Publications

2026

• Group Conversational Agents
  
  • Yeo Shunyi
  • Zhang Tianyi
  • Bateman Scott
  • Hsieh Gary
  • Kim Young-Ho
  • Perrault Simon Tangi
  • Li Jiannan
  • Tang Anthony
  , 2026, pp.2765-2779. Conversational agents that participate in or mediate group interaction introduce challenges that extend beyond supporting individual users, raising new questions about how agents participate in and influence groups. To characterise this emerging design space, we present a systematic review of 53 peer-reviewed studies on group conversational agents (GCAs). We analyse how GCAs intervene in group-level processes, including participation regulation, conflict mediation, task alignment, and execution support. Using concepts from group research as an analytic lens, we organise prior GCA work around recurring group interactional challenges (orientation, conflict, alignment, and execution), and examine the roles agents are designed to play in addressing these challenges. We find that GCAs are predominantly designed as short-term, role-bounded interventions targeting isolated challenges in bounded interactional contexts. We further identify recurring structural tensions in GCA design, including tradeoffs between visibility and discretion, proactivity and group autonomy, and agent authority and group ownership. Together, these findings clarify how current GCAs are positioned within group interaction, surface the implicit assumptions embedded in their designs, and outline open questions for future research on conversational agents as group-level interventions. (10.1145/3800645.3812934)
  DOI : 10.1145/3800645.3812934
• 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
• Beyond MERLIN: A Multi-Scale Self-Supervised Framework for Despeckling
  
  • Basso-Lacroix Stella
  • Bultingaire Thomas
  • Denis Loïc
  • Gasnier Nicolas
  • Desroches Damien
  • Tupin Florence
  , 2026. Synthetic aperture radar (SAR) images are severely affected by speckle. Numerous deep learning techniques have been developed to restore image quality. Among them, the self-supervised MERLIN strategy achieves state-of-the-art performance using only single-look complex (SLC) data for training, without imposing any particular constraint on the network architecture. While the method produces excellent results when properly trained, training the network can be challenging. The approach proposed in this study extends the MERLIN strategy by exploiting several resolution levels to increase the robustness and performance of the despeckling method. Low-resolution images are generated using a multilook approach, which mitigates speckle fluctuations and simplifies subsequent processing. The knowledge obtained from models trained at low resolution is then distilled into a higher-resolution network to effectively guide the training phase.
• Generalization of InSAR2InSAR to Sentinel-1 Multi-Channel Data: PolSAR and PolInSAR
  
  • Geara Carla
  • Gelas Colette
  • De Vitry Louis
  • Colin Elise
  • Tupin Florence
  , 2026. Despeckling SAR images is essential for improving data quality and facilitating their use in Earth observation applications. While single-channel SAR despeckling has been extensively studied, multi-channel SAR despeckling remains a challenging task. In this paper, we extend InSAR2InSAR, a semi-supervised InSAR parameter estimation method, to PolSAR and PolInSAR Sentinel-1 acquisitions. We also adapt a previously proposed uncertainty estimation principle, originally introduced for the single-channel despeckling filter MERLIN, to the covariance matrix domain in order to assess the quality of our filter by quantifying the uncertainty of its estimations.
• 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.
• Learning with Importance Weighted Variational Inference
  
  • Daudel Kamélia
  • Roueff François
  , 2026. Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Auto-Encoder (IWAE), Variational Rényi (VR) and VR-IWAE bounds. Yet, it remains unclear how the joint choice of bound and gradient estimator impacts the behavior of the resulting variational inference (VI) algorithms. This paper provides a unified theoretical comparison of reparameterized (REP) and doubly-reparameterized (DREP) gradient estimators tied to the IWAE, VR and VR-IWAE bounds. Through asymptotic analyses of the Signal-to-Noise Ratio as the number of Monter Carlo samples $N$ goes to infinity, we identify a bias-variance tradeoff in these gradient estimators and we formally justify the superiority of DREP over REP in importance-weighted VI. An additional asymptotic analysis for challenging regimes, where both $N$ and the Kullback-Leibler divergence between the variational and posterior densities go to infinity, indicates that importance-weighted VI gradient estimators point in a well-founded direction even when the variational approximation deteriorates. Together, these complementary results characterize the optimization trajectory in importance-weighted VI from poor initialization to final convergence. Importantly, our proof techniques establish general theoretical tools for the study of sample means ratios whose scope extend beyond VI and constitute an independent contribution to the field of Monte Carlo methods.
• Explaining CLIP Zero-shot Predictions Through Concepts
  
  • Ozdemir Onat
  • Christensen Anders
  • Alaniz Stephan
  • Akata Zeynep
  • Akbas Emre
  , 2026. Large-scale vision-language models such as CLIP have achieved remarkable success in zero-shot image recognition, yet their predictions remain largely opaque to human understanding. In contrast, Concept Bottleneck Models provide interpretable intermediate representations by reasoning through human-defined concepts, but they rely on concept supervision and lack the ability to generalize to unseen classes. We introduce EZPC that bridges these two paradigms by explaining CLIP's zero-shot predictions through human-understandable concepts. Our method projects CLIP's joint image-text embeddings into a concept space learned from language descriptions, enabling faithful and transparent explanations without additional supervision. The model learns this projection via a combination of alignment and reconstruction objectives, ensuring that concept activations preserve CLIP's semantic structure while remaining interpretable. Extensive experiments on five benchmark datasets, CIFAR-100, CUB-200-2011, Places365, ImageNet-100, and ImageNet-1k, demonstrate that our approach maintains CLIP's strong zero-shot classification accuracy while providing meaningful concept-level explanations. By grounding open-vocabulary predictions in explicit semantic concepts, our method offers a principled step toward interpretable and trustworthy vision-language models. Code is available at https://github.com/oonat/ezpc.
• FlowC2S: Flowing from Current to Succeeding Frames for Fast and Memory-Efficient Video Continuation
  
  • Margaryan Hovhannes
  • Bammey Quentin
  • Sandor Christian
  , 2026, pp.3861-3872. This paper introduces a novel methodology for generating fast and memory-efficient video continuations. Our method, dubbed FlowC2S, fine-tunes a pre-trained text-to-video flow model to learn a vector field between the current and succeeding video chunks. Two design choices are key. First, we introduce inherent optimal couplings, utilizing temporally adjacent video chunks during training as a practical proxy for true optimal couplings, resulting in straighter flows. Second, we incorporate target inversion, injecting the inverted latent of the target chunk into the input representation to strengthen correspondences and improve visual fidelity. By flowing directly from current to succeeding frames, instead of the common combination of current frames with noise to generate a video continuation, we reduce the dimensionality of the model input by a factor of two. The proposed method, fine-tuned from LTXV and Wan, surpasses the state-of-the-art scores across quantitative evaluations with FID and FVD, with as few as five neural function evaluations.
• 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.
• FINER: MLLMs Hallucinate under Fine-grained Negative Queries
  
  • Xiao Rui
  • Kim Sanghwan
  • Xian Yongqin
  • Akata Zeynep
  • Alaniz Stephan
  , 2026. Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image-related questions. We introduce FIne-grained NEgative queRies (FINER), alongside two benchmarks: FINER-CompreCap and FINER-DOCCI. Using FINER, we analyze hallucinations across four settings: multi-object, multi-attribute, multi-relation, and "what" questions. Our benchmarks reveal that MLLMs hallucinate when fine-grained mismatches co-occur with genuinely present elements in the image. To address this, we propose FINER-Tuning, leveraging Direct Preference Optimization (DPO) on FINER-inspired data. Finetuning four frontier MLLMs with FINER-Tuning yields up to 24.2% gains (InternVL3.5-14B) on hallucinations from our benchmarks, while simultaneously improving performance on eight existing hallucination suites, and enhancing general multimodal capabilities across six benchmarks. Code, benchmark, and models are available at https://explainableml.github.io/finer-project/.
• What can we do in a symmetry-constrained perspective? The importance of the total charge's status in quantum reference frame frameworks
  
  • Doat Guilhem
  • Vanrietvelde Augustin
  , 2025. The study of quantum reference frames has received renewed interest over the last years, leading to the parallel development of non-equivalent frameworks by different com- munities. We clarify the differences between these frameworks. At the mathematical level, they mainly differ in the kind of symmetry (either weak or strong) employed to constrain the system. We show that this mathematical difference corresponds to a fundamental physical question: whether the global charge associated to the symmetry group is acces- sible to symmetry-constrained observers. In this context, we formulate a definition of a perspective in terms of operational capacities, or lack thereof. Turning to consequences of adopting either approach, we discuss how adopting the weak approach induces an ambi- guity in the momenta included in each perspective and bars from defining reversible QRF transformations. We then review and analyze the existing arguments motivating each approach, and show how they bear upon the problem of charge accessibility. Finally, we introduce a simple operational scenario in which upholding two reasonable physical pos- tulates leads to the conclusion that internal observers could measure the global charge by 1/ performing a relativized interference measurement and 2/ classically communicating.
• Trust-based attack detection model for connected cars using a Subjective Logic based framework
  
  • Ismail Ahmad
  • Fadlallah Ahmad
  • Bassi Francesca
  • Khatoun Rida
  , 2026, pp.1507-1512. Traditional Voting Classifiers (Hard and Soft voting) assign fixed weights to individual models. In many cases, a machine learning model performs differently depending on the predicted class and the context. Hard and soft voting are static in nature which often prevents the full potential of ensemble methods from being realized. In contrast, Subjective Logic offers a probabilistic framework that effectively accounts for information uncertainty and the trustworthiness of information sources. This paper introduces a novel subjective logic based binary ensemble classifier that takes conflict between models and individual model performance into account to modify model weights in real time and improve the ensemble predictions. (10.1109/IWCMC69287.2026.11580092)
  DOI : 10.1109/IWCMC69287.2026.11580092
• 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
• Statistical wave field theory: Anisotropic wave fields under Robin's boundary condition
  
  • Badeau Roland
  Journal of the Acoustical Society of America, Acoustical Society of America, 2026, 159 (6), pp.5359-5377. 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 develop a unified version of the theory dedicated to semi-mixing billiards. In the case of Robin's boundary condition, we show that such wave fields are characterized by a directional reverberation time that is independent of the receiver's position but depends on its orientation, and we provide its closed-form expression, which improves and generalizes Eyring's formula of the reverberation time in ergodic rooms. (10.1121/10.0044104)
  DOI : 10.1121/10.0044104
• DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence
  
  • Santini Cristian
  • Barzaghi Sebastian
  • Sernani Paolo
  • Frontoni Emanuele
  • Alam Mehwish
  Journal on Computing and Cultural Heritage, Association for Computing Machinery, 2026. In spite of the remarkable advancements in the field of Natural Language Processing, the task of Entity Linking (EL) remains challenging in the field of humanities due to complex document typologies, lack of domain-specific datasets and models, and long-tail entities, i.e., entities under-represented in Knowledge Bases (KBs). The goal of this paper is to address these issues with two main contributions. The first contribution is DELICATE, a novel neuro-symbolic method for EL on historical Italian which combines a BERT-based encoder with contextual information from Wikidata to select appropriate KB entities using temporal plausibility and entity type consistency. The second contribution is ENEIDE, a multi-domain EL corpus in historical Italian semi-automatically extracted from two annotated editions spanning from the 19th to the 20th century and including literary and political texts. Results show how DELICATE outperforms other EL models in historical Italian even if compared with larger architectures with billions of parameters. Moreover, further analyses reveal how DELICATE confidence scores and features sensitivity provide results which are more explainable and interpretable than purely neural methods.
• Co-Investment under Revenue Uncertainty Based on Stochastic Coalitional Game Theory
  
  • Sakr Amal
  • Araldo Andrea
  • Chahed Tijani
  • Kofman Daniel
  Annals of Operations Research, Springer Verlag, 2026, 362 (1-3), pp.293-355. The introduction of new services, such as Mobile Edge Computing (MEC), requires a massive investment that cannot be assumed by a single stakeholder, for instance the Infrastructure Provider (InP ). Service Providers (SPs) however also have an interest in the deployment of such services. We hence propose a coinvestment scheme in which all stakeholders, i.e., the InP and the SPs, form the so-called grand coalition composed of all the stakeholders with the aim of sharing costs and revenues and maximizing their payoffs. The challenge comes from the fact that future revenues are uncertain. We devise in this case a novel stochastic coalitional game formulation which builds upon robust game theory and derive a lower bound on the probability of the stability of the grand coalition, wherein no player can be better off outside of it. In the presence of highly dependent fluctuations of revenues however, stability can be too conservative. In this case, we make use also of profitability, in which payoffs of players are non-negative, as a necessary condition for co-investment, and we derive a lower bound on the probability that co-investment is profitable. The proposed framework is showcased for MEC deployment, where computational resources need to be deployed in nodes at the edge of a telecommunication network. Numerical results show high lower bound on the probability of stability when the SPs' revenues are of similar magnitude even with high levels of uncertainty. In the case where revenues are highly variable however, the lower bound on stability can be trivially low whereas co-investment is still profitable. (10.1007/s10479-026-07222-w)
  DOI : 10.1007/s10479-026-07222-w
• 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.
• Melody-Lyrics Matching with Contrastive Alignment Loss
  
  • Wang Changhong
  • Olvera Michel
  • Richard Gaël
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2026. The connection between music and lyrics is far beyond semantic bonds. Conceptual pairs in the two modalities, such as rhythm and rhyme, note duration and syllabic stress, and structure correspondence, raise a compelling yet seldom-explored direction in the field of music information retrieval. In this paper, we present melody-lyrics matching (MLM), a new task that retrieves potential lyrics for a given symbolic melody from text sources. Rather than generating lyrics from scratch, MLM essentially exploits the relationships between melody and lyrics. We propose a self-supervised representation learning framework with contrastive alignment loss for melody and lyrics. This has the potential to leverage the abundance of existing songs with paired melody and lyrics. No alignment annotations are required. Additionally, we introduce syllable vector, a novel representation for lyrics at the syllable-level activated by phoneme identity and vowel stress. We demonstrate that our method can retrieve lyrics for symbolic melody queries with empirical results and intuitive examples. We open-source code and provide matching examples on the companion webpage: https://github.com/changhongw/mlm. (10.1109/TASLPRO.2026.3703164)
  DOI : 10.1109/TASLPRO.2026.3703164
• VOX2Surf: Faithful surface extraction from coarse binary voxels
  
  • Jetti Hari Hara Gowtham
  • Qin Leiheng
  • Huynh Chi
  • Khawand Joe
  • Sureshkumar Anandhu
  • Vining Nicholas
  • Cani Marie-Paule
  • Parakkat Amal Dev
  • Sheffer Alla
  Computers and Graphics, Elsevier, 2026, 138, pp.104649. Coarse binary voxel grids (under 100³ ) provide a simple interface enabling non-expert users to create a coarse approximation of diverse geometric content. Converting voxelized content into piecewise-smooth geometric models that reflect user intent can greatly increase the attractiveness of such interfaces. While multiple methods exist for surfacing binary voxel grids, they by and large target much higher grid resolutions. Applying these to coarse inputs often produces unintuitive results. We introduce VOX2Surf, a novel method for reconstructing user-intended surfaces from coarse binary voxel grids. We observe that a key challenge in achieving this goal is to correctly identify viewer-expected sharp features in these inputs. While human observers easily mentally separate sharp grid edges that are an artefact of the voxel representation from those depicting intended sharp features, existing techniques struggle to distinguish between them. We employ a learning-based approach, targeted at coarse data, to accurately recover the intended sharp features and utilize them for piecewise-smooth surface fitting. After identifying voxels containing sharp features, we employ a novel geometric reconstruction method to extract a curve network from these voxels. We use the loops of this network as the boundaries of our surface patches and use physically based simulation to smooth both the network curves and the surface patches. Extensive comparisons demonstrate that VOX2Surf achieves better approximation of the input voxelized surfaces compared to alternatives. More importantly, our user study confirms that our results are visually significantly better aligned with viewer expectations when presented with the input surfaces than those produced by alternative approaches. (10.1016/j.cag.2026.104649)
  DOI : 10.1016/j.cag.2026.104649
• Leveraging LiDAR datasets to improve SAR tomography: a diffusion model approach
  
  • Mendes Cristiano Ulondu
  • Denis Loïc
  • Kervazo Christophe
  • Tupin Florence
  , 2026. <div><p>Synthetic Aperture Radar (SAR) tomography is a 3D imaging technique based on the combination of multiple images acquired from slightly dierent angles. By analyzing the phase shift measured across the dierent images, it is possible to separate scatterers located at dierent heights. This requires solving an inverse problem and is typically performed independently for each pixel, producing a point cloud with large localization uncertainties in the elevation direction. Performing a singlestep tomographic reconstruction with improved spatial regularity is dicult to achieve using a supervised approach, as simulating realistic SAR tomography data corresponding to a given 3D urban scene is a highly complex task. In this paper, we suggest a two-step approach: a rst step using simple pixel-based inversion, and a second step restoring the reconstructed 3D cloud thanks to a diusion model. We train our diusion model on a large dataset of freely available LiDAR point clouds. A wellchosen geometrical projection is applied to represent the 3D points of the cloud visible from the radar as a 2D image. The degradation modeled by the diusion model corresponds both to point omission (non-detections) and to mislocalizations in the elevation direction. Our diusion model, based on the Residual Shifting method, requires as few as 20 diusion steps to produce restored reconstructions. The paper introduces a exible approach to leverage digital surface models from LiDAR datasets and improve 3D tomographic SAR reconstructions.</p></div>
• 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.
• Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations
  
  • Folco Maxime Di
  • Bernardino Gabriel
  • Clarysse Patrick
  • Duchateau Nicolas
  IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2026, pp.1-12. <div><p>Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are defined or computed (e.g. due to a lack of consensus in the scientific field) may have a crucial impact on the analysis, and are hardly considered in practice. In this paper, we propose an original strategy based on representation learning to estimate a parametric map reflecting the impact of such definitional differences on a given physiological descriptor, previously extracted from medical images. We consider the different definitions or computations of such physiological descriptors as different high-dimensional data, potentially of heterogeneous types. We specifically focus on myocardial deformation (strain), for which there is limited agreement on its definition. We first use manifold alignment to match the latent representations associated with the different definitions of this descriptor. Then, we formulate plausible distributions in the latent space to represent definitional divergence across descriptors, from which we reconstruct a high-dimensional parametric map to visualize such definitional divergence.</p><p>Due to the lack of proper ground truth for this specific clinical application, we first demonstrate this methodology on toy experiments and then expand the evaluation on right ventricular strain data from subjects obtained from 3D echocardiographic image sequences, for which different types of strain are available at each point of the right ventricle endocardial surface mesh. Beyond this illustrative application, our methodology has the potential to be generalised to many other population analyses considering heterogeneous high-dimensional descriptors.</p></div> (10.1109/TMI.2026.3698240)
  DOI : 10.1109/TMI.2026.3698240
• 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.