Tootfinder

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Md Toufique Hasan, Muhammad Waseem, Kai-Kristian Kemell, Ayman Asad Khan, Mika Saari, Pekka Abrahamsson
https://arxiv.org/abs/2506.20869

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific…

This https://arxiv.org/abs/2503.17274 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Composable Uncertainty in Symmetric Monoidal Categories for Design Problems
Applied category theory often studies symmetric monoidal categories (SMCs) whose morphisms represent open systems. These structures naturally accommodate complex wiring patterns, leveraging (co)monoidal structures for splitting and merging wires, or compact closed structures for feedback. A key example is the compact closed SMC of design problems (DP), which enables a compositional approach to co-design in engineering. However, in practice, the systems of interest may not be fully known. Recent…

Modeling Social Systems: Transparency, Reproducibility, and Responsibility
Maximino Aldana, Roni Barak Ventura, Heather Z. Brooks, Philip S. Chodrow, Filipe Georgiou, Joseph Johnson, Kre\v{s}imir Josi\'c, Zachary P. Kilpatrick, Kath Landgren, Andrew Nugent, Maurizio Porfiri, Nancy Rodriguez, Pablo Su\'arez-Serrato, David White, Alexander Wiedemann, Sam Zhang

Modeling Social Systems: Transparency, Reproducibility, and Responsibility
Mathematical models of complex social systems can enrich social scientific theory, inform interventions, and shape policy. From voting behavior to economic inequality and urban development, such models influence decisions that affect millions of lives. Thus, it is especially important to formulate and present them with transparency, reproducibility, and humility. Modeling in social domains, however, is often uniquely challenging. Unlike in physics or engineering, researchers often lack controll…

Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit
Dean Brand, Domenica Dibenedetto, Francesco Petruccione
https://arxiv.org/abs/2506.21363

Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit
We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory …

Defect Engineering the Interacting Many-body SSH Model
Lin Wang, Thomas Luu, Ulf-G. Mei{\ss}ner
https://arxiv.org/abs/2507.18806 https://arxiv.org/pdf/2507…

Defect Engineering the Interacting Many-body SSH Model
The ability to engineer topologically distinct materials opens the possibility of enabling novel phenomena in low-dimensional nano-systems, as well as manufacturing novel quantum devices. One of the simplest examples, the SSH model with both even and odd number of sites, demonstrates the connection between localized edge states and the topology of the system. We show that the SSH model hosts localized spin centers due to the interplay between the localized edge states and the on-site Hubbard in…

Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design
Jiayi Wang, Ruiwei Xiao, Xinying Hou, John Stamper
https://arxiv.org/abs/2508.16659

Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design
K-12 educators are increasingly using Large Language Models (LLMs) to create instructional materials. These systems excel at producing fluent, coherent content, but often lack support for high-quality teaching. The reason is twofold: first, commercial LLMs, such as ChatGPT and Gemini which are among the most widely accessible to teachers, do not come preloaded with the depth of pedagogical theory needed to design truly effective activities; second, although sophisticated prompt engineering can …

Towards a fundamental theory of modeling discrete systems
Peter Fettke, Wolfgang Reisig
https://arxiv.org/abs/2508.19803 https://arxiv.org/pdf/2508.19803…

Towards a fundamental theory of modeling discrete systems
Modeling is a central concern in both science and engineering. However, we need a new fundamental theory to address the challenges of the digital age. In this paper, we first explain why modeling is fundamental and which challenges must be addressed in the digital world. As a main contribution, we introduce the Heraklit modeling framework as a new approach to modeling. We conclude with some general remarks. Future work will involve the correctness of modeling, the notion of information, and the…

Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications
Naoki Ishikawa, Giuseppe Thadeu Freitas de Abreu, Petar Popovski, Robert W. Heath Jr
https://arxiv.org/abs/2506.20863

Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications
Quantum computing is poised to redefine the algorithmic foundations of communication systems. While quantum superposition and entanglement enable quadratic or exponential speedups for specific problems, identifying use cases where these advantages yield engineering benefits is, however, still nontrivial. This article presents the fundamentals of quantum computing in a style familiar to the communications society, outlining the current limits of fault-tolerant quantum computing and uncovering a …

Marvels of engineering: a pedestrian #bridge in #Owińska.
https://www.openstreetmap.org/way/522807256#map=15/52.51381/16.99722

Robust space-time multiscale upscaling via multicontinuum homogenization for evolving perforated media
Wei Xie, Viet Ha Hoang, Yin Yang, Yunqing Huang
https://arxiv.org/abs/2506.21104

Robust space-time multiscale upscaling via multicontinuum homogenization for evolving perforated media
Time-evolving perforated domains arise in many engineering and geoscientific applications, including reactive transport, particle deposition, and structural degradation in porous media. Accurately capturing the macroscopic behavior of such systems poses significant computational challenges due to the dynamic fine-scale geometries. In this paper, we develop a robust and generalizable multiscale modeling framework based on multicontinuum homogenization to derive effective macroscopic equations in…

Boundary-layer transition in the age of data: from a comprehensive dataset to fine-grained prediction
Wenhui Chang, Hongyuan Hu, Youcheng Xi, Markus Kloker, Honghui Teng, Jie Ren
https://arxiv.org/abs/2507.19120

Boundary-layer transition in the age of data: from a comprehensive dataset to fine-grained prediction
The laminar-to-turbulent transition remains a fundamental and enduring challenge in fluid mechanics. Its complexity arises from the intrinsic nonlinearity and extreme sensitivity to external disturbances. This transition is critical in a wide range of applications, including aerospace, marine engineering, geophysical flows, and energy systems. While the governing physics can be well described by the Navier-Stokes equations, practical prediction efforts often fall short due to the lack of compre…

Anemoi: A Semi-Centralized Multi-agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol
Xinxing Ren, Caelum Forder, Qianbo Zang, Ahsen Tahir, Roman J. Georgio, Suman Deb, Peter Carroll, \"Onder G\"urcan, Zekun Guo
https://arxiv.org/abs/2508.17068

Anemoi: A Semi-Centralized Multi-agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol
Recent advances in generalist multi-agent systems (MAS) have largely followed a context-engineering plus centralized paradigm, where a planner agent coordinates multiple worker agents through unidirectional prompt passing. While effective under strong planner models, this design suffers from two critical limitations: (1) strong dependency on the planner's capability, which leads to degraded performance when a smaller LLM powers the planner; and (2) limited inter-agent communication, where colla…

Disentangled representations of microscopy images
Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti, Francesca Odone
https://arxiv.org/abs/2506.20649 ht…

Disentangled representations of microscopy images
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted the possibility to acquire an escalating amount of images, requiring a consequent development of a large collection of deep learning-based automatic image analysis methods. Although deep neural networks have demonstrated great performance in this field, interpretability, an essential requirement for microscopy image an…

Highly anisotropic 1D materials supported in exfoliable 2D coordination polymers with optical anisotropy switching via twist-engineering
Eleni C. Mazarakioti, Carla Boix-Constant, Iv\'an G\'omez-Mu\~noz, Diego L\'opez-Alcal\'a, Sergio Revuelta, Marco Ballabio, Vasileios Balos, Jos\'e J. Baldov\'i, Enrique C\'anovas, Josep Canet-Ferrer, Guillermo M\'inguez Espallargas, Samuel Ma\~nas-Valero, Eugenio Coronado

Highly anisotropic 1D materials supported in exfoliable 2D coordination polymers with optical anisotropy switching via twist-engineering
Van der Waals (vdW) materials provide a platform to study and control the physical properties of low-dimensional materials. While strategies developed for two-dimensional (2D) crystals are not directly transferable to one-dimensional (1D) systems, we can benefit from them by creating layers formed by interconnected chains. Here, we develop a molecular strategy to illustrate this concept consisting of assembling 1D materials in 2D metal-organic frameworks (MOFs). Crystals of [FeX(pzX)(bpy)] (X =…

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
Haonan An, Guang Hua, Hangcheng Cao, Zhengru Fang, Guowen Xu, Susanto Rahardja, Yuguang Fang
https://arxiv.org/abs/2507.18034

Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
The intellectual property of deep generative networks (GNets) can be protected using a cascaded hiding network (HNet) which embeds watermarks (or marks) into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box (called operation network, or ONet), with only the generated and marked outputs from HNet being released to end users and deemed secure, in this paper, we reveal an overlooked vulnerability in such systems. Specifically, we show that t…

High-Density EEG Enables the Fastest Visual Brain-Computer Interfaces
Gege Ming (Department of Biomedical Engineering, Tsinghua University), Weihua Pei (Laboratory of Solid-State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, School of Future Technology, University of Chinese Academy of Sciences), Sen Tian (Suzhou Nianji Intelligent Technology Co., Ltd), Xiaogang Chen (Institute of Biomedical Engineering, Chinese Academy of Medical Sci…

High-Density EEG Enables the Fastest Visual Brain-Computer Interfaces
Brain-computer interface (BCI) technology establishes a direct communication pathway between the brain and external devices. Current visual BCI systems suffer from insufficient information transfer rates (ITRs) for practical use. Spatial information, a critical component of visual perception, remains underexploited in existing systems because the limited spatial resolution of recording methods hinders the capture of the rich spatiotemporal dynamics of brain signals. This study proposed a freque…

Anthropic details how it built its multi-agent Claude Research system, claiming significant improvements in internal evaluations over single-agent systems (Anthropic)
https://www.anthropic.com/engineering/built-multi-agent-research-system

How we built our multi-agent research system
On the the engineering challenges and lessons learned from building Claude's Research system

Exploration of Evolving Quantum Key Distribution Network Architecture Using Model-Based Systems Engineering
Hayato Ishida, Amal Elsokary, Maria Aslam, Catherine White, Michael J. de C. Henshaw, Siyuan Ji
https://arxiv.org/abs/2508.15733

Exploration of Evolving Quantum Key Distribution Network Architecture Using Model-Based Systems Engineering
Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quan…

"The Librarian Skillset and the Needs of Artificial Intelligence"
https://doi.org/10.1080/01639374.2025.2539787
"New roles in AI are emerging throughout Industry, Academia, and Government. From responsible AI management to AI systems engineering, these roles requi…

LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
Zirui Li, Stephan Husung, Haoze Wang
https://arxiv.org/abs/2508.16181

LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM…

Replaced article(s) found for cs.CE. https://arxiv.org/list/cs.CE/new
[1/1]:
- Energy-consistent integration of mechanical systems based on Livens principle
Philipp L. Kinon, Peter Betsch

Spin Polarization Control via Magnetic Field in Dissipative Bosonic Systems
Yaoyuan Fan, Shuoyu Shi, Lang Cao, Qiuxin Zhang, Dong Hu, Yu Wang, Xiaoji Zhou
https://arxiv.org/abs/2506.17962

Spin Polarization Control via Magnetic Field in Dissipative Bosonic Systems
Engineering spin polarization in dissipative bosonic systems is crucial for advancing quantum technologies, especially for applications in quantum metrology and space-based quantum simulations. This work demonstrates precise magnetic moment control in multicomponent Bose gases during evaporative cooling via tailored magnetic fields. By adjusting the magnetic field gradients, null point position, and duration, we selectively tune evaporation rates of magnetic sublevels, achieving targeted spin p…

A Survey of Context Engineering for Large Language Models
Lingrui Mei, Jiayu Yao, Yuyao Ge, Yiwei Wang, Baolong Bi, Yujun Cai, Jiazhi Liu, Mingyu Li, Zhong-Zhi Li, Duzhen Zhang, Chenlin Zhou, Jiayi Mao, Tianze Xia, Jiafeng Guo, Shenghua Liu
https://arxiv.org/abs/2507.13334

A Survey of Context Engineering for Large Language Models
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple prompt design to encompass the systematic optimization of information payloads for LLMs. We present a comprehensive taxonomy decomposing Context Engineering into its foundational components and the sophisticated implementations that integrate them into intelligent systems. We fi…

ModeliHub: A Web-based, Federated Analytics Platform for Modelica-centric, Model-based Systems Engineering
Mohamad Omar Nachawati
https://arxiv.org/abs/2506.18790

ModeliHub: A Web-based, Federated Analytics Platform for Modelica-centric, Model-based Systems Engineering
This paper introduces ModeliHub, a Web-based, federated analytics platform designed specifically for model-based systems engineering with Modelica. ModeliHub's key innovation lies in its Modelica-centric, hub-and-spoke federation architecture that provides systems engineers with a Modelica-based, unified system model of repositories containing heterogeneous engineering artifacts. From this unified system model, ModeliHub's Virtual Twin engine provides a real-time, interactive simulation environ…

"Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizations
Sanika Moharana, Cynthia L. Bennett, Erin Buehler, Michael Madaio, Vinita Tibdewal, Shaun K. Kane
https://arxiv.org/abs/2508.16607

"Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizations
The rapid emergence of generative AI has changed the way that technology is designed, constructed, maintained, and evaluated. Decisions made when creating AI-powered systems may impact some users disproportionately, such as people with disabilities. In this paper, we report on an interview study with 25 AI practitioners across multiple roles (engineering, research, UX, and responsible AI) about how their work processes and artifacts may impact end users with disabilities. We found that practiti…

Autonomy at Levels for Spacecraft
Daniel Baker (Defense Sector, BlueHalo, LLC), Jeremy Wojcik (Defense Sector, BlueHalo, LLC), Sean Phillips (Space Control Branch)
https://arxiv.org/abs/2508.14226

Autonomy at Levels for Spacecraft
Autonomy at Levels is the idea that autonomy should be embedded within and throughout a spacecraft. Using Systems Engineering methods a spacecraft is typically decomposed into systems, subsystems, assemblies, components, and so on. All these decomposition levels within all the spacecraft's systems, could and should have autonomy elements built in. As a result, the "autonomy system" is made of autonomy elements or units that are integrated, distributed and embedded within the whole spacecraft. T…

JAX-LaB: A High-Performance, Differentiable, Lattice Boltzmann Library for Modeling Multiphase Fluid Dynamics in Geosciences and Engineering
Piyush Pradhan, Pierre Gentine, Shaina Kelly
https://arxiv.org/abs/2506.17713

JAX-LaB: A High-Performance, Differentiable, Lattice Boltzmann Library for Modeling Multiphase Fluid Dynamics in Geosciences and Engineering
We present JAX-LaB, a differentiable, Python-based Lattice Boltzmann library for simulating multiphase and multiphysics flows in hydrologic, geologic, and engineered porous media. Built as an extension of the XLB library, JAX-LaB utilizes JAX for computations and offers a performant, hardware-agnostic implementation that integrates seamlessly with machine learning workflows and scales efficiently across CPUs, GPUs, and distributed systems. Multiphase interactions are modeled using the Shan-Chen…

Probing Phonon Modes in Reconstructed twisted Homo and Hetero Bilayer System
Sushil Kumar Sahu, Robin Bajaj, Syed Ummair Ali, Ajay Bhut, Roshan Jesus Mathew, Shinjan Mandal, Kenji Watanabe, Takashi Taniguchi, Manish Jain, Chandan Kumar
https://arxiv.org/abs/2506.19669

Probing Phonon Modes in Reconstructed twisted Homo and Hetero Bilayer System
Twist angle engineering in van der Waals homo and hetero-bilayers introduces profound modifications in their electronic, optical and mechanical properties due to lattice reconstruction. In these systems, the interlayer coupling and atomic rearrangement strongly depend on the twist angle, leading to the formation of periodic Moire superlattices. At small twist angles, significant lattice relaxation results in the emergence of domain structures separated by one dimensional soliton networks, influ…

AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps
Khaled M. A. Alghtus, Ayad Gannan, Khalid M. Alhajri, Ali L. A. Al Jubouri, Hassan A. I. Al-Janahi
https://arxiv.org/abs/2508.15550

AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps
This study presents a practical approach for early fault detection in industrial pump systems using real-world sensor data from a large-scale vertical centrifugal pump operating in a demanding marine environment. Five key operational parameters were monitored: vibration, temperature, flow rate, pressure, and electrical current. A dual-threshold labeling method was applied, combining fixed engineering limits with adaptive thresholds calculated as the 95th percentile of historical sensor values. …

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
Yuki Imajuku, Kohki Horie, Yoichi Iwata, Kensho Aoki, Naohiro Takahashi, Takuya Akiba
https://arxiv.org/abs/2506.09050

ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering
How well do AI systems perform in algorithm engineering for hard optimization problems in domains such as package-delivery routing, crew scheduling, factory production planning, and power-grid balancing? We introduce ALE-Bench, a new benchmark for evaluating AI systems on score-based algorithmic programming contests. Drawing on real tasks from the AtCoder Heuristic Contests, ALE-Bench presents optimization problems that are computationally hard and admit no known exact solution. Unlike short-du…

Computing stabilizing feedback gains for stochastic linear systems via policy iteration method
Xinpei Zhang, Guangyan Jia
https://arxiv.org/abs/2508.05214 https://

Computing stabilizing feedback gains for stochastic linear systems via policy iteration method
In recent years, stabilizing unknown dynamical systems has became a critical problem in control systems engineering. Addressing this for linear time-invariant (LTI) systems is an essential fist step towards solving similar problems for more complex systems. In this paper, we develop a model-free reinforcement learning algorithm to compute stabilizing feedback gains for stochastic LTI systems with unknown system matrices. This algorithm proceeds by solving a series of discounted stochastic linea…

from my link log —
Bazel’s original sins.
https://fzakaria.com/2025/06/22/bazel-s-original-sins
saved 2025-06-24 https://

ROS-related Robotic Systems Development with V-model-based Application of MeROS Metamodel
Tomasz Winiarski, Jan Kaniuka, Daniel Gie{\l}dowski, Jakub Ostrysz, Krystian Radlak, Dmytro Kushnir
https://arxiv.org/abs/2506.08706

ROS-related Robotic Systems Development with V-model-based Application of MeROS Metamodel
As robotic systems grow increasingly complex, heterogeneous, and safety-critical, the need for structured development methodologies becomes paramount. Although frameworks like the Robot Operating System (ROS) and Model-Based Systems Engineering (MBSE) offer foundational tools, they often lack integration when used together. This paper addresses that gap by aligning the widely recognized V-model development paradigm with the MeROS metamodel SysML-based modeling language tailored for ROS-based sy…

Error-resilient Reversal of Quantum Chaotic Dynamics Enabled by Scramblons
Yu-Chen Li, Tian-Gang Zhou, Shengyu Zhang, Ze Wu, Liqiang Zhao, Haochuan Yin, Xiaoxue An, Hui Zhai, Pengfei Zhang, Xinhua Peng
https://arxiv.org/abs/2506.19915

Error-resilient Reversal of Quantum Chaotic Dynamics Enabled by Scramblons
The emergence of the arrow of time in quantum many-body systems stems from the inherent tendency of Hamiltonian evolution to scramble quantum information and increase entanglement. While, in principle, one might counteract this temporal directionality by engineering a perfectly inverted Hamiltonian to reverse entanglement growth, such a scenario is fundamentally unstable because even minor imperfections in the backward evolution can be exponentially amplified, a hallmark of quantum many-body ch…

Multivariate Long-term Profile Monitoring with Application to the KW51 Railway Bridge
Philipp Wittenberg, Alexander Mendler, Sven Knoth, Jan Gertheiss
https://arxiv.org/abs/2506.20295

Multivariate Long-term Profile Monitoring with Application to the KW51 Railway Bridge
Structural Health Monitoring (SHM) plays a pivotal role in modern civil engineering, providing critical insights into the health and integrity of infrastructure systems. This work presents a novel multivariate long-term profile monitoring approach to eliminate fluctuations in the measured response quantities, e.g., caused by environmental influences or measurement error. Our methodology addresses critical challenges in SHM and combines supervised methods with unsupervised, principal component a…

Quantum Optimization for Software Engineering: A Survey
Man Zhang, Yuechen Li, Tao Yue, Kai-Yuan Cai
https://arxiv.org/abs/2506.16878 https://

Quantum Optimization for Software Engineering: A Survey
Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative sol…

innovations from the semiconductor, electronics, metallurgy, and petroleum industries played a major role in reducing both PhotoVoltaic and Balance of System (BOS) costs,
but BOS costs were also impacted by innovations in software engineering and electric utilities.
Noninnovation factors, like efficiency gains from bulk purchasing and the accumulation of knowledge in the solar power industry, also reduced some cost variables.
In addition, while most PV panel innovations origi…

Surprisingly diverse innovations led to dramatically cheaper solar panels
A new study reveals key innovations that contributed to the rapid decline of solar energy systems, showing that many of the most significant technological advances came from outside the solar sector. This work could help businesses, researchers, and policymakers identify optimal areas for future investment.

By all reports, DOGE’s tools were hot crap, and I have serious doubts about whether any of the engineers in involved were any good. But what Weissmann is saying about this •belief•? Agree completely.
That particular form of engineering arrogance that imagines Smart Boys with Fancy Tech can magically untangle complex human systems needs to die a quick death. It’s not good public policy. It’s not even good engineering.
via @…: https://toad.social/@KimPerales/114942923052855822

Parametric Amplification of Spin-Motion Coupling in Three-Dimensional Trapped-Ion Crystals
Samarth Hawaldar, N. Nikhil, Ana Maria Rey, John J. Bollinger, Athreya Shankar
https://arxiv.org/abs/2507.16741

Parametric Amplification of Spin-Motion Coupling in Three-Dimensional Trapped-Ion Crystals
Three-dimensional (3D) crystals offer a route to scale up trapped ion systems for quantum sensing and quantum simulation applications. However, engineering coherent spin-motion couplings and effective spin-spin interactions in large crystals poses technical challenges associated with decoherence and prolonged timescales to generate appreciable entanglement. Here, we explore the possibility to speed up these interactions in 3D crystals via parametric amplification. We derive a general Hamiltonia…

Large Language Models as Visualization Agents for Immersive Binary Reverse Engineering
Dennis Brown, Samuel Mulder
https://arxiv.org/abs/2508.13413 https://

Large Language Models as Visualization Agents for Immersive Binary Reverse Engineering
Immersive virtual reality (VR) offers affordances that may reduce cognitive complexity in binary reverse engineering (RE), enabling embodied and external cognition to augment the RE process through enhancing memory, hypothesis testing, and visual organization. In prior work, we applied a cognitive systems engineering approach to identify an initial set of affordances and implemented a VR environment to support RE through spatial persistence and interactivity. In this work, we extend that platfo…

Engineering Resilience: An Energy-Based Approach to Sustainable Behavioural Interventions
Arpitha Srivathsa Malavalli, Karthik Sama, Janvi Chhabra, Pooja Bassin, Srinath Srinivasa
https://arxiv.org/abs/2506.16836

Engineering Resilience: An Energy-Based Approach to Sustainable Behavioural Interventions
Addressing complex societal challenges, such as improving public health, fostering honesty in workplaces, or encouraging eco-friendly behaviour requires effective nudges to influence human behaviour at scale. Intervention science seeks to design such nudges within complex societal systems. While interventions primarily aim to shift the system toward a desired state, less attention is given to the sustainability of that state, which we define in terms of resilience: the system's ability to retai…

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry
Asif Iqbal, John Verboncoeur, Peng Zhang
https://arxiv.org/abs/2507.17881

A Supervised Machine Learning Framework for Multipactor Breakdown Prediction in High-Power Radio Frequency Devices and Accelerator Components: A Case Study in Planar Geometry
Multipactor is a nonlinear electron avalanche phenomenon that can severely impair the performance of high-power radio frequency (RF) devices and accelerator systems. Accurate prediction of multipactor susceptibility across different materials and operational regimes remains a critical yet computationally intensive challenge in accelerator component design and RF engineering. This study presents the first application of supervised machine learning (ML) for predicting multipactor susceptibility i…

Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks
Yuzhen Zhan, Li Jin
https://arxiv.org/abs/2507.12975 https://…

Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks
We consider the cyber-physical security of parallel server systems, which is relevant for a variety of engineering applications such as networking, manufacturing, and transportation. These systems rely on feedback control and may thus be vulnerable to malicious attacks such as denial-of-service, data falsification, and instruction manipulations. In this paper, we develop a learning algorithm that computes a defensive strategy to balance technological cost for defensive actions and performance d…

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Tianyue Ou, Saujas Vaduguru, Daniel Fried
https://arxiv.org/abs/2508.12981 https://a…

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Multi-agent systems (MASs) have pushed the boundaries of large language model (LLM) agents in domains such as web research and software engineering. However, long-horizon, multi-constraint planning tasks involve conditioning on detailed information and satisfying complex interdependent constraints, which can pose a challenge for these systems. In this study, we construct an LLM-based MAS for a travel planning task which is representative of these challenges. We evaluate the impact of a notebook…

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Tri Kurniawan Wijaya, Xinyang Shao, Gonzalo Fiz Pontiveros, Edoardo D'Amico
https://arxiv.org/abs/2506.03391

Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks
Recommender systems are pivotal in delivering personalized experiences across industries, yet their adoption and scalability remain hindered by the need for extensive dataset- and task-specific configurations. Existing systems often require significant manual intervention, domain expertise, and engineering effort to adapt to new datasets or tasks, creating barriers to entry and limiting reusability. In contrast, recent advancements in large language models (LLMs) have demonstrated the transform…

It's a Dragonforce and distributed systems engineering type of night.

The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
Hao Li, Haoxiang Zhang, Ahmed E. Hassan
https://arxiv.org/abs/2507.15003

The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
The future of software engineering--SE 3.0--is unfolding with the rise of AI teammates: autonomous, goal-driven systems collaborating with human developers. Among these, autonomous coding agents are especially transformative, now actively initiating, reviewing, and evolving code at scale. This paper introduces AIDev, the first large-scale dataset capturing how such agents operate in the wild. Spanning over 456,000 pull requests by five leading agents--OpenAI Codex, Devin, GitHub Copilot, Cursor…

Should we teach vibe coding? Here's why not.
2/2
To address the bigger question I started with ("should we teach AI-"assisted" coding?"), my answer is: "No, except enough to show students directly what its pitfalls are." We have little enough time as it is to cover the core knowledge that they'll need, which has become more urgent now that they're going to be expected to clean up AI bugs and they'll have less time to develop an understanding of the problems they're supposed to be solving. The skill of prompt engineering & other skills of working with AI are relatively easy to pick up on your own, given a decent not-even-mathematical understanding of how a neutral network works, which is something we should be giving to all students, not just our majors.
Reasonable learning objectives for CS majors might include explaining what types of bugs an AI "assistant" is most likely to introduce, explaining the difference between software engineering and writing code, explaining why using an AI "assistant" is likely to violate open-source licenses, listing at lest three independent ethical objections to contemporary LLMs and explaining the evidence for/reasoning behind them, explaining why we should expect AI "assistants" to be better at generating code from scratch than at fixing bugs in existing code (and why they'll confidently "claim" to have fixed problems they haven't), and even fixing bugs in AI generated code (without AI "assistance").
If we lived in a world where the underlying environmental, labor, and data commons issues with AI weren't as bad, or if we could find and use systems that effectively mitigate these issues (there's lots of piecemeal progress on several of these) then we should probably start teaching an elective on coding with an assistant to students who have mastered programming basics, but such a class should probably spend a good chunk of time on non-assisted debugging.
#AI #LLMs #VibeCoding

Engineering Hubbard models with gated two-dimensional moir\'e systems
Yiqi Yang, Yubo Yang, Miguel A. Morales, Shiwei Zhang
https://arxiv.org/abs/2508.13314 https://

Engineering Hubbard models with gated two-dimensional moiré systems
Lattice models are powerful tools for studying strongly correlated quantum many-body systems, but their general lack of exact solutions motivates efforts to simulate them in tunable platforms. Recently, a promising new candidate has emerged for such platforms from two-dimensional materials. A subset of moiré systems can be effectively described as a two-dimensional electron gas (2D EG) subject to a moiré potential, with electron-electron interactions screened by nearby metallic gates. In this…

Engineering and exploiting self-driven domain wall motion in ferrimagnets for neuromorphic computing applications
Jeffrey A. Brock, Aleksandr Kurenkov, Ale\v{s} Hrabec, Laura J. Heyderman
https://arxiv.org/abs/2508.14252

Engineering and exploiting self-driven domain wall motion in ferrimagnets for neuromorphic computing applications
Magnetic domain wall motion has recently garnered significant interest as a physical mechanism to enable energy-efficient, next-generation brain-inspired computing architectures. However, realizing all behaviors required for neuromorphic computing within standard material systems remains a significant challenge, as these functionalities often rely on competing interactions. Here, we demonstrate how spontaneous domain wall motion in response to locally engineered lateral exchange coupling in tra…

DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins
Eduardo de Conto, Blaise Genest, Arvind Easwaran, Nicholas Ng, Shweta Menon
https://arxiv.org/abs/2508.17988

DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins
Digital twins (DTs) are increasingly utilized to monitor, manage, and optimize complex systems across various domains, including civil engineering. A core requirement for an effective DT is to act as a fast, accurate, and maintainable surrogate of its physical counterpart, the physical twin (PT). To this end, machine learning (ML) is frequently employed to (i) construct real-time DT prototypes using efficient reduced-order models (ROMs) derived from high-fidelity simulations of the PT's nominal…

Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas
Francesco Sovrano, Gabriele Dominici, Rita Sevastjanova, Alessandra Stramiglio, Alberto Bacchelli
https://arxiv.org/abs/2508.11278

Is General-Purpose AI Reasoning Sensitive to Data-Induced Cognitive Biases? Dynamic Benchmarking on Typical Software Engineering Dilemmas
Human cognitive biases in software engineering can lead to costly errors. While general-purpose AI (GPAI) systems may help mitigate these biases due to their non-human nature, their training on human-generated data raises a critical question: Do GPAI systems themselves exhibit cognitive biases? To investigate this, we present the first dynamic benchmarking framework to evaluate data-induced cognitive biases in GPAI within software engineering workflows. Starting with a seed set of 16 hand-cra…

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering
Junling Fan, David Koblah, Domenic Forte
https://arxiv.org/abs/2508.08462 ht…

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering
Integrated circuits (ICs) are essential to modern electronic systems, yet they face significant risks from physical reverse engineering (RE) attacks that compromise intellectual property (IP) and overall system security. While IC camouflage techniques have emerged to mitigate these risks, existing approaches largely focus on localized gate modifications, neglecting comprehensive deception strategies. To address this gap, we present a machine learning (ML)-driven methodology that integrates cryp…

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
Mingda Zhang, Na Zhao, Jianglong Qing, Qing xu, Kaiwen Pan, Ting luo
https://arxiv.org/abs/2507.07893

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
The rapid development of artificial intelligence has positioned large language models as fundamental components of intelligent legal systems. However, these models face significant limitations in legal dispute analysis, including insufficient legal knowledge representation, limited concept understanding, and reasoning deficiencies. This research proposes an enhanced framework integrating prompt engineering with multidimensional knowledge graphs. The framework introduces a three-stage hierarchic…

Comparative Model Fidelity Evaluation to Support Design Decisions for Complex, Novel Systems of Systems
Edward Louis, Gregory Mocko, Evan Taylor
https://arxiv.org/abs/2508.02456

Comparative Model Fidelity Evaluation to Support Design Decisions for Complex, Novel Systems of Systems
Systems design processes are increasingly reliant on simulation models to inform design decisions. A pervasive issue within the systems engineering community is trusting in the models used to make decisions about complex systems. This work presents a method of evaluating the trustworthiness of a model to provide utility to a designer making a decision within a design process. Trusting the results of a model is especially important in design processes where the system is complex, novel, or displ…

Density control of multi-agent swarms via bio-inspired leader-follower plasticity
Gian Carlo Maffettone, Alain Boldini, Mario di Bernardo, Maurizio Porfiri
https://arxiv.org/abs/2507.15781

Density control of multi-agent swarms via bio-inspired leader-follower plasticity
The design of control systems for the spatial self-organization of mobile agents is an open challenge across several engineering domains, including swarm robotics and synthetic biology. Here, we propose a bio-inspired leader-follower solution, which is aware of energy constraints of mobile agents and is apt to deal with large swarms. Akin to many natural systems, control objectives are formulated for the entire collective, and leaders and followers are allowed to plastically switch their role i…

Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
Hongzhen Zhang (Department of Chemical Engineering, KU Leuven, Leuven, Belgium), Tim Kerkenhoff (Institute of Climate and Energy Systems - Energy Systems Engineering), Neil Kichler (Software and Tools for Computational Engineering), Manuel Dahmen (Institute of Climate and Energy Systems - Energy Systems Engineering), Alexander Mitsos (JARA-CSD, Aachen, Germany, Process Systems Enginering, Institute of C…

Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
Spatial Branch and Bound (B&B) algorithms are widely used for solving nonconvex problems to global optimality, yet they remain computationally expensive. Though some works have been carried out to speed up B&B via CPU parallelization, GPU parallelization is much less explored. In this work, we investigate the design of a spatial B&B algorithm that involves an interval-based GPU-parallel lower bounding solver: The domain of each B&B node is temporarily partitioned into numerous subdomains, then …

Multifrequency system model for multiport time-modulated scatterers
Aleksandr D. Kuznetsov (Aalto University), Jari Holopainen (Aalto University), Ville Viikari (Aalto University)
https://arxiv.org/abs/2507.13130

Multifrequency system model for multiport time-modulated scatterers
Utilizing scatterers in communication engineering, such as reconfigurable intelligent surfaces (RISs) and backscatter systems, requires physically consistent models for accurate performance prediction. A multiport model, which also accounts for structural scattering, has been developed for non-periodic scatterers. However, many emerging systems operate at multiple frequencies or generate intermodulation harmonics, particularly when incorporating space-time modulation (STM) or dynamic load contr…

Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
Frederik Vandeputte
https://arxiv.org/abs/2508.15411 https://

Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
Generative AI (GenAI) has emerged as a transformative technology, demonstrating remarkable capabilities across diverse application domains. However, GenAI faces several major challenges in developing reliable and efficient GenAI-empowered systems due to its unpredictability and inefficiency. This paper advocates for a paradigm shift: future GenAI-native systems should integrate GenAI's cognitive capabilities with traditional software engineering principles to create robust, adaptive, and effici…

A look at Scattered Spider, a group of mainly young UK and US hackers using targeted social engineering to gain access to company systems and install ransomware (Wired)
https://www.wired.com/story/scattered-spider-most-imminent-threat/

A Group of Young Cybercriminals Poses the ‘Most Imminent Threat’ of Cyberattacks Right Now
The Scattered Spider hacking group has caused chaos among retailers, insurers, and airlines in recent months. Researchers warn that its flexible structure poses challenges for defense.

Multi-agent systems for chemical engineering: A review and perspective
Sophia Rupprecht, Qinghe Gao, Tanuj Karia, Artur M. Schweidtmann
https://arxiv.org/abs/2508.07880 https://…

Multi-agent systems for chemical engineering: A review and perspective
Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex workflows into teams of collaborative agents with specialized knowledge and tools. This review surveys the state-of-the-art of MAS within chemical engineering. While early studies demonstrate promising results, scientific challenges remain, including the design of tailored architectures, integration of heterogeneous …

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Understanding the Issues, Their Causes and Solutions in Microservices Systems: An Empirical Study
Waseem, Liang, Ahmad, Khan, Shahin, Abrahamsson, Nasab, Mikkonen

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Technical Solutions
Parsa Omidi, Xingshuai Huang, Axel Laborieux, Bahareh Nikpour, Tianyu Shi, Armaghan Eshaghi
https://arxiv.org/abs/2508.10824

Memory-Augmented Transformers: A Systematic Review from Neuroscience Principles to Technical Solutions
Memory is fundamental to intelligence, enabling learning, reasoning, and adaptability across biological and artificial systems. While Transformer architectures excel at sequence modeling, they face critical limitations in long-range context retention, continual learning, and knowledge integration. This review presents a unified framework bridging neuroscience principles, including dynamic multi-timescale memory, selective attention, and consolidation, with engineering advances in Memory-Augment…

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
Amro M. Farid, Amirreza Hosseini, John C. Little
https://arxiv.org/abs/2505.24046

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
A defining feature of twenty first century engineering challenges is their inherent complexity, demanding the convergence of knowledge across diverse disciplines. Establishing consistent methodological foundations for engineering systems remains a challenge -- one that both systems engineering and network science have sought to address. Model-based systems engineering (MBSE) has recently emerged as a practical, interdisciplinary approach for developing complex systems from concept through imple…

Construction of Kondo Chains by Engineering Porphyrin {\pi}-Radicals on Au(111)
Yan Zhao, Kaiyue Jiang, Peng-Yi Liu, Ruoning Li, Jie Li, Xin Li, Xinchen Fang, Anjing Zhao, Yutong Zhu, Hongxiang Xu, Ting Chen, Dong Wang, Xiaodong Zhuang, Shimin Hou, Kai Wu, Song Gao, Qing-Feng Sun, Yajie Zhang, Yongfeng Wang
https://arxiv.org/abs…

Construction of Kondo Chains by Engineering Porphyrin π-Radicals on Au(111)
Quantum manipulation of molecular radical spins provides a crucial platform for exploring emergent phenomena in many-body systems. Here, we combine surface-confined synthesis with scanning tunneling microscopy (STM) tip-induced dehydrogenation to achieve atom-precise engineering of quasi-one-dimensional porphyrin-based Kondo chains (1-7 units) on Au(111). Key design innovations leverage large-sized porphyrins to suppress intrachain antiferromagnetic coupling, while ${Zn}^{2+}$ chelation at porp…

A Conceptual Framework for Requirements Engineering of Pretrained-Model-Enabled Systems
Dongming Jin, Zhi Jin, Linyu Li, Xiaohong Chen
https://arxiv.org/abs/2507.13095

A Conceptual Framework for Requirements Engineering of Pretrained-Model-Enabled Systems
Recent advances in large pretrained models have led to their widespread integration as core components in modern software systems. The trend is expected to continue in the foreseeable future. Unlike traditional software systems governed by deterministic logic, systems powered by pretrained models exhibit distinctive and emergent characteristics, such as ambiguous capability boundaries, context-dependent behavior, and continuous evolution. These properties fundamentally challenge long-standing a…

Quantum Control of Thermal Emission from Photonic Crystals with Two-Level Atoms
Chih-Wei Wang, Jhih-Sheng Wu
https://arxiv.org/abs/2508.11191 https://arxiv…

Quantum Control of Thermal Emission from Photonic Crystals with Two-Level Atoms
Thermal light engineering is a field of considerable interest and potential. We study quantum light-matter interactions in a one-dimensional photonic crystal with two-level atoms as the active medium, replacing classical oscillators in traditional blackbody models. In a thermal bath with pumping, these atoms modulate thermal emission via interactions with photonic modes. The model with quantum two-level systems enables the processes of spontaneous emission, stimulated absorption, and stimulated…

Moir\'e-Induced Magnetoelectricity in Twisted Bilayer NiI2
Haiyan Zhua, Hongyu Yua, Weiqin Zhua, Guoliang Yua, Changsong Xu, Hongjun Xiang
https://arxiv.org/abs/2507.13709

Moiré-Induced Magnetoelectricity in Twisted Bilayer NiI2
Twisted magnetic van der Waals (vdW) materials offer a promising route for multiferroic engineering, yet modeling large-scale moiré superlattices remains challenging. Leveraging a newly developed SpinGNN++ framework that effectively handles spin-lattice coupled systems, we develop a comprehensive interatomic machine learning (ML) potential and apply it to twisted bilayer NiI2 (TBN). Structural relaxation introduces moiré-periodic "bumps" that modulate the interlayer spacing by about 0.55~Å a…

Enterprise Security Incident Analysis and Countermeasures Based on the T-Mobile Data Breach
Zhuohan Cui, Zikun Song
https://arxiv.org/abs/2507.12937 https:…

Enterprise Security Incident Analysis and Countermeasures Based on the T-Mobile Data Breach
This paper presents a comprehensive analysis of T-Mobile's critical data breaches in 2021 and 2023, alongside a full-spectrum security audit targeting its systems, infrastructure, and publicly exposed endpoints. By combining case-based vulnerability assessments with active ethical hacking techniques--including Shodan reconnaissance, API misuse simulations, VNC brute-forcing, firmware reverse engineering, and web application scans--we uncover structural weaknesses persisting beyond the initial b…

Developing Shared Vocabulary System For Collaborative Software Engineering
Carey Lai Zheng Hui, Johnson Britto Jessia Esther Leena, Kumuthini Subramanian, Zhao Chenyu, Shubham Rajeshkumar Jariwala
https://arxiv.org/abs/2507.14396

Developing Shared Vocabulary System For Collaborative Software Engineering
Effective communication is a critical factor in successful software engineering collaboration. However, communication gaps remain a persistent challenge, often leading to misunderstandings, inefficiencies, and defects. This research investigates the technical factors contributing to such misunderstandings and explores the measurable benefits of establishing shared vocabulary systems within software documentation and codebases. Using a Design Science Research (DSR) framework, the study was struc…

Human-in-the-Loop Systems for Adaptive Learning Using Generative AI
Bhavishya Tarun, Haoze Du, Dinesh Kannan, Edward F. Gehringer
https://arxiv.org/abs/2508.11062 https://

Human-in-the-Loop Systems for Adaptive Learning Using Generative AI
A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefined feedback tags, fostering deeper engagement and understanding. This empowers students to actively shape their learning, with AI serving as an adaptive partner. The system uses a tagging technique and prompt engineering to personalize content, informing a Retrieval-Augmented Gen…

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Jan C. Schulze, Alexander Mitsos
https://arxiv.org/abs/2506.12819

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
Computationally cheap yet accurate enough dynamical models are vital for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order simplified model can enable such real-time applications. Herein, we review state-of-the-art nonlinear model order reduction methods and provide a theoretical comparison of method properties. Additionally, we discuss both general-purpose methods and tailored appro…

MCNP-GO: A python package for assembling MCNP input files with a systems engineering approach
Alexandre Friou
https://arxiv.org/abs/2507.05659 https://

MCNP-GO: A python package for assembling MCNP input files with a systems engineering approach
This article introduces MCNP-GO (https://github.com/afriou/mcnpgo), a Python package designed to manipulate and assemble MCNP input files, allowing users to assemble a set of independent objects, each described by a valid MCNP file, into a single cohesive file. This tool is particularly useful for applications where precise modeling and positioning of equipment are crucial. The package addresses the challenges of managing large databases of MCNP input files, ensuring reliability and traceabilit…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Towards Understanding the Challenges of Bug Localization in Deep Learning Systems
Sigma Jahan, Mehil B. Shah, Mohammad Masudur Rahman

Minimizing Structural Vibrations via Guided Flow Matching Design Optimization
Jan van Delden, Julius Schultz, Sebastian Rothe, Christian Libner, Sabine C. Langer, Timo L\"uddecke
https://arxiv.org/abs/2506.15263

Minimizing Structural Vibrations via Guided Flow Matching Design Optimization
Structural vibrations are a source of unwanted noise in engineering systems like cars, trains or airplanes. Minimizing these vibrations is crucial for improving passenger comfort. This work presents a novel design optimization approach based on guided flow matching for reducing vibrations by placing beadings (indentations) in plate-like structures. Our method integrates a generative flow matching model and a surrogate model trained to predict structural vibrations. During the generation process…

A MARL-based Approach for Easing MAS Organization Engineering
Julien Soul\'e, Jean-Paul Jamont, Michel Occello, Louis-Marie Traonouez, Paul Th\'eron
https://arxiv.org/abs/2506.05437

A MARL-based Approach for Easing MAS Organization Engineering
Multi-Agent Systems (MAS) have been successfully applied in industry for their ability to address complex, distributed problems, especially in IoT-based systems. Their efficiency in achieving given objectives and meeting design requirements is strongly dependent on the MAS organization during the engineering process of an application-specific MAS. To design a MAS that can achieve given goals, available methods rely on the designer's knowledge of the deployment environment. However, high complex…

A Representation Engineering Perspective on the Effectiveness of Multi-Turn Jailbreaks
Blake Bullwinkel, Mark Russinovich, Ahmed Salem, Santiago Zanella-Beguelin, Daniel Jones, Giorgio Severi, Eugenia Kim, Keegan Hines, Amanda Minnich, Yonatan Zunger, Ram Shankar Siva Kumar
https://arxiv.org/abs/2507.02956

A Representation Engineering Perspective on the Effectiveness of Multi-Turn Jailbreaks
Recent research has demonstrated that state-of-the-art LLMs and defenses remain susceptible to multi-turn jailbreak attacks. These attacks require only closed-box model access and are often easy to perform manually, posing a significant threat to the safe and secure deployment of LLM-based systems. We study the effectiveness of the Crescendo multi-turn jailbreak at the level of intermediate model representations and find that safety-aligned LMs often represent Crescendo responses as more benign…

Overview of Complex System Design
John W. Sheppard
https://arxiv.org/abs/2508.11026 https://arxiv.org/pdf/2508.11026

Overview of Complex System Design
This chapter serves as an introduction to systems engineering focused on the broad issues surrounding realizing complex integrated systems. What is a system? We pose a number of possible definitions and perspectives, but leave open the opportunity to consider the system from the target context where it will be used. Once we have a system in mind, we acknowledge the fact that this system needs to integrate a variety of pieces, components, subsystems, in order for it to accomplish its task. There…

Is Your Automated Software Engineer Trustworthy?
Noble Saji Mathews, Meiyappan Nagappan
https://arxiv.org/abs/2506.17812 https://arxi…

Is Your Automated Software Engineer Trustworthy?
Large Language Models (LLMs) are being increasingly used in software engineering tasks, with an increased focus on bug report resolution over the past year. However, most proposed systems fail to properly handle uncertain or incorrect inputs and outputs. Existing LLM-based tools and coding agents respond to every issue and generate a patch for every case, even when the input is vague or their own output is incorrect. There are no mechanisms in place to abstain when confidence is low. This leads…

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…

Active Digital Twins via Active Inference
Matteo Torzoni, Domenico Maisto, Andrea Manzoni, Francesco Donnarumma, Giovanni Pezzulo, Alberto Corigliano
https://arxiv.org/abs/2506.14453

Active Digital Twins via Active Inference
Digital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes. However, conventional digital twins rely primarily on passive data assimilation, which limits their adaptability in uncertain and dynamic environments. This paper introduces the active digital twin paradigm, based on active inference. Active inference is a neuroscience-inspired, Bayesian framework for probabilistic reasoning and…

Learning event-triggered controllers for linear parameter-varying systems from data
Renjie Ma, Su Zhang, Wenjie Liu, Zhijian Hu, Peng Shi
https://arxiv.org/abs/2506.08366

Learning event-triggered controllers for linear parameter-varying systems from data
Nonlinear dynamical behaviours in engineering applications can be approximated by linear-parameter varying (LPV) representations, but obtaining precise model knowledge to develop a control algorithm is difficult in practice. In this paper, we develop the data-driven control strategies for event-triggered LPV systems with stability verifications. First, we provide the theoretical analysis of $θ$-persistence of excitation for LPV systems, which leads to the feasible data-based representations. T…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead
Junda He, Christoph Treude, David Lo

DriveSimQuest: A VR Driving Simulator and Research Platform on Meta Quest with Unity
Nishanth Chidambaram, Weichen Liu, Manas Satish Bedmutha, Nadir Weibel, Chen Chen
https://arxiv.org/abs/2508.11072

DriveSimQuest: A VR Driving Simulator and Research Platform on Meta Quest with Unity
Using head-mounted Virtual Reality (VR) displays to simulate driving is critical to studying driving behavior and designing driver assistance systems. But existing VR driving simulators are often limited to tracking only eye movements. The bulky outside-in tracking setup and Unreal-based architecture also present significant engineering challenges for interaction researchers and practitioners. We present DriveSimQuest, a VR driving simulator and research platform built on the Meta Quest Pro and…

Dialogue Systems Engineering: A Survey and Future Directions
Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa, Yoichi Matsuyama, Kazunori Komatani
https://arxiv.org/abs/2508.02279

Dialogue Systems Engineering: A Survey and Future Directions
This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is impo…

Integrative, Scalable Modeling of Hydrological Systems with MBSE and HFGT
Megan Harris, Ehsanoddin Ghorbanichemazkati, Mohammad Mahdi Naderi, John C. Little, Amro M. Farid
https://arxiv.org/abs/2506.00696

Integrative, Scalable Modeling of Hydrological Systems with MBSE and HFGT
Worsening global challenges in the Anthropocene demand complex, adaptive solutions grounded in a systems-level understanding of coupled social and environmental dynamics. However, existing modeling approaches often fall short due to disciplinary silos, limited scalability, and the absence of shared ontological frameworks. Model-Based Systems Engineering (MBSE), when integrated with Hetero-functional Graph Theory (HFGT), offers a powerful methodology for modeling systems of systems while preserv…

SemAgent: A Semantics Aware Program Repair Agent
Anvith Pabba, Alex Mathai, Anindya Chakraborty, Baishakhi Ray
https://arxiv.org/abs/2506.16650 https://

SemAgent: A Semantics Aware Program Repair Agent
Large Language Models (LLMs) have shown impressive capabilities in downstream software engineering tasks such as Automated Program Repair (APR). In particular, there has been a lot of research on repository-level issue-resolution benchmarks such as SWE-Bench. Although there has been significant progress on this topic, we notice that in the process of solving such issues, existing agentic systems tend to hyper-localize on immediately suspicious lines of code and fix them in isolation, without a …

Enhancing Spatio-Temporal Resolution of Process-Based Life Cycle Analysis with Model-Based Systems Engineering \& Hetero-functional Graph Theory
Niraj Gohil, Nawshad Haque, Amgad Elgowainy, Amro M. Farid
https://arxiv.org/abs/2506.00230

Enhancing Spatio-Temporal Resolution of Process-Based Life Cycle Analysis with Model-Based Systems Engineering \& Hetero-functional Graph Theory
Life cycle analysis (LCA) has emerged as a vital tool for assessing the environmental impacts of products, processes, and systems throughout their entire lifecycle. It provides a systematic approach to quantifying resource consumption, emissions, and waste, enabling industries, researchers, and policymakers to identify hotspots for sustainability improvements. By providing a comprehensive assessment of systems, from raw material extraction to end-of-life disposal, LCA facilitates the developmen…

Microservices and Real-Time Processing in Retail IT: A Review of Open-Source Toolchains and Deployment Strategies
Aaditaa Vashisht (Department of Information Science,Engineering, RV College of Engineering, India), Rekha B S (Department of Information Science,Engineering, RV College of Engineering, India)
https://arxiv.org/abs/25…

Microservices and Real-Time Processing in Retail IT: A Review of Open-Source Toolchains and Deployment Strategies
With the rapid pace of digital transformation, the retail industry is increasingly depending on real-time, scalable, and resilient systems to manage financial transactions, analyze customer behavior, and streamline order processing. This literature review explores how modern event-driven and microservices-based architectures, particularly those leveraging Apache Kafka, Spring Boot, MongoDB, and Kubernetes are transforming retail and financial systems. By systematically reviewing academic public…

What's Really Different with AI? -- A Behavior-based Perspective on System Safety for Automated Driving Systems
Marcus Nolte, Nayel Fabian Salem, Olaf Franke, Jan Heckmann, Christoph H\"ohmann, Georg Stettinger, Markus Maurer
https://arxiv.org/abs/2507.20685

What's Really Different with AI? -- A Behavior-based Perspective on System Safety for Automated Driving Systems
Assuring safety for ``AI-based'' systems is one of the current challenges in safety engineering. For automated driving systems, in particular, further assurance challenges result from the open context that the systems need to operate in after deployment. The current standardization and regulation landscape for ``AI-based'' systems is becoming ever more complex, as standards and regulations are being released at high frequencies. This position paper seeks to provide guidance for making qualifi…

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Lu\'is Cruz, Jo\~ao Paulo Fernandes, Maja H. Kirkeby, Silverio Mart\'inez-Fern\'andez, June Sallou, Hina Anwar, Enrique Barba Roque, Justus Bogner, Joel Casta\~no, Fernando Castor, Aadil Chasmawala, Sim\~ao Cunha, Daniel Feitosa, Alexandra Gonz\'alez, Andreas Jedlitschka, Patricia Lago, Ana Oprescu, Pooja Rani, Jo\~ao Saraiva, Federica Sarro, Raghavendra…

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The "Greening AI with Software Engineering" CECAM-Lorentz workshop (no. 1358, 2025) funded by the Centre Européen de Calcul Atomique et Moléculaire and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current resul…

An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management
Simin Sun, Yuchuan Jin, Miroslaw Staron
https://arxiv.org/abs/2506.11659

An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management
Developing autonomous driving systems (ADSs) involves generating and storing extensive log data from test drives, which is essential for verification, research, and simulation. However, these high-frequency logs, recorded over varying durations, pose challenges for developers attempting to locate specific driving scenarios. This difficulty arises due to the wide range of signals representing various vehicle components and driving conditions, as well as unfamiliarity of some developers' with the…

Testing the Untestable? An Empirical Study on the Testing Process of LLM-Powered Software Systems
Cleyton Magalhaes, Italo Santos, Brody Stuart-Verner, Ronnie de Souza Santos
https://arxiv.org/abs/2508.00198

Testing the Untestable? An Empirical Study on the Testing Process of LLM-Powered Software Systems
Background: Software systems powered by large language models are becoming a routine part of everyday technologies, supporting applications across a wide range of domains. In software engineering, many studies have focused on how LLMs support tasks such as code generation, debugging, and documentation. However, there has been limited focus on how full systems that integrate LLMs are tested during development. Aims: This study explores how LLM-powered systems are tested in the context of real-wo…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Machine Learning Systems: A Survey from a Data-Oriented Perspective
Christian Cabrera, Andrei Paleyes, Pierre Thodoroff, Neil D. Lawrence

KARMA Approach supporting Development Process Reconstruction in Model-based Systems Engineering
Jiawei Li, Zan Liang, Guoxin Wang, Jinzhi Lu, Yan Yan, Shouxuan Wu, Hao Wang
https://arxiv.org/abs/2506.22037

KARMA Approach supporting Development Process Reconstruction in Model-based Systems Engineering
Model reconstruction is a method used to drive the development of complex system development processes in model-based systems engineering. Currently, during the iterative design process of a system, there is a lack of an effective method to manage changes in development requirements, such as development cycle requirements and cost requirements, and to realize the reconstruction of the system development process model. To address these issues, this paper proposes a model reconstruction method to…

Computing Floating-Point Errors by Injecting Perturbations
Youshuai Tan, Zhanwei Zhang, Jinfu Chen, Zishuo Ding, Jifeng Xuan, Weiyi Shang
https://arxiv.org/abs/2507.08467

Computing Floating-Point Errors by Injecting Perturbations
Floating-point programs form the foundation of modern science and engineering, providing the essential computational framework for a wide range of applications, such as safety-critical systems, aerospace engineering, and financial analysis. Floating-point errors can lead to severe consequences. Although floating-point errors widely exist, only a subset of inputs may trigger significant errors in floating-point programs. Therefore, it is crucial to determine whether a given input could produce s…

SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?
Xinyi He, Qian Liu, Mingzhe Du, Lin Yan, Zhijie Fan, Yiming Huang, Zejian Yuan, Zejun Ma
https://arxiv.org/abs/2507.12415

SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?
Code performance optimization is paramount in real-world software engineering and critical for production-level systems. While Large Language Models (LLMs) have demonstrated impressive capabilities in code generation and bug fixing, their proficiency in enhancing code performance at the repository level remains largely unexplored. To address this gap, we introduce SWE-Perf, the first benchmark specifically designed to systematically evaluate LLMs on code performance optimization tasks within au…

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
Ina K. Schieferdecker
https://arxiv.org/abs/2506.14640

Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening…

Machine Learning Pipeline for Software Engineering: A Systematic Literature Review
Samah Kansab
https://arxiv.org/abs/2508.00045 https://arxiv.org/pdf/2508…

Machine Learning Pipeline for Software Engineering: A Systematic Literature Review
The rapid advancement of software development practices has introduced challenges in ensuring quality and efficiency across the software engineering (SE) lifecycle. As SE systems grow in complexity, traditional approaches often fail to scale, resulting in longer debugging times, inefficient defect detection, and resource-heavy development cycles. Machine Learning (ML) has emerged as a key solution, enabling automation in tasks such as defect prediction, code review, and release quality estimati…

Towards a unified user modeling language for engineering human centered AI systems
Aaron Conrardy, Alfredo Capozucca, Jordi Cabot
https://arxiv.org/abs/2505.24697

Towards a unified user modeling language for engineering human centered AI systems
In today's digital society, personalization has become a crucial aspect of software applications, significantly impacting user experience and engagement. A new wave of intelligent user interfaces, such as AI-based conversational agents, has the potential to enable such personalization beyond what other types of interfaces could offer in the past. Personalization requires the ability to specify a complete user profile, covering as many dimensions as possible, such as potential accessibility cons…