Tootfinder

"Scientists design sustainable and more resistant asphalt using cigarette butts"
#Asphalt #Environment

Scientists design sustainable and more resistant asphalt using cigarette butts
Since the advent of filter cigarettes, cigarette butts have become a predominant form of waste, with projections indicating that about 9 trillion will be generated annually by 2025. In addition, since the emergence of low-nicotine e-cigarettes, the consumption and production of this waste has been on the rise (especially among young people aged 14 to 30).

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 …

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…

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…

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…

Enhancing Test Efficiency through Automated ATPG-Aware Lightweight Scan Instrumentation
Sudipta Paria, Md Rezoan Ferdous, Aritra Dasgupta, Atri Chatterjee, Swarup Bhunia
https://arxiv.org/abs/2505.19418

Enhancing Test Efficiency through Automated ATPG-Aware Lightweight Scan Instrumentation
Scan-based Design-for-Testability (DFT) measures are prevalent in modern digital integrated circuits to achieve high test quality at low hardware cost. With the advent of 3D heterogeneous integration and chiplet-based systems, the role of scan is becoming ever more important due to its ability to make internal design nodes controllable and observable in a systematic and scalable manner. However, the effectiveness of scan-based DFT suffers from poor testability of internal nodes for complex circ…

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…

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…

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 …

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…

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…

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…

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.

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…

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…

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…

»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.

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…

Last leg on our brief history of NLP (so far) is the advent of large language models with GPT-3 in 2020 and the introduction of learning from the prompt (aka few-shot learning).
T. B. Brown et al. (2020). Language models are few-shot learners. NIPS'20
https://…

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 …

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…

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…

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…

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…

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…

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…

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…

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, …

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…

I get the desire for live coding interviews, you can't just take people's word that they know how to do it.
But what's the point in throwing Advent of Code style problems at me and interrupting a naive or incorrect approach before I even start implementing it? Let me write unoptimized code for you and then make it better! I'm not going to write the perfect implementation first try for every problem, but I can show you my process and prove I can write _some_ code.
…

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...

This https://arxiv.org/abs/2505.12638 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneous…

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…

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 …

With the advent of ELIZA, Joseph Weizenbaum's first psychotherapist chatbot, NLP took another major step with pattern-based substitution algorithms based on simple regular expressions.
Weizenbaum, Joseph (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Com. of the ACM. 9: 36–45.

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…

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…

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…

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…

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 …

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…

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…

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…

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…

General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess
Brian Hu Zhang, Tuomas Sandholm
https://arxiv.org/abs/2506.01242

General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess
Since the advent of AI, games have served as progress benchmarks. Meanwhile, imperfect-information variants of chess have existed for over a century, present extreme challenges, and have been the focus of significant AI research. Beyond calculation needed in regular chess, they require reasoning about information gathering, the opponent's knowledge, signaling, etc. The most popular variant, Fog of War (FoW) chess (aka. dark chess) is a recognized challenge problem in AI after superhuman perform…

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…

An analysis of capital market through the lens of integral transforms: exploring efficient markets and information asymmetry
Kiran Sharma, Abhijit Dutta, Rupak Mukherjee
https://arxiv.org/abs/2506.06350

An analysis of capital market through the lens of integral transforms: exploring efficient markets and information asymmetry
Post Modigliani and Miller (1958), the concept of usage of arbitrage created a permanent mark on the discourses of financial framework. The arbitrage process is largely based on information dissemination amongst the stakeholders operating in the financial market. The advent of the efficient market Hypothesis draws close to the M&M hypothesis. Giving importance to the arbitrage process, which effects the price discovery in the stock market. This divided the market as random and efficient cohort …

ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
Yifeng Jiao, Yuchen Liu, Yu Zhang, Xin Guo, Yushuai Wu, Chen Jiang, Jiyang Li, Hongwei Zhang, Limei Han, Xin Gao, Yuan Qi, Yuan Cheng
https://arxiv.org/abs/2505.12638

ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data
The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneous…

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…

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, …

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…

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…

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…

Bringing Interpretability to Neural Audio Codecs
Samir Sadok, Julien Hauret, \'Eric Bavu
https://arxiv.org/abs/2506.04492 https://

Bringing Interpretability to Neural Audio Codecs
The advent of neural audio codecs has increased in popularity due to their potential for efficiently modeling audio with transformers. Such advanced codecs represent audio from a highly continuous waveform to low-sampled discrete units. In contrast to semantic units, acoustic units may lack interpretability because their training objectives primarily focus on reconstruction performance. This paper proposes a two-step approach to explore the encoding of speech information within the codec tokens…

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…

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…

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
Yong Xiao, Li, Zin Nandar Win, He Wang, Hla Myo Tun, Win Thu Zar
https://arxiv.org/abs/2506.04584

The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and…

This https://arxiv.org/abs/2412.15323 has been replaced.
initial toot: https://mastoxiv.page/@a…

Abelian Spectral Topology of Multifold Exceptional Points
The advent of non-Hermitian physics has enriched the plethora of topological phases to include phenomena without Hermitian counterparts. Despite being among the most well-studied uniquely non-Hermitian features, the topological properties of multifold exceptional points, $n$-fold spectral degeneracies (EP$n$s) at which also the corresponding eigenvectors coalesce, were only recently revealed in terms of topological resultant winding numbers and concomitant Abelian doubling theorems. Nevertheles…

Modeling blazar broadband emission with convolutional neural networks -- III. proton synchrotron and hybrid models
N. Sahakyan, D. B\'egu\'e, A. Casotto, H. Dereli-B\'egu\'e, V. Vardanyan, M. Khachatryan, P. Giommi, A. Pe'er
https://arxiv.org/abs/2506.23885

Modeling blazar broadband emission with convolutional neural networks -- III. proton synchrotron and hybrid models
Modeling the broadband emission of blazars has become increasingly challenging with the advent of multimessenger observations. Building upon previous successes in applying convolutional neural networks (CNNs) to leptonic emission scenarios, we present an efficient CNN-based approach for modeling blazar emission under proton synchrotron and hybrid lepto-hadronic frameworks. Our CNN is trained on extensive numerical simulations generated by SOPRANO, which span a comprehensive parameter space acco…

String Breaking Dynamics and Glueball Formation in a $2 1$D Lattice Gauge Theory
Kaidi Xu, Umberto Borla, Sergej Moroz, Jad C. Halimeh
https://arxiv.org/abs/2507.01950

String Breaking Dynamics and Glueball Formation in a $2+1$D Lattice Gauge Theory
With the advent of advanced quantum processors capable of probing lattice gauge theories (LGTs) in higher spatial dimensions, it is crucial to understand string dynamics in such models to guide upcoming experiments and to make connections to high-energy physics (HEP). Using tensor network methods, we study the far-from-equilibrium quench dynamics of electric flux strings between two static charges in the $2+1$D $\mathbb{Z}_2$ LGT with dynamical matter. We calculate the probabilities of finding …

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…

hassediagrams:an R package that generates the Hasse diagram of the layout structure and the restricted layout structure
Damianos Michaelides, Simon T. Bate, Marion J. Chatfield
https://arxiv.org/abs/2507.05949

hassediagrams:an R package that generates the Hasse diagram of the layout structure and the restricted layout structure
With the advent of modern statistical software, complex experimental designs are now routinely employed in many areas of research. Failing to correctly identify the structure of the experimental design can lead to incorrect model selection and misleading inferences. This paper describes the hassediagrams package in R that determines the structure of the design, summarised by the layout structure, and generates a Hasse diagram of the layout structure. By considering the randomisation performed, …

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…

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
Jindong Han, Yansong Ning, Zirui Yuan, Hang Ni, Fan Liu, Tengfei Lyu, Hao Liu
https://arxiv.org/abs/2507.00914

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, whic…

🇺🇦 #NowPlaying on BBCRadio3's #Breakfast
Bob Chilcott & BBC Singers:
🎵 O Radix Jesse (Advent Antiphons)
#BobChilcott #BBCSingers

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Hiren Madhu, Jo\~ao Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying
https://arxiv.org/abs/2506.11152

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data
Single-cell transcriptomics has become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and transcriptional regulation at the single-cell level. With the advent of spatial transcriptomics data we have the promise of learning about cells within a tissue context as it provides both spatial coordinates and transcriptomic readouts. However, existing models either ignore spatial resolution or the gene regula…

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 …

This https://arxiv.org/abs/2409.16349 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

No Evidence of a Dichotomy in the Elliptical Galaxy Population
The advent of large integral field spectroscopic surveys has found that elliptical galaxies (EGs) can be classified into two classes: the fast rotators (whose kinematics are dominated by rotation) and the slow rotators (which exhibit slow or no rotation pattern). It is often suggested that while the slow rotators typically have boxy isophotal shapes, have a high $α$-to-iron abundance ratio, and are quite massive, the fast rotators often exhibit the opposite properties (that is, having disky is…

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…

Wireless Home Automation Using Social Networking Websites
Divya Alok Gupta, Dwith Chenna, B. Aditya Vighnesh Ramakanth
https://arxiv.org/abs/2506.22482 htt…

Wireless Home Automation Using Social Networking Websites
With the advent of Internet of Things, Wireless Home Automation Systems WHAS are gradually gaining popularity. These systems are faced with multiple challenges such as security; controlling a variety of home appliances with a single interface and user friendliness. In this paper we propose a system that uses secure authentication systems of social networking websites such as Twitter, tracks the end-users activities on the social network and then control his or her domestic appliances. At the en…

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…

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…

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…

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…