Tootfinder

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…

Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
Yinghan Zhou, Juan Wen, Wanli Peng, Zhengxian Wu, Ziwei Zhang, Yiming Xue
https://arxiv.org/abs/2508.15848

Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
AI-generated text (AIGT) detection evasion aims to reduce the detection probability of AIGT, helping to identify weaknesses in detectors and enhance their effectiveness and reliability in practical applications. Although existing evasion methods perform well, they suffer from high computational costs and text quality degradation. To address these challenges, we propose Self-Disguise Attack (SDA), a novel approach that enables Large Language Models (LLM) to actively disguise its output, reducing…

Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search
Zhigong Zhou, Ning Ding, Xiaochuan Fan, Yue Shang, Yiming Qiu, Jingwei Zhuo, Zhiwei Ge, Songlin Wang, Lin Liu, Sulong Xu, Han Zhang
https://arxiv.org/abs/2506.20330

Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search
Semantic retrieval, which retrieves semantically matched items given a textual query, has been an essential component to enhance system effectiveness in e-commerce search. In this paper, we study the multimodal retrieval problem, where the visual information (e.g, image) of item is leveraged as supplementary of textual information to enrich item representation and further improve retrieval performance. Though learning from cross-modality data has been studied extensively in tasks such as visual…

Indirect multiphoton scattering between light and bulk plasmons via ultrafast free electrons
Ruoyu Chen, Jun Li, Qiaofei Pan, Dingguo Zheng, Bin Zhang, Ye Tian, Jianqi Li, Huaixin Yang, Yiming Pan
https://arxiv.org/abs/2507.18091

Indirect multiphoton scattering between light and bulk plasmons via ultrafast free electrons
Efficient coupling between light and bulk plasmons (BPs) remains a central challenge because of their inherent mode mismatch, limited penetration depth, and pronounced resonant energy mismatch between visible-range photons and BPs. In this work, we demonstrate that ultrafast free electrons can coherently mediate an interaction between electromagnetic fields and BPs at the nanoscale. An electron pulse emitted from the photocathode of ultrafast transmission electron microscope, functions as a qua…

Cooperative Bistatic ISAC Systems for Low-Altitude Economy
Zhenkun Zhang, Yining Xu, Cunhua Pan, Hong Ren, Yiming Yu, Jiangzhou Wang
https://arxiv.org/abs/2506.18067

Cooperative Bistatic ISAC Systems for Low-Altitude Economy
The burgeoning low-altitude economy (LAE) necessitates integrated sensing and communication (ISAC) systems capable of high-accuracy multi-target localization and velocity estimation under hardware and coverage constraints inherent in conventional ISAC architectures. This paper addresses these challenges by proposing a cooperative bistatic ISAC framework within MIMO-OFDM cellular networks, enabling robust sensing services for LAE applications through standardized 5G New Radio (NR) infrastructure…

Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook
Ping Zhang, Kai Niu, Yiming Liu, Zijian Liang, Nan Ma, Xiaodong Xu, Wenjun Xu, Mengying Sun, Yinqiu Liu, Xiaoyun Wang, Ruichen Zhang
https://arxiv.org/abs/2508.15277

Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook
Artificial intelligence (AI) is expected to serve as a foundational capability across the entire lifecycle of 6G networks, spanning design, deployment, and operation. This article proposes a native AI-driven air interface architecture built around two core characteristics: compression and adaptation. On one hand, compression enables the system to understand and extract essential semantic information from the source data, focusing on task relevance rather than symbol-level accuracy. On the other…

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
Jiachi Chen, Yiming Shen, Jiashuo Zhang, Zihao Li, John Grundy, Zhenzhe Shao, Yanlin Wang, Jiashui Wang, Ting Chen, Zibin Zheng
https://arxiv.org/abs/2506.18795

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
High-quality smart contract vulnerability datasets are critical for evaluating security tools and advancing smart contract security research. Two major limitations of current manual dataset construction are (1) labor-intensive and error-prone annotation processes limiting the scale, quality, and evolution of the dataset, and (2) absence of standardized classification rules results in inconsistent vulnerability categories and labeling results across different datasets. To address these limitatio…

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese
Yikang Liu, Wanyang Zhang, Yiming Wang, Jialong Tang, Pei Zhang, Baosong Yang, Fei Huang, Rui Wang, Hai Hu
https://arxiv.org/abs/2507.12260

Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese
In this paper, we propose the first quantitative measure for translationese -- the translationese-index (T-index) for graded and generalizable measurement of translationese, computed from the likelihood ratios of two contrastively fine-tuned language models (LMs). We use a synthesized dataset and a dataset with translations in the wild to evaluate T-index's generalizability in cross-domain settings and its validity against human judgments. Our results show that T-index is both robust and effici…

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

P/D-Device: Disaggregated Large Language Model between Cloud and Devices
Yibo Jin, Yixu Xu, Yue Chen, Chengbin Wang, Tao Wang, Jiaqi Huang, Rongfei Zhang, Yiming Dong, Yuting Yan, Ke Cheng, Yingjie Zhu, Shulan Wang, Qianqian Tang, Shuaishuai Meng, Guanxin Cheng, Ze Wang, Shuyan Miao, Ketao Wang, Wen Liu, Yifan Yang, Tong Zhang, Anran Wang, Chengzhou Lu, Tiantian Dong, Yongsheng Zhang, Zhe Wang, Hefei Guo, Hongjie Liu, Wei Lu, Zhengyong Zhang

P/D-Device: Disaggregated Large Language Model between Cloud and Devices
Serving disaggregated large language models has been widely adopted in industrial practice for enhanced performance. However, too many tokens generated in decoding phase, i.e., occupying the resources for a long time, essentially hamper the cloud from achieving a higher throughput. Meanwhile, due to limited on-device resources, the time to first token (TTFT), i.e., the latency of prefill phase, increases dramatically with the growth on prompt length. In order to concur with such a bottleneck on…

Pedestrian Dead Reckoning using Invariant Extended Kalman Filter
Jingran Zhang, Zhengzhang Yan, Yiming Chen, Zeqiang He, Jiahao Chen
https://arxiv.org/abs/2508.11396 https://

Pedestrian Dead Reckoning using Invariant Extended Kalman Filter
This paper presents a cost-effective inertial pedestrian dead reckoning method for the bipedal robot in the GPS-denied environment. Each time when the inertial measurement unit (IMU) is on the stance foot, a stationary pseudo-measurement can be executed to provide innovation to the IMU measurement based prediction. The matrix Lie group based theoretical development of the adopted invariant extended Kalman filter (InEKF) is set forth for tutorial purpose. Three experiments are conducted to compa…

Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
Dayu Fan, Rui Meng, Xiaodong Xu, Yiming Liu, Guoshun Nan, Chenyuan Feng, Shujun Han, Song Gao, Bingxuan Xu, Dusit Niyato, Tony Q. S. Quek, Ping Zhang
https://arxiv.org/abs/2507.16733

Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systemat…

DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
Boheng Li, Junjie Wang, Yiming Li, Zhiyang Hu, Leyi Qi, Jianshuo Dong, Run Wang, Han Qiu, Zhan Qin, Tianwei Zhang
https://arxiv.org/abs/2507.16329

DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
Despite the integration of safety alignment and external filters, text-to-image (T2I) generative models are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended exposure and potential misuse. Red teaming, which aims to proactively identify diverse prompts that can elicit unsafe outputs from the T2I system (including the core generative model as well as potential external safety filters and other processing components), …

Cowpox: Towards the Immunity of VLM-based Multi-Agent Systems
Yutong Wu, Jie Zhang, Yiming Li, Chao Zhang, Qing Guo, Nils Lukas, Tianwei Zhang
https://arxiv.org/abs/2508.09230 h…

Cowpox: Towards the Immunity of VLM-based Multi-Agent Systems
Vision Language Model (VLM)-based agents are stateful, autonomous entities capable of perceiving and interacting with their environments through vision and language. Multi-agent systems comprise specialized agents who collaborate to solve a (complex) task. A core security property is robustness, stating that the system should maintain its integrity under adversarial attacks. However, the design of existing multi-agent systems lacks the robustness consideration, as a successful exploit against o…

Generating and Weaving Topological Event Wavepackets in Photonic Spacetime Crystals with Fully Energy-Momentum Gapped
Liang Zhang, Zirui Zhao, Qiaofei Pan, Chenhao Pan, Qingqing Cheng, Yiming Pan
https://arxiv.org/abs/2507.15309

Generating and Weaving Topological Event Wavepackets in Photonic Spacetime Crystals with Fully Energy-Momentum Gapped
We propose a novel type of topological excitation topological event wavepackets (TEWs) emerging in photonic spacetime crystals (STCs) with spacetime modulated dielectric constants. These TEWs exhibit strong spatiotemporal localization and are topologically protected by a fully opened energy momentum (ωk) gap, within which conventional steady states are absent. We further demonstrate that TEWs are spectrally confined within the ωk-gap, providing a combined measurement for probing the emergence…

Disorder-induced spin excitation continuum and spin-glass ground state in the inverse spinel CuGa$_2$O$_4$
Zhentao Huang, Zhijun Xu, Shuaiwei Li, Qingchen Duan, Junbo Liao, Song Bao, Yanyan Shangguan, Bo Zhang, Hao Xu, Shufan Cheng, Zihang Song, Shuai Dong, Maofeng Wu, M. B. Stone, Yiming Qiu, Ruidan Zhong, Guangyong Xu, Zhen Ma, G. D. Gu, J. M. Tranquada, Jinsheng Wen

Disorder-induced spin excitation continuum and spin-glass ground state in the inverse spinel CuGa$_2$O$_4$
Spinel-structured compounds serve as prototypical examples of highly frustrated systems, and are promising candidates for realizing the long-sought quantum spin liquid (QSL) state. However, structural disorder is inevitable in many real QSL candidates and its impact remains a topic of intense debate. In this work, we conduct comprehensive investigations on CuGa$_2$O$_4$, a spinel compound with significant structural disorder, focusing on its thermodynamic properties and spectroscopic behaviors.…

EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
Ruifeng Zheng, Cong Chen, Shuang Wang, Yiming Liu, Lin You, Jindong Lu, Ruizhe Zhu, Guodao Zhang, Kejie Huang
https://arxiv.org/abs/2507.15364

EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
Epilepsy is a chronic, noncommunicable brain disorder, and sudden seizure onsets can significantly impact patients' quality of life and health. However, wearable seizure-predicting devices are still limited, partly due to the bulky size of EEG-collecting devices. To relieve the problem, we proposed a novel two-stage channel-aware Set Transformer Network that could perform seizure prediction with fewer EEG channel sensors. We also tested a seizure-independent division method which could prevent …

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Yingze Li, Xianglong Liu, Dong Wang, Zixuan Wang, Hongzhi Wang, Kaixing Zhang, Yiming Guan
https://arxiv.org/abs/2507.07438

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Learned cardinality estimators show promise in query cardinality prediction, yet they universally exhibit fragility to training data drifts, posing risks for real-world deployment. This work is the first to theoretical investigate how minimal data-level drifts can maximally degrade the accuracy of learned estimators. We propose data-centric algorithmic complexity attacks against learned estimators in a black-box setting, proving that finding the optimal attack strategy is NP-Hard. To address th…

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges
Yiming Shen, Jiashuo Zhang, Zhenzhe Shao, Wenxuan Luo, Yanlin Wang, Ting Chen, Zibin Zheng, Jiachi Chen
https://arxiv.org/abs/2508.02773

Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges
The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems. This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms. Through an analysis of 133 existing projects, we first develop a taxonomy and systematically map the current market landscape (RQ1), identifying distinct…

Reinforcement Learning in Vision: A Survey
Weijia Wu, Chen Gao, Joya Chen, Kevin Qinghong Lin, Qingwei Meng, Yiming Zhang, Yuke Qiu, Hong Zhou, Mike Zheng Shou
https://arxiv.org/abs/2508.08189

Reinforcement Learning in Vision: A Survey
Recent advances at the intersection of reinforcement learning (RL) and visual intelligence have enabled agents that not only perceive complex visual scenes but also reason, generate, and act within them. This survey offers a critical and up-to-date synthesis of the field. We first formalize visual RL problems and trace the evolution of policy-optimization strategies from RLHF to verifiable reward paradigms, and from Proximal Policy Optimization to Group Relative Policy Optimization. We then org…

SWAN: Synergistic Wavelet-Attention Network for Infrared Small Target Detection
Yuxin Jing, Jufeng Zhao, Tianpei Zhang, Yiming Zhu
https://arxiv.org/abs/2508.01322 https://

SWAN: Synergistic Wavelet-Attention Network for Infrared Small Target Detection
Infrared small target detection (IRSTD) is thus critical in both civilian and military applications. This study addresses the challenge of precisely IRSTD in complex backgrounds. Recent methods focus fundamental reliance on conventional convolution operations, which primarily capture local spatial patterns and struggle to distinguish the unique frequency-domain characteristics of small targets from intricate background clutter. To overcome these limitations, we proposed the Synergistic Wavelet-…

Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
Ruiyang Hao, Haibao Yu, Jiaru Zhong, Chuanye Wang, Jiahao Wang, Yiming Kan, Wenxian Yang, Siqi Fan, Huilin Yin, Jianing Qiu, Yao Mu, Jiankai Sun, Li Chen, Walter Zimmer, Dandan Zhang, Shanghang Zhang, Mac Schwager, Wei Huang, Xiaobo Zhang, Ping Luo, Zaiqing Nie
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Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition
With the rapid advancement of autonomous driving technology, vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety by providing visibility beyond the line of sight. However, integrating multi-source sensor data from both ego-vehicles and infrastructure under real-world constraints, such as limited communication bandwidth and dynamic environments, presents significant technical challenges. To facilitate research in this…

Towards Effective Prompt Stealing Attack against Text-to-Image Diffusion Models
Shiqian Zhao, Chong Wang, Yiming Li, Yihao Huang, Wenjie Qu, Siew-Kei Lam, Yi Xie, Kangjie Chen, Jie Zhang, Tianwei Zhang
https://arxiv.org/abs/2508.06837

Towards Effective Prompt Stealing Attack against Text-to-Image Diffusion Models
Text-to-Image (T2I) models, represented by DALL$\cdot$E and Midjourney, have gained huge popularity for creating realistic images. The quality of these images relies on the carefully engineered prompts, which have become valuable intellectual property. While skilled prompters showcase their AI-generated art on markets to attract buyers, this business incidentally exposes them to \textit{prompt stealing attacks}. Existing state-of-the-art attack techniques reconstruct the prompts from a fixed se…

Important Bit Prefix M-ary Quadrature Amplitude Modulation for Semantic Communications
Haonan Lu, Rui Meng, Xiaodong Xu, Yiming Liu, Ping Zhang, Dusit Niyato
https://arxiv.org/abs/2508.11351

Important Bit Prefix M-ary Quadrature Amplitude Modulation for Semantic Communications
M-ary Quadrature Amplitude Modulation (MQAM) is a commonly used channel modulation technology in wireless communication systems. To achieve dedicated channel modulation for semantic communication (SemCom), we propose an Important-Bit-Prefixed MQAM (IBP-MQAM) scheme and derive its approximate expression of important symbol error rate (ISER) and unimportant symbol error rate (USER). By extracting and quantifying text semantics using Latent Dirichlet Allocation (LDA), we verify that IBP-MQAM achie…

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Fangyu Lei, Jinxiang Meng, Yiming Huang, Tinghong Chen, Yun Zhang, Shizhu He, Jun Zhao, Kang Liu
https://arxiv.org/abs/2506.01710

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL)…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- EEmo-Bench: A Benchmark for Multi-modal Large Language Models on Image Evoked Emotion Assessment
Lancheng Gao, Ziheng Jia, Yunhao Zeng, Wei Sun, Yiming Zhang, Wei Zhou, Guangtao Zhai, Xiongkuo Min

Large AI Models for Wireless Physical Layer
Jiajia Guo, Yiming Cui, Shi Jin, Jun Zhang
https://arxiv.org/abs/2508.02314 https://arxiv.org/pdf/2508.02314

Large AI Models for Wireless Physical Layer
Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in LAM applications for physical layer communications, addressing limitations of conventional AI-based approaches. LAM applications are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. T…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Training-free Geometric Image Editing on Diffusion Models
Hanshen Zhu, Zhen Zhu, Kaile Zhang, Yiming Gong, Yuliang Liu, Xiang Bai

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
Shuo Shao, Yiming Li, Mengren Zheng, Zhiyang Hu, Yukun Chen, Boheng Li, Yu He, Junfeng Guo, Tianwei Zhang, Dacheng Tao, Zhan Qin
https://arxiv.org/abs/2507.05622

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to determine if a specific dataset was used to train a given suspicious model, provide promising solutions to addressing these transparency gaps. While prior work has developed various auditing methods, their r…

CircuitProbe: Dissecting Spatiotemporal Visual Semantics with Circuit Tracing
Yiming Zhang, Chengzhang Yu, Zhuokai Zhao, Kun Wang, Qiankun Li, Zihan Chen, Yang Liu, Zenghui Ding, Yining Sun
https://arxiv.org/abs/2507.19420

CircuitProbe: Dissecting Spatiotemporal Visual Semantics with Circuit Tracing
The processing mechanisms underlying language and image understanding in large vision-language models (LVLMs) have been extensively studied. However, the internal reasoning mechanisms of LVLMs for spatiotemporal understanding remain poorly understood. In this work, we introduce a systematic, circuit-based framework designed to investigate how spatiotemporal visual semantics are represented and processed within these LVLMs. Specifically, our framework comprises three circuits: visual auditing ci…

Coward: Toward Practical Proactive Federated Backdoor Defense via Collision-based Watermark
Wenjie Li, Siying Gu, Yiming Li, Kangjie Chen, Zhili Chen, Tianwei Zhang, Shu-Tao Xia, Dacheng Tao
https://arxiv.org/abs/2508.02115

Coward: Toward Practical Proactive Federated Backdoor Defense via Collision-based Watermark
Backdoor detection is currently the mainstream defense against backdoor attacks in federated learning (FL), where malicious clients upload poisoned updates that compromise the global model and undermine the reliability of FL deployments. Existing backdoor detection techniques fall into two categories, including passive and proactive ones, depending on whether the server proactively modifies the global model. However, both have inherent limitations in practice: passive defenses are vulnerable to…

Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol Ecosystem
Hao Song, Yiming Shen, Wenxuan Luo, Leixin Guo, Ting Chen, Jiashui Wang, Beibei Li, Xiaosong Zhang, Jiachi Chen
https://arxiv.org/abs/2506.02040

Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol Ecosystem
The Model Context Protocol (MCP) is an emerging standard designed to enable seamless interaction between Large Language Model (LLM) applications and external tools or resources. Within a short period, thousands of MCP services have already been developed and deployed. However, the client-server integration architecture inherent in MCP may expand the attack surface against LLM Agent systems, introducing new vulnerabilities that allow attackers to exploit by designing malicious MCP servers. In th…