Tootfinder

Ask TUI to Stop Supporting Orca Abuse #AnimalRights

Please ask TUI UK to stop supporting orca abuse
TUI UK continues to sell tickets to SeaWorld, despite being fully aware of the company's cruel treatment of animals for profit.

Discovery of energy landscapes towards optimized quantum transport: Environmental effects and long-range tunneling
Maggie Lawrence, Matthew Pocrnic, Erin Fung, Juan Carrasquilla, Erik M. Gauger, Dvira Segal
https://arxiv.org/abs/2508.09371

Discovery of energy landscapes towards optimized quantum transport: Environmental effects and long-range tunneling
Carrier transport in quantum networks is governed by a variety of factors, including network dimensionality and connectivity, on-site energies, couplings between sites and whether they are short- or long-range, and environmental effects. In this work, we identify classes of quasi-one-dimensional chains with energy profiles that optimize carrier transport under such influences. Specifically, we optimize on-site energies using Optax's optimistic gradient descent and AdaMax algorithms, enabled by …

GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
Tai Vu, Robert Yang
https://arxiv.org/abs/2508.09207 https://arxiv.o…

GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
The process of generating fully colorized drawings from sketches is a large, usually costly bottleneck in the manga and anime industry. In this study, we examine multiple models for image-to-image translation between anime characters and their sketches, including Neural Style Transfer, C-GAN, and CycleGAN. By assessing them qualitatively and quantitatively, we find that C-GAN is the most effective model that is able to produce high-quality and high-resolution images close to those created by hu…

Hey, Jörg Schoppmeyer opened his talk at the #SolarEclipse Conference in Leuven on imaging annular eclipses without filters with one of my Australian pictures from 1999 (upper right): see https://geocities.restorativland.org/CapeCanaveral/5599/aus99/story.html for how they originated. Schoppmeyer's video https://www.youtube.com/watch?v=lbtQrHdl5Og of the 2023 ASE actually found scientific use: https://www.iota-es.de/JOA/JOA2024_2.pdf#page=3 (once again confirming the larger solar diameter https://scicomm.xyz/@cosmos4u/114681336115327260 had determined by an entirely different technique). 6/n

[2025-08-14 Thu (UTC), 4 new articles found for cs.LO Logic in Computer Science]
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Week 2 inactives: Who's in and who's out? https://www.espn.com/fantasy/football/story/_/page/FFSundayInactives-46205936/nfl-fantasy-football-espn-injured-inactives-watchlist-week-2-…

Note to web designers: if you set a size for body text fonts, a substantial part of your audience won't be able to read your page at all, so you've lost them; and more tech-savvy users will either zoom the page, breaking your clever design, or remove your styling altogether (which is what I tend to do).
The user knows what size of font the user can read. The user has their preferences set up so they can read their screen. Trust the user.
And if that breaks your design, yo…

[2025-08-14 Thu (UTC), 21 new articles found for cs.RO Robotics]
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Orwell was literally a peniless immigrant in France.
It's all there in "Down and Out in Paris and London".
It taught him empathy for the poor, who he wrote about as human beings.
This is the antidote to the xenophobia that was being pedalled in London today.
https://

[2025-08-14 Thu (UTC), 45 new articles found for cs.CL Computation and Language]
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The Path-product in Morse Homology with differential graded coefficients
Robin Riegel (IRMA)
https://arxiv.org/abs/2508.09583 https://arxiv.org/pdf/2508.09…

The Path-product in Morse Homology with differential graded coefficients
We will use the tools developed in [Rie24] to give a Morse-theoretic description of a string topology product on the homology of the space of paths in a manifold Y with endpoints in a submanifold X and a module structure on this homology over the Chas-Sullivan ring of Y . These operations have been defined and studied by Stegemeyer in [Ste25] in singular homology.

[2025-08-14 Thu (UTC), 13 new articles found for cs.AI Artificial Intelligence]
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[2025-07-14 Mon (UTC), 11 new articles found for math.NT Number Theory]
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[2025-08-14 Thu (UTC), 11 new articles found for math.AG Algebraic Geometry]
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[2025-08-14 Thu (UTC), 122 new articles found for cs.CV Computer Vision and Pattern Recognition]
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Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[3/3]:
- Forecasting steam mass flow in power plants using the parallel hybrid network
Andrii Kurkin, Jonas Hegemann, Mo Kordzanganeh, Alexey Melnikov

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Verifying Quantized Graph Neural Networks is PSPACE-complete
Marco S\"alzer, Fran\c{c}ois Schwarzentruber, Nicolas Troquard

1984 - Part Two - Chapter 9
https://skywriter.blue/pages/did:plc:nywb5oene54cllowkfwouxzz/post/3lyqzxb3ojs2k

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
- BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion
Qiayuan Liao, Takara E. Truong, Xiaoyu Huang, Guy Tevet, Koushil Sreenath, C. Karen Liu

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/3]:
- Towards a Digital Twin of Noisy Quantum Computers: Calibration-Driven Emulation of Transmon Qubits
uller, et al.

Harnessing Input-Adaptive Inference for Efficient VLN
Dongwoo Kang, Akhil Perincherry, Zachary Coalson, Aiden Gabriel, Stefan Lee, Sanghyun Hong
https://arxiv.org/abs/2508.09262

Harnessing Input-Adaptive Inference for Efficient VLN
An emerging paradigm in vision-and-language navigation (VLN) is the use of history-aware multi-modal transformer models. Given a language instruction, these models process observation and navigation history to predict the most appropriate action for an agent. While they have significantly improved performance, the scale of these models can be a bottleneck in practical settings with limited computational resources. In this work, we propose a novel input-adaptive navigation method to enhance VLN …

Efficient Volume Computation for SMT Formulas
Arijit Shaw, Uddalok Sarkar, Kuldeep S. Meel
https://arxiv.org/abs/2508.09934 https://arxiv.org/pdf/2508.0993…

Efficient Volume Computation for SMT Formulas
Satisfiability Modulo Theory (SMT) has recently emerged as a powerful tool for solving various automated reasoning problems across diverse domains. Unlike traditional satisfiability methods confined to Boolean variables, SMT can reason on real-life variables like bitvectors, integers, and reals. A natural extension in this context is to ask quantitative questions. One such query in the SMT theory of Linear Real Arithmetic (LRA) is computing the volume of the entire satisfiable region defined by…

Law enforcement official on Hortman shooting:
"It was knocking on the door and asking the person to come out.
Absolutely impersonating a police officer ... we have the suspect's car. The suspect is on foot."
https://skywriter.blue/pages/did:plc:4

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
- TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
Anoushka Alavilli, Khai Nguyen, Sam Schoedel, Brian Plancher, Zachary Manchester

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/3]:
- Characterizing Universal Gate Sets via Dihedral Benchmarking
Arnaud Carignan-Dugas, Joel J. Wallman, Joseph Emerson

Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users
Jeffri Murrugarra-LLerena, Haoran Niu, K. Suzanne Barber, Hal Daum\'e III, Yang Trista Cao, Paola Cascante-Bonilla
https://arxiv.org/abs/2508.09245

Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users
As visual assistant systems powered by visual language models (VLMs) become more prevalent, concerns over user privacy have grown, particularly for blind and low vision users who may unknowingly capture personal private information in their images. Existing privacy protection methods rely on coarse-grained segmentation, which uniformly masks entire private objects, often at the cost of usability. In this work, we propose FiGPriv, a fine-grained privacy protection framework that selectively mask…

Short proofs without interference
Adrian Rebola-Pardo
https://arxiv.org/abs/2508.09851 https://arxiv.org/pdf/2508.09851

Short proofs without interference
Interference is a phenomenon on proof systems for SAT solving that is both counter-intuitive and bothersome when developing proof-logging techniques. However, all existing proof systems that can produce short proofs for all inprocessing techniques deployed by SAT present this feature. Based on insights from propositional dynamic logic, we propose a framework that eliminates interference while preserving the same expressive power of interference-based proofs. Furthermore, we propose a first buil…

Masquerade: Learning from In-the-wild Human Videos using Data-Editing
Marion Lepert, Jiaying Fang, Jeannette Bohg
https://arxiv.org/abs/2508.09976 https://…

Masquerade: Learning from In-the-wild Human Videos using Data-Editing
Robot manipulation research still suffers from significant data scarcity: even the largest robot datasets are orders of magnitude smaller and less diverse than those that fueled recent breakthroughs in language and vision. We introduce Masquerade, a method that edits in-the-wild egocentric human videos to bridge the visual embodiment gap between humans and robots and then learns a robot policy with these edited videos. Our pipeline turns each human video into robotized demonstrations by (i) est…

Improving quantum communication rates with permutation-invariant codes
Sujeet Bhalerao, Felix Leditzky
https://arxiv.org/abs/2508.09978 https://arxiv.org/p…

Improving quantum communication rates with permutation-invariant codes
In this work we improve the quantum communication rates of various quantum channels of interest using permutation-invariant quantum codes. We focus in particular on parametrized families of quantum channels and aim to improve bounds on their quantum capacity threshold, defined as the lowest noise level at which the quantum capacity of the channel family vanishes. These thresholds are important quantities as they mark the noise level up to which faithful quantum communication is theoretically po…

FineState-Bench: A Comprehensive Benchmark for Fine-Grained State Control in GUI Agents
Fengxian Ji, Jingpu Yang, Zirui Song, Yuanxi Wang, Zhexuan Cui, Yuke Li, Qian Jiang, Miao Fang, Xiuying Chen
https://arxiv.org/abs/2508.09241

FineState-Bench: A Comprehensive Benchmark for Fine-Grained State Control in GUI Agents
With the rapid advancement of generative artificial intelligence technology, Graphical User Interface (GUI) agents have demonstrated tremendous potential for autonomously managing daily tasks through natural language instructions. However, current evaluation frameworks for GUI agents suffer from fundamental flaws: existing benchmarks overly focus on coarse-grained task completion while neglecting fine-grained control capabilities crucial for real-world applications. To address this, we introduc…

On Middle Grounds for Preference Statements
Anne-Marie George, Ana Ozaki
https://arxiv.org/abs/2508.09553 https://arxiv.org/pdf/2508.09553

On Middle Grounds for Preference Statements
In group decisions or deliberations, stakeholders are often confronted with conflicting opinions. We investigate a logic-based way of expressing such opinions and a formal general notion of a middle ground between stakeholders. Inspired by the literature on preferences with hierarchical and lexicographic models, we instantiate our general framework to the case where stakeholders express their opinions using preference statements of the form I prefer 'a' to 'b', where 'a' and 'b' are alternative…

Quantum statistics of single-mode radiation emitted by superradiant Dicke states
A. Yadav, D. D. Yavuz
https://arxiv.org/abs/2508.09962 https://arxiv.org/p…

Quantum statistics of single-mode radiation emitted by superradiant Dicke states
We study the quantum statistics of single-mode radiation emitted by an atomic ensemble when the ensemble is initially prepared in a superradiant Dicke state. We show that while the radiation is well approximated by the Glauber coherent state at early times in the evolution, the emission can be truly quantum at later times. In particular, one can observe a large amount of photon-number squeezing in the emission under certain conditions; even a Fock state can be produced. We discuss the quantum s…

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Zihan Wang, Nina Mahmoudian
https://arxiv.org/abs/2508.09971 https://

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Vision-driven autonomous river following by Unmanned Aerial Vehicles is critical for applications such as rescue, surveillance, and environmental monitoring, particularly in dense riverine environments where GPS signals are unreliable. We formalize river following as a coverage control problem in which the reward function is submodular, yielding diminishing returns as more unique river segments are visited, thereby framing the task as a Submodular Markov Decision Process. First, we introduce Ma…

Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
Zheng Zhou, Yu-Jie Xiong, Chun-Ming Xia, Jia-Chen Zhang, Hong-Jian Zhan
https://arxiv.org/abs/2508.09239 https…

Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced novel view synthesis through explicit scene representation, enabling real-time photorealistic rendering. However, existing approaches manifest two critical limitations in complex scenarios: (1) Over-reconstruction occurs when persistent large Gaussians cannot meet adaptive splitting thresholds during density control. This is exacerbated by conflicting gradient directions that prevent effective splitting of these Gaussians;…

TPTP World Infrastructure for Non-classical Logics
Alexander Steen, Geoff Sutcliffe
https://arxiv.org/abs/2508.09318 https://arxiv.org/pdf/2508.09318

TPTP World Infrastructure for Non-classical Logics
The TPTP World is the well established infrastructure that supports research, development, and deployment of Automated Theorem Proving (ATP) systems. The TPTP World supports a range of classical logics, and since release v9.0.0 has supported non-classical logics. This paper provides a self-contained comprehensive overview of the TPTP World infrastructure for ATP in non-classical logics: the non-classical language extension, problems and solutions, and tool support. A detailed description of use…

Quantum recurrences and the arithmetic of Floquet dynamics
Amit Anand, Dinesh Valluri, Jack Davis, Shohini Ghose
https://arxiv.org/abs/2508.09933 https://a…

Quantum recurrences and the arithmetic of Floquet dynamics
The Poincaré recurrence theorem shows that conservative systems in a bounded region of phase space eventually return arbitrarily close to their initial state after a finite amount of time. An analogous behavior occurs in certain quantum systems where quantum states can recur after sufficiently long unitary evolution, a phenomenon known as quantum recurrence. Periodically driven (i.e. Floquet) quantum systems in particular exhibit complex dynamics even in small dimensions, motivating the study …

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

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

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Haoyang Li, Jiaqing Li, Jialun Cao, Zongyuan Yang, Yongping Xiong
https://arxiv.org/abs/2508.09220 …

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Large foundation models have achieved significant performance gains through scalable training on massive datasets. However, the field of \textbf{H}andwritten \textbf{M}athematical \textbf{E}xpression \textbf{R}ecognition (HMER) has been impeded by the scarcity of data, primarily due to the arduous and costly process of manual annotation. To bridge this gap, we propose a novel method integrating limited handwritten formulas with large-scale LaTeX-rendered formulas by developing a scalable data e…

Fault tolerant Operations in Majorana-based Quantum Codes: Gates, Measurements and High Rate Constructions
Maryam Mudassar, Alexander Schuckert, Daniel Gottesman
https://arxiv.org/abs/2508.09928

Fault tolerant Operations in Majorana-based Quantum Codes: Gates, Measurements and High Rate Constructions
Majorana-based quantum computation in nanowires and neutral atoms has gained prominence as a promising platform to encode qubits and protect them against noise. In order to run computations reliably on such devices, a fully fault-tolerant scheme is needed for state preparation, gates, and measurements. However, current fault-tolerant schemes have either been limited to specific code families or have not been developed fully. In this work, we develop a general framework for fault-tolerant comput…

[2025-07-14 Mon (UTC), 2 new articles found for cs.LO Logic in Computer Science]
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Towards Effective MLLM Jailbreaking Through Balanced On-Topicness and OOD-Intensity
Zuoou Li, Weitong Zhang, Jingyuan Wang, Shuyuan Zhang, Wenjia Bai, Bernhard Kainz, Mengyun Qiao
https://arxiv.org/abs/2508.09218

Towards Effective MLLM Jailbreaking Through Balanced On-Topicness and OOD-Intensity
Multimodal large language models (MLLMs) are widely used in vision-language reasoning tasks. However, their vulnerability to adversarial prompts remains a serious concern, as safety mechanisms often fail to prevent the generation of harmful outputs. Although recent jailbreak strategies report high success rates, many responses classified as "successful" are actually benign, vague, or unrelated to the intended malicious goal. This mismatch suggests that current evaluation standards may overestim…

PPL: Point Cloud Supervised Proprioceptive Locomotion Reinforcement Learning for Legged Robots in Crawl Spaces
Bida Ma, Nuo Xu, Chenkun Qi, Xin Liu, Yule Mo, Jinkai Wang, Chunpeng Lu
https://arxiv.org/abs/2508.09950

PPL: Point Cloud Supervised Proprioceptive Locomotion Reinforcement Learning for Legged Robots in Crawl Spaces
The legged locomotion in spatially constrained structures (called crawl spaces) is challenging. In crawl spaces, current exteroceptive locomotion learning methods are limited by large noises and errors of the sensors in possible low visibility conditions, and current proprioceptive locomotion learning methods are difficult in traversing crawl spaces because only ground features are inferred. In this study, a point cloud supervised proprioceptive locomotion reinforcement learning method for legg…

An integrated photonics platform for high-speed, ultrahigh-extinction, many-channel quantum control
Mengdi Zhao, Manuj Singh, Anshuman Singh, Henry Thoreen, Robert J. DeAngelo, Daniel Dominguez, Andrew Leenheer, Fr\'ed\'eric Peyskens, Alexander Lukin, Dirk Englund, Matt Eichenfield, Nathan Gemelke, Noel H. Wan
https://arxiv.org/abs…

An integrated photonics platform for high-speed, ultrahigh-extinction, many-channel quantum control
High-fidelity control of the thousands to millions of programmable qubits needed for utility-scale quantum computers presents a formidable challenge for control systems. In leading atomic systems, control is optical: UV-NIR beams must be fanned out over numerous spatial channels and modulated to implement gates. While photonic integrated circuits (PICs) offer a potentially scalable solution, they also need to simultaneously feature high-speed and high-extinction modulation, strong inter-channel…

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

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

A Shank Angle-Based Control System Enables Soft Exoskeleton to Assist Human Non-Steady Locomotion
Xiaowei Tan, Weizhong Jiang, Bi Zhang, Wanxin Chen, Yiwen Zhao, Ning Li, Lianqing Liu, Xingang Zhao
https://arxiv.org/abs/2508.09876

A Shank Angle-Based Control System Enables Soft Exoskeleton to Assist Human Non-Steady Locomotion
Exoskeletons have been shown to effectively assist humans during steady locomotion. However, their effects on non-steady locomotion, characterized by nonlinear phase progression within a gait cycle, remain insufficiently explored, particularly across diverse activities. This work presents a shank angle-based control system that enables the exoskeleton to maintain real-time coordination with human gait, even under phase perturbations, while dynamically shaping assistance profiles to match the bi…

Hybrid Quantum-Classical Latent Diffusion Models for Medical Image Generation
K\"ubra Yeter-Aydeniz, Nora M. Bauer, Pranay Jain, Max Masnick
https://arxiv.org/abs/2508.09903

Hybrid Quantum-Classical Latent Diffusion Models for Medical Image Generation
Generative learning models in medical research are crucial in developing training data for deep learning models and advancing diagnostic tools, but the problem of high-quality, diverse images is an open topic of research. Quantum-enhanced generative models have been proposed and tested in the literature but have been restricted to small problems below the scale of industry relevance. In this paper, we propose quantum-enhanced diffusion and variational autoencoder (VAE) models and test them on t…

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Masoumeh Sharafi, Soufiane Belharbi, Houssem Ben Salem, Ali Etemad, Alessandro Lameiras Koerich, Marco Pedersoli, Simon Bacon, Eric Granger
https://arxiv.org/abs/2508.09202

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve their performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain da…

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Yuekun Wu, Yik Lung Pang, Andrea Cavallaro, Changjae Oh
https://arxiv.org/abs/2508.09855 https://

Toward Human-Robot Teaming: Learning Handover Behaviors from 3D Scenes
Human-robot teaming (HRT) systems often rely on large-scale datasets of human and robot interactions, especially for close-proximity collaboration tasks such as human-robot handovers. Learning robot manipulation policies from raw, real-world image data requires a large number of robot-action trials in the physical environment. Although simulation training offers a cost-effective alternative, the visual domain gap between simulation and robot workspace remains a major limitation. We introduce a …

The Role of Symmetry in Generalized Hong-Ou-Mandel Interference and Quantum Metrology
\'Eloi Descamps, Arne Keller, P\'erola Milman
https://arxiv.org/abs/2508.09887 http…

The Role of Symmetry in Generalized Hong-Ou-Mandel Interference and Quantum Metrology
The Hong-Ou-Mandel interferometer is a foundational tool in quantum optics, with both fundamental and practical significance. Earlier works identified that input-state symmetry under exchange of the two spatial modes is fundamental in the understanding of the Hong-Ou-Mandel effect. We now show that this notion of symmetry is central to generalizing this effect. In particular, this point of view enables the construction of extensions beyond the standard two single-photon case to arbitrary input …

$\Delta$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Jucheng Hu, Suorong Yang, Dongzhan Zhou
https://arxiv.org/abs/2508.09199 h…

$Δ$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Visual Instruction Finetuning (VIF) is pivotal for post-training Vision-Language Models (VLMs). Unlike unimodal instruction finetuning in plain-text large language models, which mainly requires instruction datasets to enable model instruction-following ability, VIF also requires multimodal data to enable joint visual and textual understanding; therefore, it typically requires more data. Consequently, VIF imposes stricter data selection challenges: the method must scale efficiently to handle lar…

On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generators
Jasmin Frkatovic, Akash Malemath, Ivan Kankeu, Yannick Werner, Matthias Tsch\"ope, Vitor Fortes Rey, Sungho Suh, Paul Lukowicz, Nikolaos Palaiodimopoulos, Maximilian Kiefer-Emmanouilidis
https://arxiv.org/abs/2508.09844

On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generators
We investigate the capabilities of Quantum Generative Adversarial Networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of current main architectures, we find that QGANs struggle to generalize across datasets, converging on merely the average representation of the training data. When the output of the generator is a pure-state, we analytically derive a lower bound for the di…

Whole-Body Bilateral Teleoperation with Multi-Stage Object Parameter Estimation for Wheeled Humanoid Locomanipulation
Donghoon Baek, Amartya Purushottam, Jason J. Choi, Joao Ramos
https://arxiv.org/abs/2508.09846

Whole-Body Bilateral Teleoperation with Multi-Stage Object Parameter Estimation for Wheeled Humanoid Locomanipulation
This paper presents an object-aware whole-body bilateral teleoperation framework for wheeled humanoid loco-manipulation. This framework combines whole-body bilateral teleoperation with an online multi-stage object inertial parameter estimation module, which is the core technical contribution of this work. The multi-stage process sequentially integrates a vision-based object size estimator, an initial parameter guess generated by a large vision-language model (VLM), and a decoupled hierarchical …

Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation
Seyed Muhammad Hossein Mousavi, S. Younes Mirinezhad
https://arxiv.org/abs/2508.09188

Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation
Affective computing faces a major challenge: the lack of high-quality, diverse depth facial datasets for recognizing subtle emotional expressions. We propose a framework for synthetic depth face generation using an optimized GAN with Knowledge Distillation (EMA teacher models) to stabilize training, improve quality, and prevent mode collapse. We also apply Genetic Algorithms to evolve GAN latent vectors based on image statistics, boosting diversity and visual quality for target emotions. The ap…

Entanglement certification from moments of positive maps
Qing-Hua Zhang, Xiaoyu Ma, Shao-Ming Fei
https://arxiv.org/abs/2508.09766 https://arxiv.org/pdf/25…

Entanglement certification from moments of positive maps
Entanglement certification is crucial in physical experiments, particularly when only partial knowledge of the quantum state is available. In this context, we present an entanglement criterion based on positive but not completely positive maps, which eliminates the need to identify eigenvalues of the output state. Notably, the Faddeev-LeVerrier algorithm establishes a relationship between the coefficients of characteristic polynomials and the moments of a matrix. This enables the existence of n…

Embodied Tactile Perception of Soft Objects Properties
Anirvan Dutta, Alexis WM Devillard, Zhihuan Zhang, Xiaoxiao Cheng, Etienne Burdet
https://arxiv.org/abs/2508.09836 https:/…

Embodied Tactile Perception of Soft Objects Properties
To enable robots to develop human-like fine manipulation, it is essential to understand how mechanical compliance, multi-modal sensing, and purposeful interaction jointly shape tactile perception. In this study, we use a dedicated modular e-Skin with tunable mechanical compliance and multi-modal sensing (normal, shear forces and vibrations) to systematically investigate how sensing embodiment and interaction strategies influence robotic perception of objects. Leveraging a curated set of soft wa…

RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast
https://arxiv.org/abs/2508.09186 ht…

RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
The proliferation of AI-powered cameras in Intelligent Transportation Systems (ITS) creates a severe conflict between the need for rich visual data and the fundamental right to privacy. Existing privacy-preserving mechanisms, such as blurring or encryption, are often insufficient, creating an undesirable trade-off where either privacy is compromised against advanced reconstruction attacks or data utility is critically degraded. To resolve this impasse, we propose RL-MoE, a novel framework that …

Hybrid Optomechanical Cooling with Kerr Magnons and Squeezed Vacuum
Xiao-Hong Fan, Qin-Geng Chen, Jiaojiao Chen, Wei Xiong
https://arxiv.org/abs/2508.09725 https://

Hybrid Optomechanical Cooling with Kerr Magnons and Squeezed Vacuum
Ground-state cooling is essential for accessing the quantum regime and enabling quantum control of macroscopic systems. However, achieving optomechanical cooling in the unresolved-sideband regime ($ω_b < κ$) remains challenging. In this Letter, we propose an efficient cooling strategy based on a hybrid optomechanical system incorporating a yttrium iron garnet (YIG) sphere embedded in an optomechanical cavity. Under strong cavity driving, the Kerr nonlinearity of the magnons hosted in the YIG …

FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning
Dongcheng Cao, Jin Zhou, Xian Wang, Shuo Li
https://arxiv.org/abs/2508.09797 https:/…

FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning
Agile flight for the quadrotor cable-suspended payload system is a formidable challenge due to its underactuated, highly nonlinear, and hybrid dynamics. Traditional optimization-based methods often struggle with high computational costs and the complexities of cable mode transitions, limiting their real-time applicability and maneuverability exploitation. In this letter, we present FLARE, a reinforcement learning (RL) framework that directly learns agile navigation policy from high-fidelity sim…

A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Rongqian Chen, Allison Andreyev, Yanming Xiu, Mahdi Imani, Bin Li, Maria Gorlatova, Gang Tan, Tian Lan
https://arxiv.org/abs/2508.09185

A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Augmented Reality (AR) enriches perception by overlaying virtual elements on the physical world. Due to its growing popularity, cognitive attacks that alter AR content to manipulate users' semantic perception have received increasing attention. Existing detection methods often focus on visual changes, which are restricted to pixel- or image-level processing and lack semantic reasoning capabilities, or they rely on pre-trained vision-language models (VLMs), which function as black-box approaches…

Stabilizing boundary time crystals through Non-markovian dynamics
Bandita Das, Rahul Ghosh, Victor Mukherjee
https://arxiv.org/abs/2508.09688 https://arxiv…

Stabilizing boundary time crystals through Non-markovian dynamics
We study Boundary time crystals (BTCs) in the presence of non-Markovian dynamics. In contrast to BTCs observed in earlier works in the Markovian regime, we show that non-Markovian dynamics can be highly beneficial for stabilizing BTCs over a wide range of parameter values, even in the presence of intermediate rates of dissipation. We analyze the effect of non-Markovian dynamics on BTCs using quantum Fisher information, order parameter, a measure of non-Markovianity, and a dynamical phase diagra…

IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
Yanhui Li, Yunkang Cao, Chengliang Liu, Yuan Xiong, Xinghui Dong, Chao Huang
https://arxiv.org/abs/2508.09178

IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
Industrial anomaly detection is a critical component of modern manufacturing, yet the scarcity of defective samples restricts traditional detection methods to scenario-specific applications. Although Vision-Language Models (VLMs) demonstrate significant advantages in generalization capabilities, their performance in industrial anomaly detection remains limited. To address this challenge, we propose IAD-R1, a universal post-training framework applicable to VLMs of different architectures and par…

Immersive Teleoperation of Beyond-Human-Scale Robotic Manipulators: Challenges and Future Directions
Mahdi Hejrati, Jouni Mattila
https://arxiv.org/abs/2508.09700 https://

Immersive Teleoperation of Beyond-Human-Scale Robotic Manipulators: Challenges and Future Directions
Teleoperation of beyond-human-scale robotic manipulators (BHSRMs) presents unique challenges that differ fundamentally from conventional human-scale systems. As these platforms gain relevance in industrial domains such as construction, mining, and disaster response, immersive interfaces must be rethought to support scalable, safe, and effective human-robot collaboration. This paper investigates the control, cognitive, and interface-level challenges of immersive teleoperation in BHSRMs, with a f…

Procedural Generation and Games at the Dawn of Fault Tolerant Quantum Computing
Daniel Bultrini, James Wootton
https://arxiv.org/abs/2508.09683 https://arx…

Procedural Generation and Games at the Dawn of Fault Tolerant Quantum Computing
Quantum computers have long been more of a toy for researchers than a tool for solving complex problems. However, recent advances in the field make exploiting the advantages of fault-tolerant quantum computers feasible in the next 5 to 10 years. It is now time to begin imagining how such devices could be used in practice for game development and deployment. In this work we identify procedural content generation as a very promising area of application and exploration. We examine a selection of a…

A Context-aware Attention and Graph Neural Network-based Multimodal Framework for Misogyny Detection
Mohammad Zia Ur Rehman, Sufyaan Zahoor, Areeb Manzoor, Musharaf Maqbool, Nagendra Kumar
https://arxiv.org/abs/2508.09175

A Context-aware Attention and Graph Neural Network-based Multimodal Framework for Misogyny Detection
A substantial portion of offensive content on social media is directed towards women. Since the approaches for general offensive content detection face a challenge in detecting misogynistic content, it requires solutions tailored to address offensive content against women. To this end, we propose a novel multimodal framework for the detection of misogynistic and sexist content. The framework comprises three modules: the Multimodal Attention module (MANM), the Graph-based Feature Reconstruction …

Interpretable Robot Control via Structured Behavior Trees and Large Language Models
Ingrid Ma\'eva Chekam, Ines Pastor-Martinez, Ali Tourani, Jose Andres Millan-Romera, Laura Ribeiro, Pedro Miguel Bastos Soares, Holger Voos, Jose Luis Sanchez-Lopez
https://arxiv.org/abs/2508.09621

Interpretable Robot Control via Structured Behavior Trees and Large Language Models
As intelligent robots become more integrated into human environments, there is a growing need for intuitive and reliable Human-Robot Interaction (HRI) interfaces that are adaptable and more natural to interact with. Traditional robot control methods often require users to adapt to interfaces or memorize predefined commands, limiting usability in dynamic, unstructured environments. This paper presents a novel framework that bridges natural language understanding and robotic execution by combinin…

High-$Q$ superconducting resonators fabricated in an industry-scale semiconductor-fabrication facility
Nicolas Arlt, Karina Houska, Jochen Braum\"uller, Michael Kirsch, Gerhard Metzger-Br\"uckl, Wolfgang Raberg, Thomas Stangl, Stefan Filipp, Jash Banker, Florian Brandl
https://arxiv.org/abs/2508.09577

High-$Q$ superconducting resonators fabricated in an industry-scale semiconductor-fabrication facility
Universal quantum computers promise to solve computational problems that are beyond the capabilities of known classical algorithms. To realize such quantum hardware on a superconducting material platform, a vast number of physical qubits has to be manufactured and integrated at high quality and uniformity on a chip. Anticipating the benefits of semiconductor industry processes in terms of process control, uniformity and repeatability, we set out to manufacture superconducting quantum circuits i…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- Emotion-Qwen: A Unified Framework for Emotion and Vision Understanding
Huang, Li, Yan, Cheng, Han, Huang, Li, Li, Wang, Lian, Cheng, Peng

Interpreting quantum reference frame transformations through a simple example
Esteban Castro-Ruiz, Thomas D. Galley, Leon Loveridge
https://arxiv.org/abs/2508.09540 https://

Interpreting quantum reference frame transformations through a simple example
Quantum reference frame transformations have been proposed to provide a means by which to translate descriptions of quantum systems relative to each other. At present, there are several differing frameworks for describing quantum reference frames, with concomitantly different transformation rules. Here, we investigate a simple example of three qubit systems with $\mathbb{Z}_2$ symmetry in order to analyze physical and conceptual distinctions between three contemporary approaches to quantum refe…

BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots
Alejandro Posadas-Nava, Alejandro Carrasco, Richard Linares
https://arxiv.org/abs/2508.09606

BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots
\textbf{BEAVR} is an open-source, bimanual, multi-embodiment Virtual Reality (VR) teleoperation system for robots, designed to unify real-time control, data recording, and policy learning across heterogeneous robotic platforms. BEAVR enables real-time, dexterous teleoperation using commodity VR hardware, supports modular integration with robots ranging from 7-DoF manipulators to full-body humanoids, and records synchronized multi-modal demonstrations directly in the LeRobot dataset schema. Our …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- MoCA: Identity-Preserving Text-to-Video Generation via Mixture of Cross Attention
Qi Xie, Yongjia Ma, Donglin Di, Xuehao Gao, Xun Yang

Relative Wavefront Errors in Continuous-Variable Quantum Communication
Nathan K. Long, John Wallis, Alex Frost, Benjamin P. Dix-Matthews, Sascha W. Schediwy, Kenneth J. Grant, Robert Malaney
https://arxiv.org/abs/2508.09491

Relative Wavefront Errors in Continuous-Variable Quantum Communication
When undertaking continuous-variable quantum key distribution (CV-QKD) across atmospheric channels, strong classical local oscillators (LOs) are often polarization-multiplexed with the weak quantum signals for coherent measurement at the receiver. Although the wavefronts of the quantum signal and LO are often assumed to experience the same distortion across channels, previous theoretical work has shown that they can experience differential distortions, resulting in relative wavefront errors (WF…

HapticGiant: A Novel Very Large Kinesthetic Haptic Interface with Hierarchical Force Control
Michael Fennel, Markus Walker, Dominik Pikos, Uwe D. Hanebeck
https://arxiv.org/abs/2508.09595

HapticGiant: A Novel Very Large Kinesthetic Haptic Interface with Hierarchical Force Control
Research in virtual reality and haptic technologies has consistently aimed to enhance immersion. While advanced head-mounted displays are now commercially available, kinesthetic haptic interfaces still face challenges such as limited workspaces, insufficient degrees of freedom, and kinematics not matching the human arm. In this paper, we present HapticGiant, a novel large-scale kinesthetic haptic interface designed to match the properties of the human arm as closely as possible and to facilitat…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models in Compositional Spatial...
Ziyang Gong, Wenhao Li, Oliver Ma, Songyuan Li, Jiayi Ji, Xue Yang, Gen Luo, Junchi Yan, Rongrong Ji

Lifshitz-like Metastability and Optimal Dephasing in Dissipative Bosonic Lattices
Stefano Longhi
https://arxiv.org/abs/2508.09485 https://arxiv.org/pdf/250…

Lifshitz-like Metastability and Optimal Dephasing in Dissipative Bosonic Lattices
In dissipative bosonic systems, dephasing is typically expected to accelerate relaxation and suppress coherent dynamics. However, we show that in networks of coherently coupled bosonic modes with non-uniform local dissipation, the presence of quasi-dark states leads to a nontrivial response to dephasing: while weak dephasing facilitates equilibration, moderate to strong dephasing induces a pronounced slowdown of relaxation, revealing the existence of an optimal dephasing rate that enhances equi…

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Junkai Jiang, Yihe Chen, Yibin Yang, Ruochen Li, Shaobing Xu, Jianqiang Wang
https://arxiv.org/abs/2508.09581

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step-based coordinate trajectory planning method for multiple car-like robots. ESCoT incorporates two key strategies: collaborative planning for local robot groups and replanning for duplicate configurations. These strategies effectively enhance the performance of step-based MVTP methods. Through extensive ex…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- UltraRay: Introducing Full-Path Ray Tracing in Physics-Based Ultrasound Simulation
Felix Duelmer, Mohammad Farid Azampour, Magdalena Wysocki, Nassir Navab

Realizing Parrondo's Paradox in Single-Qubit Quantum Walks via Local Phase-Induced Spatial Inhomogeneity
Ran-Yu Chang, Yun-Hsuan Chen, Gooi Zi Liang, Tsung-Wei Huang
https://arxiv.org/abs/2508.09457

Realizing Parrondo's Paradox in Single-Qubit Quantum Walks via Local Phase-Induced Spatial Inhomogeneity
Parrondo's paradox describes a counterintuitive phenomenon where alternating between two individually losing games results in a winning expectation. While its classical origin relies on capital-dependent bias and noise-induced asymmetry, realizing a robust quantum version of the paradox has remained challenging, especially under the constraint of single-qubit coin systems. In this work, we demonstrate that a genuine quantum Parrondo effect can emerge in discrete-time quantum walks (DTQWs) by al…

CaRoBio: 3D Cable Routing with a Bio-inspired Gripper Fingernail
Jiahui Zuo, Boyang Zhang, Fumin Zhang
https://arxiv.org/abs/2508.09558 https://arxiv.org/p…

CaRoBio: 3D Cable Routing with a Bio-inspired Gripper Fingernail
The manipulation of deformable linear flexures has a wide range of applications in industry, such as cable routing in automotive manufacturing and textile production. Cable routing, as a complex multi-stage robot manipulation scenario, is a challenging task for robot automation. Common parallel two-finger grippers have the risk of over-squeezing and over-tension when grasping and guiding cables. In this paper, a novel eagle-inspired fingernail is designed and mounted on the gripper fingers, whi…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Prompt-aligned Gradient for Prompt Tuning
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, Hanwang Zhang

Surpassing the PLOB bound in continuous-variable quantum secret sharing using a state-discrimination detector
Qin Liao, Chong Tang, Qingquan Peng, Chao Ding, Lingjin Zhu, Yijun Wang, Xiquan Fu
https://arxiv.org/abs/2508.09445

Surpassing the PLOB bound in continuous-variable quantum secret sharing using a state-discrimination detector
Continuous-variable quantum secret sharing (CVQSS) is a promising approach to ensuring multi-party information security. While CVQSS offers practical ease of implementation, its present performance remains limited. In this paper, we propose a novel CVQSS protocol integrated with a state-discrimination detector (SDD), dubbed SDD-CVQSS. In particular, we first develop the detailed procedure of SDD-CVQSS, which replaces the traditional coherent detector with an SDD and eliminates the long-standing…

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Junyan Ye, Dongzhi Jiang, Zihao Wang, Leqi Zhu, Zhenghao Hu, Zilong Huang, Jun He, Zhiyuan Yan, Jinghua Yu, Hongsheng Li, Conghui He, Weijia Li
https://arxiv.org/abs/2508.09987

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Recently, GPT-4o has garnered significant attention for its strong performance in image generation, yet open-source models still lag behind. Several studies have explored distilling image data from GPT-4o to enhance open-source models, achieving notable progress. However, a key question remains: given that real-world image datasets already constitute a natural source of high-quality data, why should we use GPT-4o-generated synthetic data? In this work, we identify two key advantages of syntheti…

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

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

SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currents
Reema Raval, Shalabh Gupta
https://arxiv.org/abs/2508.09508

SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currents
Typical marine environments are highly complex with spatio-temporally varying currents and dynamic obstacles, presenting significant challenges to Unmanned Surface Vehicles (USVs) for safe and efficient navigation. Thus, the USVs need to continuously adapt their paths with real-time information to avoid collisions and follow the path of least resistance to the goal via exploiting ocean currents. In this regard, we introduce a novel algorithm, called Self-Morphing Adaptive Replanning Tree for dy…

Story2Board: A Training-Free Approach for Expressive Storyboard Generation
David Dinkevich, Matan Levy, Omri Avrahami, Dvir Samuel, Dani Lischinski
https://arxiv.org/abs/2508.09983

Story2Board: A Training-Free Approach for Expressive Storyboard Generation
We present Story2Board, a training-free framework for expressive storyboard generation from natural language. Existing methods narrowly focus on subject identity, overlooking key aspects of visual storytelling such as spatial composition, background evolution, and narrative pacing. To address this, we introduce a lightweight consistency framework composed of two components: Latent Panel Anchoring, which preserves a shared character reference across panels, and Reciprocal Attention Value Mixing,…

Parallel repetition of expanded, and multiplayer, Quantum games: anchoring, optimal values, generalized error bounds, dependency-breaking as symmetry-breaking
Pete Rigas
https://arxiv.org/abs/2508.09380

Parallel repetition of expanded, and multiplayer, Quantum games: anchoring, optimal values, generalized error bounds, dependency-breaking as symmetry-breaking
We demonstrate that parallel repetition of the multiplayer anchored optimal value, $ω\big( G_{\bot} \big)^{\otimes n}$, decays exponentially. Central to our approach are several probabilistic computations, pertaining to: (1) the computation of expected values for quantifying how the winning probability of the game is likely to change under the anchoring transformation; (2) the computation of positive operator valued measurements, which can be placed into direct correspondence with several prob…

Reactive Model Predictive Contouring Control for Robot Manipulators
Junheon Yoon, Woo-Jeong Baek, Jaeheung Park
https://arxiv.org/abs/2508.09502 https://ar…

Reactive Model Predictive Contouring Control for Robot Manipulators
This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executi…

Oscillating bound states in waveguide-QED system with two giant atoms
F. J. L\"u, W. Z. Jia
https://arxiv.org/abs/2508.09338 https://arxiv.org/pdf/250…

Oscillating bound states in waveguide-QED system with two giant atoms
We study the bound states in the continuum (BIC) in a system of two identical two-level giant atoms coupled to a one-dimensional waveguide. By deriving general dark-state conditions, we clarify how coupling configurations and atomic parameters influence decay suppression. Through analysis of the long-time dynamical behaviors of atoms and bound photons, we carry out a detailed classification of bound states and explore the connections between these dynamical behaviors and the system's intrinsic …

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

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

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Haoxiang Shi, Xiang Deng, Zaijing Li, Gongwei Chen, Yaowei Wang, Liqiang Nie
https://arxiv.org/abs/2508.09444

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces. Existing VLN-CE approaches typically use a two-stage waypoint planning framework, where a high-level waypoint predictor generates the navigable waypoints, and then a navigation planner suggests the intermediate goals in the high-level action space. However, this two-stage decomposition framework suffers from: (1) global sub-optimization due to the p…

Demonstration of a photonic time-frequency Fourier transform and temporal double slit using atomic quantum memory
Ankit Papneja, Jesse Everett, Cameron Trainor, Aaron D. Tranter, Ben C. Buchler
https://arxiv.org/abs/2508.09316

Demonstration of a photonic time-frequency Fourier transform and temporal double slit using atomic quantum memory
Aquantummemoryforlightisexpectedtoplayacrucialroleinquantumcommunicationprotocols and distributed quantum computing. In addition to storage and buffering, a quantum memory can be used for manipulations of stored states to allow more complex quantum network operations. In this work, we demonstrate an in-memory Fourier transform using a combination of two well-established quantum memory protocols: Gradient Echo Memory and Electromagnetically Induced Transparency. Our experiment is realised using …

A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
Shuting He, Peilin Ji, Yitong Yang, Changshuo Wang, Jiayi Ji, Yinglin Wang, Henghui Ding
https://arxiv.org/abs/2508.09977

A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
3D Gaussian Splatting (3DGS) has recently emerged as a powerful alternative to Neural Radiance Fields (NeRF) for 3D scene representation, offering high-fidelity photorealistic rendering with real-time performance. Beyond novel view synthesis, the explicit and compact nature of 3DGS enables a wide range of downstream applications that require geometric and semantic understanding. This survey provides a comprehensive overview of recent progress in 3DGS applications. It first introduces 2D foundat…

CLF-RL: Control Lyapunov Function Guided Reinforcement Learning
Kejun Li, Zachary Olkin, Yisong Yue, Aaron D. Ames
https://arxiv.org/abs/2508.09354 https://

CLF-RL: Control Lyapunov Function Guided Reinforcement Learning
Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly shaped objectives. In this work, we propose a structured reward shaping framework that leverages model-based trajectory generation and control Lyapunov functions (CLFs) to guide policy learning. We explore two model-based planners for generating reference trajectories: a reduced-order linear inverted pendulum (LIP) mode…

PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image
Geonhee Sim, Gyeongsik Moon
https://arxiv.org/abs/2508.09973 https://

PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image
Two major approaches exist for creating animatable human avatars. The first, a 3D-based approach, optimizes a NeRF- or 3DGS-based avatar from videos of a single person, achieving personalization through a disentangled identity representation. However, modeling pose-driven deformations, such as non-rigid cloth deformations, requires numerous pose-rich videos, which are costly and impractical to capture in daily life. The second, a diffusion-based approach, learns pose-driven deformations from la…

How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
https://arxiv.org/abs/2508.09346

How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Autonomous robots that rely on deep neural network controllers pose critical challenges for safety prediction, especially under partial observability and distribution shift. Traditional model-based verification techniques are limited in scalability and require access to low-dimensional state models, while model-free methods often lack reliability guarantees. This paper addresses these limitations by introducing a framework for calibrated safety prediction in end-to-end vision-controlled systems…

MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
Tianqi Xiang, Yi Li, Qixiang Zhang, Xiaomeng Li
https://arxiv.org/abs/2508.09967 https://

MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
Recent advances in histopathology vision-language foundation models (VLFMs) have shown promise in addressing data scarcity for whole slide image (WSI) classification via zero-shot adaptation. However, these methods remain outperformed by conventional multiple instance learning (MIL) approaches trained on large datasets, motivating recent efforts to enhance VLFM-based WSI classification through fewshot learning paradigms. While existing few-shot methods improve diagnostic accuracy with limited a…

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
Kelen C. Teixeira Vivaldini, Robert P\v{e}ni\v{c}ka, Martin Saska
https://arxiv.org/abs/2508.09304 https://

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
One of the most critical features for the successful operation of autonomous UAVs is the ability to make decisions based on the information acquired from their surroundings. Each UAV must be able to make decisions during the flight in order to deal with uncertainties in its system and the environment, and to further act upon the information being received. Such decisions influence the future behavior of the UAV, which is expressed as the path plan. Thus, decision-making in path planning is an e…

January Food Benchmark (JFB): A Public Benchmark Dataset and Evaluation Suite for Multimodal Food Analysis
Amir Hosseinian, Ashkan Dehghani Zahedani, Umer Mansoor, Noosheen Hashemi, Mark Woodward
https://arxiv.org/abs/2508.09966

January Food Benchmark (JFB): A Public Benchmark Dataset and Evaluation Suite for Multimodal Food Analysis
Progress in AI for automated nutritional analysis is critically hampered by the lack of standardized evaluation methodologies and high-quality, real-world benchmark datasets. To address this, we introduce three primary contributions. First, we present the January Food Benchmark (JFB), a publicly available collection of 1,000 food images with human-validated annotations. Second, we detail a comprehensive benchmarking framework, including robust metrics and a novel, application-oriented overall s…

LIA-X: Interpretable Latent Portrait Animator
Yaohui Wang, Di Yang, Xinyuan Chen, Francois Bremond, Yu Qiao, Antitza Dantcheva
https://arxiv.org/abs/2508.09959 https://

LIA-X: Interpretable Latent Portrait Animator
We introduce LIA-X, a novel interpretable portrait animator designed to transfer facial dynamics from a driving video to a source portrait with fine-grained control. LIA-X is an autoencoder that models motion transfer as a linear navigation of motion codes in latent space. Crucially, it incorporates a novel Sparse Motion Dictionary that enables the model to disentangle facial dynamics into interpretable factors. Deviating from previous 'warp-render' approaches, the interpretability of the Spars…

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

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[3/3]:
- Hollow-grams: Generalized Entanglement Wedges from the Gravitational Path Integral
Sami Kaya, Pratik Rath, Kyle Ritchie