Tootfinder

"Let it be Chaos in the Plumbing!" Usage and Efficacy of Chaos Engineering in DevOps Pipelines
Stefano Fossati, Damian Andrew Tamburri, Massimiliano Di Penta, Marco Tonnarelli
https://arxiv.org/abs/2509.14931

"Let it be Chaos in the Plumbing!" Usage and Efficacy of Chaos Engineering in DevOps Pipelines
Chaos Engineering (CE) has emerged as a proactive method to improve the resilience of modern distributed systems, particularly within DevOps environments. Originally pioneered by Netflix, CE simulates real-world failures to expose weaknesses before they impact production. In this paper, we present a systematic gray literature review that investigates how industry practitioners have adopted and adapted CE principles over recent years. Analyzing 50 sources published between 2019 and early 2024, w…

Spin-polarised surface fermiology of ohmic WSe$_2$/NbSe$_2$ interfaces
Oliver J. Clark, Thi-Hai-Yen Vu, Ben A. Chambers, Federico Mazzola, Sadhana Sridhar, Geetha Balakrishnan, Aaron Bostwick, Chris Jozwiak, Eli Rotenberg, Sarah L. Harmer, Michael S. Fuhrer, Mark T. Edmonds
https://arxiv.org/abs/2509.14937

Spin-polarised surface fermiology of ohmic WSe$_2$/NbSe$_2$ interfaces
Discovering and engineering spin-polarised surface states in the electronic structures of condensed matter systems is a crucial first step in development of spintronic devices, wherein spin-polarised bands crossing the Fermi level can facilitate information transfer. Here, we show how the spin-orbit split K-point valleys of monolayer WSe$_2$ can be made potentially suitable for this purpose, despite the semiconducting ground state. By interfacing with metallic 2H-NbSe$_2$, these valence band ex…

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…

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…

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.

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…

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-…

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…

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…

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…

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…

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…

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 …

> Let's be clear what's happening: the person is outright baiting people using this AI slop into correcting the post, incorporating said corrections without attribution to the people who corrected the post and then took the credit for said corrections silently.
https://social.treehouse.systems/@am…

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…

When "self-driving" cars were first getting some hype back in ~2015 or so, I told people who asked me that I didn't think they'd be safe, and that I wished the same money were being invested in driver-assistance systems instead.
At the time, advocates were claiming that self-driving cars would be safer than human drivers.
We now have both self-driving cars and some nifty new driver assistance things, and it turns out that the self-driving cars are in fact being developed by corporations whose attention to the bottom line results in danger to others on the road pretty regularly. I don't actually have stats here for whether they're "safer than human drivers" or not, but the opportunity for one bad software update to make *all* self-driving cars dangerous at once kinda makes me doubt that.
Here's an example of Waymo cars getting "more aggressive" as they try to balance between being too timid and obstructing traffic (including emergency vehicles) and being too dangerous:
https://archive.ph/JJuGv
Here's another example of passing stopped schoolbusses leading to a software recall:
https://abcnews.go.com/GMA/News/waymo-issue-voluntary-software-recall-after-close-calls/story?id=128207776
In the first article, Waymo claims 91% fewer serious accidents per mile. Obviously an independent audit would be actually trustworthy, but even if we take that claim at face value, it's meaningless if an update tomorrow causes 100,000 accidents.
Note that they could be using better engineering practices, and the fact that they aren't shows that they don't care enough about the risks. They could be deploying new software versions incrementally and slowly, letting new versions rack up lots of miles only on a few vehicles before pushing them to a fleet. The should also have the equivalent of a simulation unit test for "schoolbus is stopped, what do?" and if a software version fails that test, it doesn't make it to the fleet. Clearly they don't have that.
I feel pretty vindicated in my earlier prediction that this tech is a bad idea in the hands of the current advocates.

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…

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…

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…

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 …

Advances in artificial vision systems: a comprehensive review of technologies, applications, and future directions https://link.springer.com/article/10.1007/s13534-025-00513-4 "clinical value hinges on durability, effective resolution, surgical practicality, and user t…

Advances in artificial vision systems: a comprehensive review of technologies, applications, and future directions - Biomedical Engineering Letters
This review article focuses on recent advancements and persistent challenges in artificial vision prostheses designed to restore sight for patients affected by retinal diseases. It comprehensively examines various approaches, including epiretinal, subretinal, and suprachoroidal implants, as well as optic nerve and visual cortex stimulation strategies. The critical role of the retina in visual perception is explored, emphasizing how retinal degeneration affects the transmission of visual informa…

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 …

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…

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…

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…

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…

👾 Large language models can execute complete ransomware attacks autonomously, research shows
https://techxplore.com/news/2025-09-large-language-ransomware-autonomously.html

Large language models can execute complete ransomware attacks autonomously, research shows
Criminals can use artificial intelligence, specifically large language models, to autonomously carry out ransomware attacks that steal personal files and demand payment, handling every step from breaking into computer systems to writing threatening messages to victims, according to new research from NYU Tandon School of Engineering posted to the arXiv preprint server.

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…

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…

Have you ever wondered to what extend LLMs are used to support writing of scientific publications? Here is a chart indicating the fraction of LLM-modified sentences in scientific publications over time.
c.f. Liang et al, Mapping the Increasing Use of LLMs in Scientific Papers (2024)
https://arxiv.org/html/2404.01268v1

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…

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…

Casimir Stabilization of Fluctuating Electronic Nematic Order
Ola Carlsson, Sambuddha Chattopadhyay, Jonathan B. Curtis, Frieder Lindel, Lorenzo Graziotto, J\'er\^ome Faist, Eugene Demler
https://arxiv.org/abs/2510.05088

Casimir Stabilization of Fluctuating Electronic Nematic Order
Vacuum cavity control of quantum materials is the engineering of quantum materials systems through electromagnetic zero-point fluctuations. In this work we articulate a generic mechanism for vacuum optical control of correlated electronic order: Casimir control, where the zero-point energy of the electromagnetic continuum, the Casimir energy, depends on the properties of the material system. To assess the experimental viability of this mechanism we focus on the Casimir stabilization of fluctuat…

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 …

TopoSPAM: Topology grounded Simulation Platform for morphogenesis and biological Active Matter
Abhinav Singh (Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany, Center for Systems Biology Dresden, Dresden, Germany, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA), Abhijeet Krishna (Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany, Center for Systems Biology Dresden, Dresden, Germany), Aboutaleb …

TopoSPAM: Topology grounded Simulation Platform for morphogenesis and biological Active Matter
We present a topology grounded, multiscale simulation platform for morphogenesis and biological active matter. Morphogenesis and biological active matter represent keystone problems in biology with additional, far-reaching implications across the biomedical sciences. Addressing these problems will require flexible, cross-scale models of tissue shape, development, and dysfunction that can be tuned to understand, model, and predict relevant individual cases. Current approaches to simulating anato…

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…

Hyundai is making a major bet on EV batteries. The automaker is investing $900M to build a massive battery R&D campus in Korea by 2026.
The goal? Develop breakthrough battery tech in-house, optimize EV performance, and stay ahead in the global race to electrification.

Hyundai Motor Group Drives Next-Generation Battery Innovation with Future Mobility Battery Campus - CleanTechnica
Support CleanTechnica's work through a Substack subscription or on Stripe. KRW 1.2 trillion investment to establish Hyundai Motor Group’s first comprehensive battery research and development hub in Anseong, Korea, by the end of 2026 To internalize core battery technologies — cell design, process engineering and integrated control systems — to ... [continued]

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…

A Nonparametric Bayesian Solution of the Empirical Stochastic Inverse Problem
Haiyi Shi, Lei Yang, Jiarui Chi, Troy Butler, Haonan Wang, Derek Bingham, Don Estep
https://arxiv.org/abs/2509.22597

A Nonparametric Bayesian Solution of the Empirical Stochastic Inverse Problem
The stochastic inverse problem is a key ingredient in making inferences, predictions, and decisions for complex science and engineering systems. We formulate and analyze a nonparametric Bayesian solution for the stochastic inverse problem. Key properties of the solution are proved and the convergence and error of a computational solution obtained by random sampling is analyzed. Several applications illustrate the results.

Accelerating kinetic plasma simulations with machine learning generated initial conditions
Andrew T. Powis, Domenica Corona Rivera, Alexander Khrabry, Igor D. Kaganovich
https://arxiv.org/abs/2510.01977

Accelerating kinetic plasma simulations with machine learning generated initial conditions
Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with traditional first-principles simulations and high-performance computing offer many interesting pathways towards resolving this challenge. We consider acceleration of kinetic plasma simulations via machine learning generated initial conditions. The approach is demons…

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

One Filters All: A Generalist Filter for State Estimation
Shiqi Liu, Wenhan Cao, Chang Liu, Zeyu He, Tianyi Zhang, Shengbo Eben Li
https://arxiv.org/abs/2509.20051 https://

One Filters All: A Generalist Filter for State Estimation
Estimating hidden states in dynamical systems, also known as optimal filtering, is a long-standing problem in various fields of science and engineering. In this paper, we introduce a general filtering framework, \textbf{LLM-Filter}, which leverages large language models (LLMs) for state estimation by embedding noisy observations with text prototypes. In various experiments for classical dynamical systems, we find that first, state estimation can significantly benefit from the reasoning knowledg…

Hijacking Living Cells with Surface Engineering for the Internet of Bio-Nano Things
Ekin Ince, Murat Kuscu
https://arxiv.org/abs/2509.17227 https://arxiv.o…

Hijacking Living Cells with Surface Engineering for the Internet of Bio-Nano Things
The Internet of Bio-Nano Things (IoBNT) promises to revolutionize healthcare by interfacing the cyber domain with the living systems at unprecedented resolution. Realizing this vision hinges on the development of Bio-Nano Things (BNTs), i.e., functional nodes capable of sensing, actuation, and communications within biological environments. Existing BNT architectures, e.g., nanomaterial-based, biosynthetic, and passive molecular agents, face significant limitations, including toxicity, lack of a…

Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun, Mohammad Reza Nikoo, Fang Chen, Amir H. Gandomi
https://arxiv.org/abs/2509.21355

Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
This study presents domain-informed genetic superposition programming (DIGSP), a symbolic regression framework tailored for engineering systems governed by separable physical mechanisms. DIGSP partitions the input space into domain-specific feature subsets and evolves independent genetic programming (GP) populations to model material-specific effects. Early evolution occurs in isolation, while ensemble fitness promotes inter-population cooperation. To enable symbolic superposition, an adaptive …

Constrained Higher-Order Binary Optimization for Wireless Communications Systems Using Ising Machines
Gan Zheng, Ioannis Krikidis
https://arxiv.org/abs/2509.20092 https://

Constrained Higher-Order Binary Optimization for Wireless Communications Systems Using Ising Machines
This paper develops an algorithmic solution using Ising machines to solve large-scale higher-order binary optimization (HOBO) problems with inequality constraints for resource optimization in wireless communications systems. Quadratic unconstrained binary optimization (QUBO) aims to solve a special category of these problems widely encountered in engineering and science. To solve QUBO instances, specialized Ising machines have been designed, while sophisticated quantum annealing algorithm and q…

Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination
Hassen Dhrif
https://arxiv.org/abs/2510.00326 https://…

Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination
The emergence of large language models has enabled sophisticated multi-agent systems, yet coordinating their reasoning capabilities through prompt engineering remains challenging. We present a theoretically-grounded framework for dynamic prompt orchestration that enhances reasoning across multiple specialized agents. This framework addresses three core challenges: logical consistency preservation during agent transitions, reasoning-aware prompt adaptation, and scalable coordination of distribut…

OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Sunhao Dai, Jiakai Tang, Jiahua Wu, Kun Wang, Yuxuan Zhu, Bingjun Chen, Bangyang Hong, Yu Zhao, Cong Fu, Kangle Wu, Yabo Ni, Anxiang Zeng, Wenjie Wang, Xu Chen, Jun Xu, See-Kiong Ng
https://arxiv.org/abs/2509.18091

OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, w…

🏗️ Construction Biotechnology: Integrating Bacterial Systems into Civil Engineering Practices
#biotechnology

Beyond the non-Hermitian skin effect: scaling-controlled topology from Exceptional-Bound Bands
Mengjie Yang, Ching Hua Lee
https://arxiv.org/abs/2510.06338 https://

Beyond the non-Hermitian skin effect: scaling-controlled topology from Exceptional-Bound Bands
We establish a novel mechanism for topological transitions in non-Hermitian systems that are controlled by the system size. Based on a new paradigm known as exceptional-bound (EB) band engineering, its mechanism hinges on the unique critical scaling behavior near an exceptional point, totally unrelated to the well-known non-Hermitian skin effect. Through a series of ansatz models, we analytically derive and numerically demonstrate how topological transitions depend on the system size with incre…

UNAGI: A Conceptual Electrodynamic Tethered Spacecraft Demonstration Mission for Propellantless Landing on Io
Spencer Devins, Svitlana Kuklenko, Abhik Das, Cora Abrams-McCabe, Holly Herman, Madison Schooley, Matvey Zubkov, Sebastian Colussi, Dmitry Kuklenko, Abeljohn Burke-Raymond, Rae Chauvaux, Jonah Martinez, Mika Villanueva, Jorell Sakamoto, Alejandro Municio, Eduardo Duran, Elbert Chang, Advik Jain, Satvik Magganmane, Marcus Gonsalves, Antonio Corona, Anishka Chauhan, Zephra Blake

UNAGI: A Conceptual Electrodynamic Tethered Spacecraft Demonstration Mission for Propellantless Landing on Io
This paper presents UNAGI, a novel spacecraft mission developed by Spartan Space Systems, a student engineering team at San Jose State University, aiming for the first controlled landing on Jupiter's volcanically active moon Io. Inspired by the Japanese freshwater eel's agility, UNAGI employs an electrodynamic tether system that interacts with Jupiter's magnetic field to generate Lorentz forces, enabling dynamic modulation of the spacecraft's velocity without the need for traditional propellant…

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
Jihun Kim, Javad Lavaei
https://arxiv.org/abs/2509.15794 https://

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
System identification in modern engineering systems faces emerging challenges from unanticipated adversarial attacks beyond existing detection mechanisms. In this work, we obtain a provably accurate estimate of the Markov parameter matrix of order $k$ to identify partially observed linear systems, in which the probability of having an attack at each time is $O(1/k)$. We show that given the batch data accumulated up to time $T^*$, the $\ell_2$-norm estimator achieves an error decaying exponentia…

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…

Instability of the Sherman-Morrison formula and stabilization by iterative refinement
Behnam Hashemi, Yuji Nakatsukasa
https://arxiv.org/abs/2510.01696 https://

Instability of the Sherman-Morrison formula and stabilization by iterative refinement
Owing to its simplicity and efficiency, the Sherman-Morrison (SM) formula has seen widespread use across various scientific and engineering applications for solving rank-one perturbed linear systems of the form $(A+uv^T)x = b$. Although the formula dates back at least to 1944, its numerical stability properties have remained an open question and continue to be a topic of current research. We analyze the backward stability of the SM, demonstrate its instability in a scenario increasingly common …

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
Aleksandra Jekic, Afroditi Natsaridou, Signe Riemer-S{\o}rensen, Helge Langseth, Odd Erik Gundersen
https://arxiv.org/abs/2509.20191

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
Approximating solutions to partial differential equations (PDEs) is fundamental for the modeling of dynamical systems in science and engineering. Physics-informed neural networks (PINNs) are a recent machine learning-based approach, for which many properties and limitations remain unknown. PINNs are widely accepted as inferior to traditional methods for solving PDEs, such as the finite element method, both with regard to computation time and accuracy. However, PINNs are commonly claimed to show…

The (PXP)$^2$ model: long-range quantum scars in optical cavities
Hossein Hosseinabadi, Riccardo J. Valencia-Tortora, Aleksandr N. Mikheev, Darrick E. Chang, Johannes Zeiher, Roderich Moessner, Jamir Marino
https://arxiv.org/abs/2510.02246

The (PXP)$^2$ model: long-range quantum scars in optical cavities
Rydberg-cavity systems are emerging as promising platforms for quantum simulation and quantum information processing. These hybrid architectures combine two complementary interaction mechanisms: cavity photons mediate collective long-range couplings, while Rydberg excitations generate strong short-range interactions. Together, they offer a setting for engineering many-body phases characterized by a hierarchy of interactions across widely different length scales. In this work, we introduce a min…

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…

Roadmap: Emerging Platforms and Applications of Optical Frequency Combs and Dissipative Solitons
Dmitry Skryabin, Arne Kordts, Richard Zeltner, Ronald Holzwarth, Victor Torres-Company, Tobias Herr, Fuchuan Lei, Qi-Fan Yang, Camille-Sophie Br\`es, John F. Donegan, Hai-Zhong Weng, Delphine Marris-Morini, Adel Bousseksou, Markku Vainio, Thomas Bunel, Matteo Conforti, Arnaud Mussot, Erwan Lucas, Julien Fatome, Yuk Shan Cheng, Derryck T. Reid, Alessia Pasquazi, Marco Peccianti, M. Giudici, M. Marconi, A. Bartolo, N. Vigne, B. Chomet, A. Garnache, G. Beaudoin, I. Sagnes, Richard Burguete, Sarah Hammer, Jonathan Silver
https://arxiv.org/abs/2511.18231 https://arxiv.org/pdf/2511.18231 https://arxiv.org/html/2511.18231
arXiv:2511.18231v1 Announce Type: new
Abstract: The discovery of optical frequency combs (OFCs) has revolutionised science and technology by bridging electronics and photonics, driving major advances in precision measurements, atomic clocks, spectroscopy, telecommunications, and astronomy. However, current OFC systems still require further development to enable broader adoption in fields such as communication, aerospace, defence, and healthcare. There is a growing need for compact, portable OFCs that deliver high output power, robust self-referencing, and application-specific spectral coverage. On the conceptual side, progress toward such systems is hindered by an incomplete understanding of the fundamental principles governing OFC generation in emerging devices and materials, as well as evolving insights into the interplay between soliton and mode-locking effects. This roadmap presents the vision of a diverse group of academic and industry researchers and educators from Europe, along with their collaborators, on the current status and future directions of OFC science. It highlights a multidisciplinary approach that integrates novel physics, engineering innovation, and advanced researcher training. Topics include advances in soliton science as it relates to OFCs, the extension of OFC spectra into the visible and mid-infrared ranges, metrology applications and noise performance of integrated OFC sources, new fibre-based OFC modules, OFC lasers and OFC applications in astronomy.
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Cusp solitons mediated by a topological nonlinearity
Harvey Cao, Daniel Leykam
https://arxiv.org/abs/2509.18622 https://arxiv.org/pdf/2509.18622

Cusp solitons mediated by a topological nonlinearity
Nonlinearity in the Schrödinger equation gives rise to rich phenomena such as soliton formation, modulational instability, and self-organization in diverse physical systems. Motivated by recent advances in engineering nonlinear gauge fields in Bose-Einstein condensates, we introduce a nonlinear Schrödinger model whose dynamics are dependent on the curvature of the wavefunction intensity and show that this has a direct link to a topological quantity from persistent homology. Our model energeti…

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Berg\'es
https://arxiv.org/abs/2510.01475

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
Advanced control strategies like Model Predictive Control (MPC) offer significant energy savings for HVAC systems but often require substantial engineering effort, limiting scalability. Reinforcement Learning (RL) promises greater automation and adaptability, yet its practical application in real-world residential settings remains largely undemonstrated, facing challenges related to safety, interpretability, and sample efficiency. To investigate these practical issues, we performed a direct com…

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Phuong Mai Dinh, Van-Nam Huynh
https://arxiv.org/abs/2509.17889

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Multi-objective optimization (MOO) is essential for solving complex real-world problems involving multiple conflicting objectives. However, many practical applications - including engineering design, autonomous systems, and machine learning - often yield non-convex, degenerate, or discontinuous Pareto frontiers, which involve traditional scalarization and Pareto Set Learning (PSL) methods that struggle to approximate accurately. Existing PSL approaches perform well on convex fronts but tend to …

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Yining Jiang, Wenyun Xu, Qingyu Song, Yuling Lin, Xuanhao Liu, Xiaoqiang Zheng, Qiang Su, Lizhao You, Lu Tang, Wangjian Feng, Linghe Kong, Qiao Xiang, Jiwu Shu
https://arxiv.org/abs/2509.21074

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Reproducing networking research is a critical but challenging task due to the scarcity of open-source code. While Large Language Models (LLMs) can automate code generation, current approaches lack the generalizability required for the diverse networking field. To address this, we propose RePro, a semi-automated reproduction framework that leverages advanced prompt engineering to reproduce network systems from their research papers. RePro combines few-shot in-context learning with Structured and…

Deterministic Detection of Single Ion Implantation
Mason Adshead, Lok Kan Wan, Maddison Coke, Richard J Curry
https://arxiv.org/abs/2510.01035 https://arxi…

Deterministic Detection of Single Ion Implantation
Single ion implantation using focused ion beam systems enables high spatial resolution and maskless doping for rapid and scalable engineering of materials for quantum technologies, particularly qubits and colour centres in solid-state hosts. In such applications, the confidence with which a single ion can be deterministically implanted is crucial, and so the efficiency of the detection mechanism is a vital parameter. Here, we present a study of the single-ion detection efficiency for a variety …

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…

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…

Theory of Multi-photon Processes for Applications in Quantum Control
Longxiang Huang, Jacquelin Luneau, Johannes Schirk, Florian Wallner, Christian M. F. Schneider, Stefan Filipp, Klaus Liegener, Peter Rabl
https://arxiv.org/abs/2509.16074

Theory of Multi-photon Processes for Applications in Quantum Control
We present a general theoretical framework for evaluating multi-photon processes in periodically driven quantum systems, which have been identified as a versatile tool for engineering and controlling nontrivial interactions in various quantum technology platforms. To achieve the accuracy required for such applications, the resulting effective coupling rates, as well as any drive-induced frequency shifts, must be determined with very high precision. Here, we employ degenerate Floquet perturbatio…

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 …

The critical role of substrates in mitigating the power-efficiency trade-off in near-field thermophotovoltaics
Kartika N. Nimje, Julien Legendre, Michela F. Picardi, Alejandro W. Rodriguez, Georgia T. Papadakis
https://arxiv.org/abs/2509.16048

The critical role of substrates in mitigating the power-efficiency trade-off in near-field thermophotovoltaics
Near-field thermophotovoltaic systems can achieve ultra-high power densities, however, this often comes at the cost of reduced efficiency. We show that this power-efficiency trade-off can be mitigated through substrate engineering. We exploit gradient-based optimization and show that thin lossless metallic films with plasma frequencies resonantly matched to the plasmonic emitter can yield high power and spectral efficiency by spectrally enhancing and confining radiative heat transfer to a narro…

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

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…

Giant optical anisotropy in CrSBr from giant exciton oscillator strength
Georgy Ermolaev, Tagir Mazitov, Arslan Mazitov, Adilet Toksumakov, Dmitriy Grudinin, Anton Minnekhanov, Gleb Tselikov, Dmitry Yakubovsky, Gleb Tikhonowski, Nikolay Pak, Umer Ahsan, Aleksandr Slavich, Mikhail Mironov, Alexey Tsapenko, Andrey Vyshnevyy, Ivan Kruglov, Zdenek Sofer, Aleksey Arsenin, Kostya Novoselov, Andrey Katanin, Valentyn Volkov

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

Reverse Engineering User Stories from Code using Large Language Models
Mohamed Ouf, Haoyu Li, Michael Zhang, Mariam Guizani
https://arxiv.org/abs/2509.19587 https://

Reverse Engineering User Stories from Code using Large Language Models
User stories are essential in agile development, yet often missing or outdated in legacy and poorly documented systems. We investigate whether large language models (LLMs) can automatically recover user stories directly from source code and how prompt design impacts output quality. Using 1,750 annotated C++ snippets of varying complexity, we evaluate five state-of-the-art LLMs across six prompting strategies. Results show that all models achieve, on average, an F1 score of 0.8 for code up to 20…