Tootfinder

Quantifying Radio Source Morphology
Lachlan J. Barnes, Andrew M. Hopkins, Lawrence Rudnick, Heinz Andernach, Michael Cowley, Nikhel Gupta, Ray P. Norris, Stanislav S. Shabala, Tayyaba Zafar
https://arxiv.org/abs/2506.21906

Quantifying Radio Source Morphology
The advent of next-generation telescope facilities brings with it an unprecedented amount of data, and the demand for effective tools to process and classify this information has become increasingly important. This work proposes a novel approach to quantify the radio galaxy morphology, through the development of a series of algorithmic metrics that can quantitatively describe the structure of radio source, and can be applied to radio images in an automatic way. These metrics are intuitive in na…

Measurement of the Lund plane for light- and beauty-quark jets
LHCb collaboration, et al.
https://arxiv.org/abs/2505.23530 https://ar…

Measurement of the Lund plane for light- and beauty-quark jets
The substructure of jets in quantum chromodynamics (QCD) has garnered significant attention with the advent of infrared- and collinear-safe clustering algorithms and observables. A key question emerging from these studies is how in-jet emissions at soft and hard energy scales, across collinear and wide angles relative to the emitter, differ with the mass of the emitting parton. The Lund jet plane (LJP) is a perturbatively well-defined substructure observable that maps the radiation pattern of j…

Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization
Pol Francesch Huc, Emily Bates, Simone D'Amico
https://arxiv.org/abs/2507.19459 https:…

Fast Learning of Non-Cooperative Spacecraft 3D Models through Primitive Initialization
The advent of novel view synthesis techniques such as NeRF and 3D Gaussian Splatting (3DGS) has enabled learning precise 3D models only from posed monocular images. Although these methods are attractive, they hold two major limitations that prevent their use in space applications: they require poses during training, and have high computational cost at training and inference. To address these limitations, this work contributes: (1) a Convolutional Neural Network (CNN) based primitive initializer…

Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
Chandra Maddila, Adam Tait, Claire Chang, Daniel Cheng, Nauman Ahmad, Vijayaraghavan Murali, Marshall Roch, Arnaud Avondet, Aaron Meltzer, Victor Montalvao, Michael Hopko, Chris Waterson, Parth Thakkar, Renuka Fernandez, Kristian Kristensen, Sivan Barzily, Sherry Chen, Rui Abreu, Nachiappan Nagappan, Payam Shodjai, Killian Murphy, James Everingham, Aparna Raman…

Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
Aim: With the advent of LLMs, sophisticated agentic program repair has become viable at large organizations with large codebases. In this work, we develop an Engineering Agent that fixes the source code based on test failures at scale across diverse software offerings internally. Method: Using Llama as the base, we employ the ReAct harness to develop an agent. We start with a test failure that was triaged by a rule-based test failure bot. We then set up an agentic harness and allow the agent …

PE firm Advent International agrees to acquire U-blox, a SIX Swiss Exchange-listed wireless and positioning chipmaker, for about $1.3B via public tender offer (Andrea Figueras/Wall Street Journal)
https://www.wsj.com/business/de…

Modular Embedding Recomposition for Incremental Learning
Aniello Panariello, Emanuele Frascaroli, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara
https://arxiv.org/abs/2508.16463

Modular Embedding Recomposition for Incremental Learning
The advent of pre-trained Vision-Language Models (VLMs) has significantly transformed Continual Learning (CL), mainly due to their zero-shot classification abilities. Such proficiency makes VLMs well-suited for real-world applications, enabling robust performance on novel unseen classes without requiring adaptation. However, fine-tuning remains essential when downstream tasks deviate significantly from the pre-training domain. Prior CL approaches primarily focus on preserving the zero-shot capa…

32 Millionen Schulden - Stadt muss heuer sogar den Advent „einsparen“ #News #Nachrichten

Post-Quantum and Blockchain-Based Attestation for Trusted FPGAs in B5G Networks
Ilias Papalamprou, Nikolaos Fotos, Nikolaos Chatzivasileiadis, Anna Angelogianni, Dimosthenis Masouros, Dimitrios Soudris
https://arxiv.org/abs/2506.21073

Post-Quantum and Blockchain-Based Attestation for Trusted FPGAs in B5G Networks
The advent of 5G and beyond has brought increased performance networks, facilitating the deployment of services closer to the user. To meet performance requirements such services require specialized hardware, such as Field Programmable Gate Arrays (FPGAs). However, FPGAs are often deployed in unprotected environments, leaving the user's applications vulnerable to multiple attacks. With the rise of quantum computing, which threatens the integrity of widely-used cryptographic algorithms, the need…

Literature Review of the Effect of Quantum Computing on Cryptocurrencies using Blockchain Technology
Adi Mutha, Jitendra Sandu
https://arxiv.org/abs/2508.17296 https://

Literature Review of the Effect of Quantum Computing on Cryptocurrencies using Blockchain Technology
With the advent of quantum computing, cryptocurrencies that rely on blockchain technology face mounting cryptographic vulnerabilities. This paper presents a comprehensive literature review evaluating how quantum algorithms, specifically Shors and Grovers, could disrupt the foundational security mechanisms of cryptocurrencies. Shors algorithm poses a threat to public-key cryptographic schemes by enabling efficient factorization and discrete logarithm solving, thereby endangering digital signatur…

Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning
Andrea Bussolan, Oliver Avram, Andrea Pignata, Gianvito Urgese, Stefano Baraldo, Anna Valente
https://arxiv.org/abs/2506.20212

Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning
With the advent of Industry 5.0, manufacturers are increasingly prioritizing worker well-being alongside mass customization. Stress-aware Human-Robot Collaboration (HRC) plays a crucial role in this paradigm, where robots must adapt their behavior to human mental states to improve collaboration fluency and safety. This paper presents a novel framework that integrates Federated Learning (FL) to enable personalized mental state evaluation while preserving user privacy. By leveraging physiological…

Saving Energy with Relaxed Latency Constraints: A Study on Data Compression and Communication
Pietro Talli, Anup Mishra, Federico Chiariotti, Israel Leyva-Mayorga, Andrea Zanella, Petar Popovski
https://arxiv.org/abs/2508.18863

Saving Energy with Relaxed Latency Constraints: A Study on Data Compression and Communication
With the advent of edge computing, data generated by end devices can be pre-processed before transmission, possibly saving transmission time and energy. On the other hand, data processing itself incurs latency and energy consumption, depending on the complexity of the computing operations and the speed of the processor. The energy-latency-reliability profile resulting from the concatenation of pre-processing operations (specifically, data compression) and data transmission is particularly relev…

LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models
Doohee You, Andy Parisi, Zach Vander Velden, Lara Dantas Inojosa
https://arxiv.org/abs/2508.16478

LLM-as-classifier: Semi-Supervised, Iterative Framework for Hierarchical Text Classification using Large Language Models
The advent of Large Language Models (LLMs) has provided unprecedented capabilities for analyzing unstructured text data. However, deploying these models as reliable, robust, and scalable classifiers in production environments presents significant methodological challenges. Standard fine-tuning approaches can be resource-intensive and often struggle with the dynamic nature of real-world data distributions, which is common in the industry. In this paper, we propose a comprehensive, semi-supervise…

Learning measurement-induced phase transitions using attention
Hyejin Kim, Abhishek Kumar, Yiqing Zhou, Yichen Xu, Romain Vasseur, Eun-Ah Kim
https://arxiv.org/abs/2508.15895 ht…

Learning measurement-induced phase transitions using attention
Measurement-induced phase transitions (MIPTs) epitomize new intellectual pursuits inspired by the advent of quantum hardware and the emergence of discrete and programmable circuit dynamics. Nevertheless, experimentally observing this transition is challenging, often requiring non-scalable protocols, such as post-selecting measurement trajectories or relying on classical simulations. We introduce a scalable data-centric approach using Quantum Attention Networks (QuAN) to detect MIPTs without req…

Cyber Orbits of Large Scale Network Traffic
Jeremy Kepner, Hayden Jananthan, Chasen Milner, Michael Houle, Michael Jones, Peter Michaleas, Alex Pentland
https://arxiv.org/abs/2508.16847

Cyber Orbits of Large Scale Network Traffic
The advent of high-performance graph libraries, such as the GraphBLAS, has enabled the analysis of massive network data sets and revealed new models for their behavior. Physical analogies for complicated network behavior can be a useful aid to understanding these newly discovered network phenomena. Prior work leveraged the canonical Gull's Lighthouse problem and developed a computational heuristic for modeling large scale network traffic using this model. A general solution using this approach …

Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization
Yupei Zhang, Xiaofei Wang, Anran Liu, Lequan Yu, Chao Li
https://arxiv.org/abs/2508.16479

Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization
Histopathology remains the gold standard for cancer diagnosis and prognosis. With the advent of transcriptome profiling, multi-modal learning combining transcriptomics with histology offers more comprehensive information. However, existing multi-modal approaches are challenged by intrinsic multi-modal heterogeneity, insufficient multi-scale integration, and reliance on paired data, restricting clinical applicability. To address these challenges, we propose a disentangled multi-modal framework w…

GPU Implementation of Second-Order Linear and Nonlinear Programming Solvers
Alexis Montoison, Fran\c{c}ois Pacaud, Sungho Shin, Mihai Anitescu
https://arxiv.org/abs/2508.16094 h…

GPU Implementation of Second-Order Linear and Nonlinear Programming Solvers
In recent years, GPU-accelerated optimization solvers based on second-order methods (e.g., interior-point methods) have gained momentum with the advent of mature and efficient GPU-accelerated direct sparse linear solvers, such as cuDSS. This paper provides an overview of the state of the art in GPU-based second-order solvers, focusing on pivoting-free interior-point methods for large and sparse linear and nonlinear programs. We begin by highlighting the capabilities and limitations of the curre…

Three-dimensional hyperspectral imaging with optical microcombs
Stephan Amann, Edoardo Vicentini, Bingxin Xu, Weiqiang Xie, Yang He, Qiang Lin, John Bowers, Theodor W. H\"ansch, Kerry Vahala, Nathalie Picqu\'e
https://arxiv.org/abs/2508.18219

Three-dimensional hyperspectral imaging with optical microcombs
Optical frequency combs have revolutionised time and frequency metrology [1, 2]. The advent of microresonator-based frequency combs ('microcombs' [3-5]) is set to lead to the miniaturisation of devices that are ideally suited to a wide range of applications, including microwave generation [6, 7], ranging [8-10], the precise calibration of astronomical spectrographs [11], neuromorphic computing [12, 13], high-bandwidth data communications[14], and quantum-optics [15, 16] platforms. Here, we intr…

BotHash: Efficient and Training-Free Bot Detection Through Approximate Nearest Neighbor
Edoardo Di Paolo, Fabio De Gaspari, Angelo Spognardi
https://arxiv.org/abs/2506.20503

BotHash: Efficient and Training-Free Bot Detection Through Approximate Nearest Neighbor
Online Social Networks (OSNs) are a cornerstone in modern society, serving as platforms for diverse content consumption by millions of users each day. However, the challenge of ensuring the accuracy of information shared on these platforms remains significant, especially with the widespread dissemination of disinformation. Social bots -- automated accounts designed to mimic human behavior, frequently spreading misinformation -- represent one of the critical problems of OSNs. The advent of Large…

After nuclear weapons testing began in 1945, atmospheric radiation contaminated new steel production worldwide.
For decades, scientists needing radiation-free metal for sensitive instruments had to salvage steel from pre-war shipwrecks.
Scientists called this steel
"low-background steel."
Graham-Cumming sees a parallel with today's web, where AI-generated content increasingly mingles with human-created material and contaminates it.
With the advent o…

Scientists once hoarded pre-nuclear steel; now we’re hoarding pre-AI content
Newly announced catalog collects pre-2022 sources untouched by ChatGPT and AI contamination.

Classification errors distort findings in automated speech processing: examples and solutions from child-development research
Lucas Gautheron, Evan Kidd, Anton Malko, Marvin Lavechin, Alejandrina Cristia
https://arxiv.org/abs/2508.15637

Classification errors distort findings in automated speech processing: examples and solutions from child-development research
With the advent of wearable recorders, scientists are increasingly turning to automated methods of analysis of audio and video data in order to measure children's experience, behavior, and outcomes, with a sizable literature employing long-form audio-recordings to study language acquisition. While numerous articles report on the accuracy and reliability of the most popular automated classifiers, less has been written on the downstream effects of classification errors on measurements and statist…

Distinguishing Predictive and Generative AI in Regulation
Jennifer Wang, Andrew Selbst, Solon Barocas, Suresh Venkatasubramanian
https://arxiv.org/abs/2506.17347

Distinguishing Predictive and Generative AI in Regulation
Over the past decade, policymakers have developed a set of regulatory tools to ensure AI development aligns with key societal goals. Many of these tools were initially developed in response to concerns with predictive AI and therefore encode certain assumptions about the nature of AI systems and the utility of certain regulatory approaches. With the advent of generative AI, however, some of these assumptions no longer hold, even as policymakers attempt to maintain a single regulatory target tha…

Two Types of $1/f$ Range in Solar Wind Turbulence
Zesen Huang, Marco Velli, B. D. G. Chandran, Chen Shi, Yuliang Ding, Lorenzo Matteini, Kyung-Eun Choi
https://arxiv.org/abs/2506.17523

Two Types of $1/f$ Range in Solar Wind Turbulence
The $1/f$ noise is a ubiquitous phenomenon in natural systems. Since the advent of space exploration, the $1/f$ range has been consistently observed in \textit{in situ} solar wind measurements throughout the heliosphere, sparking decades of debate regarding its origin. Recent Parker Solar Probe (PSP) observations near the Alfvén surface have revealed a systematic absence of the $1/f$ range in pristine solar wind, providing a unique opportunity to investigate its origin in solar wind turbulence…

Advent International agrees to acquire and take private Nasdaq-listed Sapiens International, an Israel-based insurance software company, for $2.5B in cash (Reuters)
https://www.reuters.com/world/middle-east/insurance-softwa…

The Unreasonable Effectiveness of Mathematical Experiments: What Makes Mathematics Work
Asvin G
https://arxiv.org/abs/2506.19787 https://

The Unreasonable Effectiveness of Mathematical Experiments: What Makes Mathematics Work
This essay proposes a reconceptualization of mathematics as an experimental science exploring patterns in computation and what the advent of AI-assited provers might imply in light of this reconceptualization. Beginning with Voevodsky's crisis of confidence, where a major result remained in limbo for 24 years, neither proven nor refuted, we argue that mathematics is not the discovery of eternal truths but theory-building in response to computational experiments. Just as physicists build theorie…

Modular Embedding Recomposition for Incremental Learning
Aniello Panariello, Emanuele Frascaroli, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara
https://arxiv.org/abs/2508.16463

Modular Embedding Recomposition for Incremental Learning
The advent of pre-trained Vision-Language Models (VLMs) has significantly transformed Continual Learning (CL), mainly due to their zero-shot classification abilities. Such proficiency makes VLMs well-suited for real-world applications, enabling robust performance on novel unseen classes without requiring adaptation. However, fine-tuning remains essential when downstream tasks deviate significantly from the pre-training domain. Prior CL approaches primarily focus on preserving the zero-shot capa…

»This is an awful language. Don't program in INTERCAL«
@… is talking about his work on bringing the original INTERCAL72 compiler from 1973 back to life again. Podcast recommendation, enjoy:

Advent of Computing
Welcome to Advent of Computing, the show that talks about the shocking, intriguing, and all too often relevant history of computing. A lot of little things we take for granted today have rich stories behind their creation, in each episode we will learn how older tech has lead to our modern world.

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Dipayan Saha, Shams Tarek, Hasan Al Shaikh, Khan Thamid Hasan, Pavan Sai Nalluri, Md. Ajoad Hasan, Nashmin Alam, Jingbo Zhou, Sujan Kumar Saha, Mark Tehranipoor, Farimah Farahmandi
https://arxiv.org/abs/2506.20415…

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models (LLMs), with their remarkable capabilities in natural language understanding, code generation, and advanced reasoning, presents a new paradigm for tackling these issues. Moving beyond monolithic models, an agentic ap…

NIKA2 Cosmological Legacy Survey: Blind detection of galaxy clusters in the COSMOS field via the Sunyaev-Zel'dovich effect
D. Ch\'erouvrier, J. F. Macias-Perez, F. X. D\'esert, R. Adam, P. Ade, H. Ajeddig, S. Amarantidis, P. Andr\'e, H. Aussel, R. Barrena, A. Beelen, A. Benoit, S. Berta, M. B\'ethermin, A. Bongiovanni, J. Bounmy, O. Bourrion, L. -J. Bing, M. Calvo, A. Catalano, M. De Petris, S. Doyle, E. F. C. Driessen, G. Ejlali, A. Ferragamo, M. Fernandez-Torreiro…

NIKA2 Cosmological Legacy Survey: Blind detection of galaxy clusters in the COSMOS field via the Sunyaev-Zel'dovich effect
(Abridged) Clusters of galaxies, formed in the latest stages of structure formation, are unique cosmological probes. With the advent of large CMB surveys like those from the Planck satellite, the ACT and SPT telescopes, we now have access to a large number of galaxy clusters detected at millimeter wavelengths via the thermal Sunyaev-Zel'dovich (tSZ) effect. Nevertheless, it is interesting to complement them with high-angular-resolution (tens of arcseconds) observations to target the lowest-mass…

Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges
Liyuan Chen, Shuoling Liu, Jiangpeng Yan, Xiaoyu Wang, Henglin Liu, Chuang Li, Kecheng Jiao, Jixuan Ying, Yang Veronica Liu, Qiang Yang, Xiu Li
https://arxiv.org/abs/2507.18577

Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges
The advent of foundation models (FMs) - large-scale pre-trained models with strong generalization capabilities - has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These chal…

scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection
Zhen Yuan, Shaoqing Jiao, Yihang Xiao, Jiajie Peng
https://arxiv.org/abs/2506.20697

scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection
The advent of single-cell multi-omics technologies has enabled the simultaneous profiling of diverse omics layers within individual cells. Integrating such multimodal data provides unprecedented insights into cellular identity, regulatory processes, and disease mechanisms. However, it remains challenging, as current methods often rely on selecting highly variable genes or peaks during preprocessing, which may inadvertently discard crucial biological information. Here, we present scMamba, a foun…

Exploring Organizational Strategies in Immersive Computational Notebooks
Sungwon In, Ayush Roy, Eric Krokos, Kirsten Whitley, Chris North, Yalong Yang
https://arxiv.org/abs/2508.14346

Exploring Organizational Strategies in Immersive Computational Notebooks
Computational notebooks, which integrate code, documentation, tags, and visualizations into a single document, have become increasingly popular for data analysis tasks. With the advent of immersive technologies, these notebooks have evolved into a new paradigm, enabling more interactive and intuitive ways to perform data analysis. An immersive computational notebook, which integrates computational notebooks within an immersive environment, significantly enhances navigation performance with embo…

phylo2vec: a library for vector-based phylogenetic tree manipulation
Neil Scheidwasser, Ayush Nag, Matthew J Penn, Anthony MV Jakob, Frederik M{\o}lkj{\ae}r Andersen, Mark P Khurana, Landung Setiawan, Madeline Gordon, David A Duch\^ene, Samir Bhatt
https://arxiv.org/abs/2506.19490

phylo2vec: a library for vector-based phylogenetic tree manipulation
Phylogenetics is a fundamental component of many analysis frameworks in biology as well as linguistics to study the evolutionary relationships of different entities. Recently, the advent of large-scale genomics and the SARS-CoV-2 pandemic has underscored the necessity for phylogenetic software to handle large datasets of genomes or phylogenetic trees. While significant efforts have focused on scaling optimisation algorithms, visualization, and lineage identification, an emerging body of researc…

Bounded Memory in Distributed Networks
Ran Ben Basat, Keren Censor-Hillel, Yi-Jun Chang, Wenchen Han, Dean Leitersdorf, Gregory Schwartzman
https://arxiv.org/abs/2506.11644

Bounded Memory in Distributed Networks
The recent advent of programmable switches makes distributed algorithms readily deployable in real-world datacenter networks. However, there are still gaps between theory and practice that prevent the smooth adaptation of CONGEST algorithms to these environments. In this paper, we focus on the memory restrictions that arise in real-world deployments. We introduce the $μ$-CONGEST model where on top of the bandwidth restriction, the memory of nodes is also limited to $μ$ words, in line with rea…

Beyond Syntax: Action Semantics Learning for App Agents
Bohan Tang, Dezhao Luo, Jingxuan Chen, Shaogang Gong, Jianye Hao, Jun Wang, Kun Shao
https://arxiv.org/abs/2506.17697

Beyond Syntax: Action Semantics Learning for App Agents
The advent of Large Language Models (LLMs) enables the rise of App agents that interpret user intent and operate smartphone Apps through actions such as clicking and scrolling. While prompt-based solutions with closed LLM APIs show promising ability, they incur heavy compute costs and external API dependency. Fine-tuning smaller open-source LLMs solves these limitations. However, current fine-tuning methods use a syntax learning paradigm that forces agents to reproduce exactly the ground truth …

Nonlinear reversal of photo-excitation on the attosecond time scale improves ultrafast x-ray diffraction images
Anatoli Ulmer, Phay J. Ho, Bruno Langbehn, Stephan Kuschel, Linos Hecht, Razib Obaid, Simon Dold, Taran Driver, Joseph Duris, Ming-Fu Lin, David Cesar, Paris Franz, Zhaoheng Guo, Philip A. Hart, Andrei Kamalov, Kirk A. Larsen, Xiang Li, Michael Meyer, Kazutaka Nakahara, Robert G. Radloff, River Robles, Lara R\"onnebeck, Nick Sudar, Adam M. Summers, Linda Young, Peter Wal…

Nonlinear reversal of photo-excitation on the attosecond time scale improves ultrafast x-ray diffraction images
The advent of isolated and intense sub-femtosecond X-ray pulses enables tracking of quantummechanical motion of electrons in molecules and solids. The combination of X-ray spectroscopy and diffraction imaging is a powerful approach to visualize non-equilibrium dynamics in systems beyond few atoms. However, extreme x-ray intensities introduce significant electronic damage, limiting material contrast and spatial resolution. Here we show that newly available intense subfemtosecond (sub-fs) x-ray F…

A Reference Model and Patterns for Production Event Data Enrichment
Mark van der Pas, Remco Dijkman, Alp Ak\c{c}ay, Ivo Adan, John Walker
https://arxiv.org/abs/2506.11502

A Reference Model and Patterns for Production Event Data Enrichment
With the advent of digital transformation, organisations are increasingly generating large volumes of data through the execution of various processes across disparate systems. By integrating data from these heterogeneous sources, it becomes possible to derive new insights essential for tasks such as monitoring and analysing process performance. Typically, this information is extracted during a data pre-processing or engineering phase. However, this step is often performed in an ad-hoc manner an…

StackTrans: From Large Language Model to Large Pushdown Automata Model
Kechi Zhang, Ge Li, Jia Li, Huangzhao Zhang, Yihong Dong, Jia Li, Jingjing Xu, Zhi Jin
https://arxiv.org/abs/2507.15343

StackTrans: From Large Language Model to Large Pushdown Automata Model
The Transformer architecture has emerged as a landmark advancement within the broad field of artificial intelligence, effectively catalyzing the advent of large language models (LLMs). However, despite its remarkable capabilities and the substantial progress it has facilitated, the Transformer architecture still has some limitations. One such intrinsic limitation is its inability to effectively capture the Chomsky hierarchy, such as regular expressions or deterministic context-free grammars. Dr…

Analysis of Post-Quantum Cryptography in User Equipment in 5G and Beyond
Sanzida Hoque, Abdullah Aydeger, Engin Zeydan, Madhusanka Liyanage
https://arxiv.org/abs/2507.17074 http…

Analysis of Post-Quantum Cryptography in User Equipment in 5G and Beyond
The advent of quantum computing threatens the security of classical public-key cryptographic systems, prompting the transition to post-quantum cryptography (PQC). While PQC has been analyzed in theory, its performance in practical wireless communication environments remains underexplored. This paper presents a detailed implementation and performance evaluation of NIST-selected PQC algorithms in user equipment (UE) to UE communications over 5G networks. Using a full 5G emulation stack (Open5GS a…

My favourite hammock for on our balcony broke and landed me on my ass, last month. Just got around to fixing it with parts of a new lashing strap that's rated for much more weight than me. The hammock itself is still in good shape, so this should last me some more years, now.
(I got the hammock from Kickstarter years ago, in a time when I was still fascinated by that website...

LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
Haotian Zhang, Liu Liu, Baosheng Yu, Jiayan Qiu, Yanwei Ren, Xianglong Liu
https://arxiv.org/abs/2506.12394

LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
The advent of parameter-efficient fine-tuning methods has significantly reduced the computational burden of adapting large-scale pretrained models to diverse downstream tasks. However, existing approaches often struggle to achieve robust performance under domain shifts while maintaining computational efficiency. To address this challenge, we propose Low-rAnk Regulated Gradient Projection (LARGO) algorithm that integrates dynamic constraints into low-rank adaptation methods. Specifically, LARGO …

Eliciting Reasoning in Language Models with Cognitive Tools
Brown Ebouky, Andrea Bartezzaghi, Mattia Rigotti
https://arxiv.org/abs/2506.12115 https://

Eliciting Reasoning in Language Models with Cognitive Tools
The recent advent of reasoning models like OpenAI's o1 was met with excited speculation by the AI community about the mechanisms underlying these capabilities in closed models, followed by a rush of replication efforts, particularly from the open source community. These speculations were largely settled by the demonstration from DeepSeek-R1 that chains-of-thought and reinforcement learning (RL) can effectively replicate reasoning on top of base LLMs. However, it remains valuable to explore alte…

A Survey of Data Compression Algorithms and their Applications
Mohammad Hosseini
https://arxiv.org/abs/2506.10000 https://arxiv.org/p…

A Survey of Data Compression Algorithms and their Applications
Today, with the growing demands of information storage and data transfer, data compression is becoming increasingly important. Data Compression is a technique which is used to decrease the size of data. This is very useful when some huge files have to be transferred over networks or being stored on a data storage device and the size is more than the capacity of the data storage or would consume so much bandwidth for transmission in a network. With the advent of the Internet and mobile devices w…

SoK: Securing the Final Frontier for Cybersecurity in Space-Based Infrastructure
Nafisa Anjum, Tasnuva Farheen
https://arxiv.org/abs/2507.17064 https://arx…

SoK: Securing the Final Frontier for Cybersecurity in Space-Based Infrastructure
With the advent of modern technology, critical infrastructure, communications, and national security depend increasingly on space-based assets. These assets, along with associated assets like data relay systems and ground stations, are, therefore, in serious danger of cyberattacks. Strong security defenses are essential to ensure data integrity, maintain secure operations, and protect assets in space and on the ground against various threats. Previous research has found discrete vulnerabilities…

Millimeter-Wave Position Sensing Using Reconfigurable Intelligent Surfaces: Positioning Error Bound and Phase Shift Configuration
Xin Cheng, Guangjie Han, Menglu Li, Ruoguang Li, Feng Shu
https://arxiv.org/abs/2508.06958

Millimeter-Wave Position Sensing Using Reconfigurable Intelligent Surfaces: Positioning Error Bound and Phase Shift Configuration
Millimeter-wave (mmWave) positioning has emerged as a promising technology for next-generation intelligent systems. The advent of reconfigurable intelligent surfaces (RISs) has revolutionized high-precision mmWave localization by enabling dynamic manipulation of wireless propagation environments. This paper investigates a three-dimensional (3D) multi-input single-output (MISO) mmWave positioning system assisted by multiple RISs. We introduce a measurement framework incorporating sequential RIS …

From 5G RAN Queue Dynamics to Playback: A Performance Analysis for QUIC Video Streaming
Jashanjot Singh Sidhu, Jorge Ignacio Sandoval, Abdelhak Bentaleb, Sandra Cespedes
https://arxiv.org/abs/2508.15087

From 5G RAN Queue Dynamics to Playback: A Performance Analysis for QUIC Video Streaming
The rapid adoption of QUIC as a transport protocol has transformed content delivery by reducing latency, enhancing congestion control (CC), and enabling more efficient multiplexing. With the advent of 5G networks, which support ultra-low latency and high bandwidth, streaming high-resolution video at 4K and beyond has become increasingly viable. However, optimizing Quality of Experience (QoE) in mobile networks remains challenging due to the complex interactions among Adaptive Bit Rate (ABR) sch…

Cardiotensor: A Python Library for Orientation Analysis and Tractography in 3D Cardiac Imaging
Joseph Brunet, Lisa Chestnutt, Matthieu Chourrout, Hector Dejea, Vaishnavi Sabarigirivasan, Peter D. Lee, Andrew C. Cook
https://arxiv.org/abs/2508.07476

Cardiotensor: A Python Library for Orientation Analysis and Tractography in 3D Cardiac Imaging
Understanding the architecture of the human heart requires analysis of its microstructural organization across scales. With the advent of high-resolution imaging techniques such as synchrotron-based tomography, it has become possible to visualize entire hearts at micron-scale resolution. However, translating these large, complex volumetric datasets into interpretable, quantitative descriptors of cardiac organization remains a major challenge. Here we present cardiotensor, an open-source Python …

When Competition Helps: Achieving Optimal Traffic Flow with Multiple Autonomous Planners
Ivan Geffner, Erez Karpas, Moshe Tennenholtz
https://arxiv.org/abs/2508.07145 https://…

When Competition Helps: Achieving Optimal Traffic Flow with Multiple Autonomous Planners
The inefficiency of selfish routing in congested networks is a classical problem in algorithmic game theory, often captured by the Price of Anarchy (i.e., the ratio between the social cost of decentralized decisions and that of a centrally optimized solution.) With the advent of autonomous vehicles, capable of receiving and executing centrally assigned routes, it is natural to ask whether their deployment can eliminate this inefficiency. At first glance, a central authority could simply compute…

Overview of complex organic molecule observations in protostellar systems
P. Nazari
https://arxiv.org/abs/2508.15771 https://arxiv.org/pdf/2508.15771

Overview of complex organic molecule observations in protostellar systems
Complex organic molecules (COMs) have been detected abundantly at various stages of star formation, particularly in the warm protostellar phase. The progress in gas-phase measurements has been accelerated by the advent of the Atacama Large Millimeter/submillimeter Array and in ice measurements by the James Webb Space Telescope. Particularly, the community has moved from single-source studies of COMs to statistical analyses because of these powerful instruments. In this article, I review surveys…

Exploring Palette based Color Guidance in Diffusion Models
Qianru Qiu, Jiafeng Mao, Xueting Wang
https://arxiv.org/abs/2508.08754 https://arxiv.org/pdf/250…

Exploring Palette based Color Guidance in Diffusion Models
With the advent of diffusion models, Text-to-Image (T2I) generation has seen substantial advancements. Current T2I models allow users to specify object colors using linguistic color names, and some methods aim to personalize color-object association through prompt learning. However, existing models struggle to provide comprehensive control over the color schemes of an entire image, especially for background elements and less prominent objects not explicitly mentioned in prompts. This paper prop…

From Logic to Language: A Trust Index for Problem Solving with LLMs
Tehseen Rug, Felix B\"ohmer, Tessa Pfattheicher
https://arxiv.org/abs/2507.16028 h…

From Logic to Language: A Trust Index for Problem Solving with LLMs
Classical computation, grounded in formal, logical systems, has been the engine of technological progress for decades, excelling at problems that can be described with unambiguous rules. This paradigm, however, leaves a vast ocean of human problems -- those characterized by ambiguity, dynamic environments, and subjective context -- largely untouched. The advent of Large Language Models (LLMs) represents a fundamental shift, enabling computational systems to engage with this previously inaccessi…

Faint southern spectrophotometric standard stars
N. P. Gentile Fusillo, S. Moehler, N. Przybilla, A. K. Elms, P. -E. Tremblay, W. Kausch, F. Kerber
https://arxiv.org/abs/2508.05758

Faint southern spectrophotometric standard stars
Context. The advent of the Extremely Large Telescope (ELT) will increase the collecting area by more than an order of magnitude compared to the individual Unit Telescopes of the Very Large Telescope (VLT). Fainter spectrophotometric standard stars than those currently available in the V = 11 to 13 mag (K = 12 to 14 mag) range are required for spectroscopic observations with instruments such as the Multi-AO Imaging Camera for Deep Observations (MICADO) on the ELT, notably in the near-infrared wa…

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Florian Gr\"otschla, Luis M\"uller, Jan T\"onshoff, Mikhail Galkin, Bryan Perozzi
https://arxiv.org/abs/2507.08616

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also raises several questions on the ability of a complex network of agents to effectively self-organize and collaborate. While measuring performance on standard reasoning benchmarks indicates how well multi-agent systems can solve reasoning tasks, it is unclear whether these systems are able to leverage their topology eff…

OpenCXD: An Open Real-Device-Guided Hybrid Evaluation Framework for CXL-SSDs
Hyunsun Chung, Junhyeok Park, Taewan Noh, Seonghoon Ahn, Kihwan Kim, Ming Zhao, Youngjae Kim
https://arxiv.org/abs/2508.11477

OpenCXD: An Open Real-Device-Guided Hybrid Evaluation Framework for CXL-SSDs
The advent of Compute Express Link (CXL) enables SSDs to participate in the memory hierarchy as large-capacity, byte-addressable memory devices. These CXL-enabled SSDs (CXL-SSDs) offer a promising new tier between DRAM and traditional storage, combining NAND flash density with memory-like access semantics. However, evaluating the performance of CXL-SSDs remains difficult due to the lack of hardware that natively supports the CXL.mem protocol on SSDs. As a result, most prior work relies on hybri…

Assessing Universal Relations for Rapidly Rotating Neutron Stars: Insights from an Interpretable Deep Learning Perspective
Grigorios Papigkiotis, Georgios Vardakas, Nikolaos Stergioulas
https://arxiv.org/abs/2508.05850

Assessing Universal Relations for Rapidly Rotating Neutron Stars: Insights from an Interpretable Deep Learning Perspective
Relations between stellar properties independent of the nuclear equation of state offer profound insights into neutron star physics and have practical applications in data analysis. Commonly, these relations are derived from utilizing various realistic nuclear cold hadronic, hyperonic, and hybrid EoS models, each of which should obey the current constraints and cover a wide range of stiffnesses. Concurrently, the field of multimessenger astronomy has been significantly enhanced by the advent of…

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Yuhui Chen, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao Lu, Yanqing Ma, Shiyin Lu, Qifeng Chen
https://arxiv.org/abs/2506.09373

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful intermediary. Despite the predominance of Supervised Fine-Tuning (SFT) methods in current GUI agents for achieving spatial localization, these methods face substantial challenges due to their limited capacity to accurately perceive positional data. Existing strategies, such as reinforcement learning, often fail to assess positional accuracy effectively, …

Why Report Failed Interactions With Robots?! Towards Vignette-based Interaction Quality
Agnes Axelsson, Merle Reimann, Ronald Cumbal, Hannah Pelikan, Divesh Lala
https://arxiv.org/abs/2508.10603

Why Report Failed Interactions With Robots?! Towards Vignette-based Interaction Quality
Although the quality of human-robot interactions has improved with the advent of LLMs, there are still various factors that cause systems to be sub-optimal when compared to human-human interactions. The nature and criticality of failures are often dependent on the context of the interaction and so cannot be generalized across the wide range of scenarios and experiments which have been implemented in HRI research. In this work we propose the use of a technique overlooked in the field of HRI, eth…

Interference between non-overlapping waves
Alan C. Santos, Celso J. Villas-Boas
https://arxiv.org/abs/2508.10622 https://arxiv.org/pdf/2508.10622

Interference between non-overlapping waves
In classical mechanics and electromagnetism, interference occurs when two or more waves overlap at the same point in spacetime. However, the advent of quantum electrodynamics (QED) and its remarkable success in describing light-matter interactions at the microscopic level invites us to reconsider whether interference-like effects could arise even when the waves do not physically overlap. In this work, we extend the notion of wave interference to a novel and unconventional regime. Building upon …

Altermagnet-Superconductor Heterostructure: a Scalable Platform for Braiding of Majorana Modes
Themba Hodge, Eric Mascot, Stephan Rachel
https://arxiv.org/abs/2506.08095

Altermagnet-Superconductor Heterostructure: a Scalable Platform for Braiding of Majorana Modes
Topological quantum computation, featuring qubits built out of anyonic excitations known as Majorana zero modes (MZMs), have long presented an exciting pathway towards scalable quantum computation. Recently, the advent of altermagnetic materials has presented a new pathway towards localized MZMs on the boundary of two-dimensional materials, consisting of an altermagnetic film, subject to a superconducting proximity effect from a superconducting substrate. In this work, we demonstrate the possib…

MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
Nikolai Lund K\"uhne, Jesper Jensen, Jan {\O}stergaard, Zheng-Hua Tan
https://arxiv.org/abs/2507.00966

MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
With the advent of new sequence models like Mamba and xLSTM, several studies have shown that these models match or outperform state-of-the-art models in single-channel speech enhancement, automatic speech recognition, and self-supervised audio representation learning. However, prior research has demonstrated that sequence models like LSTM and Mamba tend to overfit to the training set. To address this issue, previous works have shown that adding self-attention to LSTMs substantially improves gen…

Attributes Shape the Embedding Space of Face Recognition Models
Pierrick Leroy, Antonio Mastropietro, Marco Nurisso, Francesco Vaccarino
https://arxiv.org/abs/2507.11372

Attributes Shape the Embedding Space of Face Recognition Models
Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, we observe a multiscale geometric structure emerging in the embedding space, influenced by interpretable facial (e.g., hair color) and image attributes (e.g., contrast). We propose a …

TRACY: Benchmarking Execution Efficiency of LLM-Based Code Translation
Zhihao Gong, Zeyu Sun, Dong Huang, Qingyuan Liang, Jie M. Zhang, Dan Hao
https://arxiv.org/abs/2508.11468 …

TRACY: Benchmarking Execution Efficiency of LLM-Based Code Translation
Automatic code translation is a fundamental task in modern software development. While the advent of Large Language Models (LLMs) has significantly improved the correctness of code translation, the critical dimension of execution efficiency remains overlooked. To address this gap, we introduce TRACY, the first comprehensive benchmark designed to evaluate the execution efficiency of LLM-translated code. TRACY is constructed through an LLM-driven two-stage pipeline: an initial stage generates a s…

Tidal-Like Concept Drift in RIS-Covered Buildings: When Programmable Wireless Environments Meet Human Behaviors
Zi-Yang Wu, Muhammad Ismail, Jiliang Zhang, Jie Zhang
https://arxiv.org/abs/2507.14876

Tidal-Like Concept Drift in RIS-Covered Buildings: When Programmable Wireless Environments Meet Human Behaviors
Indoor mobile networks handle the majority of data traffic, with their performance limited by building materials and structures. However, building designs have historically not prioritized wireless performance. Prior to the advent of reconfigurable intelligent surfaces (RIS), the industry passively adapted to wireless propagation challenges within buildings. Inspired by RIS's successes in outdoor networks, we propose embedding RIS into building structures to manipulate and enhance building wire…

The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty
Mario Brcic
https://arxiv.org/abs/2508.05867 https://arxiv.org/pd…

The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty
The advent of continuously learning Artificial Intelligence (AI) assistants marks a paradigm shift from episodic interactions to persistent, memory-driven relationships. This paper introduces the concept of "Cognitive Sovereignty", the ability of individuals, groups, and nations to maintain autonomous thought and preserve identity in the age of powerful AI systems, especially those that hold their deep personal memory. It argues that the primary risk of these technologies transcends traditional…

Dwarf Galaxies at Cosmic Noon: New JWST Constraints on Satellite Models and Subhalo Tidal Evolution
Jenny T. Wan (Kavli Institute for Particle Astrophysics & Cosmology, Stanford University), Philip Mansfield (Kavli Institute for Particle Astrophysics & Cosmology), Katherine A. Suess (University of Colorado, Boulder), Yunchong Wang (Kavli Institute for Particle Astrophysics & Cosmology, SLAC National Accelerator Laboratory), Sihan Yuan (Kavli Institute for Particle Astrophys…

Dwarf Galaxies at Cosmic Noon: New JWST Constraints on Satellite Models and Subhalo Tidal Evolution
The advent of JWST has revolutionized the study of faint satellite galaxies at $z \gtrsim 1$, enabling statistical constraints on galaxy evolution and the galaxy$-$halo connection in a previously unexplored mass and redshift regime. We compare satellite abundances at $1 < z < 3.5$ from recent JWST observations with predictions from cosmological dark matter-only zoom-in simulations. We identify and quantify several sources of biases that can impact theoretical satellite counts, finding that assu…

Eat your own KR: a KR-based approach to index Semantic Web Endpoints and Knowledge Graphs
Pierre Maillot (WIMMICS), Catherine Faron (UniCA, I3S, WIMMICS), Fabien Gandon (WIMMICS), Franck Michel (Laboratoire I3S - SPARKS, WIMMICS), Pierre Monnin (WIMMICS)
https://arxiv.org/abs/2508.08713

Eat your own KR: a KR-based approach to index Semantic Web Endpoints and Knowledge Graphs
Over the last decade, knowledge graphs have multiplied, grown, and evolved on the World Wide Web, and the advent of new standards, vocabularies, and application domains has accelerated this trend. IndeGx is a framework leveraging an extensible base of rules to index the content of KGs and the capacities of their SPARQL endpoints. In this article, we show how knowledge representation (KR) and reasoning methods and techniques can be used in a reflexive manner to index and characterize existing kn…

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
Zhanhong Jiang, Aditya Balu, Soumik Sarkar
https://arxiv.org/abs/2508.08413 https://

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
$L_0$-smoothness, which has been pivotal to advancing decentralized optimization theory, is often fairly restrictive for modern tasks like deep learning. The recent advent of relaxed $(L_0,L_1)$-smoothness condition enables improved convergence rates for gradient methods. Despite centralized advances, its decentralized extension remains unexplored and challenging. In this work, we propose the first general framework for decentralized gradient descent (DGD) under $(L_0,L_1)$-smoothness by introd…

Machine Learning-driven Multiscale MD Workflows: The Mini-MuMMI Experience
Lo\"ic Pottier, Konstantia Georgouli, Timothy S. Carpenter, Fikret Aydin, Jeremy O. B. Tempkin, Dwight V. Nissley, Frederick H. Streitz, Thomas R. W. Scogland, Peer-Timo Bremer, Felice C. Lightstone, Helgi I. Ing\'olfsson
https://arxiv.org/abs/25…

Machine Learning-driven Multiscale MD Workflows: The Mini-MuMMI Experience
Computational models have become one of the prevalent methods to model complex phenomena. To accurately model complex interactions, such as detailed biomolecular interactions, scientists often rely on multiscale models comprised of several internal models operating at difference scales, ranging from microscopic to macroscopic length and time scales. Bridging the gap between different time and length scales has historically been challenging but the advent of newer machine learning (ML) approache…

FPGA & VPU Co-Processing in Space Applications: Development and Testing with DSP/AI Benchmarks
Vasileios Leon, Charalampos Bezaitis, George Lentaris, Dimitrios Soudris, Dionysios Reisis, Elissaios-Alexios Papatheofanous, Angelos Kyriakos, Aubrey Dunne, Arne Samuelsson, David Steenari
https://arxiv.org/abs/2506.12968

FPGA & VPU Co-Processing in Space Applications: Development and Testing with DSP/AI Benchmarks
The advent of computationally demanding algorithms and high data rate instruments in new space applications pushes the space industry to explore disruptive solutions for on-board data processing. We examine heterogeneous computing architectures involving high-performance and low-power commercial SoCs. The current paper implements an FPGA with VPU co-processing architecture utilizing the CIF & LCD interfaces for I/O data transfers. A Kintex FPGA serves as our framing processor and heritage accel…

Direct Nuclear-Level Qubits using Trapped Th-229 Ions: A Platform for Entanglement and Universal Quantum Information Processing
Jingbo Wang, Haixing Miao, Shiqian Ding, Dong E. Liu
https://arxiv.org/abs/2508.10626

Direct Nuclear-Level Qubits using Trapped Th-229 Ions: A Platform for Entanglement and Universal Quantum Information Processing
The low-energy isomeric transition in Thorium-229 offers a unique interface between nuclear and atomic physics, presenting a resource for quantum technologies that is notably resilient to environmental decoherence. While early experiments focused on nuclei in solid-state crystals, the recent advent of a continuous-wave vacuum ultraviolet laser at 148.4~nm now enables direct coherent control of individual trapped Th-229 ions. Building on this breakthrough, we present a theoretical framework for …

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Marcin Pietro\'n, Rafa{\l} Olszowski, Jakub Gomu{\l}ka, Filip Gampel, Andrzej Tomski
https://arxiv.org/abs/2507.08621

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Argument mining (AM) is an interdisciplinary research field that integrates insights from logic, philosophy, linguistics, rhetoric, law, psychology, and computer science. It involves the automatic identification and extraction of argumentative components, such as premises and claims, and the detection of relationships between them, such as support, attack, or neutrality. Recently, the field has advanced significantly, especially with the advent of large language models (LLMs), which have enhanc…

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

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

Doppelg\"anger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Daewon Kang, YeongHwan Shin, Doyeon Kim, Kyu-Hwan Jung, Meong Hi Son
https://arxiv.org/abs/2506.14539

Doppelgänger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack
Since the advent of large language models, prompt engineering now enables the rapid, low-effort creation of diverse autonomous agents that are already in widespread use. Yet this convenience raises urgent concerns about the safety, robustness, and behavioral consistency of the underlying prompts, along with the pressing challenge of preventing those prompts from being exposed to user's attempts. In this paper, we propose the ''Doppelgänger method'' to demonstrate the risk of an agent being hij…

Graph Learning
Feng Xia, Ciyuan Peng, Jing Ren, Falih Gozi Febrinanto, Renqiang Luo, Vidya Saikrishna, Shuo Yu, Xiangjie Kong
https://arxiv.org/abs/2507.05636

Graph Learning
Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable architectures, dynamic graph modeling, multimodal learning, generative AI, explainable AI (XAI), and responsible AI has broadened the applicability of graph learning to various challenging environments. Gra…

StarStream: Live Video Analytics over Space Networking
Miao Zhang, Jiaxing Li, Haoyuan Zhao, Linfeng Shen, Jiangchuan Liu
https://arxiv.org/abs/2508.14222 https://

StarStream: Live Video Analytics over Space Networking
Streaming videos from resource-constrained front-end devices over networks to resource-rich cloud servers has long been a common practice for surveillance and analytics. Most existing live video analytics (LVA) systems, however, have been built over terrestrial networks, limiting their applications during natural disasters and in remote areas that desperately call for real-time visual data delivery and scene analysis. With the recent advent of space networking, in particular, Low Earth Orbit (L…

Near-Field Communication with Massive Movable Antennas: A Functional Perspective
Shicong Liu, Xianghao Yu, Jie Xu, Rui Zhang
https://arxiv.org/abs/2508.01201 https://

Near-Field Communication with Massive Movable Antennas: A Functional Perspective
The advent of massive multiple-input multiple-output (MIMO) technology has provided new opportunities for capacity improvement via strategic antenna deployment, especially when the near-field effect is pronounced due to antenna proliferation. In this paper, we investigate the optimal antenna placement for maximizing the achievable rate of a point-to-point near-field channel, where the transmitter is deployed with massive movable antennas. First, we propose a novel design framework to explore th…

A Systematic Search for Main-Sequence Dipper Stars Using the Zwicky Transient Facility
Anastasios Tzanidakis, James R. A. Davenport, Neven Caplar, Eric C. Bellm, Wilson Beebe, Doug Branton, Sandro Campos, Andrew J. Connolly, Melissa DeLucchi, Konstantin Malanchev, Sean McGuire
https://arxiv.org/abs/2508.03964…

A Systematic Search for Main-Sequence Dipper Stars Using the Zwicky Transient Facility
Main-sequence dipper stars, characterized by irregular and often aperiodic luminosity dimming events, offer a unique opportunity to explore the variability of circumstellar material and its potential links to planet formation, debris disks, and broadly star-planet interactions. The advent of all-sky time-domain surveys has enabled the rapid discovery of these unique systems. We present the results of a large systematic search for main-sequence dipper stars, conducted across a sample of 63 milli…

Exploiting Jailbreaking Vulnerabilities in Generative AI to Bypass Ethical Safeguards for Facilitating Phishing Attacks
Rina Mishra, Gaurav Varshney
https://arxiv.org/abs/2507.12185

Exploiting Jailbreaking Vulnerabilities in Generative AI to Bypass Ethical Safeguards for Facilitating Phishing Attacks
The advent of advanced Generative AI (GenAI) models such as DeepSeek and ChatGPT has significantly reshaped the cybersecurity landscape, introducing both promising opportunities and critical risks. This study investigates how GenAI powered chatbot services can be exploited via jailbreaking techniques to bypass ethical safeguards, enabling the generation of phishing content, recommendation of hacking tools, and orchestration of phishing campaigns. In ethically controlled experiments, we used Cha…

A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang
https://arxiv.org/abs/2508.05668

A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
The advent of Large Language Models (LLMs) has significantly revolutionized web search. The emergence of LLM-based Search Agents marks a pivotal shift towards deeper, dynamic, autonomous information seeking. These agents can comprehend user intentions and environmental context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web. Leading examples like OpenAI's Deep Research highlight their potential for deep information mining and real-world a…

This https://arxiv.org/abs/2412.09699 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Neutrinos from explosive transients at the dawn of multi-messenger astronomy
With the advent of time-domain astronomy and the game-changing next generation of telescopes, we have unprecedented opportunities to explore the most energetic events in our Universe through electromagnetic radiation, gravitational waves, and neutrinos. These are elementary particles, which exist in three different flavors and change the latter as they propagate in the dense core of astrophysical sources as well as en route to Earth. To capitalize on existing and upcoming multi-messenger opport…

Towards Employing FPGA and ASIP Acceleration to Enable Onboard AI/ML in Space Applications
Vasileios Leon, George Lentaris, Dimitrios Soudris, Simon Vellas, Mathieu Bernou
https://arxiv.org/abs/2506.12970

Towards Employing FPGA and ASIP Acceleration to Enable Onboard AI/ML in Space Applications
The success of AI/ML in terrestrial applications and the commercialization of space are now paving the way for the advent of AI/ML in satellites. However, the limited processing power of classical onboard processors drives the community towards extending the use of FPGAs in space with both rad-hard and Commercial-Off-The-Shelf devices. The increased performance of FPGAs can be complemented with VPU or TPU ASIP co-processors to further facilitate high-level AI development and in-flight reconfigu…

Role of Plaquette Term in Genuine $2 1$D String Dynamics on Quantum Simulators
Yizhuo Tian, N. S. Srivatsa, Kaidi Xu, Jesse J. Osborne, Umberto Borla, Jad C. Halimeh
https://arxiv.org/abs/2508.05736

Role of Plaquette Term in Genuine $2+1$D String Dynamics on Quantum Simulators
With the advent of quantum simulators of $2+1$D lattice gauge theories (LGTs), a fundamental open question is under what circumstances the observed physics is genuinely $2+1$D rather than effectively $1+1$D. Here, we address this question in the ongoing strong effort to quantum-simulate string dynamics in $2+1$D LGTs on state-of-the-art quantum hardware. Through tensor network simulations and analytic derivations, we show that the plaquette term, which represents a magnetic field and only emerg…

TURA: Tool-Augmented Unified Retrieval Agent for AI Search
Zhejun Zhao, Yuehu Dong, Alley Liu, Lixue Zheng, Pingsheng Liu, Dongdong Shen, Long Xia, Jiashu Zhao, Dawei Yin
https://arxiv.org/abs/2508.04604

TURA: Tool-Augmented Unified Retrieval Agent for AI Search
The advent of Large Language Models (LLMs) is transforming search engines into conversational AI search products, primarily using Retrieval-Augmented Generation (RAG) on web corpora. However, this paradigm has significant industrial limitations. Traditional RAG approaches struggle with real-time needs and structured queries that require accessing dynamically generated content like ticket availability or inventory. Limited to indexing static pages, search engines cannot perform the interactive q…

Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators
Kaustav Chakraborty, Zeyuan Feng, Sushant Veer, Apoorva Sharma, Wenhao Ding, Sever Topan, Boris Ivanovic, Marco Pavone, Somil Bansal
https://arxiv.org/abs/2507.22389

Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators
The advent of end-to-end autonomy stacks - often lacking interpretable intermediate modules - has placed an increased burden on ensuring that the final output, i.e., the motion plan, is safe in order to validate the safety of the entire stack. This requires a safety monitor that is both complete (able to detect all unsafe plans) and sound (does not flag safe plans). In this work, we propose a principled safety monitor that leverages modern multi-modal trajectory predictors to approximate forwar…

Multi-domain anomaly detection in a 5G network
Thomas Hoger (LAAS-SARA), Philippe Owezarski (LAAS-SARA)
https://arxiv.org/abs/2506.12070 https://

Multi-domain anomaly detection in a 5G network
With the advent of 5G, mobile networks are becoming more dynamic and will therefore present a wider attack surface. To secure these new systems, we propose a multi-domain anomaly detection method that is distinguished by the study of traffic correlation on three dimensions: temporal by analyzing message sequences, semantic by abstracting the parameters these messages contain, and topological by linking them in the form of a graph. Unlike traditional approaches, which are limited to considering …

UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench
Boxi Yu, Yuxuan Zhu, Pinjia He, Daniel Kang
https://arxiv.org/abs/2506.09289 https://

UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench
The advent of Large Language Models (LLMs) has spurred the development of coding agents for real-world code generation. As a widely used benchmark for evaluating the code generation capabilities of these agents, SWE-Bench uses real-world problems based on GitHub issues and their corresponding pull requests. However, the manually written test cases included in these pull requests are often insufficient, allowing generated patches to pass the tests without resolving the underlying issue. To addre…

Design and analysis of a set of discrete variable protocols for secure quantum communication
Arindam Dutta
https://arxiv.org/abs/2508.06380 https://arxiv.o…

Design and analysis of a set of discrete variable protocols for secure quantum communication
The advent of quantum key distribution (QKD) has revolutionized secure communication by providing unconditional security, unlike classical cryptographic methods. However, its effectiveness relies on robust identity authentication, as vulnerabilities in the authentication process can cause a compromise with the security of the entire communication system. Over the past three decades, numerous quantum identity authentication (QIA) protocols have been proposed. This thesis first presents a chronol…

Hybrid-Locked Kerr Microcombs for Flexible On-Chip Optical Clock Division
Andrei Diakonov, Konstantin Khrizman, Liron Stern
https://arxiv.org/abs/2508.07258 https://

Hybrid-Locked Kerr Microcombs for Flexible On-Chip Optical Clock Division
Optical atomic clocks deliver unrivaled precision, yet their size and complexity still confine them to specialized laboratories. Frequency combs provide the crucial optical-to-microwave division needed for clock readout, but conventional fiber- or bulk-laser combs are far too large for portable use. The advent of chip-integrated microcombs, frequency combs generated in micron-scale resonators, has revolutionized this landscape, enabling fully miniaturized, low-power clocks that bridge optical a…

Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support
Wu Hao Ran, Xi Xi, Furong Li, Jingyi Lu, Jian Jiang, Hui Huang, Yuzhuan Zhang, Shi Li
https://arxiv.org/abs/2506.06340

Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support
The advent of large language models (LLMs) has opened new avenues for analyzing complex, unstructured data, particularly within the medical domain. Electronic Health Records (EHRs) contain a wealth of information in various formats, including free text clinical notes, structured lab results, and diagnostic codes. This paper explores the application of advanced language models to leverage these diverse data sources for improved clinical decision support. We will discuss how text-based features, …

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
Ryan Barker, Fatemeh Afghah
https://arxiv.org/abs/2506.09418 https://…

Securing Open RAN: A Survey of Cryptographic Challenges and Emerging Solutions for 5G
The advent of Open Radio Access Networks (O-RAN) introduces modularity and flexibility into 5G deployments but also surfaces novel security challenges across disaggregated interfaces. This literature review synthesizes recent research across thirteen academic and industry sources, examining vulnerabilities such as cipher bidding-down attacks, partial encryption exposure on control/user planes, and performance trade-offs in securing O-RAN interfaces like E2 and O1. The paper surveys key cryptogr…

Understanding Software Engineering Agents Through the Lens of Traceability: An Empirical Study
Ira Ceka, Saurabh Pujar, Shyam Ramji, Luca Buratti, Gail Kaiser, Baishakhi Ray
https://arxiv.org/abs/2506.08311

Understanding Software Engineering Agents Through the Lens of Traceability: An Empirical Study
With the advent of large language models (LLMs), software engineering agents (SWE agents) have emerged as a powerful paradigm for automating a range of software tasks -- from code generation and repair to test case synthesis. These agents operate autonomously by interpreting user input and responding to environmental feedback. While various agent architectures have demonstrated strong empirical performance, the internal decision-making worfklows that drive their behavior remain poorly understoo…

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
Muqi Zou (Jing), Hongyu Cai (Jing), Hongwei Wu (Jing), Zion Leonahenahe Basque (Jing), Arslan Khan (Jing), Berkay Celik (Jing), Dave (Jing), Tian (Fish), Antonio Bianchi (Fish), Ruoyu (Fish), Wang, Dongyan Xu
https://arxiv.org/abs/2506.10125…

D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning
Decompilers, which reconstruct human-readable source code from binary executables, are vital to many security tasks. Yet, despite recent advances, their output often suffers from syntactic and semantic errors and remains difficult to read. Recently, with the advent of large language models (LLMs), researchers began to explore the potential of LLMs to refine decompiler output. Nevertheless, our study of these approaches reveals significant limitations, such as introducing new errors and relying …

5G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AI
Joseph H. R. Isaac, Harish Saradagam, Nallamothu Pardhasaradhi
https://arxiv.org/abs/2508.09152

5G Core Fault Detection and Root Cause Analysis using Machine Learning and Generative AI
With the advent of 5G networks and technologies, ensuring the integrity and performance of packet core traffic is paramount. During network analysis, test files such as Packet Capture (PCAP) files and log files will contain errors if present in the system that must be resolved for better overall network performance, such as connectivity strength and handover quality. Current methods require numerous person-hours to sort out testing results and find the faults. This paper presents a novel AI/ML-…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Fan Wu, Cuiyun Gao, Shuqing Li, Xin-Cheng Wen, Qing Liao
https://arxiv.org/abs/2506.10376 htt…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Converting user interfaces into code (UI2Code) is a crucial step in website development, which is time-consuming and labor-intensive. The automation of UI2Code is essential to streamline this task, beneficial for improving the development efficiency. There exist deep learning-based methods for the task; however, they heavily rely on a large amount of labeled training data and struggle with generalizing to real-world, unseen web page designs. The advent of Multimodal Large Language Models (MLLMs…

Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?
Daniele Proverbio, Alessio Buscemi, Alessandro Di Stefano, The Anh Han, German Castignani, Pietro Li\`o
https://arxiv.org/abs/2508.05670

Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?
Game theory has long served as a foundational tool in cybersecurity to test, predict, and design strategic interactions between attackers and defenders. The recent advent of Large Language Models (LLMs) offers new tools and challenges for the security of computer systems; In this work, we investigate whether classical game-theoretic frameworks can effectively capture the behaviours of LLM-driven actors and bots. Using a reproducible framework for game-theoretic LLM agents, we investigate two ca…

A double selection entanglement distillation-based state estimator
Joshua Carlo A. Casapao, Ananda G. Maity, Naphan Benchasattabuse, Michal Hajdu\v{s}ek, Akihito Soeda, Rodney Van Meter, David Elkouss
https://arxiv.org/abs/2505.24280

A double selection entanglement distillation-based state estimator
With the advent of practical quantum communication networks drawing closer, there is a growing need for reliable estimation protocols that can efficiently characterize quantum resources with minimum resource overhead requirement. A novel approach to this problem is to integrate an estimator into an existing network task, thereby removing the need for an additional characterization protocol. In this work, we show that the measurement statistics of a double selection distillation protocol alone c…

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Sebastian Monka, Irlan Grangel-Gonz\'alez, Stefan Schmid, Lavdim Halilaj, Marc Rickart, Oliver Rudolph, Rui Dias
https://arxiv.org/abs/2507.22619

Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting
Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into…

This https://arxiv.org/abs/2409.14644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

An Effective Approach to Embedding Source Code by Combining Large Language and Sentence Embedding Models
The advent of large language models (LLMs) has significantly advanced artificial intelligence (AI) in software engineering (SE), with source code embeddings playing a crucial role in tasks such as source code clone detection and source code clustering. However, existing methods for source code embedding, including those based on LLMs, often rely on costly supervised training or fine-tuning for domain adaptation. This paper proposes a novel approach to embedding source code by combining large la…

This https://arxiv.org/abs/2506.01848 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Identifying Key Expert Actors in Cybercrime Forums Based on their Technical Expertise
The advent of Big Data has made the collection and analysis of cyber threat intelligence challenging due to its volume, leading research to focus on identifying key threat actors; yet these studies have failed to consider the technical expertise of these actors. Expertise, especially towards specific attack patterns, is crucial for cybercrime intelligence, as it focuses on targeting actors with the knowledge and skills to attack enterprises. Using CVEs and CAPEC classifications to build a bimod…

Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach
Melika Sepidband, Hamed Taherkhani, Song Wang, Hadi Hemmati
https://arxiv.org/abs/2505.23953

Enhancing LLM-Based Code Generation with Complexity Metrics: A Feedback-Driven Approach
Automatic code generation has gained significant momentum with the advent of Large Language Models (LLMs) such as GPT-4. Although many studies focus on improving the effectiveness of LLMs for code generation, very limited work tries to understand the generated code's characteristics and leverage that to improve failed cases. In this paper, as the most straightforward characteristic of code, we investigate the relationship between code complexity and the success of LLM generated code. Using a la…

This https://arxiv.org/abs/2411.03079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Utilizing Precise and Complete Code Context to Guide LLM in Automatic False Positive Mitigation
Static Application Security Testing (SAST) tools are critical to software quality, identifying potential code issues early in development. However, they often produce false positive warnings that require manual review, slowing down development. Thus, automating false positive mitigation (FPM) is essential. The advent of Large Language Models (LLMs), with their strong abilities in natural language and code understanding, offers promising avenues for FPM. Yet current LLM-based FPM method faces tw…