Guo, Yan Wang
https://arxiv.org/abs/2508.08976 …

Urban-STA4CLC: Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change
Natural disasters such as hurricanes and wildfires increasingly introduce unusual disturbance on economic activities, which are especially likely to reshape commercial land use pattern given their sensitive to customer visitation. However, current modeling approaches are limited in capturing such complex interplay between human activities and commercial land use change under and following disturbances. Such interactions have been more effectively captured in current resilient urban planning the…

Approximation rates for finite mixtures of location-scale models
Hien Duy Nguyen, TrungTin Nguyen, Jacob Westerhout, Xin Guo
https://arxiv.org/abs/2508.10612 https://

Approximation rates for finite mixtures of location-scale models
Finite mixture models provide useful methods for approximation and estimation of probability density functions. In the context of location-scale mixture models, we improve upon previous results to show that it is possible to quantitatively bound the ${\cal L}_{p}$ approximation error in terms of the number of mixture components $m$ for any $p\in\left(1,\infty\right)$. In particular, for approximation on $\mathbb{R}^{d}$ of targets $f_{0}\in{\cal W}^{s,p}$, the (fractional) Sobolev spaces with s…

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
Lixin Jia, Zhiqing Guo, Gaobo Yang, Liejun Wang, Keqin Li
https://arxiv.org/abs/2508.10741

Forgery Guided Learning Strategy with Dual Perception Network for Deepfake Cross-domain Detection
The emergence of deepfake technology has introduced a range of societal problems, garnering considerable attention. Current deepfake detection methods perform well on specific datasets, but exhibit poor performance when applied to datasets with unknown forgery techniques. Moreover, as the gap between emerging and traditional forgery techniques continues to widen, cross-domain detection methods that rely on common forgery traces are becoming increasingly ineffective. This situation highlights th…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- CosForce: A Force-Based General Pedestrian Model with Anticipation and Reaction Mechanisms
Jinghui Wang, Wei Lv, Shuchao Cao, Chenglin Guo

Interplay of short-range bond order and A-type antiferromagnetic order in metallic triangular lattice GdZn$_3$P$_3$
Jiesen Guo, Fan Yang, Yuzhou He, Xinyang Liu, Zheng Deng, Qinghua Zhang, Xiancheng Wang, Xianlei Sheng, Wei Li, Changqing Jin, Kan Zhao
https://arxiv.org/abs/2507.11468

Interplay of short-range bond order and A-type antiferromagnetic order in metallic triangular lattice GdZn$_3$P$_3$
We investigate a hexagonal ScAl$_3$C$_3$-type antiferromagnet GdZn$_3$P$_3$ single crystal. Compared with antiferromagnetic topological material EuM$_2$X$_2$ (M=Zn, Cd; X=P, As), the GdZn$_3$P$_3$ features an additional ZnP$_3$ trigonal planar units. Notably, single-crystal X-ray diffraction analysis reveals that Zn and P atoms within trigonal planar layer exhibit significant anisotropic displacement parameters with a space group of P63/mmc. Meanwhile, scanning transmission electron microscopy …

DCR: Quantifying Data Contamination in LLMs Evaluation
Cheng Xu, Nan Yan, Shuhao Guan, Changhong Jin, Yuke Mei, Yibing Guo, M-Tahar Kechadi
https://arxiv.org/abs/2507.11405

DCR: Quantifying Data Contamination in LLMs Evaluation
The rapid advancement of large language models (LLMs) has heightened concerns about benchmark data contamination (BDC), where models inadvertently memorize evaluation data, inflating performance metrics and undermining genuine generalization assessment. This paper introduces the Data Contamination Risk (DCR) framework, a lightweight, interpretable pipeline designed to detect and quantify BDC across four granular levels: semantic, informational, data, and label. By synthesizing contamination sco…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/9]:
- Cowpox: Towards the Immunity of VLM-based Multi-Agent Systems
Yutong Wu, Jie Zhang, Yiming Li, Chao Zhang, Qing Guo, Nils Lukas, Tianwei Zhang

Achieving fully-compensated ferrimagnetism through two-dimensional heterojunctions
San-Dong Guo, Junjie He, Yee Sin Ang
https://arxiv.org/abs/2509.10768 https://

Achieving fully-compensated ferrimagnetism through two-dimensional heterojunctions
In addition to altermagnets, fully-compensated ferrimagnets are another category of collinear magnetic materials that possess zero-net total magnetic moment and exhibit spin-splitting, making them promising for low-energy spintronics, high-density data storage and high-sensitivity sensors. Although many methods, such as alloying, external electric field, Janus engineering, ferroelectric field and spin ordering, have been proposed to achieve fully-compensated ferrimagnetism, these approaches eit…

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Zhen Xu, Hongyu Zhou, Sida Peng, Haotong Lin, Haoyu Guo, Jiahao Shao, Peishan Yang, Qinglin Yang, Sheng Miao, Xingyi He, Yifan Wang, Yue Wang, Ruizhen Hu, Yiyi Liao, Xiaowei Zhou, Hujun Bao
https://arxiv.org/abs/2507.11540

Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR technologies. Traditional methods relying on hardware sensors like LiDAR are often limited by high costs, low resolution, and environmental sensitivity, limiting their applicability in real-world scenarios. Recent advances in vision-based methods offer a promising alternative, yet they face challenges in generalizatio…

Strong and weak solutions to a structural acoustic model with a $C^1$ source term on the plate
Andrew R. Becklin (Drake University), Yanqiu Guo (Florida International University)
https://arxiv.org/abs/2509.09822

Strong and weak solutions to a structural acoustic model with a $C^1$ source term on the plate
In this manuscript, we consider a structural acoustic model consisting of a wave equation defined in a bounded domain $Ω\subset \mathbb{R}^3$, strongly coupled with a Berger plate equation acting on the flat portion of the boundary of $Ω$. The system is influenced by an arbitrary $C^1$ nonlinear source term in the plate equation. Using nonlinear semigroup theory and monotone operator theory, we establish the well-posedness of both local strong and weak solutions, along with conditions for glo…

Large Relativistic Corrections to Nonrelativistic $M1$ Transitions in Heavy Quarkonium
Su-Yan Pe, Wei Li, Wen-Yuan Ke, Yi-Yi Rui, Qiang Li, Guo-Li Wang
https://arxiv.org/abs/2507.08228

Large Relativistic Corrections to Nonrelativistic $M1$ Transitions in Heavy Quarkonium
As double heavy quarkonia, charmonium and bottomonium are generally considered to have small relativistic corrections and can be treated using nonrelativistic models. However, this is not always the case. In this paper, we employ the relativistic Bethe-Salpeter (BS) equation method to calculate the electromagnetic (EM) radiative decays of heavy quarkonium where the $M1$ transition provides the leading-order contribution. Compared to nonrelativistic method which only computes $M1$ transition, ou…

Magnetic ground state and persistent spin fluctuations in triangular-lattice antiferromagnet NdZnAl$_{11}$O$_{19}$
Yantao Cao, Huanpeng Bu, Toni Shiroka, Helen C. Walker, Zhendong Fu, Zhaoming Tian, Jinkui Zhao, Hanjie Guo
https://arxiv.org/abs/2507.11391

Magnetic ground state and persistent spin fluctuations in triangular-lattice antiferromagnet NdZnAl$_{11}$O$_{19}$
Rare-earth triangular-lattice magnets serve as an excellent platform for investigating exotic quantum magnetic phenomena. Recently, the hexaaluminate \cmao\ has been proposed to host a $U(1)$ Dirac quantum spin liquid state with dominant Ising anisotropy. Here, we report a systematic study of its analogue, \nzao, employing ac susceptibility, inelastic neutron scattering, and muon spin relaxation measurements. Inelastic neutron scattering measurements establish a well-defined $J_\mathrm{eff}$ = …

Crosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Optimal Inference of the Mean Outcome under Optimal Treatment Regime
Shuoxun Xu, Xinzhou Guo

WPTrack: A Wi-Fi and Pressure Insole Fusion System for Single Target Tracking
Wei Guo, Shunsei Yamagishi, Lei Jing
https://arxiv.org/abs/2508.09166 https://

WPTrack: A Wi-Fi and Pressure Insole Fusion System for Single Target Tracking
As the Internet of Things (IoT) continues to evolve, indoor location has become a critical element for enabling smart homes, behavioral monitoring, and elderly care. Existing WiFi-based human tracking solutions typically require specialized equipment or multiple Wi-Fi links, a limitation in most indoor settings where only a single pair of Wi-Fi devices is usually available. However, despite efforts to implement human tracking using one Wi-Fi link, significant challenges remain, such as difficul…

Spin-Orbit Structure and Helicity Anomaly in Relativistic Electron Vortex Beams
Zhongze Guo, Bei Xu, Qiang Gu
https://arxiv.org/abs/2507.08493 https://

Spin-Orbit Structure and Helicity Anomaly in Relativistic Electron Vortex Beams
The relativistic electron vortex beam (REVB) has attracted increasing attention due to its nontrivial spin-orbit structure recently. As relativistic electrons are governed by the Dirac equation, exact solutions to this equation provide the most reliable starting point for understanding angular momentum characteristics of REVBs. In this work, a set of exact eigensolutions of the Dirac equation are derived in a complex cylindrical coordinate system using a generalized series expansion method. We …

GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation
Yifei Yao, Chengyuan Luo, Jiaheng Du, Wentao He, Jun-Guo Lu
https://arxiv.org/abs/2508.09960 https:…

GBC: Generalized Behavior-Cloning Framework for Whole-Body Humanoid Imitation
The creation of human-like humanoid robots is hindered by a fundamental fragmentation: data processing and learning algorithms are rarely universal across different robot morphologies. This paper introduces the Generalized Behavior Cloning (GBC) framework, a comprehensive and unified solution designed to solve this end-to-end challenge. GBC establishes a complete pathway from human motion to robot action through three synergistic innovations. First, an adaptive data pipeline leverages a differe…

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
Hanwen Kang, Tenglong Lu, Zhanbin Qi, Jiandong Guo, Sheng Meng, Miao Liu
https://arxiv.org/abs/2508.10505

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine learning force fields (MLFFs) with 3D potential energy surface sampling and interpolation. Our method suppresses periodic self interactions via supercell expansion, builds a continuous PES from MLFF energies on a spatial grid, and extracts minimum energy pathways without predefined NEB images. Across twelve benchmark electrode and electrolyte materials including LiCoO2, Li…

Nonrelativistic limit of ground states to $L^2$-supercritical nonlinear Dirac equations
Pan Chen, Yanheng Ding, Qi Guo
https://arxiv.org/abs/2507.11203 htt…

Nonrelativistic limit of ground states to $L^2$-supercritical nonlinear Dirac equations
In this paper, we study the existence and nonrelativistic limit of normalized ground states for the following nonlinear Dirac equation with power-type potentials \begin{equation*} \begin{cases} &-i c\sum\limits_{k=1}^3α_k\partial_k u +mc^2 β{u}- |{u}|^{p-2}{u}=ω{u}, \\ &\displaystyle\int_{\mathbb{R}^3}\vert u \vert^2 dx =1. \end{cases} \end{equation*} We demonstrate the existence of ground states for large $c$ and establish the uniqueness of the associated Lagrange multiplier f…

Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance
Yuchu Jiang, Jian Zhao, Yuchen Yuan, Tianle Zhang, Yao Huang, Yanghao Zhang, Yan Wang, Yanshu Li, Xizhong Guo, Yusheng Zhao, Jun Zhang, Zhi Zhang, Xiaojian Lin, Yixiu Zou, Haoxuan Ma, Yuhu Shang, Yuzhi Hu, Keshu Cai, Ruochen Zhang, Boyuan Chen, Yilan Gao, Ziheng Jiao, Yi Qin, Shuangjun Du, Xiao Tong, Zhekun Liu, Yu Chen, Xuankun Rong, Rui Wang, Yejie Zheng, Zhaoxin Fan, Hongyuan Zhang, Pan Zhou, Lei Jin, Hao Z…

Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance
The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, that pose significant societal risks, including misinformation, inequity, security breaches, physical harm, and eroded public trust. These challenges highlight the urgent need for robust AI governance. We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconne…

VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding
Zhaowei Liu, Xin Guo, Haotian Xia, Lingfeng Zeng, Fangqi Lou, Jinyi Niu, Mengping Li, Qi Qi, Jiahuan Li, Wei Zhang, Yinglong Wang, Weige Cai, Weining Shen, Liwen Zhang
https://arxiv.org/abs/2508.09641

VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding
Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question-answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical sc…

Efficient computation of average subsystem Bures distance in transverse field Ising chain
Zhouhao Guo, M. A. Rajabpour, Jiaju Zhang
https://arxiv.org/abs/2508.09417 https://

Efficient computation of average subsystem Bures distance in transverse field Ising chain
The average subsystem trace distance has been proposed as an indicator of quantum many-body chaos and integrability. In integrable systems, evaluating the trace distance faces two challenges: the computational cost for large systems and ambiguities in defining and ordering eigenstates. In this paper, we calculate the average subsystem Bures distance in the spin-1/2 transverse-field Ising chain. We develop an efficient algorithm to evaluate the Bures distance between two Gaussian states, which a…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/7]:
- Generative AI for Cel-Animation: A Survey
Tang, Guo, Liu, Wang, Hua, Zhong, Xiao, Huang, Song, Liang, Song, He, Bi, Feng, Li, Zhang, Xu

Replaced article(s) found for math.AP. https://arxiv.org/list/math.AP/new
[1/1]:
- The Spectrum Zero Problem of nonlinear Dirac equation with particle-antiparticle interaction
Qi Guo, Yuanyuan Ke, Bernhard Ruf

Physiological Signal-Driven QoE Optimization for Wireless Virtual Reality Transmission
Chang Wu, Yuang Chen, Yiyuan Chen, Fengqian Guo, Xiaowei Qin, Hancheng Lu
https://arxiv.org/abs/2508.09151

Physiological Signal-Driven QoE Optimization for Wireless Virtual Reality Transmission
Abrupt resolution changes in virtual reality (VR) streaming can significantly impair the quality-of-experience (QoE) of users, particularly during transitions from high to low resolutions. Existing QoE models and transmission schemes inadequately address the perceptual impact of these shifts. To bridge this gap, this article proposes, for the first time, an innovative physiological signal-driven QoE modeling and optimization framework that fully leverages users' electroencephalogram (EEG), elec…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Towards Autonomous Indoor Parking: A Globally Consistent Semantic SLAM System and A Semantic Loca...
Yichen Sha, Siting Zhu, Hekui Guo, Zhong Wang, Hesheng Wang

Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
Jiaqi Cao, Jiarui Wang, Rubin Wei, Qipeng Guo, Kai Chen, Bowen Zhou, Zhouhan Lin
https://arxiv.org/abs/2508.09874

Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
Large Language Models (LLMs) have shown strong abilities in general language tasks, yet adapting them to specific domains remains a challenge. Current method like Domain Adaptive Pretraining (DAPT) requires costly full-parameter training and suffers from catastrophic forgetting. Meanwhile, Retrieval-Augmented Generation (RAG) introduces substantial inference latency due to expensive nearest-neighbor searches and longer context. This paper introduces Memory Decoder, a plug-and-play pretrained me…

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng
https://arxiv.org/abs/2507.08800

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
We introduce NeuralOS, a neural framework that simulates graphical user interfaces (GUIs) of operating systems by directly predicting screen frames in response to user inputs such as mouse movements, clicks, and keyboard events. NeuralOS combines a recurrent neural network (RNN), which tracks computer state, with a diffusion-based neural renderer that generates screen images. The model is trained on a large-scale dataset of Ubuntu XFCE recordings, which include both randomly generated interacti…

KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence
Yun Tang, Mengbang Zou, Zeinab Nezami, Syed Ali Raza Zaidi, Weisi Guo
https://arxiv.org/abs/2507.08164

KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence
The emergence of large language models (LLMs) and agentic systems is enabling autonomous 6G networks with advanced intelligence, including self-configuration, self-optimization, and self-healing. However, the current implementation of individual intelligence tasks necessitates isolated knowledge retrieval pipelines, resulting in redundant data flows and inconsistent interpretations. Inspired by the service model unification effort in Open-RAN (to support interoperability and vendor diversity), …

LLMC : Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit
Chengtao Lv, Bilang Zhang, Yang Yong, Ruihao Gong, Yushi Huang, Shiqiao Gu, Jiajun Wu, Yumeng Shi, Jinyang Guo, Wenya Wang
https://arxiv.org/abs/2508.09981

LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit
Large Vision-Language Models (VLMs) exhibit impressive multi-modal capabilities but suffer from prohibitive computational and memory demands, due to their long visual token sequences and massive parameter sizes. To address these issues, recent works have proposed training-free compression methods. However, existing efforts often suffer from three major limitations: (1) Current approaches do not decompose techniques into comparable modules, hindering fair evaluation across spatial and temporal r…

Grasp Like Humans: Learning Generalizable Multi-Fingered Grasping from Human Proprioceptive Sensorimotor Integration
Ce Guo, Xieyuanli Chen, Zhiwen Zeng, Zirui Guo, Yihong Li, Haoran Xiao, Dewen Hu, Huimin Lu
https://arxiv.org/abs/2509.08354

Grasp Like Humans: Learning Generalizable Multi-Fingered Grasping from Human Proprioceptive Sensorimotor Integration
Tactile and kinesthetic perceptions are crucial for human dexterous manipulation, enabling reliable grasping of objects via proprioceptive sensorimotor integration. For robotic hands, even though acquiring such tactile and kinesthetic feedback is feasible, establishing a direct mapping from this sensory feedback to motor actions remains challenging. In this paper, we propose a novel glove-mediated tactile-kinematic perception-prediction framework for grasp skill transfer from human intuitive an…

A Pathway to Practical Quantum Advantage in Solving Navier-Stokes Equations
Xi-Ning Zhuang, Zhao-Yun Chen, Ming-Yang Tan, Jiaxuan Zhang, Chuang-Chao Ye, Tian-Hao Wei, Teng-Yang Ma, Cheng Xue, Huan-Yu Liu, Qing-Song Li, Tai-Ping Sun, Xiao-Fan Xu, Yun-Jie Wang, Yu-Chun Wu, Guo-Ping Guo
https://arxiv.org/abs/2509.08807

A Pathway to Practical Quantum Advantage in Solving Navier-Stokes Equations
The advent of fault-tolerant quantum computing (FTQC) promises to tackle classically intractable problems. A key milestone is solving the Navier-Stokes equations (NSE), which has remained formidable for quantum algorithms due to their high input-output overhead and nonlinearity. Here, we establish a full-stack framework that charts a practical pathway to a quantum advantage for large-scale NSE simulation. Our approach integrates a spectral-based input/output algorithm, an explicit and synthesiz…

MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
Fan Zhang, Zebang Cheng, Chong Deng, Haoxuan Li, Zheng Lian, Qian Chen, Huadai Liu, Wen Wang, Yi-Fan Zhang, Renrui Zhang, Ziyu Guo, Zhihong Zhu, Hao Wu, Haixin Wang, Yefeng Zheng, Xiaojiang Peng, Xian Wu, Kun Wang, Xiangang Li, Jieping Ye, Pheng-Ann Heng
…

MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
Recent advances in multimodal large language models (MLLMs) have catalyzed transformative progress in affective computing, enabling models to exhibit emergent emotional intelligence. Despite substantial methodological progress, current emotional benchmarks remain limited, as it is still unknown: (a) the generalization abilities of MLLMs across distinct scenarios, and (b) their reasoning capabilities to identify the triggering factors behind emotional states. To bridge these gaps, we present \te…

CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
Muqing Li, Ning Li, Xin Yuan, Wenchao Xu, Quan Chen, Song Guo, Haijun Zhang
https://arxiv.org/abs/2508.09208

CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
The proliferation of large language models (LLMs) has driven the adoption of Mixture-of-Experts (MoE) architectures as a promising solution to scale model capacity while controlling computational costs. However, deploying MoE models in resource-constrained mobile edge computing environments presents significant challenges due to their large memory footprint and dynamic expert activation patterns. To address these challenges, we propose a novel dynamic resource-aware collaborative optimization f…

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
Haoyu Zhao, Linghao Zhuang, Xingyue Zhao, Cheng Zeng, Haoran Xu, Yuming Jiang, Jun Cen, Kexiang Wang, Jiayan Guo, Siteng Huang, Xin Li, Deli Zhao, Hua Zou
https://arxiv.org/abs/2508.08896

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation. To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors…

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Mingkai Jia, Wei Yin, Xiaotao Hu, Jiaxin Guo, Xiaoyang Guo, Qian Zhang, Xiao-Xiao Long, Ping Tan
https://arxiv.org/abs/2507.07997

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental models that compress continuous visual data into discrete tokens. Existing methods have tried to improve the quantization strategy for better reconstruction quality, however, there still exists a large gap between VQ-VAEs and VAEs. To narrow this gap, we propose \NickName, a novel method to augment the representation capability of discrete codebooks, facilitating easier optimization for codebooks and minimizing information loss…

P3-SAM: Native 3D Part Segmentation
Changfeng Ma, Yang Li, Xinhao Yan, Jiachen Xu, Yunhan Yang, Chunshi Wang, Zibo Zhao, Yanwen Guo, Zhuo Chen, Chunchao Guo
https://arxiv.org/abs/2509.06784

P3-SAM: Native 3D Part Segmentation
Segmenting 3D assets into their constituent parts is crucial for enhancing 3D understanding, facilitating model reuse, and supporting various applications such as part generation. However, current methods face limitations such as poor robustness when dealing with complex objects and cannot fully automate the process. In this paper, we propose a native 3D point-promptable part segmentation model termed P3-SAM, designed to fully automate the segmentation of any 3D objects into components. Inspire…