Tootfinder

🏮 'Chinese lantern' structure shifts into more than a dozen shapes for various applications
https://techxplore.com/news/2025-10-chinese-lantern-shifts-dozen-applications.html

'Chinese lantern' structure shifts into more than a dozen shapes for various applications
Researchers have created a polymer "Chinese lantern" that can snap into more than a dozen curved, three-dimensional shapes by compressing or twisting the original structure. This rapid shape-shifting behavior can be controlled remotely using a magnetic field, allowing the structure to be used for a variety of applications.

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
Wei Ma, Yixiao Yang, Qiang Hu, Shi Ying, Zhi Jin, Bo Du, Zhenchang Xing, Tianlin Li, Junjie Shi, Yang Liu, Linxiao Jiang
https://arxiv.org/abs/2508.20737

Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
Applications of Large Language Models~(LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions. Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance. This paper decomposes LLM applications into a three-layer architecture: \textbf{\textit{System Shell Layer}}, \textbf{\textit{Prompt Orchestration Layer}}, and \textbf{\textit{LLM In…

What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework
Kimmo Eriksson, Simon Karlsson, Irina Vartanova, Pontus Strimling
https://arxiv.org/abs/2508.19317

What Makes AI Applications Acceptable or Unacceptable? A Predictive Moral Framework
As artificial intelligence rapidly transforms society, developers and policymakers struggle to anticipate which applications will face public moral resistance. We propose that these judgments are not idiosyncratic but systematic and predictable. In a large, preregistered study (N = 587, U.S. representative sample), we used a comprehensive taxonomy of 100 AI applications spanning personal and organizational contexts-including both functional uses and the moral treatment of AI itself. In particip…

Good Integers: A Comprehensive Review with Applications
Somphong Jitman
https://arxiv.org/abs/2508.19629 https://arxiv.org/pdf/2508.19629

Good Integers: A Comprehensive Review with Applications
For nonzero coprime integers $a$ and $b$, a positive integer $\ell$ is said to be \emph{good with respect to $a$ and $b$} if there exists a positive integer $k$ such that $\ell$ divides $a^{k} + b^{k}$. The concept of good integers has been the subject of continuous investigation since the 1990s due to their elegant number-theoretic properties and their significant applications in various areas, particularly in coding theory. This paper provides a comprehensive review of good integers, emphasiz…

📋 Roadmap includes expanded theme design options for styling different applications
🛠️ Config management planned for #Waybar, #Omarchy, and other applications
💻 Full development setup available with #npm

Note that this is specific to generic chatbots.
There’s plenty of useful applications for the underlying technologies—but it’s all pretty boring specialized stuff with not a lot of mass-market appeal which doesn’t lend itself to the scam the chatbot companies are running.

Solvable Tuple Patterns and Their Applications to Program Verification
Naoki Kobayashi, Ryosuke Sato, Ayumi Shinohara, Ryo Yoshinaka
https://arxiv.org/abs/2508.20365 https://

Solvable Tuple Patterns and Their Applications to Program Verification
Despite the recent progress of automated program verification techniques, fully automated verification of programs manipulating recursive data structures remains a challenge. We introduce the notion of solvable tuple patterns (STPs) to express invariants between list-like recursive data structures. A distinguishing feature of STPs is that they can be efficiently inferred from only a small number of positive samples; no negative samples are required. An SMT solver that supports the sequence theo…

Towards Reliable Neural Optimizers: Permutation-Equivariant Neural Approximation in Dynamic Data Driven Applications Systems
Meiyi Li, Javad Mohammadi
https://arxiv.org/abs/2508.19364

Towards Reliable Neural Optimizers: Permutation-Equivariant Neural Approximation in Dynamic Data Driven Applications Systems
Dynamic Data Driven Applications Systems (DDDAS) motivate the development of optimization approaches capable of adapting to streaming, heterogeneous, and asynchronous data from sensor networks. Many established optimization solvers, such as branch-and-bound, gradient descent, and Newton-Raphson methods, rely on iterative algorithms whose step-by-step convergence makes them too slow for real-time, multi-sensor environments. In our recent work, we introduced LOOP-PE (Learning to Optimize the Opti…

Benchmarking MLLM-based Web Understanding: Reasoning, Robustness and Safety
Junliang Liu, Jingyu Xiao, Wenxin Tang, Wenxuan Wang, Zhixian Wang, Minrui Zhang, Shuanghe Yu
https://arxiv.org/abs/2509.21782

Benchmarking MLLM-based Web Understanding: Reasoning, Robustness and Safety
Multimodal large language models (MLLMs) are increasingly positioned as AI collaborators for building complex web-related applications like GUI agents and front-end code generation. However, existing benchmarks largely emphasize visual perception or UI code generation, showing insufficient evaluation on the reasoning, robustness and safety capability required for end-to-end web applications. To bridge the gap, we introduce a comprehensive web understanding benchmark, named WebRSSBench, that joi…

BridgeShield: Enhancing Security for Cross-chain Bridge Applications via Heterogeneous Graph Mining
Dan Lin, Shunfeng Lu, Ziyan Liu, Jiajing Wu, Junyuan Fang, Kaixin Lin, Bowen Song, Zibin Zheng
https://arxiv.org/abs/2508.20517

BridgeShield: Enhancing Security for Cross-chain Bridge Applications via Heterogeneous Graph Mining
Cross-chain bridges play a vital role in enabling blockchain interoperability. However, due to the inherent design flaws and the enormous value they hold, they have become prime targets for hacker attacks. Existing detection methods show progress yet remain limited, as they mainly address single-chain behaviors and fail to capture cross-chain semantics. To address this gap, we leverage heterogeneous graph attention networks, which are well-suited for modeling multi-typed entities and relations,…

Diamond-loaded polyimide aerogel scattering filters and their applications in astrophysical and planetary science observations
Kyle R. Helson, Carol Yan Yan Chan, Stefan Arseneau, Alyssa Barlis, Charles L. Bennett, Thomas M. Essinger-Hileman, Haiquan Guo, Tobias Marriage, Manuel A. Quijada, Ariel E. Tokarz, Stephanie L. Vivod, Edward J. Wollack
https://

Diamond-loaded polyimide aerogel scattering filters and their applications in astrophysical and planetary science observations
Infrared-blocking, aerogel-based scattering filters have a broad range of potential applications in astrophysics and planetary science instruments in the far-infrared, sub-millimeter, and microwave regimes. This paper demonstrates the ability of conductively-loaded, polyimide aerogel filters to meet the mechanical and science instrument requirements for several experiments, including the Cosmology Large Angular Scale Surveyor (CLASS), the Experiment for Cryogenic Large-Aperture Intensity Mappin…

How Mode to Code, a nonprofit founded by a teenager, is teaching Bay Area seniors to use AI and avoid scams through free coding and tech literacy classes (CNN)
http://www.cnn.com/2025/09/27/tech/sillicon-valley-seniors-ai-course

Silicon Valley’s seniors get a crash course in AI
From tech lessons to tech support, the Mode to Code program offers a positive space for the curious seniors who might be intrigued by the possible applications of AI in their daily lives or simply looking to set up their emails on their phones.

Magnetism and nonlinear charge transport in NiFe2O4/{\gamma}-Al2O3/SrTiO3 heterostructure for spintronic applications
Amit Chanda, Thor Hvid-Olsen, Christina Hoegfeldt, Anshu Gupta, Alessandro Palliotto, Maja A. Dunstan, Kasper S. Pedersen, Dae-Sung Park, Damon J. Carrad, Thomas Sand Jespersen, Felix Trier
https://arxiv.org/abs/2508.20710

Magnetism and nonlinear charge transport in NiFe2O4/γ-Al2O3/SrTiO3 heterostructure for spintronic applications
We present the synthesis and study of the magnetic and electronic properties of NiFe2O4/γ-Al2O3/SrTiO3 heterostructure. The γ-Al2O3/SrTiO3 interface hosts a high-mobility two-dimensional electron gas (2DEG) with large spin-orbit coupling, making it promising for spintronics applications if it can be coupled to a suitable source of spin currents. Here, we synthesize a ferrimagnetic insulating NiFe2O4(001) layer on γ-Al2O3(001)/SrTiO3(001) using a low-temperature reactive sputtering at 150 deg…

Evaluating LLMs on microservice-based applications: how complex is your specification?
Daniel M. Yellin
https://arxiv.org/abs/2508.20119 https://arxiv.org/…

Evaluating LLMs on microservice-based applications: how complex is your specification?
In this paper we evaluate how far LLMs have advanced in generating code for real-world problems. Specifically, we explore code synthesis for microservice-based applications, a widely used architecture pattern. We define a standard template for specifying these applications, and we propose a metric for judging the difficulty level of a specification. The higher the score, the more difficult it is to generate code for the specification. We develop a framework to automate the process of testing LL…

SpeedMalloc: Improving Multi-threaded Applications via a Lightweight Core for Memory Allocation
Ruihao Li, Qinzhe Wu, Krishna Kavi, Gayatri Mehta, Jonathan C. Beard, Neeraja J. Yadwadkar, Lizy K. John
https://arxiv.org/abs/2508.20253

SpeedMalloc: Improving Multi-threaded Applications via a Lightweight Core for Memory Allocation
Memory allocation, though constituting only a small portion of the executed code, can have a "butterfly effect" on overall program performance, leading to significant and far-reaching impacts. Despite accounting for just approximately 5% of total instructions, memory allocation can result in up to a 2.7x performance variation depending on the allocator used. This effect arises from the complexity of memory allocation in modern multi-threaded multi-core systems, where allocator metadata becomes …

Optimal Control of ODE Car-Following Models: Applications to Mixed-Autonomy Platoon Control via Coupled Autonomous Vehicles
Arwa Alanqary, Alexandre M. Bayen, Xiaoqian Gong, Anish Gollakota, Alexander Keimer, Ashish Pandian
https://arxiv.org/abs/2508.19417

Optimal Control of ODE Car-Following Models: Applications to Mixed-Autonomy Platoon Control via Coupled Autonomous Vehicles
In this paper, we study the optimal control of a mixed-autonomy platoon driving on a single lane to smooth traffic flow. The platoon consists of autonomous vehicles, whose acceleration is controlled, and human-driven vehicles, whose behavior is described using a microscopic car-following model. We formulate the optimal control problem where the dynamics of the platoon are describing through a system of non-linear ODEs, with explicit constraints on both the state and the control variables. Theor…

Spatial Data Science Symposium 2025 is announced. Check it out, it's hybrid!
http://sdss2025.spatial-data-science.net/index.html

Spatial Data Science Symposium 2025
December 4-5 | Distributed & Online. The symposium aims to bring together researchers from both academia and industry to discuss experiences, insights, methodologies, and applications, taking spatial and temporal knowledge into account while addressing their domain-specific problems.

Higher order linear differential equations for unitary matrix integrals: applications and generalisations
Peter J. Forrester, Fei Wei
https://arxiv.org/abs/2508.20797 https://…

Higher order linear differential equations for unitary matrix integrals: applications and generalisations
We consider characterisations of the class of unitary matrix integrals $ \langle (\det U)^q e^{s^{1/2} {\rm Tr}(U + U^\dagger)} \rangle_{U(l)}$ in terms of a first order matrix linear differential equation for a vector function of size $l+1$, and in terms of a scalar linear differential equation of degree $l+1$. It will be shown that the latter follows from the former. The matrix linear differential equation provides an efficient way to compute the power series expansion of the matrix integrals…

Announcing the #Swift SDK for #Android
https://www.swift.org/blog/nightly-swift-sdk-for-androi…

Hybrid Method of Moments and Generalized Scattering Matrix: Applications to Antennas in Radomes, Reflectors, and Implantable Media
Chenbo Shi, Shichen Liang, Xin Gu, Jin Pan, Le Zuo
https://arxiv.org/abs/2509.22000

Hybrid Method of Moments and Generalized Scattering Matrix: Applications to Antennas in Radomes, Reflectors, and Implantable Media
Electromagnetic analysis of antennas embedded in or interacting with large surrounding structures poses inherent multiscale challenges: the antenna is electrically small yet geometrically detailed, while the environment is electrically large but comparatively smooth. To address this, we present a hybrid method of moments (MoM) and generalized scattering matrix (GSM) framework that achieves a clean separation between fine-scale and large-scale complexities while preserving their full mutual coup…

New Algorithmic Directions in Optimal Transport and Applications for Product Spaces
Salman Beigi, Omid Etesami, Mohammad Mahmoody, Amir Najafi
https://arxiv.org/abs/2509.21502 h…

New Algorithmic Directions in Optimal Transport and Applications for Product Spaces
We study optimal transport between two high-dimensional distributions $μ,ν$ in $R^n$ from an algorithmic perspective: given $x \sim μ$, find a close $y \sim ν$ in $poly(n)$ time, where $n$ is the dimension of $x,y$. Thus, running time depends on the dimension rather than the full representation size of $μ,ν$. Our main result is a general algorithm for transporting any product distribution $μ$ to any $ν$ with cost $Δ+ δ$ under $\ell_p^p$, where $Δ$ is the Knothe-Rosenblatt transport c…

Algorithms for Dynamic Computational Geometry with Applications
Laurence Boxer
https://arxiv.org/abs/2509.21681 https://arxiv.org/pdf/2509.21681

Algorithms for Dynamic Computational Geometry with Applications
Most of the literature of computational geometry concerns geometric properties of sets of static points. M.J. Atallah introduced dynamic computational geometry, concerned with both momentary and long-term geometric properties of sets of moving point-objects. This area of research seems to have been dormant recently. The current paper examines new problems in dynamic computational geometry.

Privacy-Preserving Performance Profiling of In-The-Wild GPUs
Ian McDougall, Michael Davies, Rahul Chatterjee, Somesh Jha, Karthikeyan Sankaralingam
https://arxiv.org/abs/2509.21762

Privacy-Preserving Performance Profiling of In-The-Wild GPUs
GPUs are the dominant platform for many important applications today including deep learning, accelerated computing, and scientific simulation. However, as the complexity of both applications and hardware increases, GPU chip manufacturers face a significant challenge: how to gather comprehensive performance characteristics and value profiles from GPUs deployed in real-world scenarios. Such data, encompassing the types of kernels executed and the time spent in each, is crucial for optimizing chi…

Malaysia's AI-Driven Education Landscape: Policies, Applications, and Comparative Insights for a Digital Future
Fadhilah Jamaluddin, Ahmad Hakiim Jamaluddin, Faridzah Jamaluddin, Faathirah Jamaluddin
https://arxiv.org/abs/2509.21858

Malaysia's AI-Driven Education Landscape: Policies, Applications, and Comparative Insights for a Digital Future
Artificial Intelligence (AI) is transforming education globally, and Malaysia is leveraging this potential through strategic policies to enhance learning and prepare students for a digital future. This article explores Malaysia's AI-driven education landscape, emphasising the National Artificial Intelligence Roadmap 2021-2025 and the Digital Education Policy. Employing a policy-driven analysis, it maps AI applications in pedagogy, curriculum design, administration, and teacher training across p…

Assistant Professor of the History(ies) of Global Christianity(ies)
https://ift.tt/RhdpVnP
Phillips Theological Seminary invites applications for a full-time, tenure-trackposition in the…
via Input 4 RELCFP

Applications of the Vendi score in genomic epidemiology
Bjarke Frost Nielsen, Amey P. Pasarkar, Qiqi Yang, Bryan T. Grenfell, Adji Bousso Dieng
https://arxiv.org/abs/2509.22520 …

Applications of the Vendi score in genomic epidemiology
The Vendi score (VS), a diversity metric recently conceived in the context of machine learning, with applications in a wide range of fields, has a few distinct advantages over the metrics commonly used in ecology. It is classification-independent, incorporates abundance information, and has a tunable sensitivity to rare/abundant types. Using rich COVID-19 sequence data as a paradigm, we develop methods for applying the VS to time-resolved sequence data. We show how the VS allows for characteriz…

My latest haul, for anyone interested, contains the following volumes:
Test Driven Development by Example: https://www.powells.com/book/test-driven-development-by-example-9780321146533
Building Serverless Applications on Knative:

Performance Analysis of IEEE 802.11bn with Coordinated TDMA on Real-Time Applications
Seungmin Lee, Changmin Lee, Si-Chan Noh, Joonsoo Lee
https://arxiv.org/abs/2508.18755 https…

Performance Analysis of IEEE 802.11bn with Coordinated TDMA on Real-Time Applications
Wi-Fi plays a crucial role in connecting electronic devices and providing communication services in everyday life. Recently, there has been a growing demand for services that require low-latency communication, such as real-time applications. The latest amendments to Wi-Fi, IEEE 802.11bn, are being developed to address these demands with technologies such as the multiple access point coordination (MAPC). In this paper, we demonstrate that coordinated TDMA (Co-TDMA), one of the MAPC techniques, e…

A configuration interaction approach to solve the Anderson impurity model; applications to elemental Ce
Basile Herzog, Patrik Thunstr\"om, Olle Eriksson
https://arxiv.org/abs/2508.20779

A configuration interaction approach to solve the Anderson impurity model; applications to elemental Ce
Accurate calculations of strongly correlated materials remain a formidable challenge in condensed matter physics, particularly due to the computational demand of conventional methods. This paper presents an efficient solver for dynamical mean field theory using configuration interaction (CI). The method is shown to have improved efficiency compared to traditional, exact diagonalization approaches. Hence, it provides an accessible, open-source alternative that can be executed on standard laptop …

OFMU: Optimization-Driven Framework for Machine Unlearning
Sadia Asif, Mohammad Mohammadi Amiri
https://arxiv.org/abs/2509.22483 https://arxiv.org/pdf/2509…

OFMU: Optimization-Driven Framework for Machine Unlearning
Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copyrighted materials, or outdated information, without retraining from scratch to ensure regulatory compliance, user privacy, and safety. This task, known as machine unlearning, aims to remove the influence of targeted data (forgetting) while maintaining performance on the remaining data (retention). A common approach is to formulate this as a multi-ob…

CCNeXt: An Effective Self-Supervised Stereo Depth Estimation Approach
Alexandre Lopes, Roberto Souza, Helio Pedrini
https://arxiv.org/abs/2509.22627 https://

CCNeXt: An Effective Self-Supervised Stereo Depth Estimation Approach
Depth Estimation plays a crucial role in recent applications in robotics, autonomous vehicles, and augmented reality. These scenarios commonly operate under constraints imposed by computational power. Stereo image pairs offer an effective solution for depth estimation since it only needs to estimate the disparity of pixels in image pairs to determine the depth in a known rectified system. Due to the difficulty in acquiring reliable ground-truth depth data across diverse scenarios, self-supervis…

If you're building distributed #dotnet and #aspnetcore applications, you should know about this #dotnet 10 feature that adds new host names to the default certificate.
It should make creating…

Duende Software - Identity and Access Management for .NET
We help companies using .NET to build identity and access control solutions for modern applications.

Potential of Quantum Computing Applications for Smart Grid Digital Twins and Future Directions
Arianne Ornella Lemo, Ahmad Mohammad Saber, Deepa Kundur, Adam W. Skorek
https://arxiv.org/abs/2508.18654 …

Potential of Quantum Computing Applications for Smart Grid Digital Twins and Future Directions
The convergence of digital twin technology and quantum computing is opening new horizons for the modeling, control, and optimization of smart grid systems. This paper reviews the current research landscape at the intersection of these fields, with a focus on how quantum algorithms can enhance the performance of digital twins in smart energy systems. We conduct a thematic literature review and identify key research trends, technical challenges, and gaps in real-world adoption. Further, a concept…

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Rohan Phanse, Yijie Zhou, Kejian Shi, Wencai Zhang, Yixin Liu, Yilun Zhao, Arman Cohan
https://arxiv.org/abs/2508.20867

MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Retrieval-augmented systems are typically evaluated in settings where information required to answer the query can be found within a single source or the answer is short-form or factoid-based. However, many real-world applications demand the ability to integrate and summarize information scattered across multiple sources, where no single source is sufficient to respond to the user's question. In such settings, the retrieval component of a RAG pipeline must recognize a variety of relevance signa…

DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View
Tian Qiu, Alan Zoubi, Yiyuan Lin, Ruiming Du, Lailiang Cheng, Yu Jiang
https://arxiv.org/abs/2508.19508

DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View
Digital twin applications offered transformative potential by enabling real-time monitoring and robotic simulation through accurate virtual replicas of physical assets. The key to these systems is 3D reconstruction with high geometrical fidelity. However, existing methods struggled under field conditions, especially with sparse and occluded views. This study developed a two-stage framework (DATR) for the reconstruction of apple trees from sparse views. The first stage leverages onboard sensors …

SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions
Jiawei Shan, Yiming Dong, Jiwei Zhao
https://arxiv.org/abs/2509.21707 https://arxiv.o…

SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions
Real-world applications often face scarce labeled data due to the high cost and time requirements of gold-standard experiments, whereas unlabeled data are typically abundant. With the growing adoption of machine learning techniques, it has become increasingly feasible to generate multiple predicted labels using a variety of models and algorithms, including deep learning, large language models, and generative AI. In this paper, we propose a novel approach that safely and adaptively aggregates mu…

A Correct by Construction Fault Tolerant Voter for Input Selection of a Control System
Arif Ali AP, Jasine Babu, Deepa Sara John
https://arxiv.org/abs/2509.22236 https://…

A Correct by Construction Fault Tolerant Voter for Input Selection of a Control System
Safety-critical systems use redundant input units to improve their reliability and fault tolerance. A voting logic is then used to select a reliable input from the redundant sources. A fault detection and isolation rules help in selecting input units that can participate in voting. This work deals with the formal requirement formulation, design, verification and synthesis of a generic voting unit for an $N$-modular redundant measurement system used for control applications in avionics systems. …

The MUG-10 Framework for Preventing Usability Issues in Mobile Application Development
Pawel Weichbroth, Tomasz Szot
https://arxiv.org/abs/2509.21914 https://

The MUG-10 Framework for Preventing Usability Issues in Mobile Application Development
Nowadays, mobile applications are essential tools for everyday life, providing users with anytime, anywhere access to up-to-date information, communication, and entertainment. Needless to say, hardware limitations and the diverse needs of different user groups pose a number of design and development challenges. According to recent studies, usability is one of the most revealing among many others. However, few have made the direct effort to provide and discuss what countermeasures can be applied…

Mod $\ell$ non-vanishing of self-dual Hecke $L$-values over CM fields and applications
Ashay Burungale, Wei He, Ye Tian, Xiangdong Ye
https://arxiv.org/abs/2508.19706 https://…

Mod $\ell$ non-vanishing of self-dual Hecke $L$-values over CM fields and applications
Let $λ$ be a self-dual Hecke character over a CM field $K$. Let $\mathfrak{p}$ be a prime of the maximal totally real subfield $F$ of $K$ and $Γ_{\mathfrak{p}}$ the Galois group of the maximal anticyclotomic ${\mathbb{Z}}_{p}^{deg \mathfrak{p}}$-extension of $K$ unramified outside $\mathfrak{p}$. We prove that $$L(1,λν)\neq 0$$ for all but finitely many finite order characters $ν$ of $Γ_{\mathfrak{p}}$ such that $ε(λν)=+1$. For an ordinary prime $\ell$ with respect to the CM quadratic …

Characterizing Trust Boundary Vulnerabilities in TEE Containers
Weijie Liu, Hongbo Chen, Shuo Huai, Zhen Xu, Wenhao Wang, Zhi Li, Zheli Liu
https://arxiv.org/abs/2508.20962 http…

Characterizing Trust Boundary Vulnerabilities in TEE Containers
Trusted Execution Environments (TEEs) have emerged as a cornerstone of confidential computing, garnering significant attention from both academia and industry. To enable the secure development, execution, and deployment, of applications on TEE platforms, TEE containers have been introduced as middleware solutions. These containers aim to shield applications from potentially malicious operating systems and orchestration interfaces while maintaining usability and reliability. In this paper, we an…

COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics
Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan
https://arxiv.org/abs/2509.22240 https://

COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics
In clinical applications, the utility of segmentation models is often based on the accuracy of derived downstream metrics such as organ size, rather than by the pixel-level accuracy of the segmentation masks themselves. Thus, uncertainty quantification for such metrics is crucial for decision-making. Conformal prediction (CP) is a popular framework to derive such principled uncertainty guarantees, but applying CP naively to the final scalar metric is inefficient because it treats the complex, n…

Hyperbinary partitions and q-deformed rationals
Thomas McConville (Kennesaw State University), James Propp (University of Massachusetts Lowell), Bruce E. Sagan (Michigan State University)
https://arxiv.org/abs/2508.20026

Hyperbinary partitions and q-deformed rationals
A hyperbinary partition of the nonnegative integer n is a partition where every part is a power of 2 and every part appears at most twice. We give three applications of the length generating function for such partitions, denoted by h_q(n). Morier-Genoud and Ovsienko defined the q-analogue of a rational number [r/s]_q in various ways, most of which depend directly or indirectly on the continued fraction expansion of r/s. As our first application we show that [r/s]_q = q h_q(n-1)/h_q(n) where r/s…

Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
Vojtech Mrazek, Konstantinos Balaskas, Paula Carolina Lozano Duarte, Zdenek Vasicek, Mehdi B. Tahoori, Georgios Zervakis
https://arxiv.org/abs/2508.19660

Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
Printed electronics offer a promising alternative for applications beyond silicon-based systems, requiring properties like flexibility, stretchability, conformality, and ultra-low fabrication costs. Despite the large feature sizes in printed electronics, printed neural networks have attracted attention for meeting target application requirements, though realizing complex circuits remains challenging. This work bridges the gap between classification accuracy and area efficiency in printed neural…

Zero-Effort Image-to-Music Generation: An Interpretable RAG-based VLM Approach
Zijian Zhao, Dian Jin, Zijing Zhou
https://arxiv.org/abs/2509.22378 https://…

Zero-Effort Image-to-Music Generation: An Interpretable RAG-based VLM Approach
Recently, Image-to-Music (I2M) generation has garnered significant attention, with potential applications in fields such as gaming, advertising, and multi-modal art creation. However, due to the ambiguous and subjective nature of I2M tasks, most end-to-end methods lack interpretability, leaving users puzzled about the generation results. Even methods based on emotion mapping face controversy, as emotion represents only a singular aspect of art. Additionally, most learning-based methods require …

VIPER: A high-resolution multimode fiber-fed VIPA spectrograph concept for characterizing exoplanet atmospheric escape
Matthew C. H. Leung, David Charbonneau, Andrew Szentgyorgyi, Colby Jurgenson, Morgan MacLeod, Surangkhana Rukdee, Shreyas Vissapragada, Fabienne Nail, Joseph Zajac, Andrea K. Dupree
https://arxiv.org/abs/2508.20169

VIPER: A high-resolution multimode fiber-fed VIPA spectrograph concept for characterizing exoplanet atmospheric escape
An increasing number of applications in exoplanetary science require spectrographs with high resolution and high throughput without the need for a broad spectral range. Examples include the search for biosignatures through the detection of the oxygen A-band at 760 nm, and the study of atmospheric escape through the helium 1083 nm triplet. These applications align well with the capabilities of a spectrograph based on a Virtually Imaged Phased Array (VIPA), a high-throughput dispersive element th…

Non-periodicity of the sequence of the last nonzero digits of factorials and its applications to transcendence
Kohta Gejima, Fumichika Takamizo
https://arxiv.org/abs/2509.21948 …

Non-periodicity of the sequence of the last nonzero digits of factorials and its applications to transcendence
We prove that the sequence of the last nonzero digits of factorials in every integer base $b>2$ is not eventually periodic. We also extend the Adamczewski--Bugeaud criterion, originally formulated for integer base expansions, to Cantor base expansions associated with a periodic Cantor base. As an application, we show that a certain real number expressed through a Cantor base expansion is transcendental when the Cantor base and the digit sequence satisfy suitable conditions.

ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Chenyu Liu, Yuqiu Deng, Tianyu Liu, Jinan Zhou, Xinliang Zhou, Ziyu Jia, Yi Ding
https://arxiv.org/abs/2509.22556 h…

ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Electroencephalography (EEG), with its broad range of applications, necessitates models that can generalize effectively across various tasks and datasets. Large EEG Models (LEMs) address this by pretraining encoder-centric architectures on large-scale unlabeled data to extract universal representations. While effective, these models lack decoders of comparable capacity, limiting the full utilization of the learned features. To address this issue, we introduce ECHO, a novel decoder-centric LEM p…

EfficientDepth: A Fast and Detail-Preserving Monocular Depth Estimation Model
Andrii Litvynchuk, Ivan Livinsky, Anand Ravi, Nima Kalantari, Andrii Tsarov
https://arxiv.org/abs/2509.22527

EfficientDepth: A Fast and Detail-Preserving Monocular Depth Estimation Model
Monocular depth estimation (MDE) plays a pivotal role in various computer vision applications, such as robotics, augmented reality, and autonomous driving. Despite recent advancements, existing methods often fail to meet key requirements for 3D reconstruction and view synthesis, including geometric consistency, fine details, robustness to real-world challenges like reflective surfaces, and efficiency for edge devices. To address these challenges, we introduce a novel MDE system, called Efficien…

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Dawei Gao, Zitao Li, Yuexiang Xie, Weirui Kuang, Liuyi Yao, Bingchen Qian, Zhijian Ma, Yue Cui, Haohao Luo, Shen Li, Lu Yi, Yi Yu, Shiqi He, Zhiling Luo, Wenmeng Zhou, Zhicheng Zhang, Xuguang He, Ziqian Chen, Weikai Liao, Farruh Isakulovich Kushnazarov, Yaliang Li, Bolin Ding, Jingren Zhou

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address real-world tasks. In line with such an evolution, AgentScope introduces major improvements in a new version (1.0), towards comprehensively supporting flexible and efficient tool-based agent-environment interactions for building agentic applications. Specifically, we abstract foundational components essential for agen…

Gain-Assisted and Dynamically Controlled Optical Bistability for Quantum Logic Gate Applications
Parkhi Bhardwaj, Poonam Yadav, Bodhaditya Santra, Shubhrangshu Dasgupta
https://arxiv.org/abs/2508.18332

Gain-Assisted and Dynamically Controlled Optical Bistability for Quantum Logic Gate Applications
The propagation of a probe field in an N-type four level cold atomic system is investigated under the influence of multiple coherent fields. Coherent control of quantum interference enables switching of the probe field between transparency and gain regimes. Subsequent analysis focuses on how the introduction of gain in the probe transition lowers the threshold for optical bistability, thereby enhancing the nonlinear response of the system at reduced input intensities. A detailed analysis of opt…

HEAL: A Hypothesis-Based Preference-Aware Analysis Framework
Yifu Huo, Chenglong Wang, Qiren Zhu, Shunjie Xing, Tong Xiao, Chunliang Zhang, Tongran Liu, Jinbo Zhu
https://arxiv.org/abs/2508.19922

HEAL: A Hypothesis-Based Preference-Aware Analysis Framework
Preference optimization methods like DPO have achieved remarkable performance in LLM alignment. However, the evaluation for these methods relies on a single response and overlooks other potential outputs, which could also be generated in real-world applications within this hypothetical space. To address this issue, this paper presents a \textbf{H}ypothesis-based Pr\textbf{E}ference-aware \textbf{A}na\textbf{L}ysis Framework (HEAL), a novel evaluation paradigm that formulates preference alignmen…

Learning-Based Collaborative Control for Bi-Manual Tactile-Reactive Grasping
Leonel Giacobbe, Jingdao Chen, Chuangchuang Sun
https://arxiv.org/abs/2509.22421 https://

Learning-Based Collaborative Control for Bi-Manual Tactile-Reactive Grasping
Grasping is a core task in robotics with various applications. However, most current implementations are primarily designed for rigid items, and their performance drops considerably when handling fragile or deformable materials that require real-time feedback. Meanwhile, tactile-reactive grasping focuses on a single agent, which limits their ability to grasp and manipulate large, heavy objects. To overcome this, we propose a learning-based, tactile-reactive multi-agent Model Predictive Controll…

In silico investigation of Ba-based ternary chalcogenides for photovoltaic applications
Ramya Kormath Madam Raghupathy, Hossein Mirhosseini, Thomas D. K\"uhne
https://arxiv.org/abs/2508.18796

In silico investigation of Ba-based ternary chalcogenides for photovoltaic applications
In solar cells, the absorbers are the key components for capturing solar energy and converting photons into electron-hole pairs. The search for high-performance absorbers with advantageous characteristics is an ongoing task for researchers. In this work, we investigated promising and environmentally benign Ba-based ternary chalcogenides for photovoltaic applications. The total number of Ba-based ternary chalcogenides in the Materials Project database was found to be 279. Materials screening bas…

IndiSeek learns information-guided disentangled representations
Yu Gui, Cong Ma, Zongming Ma
https://arxiv.org/abs/2509.21584 https://arxiv.org/pdf/2509.21…

IndiSeek learns information-guided disentangled representations
Learning disentangled representations is a fundamental task in multi-modal learning. In modern applications such as single-cell multi-omics, both shared and modality-specific features are critical for characterizing cell states and supporting downstream analyses. Ideally, modality-specific features should be independent of shared ones while also capturing all complementary information within each modality. This tradeoff is naturally expressed through information-theoretic criteria, but mutual-i…

Measuring #EnergyConsumption in #ProgrammingLanguages for #AI Applications

EMG-UP: Unsupervised Personalization in Cross-User EMG Gesture Recognition
Nana Wang, Gen Li, Zhaoxin Fan, Suli Wang
https://arxiv.org/abs/2509.21589 https://

EMG-UP: Unsupervised Personalization in Cross-User EMG Gesture Recognition
Cross-user electromyography (EMG)-based gesture recognition represents a fundamental challenge in achieving scalable and personalized human-machine interaction within real-world applications. Despite extensive efforts, existing methodologies struggle to generalize effectively across users due to the intrinsic biological variability of EMG signals, resulting from anatomical heterogeneity and diverse task execution styles. To address this limitation, we introduce EMG-UP, a novel and effective fra…

Performance measurements of modern Fortran MPI applications with Score-P
Gregor Corbin
https://arxiv.org/abs/2508.16592 https://arxiv.org/pdf/2508.16592

Performance measurements of modern Fortran MPI applications with Score-P
Version 3.0 of the Message-Passing Interface (MPI) standard, released in 2012, introduced a new set of language bindings for Fortran 2008. By making use of modern language features and the enhanced interoperability with C, there was finally a type safe and standard conforming method to call MPI from Fortran. This highly recommended use mpi_f08 language binding has since then been widely adopted among developers of modern Fortran applications. However, tool support for the F08 bindings is still …

On Lattice Diameter Segments and A Discrete Borsuk Partition Problem
Anouk E. Brose, Jes\'us A. De Loera, Gyivan Lopez-Campos, Antonio J. Torres
https://arxiv.org/abs/2508.20009

On Lattice Diameter Segments and A Discrete Borsuk Partition Problem
The lattice diameter of a bounded set $S \subset \mathbb{R}^d$ measures the maximal number of lattice points in a segment whose endpoints are lattice points in $S$. Such a segment is called a lattice diameter segment of $S$. This simple invariant yields interesting applications and challenges. We describe a polynomial-time algorithm that computes lattice diameter segments of lattice polygons and show that computing lattice diameters of semi-algebraic sets in dimensions three and higher is NP-ha…

A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications
Mohtashim Baqar, Sian Lun Lau, Mansoor Ebrahim
https://arxiv.org/abs/2508.17351

A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications
Image Quality Assessment (IQA) plays a vital role in applications such as image compression, restoration, and multimedia streaming. However, existing metrics often struggle to generalize across diverse image types - particularly between natural-scene images (NSIs) and screen-content images (SCIs) - due to their differing structural and perceptual characteristics. To address this limitation, we propose a novel full-reference IQA framework: Permutation Entropy-based Features Fused with Random For…

An Efficient ADMM Method for Ratio-Type Nonconvex and Nonsmooth Minimization in Sparse Recovery
Lang Yu, Nanjing Huang
https://arxiv.org/abs/2509.21969 https://

An Efficient ADMM Method for Ratio-Type Nonconvex and Nonsmooth Minimization in Sparse Recovery
Sparse signal recovery based on nonconvex and nonsmooth optimization problems has significant applications and demonstrates superior performance in signal processing and machine learning. This work deals with a scale-invariant $\ell_{1/2}/\ell_{2}$ sparse minimization with nonconvex, nonseparable, ratio-type regularization to enhance the accuracy and stability of sparse recovery. Within the framework of the null space property, we analyze the conditions for exact and stable recovery in constrai…

Gaussian filters in quantum lattice systems: Applications to spectral flow, local perturbations, clustering, and the quantum Hall effect
Sven Bachmann (Simon), Zhiqian (Simon), Du, Martin Fraas, Tom Wessel
https://arxiv.org/abs/2508.15913

Gaussian filters in quantum lattice systems: Applications to spectral flow, local perturbations, clustering, and the quantum Hall effect
We consider the locality and spectral properties of the smearing \[ τ_f(A) = \int_{-\infty}^\infty dt \, f(t) \, τ_t(A) \] when applied to the dynamics $τ_t$ of quantum spin systems. While recent applications of this map have used superpolynomially but not exponentially decaying functions $f$ to ensure exact spectral properties, we use here Gaussian filters. This improves the locality at the expense of errors on the spectral side. We propose a number of concrete applications, from quasi-adia…

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Platform for TinyAI Applications
Simone Machetti, Pasquale Davide Schiavone, Giovanni Ansaloni, Miguel Pe\'on-Quir\'os, David Atienza
https://arxiv.org/abs/2508.16959

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Platform for TinyAI Applications
In this work, we present X-HEEP, an open-source, configurable, and extendible RISC-V platform for ultra-low-power edge applications (TinyAI). X-HEEP features the eXtendible Accelerator InterFace (XAIF), which enables seamless integration of accelerators with varying requirements along with an extensive internal configuration of cores, memory, bus, and peripherals. Moreover, it supports various development flows, including FPGA prototyping, ASIC implementation, and mixed SystemC-RTL modeling, en…

Global Convergence in Neural ODEs: Impact of Activation Functions
Tianxiang Gao, Siyuan Sun, Hailiang Liu, Hongyang Gao
https://arxiv.org/abs/2509.22436 https://

Global Convergence in Neural ODEs: Impact of Activation Functions
Neural Ordinary Differential Equations (ODEs) have been successful in various applications due to their continuous nature and parameter-sharing efficiency. However, these unique characteristics also introduce challenges in training, particularly with respect to gradient computation accuracy and convergence analysis. In this paper, we address these challenges by investigating the impact of activation functions. We demonstrate that the properties of activation functions, specifically smoothness a…

SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization
Chien-Chun Wang, En-Lun Yu, Jeih-Weih Hung, Shih-Chieh Huang, Berlin Chen
https://arxiv.org/abs/2508.20885

SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization
Voice activity detection (VAD) is essential for speech-driven applications, but remains far from perfect in noisy and resource-limited environments. Existing methods often lack robustness to noise, and their frame-wise classification losses are only loosely coupled with the evaluation metric of VAD. To address these challenges, we propose SincQDR-VAD, a compact and robust framework that combines a Sinc-extractor front-end with a novel quadratic disparity ranking loss. The Sinc-extractor uses le…

The Thinking Spectrum: An Emperical Study of Tunable Reasoning in LLMs through Model Merging
Xiaochong Lan, Yu Zheng, Shiteng Cao, Yong Li
https://arxiv.org/abs/2509.22034 https…

The Thinking Spectrum: An Emperical Study of Tunable Reasoning in LLMs through Model Merging
The growing demand for large language models (LLMs) with tunable reasoning capabilities in many real-world applications highlights a critical need for methods that can efficiently produce a spectrum of models balancing reasoning depth and computational cost. Model merging has emerged as a promising, training-free technique to address this challenge by arithmetically combining the weights of a general-purpose model with a specialized reasoning model. While various merging techniques exist, their…

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Luca Bergamin, Giovanna Maria Dimitri, Fabio Aiolli
https://arxiv.org/abs/2509.22399

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this field has been driven by deep learning applications, which have the potential to scale these systems even in the presence of noise and artifacts. However, these systems are not yet perfected. We argue that performance can be improved by incorporating common medical knowledge into the segmentation model's loss function. To…

SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications
Joshua Lee, Ali Arastehfard, Weiran Liu, Xuegang Ban, Yuan Hong
https://arxiv.org/abs/2508.19115

SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications
Autonomous driving and V2X technologies have developed rapidly in the past decade, leading to improved safety and efficiency in modern transportation. These systems interact with extensive networks of vehicles, roadside infrastructure, and cloud resources to support their machine learning capabilities. However, the widespread use of machine learning in V2X systems raises issues over the privacy of the data involved. This is particularly concerning for smart-transit and driver safety application…

T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables
Jie Zhang, Changzai Pan, Kaiwen Wei, Sishi Xiong, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Yongxiang Li, Xuelong Li
https://arxiv.org/abs/2508.19813

T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables
Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity and diversity of tables lead to suboptimal reasoning outcomes; and 2) existing table benchmarks lack the capacity to adequately assess the practical application of this task. To fil…

Efficient Human-Aware Task Allocation for Multi-Robot Systems in Shared Environments
Maryam Kazemi Eskeri, Ville Kyrki, Dominik Baumann, Tomasz Piotr Kucner
https://arxiv.org/abs/2508.19731

Efficient Human-Aware Task Allocation for Multi-Robot Systems in Shared Environments
Multi-robot systems are increasingly deployed in applications, such as intralogistics or autonomous delivery, where multiple robots collaborate to complete tasks efficiently. One of the key factors enabling their efficient cooperation is Multi-Robot Task Allocation (MRTA). Algorithms solving this problem optimize task distribution among robots to minimize the overall execution time. In shared environments, apart from the relative distance between the robots and the tasks, the execution time is …

Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications
Patrick Loic Foalem, Leuson Da Silva, Foutse Khomh, Heng Li, Ettore Merlo
https://arxiv.org/abs/2508.17851

Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications
Machine learning (ML) is increasingly applied across industries to automate decision-making, but concerns about ethical and legal compliance remain due to limited transparency, fairness, and accountability. Monitoring through logging a long-standing practice in traditional software offers a potential means for auditing ML applications, as logs provide traceable records of system behavior useful for debugging, performance analysis, and continuous auditing. systematically auditing models for comp…

Quenched Quantum Feature Maps
Anton Simen, Carlos Flores-Garrigos, Murilo Henrique De Oliveira, Gabriel Dario Alvarado Barrios, Juan F. R. Hern\'andez, Qi Zhang, Alejandro Gomez Cadavid, Yolanda Vives-Gilabert, Jos\'e D. Mart\'in-Guerrero, Enrique Solano, Narendra N. Hegade, Archismita Dalal
https://arxiv.org/abs/2508.20975

Quenched Quantum Feature Maps
We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding a dataset information into disordered quantum many-body spin-glass problems, followed by a nonadiabatic evolution and feature extraction via measurements of expectation values, significantly enhances machine learning (ML) models. By analyzing the performance…

A Nonparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior
Trinnhallen Brisley, Gordon Ross, Daniel Paulin
https://arxiv.org/abs/2509.21996 https://

A Nonparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior
Hawkes process models are used in settings where past events increase the likelihood of future events occurring. Many applications record events as counts on a regular grid, yet discrete-time Hawkes models remain comparatively underused and are often constrained by fixed-form baselines and excitation kernels. In particular, there is a lack of flexible, nonparametric treatments of both the baseline and the excitation in discrete time. To this end, we propose the Gaussian Process Discrete Hawkes …

LLM-Augmented and Fair Machine Learning Framework for University Admission Prediction
Mohammad Abbadi, Yassine Himeur, Shadi Atalla, Dahlia Mansoor, Wathiq Mansoor
https://arxiv.org/abs/2509.22560

LLM-Augmented and Fair Machine Learning Framework for University Admission Prediction
Universities face surging applications and heightened expectations for fairness, making accurate admission prediction increasingly vital. This work presents a comprehensive framework that fuses machine learning, deep learning, and large language model techniques to combine structured academic and demographic variables with unstructured text signals. Drawing on more than 2,000 student records, the study benchmarks logistic regression, Naive Bayes, random forests, deep neural networks, and a stac…

CoFormer: Collaborating with Heterogeneous Edge Devices for Scalable Transformer Inference
Guanyu Xu, Zhiwei Hao, Li Shen, Yong Luo, Fuhui Sun, Xiaoyan Wang, Han Hu, Yonggang Wen
https://arxiv.org/abs/2508.20375

CoFormer: Collaborating with Heterogeneous Edge Devices for Scalable Transformer Inference
The impressive performance of transformer models has sparked the deployment of intelligent applications on resource-constrained edge devices. However, ensuring high-quality service for real-time edge systems is a significant challenge due to the considerable computational demands and resource requirements of these models. Existing strategies typically either offload transformer computations to other devices or directly deploy compressed models on individual edge devices. These strategies, howev…

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Luca Bergamin, Roberto Confalonieri, Fabio Aiolli
https://arxiv.org/abs/2509.22384

(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
Deep neural networks are widely used in practical applications of AI, however, their inner structure and complexity made them generally not easily interpretable. Model transparency and interpretability are key requirements for multiple scenarios where high performance is not enough to adopt the proposed solution. In this work, a differentiable approximation of $L_0$ regularization is adapted into a logic-based neural network, the Multi-layer Logical Perceptron (MLLP), to study its efficacy in r…

SeqVLM: Proposal-Guided Multi-View Sequences Reasoning via VLM for Zero-Shot 3D Visual Grounding
Jiawen Lin, Shiran Bian, Yihang Zhu, Wenbin Tan, Yachao Zhang, Yuan Xie, Yanyun Qu
https://arxiv.org/abs/2508.20758

SeqVLM: Proposal-Guided Multi-View Sequences Reasoning via VLM for Zero-Shot 3D Visual Grounding
3D Visual Grounding (3DVG) aims to localize objects in 3D scenes using natural language descriptions. Although supervised methods achieve higher accuracy in constrained settings, zero-shot 3DVG holds greater promise for real-world applications since eliminating scene-specific training requirements. However, existing zero-shot methods face challenges of spatial-limited reasoning due to reliance on single-view localization, and contextual omissions or detail degradation. To address these issues, …

The Express Lane to Spam and Centralization: An Empirical Analysis of Arbitrum's Timeboost
Johnnatan Messias, Christof Ferreira Torres
https://arxiv.org/abs/2509.22143 https…

The Express Lane to Spam and Centralization: An Empirical Analysis of Arbitrum's Timeboost
DeFi applications are vulnerable to MEV, where specialized actors profit by reordering or inserting transactions. To mitigate latency races and internalize MEV revenue, Arbitrum introduced Timeboost, an auction-based transaction sequencing mechanism that grants short-term priority access to an express lane. In this paper we present the first large-scale empirical study of Timeboost, analyzing over 11.5 million express lane transactions and 151 thousand auctions between April and July 2025. Our …

Tikhonov regularized exterior penalty methods for hierarchical variational inequalities
Meggie Marschner, Mathias Staudigl
https://arxiv.org/abs/2508.20872 https://

Tikhonov regularized exterior penalty methods for hierarchical variational inequalities
We consider nested variational inequalities con- sisting in a (upper-level) variational inequality whose feasible set is given by the solution set of another (lower-level) variational inequality. This class of hierarchical equilibrium contains a wealth of important applications, including purely hierarchical convex bilevel optimization problems and certain multi-follower games. Working within a real Hilbert space setting, we develop a double loop prox-penalization algorithm with strong conver- …

TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning
Simin Ma, Shujian Liu, Jun Tan, Yebowen Hu, Song Wang, Sathish Reddy Indurthi, Sanqiang Zhao, Liwei Wu, Jianbing Han, Kaiqiang Song
https://arxiv.org/abs/2508.20374

TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning
Diverse instruction data is vital for effective instruction tuning of large language models, as it enables the model to generalize across different types of inputs . Building such diversified instruction dataset is an essential step in this process. Existing approaches often leverage large language models to automatically explore and generate diverse instructions, ensuring both data diversity and quality. However, they tend to overlook an important factor in real-world applications: on-task rel…

Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search
Zeyu Xiong, Yixuan Nan, Li Gao, Hengzhu Tang, Shuaiqiang Wang, Junfeng Wang, Dawei Yin
https://arxiv.org/abs/2508.20559

Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search
In the dynamic landscape of large-scale web search, Query-Driven Text Summarization (QDTS) aims to generate concise and informative summaries from textual documents based on a given query, which is essential for improving user engagement and facilitating rapid decision-making. Traditional extractive summarization models, based primarily on ranking candidate summary segments, have been the dominant approach in industrial applications. However, these approaches suffer from two key limitations: 1)…

GPU-Accelerated Loopy Belief Propagation for Program Analysis
Haoyu Feng, Xin Zhang
https://arxiv.org/abs/2509.22337 https://arxiv.org/pdf/2509.22337

GPU-Accelerated Loopy Belief Propagation for Program Analysis
Loopy Belief Propagation (LBP) is a widely used approximate inference algorithm in probabilistic graphical models, with applications in computer vision, error correction codes, protein folding, program analysis, etc. However, LBP faces significant computational challenges when applied to large-scale program analysis. While GPU (Graphics Processing Unit) parallel computing provides a promising solution, existing approaches lack support for flexible update strategies and have yet to integrate log…

Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments
Liding Zhang, Zeqi Li, Kuanqi Cai, Qian Huang, Zhenshan Bing, Alois Knoll
https://arxiv.org/abs/2508.20899

Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments
Enabling robots to efficiently search for and identify objects in complex, unstructured environments is critical for diverse applications ranging from household assistance to industrial automation. However, traditional scene representations typically capture only static semantics and lack interpretable contextual reasoning, limiting their ability to guide object search in completely unfamiliar settings. To address this challenge, we propose a language-enhanced hierarchical navigation framework …

Polynomial Chaos Expansion for Operator Learning
Himanshu Sharma, Luk\'a\v{s} Nov\'ak, Michael D. Shields
https://arxiv.org/abs/2508.20886 https://…

Polynomial Chaos Expansion for Operator Learning
Operator learning (OL) has emerged as a powerful tool in scientific machine learning (SciML) for approximating mappings between infinite-dimensional functional spaces. One of its main applications is learning the solution operator of partial differential equations (PDEs). While much of the progress in this area has been driven by deep neural network-based approaches such as Deep Operator Networks (DeepONet) and Fourier Neural Operator (FNO), recent work has begun to explore traditional machine …

Spectral Gaps with Quantum Counting Queries and Oblivious State Preparation
Almudena Carrera Vazquez, Aleksandros Sobczyk
https://arxiv.org/abs/2508.21002 https://

Spectral Gaps with Quantum Counting Queries and Oblivious State Preparation
Approximating the $k$-th spectral gap $Δ_k=|λ_k-λ_{k+1}|$ and the corresponding midpoint $μ_k=\frac{λ_k+λ_{k+1}}{2}$ of an $N\times N$ Hermitian matrix with eigenvalues $λ_1\geqλ_2\geq\ldots\geqλ_N$, is an important special case of the eigenproblem with numerous applications in science and engineering. In this work, we present a quantum algorithm which approximates these values up to additive error $εΔ_k$ using a logarithmic number of qubits. Notably, in the QRAM model, its…

Assessing local deformation and computing scalar curvature with nonlinear conformal regularization of decoders
Benjamin Cou\'eraud, Vikram Sunkara, Christof Sch\"utte
https://arxiv.org/abs/2508.20413

Assessing local deformation and computing scalar curvature with nonlinear conformal regularization of decoders
One aim of dimensionality reduction is to discover the main factors that explain the data, and as such is paramount to many applications. When working with high dimensional data, autoencoders offer a simple yet effective approach to learn low-dimensional representations. The two components of a general autoencoder consist first of an encoder that maps the observed data onto a latent space; and second a decoder that maps the latent space back to the original observation space, which allows to le…

An upper bound for the size of the ideal class monoid
Sungmun Cho, Jungtaek Hong, Yuchan Lee
https://arxiv.org/abs/2509.22386 https://arxiv.org/pdf/2509.22…

An upper bound for the size of the ideal class monoid
The ideal class monoid for an order $R$ in a finite field extension $E/F$ of a number field, denoted by $\overline{\mathrm{Cl}}(R)$, is a fundamental object to study in number theory which has useful applications in algebraic geometry and topology. In this paper, we describe an upper bound for $\#\overline{\mathrm{Cl}}(R)$, in terms of the class number of $E$ and (local) orbital integrals for $\mathfrak{gl}_n$. We also describe an upper bound for the class number of $E$ in terms of the Minkowsk…

Bridging the Mobile Trust Gap: A Zero Trust Framework for Consumer-Facing Applications
Alexander Tabalipa
https://arxiv.org/abs/2508.16662 https://arxiv.or…

Bridging the Mobile Trust Gap: A Zero Trust Framework for Consumer-Facing Applications
Zero Trust Architecture (ZTA) has become a widely adopted model for securing enterprise environments, promoting continuous verification and minimal trust across systems. However, its application in mobile contexts remains limited, despite mobile applications now accounting for most global digital interactions and being increasingly targeted by sophisticated threats. Existing Zero Trust frameworks developed by organisations such as the National Institute of Standards and Technology (NIST) and th…

From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Education
Iris Delikoura (May), Yi. R (May), Fung, Pan Hui
https://arxiv.org/abs/2509.21972 …

From Superficial Outputs to Superficial Learning: Risks of Large Language Models in Education
Large Language Models (LLMs) are transforming education by enabling personalization, feedback, and knowledge access, while also raising concerns about risks to students and learning systems. Yet empirical evidence on these risks remains fragmented. This paper presents a systematic review of 70 empirical studies across computer science, education, and psychology. Guided by four research questions, we examine: (i) which applications of LLMs in education have been most frequently explored; (ii) ho…

Redesigning GROMACS Halo Exchange: Improving Strong Scaling with GPU-initiated NVSHMEM
Mahesh Doijade, Andrey Alekseenko, Ania Brown, Alan Gray, Szil\'ard P\'all
https://arxiv.org/abs/2509.21527

Redesigning GROMACS Halo Exchange: Improving Strong Scaling with GPU-initiated NVSHMEM
Improving time-to-solution in molecular dynamics simulations often requires strong scaling due to fixed-sized problems. GROMACS is highly latency-sensitive, with peak iteration rates in the sub-millisecond, making scalability on heterogeneous supercomputers challenging. MPI's CPU-centric nature introduces additional latencies on GPU-resident applications' critical path, hindering GPU utilization and scalability. To address these limitations, we present an NVSHMEM-based GPU kernel-initiated rede…

An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose
Qifeng Wang, Weigang Li, Lei Nie, Xin Xu, Wenping Liu, Zhe Xu
https://arxiv.org/abs/2509.22058 https://

An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose
As a key technology for autonomous navigation and positioning in mobile robots, light detection and ranging (LiDAR) odometry is widely used in autonomous driving applications. The Iterative Closest Point (ICP)-based methods have become the core technique in LiDAR odometry due to their efficient and accurate point cloud registration capability. However, some existing ICP-based methods do not consider the reliability of the initial pose, which may cause the method to converge to a local optimum. …

Feature- and process-based optimal control of quantum dynamics
M. Farnia, V. Rezvani, A. T. Rezakhani
https://arxiv.org/abs/2509.22401 https://arxiv.org/pd…

Feature- and process-based optimal control of quantum dynamics
Preparing desired quantum states and quantum operations (processes) is essential for numerous tasks in quantum computation. Several approaches have been developed for optimal control of quantum states, whereas optimal strategies for preparation of a given quantum process have remained fairly less explored. For some applications, rather than a specific desired state or process, it may suffice to obtain states or processes with specific desired features such as (high) coherence or purity. In such…

Enabling Content Management Systems as an Information Source in Model-driven Projects
Joan Giner-Miguelez, Abel G\'omez, Jordi Cabot
https://arxiv.org/abs/2508.19797 https:/…

Enabling Content Management Systems as an Information Source in Model-driven Projects
Content Management Systems (CMSs) are the most popular tool when it comes to create and publish content across the web. Recently, CMSs have evolved, becoming \emph{headless}. Content served by a \emph{headless CMS} aims to be consumed by other applications and services through REST APIs rather than by human users through a web browser. This evolution has enabled CMSs to become a notorious source of content to be used in a variety of contexts beyond pure web navigation. As such, CMS have become …

Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation
Ian Taylor, Juliane Mueller, Julie Bessac
https://arxiv.org/abs/2509.21711 https://

Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation
As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develo…

Investigating Faithfulness in Large Audio Language Models
Lovenya Jain, Pooneh Mousavi, Mirco Ravanelli, Cem Subakan
https://arxiv.org/abs/2509.22363 https://

Investigating Faithfulness in Large Audio Language Models
Faithfulness measures whether chain-of-thought (CoT) representations accurately reflect a model's decision process and can be used as reliable explanations. Prior work has shown that CoTs from text-based LLMs are often unfaithful. This question has not been explored for large audio-language models (LALMs), where faithfulness is critical for safety-sensitive applications. Reasoning in LALMs is also more challenging, as models must first extract relevant clues from audio before reasoning over the…

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Dawei Gao, Zitao Li, Yuexiang Xie, Weirui Kuang, Liuyi Yao, Bingchen Qian, Zhijian Ma, Yue Cui, Haohao Luo, Shen Li, Lu Yi, Yi Yu, Shiqi He, Zhiling Luo, Wenmeng Zhou, Zhicheng Zhang, Xuguang He, Ziqian Chen, Weikai Liao, Farruh Isakulovich Kushnazarov, Yaliang Li, Bolin Ding, Jingren Zhou

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address real-world tasks. In line with such an evolution, AgentScope introduces major improvements in a new version (1.0), towards comprehensively supporting flexible and efficient tool-based agent-environment interactions for building agentic applications. Specifically, we abstract foundational components essential for agen…

Prompt-in-Content Attacks: Exploiting Uploaded Inputs to Hijack LLM Behavior
Zhuotao Lian, Weiyu Wang, Qingkui Zeng, Toru Nakanishi, Teruaki Kitasuka, Chunhua Su
https://arxiv.org/abs/2508.19287

Prompt-in-Content Attacks: Exploiting Uploaded Inputs to Hijack LLM Behavior
Large Language Models (LLMs) are widely deployed in applications that accept user-submitted content, such as uploaded documents or pasted text, for tasks like summarization and question answering. In this paper, we identify a new class of attacks, prompt in content injection, where adversarial instructions are embedded in seemingly benign inputs. When processed by the LLM, these hidden prompts can manipulate outputs without user awareness or system compromise, leading to biased summaries, fabri…

Compilation-informed probabilistic quantum error cancellation
Giancarlo Camilo, Thiago O. Maciel, Allan Tosta, Abdulla Alhajri, Thais de Lima Silva, Daniel Stilck Fran\c{c}a, Leandro Aolita
https://arxiv.org/abs/2508.20174

Compilation-informed probabilistic quantum error cancellation
The potential of quantum computers to outperform classical ones in practically useful tasks remains challenging in the near term due to scaling limitations and high error rates of current quantum hardware. While quantum error correction (QEC) offers a clear path towards fault tolerance, overcoming the scalability issues will take time. Early applications will likely rely on QEC combined with quantum error mitigation (QEM). We introduce a QEM scheme against both compilation errors and logical-ga…

You Can't Steal Nothing: Mitigating Prompt Leakages in LLMs via System Vectors
Bochuan Cao, Changjiang Li, Yuanpu Cao, Yameng Ge, Ting Wang, Jinghui Chen
https://arxiv.org/abs/2509.21884

You Can't Steal Nothing: Mitigating Prompt Leakages in LLMs via System Vectors
Large language models (LLMs) have been widely adopted across various applications, leveraging customized system prompts for diverse tasks. Facing potential system prompt leakage risks, model developers have implemented strategies to prevent leakage, primarily by disabling LLMs from repeating their context when encountering known attack patterns. However, it remains vulnerable to new and unforeseen prompt-leaking techniques. In this paper, we first introduce a simple yet effective prompt leaking…

PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance
Mengxiao Wang, Yuxuan Zhang, Guofei Gu
https://arxiv.org/abs/2508.20890 https://arx…

PromptSleuth: Detecting Prompt Injection via Semantic Intent Invariance
Large Language Models (LLMs) are increasingly integrated into real-world applications, from virtual assistants to autonomous agents. However, their flexibility also introduces new attack vectors-particularly Prompt Injection (PI), where adversaries manipulate model behavior through crafted inputs. As attackers continuously evolve with paraphrased, obfuscated, and even multi-task injection strategies, existing benchmarks are no longer sufficient to capture the full spectrum of emerging threats. …

MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs
Prakhar Sharma, Haohuang Wen, Vinod Yegneswaran, Ashish Gehani, Phillip Porras, Zhiqiang Lin
https://arxiv.org/abs/2509.21634

MobiLLM: An Agentic AI Framework for Closed-Loop Threat Mitigation in 6G Open RANs
The evolution toward 6G networks is being accelerated by the Open Radio Access Network (O-RAN) paradigm -- an open, interoperable architecture that enables intelligent, modular applications across public telecom and private enterprise domains. While this openness creates unprecedented opportunities for innovation, it also expands the attack surface, demanding resilient, low-cost, and autonomous security solutions. Legacy defenses remain largely reactive, labor-intensive, and inadequate for the …