Tootfinder

LINDNER IST ZURÜCK IM BUNDESTAG
https://mastodon.social/@queerde/115055635149098169

LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models
Hongyao Tu, Liang Zhang, Yujie Lin, Xin Lin, Haibo Zhang, Long Zhang, Jinsong Su
https://arxiv.org/abs/2509.15089

LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models
The goal of open relation extraction (OpenRE) is to develop an RE model that can generalize to new relations not encountered during training. Existing studies primarily formulate OpenRE as a clustering task. They first cluster all test instances based on the similarity between the instances, and then manually assign a new relation to each cluster. However, their reliance on human annotation limits their practicality. In this paper, we propose an OpenRE framework based on large language models (…

Has GPT-5 Achieved Spatial Intelligence? An Empirical Study
Zhongang Cai, Yubo Wang, Qingping Sun, Ruisi Wang, Chenyang Gu, Wanqi Yin, Zhiqian Lin, Zhitao Yang, Chen Wei, Xuanke Shi, Kewang Deng, Xiaoyang Han, Zukai Chen, Jiaqi Li, Xiangyu Fan, Hanming Deng, Lewei Lu, Bo Li, Ziwei Liu, Quan Wang, Dahua Lin, Lei Yang
https://arxiv.org/abs/2…

Has GPT-5 Achieved Spatial Intelligence? An Empirical Study
Multi-modal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, which are fundamental capabilities to achieving artificial general intelligence. With the recent release of GPT-5, allegedly the most powerful AI model to date, it is timely to examine where the leading models stand on the path toward spatial intelligence. First, we propose a comprehensive taxonomy of spatial tasks that unifies …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[10/10]:
- SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression
Lin, Lin, Yang, Huang, Zhang, Fang, Du, Chen, Zhu, Ni

Magnetic Triple-q State in Antiferromagnetic Monolayer Interfaced with Bismuthene
Chia-Ju Chen, Yu-Tung Lin, Chieh-Lin Lee, Nitin Kumar, Hung-Chin Lee, Yen-Hui Lin, Bo-Yao Wang, Stefan Bluegel, Gustav Bihlmayer, Pin-Jui Hsu
https://arxiv.org/abs/2507.12946

Magnetic Triple-q State in Antiferromagnetic Monolayer Interfaced with Bismuthene
We have successfully fabricated the bismuthene covered Mn monolayer on Ag(111) by evaporating Mn atoms onto (pxroot3)-Bi/Ag(111) at room temperature. By using spin-polarized scanning tunneling microscopy (SP-STM), we have resolved the magnetic triple-q (3Q) state. In combination with density-functional theory (DFT) calculations, the 3Q3-like spin texture is the magnetic ground state for the bismuthene covered Mn monolayer/Ag(111). Interestingly, the uniaxial magnetic anisotropy of 3Q3 state tri…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[2/2]:
- Towards Generalized Source Tracing for Codec-Based Deepfake Speech
Xuanjun Chen, I-Ming Lin, Lin Zhang, Haibin Wu, Hung-yi Lee, Jyh-Shing Roger Jang

Tetraquark states in the quark model
Wei-Lin Wu, Yan-Ke Chen, Yao Ma, Lu Meng, Shi-Lin Zhu
https://arxiv.org/abs/2508.11161 https://arxiv.org/pdf/2508.1116…

Tetraquark states in the quark model
We perform systematical investigations of heavy flavor tetraquark systems, including fully heavy $QQ\bar Q\bar Q$ ($Q=b,c$), doubly heavy $QQ^{(\prime)}\bar q\bar q$ ($q=u,d$), and singly heavy $Qs\bar q\bar q$ tetraquark systems, within the framework of quark potential model. We employ the Gaussian expansion method and complex scaling method to solve the four-body Hamiltonian and identify bound and resonant states. We further calculate the root mean square radii to study the spatial configurat…

How Does Instrumental Music Help SingFake Detection?
Xuanjun Chen, Chia-Yu Hu, I-Ming Lin, Yi-Cheng Lin, I-Hsiang Chiu, You Zhang, Sung-Feng Huang, Yi-Hsuan Yang, Haibin Wu, Hung-yi Lee, Jyh-Shing Roger Jang
https://arxiv.org/abs/2509.14675

How Does Instrumental Music Help SingFake Detection?
Although many models exist to detect singing voice deepfakes (SingFake), how these models operate, particularly with instrumental accompaniment, is unclear. We investigate how instrumental music affects SingFake detection from two perspectives. To investigate the behavioral effect, we test different backbones, unpaired instrumental tracks, and frequency subbands. To analyze the representational effect, we probe how fine-tuning alters encoders' speech and music capabilities. Our results show tha…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Learning Lens Blur Fields
Lin, Wang, Lin, Miau, Kainz, Chen, Zhang, Lindell, Kutulakos

Unveiling the Cosmic Dance of Repeated Nuclear Transient ASASSN-14ko: Insights from Multiwavelength Observations
Shifeng Huang, Tinggui Wang, Ning Jiang, Rong-Feng Shen, Zhaohao Chen, Yuanming Wang, Jiazheng Zhu, Yibo Wang, Yunguo Jiang, Xinwen Shu, Hucheng Ding, Xiongjun Fang, Yifan Wang, Jie Lin, Jingran Xu, Xu Chen, Zheyu Lin, Zhengfeng Sheng
http…

Unveiling the Cosmic Dance of Repeated Nuclear Transient ASASSN-14ko: Insights from Multiwavelength Observations
ASASSN-14ko is a periodically repeating nuclear transient. We conducted high-cadence, multiwavelength observations of this source, revealing several recurrent early bumps and rebrightenings in its UV/optical light curves. The energy released during these bumps and rebrightenings shows a diminishing trend in recent UV/optical outbursts, which we monitored through multiwavelength observations. These features can be ascribed to the interaction between stream debris and the expanded disk in the rep…

Scientific Objectives of the Xue-shan-mu-chang 15-meter Submillimeter Telescope
XSMT Project Collaboration Group, Yiping Ao, Jin Chang, Zhiwei Chen, Xiangqun Cui, Kaiyi Du, Fujun Du, Yan Gong, Zhanwen Han, Gregory Herczeg, Luis C. Ho, Jie Hu, Yipeng Jing, Sihan Jiao, Binggang Ju, Jing Li, Xiaohu Li, Xiangdong Li, Lingrui Lin, Zhenhui Lin, Daizhong Liu, Dong Liu, Guoxi Liu, Zheng Lou, Dengrong Lu, Ruiqing Mao, Wei Miao, Yuan Qian, Keping Qiu, Zhiqiang Shen, Yong Shi, Shengcai Shi, Cheng…

Scientific Objectives of the Xue-shan-mu-chang 15-meter Submillimeter Telescope
Submillimeter astronomy is poised to revolutionize our understanding of the Universe by revealing cosmic phenomena hidden from optical and near-infrared observations, particularly those associated with interstellar dust, molecular gas, and star formation. The Xue-shan-mu-chang 15-meter submillimeter telescope (XSMT-15m), to be constructed at a premier high-altitude site (4813 m) in Qinghai, China, marks a major milestone for Chinese astronomy, establishing the China mainland's first independent…

ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism
Jia Liu, ChangYi He, YingQiao Lin, MingMin Yang, FeiYang Shen, ShaoGuo Liu, TingTing Gao
https://arxiv.org/abs/2508.11356

ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism
Recent advancements in Large Language Models have yielded significant improvements in complex reasoning tasks such as mathematics and programming. However, these models remain heavily dependent on annotated data and exhibit limited adaptability in unsupervised scenarios. To address these limitations, test-time reinforcement learning (TTRL) has been proposed, which enables self-optimization by leveraging model-generated pseudo-labels. Despite its promise, TTRL faces several key challenges, inclu…

Lin-Manuel Miranda & Jimmy Fallon Perform ‘Hamilton’ Medley With Toy Instruments
https://www.billboard.com/music/music-news/lin-manuel-miranda-ji…

Lin-Manuel Miranda & Jimmy Fallon Perform ‘Hamilton’ Medley With Toy Instruments
Lin-Manuel Miranda brought 'Hamilton' back to the stage during Tuesday night’s (Aug. 5) episode of 'The Tonight Show Starring Jimmy Fallon.

Analog Quantum Phase Estimation with Single-Mode Readout
Wei-Chen Lin, Chiao-Hsuan Wang
https://arxiv.org/abs/2506.15668 https://arxi…

Analog Quantum Phase Estimation with Single-Mode Readout
Eigenvalue estimation is a central problem for demonstrating quantum advantage, yet its implementation on digital quantum computers remains limited by circuit depth and operational overhead. We present an analog quantum phase estimation (aQPE) protocol that extracts the eigenenergies of a target Hamiltonian via continuous time evolution and single-mode cavity measurement. By encoding eigenvalue information as conditional cavity phase-space rotations, the scheme avoids deep quantum circuits and …

Visuomotor Grasping with World Models for Surgical Robots
Hongbin Lin, Bin Li, Kwok Wai Samuel Au
https://arxiv.org/abs/2508.11200 https://arxiv.org/pdf/25…

Visuomotor Grasping with World Models for Surgical Robots
Grasping is a fundamental task in robot-assisted surgery (RAS), and automating it can reduce surgeon workload while enhancing efficiency, safety, and consistency beyond teleoperated systems. Most prior approaches rely on explicit object pose tracking or handcrafted visual features, limiting their generalization to novel objects, robustness to visual disturbances, and the ability to handle deformable objects. Visuomotor learning offers a promising alternative, but deploying it in RAS presents un…

XOPS, which uses AI and knowledge graphs to automate enterprise IT tasks, emerges from stealth with $40M in funding led by Activant Capital and FPV Ventures (Belle Lin/Wall Street Journal)
https://www.wsj.com/articles/it-…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- EdiVal-Agent: An Object-Centric Framework for Automated, Scalable, Fine-Grained Evaluation of Mul...
Chen, Zhang, Zhang, Yu, Wang, Wang, Lin, Wang, Yang, Li, Lin, Xie, Leong, Wang, Wu, Zhou

NFL line movements for Week 3: Bengals vs. Vikings sees big movement with backup QBs set to start

https://www.cbssports.com/nfl/news/nfl-lin

On computing and the complexity of computing higher-order $U$-statistics, exactly
Xingyu Chen, Ruiqi Zhang, Lin Liu
https://arxiv.org/abs/2508.12627 https://

On computing and the complexity of computing higher-order $U$-statistics, exactly
Higher-order $U$-statistics abound in fields such as statistics, machine learning, and computer science, but are known to be highly time-consuming to compute in practice. Despite their widespread appearance, a comprehensive study of their computational complexity is surprisingly lacking. This paper aims to fill that gap by presenting several results related to the computational aspect of $U$-statistics. First, we derive a useful decomposition from an $m$-th order $U$-statistic to a linear combi…

A Survey of Reinforcement Learning for Software Engineering
Dong Wang, Hanmo You, Lingwei Zhu, Kaiwei Lin, Zheng Chen, Chen Yang, Junji Yu, Zan Wang, Junjie Chen
https://arxiv.org/abs/2507.12483

A Survey of Reinforcement Learning for Software Engineering
Reinforcement Learning (RL) has emerged as a powerful paradigm for sequential decision-making and has attracted growing interest across various domains, particularly following the advent of Deep Reinforcement Learning (DRL) in 2015. Simultaneously, the rapid advancement of Large Language Models (LLMs) has further fueled interest in integrating RL with LLMs to enable more adaptive and intelligent systems. In the field of software engineering (SE), the increasing complexity of systems and the ris…

Non-bipartite graphs without theta subgraphs
Longfei Fang, Huiqiu Lin
https://arxiv.org/abs/2508.12855 https://arxiv.org/pdf/2508.12855

Non-bipartite graphs without theta subgraphs
Fix a color-critical graph $H$ with $χ(H)=r+1\geq 3$. Simonovits' chromatic critical edge theorem and Nikiforov's spectral chromatic critical edge theorem imply that $T_{n,r}$ is the extremal graph with the maximum size and the maximum spectral radius over all $H$-free graphs of order $n$, respectively. Since $T_{n,r}$ is $r$-partite, it is interesting to study the Turán number and the spectral Turán number of a color-critical graph $H$ in non-$r$-partite graphs. Denote by ${\rm EX}_{r+1}(n,…

RemVerse: Supporting Reminiscence Activities for Older Adults through AI-Assisted Virtual Reality
Ruohao Li, Jiawei Li, Jia Sun, Zhiqing Wu, Zisu Li, Ziyan Wang, Ge Lin Kan, Mingming Fan
https://arxiv.org/abs/2507.13247

RemVerse: Supporting Reminiscence Activities for Older Adults through AI-Assisted Virtual Reality
Reminiscence activities, which involve recalling and sharing past experiences, have proven beneficial for improving cognitive function, mood, and overall well-being. However, urbanization has led to the disappearance of familiar environments, removing visual and audio cues for effective reminiscence. While old photos can serve as visual cues to aid reminiscence, it is challenging for people to reconstruct the reminisced content and environment that are not in the photos. Virtual reality (VR) an…

Short mollifiers of the Riemann zeta-function
J. Brian Conrey, David W. Farmer, Chung-Hang Kwan, Yongxiao Lin, Caroline L. Turnage-Butterbaugh
https://arxiv.org/abs/2508.11108 h…

Short mollifiers of the Riemann zeta-function
We apply the calculus of variations to construct a new sequence of linear combinations of derivatives of the Riemann $ζ$-function adapted to Levinson's method, which yield a positive proportion of zeros of the $ζ$-function on the critical line, regardless of how short the mollifier is. Our construction extends readily to modular $L$-functions. Even with Levinson's original choice of mollifier, our method more than doubles the proportions of zeros on the critical line for modular $L$-functions…

On Finite-Blocklength Noisy Classical-Quantum Channel Coding With Amplitude Damping Errors
Tam\'as Havas, Hsuan-Yin Lin, Eirik Rosnes, Ching-Yi Lai
https://arxiv.org/abs/2509.14852

On Finite-Blocklength Noisy Classical-Quantum Channel Coding With Amplitude Damping Errors
We investigate practical finite-blocklength classical-quantum channel coding over the quantum amplitude damping channel (ADC), aiming to transmit classical information reliably through quantum outputs. Our findings indicate that for any finite blocklength, a naive (uncoded) approach fails to offer any advantage over the ADC. Instead, sophisticated encoding strategies that leverage both classical error-correcting codes and quantum input states are crucial for realizing quantum performance gains …

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing
Zhuoqun Li, Xuanang Chen, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
https://arxiv.org/abs/2508.11116

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing
Paper search is an important activity for researchers, typically involving using a query with description of a topic to find relevant papers. As research deepens, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than being limited to coarse-grained topics. However, previous paper search systems are unable to meet these flexible-grained requirements, as these systems mainly collect paper abstracts to construct index of c…

Replaced article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Enhancing Citizen-Government Communication with AI: Evaluating the Impact of AI-Assisted Interact...
Ruiyu Zhang, Lin Nie

LSM-OPD: Boosting Scan in LSM-Trees by Enabling Direct Computing on Compressed Data
Jianfeng Huang, Ziyao Wang, Lin Yuan, Jiajie Wen, Yihao Cao, Dongjing Miao, Yong Wang, Jiahao Zhang
https://arxiv.org/abs/2508.11862

LSM-OPD: Boosting Scan in LSM-Trees by Enabling Direct Computing on Compressed Data
Scan-based operations, such as backstage compaction and value filtering, have emerged as the main bottleneck for LSM-Trees in supporting contemporary data-intensive applications. For slower external storage devices, such as HDD and SATA SSD, the scan performance is primarily limited by the I/O bandwidth (i.e., I/O bound) due to the substantial read/write amplifications in LSM-Trees. Recent adoption of high-performance storage devices, such as NVMe SSD, has transformed the main limitation to be …

Efficient Artifacts Removal for Adaptive Deep Brain Stimulation and a Temporal Event Localization Analysis
Tzu-Chi Liu, Po-Lin Chen, Yi-Chieh Chen, Po-Hsun Tu, Chih-Hua Yeh, Mun-Chun Yeap, Chiung-Chu Chen, Hau-Tieng Wu
https://arxiv.org/abs/2508.11459

Efficient Artifacts Removal for Adaptive Deep Brain Stimulation and a Temporal Event Localization Analysis
Adaptive deep brain stimulation (aDBS) leverages symptom-related biomarkers to deliver personalized neuromodulation therapy, with the potential to improve treatment efficacy and reduce power consumption compared to conventional DBS. However, stimulation-induced signal contamination remains a major technical barrier to advancing its clinical application. Existing artifact removal strategies, both front-end and back-end, face trade-offs between artifact suppression and algorithmic flexibility. Am…

Location and allocation problem of high-speed train maintenance bases
Boliang Lin, Xiang Li, Yuxue Gu, Dishen Lu
https://arxiv.org/abs/2509.13383 https://a…

Location and allocation problem of high-speed train maintenance bases
Maintenance bases are crucial for the safe and stable operation of high-speed trains, necessitating significant financial investment for their construction and operation. Planning the location and task allocation of these bases in the vast high-speed railway network is a complex combinatorial optimization problem. This paper explored the strategic planning of identifying optimal locations for maintenance bases, introducing a bi-level programming model. The upper-level objective was to minimize …

CompAir: Synergizing Complementary PIMs and In-Transit NoC Computation for Efficient LLM Acceleration
Hongyi Li, Songchen Ma, Huanyu Qu, Weihao Zhang, Jia Chen, Junfeng Lin, Fengbin Tu, Rong Zhao
https://arxiv.org/abs/2509.13710

CompAir: Synergizing Complementary PIMs and In-Transit NoC Computation for Efficient LLM Acceleration
The rapid advancement of Large Language Models (LLMs) has revolutionized various aspects of human life, yet their immense computational and energy demands pose significant challenges for efficient inference. The memory wall, the growing processor-memory speed disparity, remains a critical bottleneck for LLM. Process-In-Memory (PIM) architectures overcome limitations by co-locating compute units with memory, leveraging 5-20$\times$ higher internal bandwidth and enabling greater energy efficiency…

Three dimensional magnetic reconnection mediated with plasmoids and the resulted multi-thermal emissions in the cool atmosphere of the Sun
Guanchong Cheng, Lei Ni, Robert Cameron, Hardi Peter, Yajie Chen, Jun Lin
https://arxiv.org/abs/2508.11013

Three dimensional magnetic reconnection mediated with plasmoids and the resulted multi-thermal emissions in the cool atmosphere of the Sun
Flux emergence is ubiquitous in the Sun's lower atmosphere, where the emerging magnetic flux can reconnect with the pre-existing magnetic field. We investigate plasmoid formation and the resulting multi-thermal emissions during three-dimensional magnetic reconnection in the lower solar atmosphere. We performed 3D radiation magnetohydrodynamic simulations using the MURaM code, which incorporates solar convection and radiative transfer. A flat magnetic flux sheet was introduced into the convectio…

Agentar-DeepFinance-300K: A Large-Scale Financial Dataset via Systematic Chain-of-Thought Synthesis Optimization
Xiaoke Zhao, Zhaowen Zhou, Lin Chen, Lihong Wang, Zhiyi Huang, Kaiyuan Zheng, Yanjun Zheng, Xiyang Du, Longfei Liao, Jiawei Liu, Xiang Qi, Bo Zhang, Peng Zhang, Zhe Li, Wei Wang
https://arxiv.org/abs/2507.12901

Agentar-DeepFinance-300K: A Large-Scale Financial Dataset via Systematic Chain-of-Thought Synthesis Optimization
Recent advancements in large language models (LLMs) have demonstrated remarkable general reasoning capabilities, holding significant potential for applications in the financial domain, a field that requires robust and reliable reasoning. It has been demonstrated that distilling high-quality chain-of-thought (CoT) rationales from advanced general reasoning models offers a promising and efficient path to the financial reasoning model. However, existing CoT synthesis methods suffer from shallow Co…

Influence Prediction in Collaboration Networks: An Empirical Study on arXiv
Marina Lin, Laura P. Schaposnik, Raina Wu
https://arxiv.org/abs/2508.13029 https://

Influence Prediction in Collaboration Networks: An Empirical Study on arXiv
This paper provides an empirical study of the Social Sphere Model for influence prediction, previously introduced by the authors, combining link prediction with top-k centrality-based selection. We apply the model to the temporal arXiv General Relativity and Quantum Cosmology collaboration network, evaluating its performance under varying edge sampling rates and prediction horizons to reflect different levels of initial data completeness and network evolution. Accuracy is assessed using mean sq…

Stretchable and self-adhesive triboelectric sensor for real-time musculoskeletal monitoring and personalized recovery
Cai Lin, Yunyi Ding, Kai Lin, Ru Wang, Yichen Luo, Xiaofen Wu
https://arxiv.org/abs/2508.11655

Stretchable and self-adhesive triboelectric sensor for real-time musculoskeletal monitoring and personalized recovery
Recent advances in medical diagnostics have highlighted the importance of wearable technologies for continuous and real-time physiological monitoring. In this study, we introduce a flexible, self-powered triboelectric nanogenerator (MB-TENG) engineered from commercially available medical elastic bandages for biomechanical sensing during rehabilitation and gait analysis. Leveraging the porous and skin-friendly properties of the bandage combined with a PTFE film, the MB-TENG delivers robust elect…

Bridging Molecular Simulation and Process Modeling for Predictive Multicomponent Adsorption
Sunghyun Yoon, Jui Tu, Li-Chiang Lin, Yongchul G. Chung
https://arxiv.org/abs/2508.12200

Bridging Molecular Simulation and Process Modeling for Predictive Multicomponent Adsorption
Accurate and efficient prediction of multicomponent adsorption equilibria across pressures, temperatures, and compositions remain a central challenge for designing energy-efficient adsorption-based separation processes. Traditional approaches, including model fitting and ideal adsorbed solution theory (IAST), often fail to balance accuracy, computational efficiency, and transferability under process-relevant conditions. Here, we introduce a material-to-process modeling framework that integrates…

Property-Isometric Variational Autoencoders for Sequence Modeling and Design
Elham Sadeghi, Xianqi Deng, I-Hsin Lin, Stacy M. Copp, Petko Bogdanov
https://arxiv.org/abs/2509.14287

Property-Isometric Variational Autoencoders for Sequence Modeling and Design
Biological sequence design (DNA, RNA, or peptides) with desired functional properties has applications in discovering novel nanomaterials, biosensors, antimicrobial drugs, and beyond. One common challenge is the ability to optimize complex high-dimensional properties such as target emission spectra of DNA-mediated fluorescent nanoparticles, photo and chemical stability, and antimicrobial activity of peptides across target microbes. Existing models rely on simple binary labels (e.g., binding/non…

Error analysis of a fully discrete structure-preserving finite element scheme for a diffuse-interface model of tumour growth
Agus L. Soenjaya, Ping Lin, Thanh Tran
https://arxiv.org/abs/2509.14486

Error analysis of a fully discrete structure-preserving finite element scheme for a diffuse-interface model of tumour growth
We develop a fully discrete structure-preserving finite element method for a diffuse-interface model of tumour growth. The system couples a Cahn--Hilliard type equation with a nonlinear reaction-diffusion equation for nutrient concentration and admits a dissipative energy law at the continuous level. For the discretisation, we employ a scalar auxiliary variable (SAV) formulation together with a mixed finite element method for the Cahn--Hilliard part and standard conforming finite elements for t…

A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Zhengyu Ma, Huihui Zhou, Yonghong Tian, Zhouchen Lin
https://arxiv.org/abs/2508.12609

A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Spiking Neural Networks (SNNs) are a promising approach to low-power applications on neuromorphic hardware due to their energy efficiency. However, training SNNs is challenging because of the non-differentiable spike generation function. To address this issue, the commonly used approach is to adopt the backpropagation through time framework, while assigning the gradient of the non-differentiable function with some surrogates. Similarly, Binary Neural Networks (BNNs) also face the non-differenti…

Self-Interacting Dark Matter with Mass Segregation: A Unified Explanation of Dwarf Cores and Small-Scale Lenses
Daneng Yang, Yi-Zhong Fan, Siyuan Hou, Yue-Lin Sming Tsai
https://arxiv.org/abs/2506.14898

Self-Interacting Dark Matter with Mass Segregation: A Unified Explanation of Dwarf Cores and Small-Scale Lenses
In two-component SIDM models with inter-species interactions, mass segregation arises naturally from collisional relaxation, enhancing central densities and gravothermal evolution without requiring large cross sections. We propose a model with velocity-dependent interactions, both within and between species, that connects observations across several halo mass scales while remaining consistent with cluster-scale constraints. This combination enables modest mass segregation in low-mass and typica…

Sekai: A Video Dataset towards World Exploration
Zhen Li, Chuanhao Li, Xiaofeng Mao, Shaoheng Lin, Ming Li, Shitian Zhao, Zhaopan Xu, Xinyue Li, Yukang Feng, Jianwen Sun, Zizhen Li, Fanrui Zhang, Jiaxin Ai, Zhixiang Wang, Yuwei Wu, Tong He, Jiangmiao Pang, Yu Qiao, Yunde Jia, Kaipeng Zhang
https://arxiv.org/abs/2506.15675…

Sekai: A Video Dataset towards World Exploration
Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning ``world'' in Japanese), a high-quality first-person view worldwide video dataset with rich…

Enhancing Multi-Agent Debate System Performance via Confidence Expression
Zijie Lin, Bryan Hooi
https://arxiv.org/abs/2509.14034 https://arxiv.org/pdf/2509…

Enhancing Multi-Agent Debate System Performance via Confidence Expression
Generative Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks. Recent research has introduced Multi-Agent Debate (MAD) systems, which leverage multiple LLMs to simulate human debate and thereby improve task performance. However, while some LLMs may possess superior knowledge or reasoning capabilities for specific tasks, they often struggle to clearly communicate this advantage during debates, in part due to a lack of confidence expression. Moreove…

SeamlessFlow: A Trainer Agent Isolation RL Framework Achieving Bubble-Free Pipelines via Tag Scheduling
Jinghui Wang, Shaojie Wang, Yinghan Cui, Xuxing Chen, Chao Wang, Xiaojiang Zhang, Minglei Zhang, Jiarong Zhang, Wenhao Zhuang, Yuchen Cao, Wankang Bao, Haimo Li, Zheng Lin, Huiming Wang, Haoyang Huang, Zongxian Feng, Zizheng Zhan, Ken Deng, Wen Xiang, Huaixi Tang, Kun Wu, Mengtong Li, Mengfei Xie, Junyi Peng, Haotian Zhang, Bin Chen, Bing Yu

SeamlessFlow: A Trainer Agent Isolation RL Framework Achieving Bubble-Free Pipelines via Tag Scheduling
We introduce SeamlessFlow, a server based reinforcement learning (RL) framework that addresses two core challenges in industrial scale RL: (1) decoupling RL training from the complex execution flow of agents; (2) maximizing GPU utilization with minimal idle time while preserving the stability and scalability required for large-scale deployments. First, SeamlessFlow introduces a data plane that decouples the RL trainer from diverse, complex agent implementations while sustaining high throughput.…

Fault-tolerant mixed boundary punctures on the toric code
Yao Shen, Fu-Lin Zhang
https://arxiv.org/abs/2508.11230 https://arxiv.org/pdf/2508.11230

Fault-tolerant mixed boundary punctures on the toric code
Defects on the toric code, a well-known exactly solvable Abelian anyon model, can exhibit non-Abelian statistical properties, which can be classified into punctures and twists. Benhemou et al.[Phys. Rev. A. 105, 042417 (2022)] introduced a mixed boundary puncture model that integrates the advantages of both punctures and twists. They proposed that non-Abelian properties could be realized in the symmetric subspace {$|++\rangle$, $|--\rangle$}. This work demonstrates that the nontrivial…

Publicly Verifiable Private Information Retrieval Protocols Based on Function Secret Sharing
Lin Zhu, Lingwei Kong, Xin Ning, Xiaoyang Qu, Jianzong Wang
https://arxiv.org/abs/2509.13684

Publicly Verifiable Private Information Retrieval Protocols Based on Function Secret Sharing
Private Information Retrieval (PIR) is a fundamental cryptographic primitive that enables users to retrieve data from a database without revealing which item is being accessed, thereby preserving query privacy. However, PIR protocols also face the challenge of result verifiability, as users expect the reconstructed data to be trustworthy and authentic. In this work, we propose two effective constructions of publicly verifiable PIR (PVPIR) in the multi-server setting, which achieve query privacy…

Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation
Hongbin Lin, Juan Rojas, Kwok Wai Samuel Au
https://arxiv.org/abs/2508.11204 https://

Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation
Sampling efficiency is critical for deploying visuomotor learning in real-world robotic manipulation. While task symmetry has emerged as a promising inductive bias to improve efficiency, most prior work is limited to isometric symmetries -- applying the same group transformation to all task objects across all timesteps. In this work, we explore non-isometric symmetries, applying multiple independent group transformations across spatial and temporal dimensions to relax these constraints. We intr…

The spectral Tur\'{a}n problem: Characterizing spectral-consistent graphs
Longfei Fang, Huiqiu Lin, Mingqing Zhai
https://arxiv.org/abs/2508.12070 https://

The spectral Turán problem: Characterizing spectral-consistent graphs
Let ${\rm EX}(n,H)$ and ${\rm SPEX}(n,H)$ denote the families of $n$-vertex $H$-free graphs with the maximum size and the maximum spectral radius, respectively. A graph $H$ is said to be spectral-consistent if ${\rm SPEX}(n,H)\subseteq {\rm EX}(n,H)$ for sufficiently large $n$. A fundamental problem in spectral extremal graph theory is to determine which graphs are spectral-consistent. Cioabă, Desai and Tait [European J. Combin. 99 (2022) 103420] proposed the following conjecture: Let $H$ be…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
John Y. Kim, Chaoshun Zuo, Yanjie Zhao, Zhiqiang Lin

Integrated diffractive full-Stokes spectro-polarimetric imaging
Jingyue Ma, Zhenming Yu, Zhengyang Li, Liang Lin, Liming Cheng, Jiayu Di, Tongshuo Zhang, Ning Zhan, Kun Xu
https://arxiv.org/abs/2509.13660

Integrated diffractive full-Stokes spectro-polarimetric imaging
Spectro-polarimetric imaging provides multidimensional optical information acquisition capabilities, offering significant potential for diverse applications. Current spectro-polarimetric imaging systems typically suffer from large physical footprints, high design complexity, elevated costs, or the drawback of requiring replacement of standard components with polarization optics. To address these issues, we propose an integrated diffractive full-Stokes spectro-polarimetric imaging framework that…

Back to Ear: Perceptually Driven High Fidelity Music Reconstruction
Kangdi Wang, Zhiyue Wu, Dinghao Zhou, Rui Lin, Junyu Dai, Tao Jiang
https://arxiv.org/abs/2509.14912 https://…

Back to Ear: Perceptually Driven High Fidelity Music Reconstruction
Variational Autoencoders (VAEs) are essential for large-scale audio tasks like diffusion-based generation. However, existing open-source models often neglect auditory perceptual aspects during training, leading to weaknesses in phase accuracy and stereophonic spatial representation. To address these challenges, we propose εar-VAE, an open-source music signal reconstruction model that rethinks and optimizes the VAE training paradigm. Our contributions are threefold: (i) A K-weighting perceptual…

InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Management
Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen
https://arxiv.org/abs/2509.13704

InfraMind: A Novel Exploration-based GUI Agentic Framework for Mission-critical Industrial Management
Mission-critical industrial infrastructure, such as data centers, increasingly depends on complex management software. Its operations, however, pose significant challenges due to the escalating system complexity, multi-vendor integration, and a shortage of expert operators. While Robotic Process Automation (RPA) offers partial automation through handcrafted scripts, it suffers from limited flexibility and high maintenance costs. Recent advances in Large Language Model (LLM)-based graphical user…

Integrating Uncertainties for Koopman-Based Stabilization
Yicheng Lin, Bingxian Wu, Nan Bai, Zhiyong Sun, Yunxiao Ren, Chuanze Chen, Zhisheng Duan
https://arxiv.org/abs/2508.11533

Integrating Uncertainties for Koopman-Based Stabilization
Over the past decades, the Koopman operator has been widely applied in data-driven control, yet its theoretical foundations remain underexplored. This paper establishes a unified framework to address the robust stabilization problem in data-driven control via the Koopman operator, fully accounting for three uncertainties: projection error, estimation error, and process disturbance. It comprehensively investigates both direct and indirect data-driven control approaches, facilitating flexible met…

Shimmering Darkness: Mapping the Evolution of Supernova-Neutrino-Boosted Dark Matter within the Milky Way
Yen-Hsun Lin, Meng-Ru Wu
https://arxiv.org/abs/2506.15151

Shimmering Darkness: Mapping the Evolution of Supernova-Neutrino-Boosted Dark Matter within the Milky Way
Supernova-neutrino-boosted dark matter (SN$ν$ BDM) has emerged as a promising portal for probing sub-GeV dark matter. In this work, we investigate the behavior of BDM signatures originating from core-collapse supernovae (CCSNe) within the Milky Way (MW) over the past one hundred thousand years, examining both their temporal evolution and present-day spatial distributions. We show that while the MW BDM signature is approximately diffuse in the nonrelativistic regime, it exhibits significant tem…

Automated Building Heritage Assessment Using Street-Level Imagery
Kristina Dabrock, Tim Johansson, Anna Donarelli, Mikael Mangold, Noah Pflugradt, Jann Michael Weinand, Jochen Lin{\ss}en
https://arxiv.org/abs/2508.11486

Automated Building Heritage Assessment Using Street-Level Imagery
Detailed data is required to quantify energy conservation measures in buildings, such as envelop retrofits, without compromising cultural heritage. Novel artificial intelligence tools may improve efficiency in identifying heritage values in buildings compared to costly and time-consuming traditional inventories. In this study, the large language model GPT was used to detect various aspects of cultural heritage value in façade images. Using this data and building register data as features, mach…

Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Kun Kuang, Fei Wu
https://arxiv.org/abs/2508.11184

Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
Distractors, incorrect but plausible answer choices in multiple-choice questions (MCQs), play a critical role in educational assessment by diagnosing student misconceptions. Recent work has leveraged large language models (LLMs) to generate shared, group-level distractors by learning common error patterns across large student populations. However, such distractors often fail to capture the diverse reasoning errors of individual students, limiting their diagnostic effectiveness. To address this …

The Production Mechanism of the High-energy Emission Line in the Brightest Cosmic Burst
Jieying Liu, Jirong Mao, Shao-Lin Xiong, Yan-Qiu Zhang
https://arxiv.org/abs/2509.14715 h…

The Production Mechanism of the High-energy Emission Line in the Brightest Cosmic Burst
As a characteristic feature of the spectrum, the emission line carries critical information on the underlying physics of the radiation. After extensive efforts in decades, the first high-significant detection of a series of emission lines evolving from 37 MeV to 6 MeV has been detected in the ever-bright gamma-ray burst GRB 221009A. However, the physical mechanism of the entire evolutionary trend of the lines remains elusive. To provide a self-consistent interpretation, we propose a novel scena…

The NIAID Discovery Portal: A Unified Search Engine for Infectious and Immune-Mediated Disease Datasets
Ginger Tsueng (The Scripps Research Institute, La Jolla, CA, USA), Emily Bullen (The Scripps Research Institute, La Jolla, CA, USA), Candice Czech (The Scripps Research Institute, La Jolla, CA, USA), Dylan Welzel (The Scripps Research Institute, La Jolla, CA, USA), Leandro Collares (The Scripps Research Institute, La Jolla, CA, USA), Jason Lin (The Scripps Research Institute, La Joll…

The NIAID Discovery Portal: A Unified Search Engine for Infectious and Immune-Mediated Disease Datasets
The NIAID Data Ecosystem Discovery Portal (https://data.niaid.nih.gov) provides a unified search interface for over 4 million datasets relevant to infectious and immune-mediated disease (IID) research. Integrating metadata from domain-specific and generalist repositories, the Portal enables researchers to identify and access datasets using user-friendly filters or advanced queries, without requiring technical expertise. The Portal supports discovery of a wide range of resources, including epide…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/7]:
- A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series...
Yijun Lin, Yao-Yi Chiang

Inverse Design of Amorphous Materials with Targeted Properties
Jonas A. Finkler, Yan Lin, Tao Du, Jilin Hu, Morten M. Smedskjaer
https://arxiv.org/abs/2509.13916 https://…

Inverse Design of Amorphous Materials with Targeted Properties
Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Their lack of long-range order and complex short- and medium-range orderings, which depend on composition as well as thermal and pressure history, offer a vast materials design space. To this end, relying on machine learning methods instead of trial and error is promising, and among these, inverse design has emerged as a tool for discov…

Talk Less, Fly Lighter: Autonomous Semantic Compression for UAV Swarm Communication via LLMs
Fei Lin, Tengchao Zhang, Qinghua Ni, Jun Huang, Siji Ma, Yonglin Tian, Yisheng Lv, Naiqi Wu
https://arxiv.org/abs/2508.12043

Talk Less, Fly Lighter: Autonomous Semantic Compression for UAV Swarm Communication via LLMs
The rapid adoption of Large Language Models (LLMs) in unmanned systems has significantly enhanced the semantic understanding and autonomous task execution capabilities of Unmanned Aerial Vehicle (UAV) swarms. However, limited communication bandwidth and the need for high-frequency interactions pose severe challenges to semantic information transmission within the swarm. This paper explores the feasibility of LLM-driven UAV swarms for autonomous semantic compression communication, aiming to redu…

ToxiEval-ZKP: A Structure-Private Verification Framework for Molecular Toxicity Repair Tasks
Fei Lin, Tengchao Zhang, Ziyang Gong, Fei-Yue Wang
https://arxiv.org/abs/2508.12035 …

ToxiEval-ZKP: A Structure-Private Verification Framework for Molecular Toxicity Repair Tasks
In recent years, generative artificial intelligence (GenAI) has demonstrated remarkable capabilities in high-stakes domains such as molecular science. However, challenges related to the verifiability and structural privacy of its outputs remain largely unresolved. This paper focuses on the task of molecular toxicity repair. It proposes a structure-private verification framework - ToxiEval-ZKP - which, for the first time, introduces zero-knowledge proof (ZKP) mechanisms into the evaluation proce…

Driven-Dissipative Interpretation of Measurement-Induced State Transitions Beyond Semiclassical Predictions
Bo-Syun Pan, Yen-Hsiang Lin, Chiao-Hsuan Wang
https://arxiv.org/abs/2508.13150

Driven-Dissipative Interpretation of Measurement-Induced State Transitions Beyond Semiclassical Predictions
Dispersive readout plays a central role in superconducting quantum computing, enabling quantum nondemolition (QND) measurements of qubits through a coupled microwave resonator. However, under strong readout drives, multi-photon resonances can cause measurement-induced state transition (MIST), resulting in qubit leakage out of the computational subspace and compromising the QND character. We present a driven-dissipative interpretation of MIST using a reduced quantum model that captures the dynam…

On the Tur\'{a}n number of odd-ballooning of $3$-chromatic graphs
Longfei Fang, Xueyi Huang, Huiqiu Lin, Jinlong Shu
https://arxiv.org/abs/2508.12826 https://

On the Turán number of odd-ballooning of $3$-chromatic graphs
Given a graph $F$, the Turán number ${\rm ex}(n,F)$ is the maximum number of edges in any $n$-vertex $F$-free graph. The odd-ballooning of $F$, denoted by $F^{o}$, is a graph obtained by replacing each edge of $F$ with an odd cycle, where all new vertices of the odd cycles are distinct. The Turán number of the odd-ballooning of $F$ has been established for several important cases. For a star, it was determined by Erdős, Füredi, Gould, and Gunderson (1995), Hou, Qiu, and Liu (2018), and Yuan…

3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach
Ethan Koland, Lin Xi, Nadeev Wijesuriya, YingLiang Ma
https://arxiv.org/abs/2509.13358

3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach
X-ray angiography is widely used in cardiac interventions to visualize coronary vessels, assess integrity, detect stenoses and guide treatment. We propose a framework for reconstructing 3D vessel trees from biplanar X-ray images which are extracted from two X-ray videos captured at different C-arm angles. The proposed framework consists of three main components: image segmentation, motion phase matching, and 3D reconstruction. An automatic video segmentation method for X-ray angiography to enab…

Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback
Wenzhen Yuan, Shengji Tang, Weihao Lin, Jiacheng Ruan, Ganqu Cui, Bo Zhang, Tao Chen, Ting Liu, Yuzhuo Fu, Peng Ye, Lei Bai
https://arxiv.org/abs/2508.12338

Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback
Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), but its reliance on expensive human-labeled data or complex reward models severely limits scalability. While existing self-feedback methods aim to address this problem, they are constrained by the capabilities of a single model, which can lead to overconfidence in incorrect answers, reward hacking, and even training collapse. To this end, we propose Reinforcement Learning from Coev…

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
Chao Shuai, Gaojian Wang, Kun Pan, Tong Wu, Fanli Jin, Haohan Tan, Mengxiang Li, Zhenguang Liu, Feng Lin, Kui Ren
https://arxiv.org/abs/2509.13776

Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
While the pursuit of higher accuracy in deepfake detection remains a central goal, there is an increasing demand for precise localization of manipulated regions. Despite the remarkable progress made in classification-based detection, accurately localizing forged areas remains a significant challenge. A common strategy is to incorporate forged region annotations during model training alongside manipulated images. However, such approaches often neglect the complementary nature of local detail and…

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Shaoqing Lin, Chong Teng, Fei Li, Donghong Ji, Lizhen Qu, Zhuang Li
https://arxiv.org/abs/2506.15583

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Vision-Language Models (VLMs) now generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers originally designed for single-sentence caption-to-graph mapping. Current approaches typically merge sentence-level parsing outputs for discourse input, often missing phenomena like cross-sentence coreference, resulting in fragmented graphs and degraded downstream VLM task performance. To address this, we introduce a new task, Discourse-level text Scene Graph pars…

Sample-Constrained Black Box Optimization for Audio Personalization
Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
https://arxiv.org/abs/2507.12773

Sample-Constrained Black Box Optimization for Audio Personalization
We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter $h^*$, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimization problem since the user's satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter $h_i$, and query the user for their satisfaction scores $f(h_i)$. A family of …

Track Any Motions under Any Disturbances
Zhikai Zhang, Jun Guo, Chao Chen, Jilong Wang, Chenghuai Lin, Yunrui Lian, Han Xue, Zhenrong Wang, Maoqi Liu, Huaping Liu, He Wang, Li Yi
https://arxiv.org/abs/2509.13833

Track Any Motions under Any Disturbances
A foundational humanoid motion tracker is expected to be able to track diverse, highly dynamic, and contact-rich motions. More importantly, it needs to operate stably in real-world scenarios against various dynamics disturbances, including terrains, external forces, and physical property changes for general practical use. To achieve this goal, we propose Any2Track (Track Any motions under Any disturbances), a two-stage RL framework to track various motions under multiple disturbances in the rea…

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
Zilong Lin, Zichuan Li, Xiaojing Liao, XiaoFeng Wang
https://arxiv.org/abs/2508.12622

Consiglieres in the Shadow: Understanding the Use of Uncensored Large Language Models in Cybercrimes
The advancement of AI technologies, particularly Large Language Models (LLMs), has transformed computing while introducing new security and privacy risks. Prior research shows that cybercriminals are increasingly leveraging uncensored LLMs (ULLMs) as backends for malicious services. Understanding these ULLMs has been hindered by the challenge of identifying them among the vast number of open-source LLMs hosted on platforms like Hugging Face. In this paper, we present the first systematic study …

Scaling green hydrogen and CCUS via cement-methanol co-production in China
Yuezhang He, Hongxi Luo, Yuancheng Lin, Carl J. Talsma, Anna Li, Zhenqian Wang, Yujuan Fang, Pei Liu, Jesse D. Jenkins, Eric Larson, Zheng Li
https://arxiv.org/abs/2509.13674

Scaling green hydrogen and CCUS via cement-methanol co-production in China
High costs of green hydrogen and of carbon capture, utilization, and sequestration (CCUS) have hindered policy ambition and slowed real-world deployment, despite their importance for decarbonizing hard-to-abate sectors, including cement and methanol. Given the economic challenges of adopting CCUS in cement and green hydrogen in methanol production separately, we propose a renewable-powered co-production system that couples electrolytic hydrogen and CCUS through molecule exchange. We optimize sy…

The Physical Origin and Time Lag of Multi-Frequency Flares from SgrA*
Hong-Xuan Jiang, Yosuke Mizuno, Indu K. Dihingia, Feng Yuan, Xi Lin, Christian M. Fromm, Antonios Nathanail, Ziri Younsi
https://arxiv.org/abs/2507.12789

The Physical Origin and Time Lag of Multi-Frequency Flares from SgrA*
Sagittarius~A$^*$, the supermassive black hole at the center of our galaxy, exhibits flares across various wavelengths, yet their origins remain elusive. We performed 3D two-temperature General Relativistic Magnetohydrodynamic (GRMHD) simulations of magnetized accretion flows initialized from multi-loop magnetic field configuration onto a rotating black hole and conducted General Relativistic Radiative Transfer (GRRT) calculations considering contributions from both thermal and non-thermal sync…

Quantum Algorithm of the GLMY Homology on Digraphs
Yunpeng Zi, Muchun Yang, D. L. Zhou
https://arxiv.org/abs/2509.13862 https://arxiv.org/pdf/2509.13862

Quantum Algorithm of the GLMY Homology on Digraphs
Quantum algorithms for topological data analysis provide significant advantage over the best classical algorithm. Different from the previous simplical complex on points cloud, the GLMY homology introduced by Alexander Grigor'yan, Yong Lin, Yuri Muranov and Shing-Tung Yau, is defined on digraph and is a arising realm in Topological Data Analysis (TDA), which attracts more and more attention recently. We propose a quantum algorithm for the GLMY homology with significant advantage over the best c…

Perfect tilings with the generalised triangle in $k$-graphs
Weichan Liu, Xiangxiang Nie, Donglei Yang, Lin-Peng Zhang
https://arxiv.org/abs/2508.12311 https://

Perfect tilings with the generalised triangle in $k$-graphs
Denote by $T_k$ the generalised triangle, a $k$-uniform hypergraph on vertex set $\{1,2,\dots,2k-1\}$ with three edges $\{1,\dots,k-1,k\}$,$\{1,\dots,k-1,k+1\}$ and $\{k,k+1,\dots,2k-1\}$. Recently, Bowtell, Kathapurkar, Morrison and Mycroft [arXiv: 2505.05606] established the exact minimum codegree threshold for perfect $T_3$-tilings in $3$-graphs. In this paper, we extend their result to all $k \geq 3$, determining the optimal minimum codegree threshold for perfect $T_k$-tilings in $k$-graphs…

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Kangyu Wang, Hongliang He, Lin Liu, Ruiqi Liang, Zhenzhong Lan, Jianguo Li
https://arxiv.org/abs/2508.11452

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have ushered in a new era of AI capabilities, demonstrating near-human-level performance across diverse scenarios. While numerous benchmarks (e.g., MMLU) and leaderboards (e.g., Chatbot Arena) have been proposed to help evolve the development of LLMs and MLLMs, most rely on static datasets or crowdsourced general-domain prompts, often falling short of reflecting performance in real-world applications. To bridge this criti…

The Compositional Architecture of Regret in Large Language Models
Xiangxiang Cui, Shu Yang, Tianjin Huang, Wanyu Lin, Lijie Hu, Di Wang
https://arxiv.org/abs/2506.15617

The Compositional Architecture of Regret in Large Language Models
Regret in Large Language Models refers to their explicit regret expression when presented with evidence contradicting their previously generated misinformation. Studying the regret mechanism is crucial for enhancing model reliability and helps in revealing how cognition is coded in neural networks. To understand this mechanism, we need to first identify regret expressions in model outputs, then analyze their internal representation. This analysis requires examining the model's hidden states, wh…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/1]:
- 3D Reconstruction of Coronary Vessel Trees from Biplanar X-Ray Images Using a Geometric Approach
Ethan Koland, Lin Xi, Nadeev Wijesuriya, YingLiang Ma

ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Yufeng Zhang, Wenrui Dai, Hang Yu, Shizhan Liu, Junhui Hou, Jianguo Li, Weiyao Lin
https://arxiv.org/abs/2506.15228

ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Neural Image Compression (NIC) has revolutionized image compression with its superior rate-distortion performance and multi-task capabilities, supporting both human visual perception and machine vision tasks. However, its widespread adoption is hindered by substantial computational demands. While existing approaches attempt to address this challenge through module-specific optimizations or pre-defined complexity levels, they lack comprehensive control over computational complexity. We present A…

A Recursive Total Least Squares Solution for Bearing-Only Target Motion Analysis and Circumnavigation
Lin Li, Xueming Liu, Zhoujingzi Qiu, Tianjiang Hu, Qingrui Zhang
https://arxiv.org/abs/2508.11289

A Recursive Total Least Squares Solution for Bearing-Only Target Motion Analysis and Circumnavigation
Bearing-only Target Motion Analysis (TMA) is a promising technique for passive tracking in various applications as a bearing angle is easy to measure. Despite its advantages, bearing-only TMA is challenging due to the nonlinearity of the bearing measurement model and the lack of range information, which impairs observability and estimator convergence. This paper addresses these issues by proposing a Recursive Total Least Squares (RTLS) method for online target localization and tracking using mo…

AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
John Y. Kim, Chaoshun Zuo, Yanjie Zhao, Zhiqiang Lin
https://arxiv.org/abs/2508.12187

AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
The rise of Virtual Reality (VR) has provided developers with an unprecedented platform for creating games and applications (apps) that require distinct inputs, different from those of conventional devices like smartphones. The Meta Quest VR platform, driven by Meta, has democratized VR app publishing and attracted millions of users worldwide. However, as the number of published apps grows, there is a notable lack of robust headless tools for user interface (UI) exploration and user event testi…

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Junyan Jiang, Daniel Chin, Liwei Lin, Xuanjie Liu, Gus Xia
https://arxiv.org/abs/2506.15548

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality itself, e.g., as sequences of symbolic notes. This observation enables both understanding tasks (e.g., chord recognition) and conditional generation tasks (e.g., chord-conditioned melody generation) to be unified under a music-for-music sequence modeling paradigm. In this work, we propose parameter-efficient solutions fo…

E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Ronghao Lin, Shuai Shen, Weipeng Hu, Qiaolin He, Aolin Xiong, Li Huang, Haifeng Hu, Yap-peng Tan
https://arxiv.org/abs/2508.12854

E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved text-based ERG, challenges remain in handling multimodal emotional content and maintaining identity consistency. Thus, we propose E3RG, an Explicit Emotion-driven Empathetic Response Generation System based on multimodal LLMs which decomposes MERG task into three parts: multimodal empathy understanding, empathy memory r…

Understanding the Fundamental Trade-Off Between Age of Information and Throughput in Unreliable Wireless Networks
Lin Wang, I-Hong Hou
https://arxiv.org/abs/2508.12185 https://

Understanding the Fundamental Trade-Off Between Age of Information and Throughput in Unreliable Wireless Networks
This paper characterizes the fundamental trade-off between throughput and Age of Information (AoI) in wireless networks where multiple devices transmit status updates to a central base station over unreliable channels. To address the complexity introduced by stochastic transmission successes, we propose the throughput-AoI capacity region, which defines all feasible throughput-AoI pairs achievable under any scheduling policy. Using a second-order approximation that incorporates both mean and tem…

Inverse eigenvalue problem for discrete Schr\"odinger operators of a graph
Anzila Laikhuram, Jephian C. -H. Lin
https://arxiv.org/abs/2506.15430 https…

Inverse eigenvalue problem for discrete Schrödinger operators of a graph
A discrete Schrödinger operator of a graph $G$ is a real symmetric matrix whose $i,j$-entry, $i \neq j$, is negative if $\{i,j\}$ is an edge and zero if it is not an edge, while diagonal entries can be any real numbers. The discrete Schrödinger operators have been used to study vibration theory and the Colin de Verdière parameter. The inverse eigenvalue problem for discrete Schrödinger operators of a graph aims to characterize the possible spectra among discrete Schrödinger operators of a …

Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection
Chi Wang, Min Gao, Zongwei Wang, Junwei Yin, Kai Shu, Chenghua Lin
https://arxiv.org/abs/2508.12632 …

Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection
With the rapid development of large language models, the generation of fake news has become increasingly effortless, posing a growing societal threat and underscoring the urgent need for reliable detection methods. Early efforts to identify LLM-generated fake news have predominantly focused on the textual content itself; however, because much of that content may appear coherent and factually consistent, the subtle traces of falsification are often difficult to uncover. Through distributional di…

Massive stars exploding in a He-rich circumstellar medium. XI. Diverse evolution of five Ibn SNe 2020nxt, 2020taz, 2021bbv, 2023utc and 2024aej
Z. -Y. Wang, A. Pastorello, Y. -Z. Cai, M. Fraser, A. Reguitti, W. -L. Lin, L. Tartaglia, D. Andrew Howell, S. Benetti, E. Cappellaro, Z. -H. Chen, N. Elias-Rosa, J. Farah, A. Fiore, D. Hiramatsu, E. Kankare, Z. -T. Li, P. Lundqvist, P. A. Mazzali, C. McCully, J. Mo, S. Moran, M. Newsome, E. Padilla Gonzalez, C. Pellegrino, Z. -H. Peng, S. J. S…

Massive stars exploding in a He-rich circumstellar medium. XI. Diverse evolution of five Ibn SNe 2020nxt, 2020taz, 2021bbv, 2023utc and 2024aej
We present the photometric and spectroscopic analysis of five Type Ibn supernovae (SNe): SN 2020nxt, SN 2020taz, SN 2021bbv, SN 2023utc, and SN 2024aej. These events share key observational features and belong to a family of objects similar to the prototypical Type Ibn SN 2006jc. The SNe exhibit rise times of approximately 10 days and peak absolute magnitudes ranging from $-$16.5 to $-$19 mag. Notably, SN 2023utc is the faintest Type Ibn supernova discovered to date, with an exceptionally low r…

SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
Zichen Yan, Rui Huang, Lei He, Shao Guo, Lin Zhao
https://arxiv.org/abs/2508.12394 http…

SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
Image-goal navigation (ImageNav) tasks a robot with autonomously exploring an unknown environment and reaching a location that visually matches a given target image. While prior works primarily study ImageNav for ground robots, enabling this capability for autonomous drones is substantially more challenging due to their need for high-frequency feedback control and global localization for stable flight. In this paper, we propose a novel sim-to-real framework that leverages visual reinforcement l…

CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
Maosheng Qin, Renyu Zhu, Mingxuan Xia, Chenkai Chen, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang
https://arxiv.org/abs/2509.14030

CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources-including Large Language Models (LLMs), Small Language Models (SLMs), and human experts-they often focus narrowly on the labeling step itself. A critical gap remains in the holistic process control required to manage these sources dynamically, addressing complex scheduling and quality-cost trade-offs in a unified manner. Inspired by real-wor…

MCP-Guard: A Defense Framework for Model Context Protocol Integrity in Large Language Model Applications
Wenpeng Xing, Zhonghao Qi, Yupeng Qin, Yilin Li, Caini Chang, Jiahui Yu, Changting Lin, Zhenzhen Xie, Meng Han
https://arxiv.org/abs/2508.10991

MCP-Guard: A Defense Framework for Model Context Protocol Integrity in Large Language Model Applications
The integration of Large Language Models (LLMs) with external tools via protocols such as the Model Context Protocol (MCP) introduces critical security vulnerabilities, including prompt injection, data exfiltration, and other threats. To counter these challenges, we propose MCP-Guard, a robust, layered defense architecture designed for LLM--tool interactions. MCP-Guard employs a three-stage detection pipeline that balances efficiency with accuracy: it progresses from lightweight static scanning…

Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Yong Deng, Guoqing Wang, Zhenzhe Ying, Xiaofeng Wu, Jinzhen Lin, Wenwen Xiong, Yuqin Dai, Shuo Yang, Zhanwei Zhang, Qiwen Wang, Yang Qin, Changhua Meng
https://arxiv.org/abs/2508.12800

Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Large language models (LLMs) exhibit remarkable problem-solving abilities, but struggle with complex tasks due to static internal knowledge. Retrieval-Augmented Generation (RAG) enhances access to external information, yet remains limited in multi-hop reasoning and strategic search due to rigid workflows. Recent advancements in agentic deep research empower LLMs to autonomously reason, search, and synthesize information. However, current approaches relying on outcome-based reinforcement learnin…

MeanFlowSE: one-step generative speech enhancement via conditional mean flow
Duojia Li, Shenghui Lu, Hongchen Pan, Zongyi Zhan, Qingyang Hong, Lin Li
https://arxiv.org/abs/2509.14858

MeanFlowSE: one-step generative speech enhancement via conditional mean flow
Multistep inference is a bottleneck for real-time generative speech enhancement because flow- and diffusion-based systems learn an instantaneous velocity field and therefore rely on iterative ordinary differential equation (ODE) solvers. We introduce MeanFlowSE, a conditional generative model that learns the average velocity over finite intervals along a trajectory. Using a Jacobian-vector product (JVP) to instantiate the MeanFlow identity, we derive a local training objective that directly sup…

Scale Up Analysis of Inductively Heated Metamaterial Reactors
Chenghao Wan, Conner Cremers, Ariana B. H\"ofelmann, Zhennan Ru, Calvin H. Lin, Kesha N. Tamakuwala, Dolly Mantle, Pinak Mohapatra, Juan Rivas-Davila, Matthew W. Kanan, Jonathan A. Fan
https://arxiv.org/abs/2509.13719

Scale Up Analysis of Inductively Heated Metamaterial Reactors
Inductively heated metamaterial reactors, which utilize an open cell lattice baffle structure as a heating susceptor for magnetic induction, are promising candidates for scaled electrified thermochemical reactor operation due to their ability to support volumetric heating profiles and enhanced heat transfer properties. In this work, we present a systematic scale up analysis of inductive metamaterial reactors where we utilize a combination of analytic modeling, numerical simulations, and experim…

GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance
Jinquan Shi, Yingying Cheng, Fan Zhang, Miao Jiang, Jun Lin, Yanbai Shen
https://arxiv.org/abs/2508.12682

GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance
The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and compliance increasingly vital. Grid codes, the regulations governing grid operations, are complex and often lack automated interpretation solutions, which hinders industry expansion and undermines profitability for electricity companies. We introduce GridCodex, an end to end framework for grid code reasoning and compliance that leverages large language models…

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems
Rui Huang, Zhiyu Gao, Siyu Tang, Jialin Zhang, Lei He, Ziqian Zhang, Lin Zhao
https://arxiv.org/abs/2509.14025

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems
Modular Aerial Robot Systems (MARS) consist of multiple drone modules that are physically bound together to form a single structure for flight. Exploiting structural redundancy, MARS can be reconfigured into different formations to mitigate unit or rotor failures and maintain stable flight. Prior work on MARS self-reconfiguration has solely focused on maximizing controllability margins to tolerate a single rotor or unit fault for rectangular-shaped MARS. We propose TransforMARS, a general fault…

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Zhenhua Xu, Xubin Yue, Zhebo Wang, Qichen Liu, Xixiang Zhao, Jingxuan Zhang, Wenjun Zeng, Wengpeng Xing, Dezhang Kong, Changting Lin, Meng Han
https://arxiv.org/abs/2508.11548

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, mo…

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Jiazheng Li, Hong Lu, Kaiyue Wen, Zaiwen Yang, Jiaxuan Gao, Hongzhou Lin, Yi Wu, Jingzhao Zhang
https://arxiv.org/abs/2507.13266

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Reinforcement learning (RL) has become a key component in training large language reasoning models (LLMs). However, recent studies questions its effectiveness in improving multi-step reasoning-particularly on hard problems. To address this challenge, we propose a simple yet effective strategy via Question Augmentation: introduce partial solutions during training to reduce problem difficulty and provide more informative learning signals. Our method, QuestA, when applied during RL training on mat…

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis
Haitong Luo, Weiyao Zhang, Suhang Wang, Wenji Zou, Chungang Lin, Xuying Meng, Yujun Zhang
https://arxiv.org/abs/2508.11343

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis
The proliferation of high-quality text from Large Language Models (LLMs) demands reliable and efficient detection methods. While existing training-free approaches show promise, they often rely on surface-level statistics and overlook fundamental signal properties of the text generation process. In this work, we reframe detection as a signal processing problem, introducing a novel paradigm that analyzes the sequence of token log-probabilities in the frequency domain. By systematically analyzing …

Crosslisted article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Lifelong Learner: Discovering Versatile Neural Solvers for Vehicle Routing Problems
Feng, Lin, Zhou, Zhang, Li, Chen, Jayavelu, Ong

A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Jinyi Han, Xinyi Wang, Haiquan Zhao, Tingyun li, Zishang Jiang, Sihang Jiang, Jiaqing Liang, Xin Lin, Weikang Zhou, Zeye Sun, Fei Yu, Yanghua Xiao
https://arxiv.org/abs/2508.12903

A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most existing self-refinement methods rely on a reactive process with a fixed number of iterations, making it difficult to determine the optimal timing and content of refinement based on the evolving generation context. Inspired by the way humans dynamically refine their thoughts during execution, we propose ProActive Self-Re…

Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes
Tyler Loakman, William Thorne, Chenghua Lin
https://arxiv.org/abs/2507.13335

Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes
Humour, as a complex language form, is derived from myriad aspects of life, whilst existing work on computational humour has focussed almost exclusively on short pun-based jokes. In this work, we investigate whether the ability of Large Language Models (LLMs) to explain humour depends on the particular humour form. We compare models on simple puns and more complex topical humour that requires knowledge of real-world entities and events. In doing so, we curate a dataset of 600 jokes split across…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/4]:
- Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers
Tzu-Quan Lin, Tsung-Huan Yang, Chun-Yao Chang, Kuang-Ming Chen, Tzu-hsun Feng, Hung-yi Lee, Hao Tang