Tootfinder

Chip design software maker Cadence agrees to acquire Stockholm-based Hexagon's design and simulation software business for ~$3.18B in cash and stock (Nathan Owens/Manufacturing Dive)
https://www.manufacturingdive.com/news/cad

Cadence Design Systems to spend $3.2B on Hexagon’s simulations tech
The acquisition would bolster the industrial software maker’s position in the growing structural analysis market used widely by Boeing, Lockheed Martin, Toyota and other suppliers.

Reasonable Experiments in Model-Based Systems Engineering
Johan Cederbladh, Loek Cleophas, Eduard Kamburjan, Lucas Lima, Rakshit Mittal, Hans Vangheluwe
https://arxiv.org/abs/2509.10649

Reasonable Experiments in Model-Based Systems Engineering
With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous…

Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants
Hamied Nabizada, Lasse Beers, Alain Chahine, Felix Gehlhoff, Oliver Niggemann, Alexander Fay
https://arxiv.org/abs/2509.12091

Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants
Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack symbolic planning semantics such as preconditions, effects, and constraints related to resource availability and timing. This limits their ability to evaluate whether a given system variant can fulfill specific tasks and how efficiently it performs compared to alternatives. To address this gap, this paper presents a mo…

Detection of Anomalous Behavior in Robot Systems Based on Machine Learning
Mahfuzul I. Nissan, Sharmin Aktar
https://arxiv.org/abs/2509.09953 https://arxiv…

Detection of Anomalous Behavior in Robot Systems Based on Machine Learning
Ensuring the safe and reliable operation of robotic systems is paramount to prevent potential disasters and safeguard human well-being. Despite rigorous design and engineering practices, these systems can still experience malfunctions, leading to safety risks. In this study, we present a machine learning-based approach for detecting anomalies in system logs to enhance the safety and reliability of robotic systems. We collected logs from two distinct scenarios using CoppeliaSim and comparatively…

Stable and Fault-Tolerant Decentralized Traffic Engineering
Arjun Devraj, Umesh Krishnaswamy, Ying Zhang, Karuna Grewal, Justin Hsu, Eva Tardos, Rachee Singh
https://arxiv.org/abs/2510.11937

Stable and Fault-Tolerant Decentralized Traffic Engineering
Cloud providers have recently decentralized their wide-area network traffic engineering (TE) systems to contain the impact of TE controller failures. In the decentralized design, a controller fault only impacts its slice of the network, limiting the blast radius to a fraction of the network. However, we find that autonomous slice controllers can arrive at divergent traffic allocations that overload links by 30% beyond their capacity. We present Symphony, a decentralized TE system that addresses…

Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach
Zhenyu Mao, Jacky Keung, Fengji Zhang, Shuo Liu, Yifei Wang, Jialong Li
https://arxiv.org/abs/2510.12120 https:/…

Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach
The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs). Recent efforts extend LLMs into multi-agent systems (MAS) that emulate collaborative development workflows, but these systems often fail due to three core deficiencies: under-specification, coordination misalignment, and inappropriate verification, arising from the absence of foundational SE structuring principles. This paper introduces Software En…

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Prarthana P. Kartholy, Thandi M. Labor, Neil N. Panchal, Sean H. Wang, Hillary N. Owusu
https://arxiv.org/abs/2510.09636

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Selecting a college major is a difficult decision for many incoming freshmen. Traditional academic advising is often hindered by long wait times, intimidating environments, and limited personalization. AI Chatbots present an opportunity to address these challenges. However, AI systems also have the potential to generate biased responses, prejudices related to race, gender, socioeconomic status, and disability. These biases risk turning away potential students and undermining reliability of AI s…

Automatic Regression for Governing Equations with Control (ARGOSc)
Amir Bahador Javadi, Amin Kargarian, Mort Naraghi-Pour
https://arxiv.org/abs/2509.09784 https://

Automatic Regression for Governing Equations with Control (ARGOSc)
Learning the governing equations of dynamical systems from data has drawn significant attention across diverse fields, including physics, engineering, robotics and control, economics, climate science, and healthcare. Sparse regression techniques, exemplified by the Automatic Regression for Governing Equations (ARGOS) framework, have demonstrated effectiveness in extracting parsimonious models from time series data. However, real-world dynamical systems are driven by input control, external forc…

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…

Enhancing the Plasmonic Hotspot Density via Structural Engineering of Multi-layered MoO3-Ag-Au Systems Under Extreme Electronic Excitation Conditions for Ultra-Sensitive SERS Applications
Om Prakash, Sharmistha Dey, Mayur Khan, Abhijith T, Udai Bhan Singh, Ambuj Tripathi, Santanu Ghosh
https://arxiv.org/abs/2510.11470

Enhancing the Plasmonic Hotspot Density via Structural Engineering of Multi-layered MoO3-Ag-Au Systems Under Extreme Electronic Excitation Conditions for Ultra-Sensitive SERS Applications
We illustrate ion-beam engineering of MoO3 Ag Au multilayer plasmonic substrates to improve SERS performance, We illustrate ion-beam engineering of MoO3-Ag-Au multilayer plasmonic substrates to improve SERS performance. Orthorhombic α-MoO3 microflakes were produced via chemical vapour deposition (CVD) on Si-SiO2 substrates. Thin films of Ag (5 nm) and Au (5 nm) were thermally evaporated onto the MoO3 flakes, and the samples were subjected to 100 MeV Ag8+ swift heavy ion irradiation at fluences…

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Linfeng Gao, Baolong Bi, Zheng Yuan, Le Wang, Zerui Chen, Zhimin Wei, Shenghua Liu, Qinggang Zhang, Jinsong Su
https://arxiv.org/abs/2510.12460

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance the factuality of Large Language Models (LLMs). However, existing RAG systems often suffer from an unfaithfulness issue, where the model's response contradicts evidence from the retrieved context. Existing approaches to improving contextual faithfulness largely rely on external interventions, such as prompt engineering, decoding constraints, or reward-based fine-tuning. These works treat the LLM as a black box an…

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Stefano Riva, Carolina Introini, Jos\`e Nathan Kutz, Antonio Cammi
https://arxiv.org/abs/2510.12368

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including unobservable fields, which is agnostic to sensor positions and able to handle noisy data through an ensemble strategy, leveraging the short training times and without the need for …

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…

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 …

A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
Jiale Guo, Suizhi Huang, Mei Li, Dong Huang, Xingsheng Chen, Regina Zhang, Zhijiang Guo, Han Yu, Siu-Ming Yiu, Christian Jensen, Pietro Lio, Kwok-Yan Lam
https://arxiv.org/abs/2510.09721

A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
The integration of LLMs into software engineering has catalyzed a paradigm shift from traditional rule-based systems to sophisticated agentic systems capable of autonomous problem-solving. Despite this transformation, the field lacks a comprehensive understanding of how benchmarks and solutions interconnect, hindering systematic progress and evaluation. This survey presents the first holistic analysis of LLM-empowered software engineering, bridging the critical gap between evaluation and soluti…

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.

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…

Quivers and BPS states in 3d and 4d
Piotr Kucharski, Pietro Longhi, Dmitry Noshchenko, Sunghyuk Park, Piotr Su{\l}kowski
https://arxiv.org/abs/2508.09729 https://

Quivers and BPS states in 3d and 4d
We propose a symmetrization relation between BPS quivers encoding 4d $\mathcal{N}=2$ theories and symmetric quivers associated to 3d $\mathcal{N}=2$ theories. We analyse in detail the symmetrization of BPS quivers for a series of $A_m$ Argyres-Douglas theories by engineering 3d-4d systems in geometric backgrounds involving appropriate 3-manifolds and Riemann surfaces. We discuss properties of these geometric backgrounds and derive the corresponding quiver partition functions from the perspectiv…

Controlling complex rhythms: A hierarchical approach to limit cycle switching
Sandip Saha, Suvam Pal, Dibakar Ghosh
https://arxiv.org/abs/2508.10818 https://

Controlling complex rhythms: A hierarchical approach to limit cycle switching
Limit cycles are self-sustained, closed trajectories in phase space representing (un)-stable, periodic behavior in nonlinear dynamical systems. They underpin diverse natural phenomena, from neuronal firing patterns to engineering oscillations. The presence of multiple concentric limit cycles reflects distinct behavioral symmetries within a system. In this work, we investigate the hierarchical dynamical transitions from one limit cycle to another, driven by oscillatory excitation while preservin…

Parametric resonance and nonlinear dynamics in a coupled double-pendulum system
Yusheng Niu, Yixian Liu, Hongyan Fan, Zhenqi Bai, Yichi Zhang
https://arxiv.org/abs/2509.08509 ht…

Parametric resonance and nonlinear dynamics in a coupled double-pendulum system
Nonlinear dynamics plays a significant role in interdisciplinary fields spanning biology, engineering, mathematics, and physics. Under small-amplitude approximations, certain nonlinear systems can be effectively described by the linear Mathieu equation, which is widely recognized for modeling the response of systems with periodically modulated parameters. Here we investigated a collision-coupled double pendulum system within the framework of Lagrangian mechanics, further explored the nonlinear …

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- A MAPE-K-Based Method for Architectural Conformance Checking in Self-Adaptive Systems
Daniel San Mart\'in, Guisella Angulo, Valter Vieira de Camargo

Giant Shift Current in Electrically-Tunable Superlattice Bilayer Graphene
Nabil Atlam, Swati Chaudhary, Arpit Raj, Matthew Matzelle, Barun Ghosh, Gregory Fiete, Arun Bansil
https://arxiv.org/abs/2508.09465

Giant Shift Current in Electrically-Tunable Superlattice Bilayer Graphene
Recent introduction of superlattice potentials has opened new avenues for engineering tunable electronic band structures featuring topologically nontrivial moiré-like bands. Here we consider optoelectronic properties of Bernal-stacked graphene subjected to a superlattice potential either electrostatically or through lattice twisting to show that it exhibits a giant shift current response that is orders of magnitude larger than existing predictions in twisted mulitlayer systems. Effects of gate…

ChatGPT on the Road: Leveraging Large Language Model-Powered In-vehicle Conversational Agents for Safer and More Enjoyable Driving Experience
Yeana Lee Bond (Computer Science, Virginia Tech, Blacksburg, Virginia, USA), Mungyeong Choe (Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, USA), Baker Kasim Hasan (Computer Science, Virginia Tech, Blacksburg, Virginia, USA), Arsh Siddiqui (Computer Science, Virginia Tech, Blacksburg, Virginia, USA), Myounghoon Jeon (Com…

ChatGPT on the Road: Leveraging Large Language Model-Powered In-vehicle Conversational Agents for Safer and More Enjoyable Driving Experience
Studies on in-vehicle conversational agents have traditionally relied on pre-scripted prompts or limited voice commands, constraining natural driver-agent interaction. To resolve this issue, the present study explored the potential of a ChatGPT-based in-vehicle agent capable of carrying continuous, multi-turn dialogues. Forty drivers participated in our experiment using a motion-based driving simulator, comparing three conditions (No agent, Pre-scripted agent, and ChatGPT-based agent) as a with…

Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage
Ruochen Li (Department of Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong, China), Zhichao Chen (Department of Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong, China), Zhaoting Zhang (Department of Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong, China), Renjie Guo (Department of Decision Analytics and Operations, City Univers…

Degradation-Aware Model Predictive Control for Battery Swapping Stations under Energy Arbitrage
Battery swapping stations (BSS) offer a fast and scalable alternative to conventional electric vehicle (EV) charging, gaining growing policy support worldwide. However, existing BSS control strategies typically rely on heuristics or low-fidelity degradation models, limiting profitability and service level. This paper proposes BSS-MPC: a real-time, degradation-aware Model Predictive Control (MPC) framework for BSS operations to trade off economic incentives from energy market arbitrage and long-…

Memo: Meta Superintelligence Labs' products team, led by Nat Friedman, pushes staff to ditch Meta's slow internal systems in favor of external tools like Vercel (Pranav Dixit/Business Insider)
https://www.businessinsider.com/meta-super

Meta Superintelligence Labs executives are pushing staff to ditch slow internal systems for faster engineering tools
As part of Meta's big bet on superintelligence, it's steering its AI teams to new tools like Vercel and GitHub, as revealed by internal memos.

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

Engineering Emergence
Abel Jansma, Erik Hoel
https://arxiv.org/abs/2510.02649 https://arxiv.org/pdf/2510.02649

Engineering Emergence
One of the reasons complex systems are complex is because they have multiscale structure. How does this multiscale structure come about? We argue that it reflects an emergent hierarchy of scales that contribute to the system's causal workings. An example is how a computer can be described at the level of its hardware circuitry but also its software. But we show that many systems, even simple ones, have such an emergent hierarchy, built from a small subset of all their possible scales of descrip…

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

HECATE: An ECS-based Framework for Teaching and Developing Multi-Agent Systems
Arthur Casals, Anarosa A. F. Brand\~ao
https://arxiv.org/abs/2509.06431 https://

HECATE: An ECS-based Framework for Teaching and Developing Multi-Agent Systems
This paper introduces HECATE, a novel framework based on the Entity-Component-System (ECS) architectural pattern that bridges the gap between distributed systems engineering and MAS development. HECATE is built using the Entity-Component-System architectural pattern, leveraging data-oriented design to implement multiagent systems. This approach involves engineering multiagent systems (MAS) from a distributed systems (DS) perspective, integrating agent concepts directly into the DS domain. This …

Advancing Resource Extraction Systems in Martian Volcanic Terrain: Rover Design, Power Consumption and Hazard Analysis
Divij Gupta, Arkajit Aich
https://arxiv.org/abs/2509.06103

Advancing Resource Extraction Systems in Martian Volcanic Terrain: Rover Design, Power Consumption and Hazard Analysis
This study proposes a schematic plan for in-situ resource utilization (ISRU) in Martian volcanic terrains. The work investigated the complexity of volcanic terrains and Martian environmental hazards and suggested comprehensive engineering strategies to overcome the odds and establish a successful mining program in Martian volcanic regions. Slope stabilization methods - such as terracing and anchored drilling rigs - with terrain-adaptive rovers capable of autonomous operations on steep unstable …

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

ARM: Discovering Agentic Reasoning Modules for Generalizable Multi-Agent Systems
Bohan Yao, Shiva Krishna Reddy Malay, Vikas Yadav
https://arxiv.org/abs/2510.05746 https://

ARM: Discovering Agentic Reasoning Modules for Generalizable Multi-Agent Systems
Large Language Model (LLM)-powered Multi-agent systems (MAS) have achieved state-of-the-art results on various complex reasoning tasks. Recent works have proposed techniques to automate the design of MASes, eliminating the need for manual engineering. However, these techniques perform poorly, often achieving similar or inferior performance to simple baselines. Furthermore, they require computationally expensive re-discovery of architectures for each new task domain and expensive data annotation…

Physics-Informed Machine Learning in Biomedical Science and Engineering
Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey, George Em Karniadakis
https://arxiv.org/abs/2510.05433 htt…

Physics-Informed Machine Learning in Biomedical Science and Engineering
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations…

Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
Mohanakrishnan Hariharan, Satish Arvapalli, Seshu Barma, Evangeline Sheela
https://arxiv.org/abs/2510.10824

Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent orchestration, and enhanced contextualization. The system achieves remarkable accurac…

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…

Astra: A Multi-Agent System for GPU Kernel Performance Optimization
Anjiang Wei, Tianran Sun, Yogesh Seenichamy, Hang Song, Anne Ouyang, Azalia Mirhoseini, Ke Wang, Alex Aiken
https://arxiv.org/abs/2509.07506

Astra: A Multi-Agent System for GPU Kernel Performance Optimization
GPU kernel optimization has long been a central challenge at the intersection of high-performance computing and machine learning. Efficient kernels are crucial for accelerating large language model (LLM) training and serving, yet attaining high performance typically requires extensive manual tuning. Compiler-based systems reduce some of this burden, but still demand substantial manual design and engineering effort. Recently, researchers have explored using LLMs for GPU kernel generation, though…

Heterogeneous optimized Schwarz Methods for heat conduction in composites with thermal contact resistance
Huan Zhang, Hui Zhang, Yan Wang, Yingxiang Xu
https://arxiv.org/abs/2508.06408

Heterogeneous optimized Schwarz Methods for heat conduction in composites with thermal contact resistance
Heat transfer in composites is critical in engineering, where imperfect layer contact causes thermal contact resistance (TCR), leading to interfacial temperature discontinuity. We propose solving this numerically using the optimized Schwarz method (OSM), which decouples the heterogeneous problem into homogeneous subproblems. This avoids ill-conditioned systems from monolithic solving due to high contrast and interface jumps. Both energy estimate and Fourier analysis are used to prove the conver…

Fast and Robust Non-Adiabatic Holonomic Gates for Qutrit Systems
Jie Lu, Jie-Dong Huang, Yang Qian, Ying Yan
https://arxiv.org/abs/2510.05905 https://arxiv…

Fast and Robust Non-Adiabatic Holonomic Gates for Qutrit Systems
Motivated by shortcuts to adiabaticity, we develop a framework that combines inverse engineering with time-dependent perturbation theory to implement non-adiabatic holonomic quantum computing (NHQC) in qutrit systems under realistic error sources. We derive analytical conditions that suppress second-order Rabi errors through tailored pulse parameters and eliminate detuning errors via a compensation pulse. Our analysis reveals the cancellation from the compensation pulse, which ensures robust ga…

Fractal analysis of slow-fast and regular systems: A survey of recent results and future perspectives
Renato Huzak (Hasselt University), Goran Radunovi\'c (University of Zagreb, Faculty of Science), Vesna \v{Z}upanovi\'c (University of Zagreb, Faculty of Electrical Engineering and Computing)
https://arxiv.org/abs/2508.19859

Fractal analysis of slow-fast and regular systems: A survey of recent results and future perspectives
We survey recent developments in fractal analysis of regular and slow-fast dynamical systems using Minkowski dimension. Our focus is on spiral trajectories near monodromic limit periodic sets in regular systems and entry-exit sequences in slow-fast systems with degenerate singularities. For regular systems, we recall connections between Minkowski dimension and cyclicity. Key results include fractal classifications of weak foci, degenerate foci, and polycycles, where dimensional relationships pr…

What Slows Down FMware Development? An Empirical Study of Developer Challenges and Resolution Times
Zitao Wang, Zhimin Zhao, Michael W. Godfrey
https://arxiv.org/abs/2510.11138 …

What Slows Down FMware Development? An Empirical Study of Developer Challenges and Resolution Times
Foundation Models (FMs), such as OpenAI's GPT, are fundamentally transforming the practice of software engineering by enabling the development of \emph{FMware} -- applications and infrastructures built around these models. FMware systems now support tasks such as code generation, natural-language interaction, knowledge integration, and multi-modal content creation, underscoring their disruptive impact on current software engineering workflows. However, the design, implementation, and evolution …

Topology and criticality in non-Hermitian multimodal optical resonators through engineered losses
Elizabeth Louis Pereira, Hongwei Li, Andrea Blanco-Redondo, Jose L. Lado
https://arxiv.org/abs/2509.05163

Topology and criticality in non-Hermitian multimodal optical resonators through engineered losses
Non-Hermitian topological matter provides a platform for engineering phenomena that go beyond the capabilities of Hermitian systems, enabling the use of losses to engineer topological phenomena. Non-Hermitian models often rely on artificial platforms made of engineered lattices because controlling losses in natural compounds is challenging. Although typical models for non-Hermitian photonic matter are often single mode, photonic systems are often multimodal, producing mixing between different n…

Constrained Natural Language Action Planning for Resilient Embodied Systems
Grayson Byrd, Corban Rivera, Bethany Kemp, Meghan Booker, Aurora Schmidt, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath
https://arxiv.org/abs/2510.06357

Constrained Natural Language Action Planning for Resilient Embodied Systems
Replicating human-level intelligence in the execution of embodied tasks remains challenging due to the unconstrained nature of real-world environments. Novel use of large language models (LLMs) for task planning seeks to address the previously intractable state/action space of complex planning tasks, but hallucinations limit their reliability, and thus, viability beyond a research context. Additionally, the prompt engineering required to achieve adequate system performance lacks transparency, a…

Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications
Jiacheng Wang, Changyuan Zhao, Zehui Xiong, Tao Xiang, Dusit Niyato, Xianbin Wang, Shiwen Mao, Dong In Kim
https://arxiv.org/abs/2507.19837

Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications
AI-enabled wireless communications have attracted tremendous research interest in recent years, particularly with the rise of novel paradigms such as low-altitude integrated sensing and communication (ISAC) networks. Within these systems, feature engineering plays a pivotal role by transforming raw wireless data into structured representations suitable for AI models. Hence, this paper offers a comprehensive investigation of feature engineering techniques in AI-driven wireless communications. Sp…

Exploit Tool Invocation Prompt for Tool Behavior Hijacking in LLM-Based Agentic System
Yu Liu, Yuchong Xie, Mingyu Luo, Zesen Liu, Zhixiang Zhang, Kaikai Zhang, Zongjie Li, Ping Chen, Shuai Wang, Dongdong She
https://arxiv.org/abs/2509.05755

Exploit Tool Invocation Prompt for Tool Behavior Hijacking in LLM-Based Agentic System
LLM-based agentic systems leverage large language models to handle user queries, make decisions, and execute external tools for complex tasks across domains like chatbots, customer service, and software engineering. A critical component of these systems is the Tool Invocation Prompt (TIP), which defines tool interaction protocols and guides LLMs to ensure the security and correctness of tool usage. Despite its importance, TIP security has been largely overlooked. This work investigates TIP-rela…

Intelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
Jarne Mathi Decker
https://arxiv.org/abs/2509.20134 https://

Intelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
The "No Free Lunch" theorem dictates that no single recommender algorithm is optimal for all users, creating a significant Algorithm Selection Problem. Standard meta-learning approaches aim to solve this by selecting an algorithm based on user features, but treat the fundamentally diverse algorithms themselves as equivalent, "black-box" choices. This thesis investigates the impact of overcoming this limitation by engineering a comprehensive feature set to explicitly characterize the algorithms …

AI-Driven Generation of Data Contracts in Modern Data Engineering Systems
Harshraj Bhoite
https://arxiv.org/abs/2507.21056 https://arxiv.org/pdf/2507.21056…

AI-Driven Generation of Data Contracts in Modern Data Engineering Systems
Data contracts formalize agreements between data producers and consumers regarding schema, semantics, and quality expectations. As data pipelines grow in complexity, manual authoring and maintenance of contracts becomes error-prone and labor-intensive. We present an AI-driven framework for automatic data contract generation using large language models (LLMs). Our system leverages parameter-efficient fine-tuning methods, including LoRA and PEFT, to adapt LLMs to structured data domains. The mode…

A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms
Mira Raheem, Amal Elgammal, Michael Papazoglou, Bernd Kr\"amer, Neamat El-Tazi
https://arxiv.org/abs/2510.09308

A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms
Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model driven engineering (MDE) framework designed specifically for healthcare AI. The framework relies on formal metamodels, domain-specific lang…

From Ethical Declarations to Provable Independence: An Ontology-Driven Optimal-Transport Framework for Certifiably Fair AI Systems
Sukriti Bhattacharya, Chitro Majumdar
https://arxiv.org/abs/2510.08086

From Ethical Declarations to Provable Independence: An Ontology-Driven Optimal-Transport Framework for Certifiably Fair AI Systems
This paper presents a framework for provably fair AI that overcomes the limits of current bias mitigation methods by systematically removing all sensitive information and its proxies. Using ontology engineering in OWL 2 QL, it formally defines sensitive attributes and infers their proxies through logical reasoning, constructing a sigma algebra G that captures the full structure of biased patterns. Fair representations are then obtained via Delbaen Majumdar optimal transport, which generates var…

Viscoelastic flow of an Oldroyd-B fluid through a slowly varying contraction-expansion channel: pressure drop and elastic stress relaxation
Yali Kedem, Bimalendu Mahapatra, Evgeniy Boyko
https://arxiv.org/abs/2510.07166

Viscoelastic flow of an Oldroyd-B fluid through a slowly varying contraction-expansion channel: pressure drop and elastic stress relaxation
Viscoelastic fluid flows in narrow non-uniform geometries are ubiquitous in various engineering applications and physiological flow systems. For such flows, one of the key interests is understanding how fluid viscoelasticity affects the flow rate-pressure drop relation, which remains not fully understood. We analyze the flow of the Oldroyd-B fluid in slowly varying contraction-expansion channels, commonly referred to as constrictions. Unlike most previous theoretical studies focusing on contrac…

Analyzing Computational Approaches for Differential Equations: A Study of MATLAB, Mathematica, and Maple
Arhonefe Joseph Ogethakpo, Ignatius Nkonyeasua Njoseh
https://arxiv.org/abs/2510.02346

Analyzing Computational Approaches for Differential Equations: A Study of MATLAB, Mathematica, and Maple
Differential equations are fundamental to modeling dynamic systems in physics, engineering, biology, and economics. While analytical solutions are ideal, most real-world problems necessitate numerical approaches. This study conducts a detailed comparative analysis of three leading computational software packages: MATLAB, Mathematica, and Maple in solving various differential equations, including ordinary differential equations (ODEs), partial differential equations (PDEs), and systems of differ…

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…

Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
Arnaud Vadeboncoeur, Gregory Duth\'e, Mark Girolami, Eleni Chatzi
https://arxiv.org/abs/2509.19929

Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later
Uncertainty Quantification (UQ) is paramount for inference in engineering applications. A common inference task is to recover full-field information of physical systems from a small number of noisy observations, a usually highly ill-posed problem. Critically, engineering systems often have complicated and variable geometries prohibiting the use of standard Bayesian UQ. In this work, we introduce Geometric Autoencoders for Bayesian Inversion (GABI), a framework for learning geometry-aware genera…

Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging
Tyler Smith, Tanvi P. Gujarati, Mario Motta, Ben Link, Ieva Liepuoniute, Triet Friedhoff, Hiromichi Nishimura, Nam Nguyen, Kristen S. Williams, Javier Robledo Moreno, Caleb Johnson, Kevin J. Sung, Abdullah Ash Saki, Marna Kagele
https://

Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging
The simulation of electronic systems is an anticipated application for quantum-centric computers, i.e. heterogeneous architectures where classical and quantum processing units operate in concert. An important application is the computation of radical chain reactions, including those responsible for the photodegradation of composite materials used in aerospace engineering. Here, we compute the activation energy and reaction energy for hydrogen abstraction from 2,2-diphenyldipropane, used as a mi…

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Carl Philipp Hohl, Philipp Reis, Tobias Sch\"urmann, Stefan Otten, Eric Sax
https://arxiv.org/abs/2510.00963

Structuring Automotive Data for Systems Engineering: A Taxonomy-Based Approach
Vehicle data is essential for advancing data-driven development throughout the automotive lifecycle, including requirements engineering, design, verification, and validation, and post-deployment optimization. Developers currently collect data in a decentralized and fragmented manner across simulations, test benches, and real-world driving, resulting in data silos, inconsistent formats, and limited interoperability. This leads to redundant efforts, inefficient integration, and suboptimal use of …

ZKProphet: Understanding Performance of Zero-Knowledge Proofs on GPUs
Tarunesh Verma (Computer Science and Engineering, University of Michigan, USA), Yichao Yuan (Computer Science and Engineering, University of Michigan, USA), Nishil Talati (Computer Science and Engineering, University of Michigan, USA), Todd Austin (Computer Science and Engineering, University of Michigan, USA)

ZKProphet: Understanding Performance of Zero-Knowledge Proofs on GPUs
Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Mul…

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 …

Data-efficient Kernel Methods for Learning Hamiltonian Systems
Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi, Peyman Tavallali, Houman Owhadi
https://arxiv.org/abs/2509.17154

Data-efficient Kernel Methods for Learning Hamiltonian Systems
Hamiltonian dynamics describe a wide range of physical systems. As such, data-driven simulations of Hamiltonian systems are important for many scientific and engineering problems. In this work, we propose kernel-based methods for identifying and forecasting Hamiltonian systems directly from data. We present two approaches: a two-step method that reconstructs trajectories before learning the Hamiltonian, and a one-step method that jointly infers both. Across several benchmark systems, including …

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…

Twisted bi-layer magnetic photonic crystals
You-Ming Liu, Shi-Kai Lin, Pei-Shi Li, Yi-Ran Hao, Biao Yang
https://arxiv.org/abs/2510.07714 https://arxiv.org…

Twisted bi-layer magnetic photonic crystals
In photonics, twisted bi-layer systems have demonstrated unprecedented control over light-matter interactions, primarily through the modulation of photonic band structures and the formation of Moiré patterns. Meanwhile, magnetic photonic crystals have served as cornerstone platforms for manipulating light propagation, facilitating key applications such as Faraday rotation-based isolators and non-reciprocal devices. Nevertheless, the synergistic integration of twist engineering and magneto-opti…

MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems
Sri Ram Macharla, Sridhar Murthy J, Anjaneyulu Pasala
https://arxiv.org/abs/2508.21307

MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems
MultiFluxAI is an innovative AI platform developed to address the challenges of managing and integrating vast, disparate data sources in product engineering across application domains. It addresses both current and new service related queries that enhance user engagement in the digital ecosystem. This platform leverages advanced AI techniques, such as Generative AI, vectorization, and agentic orchestration to provide dynamic and context-aware responses to complex user queries.

Single photon emission from lithographically-positioned engineered nanodiamonds for cryogenic applications
Vivekanand Tiwari, Zhaojin Liu, Hao-Cheng Weng, Krishna C Balram, John G Rarity, Soumen Mandal, Oliver A Williams, Gavin W Morley, Joe A Smith
https://arxiv.org/abs/2508.06424

Single photon emission from lithographically-positioned engineered nanodiamonds for cryogenic applications
Nitrogen-vacancy centres in nanodiamonds (NDs) provide a promising resource for quantum photonic systems. However, developing a technology beyond proof-of-principle physics requires optimally engineering its component parts. In this work, we present a hybrid materials platform by photolithographically positioning ball-milled isotopically-enriched NDs on broadband metal reflectors. The structure enhances the photonic collection efficiency, enabling cryogenic characterisation despite the limited …

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…

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…

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…

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 …

COBRA: Multimodal Sensing Deep Learning Framework for Remote Chronic Obesity Management via Wrist-Worn Activity Monitoring
Zhengyang Shen (Department of Electrical and Electronic Engineering, Imperial College London, UK), Bo Gao (Department of Electrical and Electronic Engineering, Imperial College London, UK), Mayue Shi (Department of Electrical and Electronic Engineering, Imperial College London, UK)

COBRA: Multimodal Sensing Deep Learning Framework for Remote Chronic Obesity Management via Wrist-Worn Activity Monitoring
Chronic obesity management requires continuous monitoring of energy balance behaviors, yet traditional self-reported methods suffer from significant underreporting and recall bias, and difficulty in integration with modern digital health systems. This study presents COBRA (Chronic Obesity Behavioral Recognition Architecture), a novel deep learning framework for objective behavioral monitoring using wrist-worn multimodal sensors. COBRA integrates a hybrid D-Net architecture combining U-Net spati…

Digital Twins for Software Engineering Processes
Robin Kimmel, Judith Michael, Andreas Wortmann, Jingxi Zhang
https://arxiv.org/abs/2510.05768 https://arxi…

Digital Twins for Software Engineering Processes
Digital twins promise a better understanding and use of complex systems. To this end, they represent these systems at their runtime and may interact with them to control their processes. Software engineering is a wicked challenge in which stakeholders from many domains collaborate to produce software artifacts together. In the presence of skilled software engineer shortage, our vision is to leverage DTs as means for better rep- resenting, understanding, and optimizing software engineering proce…

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Abhiram Bhupatiraju, Sung Bum Ahn
https://arxiv.org/abs/2507.22220

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essential for long-term planning. This paper presents a comparative analysis of machine learning models, Linear Regression, XGBoost, LightGBM, and Long Short-Term Memory (LSTM), for forecasting system-wide electricity load up to one year in advance. Midterm forecasting has shown to be …

Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination
Kim Hammar, Tansu Alpcan, Emil C. Lupu
https://arxiv.org/abs/2508.05188 https://…

Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination
Timely and effective incident response is key to managing the growing frequency of cyberattacks. However, identifying the right response actions for complex systems is a major technical challenge. A promising approach to mitigate this challenge is to use the security knowledge embedded in large language models (LLMs) to assist security operators during incident handling. Recent research has demonstrated the potential of this approach, but current methods are mainly based on prompt engineering o…

Coral: A Unifying Abstraction Layer for Composable Robotics Software
Steven Swanbeck, Mitch Pryor
https://arxiv.org/abs/2509.02453 https://arxiv.org/pdf/25…

Coral: A Unifying Abstraction Layer for Composable Robotics Software
Despite the multitude of excellent software components and tools available in the robotics and broader software engineering communities, successful integration of software for robotic systems remains a time-consuming and challenging task for users of all knowledge and skill levels. And with robotics software often being built into tightly coupled, monolithic systems, even minor alterations to improve performance, adjust to changing task requirements, or deploy to new hardware can require signif…

Towards a Taxonomy of Sustainability Requirements for Software Design
Mandira Roy, Novarun Deb, Nabendu Chaki, Agostino Cortesi
https://arxiv.org/abs/2510.08990 https://

Towards a Taxonomy of Sustainability Requirements for Software Design
Software systems are a significant contributor to global sustainability concerns, demanding that environmental, social, technical, and economic factors be systematically addressed from the initial requirements engineering phase. Although existing research provides various sustainability requirements (SRs), these contributions are often fragmented, specific to certain dimensions, or limited to particular application domains, resulting in a critical lack of a unified, comprehensive taxonomy for t…

Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty
Enrico Ampellio, Blazhe Gjorgiev, Giovanni Sansavini
https://arxiv.org/abs/2510.07904

Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty
Engineering design involves demanding models encompassing many decision variables and uncontrollable parameters. In addition, unavoidable aleatoric and epistemic uncertainties can be very impactful and add further complexity. The state-of-the-art adopts two steps, uncertainty quantification and design optimization, to optimize systems under uncertainty by means of robust or stochastic metrics. However, conventional scenario-based, surrogate-assisted, and mathematical programming methods are not…

Quantum Hamiltonian Descent based Augmented Lagrangian Method for Constrained Nonconvex Nonlinear Optimization
Mingze Li, Lei Fan, Zhu Han
https://arxiv.org/abs/2508.02969 https…

Quantum Hamiltonian Descent based Augmented Lagrangian Method for Constrained Nonconvex Nonlinear Optimization
Nonlinear programming (NLP) plays a critical role in domains such as power energy systems, chemical engineering, communication networks, and financial engineering. However, solving large-scale, nonconvex NLP problems remains a significant challenge due to the complexity of the solution landscape and the presence of nonlinear nonconvex constraints. In this paper, we develop a Quantum Hamiltonian Descent based Augmented Lagrange Method (QHD-ALM) framework to address largescale, constrained noncon…

Description and Comparative Analysis of QuRE: A New Industrial Requirements Quality Dataset
Henning Femmer, Frank Houdek, Max Unterbusch, Andreas Vogelsang
https://arxiv.org/abs/2508.08868

Description and Comparative Analysis of QuRE: A New Industrial Requirements Quality Dataset
Requirements quality is central to successful software and systems engineering. Empirical research on quality defects in natural language requirements relies heavily on datasets, ideally as realistic and representative as possible. However, such datasets are often inaccessible, small, or lack sufficient detail. This paper introduces QuRE (Quality in Requirements), a new dataset comprising 2,111 industrial requirements that have been annotated through a real-world review process. Previously used…

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…

Fundamental Costs of Noise-Robust Quantum Control: Speed Limits and Complexity
Junkai Zeng, Xiu-Hao Deng
https://arxiv.org/abs/2510.07183 https://arxiv.org…

Fundamental Costs of Noise-Robust Quantum Control: Speed Limits and Complexity
Noise is ubiquitous in quantum systems and is a major obstacle for the advancement of quantum information science. Noise-robust quantum control achieves high-fidelity operations by engineering the evolution path so that first-order noise contributions cancel at the final time. Such dynamical error correction typically incurs a time overhead beyond standard quantum speed limits. We derive general lower bounds on control complexity that quantify this overhead for quasi-static coherent noise under…

Reusable Surrogate Models for Distillation Columns
Martin Bubel, Tobias Seidel, Michael Bortz
https://arxiv.org/abs/2509.06638 https://arxiv.org/pdf/2509.0…

Reusable Surrogate Models for Distillation Columns
Surrogate modeling is a powerful methodology in chemical process engineering, frequently employed to accelerate optimization tasks where traditional flowsheet simulators are computationally prohibitive. However, the state-of-the-art is dominated by surrogate models trained for a narrow range of fixed chemical systems and operating conditions, limiting their reusability. This work introduces a paradigm shift towards reusable surrogates by developing a single model for distillation columns that g…

Knock-Knock: Black-Box, Platform-Agnostic DRAM Address-Mapping Reverse Engineering
Antoine Plin, Lorenzo Casalino, Thomas Rokicki, Ruben Salvador
https://arxiv.org/abs/2509.19568

Knock-Knock: Black-Box, Platform-Agnostic DRAM Address-Mapping Reverse Engineering
Modern Systems-on-Chip (SoCs) employ undocumented linear address-scrambling functions to obfuscate DRAM addressing, which complicates DRAM-aware performance optimizations and hinders proactive security analysis of DRAM-based attacks; most notably, Rowhammer. Although previous work tackled the issue of reversing physical-to-DRAM mapping, existing heuristic-based reverse-engineering approaches are partial, costly, and impractical for comprehensive recovery. This paper establishes a rigorous theor…

Certifying the Nonexistence of Feasible Path Between Power System Operating Points
Mohammad Rasoul Narimani, Katherine R. Davis, Daniel K. Molzahn
https://arxiv.org/abs/2509.05935

Certifying the Nonexistence of Feasible Path Between Power System Operating Points
By providing the optimal operating point that satisfies both the power flow equations and engineering limits, the optimal power flow (OPF) problem is central to the operation of electric power systems. While extensive research efforts have focused on reliably computing high-quality OPF solutions, assessing the feasibility of transitioning between operating points remains challenging since the feasible spaces of OPF problems may consist of multiple disconnected components. It is not possible to …

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…

Crosslisted article(s) found for cs.CE. https://arxiv.org/list/cs.CE/new
[1/1]:
- Neural Network Surrogates for Free Energy Computation of Complex Chemical Systems
Wasut Pornpatcharapong

Engineering Non-Gaussian Bosonic Gates through Quantum Signal Processing
Pak-Tik Fong, Hoi-Kwan Lau
https://arxiv.org/abs/2508.20261 https://arxiv.org/pdf/…

Engineering Non-Gaussian Bosonic Gates through Quantum Signal Processing
Non-Gaussian operations are essential for most bosonic quantum technologies. Yet, realizable non-Gaussian gates are rather limited in type and generally suffer from accuracy-duration trade-offs. In this work, we propose to use quantum signal processing (QSP) techniques to engineer non-Gaussian gates on hybrid qumode-qubit systems. For systems with dispersive coupling, our scheme can generate a new non-Gaussian gate that produces a phase shift depending on the modulus of the boson number. This g…

Quantum Computing as a Service - a Software Engineering Perspective
Aakash Ahmad, Muhammad Waseem, Bakheet Aljedaani, Mahdi Fahmideh, Peng Liang, Feras Awaysheh
https://arxiv.org/abs/2510.04982

Quantum Computing as a Service - a Software Engineering Perspective
Quantum systems have started to emerge as a disruptive technology and enabling platforms - exploiting the principles of quantum mechanics via programmable quantum bits (QuBits) - to achieve quantum supremacy in computing. Academic research, industrial projects (e.g., Amazon Braket, IBM Qiskit), and consortiums like 'Quantum Flagship' are striving to develop practically capable and commercially viable quantum computing (QC) systems and technologies. Quantum Computing as a Service (QCaaS) is view…

Deep Inverse Rosenblatt Transport for Structural Reliability Analysis
Aryan Tyagi, Jan N. Fuhg
https://arxiv.org/abs/2509.05061 https://arxiv.org/pdf/2509.…

Deep Inverse Rosenblatt Transport for Structural Reliability Analysis
Accurately estimating the probability of failure in engineering systems under uncertainty is a fundamental challenge, particularly in high-dimensional settings and for rare events. Conventional reliability analysis methods often become computationally intractable or exhibit high estimator variance when applied to problems with hundreds of uncertain parameters or highly concentrated failure regions. In this work, we investigate the use of the recently proposed Deep Inverse Rosenblatt Transport (…

Efficiently Ranking Software Variants with Minimal Benchmarks
Th\'eo Matricon, Mathieu Acher, Helge Spieker, Arnaud Gotlieb
https://arxiv.org/abs/2509.06716 https://

Efficiently Ranking Software Variants with Minimal Benchmarks
Benchmarking is a common practice in software engineering to assess the qualities and performance of software variants, coming from multiple competing systems or from configurations of the same system. Benchmarks are used notably to compare and understand variant performance, fine-tune software, detect regressions, or design new software systems. The execution of benchmarks to get a complete picture of software variants is highly costly in terms of computational resources and time. In this pape…

AI-Guided Exploration of Large-Scale Codebases
Yoseph Berhanu Alebachew
https://arxiv.org/abs/2508.05799 https://arxiv.org/pdf/2508.05799

AI-Guided Exploration of Large-Scale Codebases
Understanding large-scale, complex software systems is a major challenge for developers, who spend a significant portion of their time on program comprehension. Traditional tools such as static visualizations and reverse engineering techniques provide structural insights but often lack interactivity, adaptability, and integration with contextual information. Recent advancements in large language models (LLMs) offer new opportunities to enhance code exploration workflows, yet their lack of groun…

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…

Building an Open AIBOM Standard in the Wild
Gopi Krishnan Rajbahadur, Keheliya Gallaba, Elyas Rashno, Arthit Suriyawongkul, Karen Bennet, Kate Stewart, Ahmed E. Hassan
https://arxiv.org/abs/2510.07070 …

Building an Open AIBOM Standard in the Wild
Modern software engineering increasingly relies on open, community-driven standards, yet how such standards are created in fast-evolving domains like AI-powered systems remains underexplored. This paper presents a detailed experience report on the development of the AI Bill of Materials AIBOM specification, an extension of the ISO/IEC 5962:2021 Software Package Data Exchange (SPDX) software bill of materials (SBOM) standard, which captures AI components such as datasets and iterative training a…

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…

Learning From Software Failures: A Case Study at a National Space Research Center
Dharun Anandayuvaraj, Zain Hammadeh, Andreas Lund, Alexandra Holloway, James C. Davis
https://arxiv.org/abs/2509.06301 …

Learning From Software Failures: A Case Study at a National Space Research Center
Software failures can have significant consequences, making learning from failures a critical aspect of software engineering. While software organizations are recommended to conduct postmortems, the effectiveness and adoption of these practices vary widely. Understanding how engineers gather, document, share, and apply lessons from failures is essential for improving reliability and preventing recurrence. High-reliability organizations (HROs) often develop software systems where failures carry …

M&SCheck: Towards a Checklist to Support Software Engineering Newcomers to the Modeling and Simulation Area
Luiza Martins de Freitas Cintra, Philipp Zech, Mohamad Kassab, Eliomar Ara\'ujo Lima, Sofia Larissa da Costa Paiva, Valdemar Vicente Graciano Neto
https://arxiv.org/abs/2509.25625

M&SCheck: Towards a Checklist to Support Software Engineering Newcomers to the Modeling and Simulation Area
The advent of increasingly complex and dynamic ecosystems, such as digital twins (DT), smart cities and Industry 4.0 and 5.0, has made evident the need to include modeling and simulation (M&S) in the software development life cycle. Such disruptive systems include simulation models in their own architecture (such as DT) or require the use of simulation models to represent the high degree of movement and the multiplicity of interactions that occur between the involved systems. However, when soft…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/2]:
- Test Schedule Generation for Acceptance Testing of Mission-Critical Satellite Systems
Rapha\"el Ollando, Seung Yeob Shin, Mario Minardi, Nikolas Sidiropoulos

Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
Jingzhi Gong, Rafail Giavrimis, Paul Brookes, Vardan Voskanyan, Fan Wu, Mari Ashiga, Matthew Truscott, Mike Basios, Leslie Kanthan, Jie Xu, Zheng Wang
https://arxiv.org/abs/2508.01443

Tuning LLM-based Code Optimization via Meta-Prompting: An Industrial Perspective
There is a growing interest in leveraging large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM optimization systems in production environments. To address this, we introduce Meta-Prompted Code Optimi…

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…

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…

Lost in Transition: The Struggle of Women Returning to Software Engineering Research after Career Breaks
Shalini Chakraborty, Sebastian Baltes
https://arxiv.org/abs/2509.21533 h…

Lost in Transition: The Struggle of Women Returning to Software Engineering Research after Career Breaks
The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration …

DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn, Chetan Arora, Gunel Gulmammadova, Joey Chua
https://arxiv.org/abs/2509.16870

DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an industry-academic collaboration when deploying an LLM-powered virtual customer assistant, a critical software engineering challenge emerged: how to enhance a safer deployment of LLM-powered software systems at runtime? While LlamaGuard, the current state-of-the-art runtime guardrail, offers protection against unsafe in…

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…

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…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Methodological Framework for Quantifying Semantic Test Coverage in RAG Systems
Noah Broestl, Adel Nasser Abdalla, Rajprakash Bale, Hersh Gupta, Max Struever