Publications

2026

• Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation
  
  • Lim Gionnieve
  • Tan Bryan Cheng Zhengyu
  • Sim Kellie Yu Hui
  • Shi Weiyan
  • Chew Ming Hui
  • Hee Ming Shan
  • Lee Roy Ka-Wei
  • Perrault Simon
  • Choo Kenny Tsu Wei
  , 2026. The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated disinformation, yet they also hold promise for enhancing mitigation strategies. This paper investigates the complex dynamics between LLMs and disinformation in small, localised settings through a communication game based on online forums, inspired by Werewolf, with 25 participants. We analyse how Disinformers, Moderators, and Users leverage LLMs to advance their goals, revealing both the potential for misuse and combating disinformation. Our findings highlight the varying uses of LLMs depending on the participants' roles and strategies, underscoring the importance of understanding their effectiveness in this context. We conclude by discussing implications for future LLM development and online platform design, advocating for a balanced approach that empowers users and fosters trust while mitigating the risks of LLM-assisted disinformation.
• Feature Toggle Dynamics in Large-Scale Systems: Prevalence, Growth, Lifespan, and Benchmarking
  
  • Tërnava Xhevahire
  , 2026. <div><p>Feature toggles enable gradual rollouts and experimentation in software systems, yet often persist beyond their intended lifecycle, accumulating as technical debt. Prior research has examined feature toggle interactions and complexity, but no longitudinal study has quantified how toggles evolve over time across different organizational contexts. We analyse over 4,000 toggle events in Kubernetes (10 MLoC, 8.5 years) and GitLab (5 MLoC, 5 years). We find that feature toggle removals lags behind additions in both systems (by roughly 35% and 13%, respectively), leading to growing toggle inventories. Their lifespan patterns also differ notably, with Kubernetes toggles lasting a median of 734 days versus 185 in GitLab. Then, some feature toggles (1.33% and 0.73%, respectively) exceed all previously observed removal durations, becoming de facto permanent. Building on these findings, we propose a benchmarking framework with five key metrics and their empirically derived threshold zones, enabling practitioners to assess and compare toggle management practices across projects. All scripts and data are publicly available.</p></div> (10.5281/zenodo.18773811)
  DOI : 10.5281/zenodo.18773811
• A posteriori closure of turbulence models: Are symmetries preserved?
  
  • Freitas André
  • Um Kiwon
  • Desbrun Mathieu
  • Buzzicotti Michele
  • Biferale Luca
  European Journal of Mechanics - B/Fluids, Elsevier, 2026, 119, pp.204496:1-204496:8. Turbulence modeling remains a longstanding challenge in fluid dynamics. Recent advances in data- driven methods have led to a surge of novel approaches aimed at addressing this problem. This work builds upon our recent work [Phys. Rev. Fluids 10, 044602 (2025)], where we introduced a new closure for a shell model of turbulence using an a posteriori (or solver-in-the-loop) approach. Unlike most deep learning-based models, our method explicitly incorporates physical equations into the neural network framework, ensuring that the closure remains constrained by the underlying physics benefiting from enhanced stability and generalizability. In this paper, we further analyze the learned closure, probing its capabilities and limitations. In particular, we look at joint probability density functions between resolved and unresolved variables, as well as the scale invariance of multipliers (ratios between adjacent shells) within the inertial range. Although our model excels in reproducing high-order statistical moments, it breaks this known symmetry near the cutoff, indicating a fundamental limitation. We discuss the implications of these findings for subgrid-scale modeling in 3D turbulence and outline directions for future research. (10.1016/j.euromechflu.2026.204496)
  DOI : 10.1016/j.euromechflu.2026.204496
• LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation
  
  • Haffoudhi Samy
  • Dobričić Nikola
  • Suchanek Fabian
  • Holzenberger Nils
  , 2026. Entity linking is a key component of many downstream NLP systems, yet existing approaches are often tied to the specific target knowledge bases and domains, limiting their real world application. In this paper, we extend LELA, a modular and domain-agnostic LLM-based entity disambiguation method, into a practical Python library that integrates zero-shot Named Entity Recognition (NER) -thereby providing a complete end-toend pipeline for entity-linking in real-world usage. We provide experimental results validating LELA's performance and robustness across diverse entity linking settings. In our demo, users can play with the system on their own input texts.
• NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches
  
  • Ye Hongsheng
  • Sureshkumar Anandhu
  • Zhonghan Wang
  • Cani Marie-Paule
  • Hahmann Stefanie
  • Bonneau Georges-Pierre
  • Parakkat Amal Dev
  , 2026. With recent advances in VR, 3D sketches have emerged as a powerful medium for 3D model creation. However, while they already provide the user with a good perception of the intended shape, they must be surfaced before any reuse in a downstream application. This remains a challenge when sketches are unoriented, i.e., when they are simply sets of unsorted 3D strokes, without any additional normal information. We introduce NeuralSketch2Surf, the first fast and robust neural surfacing solution that processes arbitrarily unoriented sketches at interactive rates. Our approach uses S2V-Net, a new transformer network designed to mesh 3D sketches. Instead of directly inferring complex functions to represent shapes, we focus on predicting an occupancy grid, then refined using a custom smoothing function to create the desired surface. Thanks to a lightweight architecture that enables fast predictions, our method produces results in less than 2 seconds, in contrast to SOTA techniques that can take minutes or even hours. Extensive evaluations demonstrate that our method is not only fast but also generates closed surfaces with high geometric, topological, and perceptual accuracy. (10.1145/3799902.3811227)
  DOI : 10.1145/3799902.3811227
• Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
  
  • Plaud Roman
  • Perez-Lebel Alexandre
  • Saillenfest Antoine
  • Bonald Thomas
  • Le Morvan Marine
  • Varoquaux Gaël
  • Labeau Matthieu
  , 2026. Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near 0 and 1 often lead to high bias and variance. We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric. We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its corresponding canonical probability mapping that can be readily integrated with any model like a neural network or a gradient boosting algorithm. Extensive evaluations on causal inference benchmarks demonstrate that our tailored objective consistently outperforms standard likelihood-based and covariate-balancing approaches.
• Microarchitectural Analysis of Speculative Execution Patterns in RISC-V using Machine Learning and ISA-Level Masking Wrappers
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Haj Jawad
  • Bruguier Florent
  , 2026, pp.In press. Speculative execution attacks, such as Spectre Variant 1, leak sensitive data through transient microarchitectural effects. This paper presents a machine learning-based framework for analyzing and mitigating speculative execution vulnerability patterns in RISC-V systems. We extract branch, cache, and timing features from gem5 simulations and validate leakage behavior on a SiFive HiFive Premier P550 platform. Supervised models achieve up to 97.1% accuracy in distinguishing benign and speculative-leak execution windows, while unsupervised methods (K-means, HDBSCAN, and Isolation Forest) independently reveal structural and anomaly-based separation. Feature analysis shows that speculative loads and memory latency are the strongest leakage indicators. We further propose lightweight RISC-V assembly wrappers based on index masking and fence instructions. Results demonstrate that these wrappers shift vulnerable execution toward benign microarchitectural behavior. The proposed approach combines simulation, hardware validation, and learning-based analysis for practical speculative vulnerability analysis and mitigation in RISC-V processors.
• MEDIATE: Multi-Faceted Implementation of A Mixed Software/Hardware-Based Zero Trust Framework for the Computing Continuum
  
  • Fournaris Apostolos P
  • Haleplidis Evangelos
  • Abdoul-Soukour Shahin
  • Huang Chih-Kai
  • Khoder Niemat
  • Bouloukakis Georgios
  • Brokalakis Andreas
  • Georgopoulos Konstantinos
  • Ioannidis Sotiris
  , 2026. This paper introduces MEDIATE, a framework that brings zero trust principles to the Cloud-Edge-IoT Computing Continuum. MEDIATE integrates several software-based security and policy services operating at the cloud and edge layers with hardware-and software-based monitoring components as IoT devices deployed close to the assets that require security protection. The framework is designed to a three-layer architecture with Orchestrator (Cloud), Overwatch (Edge), and Sentinel (IoT) that collectively enables cross-layer policy enforcement and coordinated threat mitigation. The Orchestrator provides global view and policy consistency, the Overwatch performs local analysis and threat correlation, and the Sentinel acts as lightweight agent for real-time monitoring near critical assets. These components can provide an adaptive security foundation suitable for distributed and large-scale operational domains such as Fourth-Party Logistics (4PL).
• Towards Reliable and Secure RISC-V Systems: Survey of Testability and Security Mechanisms
  
  • Khan Mahreen
  • Mushtaq Maria
  • Apvrille Ludovic
  , 2026. <div><p>RISC-V has emerged as a versatile open-source instruction set architecture, enabling extensible microarchitectures, custom accelerators, and domain-specific processors. Its openness facilitates innovation in testability, safety, and security for safety-critical and security-sensitive applications. This survey provides a comprehensive review of recent research in RISC-V verification and protection mechanisms. We analyze AI-assisted test generation, statistical fault injection frameworks, systemlevel testing, design-for-test architectures, and hardware-software co-verification methods. In the safety domain, we discuss temporal isolation, performance monitoring, debug support, and fault containment strategies. Security mechanisms, including trusted execution environments, memory protection, cryptographic ISA extensions, post-quantum acceleration, and secure debug practices, are evaluated. Open challenges in scalable test coverage, AI-enabled certification, side-channel resilience, and lifecycle management are highlighted. Finally, we outline future research directions that leverage RISC-V's modularity and openness to enable trustworthy computing systems in automotive, aerospace, telecommunications, and edge computing domains.</p></div>
• Green Energy Driven Integrated Smart Grid and Wireless Networks
  
  • Wang Li-Chun
  • De Swades
  • Balakrishnan Ashutosh
  , 2026, pp.XXIII, 140. Green energy and next-generation wireless systems are no longer independent domains, rather they are rapidly converging to redefine the future of sustainable connectivity. This book presents a forward looking study outlining the design of green energy driven, integrated smart grid and wireless networks. It reimagines the integration of renewable energy with the traditional power grid, by enabling each grid user to not only consume grid energy but to also be a potential energy source, thereby revising the conventional idea of power grids. A grid networked system of such distributed ambient powered nodes is hence envisioned to potentially act as a carbon-free energy producer system to the power grid, in addition to an energy prosumer system. Through its chapters, the book outlines and highlights the importance of sustainability in 6G networks. The authors present insights on the design constraints and challenges in system analysis. The study broadly pertains to analytical modeling of spatio-temporal randomness in cellular networks, presenting novel strategies to mitigate the effects of the dual randomness on network performance. The key ideas presented include joint load and energy balancing, optimum resource provisioning, and aerial offloading. The authors also demonstrate a wider perspective by extending the concept of energy balancing to energy aware residential networks. The book concludes by discussing the scope of integration of AI in wireless networks and motivate the need for green-AI aided future networks. Written for graduate level students in computer science and electrical engineering, as well as industry professionals deploying large-scale green service solutions, this book serves as both a foundational reference and a roadmap toward sustainable, scalable, and intelligent future communication networks.
• Parser Instrumentation for Semantic-Aware Applicative Intrusion Detection
  
  • Quetel Grégor
  • Gimenez Pierre-François
  • Robert Thomas
  • Pautet Laurent
  , 2026, 787, pp.359–373. Intrusion Detection Systems (IDS) are common security tools for protecting modern information systems, yet their effectiveness at detecting application-layer attacks is often limited by the semantic gap between low-level host or network observations and the actual behavior of applications. Existing work overlooks the data collection phase and typically focuses on designing complex decision engines and preprocessing functions such as embedding-based representations. Unfortunately, these approaches incur significant computational overhead at inference time and remain brittle against adversarial inputs. In this paper, we present a parser-based instrumentation approach for application-level intrusion detection that provides lexical, syntactic and explicit semantic observation with minimal overhead. We introduce gaur, an implementation for instrumenting parsers, it produces observations during parsing by associating semantic tags to grammar rules, eliminating the need for runtime natural language processing. Our evaluation demonstrates the low overhead and collection time of our data collector. Furthermore, empirical results show that incorporating explicit semantic information into decision engines not only improves detection performance over traditional mechanisms but also enables faster inference and greater robustness than approaches relying on implicit semantic representations. (10.1007/978-3-032-27993-4_25)
  DOI : 10.1007/978-3-032-27993-4_25
• On the Informativeness of Security Commit Messages: A Large-scale Replication Study
  
  • Islam Syful
  • Zacchiroli Stefano
  , 2026. The informativeness of security-related commit messages is crucial for patch triage: when high, it enables the rapid distribution and deployment of security fixes. Prior research (Reis et al., 2023) reported, however, that commit messages are often too uninformative to support these activities. To assess the robustness of this negative result, we independently replicate the original study using only the information provided in the paper, without reusing any of the original artifacts (data, analysis pipeline, etc.). Unlike the original study, we source commit data not only from GitHub, but from the entire Software Heritage archive, which includes projects hosted on many other platforms and using multiple version control systems. We retrieve 50673 security-related commits and analyze their informativeness using an independent re-implementation of the techniques introduced by Reis et al. For the same source (i.e., GitHub) and time period (from June 1999 to August 2022) as the original study, our replication confirms the original findings in a statistically significant way: security-related commit messages are, in general, not informative enough for security-focused purposes. We then extend the original study in several ways. Over a longer time period (from June 1999 to October 2025), we find that commit-message informativeness is worsening. Breaking results down by software ecosystem (Linux kernel, Ubuntu, Go, PyPI, etc.), we observe significant differences in informativeness. Finally, we examine emerging best practices for writing commit messages, such as the Conventional Commits Specification (CCS), and again find significant differences in an unexpected direction: CCS-compliant commits are less informative than non-compliant ones. Our findings highlight the need for cross-ecosystem analyses to understand platform- and community-specific commit-message practices, and to inform the development and adoption of universally applicable guidelines for writing informative security-related commit messages. (10.1145/nnnnnnn.nnnnnnn)
  DOI : 10.1145/nnnnnnn.nnnnnnn
• On the Use of Commit Messages for Corrective Software Maintenance: A Systematic Mapping Study
  
  • Islam Syful
  • Zacchiroli Stefano
  , 2026. Corrective maintenance is crucial to ensure the quality of software, thereby improving reliability and user experience. In a version control system (VCS), developers write commit messages to document their changes and support later maintenance. Therefore, the utilization of commit messages to accomplish corrective maintenance has become a common practice among software engineering practitioners and researchers. Still, to this day, no secondary study has mapped the research landscape of how commit messages have been used in corrective software maintenance. We present a systematic mapping study of 97 primary sources published between 2004 and May 2025, where we examine the goals, potential utilization of source code artifacts along with commit messages, methodologies, stakeholders, and the key findings about their influence on corrective maintenance. Our analysis reveals a growing interest in the usage of commit messages to perform corrective maintenance tasks, in particular for bug analysis and bug fix identification goals. Surprisingly few studies address other themes such as automated program repair, security development practices, etc. We find that the software artifacts most used in combination with commit messages are commit "diffs" and that repository mining, together with natural language processing (NLP) and artificial intelligence/machine learning (AI/ML) are the methodological foundations of studies in this field. Among stakeholders considered in previous studies, developers play the most important role in shaping corrective maintenance practices. Key findings in previous studies about commit messages establish their significant role in corrective maintenance, due to the fact that they carry crucial information helpful for stakeholders to understand and improve the code base through the software evolution process. Often, though, commit messages lack important information and are not enough to convey the intent of code changes to future readers. Therefore, developers should be aware of commit message contextual richness while committing code changes in VCS. (10.1145/nnnnnnn.nnnnnnn)
  DOI : 10.1145/nnnnnnn.nnnnnnn
• Convergence rates of Sum-of-Hermitian-Squares Hierarchies for the Pauli algebra
  
  • Almasi Ali
  • Bugár Dávid
  • Rouzé Cambyse
  • Brown Peter
  , 2026. Moment/Sum-of-Hermitian-Squares relaxations for noncommutative polynomial optimization problems have become an important tool for analyzing problems within quantum theory. Despite their widespread success, little is known about their rate of convergence and, consequently, their accuracy. In this work, we develop explicit convergence rates for relaxations of noncommutative polynomial optimization problems generated from the Pauli algebra -- covering applications to the ground state energy problem for n-qubit systems. In particular, we show that the rate of convergence can be bounded in terms of the smallest roots of a family of orthogonal polynomials known as Krawtchouk polynomials. Our result represents the first quantitative analysis of the rate of convergence for relaxations of noncommutative polynomial optimization problems.
• On the explainability of max-plus neural networks
  
  • Enaieh Ikhlas
  • Fercoq Olivier
  • Ángel García
  , 2026. We investigate the explanability properties of the recently proposed linear-min-max neural networks. At initialization, they can be interpreted as k-medoids with the infinity norm as a distance. Then, they are trained using subgradient descent to better fit the data. The model has been shown to be a universal approximator. Yet, we can trace the decision process because a single most activated neuron is responsible for the value of the output. Using this property, we designed a pixel fragility measure that determines whether changes to a single pixel may be responsible to a change in the classification output. Experiments on the PneumoniaMnist dataset show that this explanation for the output of the neural network compares favorably to SHAP and Integrated Gradient.
• Using Locally Learnt Word Representations for better Textual Anomaly Detection
  
  • Breidenstein Alicia
  • Labeau Matthieu
  , 2024, pp.82-91. <div><p>The literature on general purpose textual Anomaly Detection is quite sparse, as most textual anomaly detection methods are implemented as out of domain detection in the context of pre-established classification tasks. Notably, in a field where pre-trained representations and models are of common use, the impact of the pre-training data on a task that lacks supervision has not been studied. In this paper, we use the simple setting of k-classes out anomaly detection and search for the best pairing of representation and classifier. We show that well-chosen embeddings allow a simple anomaly detection baseline such as OC-SVM to achieve similar results and even outperform deep state-of-the-art models.</p></div> (10.18653/v1/2024.insights-1.11)
  DOI : 10.18653/v1/2024.insights-1.11
• Répliquer sans Attendre mais Équitablement
  
  • Kuznetsov Petr
  • Perion Maxence
  • Tucci Piergiovanni Sara
  , 2026. La réplication assure la disponibilité des systèmes distribués sujets aux pannes et ceux dont la convergence est garantie uniquement à terme (eventual consistency) comme les CRDTs (Conflict-free Replicated Data Types), peuvent répondre aux requêtes sans attendre. Cependant, l'asynchronisme et la concurrence forcent les opérations à être réordonnées, altérant les effets originaux et bloquant la stabilisation des résultats. De plus, un utilisateur du système peut être en famine si toutes ses opérations sont réordonnées au moins une fois. Nous formalisons le problème résolu par les types de données répliqués sans attente en tant que réplication à terme de machine à état. Nous l'augmentons ensuite avec les propriétés de stabilité et d'équité assurant, respectivement, que les répliques partagent un préfixe stable grandissant d'opérations, et qu'aucun utilisateur n'est en famine. Nous présentons finalement une construction générique où les répliques échangent leurs vues locales sous forme de graphe et les unifient avec une fonction de réconciliation. Nous proposons une fonction de réconciliation assurant stabilité et équité.
• Chain rules for conditional entropies in quantum cryptography: limitations and improvements
  
  • Wooltorton Lewis
  • Brown Peter
  • Fawzi Omar
  , 2026. Security proofs in quantum cryptography rely on conditional entropies. In a many-round protocol, their estimation is a challenging task; one must account for the most general attacks by an eavesdropper, including those that are not independently and identically distributed (i.i.d.) across all rounds. Chain rules address this problem by relating the conditional entropy of a structured, but non-i.i.d. process to a sum of entropy contributions from each round. They are a key ingredient in entropy accumulation theorems (EATs), which provide a versatile security proof framework for many protocols in quantum cryptography. Recently, chain rules in the setting of trusted devices have lead to tight i.i.d. reductions at a finite number of rounds, and whether analogous results can be recovered in the device-independent (DI) setting has not been addressed. Surprisingly, we show that a natural tightening of the chain rule of Dupuis et al. [Commun. Math. Phys. 379, 867-913, (2020)] that would answer this question affirmatively cannot hold, highlighting a limitation of the current DI security proof approach. Nonetheless, we show that an intermediate improvement is possible by proving a new chain rule in this setting. Following the framework of Arqand et al. [Phys. Rev. X 15, 041013 (2025)], we use our chain rule to provide a slightly tighter version of the Rényi EAT in certain contexts. In addition, we provide a self-contained framework that unifies existing chain rules and compares their applications, framing our results in a broader context.
• Reinforcement learning for quantum processes with memory
  
  • Lumbreras Josep
  • Huang Ruo Cheng
  • Hu Yanglin
  • Fanizza Marco
  • Gu Mile
  , 2026. In reinforcement learning, an agent interacts sequentially with an environment to maximize a reward, receiving only partial, probabilistic feedback. This creates a fundamental exploration-exploitation trade-off: the agent must explore to learn the hidden dynamics while exploiting this knowledge to maximize its target objective. While extensively studied classically, applying this framework to quantum systems requires dealing with hidden quantum states that evolve via unknown dynamics. We formalize this problem via a framework where the environment maintains a hidden quantum memory evolving via unknown quantum channels, and the agent intervenes sequentially using quantum instruments. For this setting, we adapt an optimistic maximum-likelihood estimation algorithm. We extend the analysis to continuous action spaces, allowing us to model general positive operator-valued measures (POVMs). By controlling the propagation of estimation errors through quantum channels and instruments, we prove that the cumulative regret of our strategy scales as $\tilde{O}(\sqrt{K})$ over $K$ episodes. Furthermore, via a reduction to the multi-armed quantum bandit problem, we establish information-theoretic lower bounds demonstrating that this sublinear scaling is strictly optimal up to polylogarithmic factors. As a physical application, we consider state-agnostic work extraction. When extracting free energy from a sequence of non-i.i.d. quantum states correlated by a hidden memory, any lack of knowledge about the source leads to thermodynamic dissipation. In our setting, the mathematical regret exactly quantifies this cumulative dissipation. Using our adaptive algorithm, the agent uses past energy outcomes to improve its extraction protocol on the fly, achieving sublinear cumulative dissipation, and, consequently, an asymptotically zero dissipation rate.
• Convex combinations of bosonic pure-loss channels
  
  • Catalano Giuseppe
  • Fanizza Marco
  • Mele Francesco Anna
  • de Palma Giacomo
  • Giovannetti Vittorio
  , 2026. The pure-loss channel is a fundamental model for describing noise in bosonic quantum platforms. It is characterised by a single parameter, the transmissivity, which quantifies the fraction of the input energy that reaches the output of the channel. In realistic scenarios, however, such as free-space quantum communication, the transmissivity is not fixed but fluctuates from one channel use to another. In this setting, the overall channel is effectively described as a convex combination of pure-loss channels, known as a fading channel. Despite its practical relevance, the quantum Shannon theory of the fading channel has remained largely unexplored. Here, we address this gap, specifically investigating degradability, anti-degradability, entanglement breakingness, and capacities of the fading channel. Of particular relevance to practical quantum-internet applications, we prove that entanglement distribution and quantum key distribution can always be achieved at a strictly positive rate over any fading channel, no matter how noisy it is or how strongly the transmissivity fluctuates, provided the channel is not completely noisy. Moreover, we prove that thermal states, which are optimal for a broad class of static bosonic Gaussian channels, fail to achieve the entanglement-assisted classical capacity of fading channels: non-Gaussian Fock-diagonal states strictly outperform all Gaussian encodings. Most strikingly, we identify regimes where the coherent information of thermal inputs vanishes, while optimized non-Gaussian states achieve strictly positive values, thereby activating the channel for quantum communication. For a paradigmatic binary fading model we establish this result analytically, deriving the exact capacity-achieving state in closed form. For general fading distributions, we design an iterative variational algorithm to optimize the coherent and mutual information.
• Towards sample-optimal learning of bosonic Gaussian quantum states
  
  • Chen Senrui
  • Mele Francesco Anna
  • Fanizza Marco
  • Li Alfred
  • Mann Zachary
  • Huang Hsin-Yuan
  • Chen Yanbei
  • Preskill John
  , 2026. Continuous-variable systems enable key quantum technologies in computation, communication, and sensing. Bosonic Gaussian states emerge naturally in various such applications, including gravitational-wave and dark-matter detection. A fundamental question is how to characterize an unknown bosonic Gaussian state from as few samples as possible. Despite decades-long exploration, the ultimate efficiency limit remains unclear. In this work, we study the necessary and sufficient number of copies to learn an $n$-mode Gaussian state, with energy less than $E$, to $\varepsilon$ trace distance closeness with high probability. We prove a lower bound of $\Omega(n^3/\varepsilon^2)$ for Gaussian measurements, matching the best known upper bound up to doubly-log energy dependence, and ${\Omega}(n^2/\varepsilon^2)$ for arbitrary measurements. We further show an upper bound of $\widetilde{O}(n^2/\varepsilon^2)$ given that the Gaussian state is promised to be either pure or passive. Interestingly, while Gaussian measurements suffice for nearly optimal learning of pure Gaussian states, non-Gaussian measurements are provably required for optimal learning of passive Gaussian states. Finally, focusing on learning single-mode Gaussian states via non-entangling Gaussian measurements, we provide a nearly tight bound of $\widetilde\Theta(E/\varepsilon^2)$ for any non-adaptive schemes, showing adaptivity is indispensable for nearly energy-independent scaling. As a key technical tool of independent interest, we establish stringent bounds on the trace distance between Gaussian states in terms of the total variation distance between their Wigner functions. In particular, this yields a nearly tight sample complexity of $\widetilde{\Theta}(n^{2}/\varepsilon^{2})$ for learning the Wigner distribution of any Gaussian state to $\varepsilon$ total variation distance, achievable with Gaussian measurements. Our results greatly advance quantum learning theory in the bosonic regimes and have practical impact in quantum sensing and benchmarking applications.
• A Stable SVM Quantile Regression Algorithm for Heavily Censored Data
  
  • Lamalle Florian
  • Clémençon Stéphan
  • Feuillard Vincent
  • Sabourin Anne
  , 2026. This paper introduces a novel framework for quantile regression with censored observation. Our primary focus is on addressing the challenges posed by heavily censored datasets, which are prevalent in many real-world applications yet remain underexplored in the existing literature. The proposed approach, TIQ-SVM (Truncated IPCW Quantile SVM) relies on an adaptive truncation mechanism aimed at stabilizing the Inverse Probability of Censoring Weighting (IPCW) strategy in a quantile SVM framework. While theoretical guarantees within a non-asymptotic and model-agnostic framework are limited, notable exceptions include the work by Kosorok (2017). A significant limitation of existing approaches is their inability to handle heavy censoring effectively, primarily due to a central requirement that the survival function of the censoring should be bounded from below. This limitation often leads to numerical instabilities in heavily censored settings, restricting the applicability of these methods. In response to these challenges, our contribution introduces an adaptive truncation technique designed to stabilize the IPCW cost function, thereby accommodating heavy censoring scenarios. This innovative approach not only enhances the robustness of the regression framework but also broadens its applicability to datasets with substantial censoring. Beyond the theoretical guarantees in the form of generalization bounds we establish, through extensive numerical experiments, we demonstrate the efficacy and stability of our proposed method, showcasing its potential to advance the field of quantile regression for censored data. Our findings suggest that this approach can significantly improve the handling of heavily censored datasets, offering a promising direction for future research and practical applications.
• Hardness of M-LWE with General Distributions and Applications to Leaky Variants
  
  • Boudgoust Katharina
  • Jeudy Corentin
  • Tairi Erkan
  • Wen Weiqiang
  , 2026, 16551, pp.3-37. The Module Learning With Errors (M-LWE) problem has become a fundamental hardness assumption for lattice-based cryptography. It offers an attractive trade-o between strong robustness guarantees, sometimes directly based on worst-case lattice problems, and efficiency of the subsequent cryptographic primitives. Different flavors of M-LWE have then been introduced towards improving performance. Such variants look at different secret-error distributions and might allow for additional hints on the secret-error vector. Existing hardness results however only cover restricted classes of said distributions, or are tailored to specific leakage models. This lack of generality hinders the design of efficient and versatile cryptographic schemes, as each new distribution or leakage model requires a separate and nontrivial hardness evaluation In this work, we address this limitation by establishing the hardness of M-LWE under general distributions. As a first step, we show that M-LWE remains hard when the error vector follows an arbitrary bounded distribution with sufficient entropy, with some restriction on the number of samples. Building on this, we then reduce to the Hermite Normal Form (HNF) where the secret-error vector follows said arbitrary distribution. Overall, our result shows the actual shape of the distribution does not matter, as long as it keeps sufficient entropy. To demonstrate the versatility of our framework, we further analyze a range of leakage scenarios. By examining the residual entropy given the leakage, we show that our results of M-LWE with general distributions encompass various types of leakage. More precisely, we cover exact and approximate linear hints which are widely used in recent cryptographic designs, as well as quadratic, and even non-algebraic forms, some of which were not yet covered by any theoretical hardness guarantees. The generality of our results aims at facilitating future cryptographic designs and security analyses. (10.1007/978-3-032-26731-3_1)
  DOI : 10.1007/978-3-032-26731-3_1
• Arena: a kubernetes-based testbed for evaluating application deployment across the computing continuum
  
  • Huang Chih-Kai
  • Krouti Konstantinos
  • Markopoulou Stella
  • Tserpes Konstantinos
  • Bouloukakis Georgios
  , 2026. This paper introduces Arena, a Kubernetes-based testbed for evaluating application deployment across computing continuum environments (IoT/Edge/Cloud). Arena enables the emulation of diverse computing nodes using Docker containers and leverages Kubernetes for testbed management. Arena integrates the Chaos Mesh framework to simulate network characteristics and Prometheus with Grafana tools for monitoring and visualization purposes. Experiments on the Grid'5000 platform with a Google microservice application demonstrate that Arena's container-based emulation achieves similar resource usage patterns to virtual machine-based nodes, and its network chaos injection effectively enforces network constraints. Results highlight Arena's capability to provide a practical and reproducible environment for testing containerized applications across diverse computing continuum nodes.
• Co-Investment in Mobile Edge Computing with Infrastructure Update and Dynamic Participation
  
  • Sakr Amal
  • Araldo Andrea
  • Chahed Tijani
  • Kofman Daniel
  , 2026. Mobile Edge Computing (MEC) requires Network Operators (NOs) to undertake substantial infrastructure investments, while most revenues are captured by Service Providers (SPs) offering end-user applications. This cost-revenue imbalance discourages NOs from investing in MEC deployment, despite increasing demand for low-latency and bandwidth-intensive services. This paper proposes a co-investment scheme in which players, i.e., one NO and multiple SPs, jointly deploy, maintain, and share MEC infrastructure over multiple decision epochs. We devise a new coalitional game model that captures the planning of resources, their allocation among players, and cost and revenue sharing. To address fluctuating user demand and evolving participation incentives, we design a mechanism that updates resources and allows the dynamic entrance and exit of players over time. We sustain cooperation through a compensation scheme. Numerical results show that combining resource updates with dynamic participation increases the total payoff and strengthens the NO's incentive to invest.