Tootfinder

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…

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Jane Luo, Xin Zhang, Steven Liu, Jie Wu, Yiming Huang, Yangyu Huang, Chengyu Yin, Ying Xin, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Qi Chen, Scarlett Li, Mao Yang
https://arxiv.org/abs/2509.16198

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation
Large language models excel at function- and file-level code generation, yet generating complete repositories from scratch remains a fundamental challenge. This process demands coherent and reliable planning across proposal- and implementation-level stages, while natural language, due to its ambiguity and verbosity, is ill-suited for faithfully representing complex software structures. To address this, we introduce the Repository Planning Graph (RPG), a persistent representation that unifies pr…

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 …

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…

Enhancing Retrieval Augmentation via Adversarial Collaboration
Letian Zhang, Guanghao Meng, Xudong Ren, Yiming Wang, Shu-Tao Xia
https://arxiv.org/abs/2509.14750 https://…

Enhancing Retrieval Augmentation via Adversarial Collaboration
Retrieval-augmented Generation (RAG) is a prevalent approach for domain-specific LLMs, yet it is often plagued by "Retrieval Hallucinations"--a phenomenon where fine-tuned models fail to recognize and act upon poor-quality retrieved documents, thus undermining performance. To address this, we propose the Adversarial Collaboration RAG (AC-RAG) framework. AC-RAG employs two heterogeneous agents: a generalist Detector that identifies knowledge gaps, and a domain-specialized Resolver that provides …

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…

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…

SN 2024aecx: Double-Peaked Light Curves and Rapid Evolution in a Nearby Type IIb Supernova
Qiang Xi, Ning-Chen Sun, David Aguado, Ismael P'erez-Fournon, Fr'ed'erick Poidevin, Junjie Jin, Yiming Mao, Zexi Niu, Beichuan Wang, Yu Zhang, Kuntal Misra, Divyanshu Janghel, Justyn R. Maund, Amit Kumar, Samaporn Tinyanont, Liang-Duan Liu, Yu-Hao Zhang, Bhavya Ailawadhi, Monalisa Dubey, Zhen Guo, Anshika Gupta, Min He, Dhruv Jain, Debalina Kar, Wenxiong Li, Joe D. Lyman, Haiyang Mu, …

SN 2024aecx: Double-Peaked Light Curves and Rapid Evolution in a Nearby Type IIb Supernova
SN 2024aecx is a nearby ($\sim$11 Mpc) Type IIb SN discovered within $\sim$1 d after explosion. In this paper we report high-cadence photometric and spectroscopic follow-up observations, conducted from as early as 0.27 d post discovery out to the nebular phase at 158.4 d. We analyze the environment of SN 2024aecx and derive a new distance, metallicity and host extinction. The light curve exhibits a hot and luminous shock-cooling peak at the first few days, followed by a main peak with very rapi…

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…

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…

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…

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.…

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

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…

Terahretz Channel Modeling in ULEO Satellite-to-Ground Communications
Mingxia Zhang, Wanzhu Chang, Jie Yang, Hong Liang, Yiming Zhao, Houjun Sun, Jianjun Ma
https://arxiv.org/abs/2509.12769

Terahretz Channel Modeling in ULEO Satellite-to-Ground Communications
The exponential growth in satellite data traffic demands communication systems exceeding current microwave capacity limitations, while the terahertz (THz) frequency band (0.1-10 THz) offers unprecedented bandwidth potential with superior weather resilience compared to optical systems, particularly when combined with ultra-low Earth orbit (ULEO) satellite deployments below 300 km altitude. This article presents comprehensive channel modeling and performance evaluation for ULEO-THz satellite-to-g…

Neural Video Compression with In-Loop Contextual Filtering and Out-of-Loop Reconstruction Enhancement
Yaojun Wu, Chaoyi Lin, Yiming Wang, Semih Esenlik, Zhaobin Zhang, Kai Zhang, Li Zhang
https://arxiv.org/abs/2509.04051

Neural Video Compression with In-Loop Contextual Filtering and Out-of-Loop Reconstruction Enhancement
This paper explores the application of enhancement filtering techniques in neural video compression. Specifically, we categorize these techniques into in-loop contextual filtering and out-of-loop reconstruction enhancement based on whether the enhanced representation affects the subsequent coding loop. In-loop contextual filtering refines the temporal context by mitigating error propagation during frame-by-frame encoding. However, its influence on both the current and subsequent frames poses ch…

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…

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…

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Yiming Yao, Fei Liu, Liang Zhao, Xi Lin, Qingfu Zhang
https://arxiv.org/abs/2509.03244 https://

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. Existing works either involve rebuilding Gaussian process surrogates from scratch for each objective in each new problem encountered, or rely on extensive past domain experiments for pre-training deep learning models, making them hard to generalize and impractical to cope with…

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…

An affinity based opinion dynamics model for the evolving pattern of political polarization
Zhang Xiaoming, Hu Yuzhong, Zhang Yiming
https://arxiv.org/abs/2509.01244 https://

An affinity based opinion dynamics model for the evolving pattern of political polarization
Political polarization has been a subject that has attracted many studies in recent years. We have developed an opinion dynamics model with affective homophily effect and national social norm effect to describe this phenomenon. The time evolution of the polarization between the two parties and the spread of opinions within each party are affected by three factors: the repulsive effect between the two parties, the attractive and repulsive effects between the members in each party, and the nation…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- CoreMark: Toward Robust and Universal Text Watermarking Technique
Jiale Meng, Yiming Li, Zheming Lu, Zewei He, Hao Luo, Tianwei Zhang

Deep Learning Based Concurrency Bug Detection and Localization
Zuocheng Feng, Kaiwen Zhang, Miaomiao Wang, Yiming Cheng, Yuandao Cai, Xiaofeng Li, Guanjun Liu
https://arxiv.org/abs/2508.20911

Deep Learning Based Concurrency Bug Detection and Localization
Concurrency bugs, caused by improper synchronization of shared resources in multi-threaded or distributed systems, are notoriously hard to detect and thus compromise software reliability and security. The existing deep learning methods face three main limitations. First, there is an absence of large and dedicated datasets of diverse concurrency bugs for them. Second, they lack sufficient representation of concurrency semantics. Third, binary classification results fail to provide finer-grained …

Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework
Tianpei Zhang, Jufeng Zhao, Yiming Zhu, Guangmang Cui
https://arxiv.org/abs/2509.05000

Dual-Domain Perspective on Degradation-Aware Fusion: A VLM-Guided Robust Infrared and Visible Image Fusion Framework
Most existing infrared-visible image fusion (IVIF) methods assume high-quality inputs, and therefore struggle to handle dual-source degraded scenarios, typically requiring manual selection and sequential application of multiple pre-enhancement steps. This decoupled pre-enhancement-to-fusion pipeline inevitably leads to error accumulation and performance degradation. To overcome these limitations, we propose Guided Dual-Domain Fusion (GD^2Fusion), a novel framework that synergistically integrate…

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
htt…

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…

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…

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…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
Kaixin Zhang, Hongzhi Wang, Ziqi Li, Yabin Lu, Yingze Li, Yu Yan, Yiming Guan

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- CoreMark: Toward Robust and Universal Text Watermarking Technique
Jiale Meng, Yiming Li, Zheming Lu, Zewei He, Hao Luo, Tianwei Zhang

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-…

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…

Jointly Reinforcing Diversity and Quality in Language Model Generations
Tianjian Li, Yiming Zhang, Ping Yu, Swarnadeep Saha, Daniel Khashabi, Jason Weston, Jack Lanchantin, Tianlu Wang
https://arxiv.org/abs/2509.02534

Jointly Reinforcing Diversity and Quality in Language Model Generations
Post-training of Large Language Models (LMs) often prioritizes accuracy and helpfulness at the expense of diversity. This creates a tension: while post-training improves response quality, it also sharpens output distributions and reduces the range of ideas, limiting the usefulness of LMs in creative and exploratory tasks such as brainstorming, storytelling, or problem solving. We address this challenge with Diversity-Aware Reinforcement Learning (DARLING), a framework that jointly optimizes for…

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…

SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication
Song Gao, Rui Meng, Xiaodong Xu, Haixiao Gao, Yiming Liu, Chenyuan Feng, Ping Zhang, Tony Q. S. Quek, Dusit Niyato
https://arxiv.org/abs/2509.04803

SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication
Semantic communication (SemCom), as a novel paradigm for future communication systems, has recently attracted much attention due to its superiority in communication efficiency. However, similar to traditional communication, it also suffers from eavesdropping threats. Intelligent eavesdroppers could launch advanced semantic analysis techniques to infer secret semantic information. Therefore, some researchers have designed Semantic Steganography Communication (SemSteCom) scheme to confuse semanti…

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

The Ramon Llull's Thinking Machine for Automated Ideation
Xinran Zhao, Boyuan Zheng, Chenglei Si, Haofei Yu, Ken Liu, Runlong Zhou, Ruochen Li, Tong Chen, Xiang Li, Yiming Zhang, Tongshuang Wu
https://arxiv.org/abs/2508.19200

The Ramon Llull's Thinking Machine for Automated Ideation
This paper revisits Ramon Llull's Ars combinatoria - a medieval framework for generating knowledge through symbolic recombination - as a conceptual foundation for building a modern Llull's thinking machine for research ideation. Our approach defines three compositional axes: Theme (e.g., efficiency, adaptivity), Domain (e.g., question answering, machine translation), and Method (e.g., adversarial training, linear attention). These elements represent high-level abstractions common in scientific …

Replaced article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new
[1/1]:
- Celler:A Genomic Language Model for Long-Tailed Single-Cell Annotation
Huan Zhao, Yiming Liu, Jina Yao, Ling Xiong, Zexin Zhou, Zixing Zhang

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

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…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/3]:
- RePPL: Recalibrating Perplexity by Uncertainty in Semantic Propagation and Language Generation fo...
Yiming Huang, Junyan Zhang, Zihao Wang, Biquan Bie, Yunzhong Qiu, Yi R. Fung, Xinlei He

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…

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…

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…

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…

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…

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), …

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…