https://arxiv.org/api/U1q8/KcOTqAG2gLsNywXPPpIAjs 2026-09-21T19:52:15Z 25 122 0 http://arxiv.org/abs/2608.08607v1 2026-08-09T09:39:12Z

Mental health disorders are a leading cause of disability worldwide, yet Natural Language Processing (NLP) research for mental healthcare has remained concentrated in high-income, English-language settings. North Africa, and Algeria in particular, is largely absent from this literature despite its unique linguistic, historical, and healthcare context. We present the first conceptual framework examining the potential role of NLP within Algeria's mental healthcare system. Drawing on narrative synthesis of global NLP mental health research, Algerian healthcare literature, and low-resource NLP methodologies, we identify four structural barriers to mental healthcare: the language-of-care gap, geographic inequities in access, stigma-related barriers to help-seeking, and the absence of research and digital infrastructure. We then map existing NLP capabilities to each barrier, outlining their potential applications, implementation constraints, and the technical, institutional, and governance requirements necessary for deployment. Based on this analysis, we propose a research and policy roadmap that prioritizes data resources, multilingual language technologies, evaluation frameworks, and regulatory capacity. Although grounded in the Algerian context, the framework addresses challenges common to many multilingual, low-resource, and post-colonial settings. This work provides a foundation for future research on culturally and linguistically appropriate NLP for mental healthcare and offers a practical roadmap for developing responsible AI-enabled mental health systems in underrepresented regions.

2026-08-09T09:39:12Z Meriem Laifa Abdallah Bengueddoudj http://arxiv.org/abs/2608.08175v1 2026-08-08T15:11:32Z

Many research fields share a common structure: a set of agents, each pursuing its own goal, whose actions must be coordinated so that no two of them conflict. Multi-Agent Path Finding (MAPF) is a concrete instance of this structure, with applications from warehouses to road traffic and airports. Much of MAPF research assumes discrete time, circular agents sharing one spatial graph, a single goal per agent, and that an agent must remain at its goal once reached, precluding heterogeneous fleets, non-geometric conflicts, task sequences, and agents that move on after completing them. We generalise the MAPF formulation to lift these assumptions, and present Anytime-Optimal Continuous-time Conflict-Based Search (AOC-CBS), an exact and solution-complete solver for it. AOC-CBS guarantees the eventual return of an optimal solution, while reporting an incumbent with a known optimality gap upper bound throughout its runtime; it is configurable with a portfolio of repair functions, one of which we introduce (Tier-Prioritized Safe Interval Path Planning), and can exploit multiple processor cores. We demonstrate AOC-CBS on a mixed fleet of non-convex agents moving along smooth, kinodynamically feasible trajectories. Preliminary experiments against the exact solver OC-CBS, on well-known benchmarks and roadmaps we sample from them, show AOC-CBS is comparable at finding optimal solutions while extending scalability from the tens to the hundreds of agents when a bounded optimality gap is accepted.

2026-08-08T15:11:32Z 65 pages, 19 figures Alvin Combrink Sabino Francesco Roselli Martin Fabian http://arxiv.org/abs/2607.23705v2 2026-08-08T18:01:46Z

Societies often struggle to prioritize important challenges in a timely manner, with substantial costs from delayed action on issues like climate change and pandemic mitigation. A persistent puzzle is that broad concern on issues often fails to translate into collective priority. We argue that a key driver lies in how concern is formed across competing issue domains. Some issues depend heavily on social learning, where individuals infer importance from others, often because direct experience is limited. Others depend more on individual learning from firsthand experience. We develop a dynamic model in which two subgroups form issue-specific concerns through individual and social learning, and these concerns are aggregated into collective priority. The model yields three insights. First, with two issues of equal objective severity, the one that depends more on social learning tends to be underprioritized when both issues are severe. Second, gradual increases in severity delay reprioritization of the issue, with the delay growing as reliance on social learning increases. Third, this bias can be reduced by reducing social learning or by increasing intergroup learning beyond a critical threshold. These results offer a general mechanism for why severe problems can remain neglected in collective action despite widespread concern, and why intergroup interaction or experiential simulations may help align collective priorities with objective risks.

2026-07-26T15:02:22Z 24 pages, 7 figures; Supplementary Information: 7 pages, 3 figures Russ Yoon Vicky Chuqiao Yang http://arxiv.org/abs/2606.22670v1 2026-06-21T21:07:40Z

Autonomous exploration in unknown environments requires unmanned aerial vehicles (UAVs) to efficiently generate informative trajectories while simultaneously constructing accurate maps. Although many existing exploration methods rely on geometric information, they often lack semantic awareness, resulting in suboptimal exploration efficiency and limited environmental understanding. To address this limitation, this paper proposes a semantic-aware exploration framework that adds semantic information to a roadmap-based exploration strategy. The proposed method builds on the Dynamic Exploration Planner (DEP), which incrementally constructs a Probabilistic Roadmap (PRM), and augments this roadmap with a semantic layer. A semantic reward function is introduced to prioritize regions containing meaningful objects and structures, enabling the UAV to focus on areas with higher information value. Furthermore, the roadmap is continuously updated to support efficient frontier selection and path planning during exploration. The proposed framework is implemented in ROS Noetic and Gazebo using an RGB-D sensor for simultaneous acquisition of geometric and semantic information. Experimental results in multiple simulated environments demonstrate that the proposed approach achieves exploration coverage rates between 90% and 94% while reducing exploration time and travel distance compared with conventional geometry-based exploration methods.

2026-06-21T21:07:40Z Duc-Thien Nguyen Ngoc Minh Do Xiem HoangVan Thanh Nguyen Canh http://arxiv.org/abs/2607.14123v1 2026-06-18T21:46:20Z

Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice, they are often generated and discarded without guiding meaningful action. This gap reflects foundational shortcomings: research has not yet established methodologies for integrating explanations into end-to-end, human-in-the-loop systems. This position paper argues that the machine learning community must pivot from ad-hoc XAI methods toward addressing foundational & structural challenges, including unclear problem formulations, underspecified evaluation objectives, and the absence of pipelines for explanation-driven feedback. We support this claim through an analysis of recent ICML, NeurIPS, and ICLR papers and a survey of XAI practitioners, revealing recurring issues that limit cumulative progress. We conclude by outlining a practical checklist designed to shift XAI toward a more human-centered, action-oriented paradigm. By emphasizing foundational clarity over the development of ad-hoc methods, we hope to provide a roadmap for integrating explanations into actionable, feedback-driven AI systems.

2026-06-18T21:46:20Z Accepted to ICML 2026 Position Paper Track Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026 Michal Moshkovitz Suraj Srinivas Lesia Semenova Nave Frost Cyrus Rashtchian Valentyn Boreiko Shichang Zhang Himabindu Lakkaraju Cynthia Rudin Jennifer Wortman Vaughan http://arxiv.org/abs/2606.19002v1 2026-06-17T12:28:47Z

Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance. In other words, the one-size-fits-all merging strategy may not align with the characteristics of different inputs which may require prioritizing certain models over others. To this end, we propose a Steerable Model Merging (ST-Merge) framework to modulate the contribution of each source model. To realize this idea, we introduce a gated cross-attention mechanism to weight or filter the two attended source models in an adaptive manner. Extensive experiments demonstrate that ST-Merge consistently outperforms multiple strong baselines on four multilingual reasoning benchmarks across 21 different languages.

2026-06-17T12:28:47Z 12 pages, 7 figures, 8 tables. Accepted by ACL2026 Findings Zhuoran Li Rui Xu Jian Yang Junnan Liu Zhijun Chen Qianren Mao Hongcheng Guo Jiaheng Liu Likang Xiao Ming Li Xiaojie Wang http://arxiv.org/abs/2606.15513v1 2026-06-13T23:52:35Z

This paper presents the system architecture and operating logic of The Home-Energy Check-Up (New Zealand), a web-based public decision-support prototype designed to help New Zealand households identify avoidable energy-cost leakage, complete a short guided home inspection, and generate a prioritized behavior-first energy roadmap. The application is implemented as a single-file Python Streamlit system with session-state navigation, a household input dataclass, conservative low-high saving estimators, a seven-check inspection layer, a recommendation-ranking layer, visual analytics, anonymous Google Sheets persistence, downloadable reports, and a certificate-of-completion interface. The system does not claim to be a certified energy audit, New Zealand Building Code H1 verification method, Healthy Homes compliance statement, or guaranteed bill-forecasting engine. Instead, it operationalizes a practical educational workflow: start with money, collect only the minimum required household profile, convert user answers into a score and action set, estimate annual savings using transparent formulas, and convert behavior savings into a staged save-to-upgrade pathway. The manuscript details the front-end, state-management, calculation, data-storage, visualization, recommendation, deployment, privacy, and limitation layers of the prototype. It also identifies research-grade improvements required before the tool is used for validated impact assessment, including external validation against measured energy data, robust concurrent data writes, clearer uncertainty calibration, accessibility testing, and formal user evaluation. The contribution is a reproducible architecture for translating household energy advice into an interactive, gamified, data-light decision-support pathway for New Zealand homes.

2026-06-13T23:52:35Z Abdollah Baghaei Daemei http://arxiv.org/abs/2606.15091v3 2026-08-19T02:31:07Z

Brain-computer interfaces (BCIs) can restore sensory and motor function in individuals with severe neurological impairment, but the literature is fragmented between invasive neuroprosthetics and non-invasive electrophysiological decoders, with inconsistent terminology and metrics. This scoping review maps BCI-mediated sensory restoration along a unified 2x2 framework (invasiveness x signal direction), charts representative modalities and their trade-offs, and synthesizes a convergence roadmap for the field. Eligible sources were peer-reviewed studies, clinical trials, and authoritative reviews on BCI or neuroprosthetic systems for sensory or motor restoration, substitution, or augmentation, published in English between 1969 and 2025, restricted to high-impact venues to prioritize landmark evidence. Rather than an exhaustive database search, we charted a purposively assembled, citation-chained corpus of 31 pivotal sources for modality, signal type, invasiveness, signal direction, resolution, clinical risk, cost, and regulatory maturity. We define and distinguish restoration, substitution, and augmentation, and map the corpus onto the four quadrants of the framework. The corpus is dominated by efferent restoration (21 of 31) and invasive interfaces (22 of 31), and is concentrated after 2015 (25 of 31). Non-invasive, AI-augmented silent-speech decoding has matured rapidly since 2023, while invasive speech and motor neuroprostheses have achieved near-conversational communication rates. The unified taxonomy clarifies trade-offs between pathways and the role of foundation models in closing the gap between them. We outline a near-, medium-, and long-term roadmap toward closed-loop, bidirectional restoration, and identify gaps in metric standardization, longitudinal evidence, and cross-community collaboration as priorities for future research.

2026-06-13T03:53:08Z Xuan-The Tran http://arxiv.org/abs/2606.12986v1 2026-06-11T07:23:49Z

Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), and emerging Agentic AI constitute the most disruptive transformation in the history of software engineering (SE), reshaping development processes, required competencies, professional roles, and the educational outcomes that universities must deliver. This paper presents a systematic review of 48 verified, influential peer-reviewed publications (2016--2026) drawn from leading venues in software engineering, machine learning, computing education, human--AI collaboration, and software productivity. Studies were discovered, screened, and analyzed through a four-agent research workflow (Literature Discovery, Scientometric Analysis, Curriculum Transformation, and Workforce Impact) and were verified against primary sources. We synthesize the evidence along nine themes and three trajectories -- practice, education, and workforce -- and report a scientometric inflection in which annual LLM-for-SE output grew roughly five-fold after late 2022. From this synthesis we contribute: (i) a conceptual framework for AI-native software engineering organized around \emph{intent}, \emph{collaboration}, and \emph{verification}; (ii) a nine-dimension competency model spanning specification, critical evaluation, agent orchestration, and metacognition; (iii) a four-phase university curriculum roadmap with AI-resilient assessment; (iv) faculty-development and workforce-transformation strategies; and (v) a prioritized agenda of eleven research gaps. The evidence base is internally contradictory on the magnitude and direction of productivity effects, underscoring that benefits are strongly context-dependent and that educating engineers for judgment, verification, and orchestration -- rather than code production alone -- is the central challenge of the AI-native era.

2026-06-11T07:23:49Z Mamdouh Alenezi http://arxiv.org/abs/2606.10544v1 2026-06-09T08:11:25Z

Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs. As the "Twin Transition" of green and digital transformation accelerates, the industry faces technology gaps - including Scope 3 emissions and e-waste recycling - that impede sustainable scaling and lead to social tensions. This study proposes a Regenerative Socio-Technical roadmap that repurposes the Sustainable Production and Consumption system map to reframe artificial intelligence infrastructure as a system-of-systems governed ultimately by planetary limits. By integrating the Institute of Electrical and Electronics Engineers International Roadmap for Devices and Systems (IEEE IRDS) sustainability considerations for semiconductor facilities, the study proposes a metabolic circuit framework that centers "Values and Needs" within production and consumption relationship loops. This study identifies critical gaps in current Nvidia-centric roadmaps and proposes a competing reference architecture. It demonstrates how a spontaneous order of resource parsimony and planetary accountability can provide an actionable pathway for regulatory compliance and industrial resilience in the digital circular economy.

2026-06-09T08:11:25Z This document is a working paper and reflects the state of research as of May 2026. Comments are welcome and should be directed to the corresponding author at h.liao@ieee.org. This work is accepted for presentation at the 32nd IEEE ICE/ITMC Conference, Porto, Portugal 2026 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC), forthcoming 2026 Han-Teng Liao Karen Ang http://arxiv.org/abs/2606.05364v1 2026-06-03T19:12:02Z

The Habitable Worlds Observatory (HWO) will provide the first opportunity to directly image and spectrally characterize terrestrial exoplanets in the habitable zones of nearby stars. Maximizing its scientific return requires a comprehensive understanding of the high-energy radiation environments of target stars, which shape planetary atmospheres and govern the production, destruction, and detectability of biosignatures. Ultraviolet (UV) radiation plays a particularly critical role in atmospheric chemistry. Far-ultraviolet (FUV) and near-ultraviolet (NUV) photons regulate key photochemical pathways, influence ozone stability, and drive the formation of prebiotic molecules. However, the majority of high-priority HWO target stars lack high-quality UV observations. Existing datasets are sparse, heterogeneous, or limited by calibration uncertainties, and no comparable UV observatory is expected for at least 5-10 years (with UVEX offering more limited spectral resolution, wavelength coverage, and sensitivity). The Hubble Space Telescope (HST) remains the only observatory capable of acquiring high-resolution FUV and NUV spectra for these targets over the next 10-15 years. We therefore advocate for a coordinated HST program to systematically obtain UV spectra of high-priority HWO targets, ideally in conjunction with X-ray observations. This effort is essential for enabling accurate target prioritization, constraining stellar radiation environments, and ensuring robust interpretation of future HWO observations.

2026-06-03T19:12:02Z Building a Roadmap for Hubble science into the 2030s White Paper, 5 pages Sarah Peacock Aiden S. Zelakewicz Lisa Kaltenegger Breanna A. Binder José A. Caballero Lía Corrales Kevin France Cynthia Froning Eric Mamajek Seth Redfield Tyler Richey-Yowell Keighley Rockcliffe Edward Schwieterman Riccardo Spinelli Noah Tuchow David Wilson Allison Youngblood http://arxiv.org/abs/2606.04490v1 2026-06-03T06:14:41Z

Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritization: we must understand which risks are most severe, who is most vulnerable, and who is most responsible for addressing them. We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts. Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern. Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information. In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030). In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization. All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes. AI users and the general public were judged the most vulnerable to these risks, but experts assigned the highest responsibility for addressing them to general-purpose AI developers and governance actors (including governments, regulators, and standards bodies). Across most risks, experts identified information, finance, and national security as the most vulnerable sectors. These findings can guide AI risk prioritization and clarify expert expectations about who should bear responsibility for mitigation.

2026-06-03T06:14:41Z Access data at https://osf.io/pj2qr Alexander K. Saeri Jess Graham Michael Noetel Peter Slattery Dennis Ah-king Edla Aittokallio Ibitola Akindehin Abbas Al Mahdi Elie Alhajjar Rafael Andersson Lipcsey Gary Ang Catherine M. Azam Amos Azaria Rishal Balkissoon Isabel Barberá Claudio Bareato Jonathan Barry Michael Basehart Andrew M. Bean Danny Belitz Samantha Augusta Bennett Kayla Blomquist Damian Borstel Ben Bucknall Tomas Bueno Momcilovic Aurelie Bugeau Nicholas Caputo Stephen Casper Gulam Chagani Ze Shen Chin Jiyeon Cho Jay Chooi Joel N. Christoph Dmytro Chumachenko Kieran Conboy Elizabeth M. Daly Tom David Paul de Font-Reaulx Antonio De Santis Fabrizio Degni Christopher W. DiCarlo Yawen Duan Janet Egan Ian W. Eisenberg Sherif M. Elsafty Adam Ennamli Mark Esposito Nicola Fabiano Gallo Fall Neil R. Fernandes Pip Foweraker Chiara Gallese Sandra Galletti Andrew Gamino-Cheong Rokas Gipiškis Gwyn Glasser Delaram Golpayegani Jeff Grayson Hans Gundlach Josiah Hagen Alexander Hagenah Amelia S. Haines The Anh Han Yixiong Hao Kasii Harris Tianxing He Koen Holtman Giorgos Iacovides Kenneth L. Ingham Krystal Jackson Adam Jones Himanshu Joshi Brian Judge Arturs Kanepajs Shreya Kapoor Win Myat Nwe Khine Aidan Kierans Aleksandra Korolova Markus Krebsz Nicholas Kruus Joe Kwon Valeria Lazzaroli Ray X. Lee Evelina Leivada Stephan Lewandowsky Michael B. Li Xiaojian Li Geunsik Lim Henrique Lisakowski Fabio Lonardoni Todd C. Lowe Jackson G. Lu Alexander Lyzhov Nada Madkour Parv Mahajan David Manheim Kareem Mathias Claudio Mayrink Verdun Sean McGregor Scott McLean Matthew J. McMahon Minas Megalokonomos Nicolas Moës Fernando Mourao Yaroslav Mukhin Malcolm Murray Simon Mylius Neeraj Nagpal Koichi Nakada Anna Neumann Jessica Newman Kwan Yee Ng Minh N. Nguyen Quynh Phuong Nguyen Seán S. Ó hÉigeartaigh Daria Onitiu Kelly Onu Oscar Oviedo-Trespalacios Ugur Ozer Chanwoo Park M. Alejandra Parra-Orlandoni Patricia Paskov Anna M. Pastwa Burak Piskin Jacob Pratt Claudiu A. Predincea Marjana Prifti Skenduli Kenneth Priore Mukunda Madhab Pujari Zhenting Qi Preethi Raghunathan Robi Rahman Deepika Raman Max Reddel Jyoti Ruparel Emma B. Ruttkamp-Bloem Tiffany Saade Greg Sadler Said Saillant Paul M. Salmon Ayrton San Joaquin Lama Saouma Maziya Sarangpurwala Supheakmungkol Sarin Daniel S. Schiff Anna D. Schilling Chris Schmitz Reva Schwartz Abeer Sharma Tianhao Shen Kehan Sheng Maury D. Shenk Eli Sherman Chandler Smith Julie M. Smith Estevenson Solano Oliver Sourbut Madhulika Srikumar Ryan Stendall Jakob Stenseke Michael Stern Joshua Sternfeld Nikko Stevens Ilia Sucholutsky Yuanyuan Sun Mariami Tkeshelashvili Cristian Trout Brian Tse Nikolaos Tsinganos Michelle Vaccaro Anthony R. Valiaveedu Ramakrishnan Veeramony Jeremy Verdo Pulkit Verma Andrea Luigi Vitali Jinge Wang JR Washebek Yonah Welker George F. Westerman James Williams Tristan Williams Rongwu Xu Mick Yang Xuemeng Yang Sander Zeijlemaker Jingyu Zhang Marta Ziosi Neil Thompson http://arxiv.org/abs/2606.03386v1 2026-06-02T09:29:53Z

While AI and machine learning for cyber attack prediction have advanced, a critical gap persists between theoretical research and practical operational deployment. Building on Ankalaki et al. (2025), this paper provides a comprehensive analysis of 150+ benchmark datasets and 200+ studies to identify and prioritize five implementation hurdles: (1) temporal dataset obsolescence, (2) narrow attack scope, (3) real-time model interpretability, (4) inadequate adversarial robustness, and (5) privacy/ethical concerns. We introduce a novel gap-prioritization framework that evaluates these limitations based on detection impact, implementation cost, and remediation time. Our analysis identifies dataset obsolescence and adversarial robustness as the highest-priority gaps, while highlighting model interpretability as the most cost-effective path for resource-constrained environments. To bridge the research-practice divide, we provide a practical implementation roadmap and a dataset quality assessment framework that classifies 45 benchmarks into production-ready, research-only, and unusable categories. This work translates academic findings into actionable decision-support tools for robust, production-oriented AI-driven cyber defense.

2026-06-02T09:29:53Z Aminu Muhammad Auwal http://arxiv.org/abs/2605.30035v1 2026-05-28T14:56:13Z

Current observations with the James Webb Space Telescope (JWST) suggest that star-forming galaxies produce enough ionizing (LyC; $λ< 91.2$ nm) photons to drive cosmic reionization, but the efficiency with which these photons escape their host galaxies remains uncertain. Absorption by the neutral intergalactic medium progressively suppresses direct LyC detections above redshift $z\sim3$, forcing astronomers to rely on indirect diagnostics of LyC escape calibrated at low redshift. Low-resolution ultraviolet observations of high-redshift analogs obtained with the Cosmic Origins Spectrograph onboard the Hubble Space Telescope (HST) have been critical for developing these diagnostics. These studies suggest that stellar feedback plays a central role in regulating LyC escape, although the role of galactic winds and the underlying physical mechanisms remain poorly constrained. High-resolution spectroscopy blueward of 160.0 nm (rest-frame) is required to resolve the kinematic structure of the winds and reveal the physics governing LyC escape. Such observations are currently only possible with HST and represent a major science driver for the future Habitable Worlds Observatory (HWO). Extending the lifetime of HST and prioritizing ultraviolet observations are essential for interpreting current JWST studies of the early Universe and important preparatory science for HWO.

2026-05-28T14:56:13Z 8 pages, 3 figures, Space Telescope Science Institute (STScI) - Building a Roadmap for Hubble science into the 2030s - White Paper Cody Carr Stephan McCandliss Michelle Berg Renyue Cen Kevin France Matthew Hayes Alaina Henry M. S. Oey Alberto Saldana-Lopez http://arxiv.org/abs/2605.26142v1 2026-05-22T19:49:03Z

White dwarf stars, the endpoint of stellar evolution for 97% of stars in our Milky Way, offer a unique and powerful window into the bulk elemental composition of rocky exoplanetary bodies. Up to 50% of single white dwarfs are observed with photospheric metal lines from accreted exoplanetary bodies (called 'polluted' white dwarfs), and spectroscopic observations reveal the bulk composition of this material. High-resolution (R>15,000) UV spectra are essential for detecting many elements present in the material, such as the volatile elements imperative for habitability studies (C, N, O, P, S) and key rock-forming elements required to constrain interior structure (e.g. Fe, Si, Mg, Al, Ni). HST, through its COS and STIS spectrographs, remains the only facility capable of performing this science in the near future. Looking to the next decade, the scientific case for continued HST UV observations of polluted white dwarfs is compelling on three fronts (i) as a standalone to enable the bulk composition of exoplanetary material to be measured in a statistically significant sample, (ii) as essential groundwork for the Habitable Worlds Observatory (HWO), and (iii) in a powerful synergy with JWST, to enable characterization of the bulk mineralogy and bulk elemental composition of exoplanetary material. This white paper argues that continued UV spectroscopic capabilities with HST is a high-return investment for white dwarf and exoplanet science, and preserving and prioritizing HST's UV capabilities through at least 2035 is crucial to maximize the scientific return from HST, JWST, and HWO.

2026-05-22T19:49:03Z White paper submitted to STScI call: Building a Roadmap for Hubble science into the 2030s Laura K. Rogers Siyi Xu Martin Barstow Simon Blouin Amy Bonsor Andrew M. Buchan Sarah L. Casewell Tim Cunningham John Debes Patrick Dufour Boris Gansicke Joseph Guidry Ted von Hippel Mukremin Kilic Erika Le Bourdais Carl Melis Lou Baya Ould Rouis Judith Provencal Melinda Soares-Furtado Andrew Swan Isabella Trierweiler Zachary Vanderbosch Jamie Williams http://arxiv.org/abs/2605.14503v1 2026-05-14T07:47:44Z

While Retrieval-Augmented Generation (RAG) is increasingly adopted to ground Large Language Models (LLMs) in software artifacts, the optimal configuration of its components remains an open question for software engineering (SE) tasks. The lack of systematic guidance forces practitioners into costly, ad-hoc experimentation. This paper presents a comprehensive, component-wise empirical study that dissects the RAG pipeline, evaluating over 21 distinct models and methods. Our study systematically isolates and evaluates 4 query processing techniques, 7 retrieval models spanning sparse, dense, and hybrid paradigms, 4 context refinement methods, and 6 distinct generators. We test these components on a suite of 3 core SE tasks: code generation, summarization, and repair. Our empirical findings reveal a crucial insight: the retriever-side components, particularly the choice of the retrieval algorithm, often exert a more significant influence on final system performance than the selection of the generator model. Strikingly, the classic lexical retriever BM25 demonstrates exceptionally robust performance across diverse tasks. Our analysis provides a practical, data-driven roadmap for researchers and practitioners, offering clear guidance on prioritizing optimization efforts when constructing effective RAG systems for software engineering contexts.

2026-05-14T07:47:44Z Qiang Ke Yanjie Zhao Hongjin Leng Shengming Zhao Haoyu Wang http://arxiv.org/abs/2605.06713v1 2026-05-06T23:23:26Z

Agentic AI systems can plan, call tools, inspect code, interact with web applications, and coordinate multi-step workflows. These same capabilities change the economics of cyber offense. The central near-term risk is not that every low-skill criminal immediately becomes a frontier exploit researcher; it is that agentic AI compresses the attack lifecycle by lowering the cost of reconnaissance, phishing, credential abuse, vulnerability triage, exploit adaptation, and post-compromise decision support. This paper synthesizes current public evidence from national cybersecurity agencies, industry threat reports, agent security guidance, and research on LLM agents cyber capabilities. It introduces a Three Channel Agentic Cyber Risk Model and an Agentic Attack Compression Model, uses the 2026 Linux kernel Copy Fail incident as a case study for foothold-to-root acceleration, and develops a 2026 to 2028 forecast for large enterprises and the German and European Mittelstand. The paper concludes with a prioritized defense roadmap. Organizations should treat agentic AI security as an immediate operational problem: identity, phishing resistant authentication, patch velocity, CI/CD and Linux/container hardening, agent governance, telemetry, and recovery readiness must be strengthened now.

2026-05-06T23:23:26Z 7 pages Christopher Koch http://arxiv.org/abs/2605.03138v1 2026-05-04T20:24:30Z

Zero-day attacks pose severe cybersecurity risks due to their high success rates and stealth. Because signature-based approaches struggle to detect such attacks, building Intrusion Detection Systems (IDSs) for detecting zero-day attacks is essential. We contend that for an IDS to be effective it must be grounded in an understanding of how zero-day attacks manifest in real-world networks. To this end, we review documented zero-day incidents spanning 20 years, finding that these attacks arise from the exploitation of undisclosed vulnerabilities rather than novel attack behavior. Guided by this insight, we propose a taxonomy of zero-day vulnerability types and analyze assumptions of ML-based intrusion detection approaches. Our analysis shows that incidents consistently involve vulnerability exploitation, with memory-corruption flaws being most used; additionally, attacks targeting defensive-mechanism vulnerabilities have increased in recent years. We also identify a mismatch: while incident reports emphasize vulnerability exploitation, many ML-based detectors are designed to detect hypothetical "novel behaviors" during attack execution. Our findings indicate that vulnerability-centric methods are more aligned with real-world attack mechanisms. Consequently, reliance on behavior-based detection alone may overstate zero-day detection capabilities in ML-based IDSs. We advocate for cautious interpretation of such claims and call for improved automated vulnerability detection frameworks aligned with real-world exploit characteristics. Effective defense against zero-day attacks requires prioritizing vulnerability-centeric approaches that enable early identification and mitigation across the lifecycle. The ability to detect attacks that utilize novel behaviors (Tactics, Techniques, and Procedures (TTPs)) is useful, but it does necessarily equate to the ability to detect zero-day attacks.

2026-05-04T20:24:30Z Nnamdi Jibunoh Sara Khanchi Adetokunbo Makanju http://arxiv.org/abs/2604.14360v1 2026-04-15T19:23:40Z

Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks, environmental disruptions, and hardware or software failures. This survey provides a comprehensive review of CPS resilience, framing the field through five interconnected themes that are required in an integrated whole to achieve real-world resilience. The article first posits that resilience is a system-wide property emerging from interactions between hardware, software, and human users. Second, it addresses the challenges of learning-enabled CPS, which often operate in data-scarce environments characterized by imbalanced or noisy data, requiring innovative solutions like synthetic data generation and foundation model adaptation. Third, the survey examines proactive measures for resilience, which include distinctive aspects of verification, testing, and redundancy. Fourth, it explores recovery mechanisms, moving beyond traditional fault models to design "just good enough" recovery strategies that prioritize safety-critical functions during perturbations. Finally, it highlights the central role of the human, focusing on the different levels of human intervention, the necessity of trust calibration, and the requirement for explainable AI to support human-CPS teaming. These themes are illustrated through representative application domains, primarily Connected and Autonomous Transportation Systems (CATS) and Medical CPS (MCPS). By integrating the five interconnected themes, this survey provides a systematic roadmap for achieving the resilient CPS in increasingly complex and adversarial environments.

2026-04-15T19:23:40Z Submitted to ACM CSUR; 32 pages + 10 pages of references Saurabh Bagchi Hyunseung Kim Tarek Abdelzaher Homa Alemzadeh Somali Chaterji Glen Chou Yuying Duan Fanxin Kong Michael Lemmon Yin Li Mengyu Liu Wenhao Luo Meiyi Ma Sibin Mohan Ayan Mukhopadhyay Melkior Ornik Dimitra Panagou Kristin Yvonne Rozier Ivan Ruchkin Huajie Shao Sze Zheng Yong Majid Zamani Xugui Zhou http://arxiv.org/abs/2604.11337v1 2026-04-13T11:37:46Z

The dominant paradigm of local multi-agent systems -- orchestrated, enterprise-bounded pipelines -- is being superseded by internet-wide agent societies in which autonomous agents discover each other through open registries, interact without central orchestrators, and generate emergent social behaviors. We argue that governing such societies requires institutional design, not merely risk enumeration or process compliance. Applying Talcott Parsons' AGIL framework -- four functional imperatives (Adaptation, Goal Attainment, Integration, Latency) every viable social system must satisfy -- we derive a prescriptive sixteen-cell institutional architecture for internet-wide agent governance. Diagnostically applied to the OpenClaw ecosystem (250,000+ GitHub stars, 2M+ monthly users, 770,000+ registered agents) via a recursive sub-function analysis (64 binary indicators across 16 cells), we find at most 19% sub-function coverage (sensitivity range 17-30%) -- potential rather than operative capacity, since zero inter-cell coordination prevents existing infrastructure from participating in inter-pillar interchange. A complementary interchange media assessment finds zero of twelve inter-pillar pathways functional: the ecosystem has technical infrastructure but no active governance, no coordination layer, and no normative grounding, with the Fiduciary and Political pillars most severely underserved. Extending the diagnostic to the broader agent-native protocol stack (MCP, A2A, ANP, x402, ERC-8004), independent development teams reproduce the same structural pattern -- confirming the governance gap is a feature of market-driven development, not ecosystem immaturity. Institutional design is most effective before social patterns calcify; we conclude with a prioritized roadmap for the missing governance infrastructure.

2026-04-13T11:37:46Z Anbang Ruan http://arxiv.org/abs/2604.02842v1 2026-04-03T08:01:02Z

Protein-protein interactions (PPIs) between a virus and its host govern infection, replication, and pathogenesis. While high-throughput mapping has identified thousands of virus-host associations, much of the virus-host interactome remains uncharacterized due to the labor-intensive nature of experimental screens, the inherent difficulty in capturing transient interactions, and the limited sequence homology across divergent viral families. Here, we introduce ViraHinter, a dual-modal deep learning framework for the precise prediction of virus-host interactions and large-scale inference of interaction landscapes. ViraHinter couples a structure-generation branch with a sequence-representation branch, integrating structure-informed pair representations with ESM-derived embeddings to learn generalizable interaction rules across unseen viruses. We benchmark ViraHinter on pathogenic coronaviruses and influenza A viruses and show that it consistently outperforms RoseTTAFold2-PPI, AlphaFold 3 and RoseTTAFold2-Lite in prioritizing high-confidence candidates even under severe class imbalance and across diverse interface regimes. Notably, it successfully identifies novel functionally relevant host factors and recapitulates the structural plasticity of the complex interfaces. By intersecting predictions across multiple influenza subtypes, ViraHinter reveals 33 shared host factors, offering a roadmap for broad-spectrum antiviral discovery. ViraHinter therefore serves as a robust computational approach for studying virus-host interactions, enabling systematic screening of host factors for all known human-infecting viruses, providing new insights into the shared mechanisms of viral pathogenesis, and accelerating the discovery of novel therapeutic targets and the development of broad-spectrum antivirals.

2026-04-03T08:01:02Z Weiqiang Bai Fei Wang Jialin Wang Sheng Xu Lifeng Qiao Juan Li Zhuyi Guo Xiangyun Hou Lei Bai Bowen Zhou Edward C. Holmes Weifeng Shi Siqi Sun http://arxiv.org/abs/2603.29316v4 2026-06-21T19:10:52Z

Clustering mixed-type data remains a major challenge in biomedical research to uncover clinically meaningful subgroups within heterogeneous patient populations. Most existing clustering methods impose restrictive assumptions like local independence, fail to accommodate censored biomarkers, or unable to quantify variable importance. We propose a Bayesian finite mixture model (BFMM) clustering framework that addresses these limitations. BFMM flexibly models both continuous and categorical variables, incorporates three covariance structures to capture cluster-specific dependencies among continuous features, and handles censored observations through likelihood-based imputation. To facilitate feature prioritization, BFMM uses spike-and-slab priors to estimate variable importance on a continuous 0-1 scale. Simulation studies demonstrate that BFMM outperforms existing methods in clustering accuracy, particularly given strong within-cluster correlation or censored variables, and reliably distinguishes informative features from noise under varying conditions. We applied BFMM to two real-world datasets: (1) the SENECA cohort integrating electronic health records from patients with Sepsis; and (2) the EDEN randomized trial of patients with acute lung injury. In both settings, BFMM identified clinically interpretable phenotypes and revealed variable-specific contributions to subgroup differentiation. In the EDEN trial, it also uncovered evidence of treatment heterogeneity. These findings validate BFMM as an effective, interpretable, and practically useful clustering tool for complex biomedical datasets.

2026-03-31T06:34:04Z 55 pages (including 17-page Appendices), 8 figures (including 1 figure in Appendix B) Yueting Wang Shu Wang Jonathan G. Yabes Chung-Chou H. Chang http://arxiv.org/abs/2603.28677v2 2026-08-02T01:32:00Z

Context: Requirements prioritization is a challenging problem that is aimed to deliver the most suitable subset from a pool of candidate requirements. The problem is NP-hard when formulated as an optimization problem. Feedback from end users can offer valuable support for software evolution, and ReFeed represents a state-of-the-art in automatically inferring a requirement's priority via quantifiable properties of the feedback messages associated with a candidate requirement. Objectives: In this paper, we enhance ReFeed by shifting the focus of prioritization from treating requirements as independent entities toward interconnecting them. Additionally, we explore if interconnecting requirements provide additional value for search-based solutions. Methods: We leverage user feedback from mobile app stores to group requirements into topically coherent clusters. Such interconnectedness, in turn, helps to auto-generate additional "requires" relations in candidate requirements. These "requires" pairs are then integrated into a search-based software engineering solution. Results: The experiments on 94 requirements prioritization instances from four real-world software applications show that our enhancement outperforms ReFeed. In addition, we illustrate how incorporating interconnectedness among requirements improves search-based solutions. Conclusion: Our findings show that requirements interconnectedness improves user feedback driven requirements prioritization, helps uncover additional "requires" relations in candidate requirements, and also strengthens search-based release planning.

2026-03-30T16:58:07Z Revised manuscript submitted to Information and Software Technology Aurek Chattopadhyay Nan Niu Hui Liu Jianzhang Zhang http://arxiv.org/abs/2603.20829v1 2026-03-21T14:15:34Z

Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes. While the advent of graph neural networks and self-supervised learning has catalyzed a proliferation of AGC methodologies, a widening chasm persists between academic benchmark performance and the stringent demands of real-world industrial deployment. To bridge this gap, this survey provides a comprehensive, industrially grounded review of AGC from three complementary perspectives. First, we introduce the Encode-Cluster-Optimize taxonomic framework, which decomposes the diverse algorithmic landscape into three orthogonal, composable modules: representation encoding, cluster projection, and optimization strategy. This unified paradigm enables principled architectural comparisons and inspires novel methodological combinations. Second, we critically examine prevailing evaluation protocols to expose the field's academic monoculture: a pervasive over-reliance on small, homophilous citation networks, the inadequacy of supervised-only metrics for an inherently unsupervised task, and the chronic neglect of computational scalability. In response, we advocate for a holistic evaluation standard that integrates supervised semantic alignment, unsupervised structural integrity, and rigorous efficiency profiling. Third, we explicitly confront the practical realities of industrial deployment. By analyzing operational constraints such as massive scale, severe heterophily, and tabular feature noise alongside extensive empirical evidence from our companion benchmark, we outline actionable engineering strategies. Furthermore, we chart a clear roadmap for future research, prioritizing heterophily-robust encoders, scalable joint optimization, and unsupervised model selection criteria to meet production-grade requirements.

2026-03-21T14:15:34Z Yunhui Liu Yue Liu Yongchao Liu Tao Zheng Stan Z. Li Xinwang Liu Tieke He http://arxiv.org/abs/2603.12406v1 2026-03-12T19:38:18Z

\textbf{Background:} Fairness and diversity are receiving growing attention in software engineering, particularly as AI and machine learning systems increasingly influence decision-making processes. While fairness is often examined at the algorithmic or data level, there is limited understanding of how it is addressed during the early stages of software development. Moreover, little is known about how team diversity affects fairness-related decisions in software projects. \textbf{Aims:} This study investigates how diversity in software teams influences fairness-aware behavior during requirements prioritization. \textbf{Method:} A controlled experiment was conducted with 27 pairs of software engineering students, including 13 LGBTQ diverse pairs and 14 non diverse pairs. Each pair prioritized user stories with varying fairness implications. Descriptive statistics were used to analyze attitudes and prioritization outcomes, and thematic analysis was applied to examine the reasoning behind participants' decisions. \textbf{Results:} Both groups demonstrated general alignment with fairness principles, prioritizing features that promoted equitable treatment and rejecting those that posed fairness risks. However, LGBTQ diverse pairs were more consistent in rejecting fairness risking stories and made fewer fairness related misprioritization errors. Their reasoning emphasized inclusion, non discrimination, and ethical responsibility, whereas non diverse pairs adopted a more pragmatic, goal oriented perspective. \textbf{Conclusions:} The findings indicate that fairness should be considered from the earliest stages of software development. Team diversity can enhance the identification and interpretation of fairness issues during requirements analysis, fostering more reflective and inclusive decision making.

2026-03-12T19:38:18Z Cleyton Magalhes Ronnie de Souza Santos Bimpe Ayoola Brody Stuart-Verner Italo Santos