Publications

2026

• Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
  
  • Noble Maxence
  • Quintana Gonzalo Iñaki
  • Aubin Benjamin
  • Chadebec Clément
  , 2026. Diffusion-based image super-resolution (SR) has recently attracted significant attention by leveraging the expressive power of large pre-trained text-to-image diffusion models (DMs). A central practical challenge is resolving the trade-off between reconstruction faithfulness and photorealism. To address inference efficiency, many recent works have explored knowledge distillation strategies specifically tailored to SR, enabling one-step diffusion-based approaches. However, these teacher-student formulations are inherently constrained by information compression, which can degrade perceptual cues such as lifelike textures and depth of field, even with high overall perceptual quality. In parallel, self-distillation DMs, known as Flow Map models, have emerged as a promising alternative for image generation tasks, enabling fast inference while preserving the expressivity and training stability of standard DMs. Building on these developments, we propose FlowMapSR, a novel diffusion-based framework for image super-resolution explicitly designed for efficient inference. Beyond adapting Flow Map models to SR, we introduce two complementary enhancements: (i) positive-negative prompting guidance, based on a generalization of classifier free-guidance paradigm to Flow Map models, and (ii) adversarial fine-tuning using Low-Rank Adaptation (LoRA). Among the considered Flow Map formulations (Eulerian, Lagrangian, and Shortcut), we find that the Shortcut variant consistently achieves the best performance when combined with these enhancements. Extensive experiments show that FlowMapSR achieves a better balance between reconstruction faithfulness and photorealism than recent state-of-the-art methods for both x4 and x8 upscaling, while maintaining competitive inference time. Notably, a single model is used for both upscaling factors, without any scale-specific conditioning or degradation-guided mechanisms.
• Sparse recovery of Diffusion Dynamics: Handling High-Dimensionality in Repeated Short Trajectories
  
  • Bayraktar Elise
  • Dion-Blanc Charlotte
  , 2026. Viewed as systems of interacting particles, high-dimensional stochastic differential equations encode complex interaction structures within their drift component. We propose a novel approach to estimate this drift from independent high-frequency trajectory data observed over a short time horizon. Each trajectory is modelled as the solution of a Brownian-driven stochastic differential equation, while the number of time points within each path tends to infinity. We further assume that the drift function governing the dynamics can be expressed as a linear combination of a growing number of Lipschitz basis functions. To promote accurate recovery of the underlying dynamics under sparsity constraints, we propose a Lasso-regularised likelihood criterion. Under suitable regularity conditions, we establish convergence rates for the resulting estimator and emphasise how they depend on the dimensional parameters of the problem, in particular on the number of observed trajectories. We assess the performance of the estimator on synthetic datasets, both from an estimation and a generative perspective. Finally, we illustrate the practical relevance of the approach on a real-world climate dataset, highlighting its ability to perform variable selection.
• Bigraded Castelnuovo-Mumford regularity and Groebner bases
  
  • Bender Matías R
  • Busé Laurent
  • Checa Carles
  • Tsigaridas Elias
  Journal of Symbolic Computation, Elsevier, 2026, 133, pp.26. We study the relation between the bigraded Castelnuovo-Mumford regularity of a bihomogeneous ideal $I$ in the coordinate ring of the product of two projective spaces and the bidegrees of a Groebner basis of $I$ with respect to the degree reverse lexicographical monomial order in generic coordinates. For the single-graded case, Bayer and Stillman unraveled all aspects of this relationship forty years ago and these results led to complexity estimates for computations with Groebner bases. We build on this work to introduce a bounding region of the bidegrees of minimal generators of bihomogeneous Groebner bases for $I$. We also use this region to certify the presence of some minimal generators close to its boundary. Finally, we show that, up to a certain shift, this region is related to the bigraded Castelnuovo-Mumford regularity of $I$. (10.1016/j.jsc.2025.102487)
  DOI : 10.1016/j.jsc.2025.102487
• Robust a posteriori estimation of probit-lognormal seismic fragility curves via sequential design of experiments and constrained reference prior
  
  • Van Biesbroeck Antoine
  • Gauchy Clément
  • Feau Cyril
  • Garnier Josselin
  Nuclear Engineering and Design, Elsevier, 2026, 448, pp.114695. A seismic fragility curve expresses the probability of failure of a structure conditional to an intensity measure (IM) derived from seismic signals. When only limited data is available, the practitioner often refers to the probit-lognormal model coupled with maximum likelihood estimation (MLE) to obtain estimates of these curves. This means that only a binary indicator of the state (BIS) of the structure is known, namely a failure or non-failure state indicator, when it is subjected to a seismic signal with an intensity measure IM. In this context, the objective of this work is to propose a method for optimally estimating such curves by obtaining the most precise estimate possible with the minimum of data. The novelty of our work is twofold. First, we present and show how to mitigate the likelihood degeneracy problem which is ubiquitous with small data sets and hampers frequentist approaches such as MLE. Second, we propose a novel strategy for sequential design of experiments (DoE) that selects seismic signals from a large database of synthetic or real signals via their IM values, to be applied to structures to evaluate the corresponding BISs. This strategy relies on a criterion based on information theory in a Bayesian framework. It therefore aims to sequentially designate the IM value such that the pair (IM, BIS) has on average, with respect to the BIS of the structure, the greatest impact on the posterior distribution of the fragility curve. The methodology is applied to a case study from the nuclear industry. The results demonstrate its ability to efficiently and robustly estimate the fragility curve, and to avoid degeneracy even with a limited amount of data, i.e., less than 100. Furthermore, we demonstrate that the estimates quickly reach the model bias induced by the probit-lognormal modeling. Eventually, two criteria are suggested to help the user stop the DoE algorithm. (10.1016/j.nucengdes.2025.114695)
  DOI : 10.1016/j.nucengdes.2025.114695
• Asymptotic behavior of some stochastic models in population dynamics: a Hamilton-Jacobi approach
  
  • Jeddi Anouar
  , 2026. In this paper, we investigate the asymptotic behavior of individual-based models describing the evolution of a population structured by a real trait, subject to selection and mutation. We consider two different sets of assumptions: first, the case of critical or subcritical branching population processes in a regime combining a discretization of the trait space, small mutations, large time and large initial population size, where we are able to characterize using a Hamilton-Jacobi approach, the survival set of the population, and the asymptotic of the logarithmic scaling of subpopulation sizes. Second, we generalize by a direct method the convergence to the classical Hamilton-Jacobi equation obtained in the super-critical branching regime considered in [6] to a more general trait space and under weaker assumptions. Moreover, we establish that the stochastic and the deterministic dynamics are asymptotically equivalent in large population.
• samurai/ponio: two complementary software libraries for the efficient implementation of adaptive space/time numerical schemes
  
  • Dubois Sébastien
  • Gouarin Loïc
  • Hoffmann Alexandre
  • Massot Josselin
  • Massot Marc
  • Matalon Pierre
  • Series Laurent
  , 2026. We present two complementary open-source C ++ libraries for the numerical simulation of multiscale physical systems. samurai provides an efficient adaptive Cartesian meshing framework, while ponio offers a wide range of time-integration schemes for ordinary differential equations. Together, they enable the rapid implementation and testing of high-performance, adaptive space-time numerical methods for PDEs. Both libraries emphasize modularity, data locality, and modern C ++ design. Their capabilities are illustrated through applications developed during the Modeling Summer Visit 2025, including two-phase flows, rarefied gases, and reactive flows for aerospace applications relevant to NASA.
• A projection scheme for an incompressible soft material poromechanics model
  
  • Barré Mathieu
  • Grandmont Céline
  • Moireau Philippe
  IMA Journal of Numerical Analysis, Oxford University Press (OUP), 2026. In this work, we propose and analyse a new scheme to discretize the linearized version of a rather general poromechanics model adapted to biological tissues perfusion. This model, which is related to – albeit different from – Biot equations, involves unsteady solid and fluid momentum balance equations that are further coupled through an incompressibility constraint, a pore pressure and permeability terms. The key feature of the scheme is to decouple the solid, fluid and pressure unknowns at each time step by means of a projection method, composed of a prediction and a correction step. We perform a complete stability analysis of the scheme depending on the implicit or explicit treatment of friction and pressure in the prediction step. Several boundary conditions are considered, including conditions coupling the solid and fluid phases on the boundary that are imposed at the discrete level using a Robin-Robin method. In the case of Dirichlet boundary conditions, we also provide a fully discrete error estimate as long as a discrete inf-sup condition is satisfied. The scheme properties and robustness with respect to physical parameters are illustrated by numerical experiments. Finally, its computational performance is compared with that of a monolithic approach.
• Validation du simulateur ICI de propagation d'épidémies à l'aide des données publiques recueillies pendant la première vague de la pandémie de COVID-19
  
  • Colomb Maxime
  • Talay Denis
  • Carneiro Viana Aline
  • Cormier Quentin
  • Garnier Josselin
  • Gilet Nicolas
  • Graham Carl
  • Grigori Laura
  • Perret Julien
  • Porcher Raphael
  • Ravaud Philippe
  • Stanica Razvan
  • Tomasevic Milica
  • Tran Viet-Thi
  , 2026. Ce rapport a pour premier objectif de présenter le simulateur ICI de propagation d'épidémies fondé sur des jumeaux numériques de territoires géographiques, de populations synthétiques statistiquement conformes aux populations réelles, et à la simulation numérique des agendas horaires et des interactions sociales des individus, ainsi que des contaminations entre individus. Par méthode de Monte-Carlo ICI fournit des informations statistiques précises, différenciées par zones géographiques et par catégories de population, sur les évolutions d'épidémies. Ces informations permettent de comparer quantitativement les impacts attendus de politiques sanitaires variées. Le second objectif est de dresser le bilan de nos tests sur la capacité d’ICI à reproduire quantitativement la dynamique épidémiologique et hospitalière observée lors de la première vague de Covid-19 à Paris, et d'analyser les capacités et les limites d'ICI pour des analyses contrefactuelles. Nos résultats montrent que d'ores et déjà ICI est un outil numérique opérationnel d'aide à la décision préalable aux interventions publiques contre les épidémies futures, prêt à être déployé sur des territoires multiples et pour des types variés d'épidémies.
• Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
  
  • Dufraiche Jean
  • Mangold Paul
  • Perrot Michaël
  • Tommasi Marc
  , 2026. <div><p>Releasing data once and for all under noninteractive Local Differential Privacy (LDP) enables complete data reusability, but the resulting noise may create bias in subsequent analyses. In this work, we leverage the Weierstrass transform to characterize this bias in binary classification. We prove that inverting this transform leads to a biascorrection method to compute unbiased estimates of nonlinear functions on examples released under LDP. We then build a novel stochastic gradient descent algorithm called Inverse Weierstrass Private SGD (IWP-SGD). It converges to the true population risk minimizer at a rate of O(1/n), with n the number of examples. We empirically validate IWP-SGD on binary classification tasks using synthetic and real-world datasets.</p></div>
• Hematopoiesis as a continuum: from stochastic compartmental model to hydrodynamic limit
  
  • Bansaye Vincent
  • Fernández Baranda Ana
  • Giraudier Stéphane
  • Méléard Sylvie
  , 2026. We consider a multiscale stochastic compartmental model with three types of cells (stem cells, immature cells and mature cells) which combines cell proliferation and cell differentiation. We derive a hydrodynamic limit when the number of immature compartments goes to infinity obtaining a partial differential equations system with boundary conditions, modelling hematopoiesis as a continuum. We assume that proliferation and differentiation are regulated and let the corresponding rates depend on the number of mature cells. This leads us to model the dynamics of the population by a Markov process in continuous time and discrete space, which does not satisfy the branching property. We prove the convergence in law of the stem and mature cells population size processes and of the empirical measures of the immature cells dynamics, conveniently rescaled, to the unique triplet involving coupled functions and a measure, which are solutions of a deterministic measure valued equation with boundary dynamics. The cell differentiation induces a transport term in space and the main difficulty comes from the boundary effects coming from stem and mature cells. We also prove that the limiting measure admits at each time a density with respect to Lebesgue measure and can be characterized as solution of a partial differential equation.
• Empirical distribution of ancestral lineages in populations with density-dependent interactions
  
  • Kubasch Madeleine
  , 2025. We study a density-dependent Markov jump process describing a population where each individual is characterized by a type, and reproduces at rates depending both on its type and on the population type distribution. We are interested in the empirical distribution of ancestral lineages in the population process. First, we exhibit a time-inhomogeneous Markov process, which allows to capture the behavior of a sampled lineage in the population process. This is achieved through a many-to-one formula, which relates the expected value of a functional evaluated over the lineages in the population process to the expectation of the functional evaluated along this time-inhomogeneous process. This provides a direct interpretation of the underlying survivorship bias, as illustrated on a minimalistic population process. Second, we consider the large population regime, when the population size grows to infinity. Under classical assumptions, the population type distribution converges to a deterministic limit. Here, we focus on the empirical distribution of ancestral lineages in this large population limit, for which we establish a many-to-one formula. Using coupling arguments, we further quantify the approximation error which arises when sampling in this large population approximation instead of the finite-size population process.
• Quantitative approximation of a Keller–Segel PDE by a branching moderately interacting particle system and suppression of blow-up
  
  • Cavallazzi Thomas
  • Richard Alexandre
  • Tomasevic Milica
  , 2026. The Keller–Segel PDE is a model for chemotaxis known to exhibit possible finite-time blow- up. Following a seminal work by Tello and Winkler [43], a logistic damping term is added in this PDE and local well-posedness of mild solutions is proven. When the space dimension is 2 or when the damping is strong enough, the solution is global in time. In the second part of this work, a microscopic description of this model is introduced in terms of a system of stochastic moderately interacting particles. This system features two main characteristics: the interaction between particles happens through a singular (Coulomb-type) kernel which is attractive; and the particles are subject to demographic events, birth and death due to local competition with other particles. The latter induces a branching structure of the particle system. Then the main result of this work is the convergence of the empirical measure of the particle system towards the Keller–Segel PDE with logistic damping, with a rate of order N − 1 2(d+1) .
• SAM-Guide : vers un premier Laser Run à destination de sportifs non-voyants
  
  • Romeo-Pakker Katerin
  • Abdellatif Ennaji
  • Christèle Lecomte
  • Wenqi Luo
  • Christian Graff
  • Huet Sylvain
  • Pellerin Denis
  • Faugloire Elise
  • Bruno Mantel
  • Mathieu Thomas
  • François Alouges
  • Sylvain Ferrand
  • Clément Francqueville
  • Fons Coline
  , 2026. <div><p>Le projet ANR SAM-Guide vise à rendre le Laser Run praticable par des personnes aveugles et malvoyantes grâce à la combinaison multimodale de systèmes d'aide à la navigation et à l'atteinte de cible. Le dispositif fournit un retour auditif et/ou tactile en temps réel sur la position du corps et l'orientation du pistolet vers une cible virtuelle. Nous décrivons l'architecture du système et rapportons les premiers résultats obtenus auprès de participants avec ou sans capacité visuelle.</p></div>
• Stochastic invariance in infinite dimension beyond Lipschitz coefficients
  
  • Abi Jaber Eduardo
  • Tappe Stefan
  , 2026. We establish necessary and sufficient conditions for stochastic invariance of closed subsets in Hilbert spaces for solutions to infinite-dimensional stochastic differential equations (SDEs) under mild assumptions on the coefficients. Our first characterization is formulated in terms of certain normal vectors to the invariance set and requires differentiability only of the dispersion operator, but not of the diffusion coefficient itself. The condition involves a suitable corrected drift expressed through the dispersion operator and its Moore-Penrose pseudoinverse, extending the classical Stratonovich correction term to the present low-regularity setting. Our second characterization is given in terms of the positive maximum principle for the infinitesimal generator of the associated diffusion process. We illustrate our characterizations in the case of invariant manifolds.
• Unbiased Approximate Vector-Jacobian Products for Efficient Backpropagation
  
  • Bakong Killian
  • Massoulié Laurent
  • Oyallon Edouard
  • Scaman Kevin
  , 2026. In this work we introduce methods to reduce the computational and memory costs of training deep neural networks. Our approach consists in replacing exact vector-jacobian products by randomized, unbiased approximations thereof during backpropagation. We provide a theoretical analysis of the trade-off between the number of epochs needed to achieve a target precision and the cost reduction for each epoch. We then identify specific unbiased estimates of vector-jacobian products for which we establish desirable optimality properties of minimal variance under sparsity constraints. Finally we provide in-depth experiments on multi-layer perceptrons, BagNets and Visual Transfomers architectures. These validate our theoretical results, and confirm the potential of our proposed unbiased randomized backpropagation approach for reducing the cost of deep learning.
• Riemannian Stochastic Interpolants for Amorphous Particle Systems
  
  • Grenioux Louis
  • Galliano Leonardo
  • Berthier Ludovic
  • Biroli Giulio
  • Gabrié Marylou
  , 2025. Modern generative models hold great promise for accelerating diverse tasks involving the simulation of physical systems, but they must be adapted to the specific constraints of each domain. Significant progress has been made for biomolecules and crystalline materials. Here, we address amorphous materials (glasses), which are disordered particle systems lacking atomic periodicity. Sampling equilibrium configurations of glass-forming materials is a notoriously slow and difficult task. This obstacle could be overcome by developing a generative framework capable of producing equilibrium configurations with well-defined likelihoods. In this work, we address this challenge by leveraging an equivariant Riemannian stochastic interpolation framework which combines Riemannian stochastic interpolant and equivariant flow matching. Our method rigorously incorporates periodic boundary conditions and the symmetries of multi-component particle systems, adapting an equivariant graph neural network to operate directly on the torus. Our numerical experiments on model amorphous systems demonstrate that enforcing geometric and symmetry constraints significantly improves generative performance. (10.48550/arXiv.2512.16607)
  DOI : 10.48550/arXiv.2512.16607
• Non-Exchangeable Mean Field Markov Decision Processes with common noise : from Bellman equation to quantitative propagation of chaos
  
  • Mekkaoui Samy
  • Pham Huyên
  , 2026. <div><p>We study infinite-horizon Markov Decision Processes (MDPs) with a continuum of heterogeneous agents interacting through a common noise, without assuming exchangeability. We introduce the framework of Conditional Non-Exchangeable Mean Field MDPs (CNEMF-MDPs) in both a strong formulation and a label-state formulation. We establish the equivalence between these two formulations by showing that the control problem can be lifted to a standard MDP defined on the Wasserstein space P λ (I ×X ), where I denotes the label (heterogeneity) space, X is the individual state space, and λ specifies the fixed distribution of agent labels. Within this framework, we characterize the value function as the unique fixed point of an appropriate Bellman operator acting on P λ (I × X ).</p><p>Our second contribution is a quantitative analysis of the propagation of chaos for this non-exchangeable setting with common noise. We derive sharp finite-population bounds by comparing the Bellman operator of the finite N -agent MDP, defined on the high-dimensional space X N , with its infinite-agent counterpart. This comparison yields explicit constructions of near-optimal policies for the N -agent system from -optimal policies of the limiting CNEMF-MDP.</p></div>
• Two-Temperature and Thermal Plasma Kinetic Theories
  
  • Giovangigli Vincent
  , 2026. We first review a two-temperature kinetic theory of multicomponent magnetized reactive plasmas where electrons and heavy species have their own temperature. The Knudsen number is taken to be proportional to the square root of the mass ratio and polyatomic species are taken into account.</p><p>We then review the one-temperature kinetic theory of multicomponent magnetized reactive plasmas when the mass ratio remains of order unity. The complex tensorial structure of the transport fluxes is addressed as well as the symmetry properties of the multicomponent transport coefficients. We then establish new links between these two theories by using the two-temperature scaling in the transport linear system obtained from the one-temperature kinetic theory. The flux structure of the two-temperature theory is recovered from the equilibrium theory as well as the second order corrector terms. We also address the solution of transport linear systems by using fast and convergent iterative algorithms and their improvement for ionized mixtures.
• A fictitious domain method with enhanced interfacial mass conservation for immersed FSI with thin-walled solids
  
  • Corti Daniele
  • Diaz Jérôme
  • Vidrascu Marina
  • Chapelle Dominique
  • Moireau Philippe
  • Fernández Miguel Angel
  Journal of Computational Physics, Elsevier, 2026, 556, pp.114754. In this paper, we extend the low-order fictitious domain method with enhanced mass conservation, introduced in [ESAIM: Math. Model. Numer. Anal., 58(1):303--333, 2024], to fluid-structure interaction with immersed thin-walled solids. The key idea is to improve mass conservation across the interface by imposing a single global velocity constraint on one side of the interface using a scalar Lagrange multiplier. Both 2D and 3D shell models are considered for the description of the solid, including contact between solids. For both models, the interface coupling is enforced on the mid-surface of the shell using a stabilized Lagrange multiplier formulation. Numerical examples in two and three dimensions illustrate the effectiveness of the proposed method, including its successful application to the simulation of aortic heart valve dynamics. (10.1016/j.jcp.2026.114754)
  DOI : 10.1016/j.jcp.2026.114754
• Asymptotic approaches in inverse problems for depolymerization estimation
  
  • Doumic Marie
  • Moireau Philippe
  Inverse Problems and Imaging, AIMS American Institute of Mathematical Sciences, 2026, 20, pp.105-155. Depolymerization reactions constitute frequent experiments, for instance in biochemistry for the study of amyloid fibrils. The quantities experimentally observed are related to the time dynamics of a quantity averaged over all polymer sizes, such as the total polymerised mass or the mean size of particles. The question analysed here is to link this measurement to the initial size distribution. To do so, we first derive, from the initial reaction system<p>two asymptotic models: at first order, a backward transport equation, and at second order, an advection-diffusion/Fokker-Planck equation complemented with a mixed boundary condition at x = 0. We estimate their distance to the original system solution. We then turn to the inverse problem, i.e., how to estimate the initial size distribution from the time measurement of an average quantity, given by a moment of the solution. This question has been already studied for the first order asymptotic model, and we analyse here the second order asymptotic. Thanks to Carleman inequalities and to log-convexity estimates, we prove observability results and error estimates for a Tikhonov regularization.</p><p>We then develop a Kalman-based observer approach, and implement it on simulated observations. Despite its severely ill-posed character, the secondorder approach appears numerically more accurate than the first-order one.</p> (10.3934/ipi.2025020)
  DOI : 10.3934/ipi.2025020
• A 3D-shell model of left atrial electromechanics
  
  • Ruz Oscar
  • Brito-Pacheco Carlos
  • Vidrascu Marina
  • Chapelle Dominique
  • Fernández Miguel Angel
  , 2026. The thin-walled nature of the atrial myocardium can lead to artificial stiffening when full 3D electromechanical models are discretized using standard finite elements. In this work, we propose an electromechanical model of the left atrium based on a 3D-shell formulation that overcomes these limitations. The model incorporates both passive and active components of atrial tissue mechanics, while atrioventricular interaction is described by the coupling with a 0D electromechanical model of the left ventricle. The proposed approach is assessed under physiological and pathological conditions and systematically compared with the standard full 3D formulation. The results demonstrate the superior robustness and computational efficiency of the proposed 3D-shell electromechanical model.
• A benchmark of expert-level academic questions to assess AI capabilities
  
  • Phan Long
  • Gatti Alice
  • Han Ziwen
  • Li Nathaniel
  • Hu Josephina
  • Zhang Hugh
  • Shi Sean
  • Choi Michael
  • Agrawal Anish
  • Chopra Arnav
  • Khoja Adam
  • Kim Ryan
  • Hausenloy Jason
  • Zhang Oliver
  • Mazeika Mantas
  • Anderson Daron
  • Nguyen Tung
  • Mahmood Mobeen
  • Feng Fiona
  • Feng Steven Y.
  • Zhao Haoran
  • Yu Michael
  • Gangal Varun
  • Zou Chelsea
  • Wang Zihan
  • Wang Jessica P.
  • Kumar Pawan
  • Pokutnyi Oleksandr
  • Gerbicz Robert
  • Popov Serguei
  • Levin John-Clark
  • Kazakov Mstyslav
  • Schmitt Johannes
  • Galgon Geoff
  • Sanchez Alvaro
  • Lee Yongki
  • Yeadon Will
  • Sauers Scott
  • Roth Marc
  • Agu Chidozie
  • Riis Søren
  • Giska Fabian
  • Utpala Saiteja
  • Giboney Zachary
  • Goshu Gashaw M.
  • Xavier Joan of Arc
  • Crowson Sarah-Jane
  • Naiya Mohinder Maheshbhai
  • Burns Noah
  • Finke Lennart
  • Cheng Zerui
  • Park Hyunwoo
  • Fournier-Facio Francesco
  • Wydallis John
  • Nandor Mark
  • Singh Ankit
  • Gehrunger Tim
  • Cai Jiaqi
  • Mccarty Ben
  • Duclosel Darling
  • Nam Jungbae
  • Zampese Jennifer
  • Hoerr Ryan G.
  • Bacho Aras
  • Loume Gautier Abou
  • Galal Abdallah
  • Cao Hangrui
  • Garretson Alexis C
  • Sileo Damien
  • Ren Qiuyu
  • Cojoc Doru
  • Arkhipov Pavel
  • Qazi Usman
  • Li Lianghui
  • Motwani Sumeet
  • de Witt Christian Schroeder
  • Taylor Edwin
  • Veith Johannes
  • Singer Eric
  • Hartman Taylor D.
  • Rissone Paolo
  • Jin Jaehyeok
  • Shi Jack Wei Lun
  • Willcocks Chris G.
  • Robinson Joshua
  • Mikov Aleksandar
  • Prabhu Ameya
  • Tang Longke
  • Alapont Xavier
  • Uro Justine Leon
  • Zhou Kevin
  • Santos Emily de Oliveira
  • Maksimov Andrey Pupasov
  • Vendrow Edward
  • Zenitani Kengo
  • Guillod Julien
  • Li Yuqi
  • Vendrow Joshua
  • Kuchkin Vladyslav
  • Ze-An Ng
  • Marion Pierre
  • Efremov Denis
  • Lynch Jayson
  • Liang Kaiqu
  • Gritsevskiy Andrew
  • Martinez Dakotah
  • Pageler Ben
  • Crispino Nick
  • Zvonkine Dimitri
  • Fraga Natanael Wildner
  • Soori Saeed
  • Press Ori
  • Tang Henry
  • Salazar Julian
  • Green Sean R.
  • Brüssel Lina
  • Twayana Moon
  • Dieuleveut Aymeric
  • Rogers T. Ryan
  • Zhang Wenjin
  • Li Bikun
  • Yang Jinzhou
  • Rao Arun
  • Loiseau Gabriel
  • Kalinin Mikhail
  • Lukas Marco
  • Manolescu Ciprian
  • Mishra Subrata
  • Kamdoum Ariel Ghislain Kemogne
  • Kreiman Tobias
  • Hogg Tad
  • Jin Alvin
  • Bosio Carlo
  • Sun Gongbo
  • Coppola Brian P
  • Tarver Tim
  • Heidinger Haline
  • Sayous Rafael
  • Ivanov Stefan
  • Cavanagh Joseph M
  • Shen Jiawei
  • Imperial Joseph Marvin
  • Schwaller Philippe
  • Senthilkuma Shaipranesh
  • Bran Andres M
  • Dehghan Ali
  • Algaba Andres
  • Verbeken Brecht
  • Noever David
  • P V Ragavendran
  • Schut Lisa
  • Sucholutsky Ilia
  • Zheltonozhskii Evgenii
  • Lim Derek
  • Stanley Richard
  • Sivarajan Shankar
  • Yang Tong
  • Maar John
  • Wykowski Julian
  • Oller Martí
  • Sandlin Jennifer
  • Sahu Anmol
  • Hu Yuzheng
  • Fish Sara
  • Heydari Nasser
  • Apronti Archimedes
  • Rawal Kaivalya
  • Vilchis Tobias Garcia
  • Zu Yuexuan
  • Lackner Martin
  • Koppel James
  • Nguyen Jeremy
  • Antonenko Daniil S.
  • Chern Steffi
  • Zhao Bingchen
  • Arsene Pierrot
  • Goldfarb Alan
  • Ivanov Sergey
  • Poświata Rafał
  • Wang Chenguang
  • Li Daofeng
  • Crisostomi Donato
  • Achilleos Andrea
  • Myklebust Benjamin
  • Sen Archan
  • Perrella David
  • Kaparov Nurdin
  • Inlow Mark H
  • Zang Allen
  • Thornley Elliott
  • Orel Daniil
  • Poritski Vladislav
  • Ben-David Shalev
  • Berger Zachary
  • Whitfill Parker
  • Foster Michael
  • Munro Daniel
  • Ho Linh
  • Hava Dan Bar
  • Kuchkin Aleksey
  • Lauff Robert
  • Holmes David
  • Sommerhage Frank
  • Schneider Keith
  • Kazibwe Zakayo
  • Stambaugh Nate
  • Singh Mukhwinder
  • Magoulas Ilias
  • Clarke Don
  • Kim Dae Hyun
  • Dias Felipe Meneguitti
  • Elser Veit
  • Agarwal Kanu Priya
  • Vilchis Victor Efren Guadarrama
  • Klose Immo
  • Demian Christoph
  • Anantheswaran Ujjwala
  • Zweiger Adam
  • Albani Guglielmo
  • Li Jeffery
  • Daans Nicolas
  • Radionov Maksim
  • Rozhoň Václav
  • Ma Ziqiao
  • Stump Christian
  • Berkani Mohammed
  • Platnick Jacob
  • Nevirkovets Volodymyr
  • Basler Luke
  • Piccardo Marco
  • Jeanplong Ferenc
  • Cohen Niv
  • Tkadlec Josef
  • Rosu Paul
  • Padlewski Piotr
  • Barzowski Stanislaw
  • Montgomery Kyle
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  Nature, Nature Publishing Group, 2026, 649 (8099), pp.1139-1146. Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve more than 90% accuracy on popular benchmarks such as Measuring Massive Multitask Language Understanding1, limiting informed measurement of state-of-the-art LLM capabilities. Here, in response, we introduce Humanity’s Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be an expert-level closed-ended academic benchmark with broad subject coverage. HLE consists of 2,500 questions across dozens of subjects, including mathematics, humanities and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable but cannot be quickly answered by internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a marked gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai. (10.1038/s41586-025-09962-4)
  DOI : 10.1038/s41586-025-09962-4
• Synchronous vs Asynchronous Active Learning
  
  • Binois Mickael
  • Le Riche Rodolphe
  , 2026. These slides are a short class summarizing the main result in synchronous and asynchronous optimization. The material covers batch acquisition criteria, their theoretical step-ahead pendants, and the asynchronous versions.
• On the Cutoff Phenomenon for Dyson-Jacobi Processes
  
  • Chan-Ashing Samuel
  , 2026. <div><p>We study the convergence to equilibrium of the Dyson-Jacobi process, a system of n interacting particles on the segment [0, 1] arising from Random Matrix Theory. We establish the occurence of a cutoff phenomenon for the intrinsic Wasserstein distance and provide an explicit formula for the associated mixing time.</p><p>Our approach relies on the interplay between the Riemannian geometry of the process and a flattened Euclidean representation obtained via a diffeomorphic deformation. This transformation allows us to transfer curvature-dimension inequalities from the Euclidean setting to the original space, thereby yielding sharp quantitative estimates.</p></div>
• A model for a population of trees structured by phenological traits
  
  • Boucenna Sirine
  • Dakos Vasilis
  • Raoul Gaël
  , 2026. In the context of global warming, tree populations rely on two primary mechanisms of adaptation: phenotypic plasticity, which enables individuals to adjust their behavior in response to environmental stress, and genetic evolution, driven by natural selection and genetic diversity within the population. Understanding the interplay between these mechanisms is crucial for assessing the impacts of climate change on forest ecosystems and for informing sustainable management strategies. In this manuscript, we focus on a specific phenological adaptation: the ability of trees to enter summer dormancy once a critical temperature threshold is exceeded. Individuals are characterized by this threshold temperature and by their seed production capacity. We first establish a detailed mathematical model describing the population dynamics under these traits, and progressively reduce it to a system of two coupled ordinary differential equations. This simpler macroscopic model is then analyzed numerically, to investigate how the population reacts to a shift in its environment: an temperature increase, a drop in precipitation levels, or a combination of the two. Our results highlight contrasting effects of water stress and temperature stress on population dynamics, as well as the ambivalent effect of the plasticity.