@article{GedeonHuber2025, author = {Gedeon, Steven and Huber, Florian}, title = {Using AI-Enabled Images and Comic Narrative to Enhance Entrepreneurship Education}, volume = {9}, journal = {Entrepreneurship Education and Pedagogy}, number = {2}, publisher = {Sage}, address = {London}, issn = {2515-1274}, doi = {https://doi.org/10.1177/25151274251358708}, pages = {327 -- 345}, year = {2025}, abstract = {This learning innovation is an engaging technique to help students learn abstract concepts such as entrepreneurship theory, mindsets, and methods in a playful and exciting way. We explore how current AI tools can be used by entrepreneurship educators to enhance their course materials by creating memorable images and story-based, highly visual comic book content ("graphic novels"). We implemented these enhancements in an interactive learning platform based on Articulate Storyline and tested them in an introductory entrepreneurship course with over 2000 students per year on building an entrepreneurial mindset. In this article, we include sample copies of the images and graphic novel comics and highlight key methods, lessons, results, and design artifacts created during the course of our learning journey. Educators may add these free Open Educational Resource (OER) assets to their own courses by linking to the online version. We believe this approach to entrepreneurship education enhances students' engagement, memory retention, and the ability to identify and name underlying concepts and entrepreneurship theories. We hope to spark the interest of other entrepreneurship educators to use similar approaches at their universities.}, language = {en} } @article{AzzamSauerEndischetal.2025, author = {Azzam, Mohamed and Sauer, Dirk Uwe and Endisch, Christian and Lewerenz, Meinert}, title = {Comprehensive Analysis of Float Current Behavior and Calendar Aging Mechanisms in Lithium-Ion Batteries}, volume = {9}, pages = {e202500349}, journal = {Batteries \& Supercaps}, number = {1}, publisher = {Wiley}, address = {Weinheim}, issn = {2566-6223}, doi = {https://doi.org/10.1002/batt.202500349}, year = {2025}, abstract = {Aiming to quantify degradation currents from solid electrolyte interphase formation (ISEIgrowth) and gain of active lithium due to cathode lithiation (ICL), resulting from electrolyte decomposition, the float current behavior of lithium-ion batteries is investigated with different cathode materials. The float current, IFloat , represents the recharge current required to maintain the cell at a fixed potential during calendar aging. This current arises as lithium is irreversibly consumed at the anode or inserted into the cathode, shifting the electrode potentials. To account for the asymmetric response of the electrodes, a voltage-dependent scaling factor, SF, is introduced, derived from the slopes of the electrode-specific voltage curves. Using this factor in combination with measured float currents and capacity loss rates from check-up tests, ISEIgrowth and ICL is quantified at 30 °C across various float voltages. Although the SF and capacity data are limited to 30 °C, the model is extended to a range of 5-50 °C using only float current measurements. The results show that using capacity loss rates alone underestimate ISEIgrowth and that ICL, contributes significantly to the observed float current at elevated voltages, indicating that cathode lithiation plays an increasingly important role in high-voltage calendar aging.}, language = {en} } @inproceedings{HornAdamSchmidtner2026, author = {Horn, Alexander and Adam, Philip-Roman and Schmidtner, Stefanie}, title = {A Benchmark Dataset for Bus Travel and Dwell Time Prediction}, booktitle = {2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-2418-0}, doi = {https://doi.org/10.1109/ITSC60802.2025.11423733}, pages = {2047 -- 2054}, year = {2026}, language = {en} } @book{BrandvonGrooteBidlingmaierKaiseretal.2011, author = {Brand, Klaus and von Groote-Bidlingmaier, Carolin and Kaiser, Peter and Strauß, Albert and Hutner, Petra}, title = {ArcGIS 10}, subtitle = {das deutschsprachige Handbuch f{\"u}r ArcView und ArcEditor, mit zahlreichen {\"U}bungsbeispielen und Gutschein f{\"u}r die ESRI-Evaluation-DVD in der aktuellen Programmversion}, edition = {3. Aufl.}, publisher = {Wichmann}, address = {Berlin}, isbn = {978-3-87907-500-3}, pages = {XX, 705}, year = {2011}, language = {de} } @book{Hutner2017, author = {Hutner, Petra}, title = {Transdisziplin{\"a}rer Ansatz zur F{\"o}rderung einer Circular Economy durch nachhaltiges Ressourcenmanagement}, subtitle = {Identifikation von Handlungsoptionen und Evaluierung von Umweltauswirkungen der Abfallvermeidung}, publisher = {Verlag Dr. Kovač}, address = {Hamburg}, isbn = {978-3-8300-9622-1}, pages = {IX, 181}, year = {2017}, language = {de} } @article{HutnerHelbigStindtetal.2018, author = {Hutner, Petra and Helbig, Christoph and Stindt, Dennis and Thorenz, Andrea and Tuma, Axel}, title = {Transdisciplinary Development of a Life Cycle-Based Approach to Measure and Communicate Waste Prevention Effects in Local Authorities}, volume = {22}, journal = {Journal of Industrial Ecology}, number = {5}, publisher = {Wiley}, address = {Oxford}, issn = {1530-9290}, doi = {https://doi.org/10.1111/jiec.12781}, pages = {1050 -- 1065}, year = {2018}, language = {en} } @thesis{Schlereth2024, author = {Schlereth, Dunja Veronika}, title = {Die Transformation interner Kreationsprozesse bei der Ideenfindung in Marketing- und Kreativagenturen durch die Verschmelzung von k{\"u}nstlicher und menschlicher Kreativit{\"a}t}, subtitle = {eine Analyse von Best-Practice-Modellen}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-55632}, pages = {60, XI}, school = {Technische Hochschule Ingolstadt}, year = {2024}, abstract = {Angesichts der dynamischen Fortschritte in der KI-Technologie und den damit einhergehenden neuen M{\"o}glichkeiten in der Kreativwirtschaft, strebt diese Arbeit danach, zu erforschen, wie der Ideenfindungsprozess von Marketingagenturen zwischen KI und menschlicher Kreativit{\"a}t mit Hinblick auf die spezifischen Kreativit{\"a}tsanforderungen verschiedener Teilprozesse gestaltet werden kann. Die methodologische Herangehensweise kombiniert theoretische Forschung, die sich insbesondere auf die Analyse etablierter Modelle fokussiert, mit qualitativen Experteninterviews. Dabei wurde ein Schwerpunkt auf die Analyse von Best- Practice-Modellen gelegt, um bestehende Praktiken und das Potenzial der KI- Integration zu untersuchen. Im Rahmen dieser Studie wurden verschiedene Phasen des Ideenfindungsprozesses und die spezifischen Kreativit{\"a}tsanforderungen an diese Phasen identifiziert, sowie die effektive Integration von KI in diese Prozesse evaluiert. Die zentralen Erkenntnisse zeigen, dass KI insbesondere in den fr{\"u}hen Stadien des Ideenfindungsprozesses effektiv zur Generierung erg{\"a}nzender Impulse und zur Unterst{\"u}tzung menschlicher Kreativit{\"a}t eingesetzt werden kann. Ihre F{\"a}higkeiten in fortgeschrittenen Kreativit{\"a}tsstufen, vor allem in den Bereichen der assoziativen, transformationellen Kreativit{\"a}t, sind jedoch begrenzt. Es wurde deutlich, dass die tiefgreifende kognitive Verarbeitung und emotionale Bewertung, die in den sp{\"a}teren Phasen der Kreation erforderlich sind, sowie eine sorgf{\"a}ltige Abstimmung und menschliche {\"U}berwachung w{\"a}hrend des gesamten Prozesses entscheidend von menschlichen F{\"a}higkeiten abh{\"a}ngen. Die Arbeit entwickelt ein Rahmenwerk, das darlegt, wie KI systematisch in den kreativen Prozess integriert werden kann, um sowohl die Effizienz als auch die kreative Qualit{\"a}t zu verbessern, ohne dabei die wesentliche Rolle menschlicher Intuition und emotionaler Intelligenz zu schm{\"a}lern.}, language = {de} } @article{VolpatodeSouzaPintoPereiraNeumeieretal.2025, author = {Volpato, Guilherme and de Souza Pinto Pereira, Adriano and Neumeier, Steffen and Hotza, Dachamir and G{\"o}ken, Mathias and Tetzlaff, Ulrich and Fredel, M{\´a}rcio Celso}, title = {Influence of a Standard Heat Treatment on the Microstructure and Properties of Inconel X-750 Produced by Laser Powder Bed Fusion}, volume = {27}, pages = {2500306}, journal = {Advanced Engineering Materials}, number = {24}, publisher = {Wiley}, address = {Weinheim}, issn = {1527-2648}, doi = {https://doi.org/10.1002/adem.202500306}, year = {2025}, abstract = {The production of nickel-base superalloys through powder bed fusion using a laser beam (PBF-LB) offers a promising method for reducing the cost of aeronautical components, especially those with complex geometries. However, when these materials are processed using PBF-LB, the standard heat treatments developed for conventional manufacturing routes do not consistently yield predictable microstructures or mechanical properties. Thus, this study analyzes how PBF-LB affects the microstructure and mechanical behavior of Inconel alloy X-750 when compared to a hot-rolled counterpart after undergoing a standard precipitation-hardening heat treatment (solution annealing, stabilization, and precipitation). Microstructure is examined using analytical electron microscopy and high-energy X-ray diffraction, while mechanical behavior is assessed through microhardness. Significant differences between both manufacturing routes are detected, so that PBF-LB processing led to an increase in grain size, anisotropy, fraction of low-angle grain boundaries, mean angle of misorientation, and dislocation density. Moreover, PBF-LB promoted the formation of γ′-Ni3(Al,Ti) and η-Ni3Ti phases during solution annealing and stabilization, respectively. Notably, the formation of γ′ contributes to an anomalous microhardness increase observed between these steps. These findings suggest that PBF-LB may inhibit recrystallization and facilitate precipitation during heat treatment, thereby affecting the performance of components produced using this technology.}, language = {en} } @unpublished{WurstBalasubramanianBotschetal.2021, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2105.01924}, year = {2021}, language = {en} } @unpublished{BauderPaulaPfeilschifteretal.2023, author = {Bauder, Maximilian and Paula, Daniel and Pfeilschifter, Claus and Petermeier, Franziska and Kubjatko, Tibor and Riener, Andreas and Schweiger, Hans-Georg}, title = {Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis}, titleParent = {SSRN}, publisher = {Elsevier}, address = {Amsterdam}, doi = {https://dx.doi.org/10.2139/ssrn.4588275}, year = {2023}, language = {en} } @inproceedings{WeiJiangChanetal.2026, author = {Wei, Zixiang and Jiang, Yunruo and Chan, Pak Hung and Wang, Yiting and Poledna, Yuri and Donzella, Valentina}, title = {Darwick: A Paired Dataset in Low-Light Driving Scenarios for Advanced Perceptual Enhancement and Benchmarking Assessment}, booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3315-0592-9}, doi = {https://doi.org/10.1109/ITSC58415.2024.11414063}, year = {2026}, language = {en} } @inbook{BauerGleissnerKotonski2025, author = {Bauer, Ingrid and Gleissner, Christian and Kotonski, Julia}, title = {On Rigid Manifolds of Kodaira Dimension 1}, booktitle = {Perspectives on Four Decades of Algebraic Geometry, Volume 1: In Memory of Alberto Collino}, editor = {Albano, Alberto and Aluffi, Paolo and Bolognesi, Michele and Casagrande, Cinzia and Colombo, Elisabetta and Conte, Alberto and Grassi, Antonella and Pedrini, Claudio and Pirola, Gian Pietro and Verra, Alessandro}, publisher = {Birkh{\"a}user}, address = {Cham}, isbn = {978-3-031-66229-4}, doi = {https://doi.org/10.1007/978-3-031-66230-0_2}, pages = {43 -- 71}, year = {2025}, language = {en} } @article{LangerTentrupSchweiger2026, author = {Langer, Robin and Tentrup, Thomas and Schweiger, Hans-Georg}, title = {A Vehicle-in-the-Loop Approach for Front Camera Verification Using Adaptive High Beam}, journal = {IEEE Open Journal of Intelligent Transportation Systems}, publisher = {IEEE}, address = {New York}, issn = {2687-7813}, doi = {https://doi.org/10.1109/OJITS.2026.3672438}, year = {2026}, abstract = {As automated driving functions based on environmental sensors become increasingly deployed, ensuring reliable performance over the vehicle lifetime is essential. Currently, verification is carried out through internal self-diagnostics, which do not always operate correctly, and periodic technical inspection, which assesses only the test criteria installation and condition. Test criteria for function and efficiency of environmental sensors are neither standardized nor routinely assessed, creating the need for new testing approaches. Previous low-cost research approaches defined a method and conducted experiments to verify a vehicle's front camera by displaying visual stimuli and evaluating the high beam assist response. Whereas the camera's function could be verified through a basic qualitative check, the approach did not enable a quantitative evaluation of its performance. The aim of this work was therefore to advance this approach and investigate the added value of a Vehicle-in-the-Loop test bench for front camera verification. Three tests were conducted. A supporting method was introduced to reproducibly detect and define the position of the headlight cutoff line, enabling consistent evaluation of the vehicle's reaction. With static camera stimuli (Test I), the function of the front camera could be verified, and the influence of the vehicle geometry on the reaction was assessed. Dynamic stimuli (Test II) additionally enabled an efficiency evaluation, allowing quantitative comparison between vehicles. However, transferring the stimuli into a reproducible virtual simulation (Test III) remained challenging, as the vehicles under test did not respond consistently. Further research is required to refine and simplify the method toward a standardized periodic technical inspection procedure.}, language = {en} } @inproceedings{BanerjeeGoschHesteretal.2026, author = {Banerjee, Sweta and Gosch, Timo and Hester, Sara and Weiss, Viktoria and Conrad, Thomas and Donovan, Taryn and Porsche, Nils and Ammeling, Jonas and Stroblberger, Christoph and Klopfleisch, Robert and Kaltenecker, Christopher and Bertram, Christof and Breininger, Katharina and Aubreville, Marc}, title = {Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces SWAN}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2026: Proceedings, German Conference on Medical Image Computing, L{\"u}beck, March 15-17, 2026}, editor = {Handels, Heinz and Breininger, Katharina and Deserno, Thomas Martin and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-51100-5}, doi = {https://doi.org/10.1007/978-3-658-51100-5_42}, pages = {203 -- 209}, year = {2026}, language = {en} } @article{GoebelMertensRiener2026, author = {G{\"o}bel, Jan-Philipp and Mertens, Jan Cedric and Riener, Andreas}, title = {Thermal imaging for the detection of driver impairment: evidence from a high-fidelity driving simulator study}, journal = {Traffic Injury Prevention}, publisher = {Taylor \& Francis}, address = {Philadelphia}, issn = {1538-9588}, doi = {https://doi.org/10.1080/15389588.2026.2624006}, year = {2026}, abstract = {Objectives Driving under the influence of alcohol (DUI) remains a major contributor to fatal traffic crashes worldwide. With increasing regulatory pressure, such as requirements by Euro NCAP for in-vehicle impairment detection, there is a growing need for reliable, real-time monitoring solutions. While traditional DUI detection approaches focus on driving behavior or eye movement analysis, this study explores the potential of thermal imaging as a noninvasive alternative for detecting alcohol impairment. Methods We conducted a large-scale experimental study with 120 participants in a high-fidelity driving simulator, capturing thermal facial data under both non-impaired and alcohol-impaired conditions. A novel temperature extraction method was developed based on facial landmarks, incorporating multiple frames to reduce noise and improve temporal stability. Ambient cabin temperature was also recorded to normalize facial temperature readings and control for environmental influences. Several machine learning classifiers, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient-Boosting Models, were trained using five facial temperature features (cheek, temple, ear, forehead, and nasal tip) and evaluated via five-fold subject-wise cross-validation. Results Significant temperature changes were observed in specific facial regions (particularly the cheek, ear, temple, and nasal tip) under alcohol influence. Among the evaluated models, Logistic Regression achieved the highest average classification accuracy (62\%), while SVM demonstrated the most stable performance across folds. The model showed a slight conservative bias toward predicting the baseline (non-impaired) class, thereby reducing the risk of false positive classifications. Environmental conditions, including cabin temperature, were verified to be stable across both driving sessions, ensuring the validity of the physiological measurements. Conclusions This study demonstrates the feasibility of using thermal imaging for in-vehicle DUI detection under realistic conditions. Our contributions include the development of a robust facial temperature processing pipeline, the creation of a unique dataset collected under ecologically valid conditions, and a comprehensive comparison of seven state-of-the-art classification models. Thermal imaging represents a promising complementary modality for future driver monitoring systems focused on safety and impairment detection.}, language = {en} } @article{IvanHirlingGrexaetal.2026, author = {Ivan, Zsanett Zsofia and Hirling, Dominik and Grexa, Istvan and Ammeling, Jonas and Molnar, Csaba and Micsik, Tamas and Dobra, Katalin and Kuthi, Levente and Sukosd, Farkas and Fillinger, Janos and Moldvay, Judit and Toth, Erika and Aubreville, Marc and Miczan, Vivien and Horvath, Peter}, title = {A Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis}, journal = {Scientific Data}, publisher = {Springer}, address = {London}, issn = {2052-4463}, doi = {https://doi.org/10.1038/s41597-026-07007-7}, pages = {17}, year = {2026}, abstract = {Mitosis detection represents a critical task in digital pathology, as it plays an important role in the tumor grading and prognosis of patients. Manual determination is a labor-intensive task for practitioners with high interobserver variability, thus, automation is a priority. There has been substantial progress towards creating robust mitosis detection algorithms, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. Also, there has been growing interest in the molecular characterization of mitosis to achieve a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. We introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability. We envision a new perspective for domain generalization by improving model performance with subtyping mitosis, complemented with an atypical mitotic class. Our work has implications in two main areas: subtyping information can provide helpful information in mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of subphases.}, language = {en} } @article{AlessandroGleissnerKotonski2023, author = {Alessandro, Massimiliano and Gleissner, Christian and Kotonski, Julia}, title = {Semi-projective representations and twisted representation groups}, volume = {51}, journal = {Communications in Algebra}, number = {10}, publisher = {Taylor \& Francis}, address = {Philadelphia}, issn = {0092-7872}, doi = {https://doi.org/10.1080/00927872.2023.2211175}, pages = {4471 -- 4480}, year = {2023}, language = {en} } @article{RosbachAmmelingGanzetal.2026, author = {Rosbach, Emely and Ammeling, Jonas and Ganz, Jonathan and Bertram, Christof and Conrad, Thomas and Riener, Andreas and Aubreville, Marc}, title = {Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology}, volume = {3}, pages = {2026:007}, journal = {Machine Learning for Biomedical Imaging}, number = {MELBA-BVM 2025 Special Issue}, publisher = {Melba editors}, address = {[s. l.]}, issn = {2766-905X}, doi = {https://doi.org/10.59275/j.melba.2026-87b1}, pages = {126 -- 147}, year = {2026}, abstract = {Artificial intelligence (AI)-driven clinical decision support systems (CDSS) hold promise to improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may give rise to cognitive biases, such as automation and anchoring bias, wherein users may be inclined to blindly adopt system recommendations or be disproportionately influenced by the presence of AI predictions, even when they are inaccurate. These biases may be exacerbated under time pressure, pervasive in routine pathology diagnostics, or shaped by individual user characteristics. To investigate these effects, we conducted a web-based experiment in which trained pathology experts (n = 28) estimated tumor cell percentages twice: once independently and once with the aid of an AI. A subset of the estimates in each condition was performed under time constraints. Our findings indicate that AI integration generally enhances diagnostic performance. However, it also introduced a 7\% automation bias rate, quantified as the number of accepted negative consultations, where a previously correct independent assessment gets overturned by inaccurate AI guidance. While time pressure did not increase the frequency of automation bias occurrence, it appeared to intensify its severity, as evidenced by a performance decline linked to increased automation reliance under cognitive load. A linear mixed-effects model (LMM) analysis, simulating weighted averaging, revealed a statistically significant positive coefficient for AI advice, indicating a moderate degree of anchoring on system output. This effect was further intensified under time pressure, suggesting that anchoring bias may become more pronounced when cognitive resources are limited. A secondary LMM evaluation assessing automation reliance, used as a proxy for both automation and anchoring bias, demonstrated that professional experience and self-efficacy were associated with reduced dependence on system support, whereas higher confidence during AI-assisted decision-making was linked to increased automation reliance. Together, these findings underscore the dual nature of AI integration in clinical workflows, offering performance benefits while also introducing risks of cognitive bias-driven diagnostic errors. As an initial investigation focused on a single medical specialty and diagnostic task, this study aims to lay the groundwork for future research to explore these phenomena across diverse clinical contexts, ultimately supporting the establishment of appropriate reliance on automated systems and the safe, effective integration of human-AI collaboration in medical decision-making.}, language = {en} } @article{RappsilberKruegerRaspeetal.2026, author = {Rappsilber, Tim and Kr{\"u}ger, Simone and Raspe, Tina and Reclo, Rudolf and Schweiger, Hans-Georg}, title = {Toxic gas emission in electric vehicles: What a battery fire means for occupant safety}, volume = {2026}, pages = {104717}, journal = {Fire Safety Journal}, number = {162}, publisher = {Elsevier}, address = {New York}, issn = {1873-7226}, doi = {https://doi.org/10.1016/j.firesaf.2026.104717}, year = {2026}, abstract = {This work investigates the ability of occupants to escape from a battery electric vehicle during a thermal runaway of the traction battery initiated by nail penetration. Such events generate intense fires and large amounts of toxic gases, rapidly reducing the time available for safe evacuation. In controlled full-scale outdoor experiments on two identical mid-range battery electric vehicles, the smoke gas composition inside the cabin is examined. Using FTIR spectrometers and an oxygen analyzer, temporal and spatial concentrations of organic carbonates, hydrocarbons, hydrogen fluoride, hydrogen cyanide, acetylene, and oxygen are measured. Sampling locations include the driver's breathing zone, the right rear passenger's breathing zone, and the exterior right rear wheel housing. The study further evaluates occupant's escape capability using the fractional effective dose (FED) model in accordance with ISO 13571. Results show that toxic gases can reach harmful concentrations within minutes after smoke enters the cabin, though smoke entry times vary widely with fire progression. Differences in fire development and smoke dispersion strongly affect FED values and thus the time available for self-rescue. Overall, the findings provide an important basis for assessing occupant safety during battery electric vehicle fires and highlight the need for improved mitigation strategies.}, language = {en} } @unpublished{SchlampGernerBogenbergeretal.2026, author = {Schlamp, Anna-Lena and Gerner, Jeremias and Bogenberger, Klaus and Huber, Werner and Schmidtner, Stefanie}, title = {ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2602.14780}, year = {2026}, language = {en} }