Tootfinder

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Large Language Model Powered Symbolic Execution
Yihe Li, Ruijie Meng, Gregory J. Duck

edit_wikibooks: Wikipedia book edits (2010)
Two bipartite user-page networks extracted from Wikipedia, about books. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 3308 nodes and 7700 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Week 3 inactives: Who's in and who's out? https://www.espn.com/fantasy/football/story/_/page/FFSundayInactives-46311253/nfl-fantasy-football-espn-injured-inactives-watchlist-week-3-…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/3]:
- MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling
Qian Wang, Ziqi Huang, Ruoxi Jia, Paul Debevec, Ning Yu

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- Is neural semantic parsing good at ellipsis resolution, or isn't it?
Xiao Zhang, Johan bos

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- Affine Frobenius Brauer Categories
Saima Samchuck-Schnarch
https://

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Neural reproducing kernel Banach spaces and representer theorems for deep networks
Francesca Bartolucci, Ernesto De Vito, Lorenzo Rosasco, Stefano Vigogna

Replaced article(s) found for gr-qc. https://arxiv.org/list/gr-qc/new
[1/1]:
- Completely Deformed Complexity-free Anisotropic Fluid Sphere
Z. Yousaf, Kazuharu Bamba, M. Z. Bhatti, S. Khan

Replaced article(s) found for math.FA. https://arxiv.org/list/math.FA/new
[1/1]:
- The Topology of the Unitary Dual of Crystallography Groups
Frankie Chan, Ellen Weld

Replaced article(s) found for physics.ins-det. https://arxiv.org/list/physics.ins-det/new
[1/1]:
- High Voltage Delivery and Distribution for the NEXT-100 Time Projection Chamber
NEXT Collaboration, et al.

Replaced article(s) found for math.AG. https://arxiv.org/list/math.AG/new
[1/1]:
- Towards the dual motivic Steenrod algebra in positive characteristic
Martin Frankland, Markus Spitzweck

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Poisson Midpoint Method for Log Concave Sampling: Beyond the Strong Error Lower Bounds
Rishikesh Srinivasan, Dheeraj Nagaraj

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
- When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs
Kim, Dingeto, Kwon, Choi, Lee, Park, Lee, Shin

Replaced article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- Chernoff Information as a Privacy Constraint for Adversarial Classification and Membership Advantage
Ay\c{s}e \"Unsal

Replaced article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- Stop Playing the Guessing Game! Target-free User Simulation for Evaluating Conversational Recomme...
Sunghwan Kim, Tongyoung Kim, Kwangwook Seo, Jinyoung Yeo, Dongha Lee

Breakable Machine: A K-12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)
Olli Hilke, Nicolas Pope, Juho Kahila, Henriikka Vartiainen, Teemu Roos, Tuomo Parkki, Matti Tedre
https://arxiv.org/abs/2508.14201

Breakable Machine: A K-12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)
This paper, submitted to the special track on resources for teaching AI in K-12, presents an eXplainable AI (XAI)-based classroom game "Breakable Machine" for teaching critical, transformative AI literacy through adversarial play and interrogation of AI systems. Designed for learners aged 10-15, the game invites students to spoof an image classifier by manipulating their appearance or environment in order to trigger high-confidence misclassifications. Rather than focusing on building AI models,…

edit_wikibooks: Wikipedia book edits (2010)
Two bipartite user-page networks extracted from Wikipedia, about books. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 328993 nodes and 2233443 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/3]:
- VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
Zheng, Huang, Liu, Zou, He, Zhang, Gu, Zhang, He, Zheng, Qiao, Liu

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
Gao, Pi, Zhang, Ye, Zhong, Wang, Hong, Han, Xu, Li, Kong

Crosslisted article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Evaluating the Limitations of Local LLMs in Solving Complex Programming Challenges
Kadin Matotek, Heather Cassel, Md Amiruzzaman, Linh B. Ngo

Crosslisted article(s) found for physics.ins-det. https://arxiv.org/list/physics.ins-det/new
[1/1]:
- Simultaneous Speedmeter and Position-Meter Response in a Single Tabletop Interferometer
Mikhail Korobko, Xiang Li, Torben Sobottke, Yiqiu Ma, Yanbei Chen, Roman Schnabel

Similarities of subspace lattices in Banach spaces
Janko Bra\v{c}i\v{c}, Marko Kandi\'c
https://arxiv.org/abs/2508.14603 https://arxiv.org/pdf/2508.146…

Similarities of subspace lattices in Banach spaces
A collineation of a subspace lattice $\fL$ in a complex Banach space $\eX$ is an invertible operator $S$ on $\eX$ with the property that the image $S\eM$ of a subspace $\eM$ belongs to $\fL$ if and and only if $\eM$ belongs to it. Hence, $S$ is a collineation of $\fL$ if and only if it implements an order automorphism of $\fL$. We study the group $\Col(\fL)$ of all collineations of $\fL$ and its subgroup $\Grp(\Alg(\fL))$ of all invertible operators that fix every subspace in $\fL$. We show tha…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- The Kikuchi Hierarchy and Tensor PCA
Alexander S. Wein, Ahmed El Alaoui, Cristopher Moore

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/4]:
- FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for \"U-Tsang, Amdo and K...
Liu, Zhang, Ma-bao, Cai, Yu, Duojie, Wang, Gao, Huang, Tashi

3D Near-Field Beam Training for Uniform Planar Arrays through Beam Diverging
Ran Li, Ziyi Xu, Ying-Jun Angela Zhang
https://arxiv.org/abs/2509.16055 https://

3D Near-Field Beam Training for Uniform Planar Arrays through Beam Diverging
In future 6G communication systems, large-scale antenna arrays promise enhanced signal strength and spatial resolution, but they also increase the complexity of beam training. Moreover, as antenna counts grow and carrier wavelengths shrink, the channel model transits from far-field (FF) planar waves to near-field (NF) spherical waves, further complicating the beam training process. This paper focuses on millimeter-wave (mmWave) systems equipped with large-scale uniform planar arrays (UPAs), whi…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/3]:
- Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
Yifan Zhang, Junhui Hou, Siyu Ren, Jinjian Wu, Yixuan Yuan, Guangming Shi

Automatic layout of railroad diagrams
Shardul Chiplunkar, Cl\'ement Pit-Claudel
https://arxiv.org/abs/2509.15834 https://arxiv.org/pdf/2509.15834

Automatic layout of railroad diagrams
Railroad diagrams (also called "syntax diagrams") are a common, intuitive visualization of grammars, but limited tooling and a lack of formal attention to their layout mostly confines them to hand-drawn documentation. We present the first formal treatment of railroad diagram layout along with a principled, practical implementation. We characterize the problem as compiling a *diagram language* (specifying conceptual components and how they connect and compose) to a *layout language* (specifying …

Barnsley-Navascu\'es fractal operators on Banach spaces on the Sierpi\'nski gasket
Asheesh Kumar Yadav, Himanshu Kumar, Saurabh Verma, Bilel Selmi
https://arxiv.org/abs/2508.14436

Barnsley-Navascués fractal operators on Banach spaces on the Sierpiński gasket
In this article, we define fractal operators motivated by the works of Barnsley and Navascués on various function spaces such as energy space, Lebesgue space, and oscillation space on the well-known fractal domain Sierpiński gasket. We further explore the properties of these operators from the perspectives of operator and approximation theory.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections
Frederik L. Dennig, Nina Geyer, Daniela Blumberg, Yannick Metz, Daniel A. Keim

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness
Ningyi Liao, Haoyu Liu, Zulun Zhu, Siqiang Luo, Laks V. S. Lakshmanan

Repeating Words for Video-Language Retrieval with Coarse-to-Fine Objectives
Haoyu Zhao, Jiaxi Gu, Shicong Wang, Xing Zhang, Hang Xu, Zuxuan Wu, Yu-Gang Jiang
https://arxiv.org/abs/2508.14812

Repeating Words for Video-Language Retrieval with Coarse-to-Fine Objectives
The explosive growth of video streaming presents challenges in achieving high accuracy and low training costs for video-language retrieval. However, existing methods rely on large-scale pre-training to improve video retrieval performance, resulting in significant computational demands. Additionally, the fine-grained information in videos and texts remains underexplored. To alleviate these problems, we propose a novel framework to learn fine-grained features for better alignment and introduce an…

[2025-09-22 Mon (UTC), 1 new article found for cs.PL Programming Languages]
toXiv_bot_toot

[2025-08-21 Thu (UTC), 4 new articles found for math.FA Functional Analysis]
toXiv_bot_toot

Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization
Canyu Zhao, Xiaoman Li, Tianjian Feng, Zhiyue Zhao, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2508.14811

Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization
We introduce Tinker, a versatile framework for high-fidelity 3D editing that operates in both one-shot and few-shot regimes without any per-scene finetuning. Unlike prior techniques that demand extensive per-scene optimization to ensure multi-view consistency or to produce dozens of consistent edited input views, Tinker delivers robust, multi-view consistent edits from as few as one or two images. This capability stems from repurposing pretrained diffusion models, which unlocks their latent 3D …

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Lucic, Ahmad, Brinhosa, Liao, Agrawal, Bhatt, Kenthapadi, Xiang, de Rijke, Drabowski

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/4]:
- Benchmarking graph construction by large language models for coherence-driven inference
Steve Huntsman, Jewell Thomas

DINOv3 with Test-Time Training for Medical Image Registration
Shansong Wang, Mojtaba Safari, Mingzhe Hu, Qiang Li, Chih-Wei Chang, Richard LJ Qiu, Xiaofeng Yang
https://arxiv.org/abs/2508.14809

DINOv3 with Test-Time Training for Medical Image Registration
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this limitation, we propose a training-free pipeline that relies on a frozen DINOv3 encoder and test-time optimization of the deformation field in feature space. Across two representative benchmarks, the method is accurate and yields regular deformations. On Abdomen MR-CT, it attained the best mean Dice score (DSC) of 0.7…

Crosslisted article(s) found for math.FA. https://arxiv.org/list/math.FA/new
[1/1]:
- A comparative study of some wavelet and sampling operators on various features of an image
Digvijay Singh, Rahul Shukla, Karunesh Kumar Singh

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- On the Fundamental Impossibility of Hallucination Control in Large Language Models
Micha{\l} P. Karpowicz

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/6]:
- EmoHeal: An End-to-End System for Personalized Therapeutic Music Retrieval from Fine-grained Emot...
Xinchen Wan, Jinhua Liang, Huan Zhang

MF-LPR$^2$: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow
Kihyun Na, Junseok Oh, Youngkwan Cho, Bumjin Kim, Sungmin Cho, Jinyoung Choi, Injung Kim
https://arxiv.org/abs/2508.14797

MF-LPR$^2$: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow
License plate recognition (LPR) is important for traffic law enforcement, crime investigation, and surveillance. However, license plate areas in dash cam images often suffer from low resolution, motion blur, and glare, which make accurate recognition challenging. Existing generative models that rely on pretrained priors cannot reliably restore such poor-quality images, frequently introducing severe artifacts and distortions. To address this issue, we propose a novel multi-frame license plate re…

Replaced article(s) found for math.FA. https://arxiv.org/list/math.FA/new
[1/1]:
- Perturbations of embedded eigenvalues of asymptotically periodic magnetic Schr\"odinger operators...
Jonas Jansen, Sara Maad Sasane, Wilhelm Treschow

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Diffusion MRI with Machine Learning
Davood Karimi, Simon K. Warfield
https://

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/6]:
- DeepMech: A Machine Learning Framework for Chemical Reaction Mechanism Prediction
Manajit Das, Ajnabiul Hoque, Mayank Baranwal, Raghavan B. Sunoj

Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification
Mengliang Zhang, Jacob M. Luber
https://arxiv.org/abs/2508.14779 https://arxiv…

Adversarial Hospital-Invariant Feature Learning for WSI Patch Classification
Pathology foundation models (PFMs) have demonstrated remarkable potential in whole-slide image (WSI) diagnosis. However, pathology images from different hospitals often vary due to differences in scanning hardware and preprocessing styles, which may lead PFMs to inadvertently learn hospital-specific features, posing risks for clinical deployment. In this work, we present the first systematic study of domain bias in PFMs arising from hospital source characteristics. Specifically, we (1) construc…

Crosslisted article(s) found for math.FA. https://arxiv.org/list/math.FA/new
[1/1]:
- Uncertainty Relation for the Wigner-Yanase Skew Information and Quantum Sobolev Inequalities
Laurent Lafleche

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony
James Bagrow, Josh Bongard

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/6]:
- SightSound-R1: Cross-Modal Reasoning Distillation from Vision to Audio Language Models
Qiaolin Wang, Xilin Jiang, Linyang He, Junkai Wu, Nima Mesgarani

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- Robust Sparse Mean Estimation via Incremental Learning
Jianhao Ma, Rui Ray Chen, Yinghui He, Salar Fattahi, Wei Hu

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/6]:
- Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems
Reza Pirayeshshirazinezhad, Nima Fathi

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- MaskSDM with Shapley values to improve flexibility, robustness, and explainability in species dis...
Robin Zbinden, Nina van Tiel, Gencer Sumbul, Chiara Vanalli, Benjamin Kellenberger, Devis Tuia

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/6]:
- Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference ...
Zichen Wu, Hsiu-Yuan Huang, Yunfang Wu

Understanding Data Influence with Differential Approximation
Haoru Tan, Sitong Wu, Xiuzhe Wu, Wang Wang, Bo Zhao, Zeke Xie, Gui-Song Xia, Xiaojuan Qi
https://arxiv.org/abs/2508.14648

Understanding Data Influence with Differential Approximation
Data plays a pivotal role in the groundbreaking advancements in artificial intelligence. The quantitative analysis of data significantly contributes to model training, enhancing both the efficiency and quality of data utilization. However, existing data analysis tools often lag in accuracy. For instance, many of these tools even assume that the loss function of neural networks is convex. These limitations make it challenging to implement current methods effectively. In this paper, we introduce …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- AURA: A Fine-Grained Benchmark and Decomposed Metric for Audio-Visual Reasoning
Siminfar Samakoush Galougah, Rishie Raj, Sanjoy Chowdhury, Sayan Nag, Ramani Duraiswami

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/6]:
- Pre-Forgettable Models: Prompt Learning as a Native Mechanism for Unlearning
Hendrix, Patan\`e, Russo, Carnemolla, Bellitto, Salanitri, Spampinato, Pennisi

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
Salman Habib, Remi Chou, Taejoon Kim
https://arxiv.org/abs/2508.15523 https://

Stabilization of Perturbed Loss Function: Differential Privacy without Gradient Noise
We propose SPOF (Stabilization of Perturbed Loss Function), a differentially private training mechanism intended for multi-user local differential privacy (LDP). SPOF perturbs a stabilized Taylor expanded polynomial approximation of a model's training loss function, where each user's data is privatized by calibrated noise added to the coefficients of the polynomial. Unlike gradient-based mechanisms such as differentially private stochastic gradient descent (DP-SGD), SPOF does not require inject…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- Omni$^2$: Unifying Omnidirectional Image Generation and Editing in an Omni Model
Yang, Duan, Zhu, Liu, Liu, Xu, Ma, Min, Zhai, Le Callet

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
Choi, Kwon, Lopez-Lira, Kim, Kim, Hwang, Ha, Choi, Yun, Kim, Lee

Jointly Computation- and Communication-Efficient Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson, Thomas Parisini
https://arxiv.org/abs/2508.15509 https…

Jointly Computation- and Communication-Efficient Distributed Learning
We address distributed learning problems over undirected networks. Specifically, we focus on designing a novel ADMM-based algorithm that is jointly computation- and communication-efficient. Our design guarantees computational efficiency by allowing agents to use stochastic gradients during local training. Moreover, communication efficiency is achieved as follows: i) the agents perform multiple training epochs between communication rounds, and ii) compressed transmissions are used. We prove exac…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Translating Images to Road Network: A Sequence-to-Sequence Perspective
Jiachen Lu, Ming Nie, Bozhou Zhang, Reyuan Peng, Xinyue Cai, Hang Xu, Feng Wen, Wei Zhang, Li Zhang

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles
Ng, Hsu, Ramakrishnan, Kveton, Lipka, Dernoncourt, Lee, Yu, Kim, Rossi, Huang

Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links
Jiahua Lu, Huaxiao Liu, Shuotong Bai, Junjie Xu, Renqiang Luo, Enyan Dai
https://arxiv.org/abs/2508.15499

Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links
Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications. However, due to the biases in the graph structures, graph neural networks face significant challenges in fairness. Although the original user graph structure is generally biased, it is promising to guide these existing structures toward unbiased ones by introducing new links. The fairness guidance via new links could foster unbiased communities, thereby enhancing fairness in downstream applications. To ad…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language
Xi Chen, Shuo Wang

MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration
Fulden Ece U\u{g}ur, Rafael Redondo, Albert Barreiro, Stefan Hristov, Roger Mar\'i
https://arxiv.org/abs/2508.15500

MExECON: Multi-view Extended Explicit Clothed humans Optimized via Normal integration
This work presents MExECON, a novel pipeline for 3D reconstruction of clothed human avatars from sparse multi-view RGB images. Building on the single-view method ECON, MExECON extends its capabilities to leverage multiple viewpoints, improving geometry and body pose estimation. At the core of the pipeline is the proposed Joint Multi-view Body Optimization (JMBO) algorithm, which fits a single SMPL-X body model jointly across all input views, enforcing multi-view consistency. The optimized body …

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/2]:
- Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
Haitong Ma, Bo Dai, Zhaolin Ren, Yebin Wang, Na Li

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- Unplug and Play Language Models: Decomposing Experts in Language Models at Inference Time
Nakyeong Yang, Jiwon Moon, Junseok Kim, Yunah Jang, Kyomin Jung

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Chengqi Dong, Fenghe Tang, Rongge Mao, Xinpei Gao, S. Kevin Zhou
https://arxiv.org/abs/2508.15476

LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion
Medical image segmentation plays a pivotal role in disease diagnosis and treatment planning, particularly in resource-constrained clinical settings where lightweight and generalizable models are urgently needed. However, existing lightweight models often compromise performance for efficiency and rarely adopt computationally expensive attention mechanisms, severely restricting their global contextual perception capabilities. Additionally, current architectures neglect the channel redundancy issu…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- CRISPR-GPT for Agentic Automation of Gene-editing Experiments
Qu, Huang, Yin, Zhan, Liu, Yin, Cousins, Johnson, Wang, Shah, Altman, Zhou, Wang, Cong

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine
Chen, Cai, Liu, Yang, Wang, Xiao, Feng, Su, Guo, Wan, Yu, Li, Wang

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- From Image Captioning to Visual Storytelling
Admitos Passadakis, Yingjin Song, Albert Gatt

Virtual Community: An Open World for Humans, Robots, and Society
Qinhong Zhou, Hongxin Zhang, Xiangye Lin, Zheyuan Zhang, Yutian Chen, Wenjun Liu, Zunzhe Zhang, Sunli Chen, Lixing Fang, Qiushi Lyu, Xinyu Sun, Jincheng Yang, Zeyuan Wang, Bao Chi Dang, Zhehuan Chen, Daksha Ladia, Jiageng Liu, Chuang Gan
https://arxiv.org/abs/2508.14893

Virtual Community: An Open World for Humans, Robots, and Society
The rapid progress in AI and Robotics may lead to a profound societal transformation, as humans and robots begin to coexist within shared communities, introducing both opportunities and challenges. To explore this future, we present Virtual Community-an open-world platform for humans, robots, and society-built on a universal physics engine and grounded in real-world 3D scenes. With Virtual Community, we aim to study embodied social intelligence at scale: 1) How robots can intelligently cooperat…

GaussianArt: Unified Modeling of Geometry and Motion for Articulated Objects
Licheng Shen, Saining Zhang, Honghan Li, Peilin Yang, Zihao Huang, Zongzheng Zhang, Hao Zhao
https://arxiv.org/abs/2508.14891

GaussianArt: Unified Modeling of Geometry and Motion for Articulated Objects
Reconstructing articulated objects is essential for building digital twins of interactive environments. However, prior methods typically decouple geometry and motion by first reconstructing object shape in distinct states and then estimating articulation through post-hoc alignment. This separation complicates the reconstruction pipeline and restricts scalability, especially for objects with complex, multi-part articulation. We introduce a unified representation that jointly models geometry and …

MS-CLR: Multi-Skeleton Contrastive Learning for Human Action Recognition
Mert Kiray, Alvaro Ritter, Nassir Navab, Benjamin Busam
https://arxiv.org/abs/2508.14889 https://…

MS-CLR: Multi-Skeleton Contrastive Learning for Human Action Recognition
Contrastive learning has gained significant attention in skeleton-based action recognition for its ability to learn robust representations from unlabeled data. However, existing methods rely on a single skeleton convention, which limits their ability to generalize across datasets with diverse joint structures and anatomical coverage. We propose Multi-Skeleton Contrastive Learning (MS-CLR), a general self-supervised framework that aligns pose representations across multiple skeleton conventions …

Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging
Lucas W. Remedios, Chloe Cho, Trent M. Schwartz, Dingjie Su, Gaurav Rudravaram, Chenyu Gao, Aravind R. Krishnan, Adam M. Saunders, Michael E. Kim, Shunxing Bao, Thomas A. Lasko, Alvin C. Powers, Bennett A. Landman, John Virostko
https://arxi…

Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging
Purpose: Understanding how the pancreas changes is critical for detecting deviations in type 2 diabetes and other pancreatic disease. We measure pancreas size and shape using morphological measurements from ages 0 to 90. Our goals are to 1) identify reliable clinical imaging modalities for AI-based pancreas measurement, 2) establish normative morphological aging trends, and 3) detect potential deviations in type 2 diabetes. Approach: We analyzed a clinically acquired dataset of 2533 patients …

EventSSEG: Event-driven Self-Supervised Segmentation with Probabilistic Attention
Lakshmi Annamalai, Chetan Singh Thakur
https://arxiv.org/abs/2508.14856 https://

EventSSEG: Event-driven Self-Supervised Segmentation with Probabilistic Attention
Road segmentation is pivotal for autonomous vehicles, yet achieving low latency and low compute solutions using frame based cameras remains a challenge. Event cameras offer a promising alternative. To leverage their low power sensing, we introduce EventSSEG, a method for road segmentation that uses event only computing and a probabilistic attention mechanism. Event only computing poses a challenge in transferring pretrained weights from the conventional camera domain, requiring abundant labeled…

TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
Zongming Li, Lianghui Zhu, Haocheng Shen, Longjin Ran, Wenyu Liu, Xinggang Wang
https://arxiv.org/abs/2508.14814

TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
Most existing illumination-editing approaches fail to simultaneously provide customized control of light effects and preserve content integrity. This makes them less effective for practical lighting stylization requirements, especially in the challenging task of transferring complex light effects from a reference image to a user-specified target image. To address this problem, we propose TransLight, a novel framework that enables high-fidelity and high-freedom transfer of light effects. Extract…

6-DoF Object Tracking with Event-based Optical Flow and Frames
Zhichao Li, Arren Glover, Chiara Bartolozzi, Lorenzo Natale
https://arxiv.org/abs/2508.14776 https://

6-DoF Object Tracking with Event-based Optical Flow and Frames
Tracking the position and orientation of objects in space (i.e., in 6-DoF) in real time is a fundamental problem in robotics for environment interaction. It becomes more challenging when objects move at high-speed due to frame rate limitations in conventional cameras and motion blur. Event cameras are characterized by high temporal resolution, low latency and high dynamic range, that can potentially overcome the impacts of motion blur. Traditional RGB cameras provide rich visual information tha…

Fusing Monocular RGB Images with AIS Data to Create a 6D Pose Estimation Dataset for Marine Vessels
Fabian Holst, Emre G\"ulsoylu, Simone Frintrop
https://arxiv.org/abs/2508.14767

Fusing Monocular RGB Images with AIS Data to Create a 6D Pose Estimation Dataset for Marine Vessels
The paper presents a novel technique for creating a 6D pose estimation dataset for marine vessels by fusing monocular RGB images with Automatic Identification System (AIS) data. The proposed technique addresses the limitations of relying purely on AIS for location information, caused by issues like equipment reliability, data manipulation, and transmission delays. By combining vessel detections from monocular RGB images, obtained using an object detection network (YOLOX-X), with AIS messages, t…

Improved Mapping Between Illuminations and Sensors for RAW Images
Abhijith Punnappurath, Luxi Zhao, Hoang Le, Abdelrahman Abdelhamed, SaiKiran Kumar Tedla, Michael S. Brown
https://arxiv.org/abs/2508.14730

Improved Mapping Between Illuminations and Sensors for RAW Images
RAW images are unprocessed camera sensor output with sensor-specific RGB values based on the sensor's color filter spectral sensitivities. RAW images also incur strong color casts due to the sensor's response to the spectral properties of scene illumination. The sensor- and illumination-specific nature of RAW images makes it challenging to capture RAW datasets for deep learning methods, as scenes need to be captured for each sensor and under a wide range of illumination. Methods for illuminatio…

Multiscale Video Transformers for Class Agnostic Segmentation in Autonomous Driving
Leila Cheshmi, Mennatullah Siam
https://arxiv.org/abs/2508.14729 https://

Multiscale Video Transformers for Class Agnostic Segmentation in Autonomous Driving
Ensuring safety in autonomous driving is a complex challenge requiring handling unknown objects and unforeseen driving scenarios. We develop multiscale video transformers capable of detecting unknown objects using only motion cues. Video semantic and panoptic segmentation often relies on known classes seen during training, overlooking novel categories. Recent visual grounding with large language models is computationally expensive, especially for pixel-level output. We propose an efficient vide…

GSFix3D: Diffusion-Guided Repair of Novel Views in Gaussian Splatting
Jiaxin Wei, Stefan Leutenegger, Simon Schaefer
https://arxiv.org/abs/2508.14717 https://

GSFix3D: Diffusion-Guided Repair of Novel Views in Gaussian Splatting
Recent developments in 3D Gaussian Splatting have significantly enhanced novel view synthesis, yet generating high-quality renderings from extreme novel viewpoints or partially observed regions remains challenging. Meanwhile, diffusion models exhibit strong generative capabilities, but their reliance on text prompts and lack of awareness of specific scene information hinder accurate 3D reconstruction tasks. To address these limitations, we introduce GSFix3D, a novel framework that improves the …

Seeing Further on the Shoulders of Giants: Knowledge Inheritance for Vision Foundation Models
Jiabo Huang, Chen Chen, Lingjuan Lyu
https://arxiv.org/abs/2508.14707 https://

Seeing Further on the Shoulders of Giants: Knowledge Inheritance for Vision Foundation Models
Vision foundation models (VFMs) are predominantly developed using data-centric methods. These methods require training on vast amounts of data usually with high-quality labels, which poses a bottleneck for most institutions that lack both large-scale data and high-end GPUs. On the other hand, many open-source vision models have been pretrained on domain-specific data, enabling them to distill and represent core knowledge in a form that is transferable across diverse applications. Even though th…

GeMS: Efficient Gaussian Splatting for Extreme Motion Blur
Gopi Raju Matta, Trisha Reddypalli, Vemunuri Divya Madhuri, Kaushik Mitra
https://arxiv.org/abs/2508.14682 https://

GeMS: Efficient Gaussian Splatting for Extreme Motion Blur
We introduce GeMS, a framework for 3D Gaussian Splatting (3DGS) designed to handle severely motion-blurred images. State-of-the-art deblurring methods for extreme blur, such as ExBluRF, as well as Gaussian Splatting-based approaches like Deblur-GS, typically assume access to sharp images for camera pose estimation and point cloud generation, an unrealistic assumption. Methods relying on COLMAP initialization, such as BAD-Gaussians, also fail due to unreliable feature correspondences under sever…

Towards PerSense : Advancing Training-Free Personalized Instance Segmentation in Dense Images
Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid, Kevin Henry, Muhammad Haris Khan
https://arxiv.org/abs/2508.14660

Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images
Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance Segmentation in dense images. PerSense employs a novel Instance Detection Module (IDM) that leverages density maps (DMs) to generate instance-level candidate point prompts, followed by a Point Prompt Selection Module (PPSM) that filters fa…

AnchorSync: Global Consistency Optimization for Long Video Editing
Zichi Liu, Yinggui Wang, Tao Wei, Chao Ma
https://arxiv.org/abs/2508.14609 https://arxiv…

AnchorSync: Global Consistency Optimization for Long Video Editing
Editing long videos remains a challenging task due to the need for maintaining both global consistency and temporal coherence across thousands of frames. Existing methods often suffer from structural drift or temporal artifacts, particularly in minute-long sequences. We introduce AnchorSync, a novel diffusion-based framework that enables high-quality, long-term video editing by decoupling the task into sparse anchor frame editing and smooth intermediate frame interpolation. Our approach enforce…

SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos
Pengzhi Zhong, Xinzhe Wang, Dan Zeng, Qihua Zhou, Feixiang He, Shuiwang Li
https://arxiv.org/abs/2508.14607

SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos
Brain-inspired Spiking Neural Networks (SNNs) exhibit significant potential for low-power computation, yet their application in visual tasks remains largely confined to image classification, object detection, and event-based tracking. In contrast, real-world vision systems still widely use conventional RGB video streams, where the potential of directly-trained SNNs for complex temporal tasks such as multi-object tracking (MOT) remains underexplored. To address this challenge, we propose SMTrack…

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Peiming Li, Ziyi Wang, Yulin Yuan, Hong Liu, Xiangming Meng, Junsong Yuan, Mengyuan Liu
https://arxiv.org/abs/2508.14604

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity, the spatio-temporal disorder of point cloud videos hinders their unidirectional modeling when directly unfolding the point cloud video into a 1D sequence through temporally sequential scann…

Incremental Object Detection with Prompt-based Methods
Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Pani Paudel, Luc Van Gool
https://arxiv.org/abs/2508.14599 https://

Incremental Object Detection with Prompt-based Methods
Visual prompt-based methods have seen growing interest in incremental learning (IL) for image classification. These approaches learn additional embedding vectors while keeping the model frozen, making them efficient to train. However, no prior work has applied such methods to incremental object detection (IOD), leaving their generalizability unclear. In this paper, we analyze three different prompt-based methods under a complex domain-incremental learning setting. We additionally provide a wide…

Reliable Smoke Detection via Optical Flow-Guided Feature Fusion and Transformer-Based Uncertainty Modeling
Nitish Kumar Mahala, Muzammil Khan, Pushpendra Kumar
https://arxiv.org/abs/2508.14597

Reliable Smoke Detection via Optical Flow-Guided Feature Fusion and Transformer-Based Uncertainty Modeling
Fire outbreaks pose critical threats to human life and infrastructure, necessitating high-fidelity early-warning systems that detect combustion precursors such as smoke. However, smoke plumes exhibit complex spatiotemporal dynamics influenced by illumination variability, flow kinematics, and environmental noise, undermining the reliability of traditional detectors. To address these challenges without the logistical complexity of multi-sensor arrays, we propose an information-fusion framework by…

Controllable Latent Space Augmentation for Digital Pathology
Sofi\`ene Boutaj, Marin Scalbert, Pierre Marza, Florent Couzinie-Devy, Maria Vakalopoulou, Stergios Christodoulidis
https://arxiv.org/abs/2508.14588

Controllable Latent Space Augmentation for Digital Pathology
Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multiple Instance Learning (MIL) is a natural fit for slide-level tasks, training robust models requires large and diverse datasets. Even though image augmentation techniques could be utilized to increase data variability and reduce overfitting, implementing them effectively is not a trivial task. Traditional patch-level au…

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
Walter Zimmer, Ross Greer, Xingcheng Zhou, Rui Song, Marc Pavel, Daniel Lehmberg, Ahmed Ghita, Akshay Gopalkrishnan, Mohan Trivedi, Alois Knoll
https://arxiv.org/abs/2508.14567

Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Dataset
Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as an unavoidable and sporadic outcome of traffic networks. We present the TUM Traffic Accident (TUMTraf-A) dataset, a collection of real-world highway accidents. It contains ten sequences of vehicle crashes at high-speed driving with 294,924 labeled 2D and 93,012 labeled 3D boxes and track IDs within 48,144 labeled frames recorded fr…

GOGS: High-Fidelity Geometry and Relighting for Glossy Objects via Gaussian Surfels
Xingyuan Yang, Min Wei
https://arxiv.org/abs/2508.14563 https://arxiv.o…

GOGS: High-Fidelity Geometry and Relighting for Glossy Objects via Gaussian Surfels
Inverse rendering of glossy objects from RGB imagery remains fundamentally limited by inherent ambiguity. Although NeRF-based methods achieve high-fidelity reconstruction via dense-ray sampling, their computational cost is prohibitive. Recent 3D Gaussian Splatting achieves high reconstruction efficiency but exhibits limitations under specular reflections. Multi-view inconsistencies introduce high-frequency surface noise and structural artifacts, while simplified rendering equations obscure mate…

Locality-aware Concept Bottleneck Model
Sujin Jeon, Hyundo Lee, Eungseo Kim, Sanghack Lee, Byoung-Tak Zhang, Inwoo Hwang
https://arxiv.org/abs/2508.14562 https://

Locality-aware Concept Bottleneck Model
Concept bottleneck models (CBMs) are inherently interpretable models that make predictions based on human-understandable visual cues, referred to as concepts. As obtaining dense concept annotations with human labeling is demanding and costly, recent approaches utilize foundation models to determine the concepts existing in the images. However, such label-free CBMs often fail to localize concepts in relevant regions, attending to visually unrelated regions when predicting concept presence. To th…

Making Pose Representations More Expressive and Disentangled via Residual Vector Quantization
Sukhyun Jeong, Hong-Gi Shin, Yong-Hoon Choi
https://arxiv.org/abs/2508.14561 https:…

Making Pose Representations More Expressive and Disentangled via Residual Vector Quantization
Recent progress in text-to-motion has advanced both 3D human motion generation and text-based motion control. Controllable motion generation (CoMo), which enables intuitive control, typically relies on pose code representations, but discrete pose codes alone cannot capture fine-grained motion details, limiting expressiveness. To overcome this, we propose a method that augments pose code-based latent representations with continuous motion features using residual vector quantization (RVQ). This d…

A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives
Juepeng Zheng, Zi Ye, Yibin Wen, Jianxi Huang, Zhiwei Zhang, Qingmei Li, Qiong Hu, Baodong Xu, Lingyuan Zhao, Haohuan Fu
https://arxiv.org/abs/2508.14558

A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives
Powered by advances in multiple remote sensing sensors, the production of high spatial resolution images provides great potential to achieve cost-efficient and high-accuracy agricultural inventory and analysis in an automated way. Lots of studies that aim at providing an inventory of the level of each agricultural parcel have generated many methods for Agricultural Parcel and Boundary Delineation (APBD). This review covers APBD methods for detecting and delineating agricultural parcels and syst…

Improving OCR using internal document redundancy
Diego Belzarena, Seginus Mowlavi, Aitor Artola, Camilo Mari\~no, Marina Gardella, Ignacio Ram\'irez, Antoine Tadros, Roy He, Natalia Bottaioli, Boshra Rajaei, Gregory Randall, Jean-Michel Morel
https://arxiv.org/abs/2508.14557

Improving OCR using internal document redundancy
Current OCR systems are based on deep learning models trained on large amounts of data. Although they have shown some ability to generalize to unseen data, especially in detection tasks, they can struggle with recognizing low-quality data. This is particularly evident for printed documents, where intra-domain data variability is typically low, but inter-domain data variability is high. In that context, current OCR methods do not fully exploit each document's redundancy. We propose an unsupervis…

WISE-FUSE: Efficient Whole Slide Image Encoding via Coarse-to-Fine Patch Selection with VLM and LLM Knowledge Fusion
Yonghan Shin, SeungKyu Kim, Won-Ki Jeong
https://arxiv.org/abs/2508.14537

WISE-FUSE: Efficient Whole Slide Image Encoding via Coarse-to-Fine Patch Selection with VLM and LLM Knowledge Fusion
Whole slide images (WSIs) in computational pathology (CPath) pose a major computational challenge due to their gigapixel scale, often requiring the processing of tens to hundreds of thousands of high-resolution patches per slide. This results in prohibitive encoding costs, with preprocessing and training times extending to days or even weeks-making WSI encoding the most significant bottleneck in real-world deployment. In this work, we propose WISE-FUSE, an adaptive WSI encoding framework that l…

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
Jiangfan Liu, Yongkang Guo, Fangzhi Zhong, Tianyuan Zhang, Zonglei Jing, Siyuan Liang, Jiakai Wang, Mingchuan Zhang, Aishan Liu, Xianglong Liu
https://arxiv.org/abs/2508.14527

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
The generation of safety-critical scenarios in simulation has become increasingly crucial for safety evaluation in autonomous vehicles prior to road deployment in society. However, current approaches largely rely on predefined threat patterns or rule-based strategies, which limit their ability to expose diverse and unforeseen failure modes. To overcome these, we propose ScenGE, a framework that can generate plentiful safety-critical scenarios by reasoning novel adversarial cases and then amplif…

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments
Bernd Hofmann, Albert Scheck, Joerg Franke, Patrick Bruendl
https://arxiv.org/abs/2508.14504

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments
The detection of anomalies in manufacturing processes is crucial to ensure product quality and identify process deviations. Statistical and data-driven approaches remain the standard in industrial anomaly detection, yet their adaptability and usability are constrained by the dependence on extensive annotated datasets and limited flexibility under dynamic production conditions. Recent advances in the perception capabilities of foundation models provide promising opportunities for their adaptatio…

SATURN: Autoregressive Image Generation Guided by Scene Graphs
Thanh-Nhan Vo, Trong-Thuan Nguyen, Tam V. Nguyen, Minh-Triet Tran
https://arxiv.org/abs/2508.14502 https://…

SATURN: Autoregressive Image Generation Guided by Scene Graphs
State-of-the-art text-to-image models excel at photorealistic rendering but often struggle to capture the layout and object relationships implied by complex prompts. Scene graphs provide a natural structural prior, yet previous graph-guided approaches have typically relied on heavy GAN or diffusion pipelines, which lag behind modern autoregressive architectures in both speed and fidelity. We introduce SATURN (Structured Arrangement of Triplets for Unified Rendering Networks), a lightweight exte…