@inbook{21257,
  abstract     = {We investigate the problem of accurate sparse fine-tuning of large language models (LLMs), that is, fine-tuning pre-trained LLMs on specialized tasks, while inducing sparsity in their weights. Our work is motivated by experiments showing that standard loss-based fine-tuning methods are not able to achieve high accuracy in this setting, especially at high sparsity targets. To address this issue, we perform a detailed study of knowledge distillation losses for fine-tuning of sparse models. We determine an L2-based distillation approach that we term ‘SquareHead’, which enables accurate recovery even at higher sparsities. Investigating the question of efficient inference, we show that sparse LLMs can be executed faster by taking advantage of sparsity. Specifically, we exhibit end-to-end results showing speedups enabled by sparsity, while recovering accuracy, on the following models and tasks, respectively: T5 for language translation, Whisper for speech translation, and open GPT-type models such as the Mosaic Pre-Trained Transformer (MPT) and Llama-2 models for text generation. In particular, for popular generative tasks, we show for the first time that sparse fine-tuning can reach 75% sparsity without drops in accuracy, and provide notable end-to-end speedups for inference on CPUs. Moreover, we also highlight that sparsity is compatible with other compression approaches, such as quantization.},
  author       = {Kurtic, Eldar and Kuznedelev, Denis and Frantar, Elias and Goinv, Michael and Pandit, Shubhra and Agarwalla, Abhinav and Nguyen, Tuan and Marques, Alexandre and Kurtz, Mark and Alistarh, Dan-Adrian},
  booktitle    = {Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques},
  editor       = {Passban, Peyman and Way, Andy and Rezagholizadeh, Mehdi},
  isbn         = {9783031857461},
  issn         = {2522-803X},
  pages        = {83--97},
  publisher    = {Springer Nature},
  title        = {{Sparse Fine-Tuning for Inference Acceleration of Large Language Models}},
  doi          = {10.1007/978-3-031-85747-8_6},
  year         = {2025},
}

@article{21269,
  abstract     = {The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing seven distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data.},
  author       = {Zhang, Chen Y and Rosa, Angelo and Sanguinetti, Guido},
  issn         = {2835-8279},
  journal      = {PRX Life},
  number       = {4},
  publisher    = {American Physical Society},
  title        = {{bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction}},
  doi          = {10.1103/gy1p-4256},
  volume       = {3},
  year         = {2025},
}

@article{21270,
  abstract     = {The one-dimensional Fröhlich model describing the motion of a single electron interacting with optical phonons is a paradigmatic model of quantum many-body physics. We predict the existence of an arbitrarily large number of bound excited states in the strong-coupling limit and calculate their excitation energies. Numerical simulations of a discretized model demonstrate the complete amelioration of the projector Monte Carlo sign problem by walker annihilation in an infinite Hilbert space. They reveal the threshold for the occurrence of the first bound excited states at a value of 𝛼≈1.73 for the dimensionless coupling constant. This puts the threshold into the regime of intermediate interaction strength. We find a significant spectral weight and increased phonon number of the bound excited state at threshold.},
  author       = {Taylor, J. and Čufar, M. and Mitrouskas, David Johannes and Seiringer, Robert and Pahl, E. and Brand, J.},
  issn         = {2469-9969},
  journal      = {Physical Review B},
  number       = {18},
  publisher    = {American Physical Society},
  title        = {{Bound excited states of Fröhlich polarons in one dimension}},
  doi          = {10.1103/s9p9-jflq},
  volume       = {112},
  year         = {2025},
}

@article{21271,
  abstract     = {For general non-Hermitian large random matrices X and deterministic deformation matrices A, we prove that the local eigenvalue statistics of A+X close to the typical edge points of its spectrum are universal. Furthermore, we show that, under natural assumptions, on A the spectrum of A+X does not have outliers at a distance larger than the natural fluctuation scale of the eigenvalues. As a consequence, the number of eigenvalues in each component of Spec(A+X) is deterministic.},
  author       = {Campbell, Andrew J and Cipolloni, Giorgio and Erdös, László and Ji, Hong Chang},
  issn         = {2168-894X},
  journal      = {The Annals of Probability},
  number       = {6},
  pages        = {2256--2308},
  publisher    = {Institute of Mathematical Statistics},
  title        = {{On the spectral edge of non-Hermitian random matrices}},
  doi          = {10.1214/25-aop1761},
  volume       = {53},
  year         = {2025},
}

@inproceedings{21272,
  abstract     = {Finding the ground state of Ising spin glasses is notoriously difficult due to disorder and frustration. Often, this challenge is framed as a combinatorial optimization problem, for which a common strategy employs simulated annealing, a Monte Carlo (MC)-based algorithm that updates spins one at a time. Yet, these localized updates can cause the system to become trapped in local minima. Cluster algorithms (CAs) were developed to address this limitation and have demonstrated considerable success in studying ferromagnetic systems; however, they tend to encounter percolation issues when applied to generic spin glasses. In this work, we introduce a novel CA designed to tackle these challenges by leveraging precomputed two-point correlations, aiming solve combinatorial optimization problems in the form of Max-Cut more efficiently. In our approach, clusters are formed probabilistically based on these correlations. Various classical and quantum algorithms can be employed to generate correlations that embody information about the energy landscape of the problem. By utilizing this information, the algorithm aims to identify groups of spins whose simultaneous flipping induces large transitions in configuration space with high acceptance probability - even at low energy levels - thereby escaping local minima more effectively. Notably, clusters generated using correlations from the Quantum Approximate Optimization Algorithm exhibit high acceptance rates at low temperatures. These acceptance rates often increase with circuit depth, accelerating the algorithm and enabling more efficient exploration of the solution space.},
  author       = {Eder, Peter J. and Kerschbaumer, Aron and Finžgar, Jernej Rudi and Medina Ramos, Raimel A and Schuetz, Martin J. A. and Katzgraber, Helmut G. and Braun, Sarah and Mendl, Christian B.},
  booktitle    = {2025 IEEE International Conference on Quantum Computing and Engineering},
  location     = {Albuquerque, NM, United States},
  publisher    = {IEEE},
  title        = {{Quantum-guided cluster algorithms for combinatorial optimization}},
  doi          = {10.1109/qce65121.2025.00033},
  year         = {2025},
}

@unpublished{21309,
  abstract     = {The polarization of light is a critically under-utilized, rich source of information in astronomy. For stars in particular, surface magnetism polarization that can be detected and measured with spectro-polarimetry. Many questions about these surface fields remain unanswered due to a lack of dedicated instruments capable of probing weak and strong surface magnetic fields for the entire mass range of stars, from M-dwarfs (and even substellar objects) to massive O-type stars at different evolutionary stages and metallicities. These questions range from the origin of these fields to their true incidence rate throughout the stellar population and the dependence on metallicity. Magnetic fields, although currently often excluded from stellar evolution models, play an important role in stellar evolution. Connecting the surface fields to internal fields through asteroseismology will instigate a new era of understanding stellar evolution and the transport of angular momentum and chemical elements throughout stellar interiors, also impacting our understanding of star-planet interactions and stellar remnants. Polarimetry is also an under-utilized tool to observationally constrain the mode identification of nonradial oscillations, which lies at the basis of accurate asteroseismic parameter estimation at percentage-level for stellar radii, masses, ages, internal rotation, and magnetic field strengths. Combining strong constraints on mode identification and surface magnetic properties through the acquisition of time-resolved, high-resolution and high-signal-to-noise (S/N) spectro-polarimetry and spectroscopy promises to bring leaps forward in our understanding of stellar structure, particularly when combined with long-term space photometric data from past, current, and future missions.},
  author       = {Vandersnickt, J. and Armenta, R. Ochoa and Vanlaer, V. and A. David-Uraz, A. David-Uraz and Aerts, C. and Das, S. B. and Bouret, J. -C. and Bowman, D. M. and Bugnet, Lisa Annabelle and Khalack, V. and J. Labadie-Bartz, J. Labadie-Bartz and Mathis, S. and Nazé, Y. and Neiner, C. and Petit, P. and Petit, V. and K. Thomson-Paressant, K. Thomson-Paressant and Doorsselaere, T. Van and Vanrespaille, M.},
  booktitle    = {arXiv},
  title        = {{Expanding stellar horizons with polarized light}},
  doi          = {10.48550/arXiv.2512.15170},
  year         = {2025},
}

@article{21317,
  abstract     = {Accreting white dwarfs (WDs) in close binary systems, commonly known as cataclysmic variables (CVs), with orbital periods below the canonical period minimum (≈80 minutes) are rare. Such short periods can only be reached if the donor star in the CV is either significantly evolved before initiating mass transfer to the WD or is metal-poor. We present optical photometry and spectroscopy of Gaia19bxc, a high-amplitude variable identified as a polar CV with an exceptionally short orbital period of 64.42 minutes—well below the canonical CV period minimum. High-speed photometry confirms persistent double-peaked variability consistent with cyclotron beaming, thus indicating the presence of a magnetic WD. Phase-resolved Keck/Low-Resolution Imaging Spectrometer (LRIS) spectroscopy reveals strong hydrogen and helium emission lines but no donor features, indicating the accretor is a magnetic WD and the donor is hydrogen-rich, but cold and faint. The absence of a detectable donor and the low inferred temperature (≲3500 K) disfavor an evolved donor scenario. Instead, the short period and the system’s halo-like kinematics suggest Gaia19bxc may be the first known metal-poor polar. Because metal-poor donors are more compact than solar-metallicity donors of the same mass, they can reach shorter minimum periods. Gaia19bxc is one of only a handful of known metal-poor CVs below the canonical period minimum and has the shortest period of any such magnetic system discovered to date.},
  author       = {Galiullin, Ilkham and Rodriguez, Antonio C. and El-Badry, Kareem and Caiazzo, Ilaria and Szkody, Paula and Nagarajan, Pranav and Whitebook, Samuel},
  issn         = {2041-8213},
  journal      = {The Astrophysical Journal Letters},
  number       = {2},
  publisher    = {IOP Publishing},
  title        = {{Optical spectroscopy of the most compact accreting binary harboring a magnetic White Dwarf and a hydrogen-rich donor}},
  doi          = {10.3847/2041-8213/adff82},
  volume       = {990},
  year         = {2025},
}

@article{21318,
  abstract     = {Matter waves have been observed in double-slit experiments with microscopic objects, such as atoms or molecules. The wave function describing the motion of these objects must extend over a distance comparable to the slit separation, much larger than the characteristic size of the objects. Preparing such states for more massive objects, such as mechanical oscillators, remains an outstanding challenge. Here we delocalize the quantum ground state of an optically levitated nanosphere by modulating the stiffness of the confining potential. We show a more than threefold increase of the initial coherence length, which corresponds to mechanical momentum squeezing of more than 7 dB. Our work is a stepping stone toward the generation of coherence lengths comparable to the object size, a crucial regime for macroscopic quantum experiments.},
  author       = {Rossi, M. and Militaru, Andrei and Carlon Zambon, N. and Riera-Campeny, A. and Romero-Isart, O. and Frimmer, M. and Novotny, L.},
  issn         = {1079-7114},
  journal      = {Physical Review Letters},
  number       = {8},
  publisher    = {American Physical Society},
  title        = {{Quantum delocalization of a levitated nanoparticle}},
  doi          = {10.1103/2yzc-fsm3},
  volume       = {135},
  year         = {2025},
}

@article{21321,
  abstract     = {The development of cost-effective and high-performance thermoelectric (TE) materials faces significant challenges, particularly in improving the properties of promising copper-based TE materials such as Cu3SbSe4, which are limited by their poor electrical conductivity. This study presents a detailed comparative analysis of three strategies to promote the electrical transport properties of Cu3SbSe4 through Sn doping: conventional Sn atomic doping, surface treatment with SnSe molecular complexes, and blending with SnSe nanocrystals to form nanocomposites, all followed by annealing and hot pressing under identical conditions. Our results reveal that a surface treatment using SnSe molecular complexes significantly enhances TE performance over atomic doping and nanocomposite formation, achieving a power factor of 1.1 mW·m−1·K−2 and a maximum dimensionless figure of merit zT value of 0.80 at 640 K, representing an excellent performance among Cu3SbSe4-based materials produced via solution-processing methods. This work highlights the effectiveness of surface engineering in optimizing the transport properties of nanostructured materials, demonstrating the versatility and cost-efficiency of solution-based technologies in the development of advanced nanostructured materials for application in the field of TE among others.},
  author       = {Xiao, Shanshan and Zhao, Mingjun and Li, Mingquan and Wan, Shanhong and Genç, Aziz and Huang, Lulu and Chen, Lei and Zhang, Yu and Ibáñez, Maria and Lim, Khak Ho and Hong, Min and Liu, Yu and Cabot, Andreu},
  issn         = {1998-0000},
  journal      = {Nano Research},
  number       = {1},
  publisher    = {Tsinghua University Press},
  title        = {{Band and defect engineering in solution-processed nanocrystal building blocks to promote transport properties in nanomaterials: The case of thermoelectric Cu            <sub>3</sub>SbSe            <sub>4</sub>}},
  doi          = {10.26599/nr.2025.94907072},
  volume       = {18},
  year         = {2025},
}

@article{21322,
  abstract     = {Habitat fragmentation poses a significant risk to population survival, causing both demographic stochasticity and genetic drift within local populations to increase, thereby increasing genetic load. Higher load causes population numbers to decline, which reduces the efficiency of selection and further increases load, resulting in a positive feedback that may drive entire populations to extinction. Here, we investigate this eco-evolutionary feedback in a metapopulation consisting of local demes connected via migration, with individuals subject to deleterious mutation at a large number of loci. We first analyze the determinants of load under soft selection, where population sizes are fixed, and then build on this to understand hard selection, where population sizes and load coevolve. We show that under soft selection, very little gene flow (less than one migrant per generation) is enough to prevent fixation of deleterious alleles. By contrast, much higher levels of migration are required to mitigate load and prevent extinction when selection is hard, with critical migration thresholds for metapopulation persistence increasing sharply as the genome-wide deleterious mutation rate becomes comparable to the baseline population growth rate. Moreover, critical migration thresholds are highest if deleterious mutations have intermediate selection coefficients but lower if alleles are predominantly recessive rather than additive (due to more efficient purging of recessive load within local populations). Our analysis is based on a combination of analytical approximations and simulations, allowing for a more comprehensive understanding of the factors influencing load and extinction in fragmented populations.},
  author       = {Olusanya, Oluwafunmilola O and Khudiakova, Kseniia and Sachdeva, Himani},
  issn         = {1537-5323},
  journal      = {The American Naturalist},
  number       = {6},
  pages        = {617--636},
  publisher    = {University of Chicago Press},
  title        = {{Genetic load, eco-evolutionary feedback, and extinction in metapopulations}},
  doi          = {10.1086/735562},
  volume       = {205},
  year         = {2025},
}

@inproceedings{21323,
  abstract     = {We present a unifying framework for proving the knowledge-soundness of KZG-like polynomial commitment schemes, encompassing both univariate and multivariate variants. By conceptualizing the proof technique of Lipmaa, Parisella, and Siim for the univariate KZG scheme (EUROCRYPT 2024), we present tools and falsifiable hardness assumptions that permit black-box extraction of the multivariate KZG scheme. Central to our approach is the notion of a canonical Proof-of-Knowledge of a Polynomial (PoKoP) of a polynomial commitment scheme, which we use to capture the extractability notion required in constructions of practical zk-SNARKs. We further present an explicit polynomial decomposition lemma for multivariate polynomials, enabling a more direct analysis of interpolating extractors and bridging the gap between univariate and multivariate commitments. Our results provide the first standard-model proofs of extractability for the multivariate KZG scheme and many of its variants under falsifiable assumptions.},
  author       = {Belohorec, Juraj and Dvořák, Pavel and Hoffmann, Charlotte and Hubáček, Pavel and Mašková, Kristýna and Pastyřík, Martin},
  booktitle    = {45th Annual International Cryptology Conference},
  isbn         = {9783032018861},
  issn         = {1611-3349},
  location     = {Santa Barbara, CA, United States},
  pages        = {584--616},
  publisher    = {Springer Nature},
  title        = {{On extractability of the KZG family of polynomial commitment schemes}},
  doi          = {10.1007/978-3-032-01887-8_19},
  volume       = {16005},
  year         = {2025},
}

@inproceedings{21325,
  abstract     = {Test-time training (TTT) methods explicitly update the weights of a model to adapt to the specific test instance, and they have found success in a variety of settings, including most recently language modeling and reasoning. To demystify this success, we investigate a gradient-based TTT algorithm for in-context learning, where we train a transformer model on the in-context demonstrations provided in the test prompt. Specifically, we provide a comprehensive theoretical characterization of linear transformers when the update rule is a single gradient step. Our theory (i) delineates the role of alignment between pretraining distribution and target task, (ii) demystifies how TTT can alleviate distribution shift, and (iii) quantifies the sample complexity of TTT including how it can significantly reduce the eventual sample size required for in-context learning. As our empirical contribution, we study the benefits of TTT for TabPFN, a tabular foundation model. In line with our theory, we demonstrate that TTT significantly reduces the required sample size for tabular classification (3 to 5 times fewer) unlocking substantial inference efficiency with a negligible training cost.},
  author       = {Gozeten, Halil Alperen and Ildiz, Muhammed Emrullah and Zhang, Xuechen and Soltanolkotabi, Mahdi and Mondelli, Marco and Oymak, Samet},
  booktitle    = {Proceedings of the 42nd International Conference on Machine Learning},
  issn         = {2640-3498},
  location     = {Vancouver, Canada},
  pages        = {20266--20295},
  publisher    = {ML Research Press},
  title        = {{Test-time training provably improves transformers as in-context learners}},
  volume       = {267},
  year         = {2025},
}

@inproceedings{21327,
  abstract     = {Proteins exist as a dynamic ensemble of multiple conformations, and these motions are often crucial for their functions. However, current structure prediction methods predominantly yield a single conformation, overlooking the conformational heterogeneity revealed by diverse experimental modalities. Here, we present a framework for building experiment-grounded protein structure generative models that infer conformational ensembles consistent with measured experimental data. The key idea is to treat stateof-the-art protein structure predictors (e.g., AlphaFold3) as sequence-conditioned structural priors, and cast ensemble modeling as posterior inference of protein structures given experimental measurements. Through extensive real-data experiments, we demonstrate the generality of our method to incorporate a variety of experimental measurements. In particular, our framework uncovers previously unmodeled conformational heterogeneity from crystallographic densities, and generates high-accuracy NMR ensembles orders of magnitude faster than the status quo. Notably, we demonstrate that our ensembles outperform AlphaFold3 (Abramson et al., 2024) and sometimes better fit experimental data than publicly deposited structures to the Protein Data Bank (PDB, Burley et al. (2017)). We believe that this approach will unlock building predictive models that fully embrace experimentally observed conformational diversity.},
  author       = {Maddipatla, Sai A and Sellam, Nadav E and Bojan, Meital I and Vedula, Sanketh and Schanda, Paul and Marx, Ailie and Bronstein, Alexander},
  booktitle    = {Proceedings of the 42nd International Conference on Machine Learning},
  issn         = {2640-3498},
  location     = {Vancouver, Canada},
  pages        = {42366 -- 42393},
  publisher    = {ML Research Press},
  title        = {{Inverse problems with experiment-guided AlphaFold}},
  volume       = {267},
  year         = {2025},
}

@inproceedings{21328,
  abstract     = {Multi-index models provide a popular framework to investigate the learnability of functions with low-dimensional structure and, also due to their connections with neural networks, they have been object of recent intensive study. In this paper, we focus on recovering the subspace spanned by the signals via spectral estimators – a family of methods routinely used in practice, often as a warm-start for iterative algorithms. Our main technical contribution is a precise asymptotic characterization of the performance of spectral methods, when sample size and input dimension grow proportionally and the dimension p of the space to recover is fixed. Specifically, we locate the top-p eigenvalues of the spectral matrix and establish the overlaps between the corresponding eigenvectors (which give the spectral estimators) and a basis of the signal subspace. Our analysis unveils a phase transition phenomenon in which, as the sample complexity grows, eigenvalues escape from the bulk of the spectrum and, when that happens, eigenvectors recover directions of the desired subspace. The precise characterization we put forward enables the optimization of the data preprocessing, thus allowing to identify the spectral estimator that requires the minimal sample size for weak recovery.},
  author       = {Kovačević, Filip and Yihan, Zhang and Mondelli, Marco},
  booktitle    = {Proceedings of 38th Conference on Learning Theory},
  issn         = {2640-3498},
  location     = {Lyon, France},
  pages        = {3354--3404},
  publisher    = {ML Research Press},
  title        = {{Spectral estimators for multi-index models: Precise asymptotics and optimal weak recovery}},
  volume       = {291},
  year         = {2025},
}

@article{21413,
  abstract     = {We present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs).
The primary goal of our techniques is to improve performance by avoiding an exhaustive exploration of the state space, instead focussing on particularly relevant areas of the system, guided by heuristics. Our work builds on the previous results of Br{á}zdil et al., significantly extending it as well as refining several details and fixing errors.
The presented framework focuses on probabilistic reachability, which is a core problem in verification, and is instantiated in two distinct scenarios.
The first assumes that full knowledge of the MDP is available, in particular precise transition probabilities. It performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP without knowing the exact transition dynamics. Here, we obtain probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. In particular, the latter is an extension of statistical model-checking (SMC) for unbounded properties in MDPs. In contrast to other related approaches, we do not restrict our attention to time-bounded (finite-horizon) or discounted properties, nor assume any particular structural properties of the MDP.},
  author       = {Brázdil, Tomáš and Chatterjee, Krishnendu and Chmelik, Martin and Forejt, Vojtěch and Kretinsky, Jan and Kwiatkowska, Marta and Meggendorfer, Tobias and Parker, David and Ujma, Mateusz},
  issn         = {2751-4838},
  journal      = {TheoretiCS},
  publisher    = {TheoretiCS Foundation},
  title        = {{Learning algorithms for verification of Markov decision processes}},
  doi          = {10.46298/theoretics.25.10},
  volume       = {4},
  year         = {2025},
}

@unpublished{21435,
  abstract     = {Multiferroic materials, in which electric polarization and magnetic order coexist and couple, offer rich opportunities for both fundamental discovery and technology. However, multiferroicity remains rare due to conflicting electronic requirements for ferroelectricity and magnetism. One route to circumvent this challenge is to exploit the noncollinear ordering of spin cycloids, whose symmetry permits the emergence of polar order. In this work, we introduce another pathway to multiferroic order in which strain generates polarization in materials that host nonpolar spin spirals. To demonstrate this phenomenon, we chose the spin spiral in the well-studied helimagnet Cr1/3NbS2. To detect the induced polarization, we introduce the technique of magnetoelectric birefringence (MEB), an optical probe that enables spatially-resolved and unambiguous detection of polar order. By combining MEB imaging with strain engineering, we confirm the onset of a polar vector at the magnetic transition, establishing strained Cr1/3NbS2 as a type-II multiferroic.},
  author       = {Sun, Y. and Ahn, Y. and Sapkota, D. and Arachchige, H. S. and Xue, R. and Mozaffari, S. and Mandrus, D. G. and Zhao, L. and Orenstein, J. and Sunko, Veronika},
  booktitle    = {arXiv},
  title        = {{Strain-induced multiferroicity in Cr1/3NbS2}},
  doi          = {10.48550/arXiv.2510.11619},
  year         = {2025},
}

@inproceedings{21474,
  abstract     = {Rendering novel, relit views of a human head, given a monocular portrait image as input, is an inherently underconstrained problem. The traditional graphics solution is to explicitly decompose the input image into geometry, material and lighting via differentiable rendering; but this is constrained by the multiple assumptions and approximations of the underlying models and parameterizations of these scene components. We propose 3DPR, an image-based relighting model that leverages generative priors learnt from multi-view One-Light-at-A-Time (OLAT) images captured in a light stage. We introduce a new diverse and large-scale multi-view 4K OLAT dataset of 139 subjects to learn a high-quality prior over the distribution of high-frequency face reflectance. We leverage the latent space of a pre-trained generative head model that provides a rich prior over face geometry learnt from in-the-wild image datasets. The input portrait is first embedded in the latent manifold of such a model through an encoder-based inversion process. Then a novel triplane-based reflectance network trained on our lightstage data is used to synthesize high-fidelity OLAT images to enable image-based relighting. Our reflectance network operates in the latent space of the generative head model, crucially enabling a relatively small number of lightstage images to train the reflectance model. Combining the generated OLATs according to a given HDRI environment maps yields physically accurate environmental relighting results. Through quantitative and qualitative evaluations, we demonstrate that 3DPR outperforms previous methods, particularly in preserving identity and in capturing lighting effects such as specularities, self-shadows, and subsurface scattering.},
  author       = {Rao, Pramod and Meka, Abhimitra and Zhou, Xilong and Fox, Gereon and Mallikarjun, B. R. and Zhan, Fangneng and Weyrich, Tim and Bickel, Bernd and Pfister, Hanspeter and Matusik, Wojciech and Beeler, Thabo and Elgharib, Mohamed and Habermann, Marc and Theobalt, Christian},
  booktitle    = {Proceedings SIGGRAPH Asia 2025 Conference Papers 2025},
  isbn         = {9798400721373},
  location     = {Hong Kong, Hong Kong},
  publisher    = {Association for Computing Machinery},
  title        = {{3DPR: Single image 3D portrait relighting with generative priors}},
  doi          = {10.1145/3757377.3763962},
  year         = {2025},
}

@misc{21668,
  abstract     = {This artifact allows to review and reproduce the experiments from the paper *A Revised Practitioner's Guide to MDP Model Checking Algorithms*.
The package contains all original logfiles and derived data used to generate the plots as in the paper. Furthermore, the artifact contains the model checking tools `Storm` and `mcsta` in the version exercised in the paper, the used Docker container, as well as benchmark instances and execution scripts to reproduce the experiments.

See also the artifact of the conference paper: https://zenodo.org/records/7509474},
  author       = {Hartmanns, Arnd and Junges, Sebastian and Quatmann, Tim and Weininger, Maximilian},
  publisher    = {Zenodo},
  title        = {{Benchmark data for the revised practitioner's guide to MDP model checking algorithms}},
  doi          = {10.5281/ZENODO.14500423},
  year         = {2025},
}

@article{21706,
  abstract     = {Consider a quadratic polynomial Q(ξ1, . . . , ξn) of independent Rademacher random variables ξ1, . . . , ξn. To what extent can Q(ξ1, . . . , ξn) concentrate on a single value? This quadratic version of the classical Littlewood–Offord problem was popularised by Costello, Tao and Vu in their study of symmetric random matrices. In this paper, we obtain an essentially optimal bound for this problem, as conjectured by Nguyen and Vu. Specifically, if Q(ξ1, . . . , ξn) ‘robustly depends on at least m of the ξi’ in the sense that there is no way to pin down the value of Q(ξ1, . . . , ξn) by fixing values for fewer than m of the variables ξi, then we have Pr[Q(ξ1, . . . , ξn) = 0] ≤ O(1/√m). This also implies a similar result in the case where ξ1, . . . , ξn have arbitrary distributions. Our proof combines a number of ideas that may be of independent interest, including an inductive decoupling scheme that reduces quadratic anticoncentration problems
to high-dimensional linear anticoncentration problems. Also, one application of our main result is the resolution of a conjecture of Alon, Hefetz, Krivelevich and Tyomkyn related to graph inducibility. },
  author       = {Kwan, Matthew Alan and Sauermann, Lisa},
  issn         = {1570-5846},
  journal      = {Compositio Mathematica},
  number       = {12},
  pages        = {3089--3139},
  publisher    = {Cambridge University Press},
  title        = {{Resolution of the quadratic Littlewood–Offord problem}},
  doi          = {10.1112/S0010437X25102789},
  volume       = {161},
  year         = {2025},
}

@article{21724,
  abstract     = {The next generation of weak-gravitational-lensing surveys has the potential to place stringent constraints on cosmological parameters. However, their analysis is limited by systematics such as the intrinsic alignments of galaxies, which alter weak-lensing convergence and can lead to biases in cosmological parameter estimations. For the first time, in this work, we investigate the impact of intrinsic alignments on non-Gaussian statistics of the weak-lensing field using galaxy shapes derived from the IllustrisTNG hydrodynamical simulation. We create two catalogs of ray-traced convergence maps: one that includes the measured intrinsic shape of each galaxy and another where all galaxies are randomly rotated to eliminate intrinsic alignments. We compare a range of weak-lensing statistics between the two catalogs, including the shear–shear correlation function, the map-level angular power spectrum, one-point, peak count, and minimum distribution functions, and Minkowski functionals. For each statistic, we assess the level of statistical distinguishability between catalogs for a set of future survey angular areas. Our results reveal strong small-scale correlation in the alignment of galaxies and statistically significant boosts in weak-lensing convergence in both positive and negative directions for high-significance peaks and minima, respectively. We note that our analysis is at a fixed number density of  ˜ 5 arcmin^-2, drawn from a single realization of initial conditions, and does not include observational uncertainties or supersample covariance contributions. Weak-lensing analyses utilizing non-Gaussian statistics must account for intrinsic alignments to avoid significantly compromised cosmological inferences.},
  author       = {Lee, Max E. and Haiman, Zoltán and Pandey, Shivam and Genel, Shy},
  issn         = {1538-4357},
  journal      = {The Astrophysical Journal},
  number       = {1},
  publisher    = {IOP Publishing},
  title        = {{The effect of intrinsic alignments on weak-lensing statistics in hydrodynamical simulations}},
  doi          = {10.3847/1538-4357/ae1ca7},
  volume       = {996},
  year         = {2025},
}