Publications

2026

• Analytical Logit Scaling for High-Resolution Sea Ice Topology Retrieval from Weakly Labeled SAR Imagery
  
  • Elwaradi Reda
  • Gimenez Julien
  • Hordoir Stéphane
  • Ait Hamma Mehdi
  • Chan-Hon-Tong Adrien
  • Weissgerber Flora
  , 2026. High-resolution sea ice mapping using Synthetic Aperture Radar (SAR) is crucial for Arctic navigation and climate monitoring. However, operational ice charts provide only coarse, region-level polygons (weak labels), forcing automated segmentation models to struggle with pixel-level accuracy and often yielding under-confident, blurred concentration maps. In this paper, we propose a weakly supervised deep learning pipeline that fuses Sentinel-1 SAR and AMSR-2 radiometry data using a U-Net architecture trained with a region-based loss. To overcome the severe under-confidence caused by weak labels, we introduce an Analytical Logit Scaling method applied post-inference. By dynamically calculating the temperature and bias based on the latent space percentiles (2% and 98%) of each scene, we force a physical binarization of the predictions. This adaptive scaling acts as a topological extractor, successfully revealing fine-grained sea ice fractures (leads) at a 40-meter resolution without requiring any manual pixel-level annotations. Our approach not only resolves local topology but also perfectly preserves regional macroscopic concentrations, achieving a 78% accuracy on highly fragmented summer scenes, thereby bridging the gap between weakly supervised learning and high-resolution physical segmentation.
• Constant Time with Minimal Preprocessing, a Robust and Extensive Complexity Class
  
  • Grandjean Étienne
  • Jachiet Louis
  , 2025. In this paper, we study the class $\mathtt{cstPP}$ of operations $\mathtt{op}: \mathbb{N}^k\to\mathbb{N}$, of any fixed arity $k\ge 1$, satisfying the following property: for each fixed integer $d\ge 1$, there exists an algorithm for a RAM machine which, for any input integer $N\ge 2$, - pre-computes some tables in $O(N)$ time, - then reads $k$ operands $x_1,\ldots,x_k1$, or conversely, is reduced to $N^{\varepsilon}$, for any positive $\varepsilon<1$ (provided the set of primitive operation includes $+$, $\mathtt{div}$ and $\mathtt{mod}$). To complete the picture, we demonstrate that the $\mathtt{cstPP}$ class degenerates if the preprocessing time reduces to $N^{o(1)}$. (10.48550/ARXIV.2509.10188)
  DOI : 10.48550/ARXIV.2509.10188
• WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift
  
  • Santamaria Julian
  • Isaza Claudia
  • Giraldo Jhony
  International Journal of Computer Vision, Springer Verlag, 2026, 134 (4), pp.183. Abstract Wildlife monitoring is crucial for studying biodiversity loss and climate change. Camera trap images provide a non-intrusive method for analyzing animal populations and identifying ecological patterns over time. However, manual analysis is time-consuming and resource-intensive. Deep learning, particularly foundation models, has been applied to automate wildlife identification, achieving strong performance when tested on data from the same geographical locations as their training sets. Yet, despite their promise, these models struggle to generalize to new geographical areas, leading to significant performance drops. For example, training an advanced vision-language model, such as CLIP with an adapter, on an African dataset achieves an accuracy of 84.77%. However, this performance drops significantly to 16.17% when the model is tested on an American dataset. This limitation partly arises because existing models rely predominantly on image-based representations, making them sensitive to geographical data distribution shifts, such as variation in background, lighting, and environmental conditions. To address this, we introduce WildIng, a Wild life image In variant representation model for g eographical domain shift. WildIng integrates text descriptions with image features, creating a more robust representation to geographical domain shifts. By leveraging textual descriptions, our approach captures consistent semantic information, such as detailed descriptions of the appearance of the species, improving generalization across different geographical locations. Experiments show that WildIng enhances the accuracy of foundation models such as BioCLIP by 30% under geographical domain shift conditions. We evaluate WildIng on two datasets collected from different regions, namely America and Africa. The code and models are publicly available at https://github.com/Julian075/CATALOG/tree/WildIng . (10.1007/s11263-026-02739-w)
  DOI : 10.1007/s11263-026-02739-w
• Additivity and Chain Rules for Quantum Entropies via Multi-index Schatten Norms
  
  • Fawzi Omar
  • Kochanowski Jan
  • Rouzé Cambyse
  • van Himbeeck Thomas
  Communications in Mathematical Physics, Springer Verlag, 2026, 407 (4), pp.75 (1-39). The primary entropic measures for quantum states are additive under the tensor product. In the analysis of quantum information processing tasks, the minimum entropy of a set of states, e.g., the minimum output entropy of a channel, often plays a crucial role. A fundamental question in quantum information and cryptography is whether the minimum output entropy remains additive under the tensor product of channels. Here, we establish a general additivity statement for the optimized sandwiched Rényi entropy of quantum channels. For that, we generalize the results of Devetak et al. (Commun Math Phys 266(1):37–63, 2006) to multi-index Schatten norms. As an application, we strengthen the additivity statement of Van Himbeeck and Brown (A tight and general finite-size security proof for quantum key distribution, 2025) thus allowing the analysis of time-adaptive quantum cryptographic protocols. In addition, we establish chain rules for Rényi conditional entropies that are similar to the ones used for the generalized entropy accumulation theorem of Metger et al. (Commun Math Phys 405(11):261, 2024). (10.1007/s00220-026-05567-8)
  DOI : 10.1007/s00220-026-05567-8
• Continuous AI Assistance for Model-Driven Engineering
  
  • Apvrille Ludovic
  • Sultan Bastien
  , 2026. Proactive AI-based assistants are now common in software engineering tools; however, few exist for Model-Driven Engineering (MDE) environments. Most existing AI assistants for MDE, particularly those based on large language models, require user interactions that can interrupt the modeling workflow. However, MDE is inherently a continuous process, involving successive cycles of diagram construction, verification, and modification. Relying on supplementary tools that require intensive interaction can therefore be time-consuming and disrupt engineers focus. Consequently, there is a need to shift AI-based modeling assistance paradigms to mechanisms that integrate naturally into the continuous MDE workflow. To address this need, the paper introduces ContinuousAI, a framework for AI-based continuous MDE assistance. Working alongside MDE engineers, ContinuousAI generates modeling suggestions either on demand or continuously, supporting the improvement of model quality throughout the engineering process. ContinuousAI has been implemented within the MDE toolkit TTool. Evaluation results show that ContinuousAI provides highly relevant suggestions while maintaining computation times and environmental footprints compatible with real-world continuous MDE usage.
• How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
  
  • Roos Nathan
  • Iakovleva Ekaterina
  • Gjergji Ani
  • Pastore Vito Paolo
  • Tartaglione Enzo
  , 2026, pp.5374-5383. Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains poorly understood. Although previous research has viewed bias amplification as an inherent characteristic of diffusion models, this work provides the first analysis of how sampling algorithms and their hyperparameters influence bias amplification. We empirically demonstrate that samplers for diffusion models – commonly optimized for sample quality and speed – have a significant and measurable effect on bias amplification. Through controlled studies with models trained on Biased MNIST, Multi-Color MNIST and BFFHQ, and with Stable Diffusion, we show that sampling hyperparameters can induce both bias reduction and amplification, even when the trained model is fixed. Source code is available at https://github.com/How-I-met-your-bias/how_i_met_your_bias. (10.1109/WACV61042.2026.00521)
  DOI : 10.1109/WACV61042.2026.00521
• Occlusion Boundary and Depth: Mutual Enhancement via Multi-Task Learning
  
  • Xu Lintao
  • Wang Yinghao
  • Wang Chaohui
  , 2026, pp.4427-4437. Occlusion Boundary Estimation (OBE) identifies boundaries arising from both inter-object occlusions and selfocclusion within individual objects. This task is closely related to Monocular Depth Estimation (MDE), which infers depth from a single image, as Occlusion Boundaries (OBs) provide critical geometric cues for resolving depth ambiguities, while depth can conversely refine occlusion reasoning. In this paper, we aim to systematically model and exploit this mutually beneficial relationship. To this end, we propose MoDOT, a novel framework for joint estimation of depth and OBs, which incorporates a new Cross-Attention Strip Module (CASM) to leverage mid-level OB features for depth prediction, and a novel OB-Depth Constraint Loss (OBDCL) to enforce geometric consistency.<p>To facilitate this study, we contribute OB-Hypersim, a large-scale photorealistic dataset with precise depth and self-occlusion-handled OB annotations. Extensive experiments on two synthetic datasets and NYUD-v2 demonstrate that MoDOT achieves significantly better performance than single-task baselines and multi-task competitors. Furthermore, models trained solely on our synthetic data demonstrate strong generalization to real-world scenes without fine-tuning, producing depth maps with sharper boundaries and improved geometric fidelity. Collectively, these results underscore the significant benefits of jointly modeling OBs and depth. Code and resources are available at HERE.</p> (10.1109/WACV61042.2026.00431)
  DOI : 10.1109/WACV61042.2026.00431
• Optical Functionalities to Curb Network Power Consumption: Can Digital Twins Solve the Cross-Layer Problem?
  
  • Ware Cédric
  • Lourdiane Mounia
  , 2026 (PT-Sustain-ICT26-96). After decades of research, still few all-optical functionalities have been deployed outside the lab, despite their potential to reduce network power consumption by leveraging optical systems' lower energy per bit. A major issue is that optical functionalities typically don't fit neatly into the conventional layered-network paradigm — which on the other hand enables modularity and interoperability. In a complex network environment, operators are pushing to develop digital twins and other types of software tools for network planning and operational management. This raises the question: can such tools also enable integration of more all-optical functionalities by wrapping software layers around their specificities? We are currently working on network simulation tools sufficiently generic and flexible to allow testing this potential in a variety of scenarios and use cases.
• 5G-EcoSim: A Simulation Framework for Estimating 5G Energy Consumption Using Real-World Data and Analytical Models
  
  • Ghali Meriem
  • Busson Anthony
  • Coupechoux Marceau
  , 2026, pp.1-8. The definition and deployment of next-generation mobile networks must incorporate considerations of sustainability and environmental impact. In this context, estimating the energy consumption of a mobile network deployment across a city or region is of utmost importance. Nationwide historical aggregate values are informative but do not allow to understand the underlying dynamics or to perform prospective studies. There is hence a need for bottom-up approaches to assess the energy consumption of mobile networks. However, this is a complex task, as it depends on numerous factors, including the number and spatial distribution of base stations, the underlying technologies and their configuration, as well as user demand patterns and their geographic distribution. This paper thus introduces a simulation framework aimed at estimating the energy consumption of 5G networks at urban, regional or national scale. The framework integrates radio propagation models operating in the 3.5 GHz band with publicly available datasets describing user and base station locations, as well as network traffic volumes. As a case study, we consider the 5G deployment in France and examine the spatial distribution of network load and the resulting energy consumption. The source code and datasets are publicly available, ensuring that the simulator is fully reproducible and easily adaptable to other use cases, countries, or regions. (10.23919/WONS68803.2026.11501828)
  DOI : 10.23919/WONS68803.2026.11501828
• Statistical wave field theory: Anisotropic wave fields under Neumann's boundary condition
  
  • Badeau Roland
  Journal of the Acoustical Society of America, Acoustical Society of America, 2026, 159 (3), pp.2265-2280. The statistical wave field theory mathematically establishes the statistical laws of the solutions to the wave equation in a bounded domain. It provides the closed-form expressions of the power distribution and the correlations of the wave field jointly over time, frequency, and space, which hold at high frequency and after many reflections, in terms of the geometry and the specific admittance of the boundary surface. This theory was originally developed in the particular case of mixing rooms, which are characterized by a diffuse wave field, based on the theory of dynamical billiards and on Weyl-like asymptotic laws. Then it was extended to the finite family of special polyhedra, where the wave field is anisotropic, based on a simpler geometric approach related to mathematical crystallography. In this paper, we introduce a unified version of the theory dedicated to a class of semi-mixing billiards. In the case of Neumann's boundary condition, we show that the wave field is stationary, but it is generally anisotropic. In particular, the correlation between two spatial positions at a given frequency is different from the well-known cardinal sine formula that characterizes diffuse acoustic fields. (10.1121/10.0042450)
  DOI : 10.1121/10.0042450
• Capsule networks do not need to model everything
  
  • Renzulli Riccardo
  • Tartaglione Enzo
  • Grangetto Marco
  Pattern Recognition, Elsevier, 2026, 171, Part A, pp.112119 (1-11). Capsule networks are biologically inspired neural networks that group neurons into vectors called capsules, each explicitly representing an object or one of its parts. The routing mechanism connects capsules in consecutive layers, forming a hierarchical structure between parts and objects, also known as a parse tree. Capsule networks often attempt to model all elements in an image, requiring large network sizes to handle complexities such as intricate backgrounds or irrelevant objects. However, this comprehensive modeling leads to increased parameter counts and computational inefficiencies. Our goal is to enable capsule networks to focus only on the object of interest, reducing the number of parse trees. We accomplish this with REM (Routing Entropy Minimization), a technique that minimizes the entropy of the parse tree-like structure. REM drives the model parameters distribution towards low entropy configurations through a pruning mechanism, significantly reducing the generation of intra-class parse trees. This empowers capsules to learn more stable and succinct representations with fewer parameters and negligible performance loss. (10.1016/j.patcog.2025.112119)
  DOI : 10.1016/j.patcog.2025.112119
• Carbon Footprint of Urban 5G Traffic in Lyon Based on Real-World Data and Analytical Modeling
  
  • Ghali Mériem
  • Busson Anthony
  • Coupechoux Marceau
  Annals of Telecommunications - annales des télécommunications, Springer, 2026, pp.1-16. The deployment of 5G networks has generated significant debate regarding its environmental implications, particularly concerning carbon emissions. Although 5G technology offers improved energy efficiency per bit transmitted, concerns persist due to potential rebound effects and the significant carbon footprint associated with infrastructure deployment and baseline energy consumption. This paper presents a bottom-up approach combining a detailed radio load model and spatial distribution of users to precisely estimate the energy usage and carbon emissions of 5G networks using the 3.5 GHz band. Although the model is valid for other regions, we focus specifically on the city of Lyon in France, providing a detailed assessment of current emissions (2021-2024) and projections up to 2050, incorporating traffic growth and national energy decarbonization scenarios. At the base station scale, our results show that emissions generated by hardware manufacturing and baseline energy consumption constitute the dominant contributors to the overall carbon footprint, compared with emissions induced by traffic load. As a result, our projections indicate that an increase in traffic demand does not significantly impact the carbon footprint unless it necessitates the deployment of additional base stations. By 2050, infrastructure-related emissions could constitute up to 70% of total network emissions, highlighting a major challenge in the management of network growth to avoid rebound effect. The study demonstrates that decarbonizing electricity and enhancing energy efficiency alone are insufficient. (10.1007/s12243-026-01166-9)
  DOI : 10.1007/s12243-026-01166-9
• Exploiting Subgradient Sparsity in Max-Plus Neural Networks
  
  • Enaieh Ikhlas
  • Fercoq Olivier
  , 2026. Deep Neural Networks are powerful tools for solving machine learning problems, but their training often involves dense and costly parameter updates. In this work, we use a novel Max-Plus neural architecture in which classical addition and multiplication are replaced with maximum and summation operations respectively. This is a promising architecture in terms of interpretability, but its training is challenging. A particular feature is that this algebraic structure naturally induces sparsity in the subgradients, as only neurons that contribute to the maximum affect the loss. However, standard backpropagation fails to exploit this sparsity, leading to unnecessary computations. In this work, we focus on the minimization of the worst sample loss which transfers this sparsity to the optimization loss. To address this, we propose a sparse subgradient algorithm that explicitly exploits the algebraic sparsity. By tailoring the optimization procedure to the non-smooth nature of Max-Plus models, our method achieves more efficient updates while retaining theoretical guarantees. This highlights a principled path toward bridging algebraic structure and scalable learning.
• Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus
  
  • van Elst Anna
  • Le Caillec Kerrian
  • Colin Igor
  • Clémençon Stéphan
  , 2026. <div><p>The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory, have been documented in the literature, offering theoretical guarantees in a centralized setting, i.e., when all the ranking data to be aggregated can be brought together in a single computing unit. For many technologies (e.g. peer-to-peer networks, IoT, multi-agent systems), extending the ability to calculate consensus rankings with guarantees in a decentralized setting, i.e., when preference data is initially distributed across a communicating network, remains a major methodological challenge. Indeed, in recent years, the literature on decentralized computation has mainly focused on computing or optimizing statistics such as arithmetic means using gossip algorithms. The purpose of this article is precisely to study how to achieve reliable consensus on collective rankings using classical rules (e.g. Borda, Copeland) in a decentralized setting, thereby raising new questions, robustness to corrupted nodes, and scalability through reduced communication costs in particular. The approach proposed and analyzed here relies on random gossip communication, allowing autonomous agents to compute global ranking consensus using only local interactions, without coordination or central authority. We provide rigorous convergence guarantees, including explicit rate bounds, for the Borda and Copeland consensus methods. Beyond these rules, we also provide a decentralized implementation of consensus according to the median rank rule and local Kemenization. Extensive empirical evaluations on various network topologies and real and synthetic ranking datasets demonstrate that our algorithms converge quickly and reliably to the correct ranking aggregation. This work paves the way for principled collective decision-making in fully decentralized systems.</p></div>
• On A Class Of Dynamical Poisson-Voronoi Tessellations
  
  • Baccelli François
  • Kumar Jhawar Sanjoy
  , 2025. Consider a dynamical network model featuring mobile stations on the Euclidean plane. The initial locations of the stations are given by a homogeneous Poisson point process. The stations are all moving at a constant speed and in a random direction. Consider fixed users located in the Euclidean plane, which are served by the mobile stations. Each user stays connected to the nearest station at any given point of time. Since the stations are moving, an user disconnects and connects with different stations over time, by always selecting which ever station is the closest. This gives rise to a dynamical version of the Poisson-Voronoi tessellation. The focus of this paper is on the sequence of "handover" events of a typical user, which are the epochs when its association changes. This defines a point process on the time-axis, the "handover point process". We show that this point process is stationary and we determine its main properties, in particular its intensity and the joint distribution of its inter-event times. We also analyze the handover Palm distributions of several variables of practical interest. This includes the distance to the closest mobile stations and the point process of all other mobile stations at handover epochs. The analysis is conducted both in the single-speed and in the multi-speed scenarios. It leads to the identification of the three dimensional state variables that "Markovize" the association dynamics. The analysis is based on a specific system of non-compact particles. The motivations are in the modeling of low or medium orbit satellite wireless communication networks. The model studied here is a planar "caricature" of this problem, which is initially defined on the sphere.
• D3.2 – Certification Roadmap
  
  • Alleaume Romain
  , 2026. This document gives, in a first part, an overview of the efforts undertaken towards the security certification of Quantum Key Distribution (QKD), and presents a gap analysis of the first QKD Protection Profile published in 2023. In a second part, a new methodological framework for QKD security proof, well adapted for a furture standardization is presented, and compared to previous approaches.
• Texo: Formula Recognition within 20M Parameters
  
  • Mao Sicheng
  , 2026. In this paper we present Texo, a minimalist yet highperformance formula recognition model that contains only 20 million parameters. By attentive design, distillation and transfer of the vocabulary and the tokenizer, Texo achieves comparable performance to state-of-the-art models such as UniMERNet-T and PPFormulaNet-S, while reducing the model size by 80% and 65%, respectively. This enables real-time inference on consumer-grade hardware and even in-browser deployment. We also developed a web application to demonstrate the model capabilities and facilitate its usage for end users.
• Qiana: A First-Order Formalism to Quantify over Contexts and Formulas with Temporality
  
  • Coumes Simon
  • Paris Pierre-Henri
  • Schwarzentruber François
  • Suchanek Fabian
  Journal of Artificial Intelligence Research, Association for the Advancement of Artificial Intelligence, 2026, 85, pp.12:1-12:35. We introduce Qiana, a logic framework for reasoning on formulas that are true only in specific contexts. In Qiana, it is possible to quantify over both formulas and contexts to express, e.g., that “everyone knows everything Alice says”. Qiana also permits paraconsistent logics within contexts, so that contexts can contain contradictions. Furthermore, Qiana is based on first-order logic, and is finitely axiomatizable, so that Qiana theories are compatible with pre-existing first-order logic theorem provers. We show how Qiana can be used to represent temporality, event calculus, and modal logic. We also discuss different design alternatives of Qiana. (10.1613/jair.1.18402)
  DOI : 10.1613/jair.1.18402
• Deep learning of self-supervised audio representation for general audio and music tagging
  
  • Quélennec Aurian
  , 2026. The objective of this dissertation is to develop new deep learning methods for building general-purpose audio foundation models capable of learning meaningful representations without annotated data. We first conduct a systematic analysis of both self-supervised and supervised audio foundation models within a shared evaluation protocol. In particular, we study their inference behavior and measure how robust they are to variations in input segment duration. This analysis provides practical guidance for choosing pre-trained audio embeddings and highlights the strong robustness of Audio Spectrogram Transformer models when processing shorter audio segments. Building on these observations, we propose MATPAC, a self-supervised method based on Masked Latent Prediction combined with an unsupervised classification objective. This design encourages the model to learn both predictive and semantic structure, leading to representations that outperform previous self-supervised approaches and rival supervised models. We then introduce an improved version, MATPAC++, which addresses the ambiguity of predicting masked content by generating multiple hypotheses and selecting among them with Multiple Choice Learning. This variant achieves state-of-the-art performance on various audio tagging tasks. Finally, we explore how these models can be reused in different contexts. We demonstrate that their learned representations can be efficiently compressed into small models without labels, and we show that they can serve as conditioning modules in multimodal systems. In particular, we introduce TinyMU, a compact music language model that leverages MATPAC++ to achieve high-level music reasoning while remaining lightweight and deployable.
• RV-Sec5: Enhancing RISC-V Security Evaluation via Targeted ISA-Level Instrumentation using gem5
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Bruguier Florent
  • Haj Yahya Jawad
  , 2026, pp.10-19. The modularity of the RISC-V Instruction Set Architecture (ISA) has accelerated its adoption in security-critical domains, yet it introduces significant challenges for pre-silicon security validation. Current evaluation methods often rely on high-level emulation that overlooks microarchitectural side effects or post-silicon testing that identifies vulnerabilities too late in the design cycle. This paper presents RV-Sec5, a systematic framework for ISA-level security evaluation that leverages the gem5 simulator. Unlike standard simulators, RV-Sec5 introduces a methodology to map high-level security invariants-such as privilege isolation and memory protection-directly to automated, cycle-accurate instrumentation points within the ISA decoder. This approach bridges the semantic gap between abstract security policies and low-level hardware execution. We demonstrate the framework's efficacy through a case study involving unauthorized Control and Status Register (CSR) modifications, showing how RV-Sec5 detects privilege escalation attempts and monitors microarchitectural anomalies, such as TLB flushes and cache state changes, in real-time. (10.1145/3793638.3793640)
  DOI : 10.1145/3793638.3793640
• Optimal Quantum Algorithm for Gibbs State Preparation
  
  • Rouzé Cambyse
  • Stilck França Daniel
  • Alhambra Álvaro
  Physical Review Letters, American Physical Society, 2026, 136 (6), pp.060601. It is of great interest to understand the thermalization of open quantum many-body systems, and how quantum computers are able to efficiently simulate that process. A recently introduced dissipative evolution, inspired by existing models of open system thermalization, has been shown to be efficiently implementable on a quantum computer. Here, we prove that, at high enough temperatures, this evolution reaches the Gibbs state in time scaling logarithmically with system size. The result holds for Hamiltonians that satisfy the Lieb-Robinson bound, such as local Hamiltonians on a lattice, and includes long-range systems. To the best of our knowledge, these are the first results rigorously establishing the rapid mixing property of high-temperature quantum Gibbs samplers, which is known to give the fastest possible speed for thermalization in the many-body setting. We then apply our result to the problem of estimating partition functions at high temperature, showing an improved performance over previous classical and quantum algorithms. (10.1103/lhht-svmn)
  DOI : 10.1103/lhht-svmn
• DRAGON: Robust Classification for Very Large Collections of Software Repositories
  
  • Balla Stefano
  • Zacchiroli Stefano
  • Degueule Thomas
  • Falleri Jean-Rémy
  • Robbes Romain
  , 2026. The ability to automatically classify source code repositories with "topics" that reflect their content and purpose is very useful, especially when navigating or searching through large software collections. However, existing approaches often rely heavily on README files and other metadata, which are frequently missing, limiting their applicability in real-world large-scale settings. We present DRAGON, a repository classifier designed for very large and diverse software collections. It operates entirely on lightweight signals commonly stored in version control systems: file and directory names, and optionally the README when available. In repository classification at scale, DRAGON improves F1@5 from 54.8% to 60.8%, surpassing the state of the art. DRAGON remains effective even when README files are absent, with performance degrading by only 6% w.r.t. when they are present. This robustness makes it practical for real-world settings where documentation is sparse or inconsistent. Furthermore, many of the remaining classification errors are near misses, where predicted labels are semantically close to the correct topics. This property increases the practical value of the predictions in real-world software collections, where suggesting a few related topics can still guide search and discovery. As a byproduct of developing DRAGON, we also release the largest open dataset to date for repository classification, consisting of 825 thousand repositories with associated ground-truth topics, sourced from the Software Heritage archive, providing a foundation for future large-scale and language-agnostic research on software repository understanding.
• Efficient Online Variational Estimation via Monte Carlo Sampling
  
  • Chagneux Mathis
  • Müller Mathias
  • Gloaguen Pierre
  • Le Corff Sylvain
  • Olsson Jimmy
  , 2026. This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradient in a streaming-data setting, where observations arrive sequentially. The algorithm allows for the simultaneous training of the model parameters and the distribution of the latent states given the observations. It is based on i.i.d. Monte Carlo sampling, coupled with a well-chosen deep architecture, enabling both computational efficiency and flexibility. The performance of the method is illustrated on both synthetic data and real-world air-quality data. The proposed approach is theoretically motivated by the existence of an asymptotic contrast function and the ergodicity of the underlying Markov chain, and applies more generally to the computation of additive expectations under posterior distributions in state-space models.
• Towards Reliable LLM-Based Model Driven Engineering: when Full Syntax Checking and Formal Verification Join the Loop
  
  • Sultan Bastien
  • Apvrille Ludovic
  , 2026. Model-Driven Engineering facilitates the design of embedded systems by promoting abstraction and enabling early verification of design correctness. Recent approaches have integrated Large Language Models into MDE workflows to automatically generate models from textual specifications. However, these models often require extensive prompt refinement and lack formal guarantees of correctness. This paper introduces an enhanced LLM-based generation process in TTool-AI, incorporating a novel dual feedback loop that combines automated syntactic checking with formal verification of safety properties. The loop iteratively refines LLM-generated SysML block and state-machine diagrams to ensure syntactic validity and verify safety properties. First experimental evaluation on both academic and industrial-grade specifications demonstrates that the proposed mechanism reliably produces syntactically correct models, enabling direct model checking of LLM-produced models and reducing the effort required by engineers to obtain correct-by-construction designs.
• Collaborative Action on Timing InterferenCes: Summary and Perspectives at Mid-term
  
  • Maiza Claire
  • Rieg Lionel
  • Asavoae Mihail
  • Béchennec Jean-Luc
  • Blouin Dominique
  • Brandner Florian
  • Carle Thomas
  • Cassé Hugues
  • Faucou Sébastien
  • Ferres Bruno
  • Hladik Pierre-Emmanuel
  • Erwan Jahier
  • Jan Mathieu
  • Jenn Éric
  • Jezequel Loïg
  • Potop Butucaru Dumitru
  • Puaut Isabelle
  • Raymond Pascal
  • Rochange Christine
  • Sotin Pascal
  • Parent-Vigouroux Catherine
  • Zahaf Houssam
  • Chabot Hector
  • Essabyr Maha
  • Jeanmougin Louison
  • Rebhi Hichem
  , 2026, pp.1-11. CAOTIC is an ambitious initiative aimed at pooling and coordinating the efforts of major French research teams working on timing analysis of multicore real-time systems, with a focus on interference due to shared resources. The objective is to enable the efficient use of multicores in critical systems. Based on a better understanding of timing anomalies and interference, considering the specificities of applications (structural properties and execution model), and revisiting the links between timing analysis and synthesis processes (code generation, mapping, scheduling), we target significant progresses in timing analysis models and techniques for critical systems, as well as in methodologies for their application in industry. In this paper, at project mid-term, we show the progress of the project. We also present some original work, about the use of a Tricore plaform and its timing model, and discuss open questions and future work. (10.82331/ERTS.2026.27)
  DOI : 10.82331/ERTS.2026.27