March 2026 arXiv papers — page 89

Le Fang, Wangkun Xu, Fei Teng

The large-scale integration of renewable energy sources introduces significant operational uncertainty into power systems. Although Polynomial Chaos Expansion (PCE) provides an efficient tool for uncertainty quantification (UQ) in power system dynamics, its accuracy depends critically on the faithful representation of input uncertainty, an assumption that is

Sami Mabrouk

An algebra with bracket ({\sf AWB} for short) is an associative algebra endowed with a bilinear bracket satisfying a Leibniz-type compatibility condition, as introduced in \cite{casas}. It can be viewed as a noncommutative generalization of an almost Poisson algebra; indeed, when the associative product is commutative and the bracket is skew-symmetric, one r

Dinesha Dharmathilaka, Janaka Adassuriya, Chandana Jayaratne, Jordi Gutiérrez

Eclipsing binary systems with a Delta (${\delta}$) Scuti component serve a vital role in deriving precise fundamental stellar parameters and testing stellar evolution models. This study mainly focuses on the Kepler target KIC 8569819, a detached eclipsing binary system that consists of a ${\delta}$ Scuti pulsating component. The quarter 9 photometric data ob

Zhuoling Li, Hossein Rahmani, Jiarui Zhang, Yu Xue

The rapid growth of the text-to-image (T2I) community has fostered a thriving online ecosystem of expert models, which are variants of pretrained diffusion models specialized for diverse generative abilities. Yet, existing model merging methods remain limited in fully leveraging abundant online expert resources and still struggle to meet diverse in-the-wild

German Mino Galaz, Juan Pablo Pena, Javier Patino Baez, Nicolas Mino Berdu

This research reports the entropy and information transfer throughout seven different enzymatic systems, namely, TIM-Barrel, Human Lysozyme, Ribonuclease A1, Pepsin , b-lactamase, Human Glucokinase and Carbonic anhydrase II. A general trend is detected: entropy and information is transported form the peripherical regions towards the catalytic site of the ana

Nurjanna Joko Trilaksono, Sandy Hardian Susanto Herho, I Putu Ferry Wistika, Faiz Rohman Fajary

This study evaluates the effectiveness of three-dimensional variational (3D-Var) data assimilation coupled with a Rapid Update Cycle (RUC) framework for improving short-range precipitation forecasts over the Indonesian Maritime Continent (IMC). We employ the Weather Research and Forecasting (WRF) model and its data assimilation component (WRFDA) to assimilat

Şuayp Talha Kocabay, Talha Rüzgar Akkuş

Masked Diffusion Language Models (MDLMs) have emerged as a compelling non-autoregressive alternative to standard large language models; however, their application to morphologically rich languages remains limited. In this paper, we introduce $\textit{Diffutron}$, a masked diffusion language model specifically designed for Turkish. Our approach leverages a re

Zachary Logan, Andrew Dudash, Daniel Negrón

We present an open-source robotic framework that integrates computer vision and machine learning based inverse kinematics to enable low-cost laboratory automation tasks such as colony picking and liquid handling. The system uses a custom trained U-net model for semantic segmentation of microbial cultures, combined with Mixture Density Network for predicating

Anna Baiardi, Paul S. Clarke, Andrea A. Naghi, Annalivia Polselli

Panel data methods are widely used in empirical analysis to address unobserved heterogeneity, but causal inference remains challenging when treatments are endogenous and confounding variables high-dimensional and potentially nonlinear. Standard instrumental variables (IV) estimators, such as two-stage least squares (2SLS), become unreliable when instrument v

Yalemzerf Getnet, Abiy Tasissa, Waltenegus Dargie

Assigning relevance scores to the input features of a machine learning model enables to measure the contributions of the features in achieving a correct outcome. It is regarded as one of the approaches towards developing explainable models. For biomedical assignments, this is very useful for medical experts to comprehend machine-based decisions. In the analy

Rebecca Pattichis, Sebastian Janampa, Constantinos S. Pattichis, Marios S. Pattichis

Neural network systems describe complex mappings that can be very difficult to understand. In this paper, we study the inverse problem of determining the input images that get mapped to specific neural network classes. Ultimately, we expect that these images contain recognizable features that are associated with their corresponding class classifications. We

Boyang Zhao, Youngjun Ahn, Qinwen Deng, Yidai Liu

The intertwining of charge, spin, and lattice degrees of freedom underlies the emergent properties of correlated materials. A recent prominent example is the kagome metal FeGe, which hosts coexisting charge density wave (CDW) and antiferromagnetic orders, accompanied by a lattice distortion associated with partial Ge-Ge dimerization. Using temperature-depend

Marko Milivojević, Martin Gmitra

We propose encapsulating type-A antiferromagnetic semiconductors between graphene layers to realize a gate-tunable synthetic antiferromagnet with nonrelativistic spin splitting, enabling efficient spintronic transport via graphene. Ab initio calculations and tight-binding models of graphene/MnS/graphene heterostructure reveal that gate-tuning of the heterost

Avinash Krishna, Kalyana Chadalavada, Unso Eun Seo Jo

LLM assistant personalities play a critical role in user experience and perceived response quality. We present a large-scale experiment of frontier LLM personalities using external ELO-based traits scoring across 144 traits. We find that all models tested converge on a form of trait expression that is systematic, methodical, and analytical and suppress trait

Satyabrata Bera, Sudipta Chatterjee, Suman Kalyan Pradhan, Subhadip Pradhan

The interplay between spin reorientation and topological electronic structure in two-dimensional (2D) van der Waals (vdW) ferromagnets is central to understanding how magnetic anisotropy shapes charge transport. Although spin-reorientation transitions (SRTs) are common in 2D metallic ferromagnets, their impact on electronic-topology-driven thermodynamic and

Tianzuo Hu

We propose a Neural Hidden Markov Model (HMM) with Adaptive Granularity Attention (AGA) for high-frequency order flow modeling. The model addresses the challenge of capturing multi-scale temporal dynamics in financial markets, where fine-grained microstructure signals and coarse-grained liquidity trends coexist. The proposed framework integrates parallel mul

Michael J. Kurtz, Carlolyn S. Grant, Matthew R. Templeton, The ADS/SciX Team

As many research papers in astronomy have been written since the beginning of the 21st century as had been written previously. This exponential growth has been accompanied by substantial changes in the structure of astrophysics research, which organizations perform it and where they are located. Using data from the Smithsonian/NASA Astrophysics Data System/S

Ming Shi, Yingbin Liang, Ness B. Shroff, Ananthram Swami

Reinforcement learning from human feedback (RLHF) replaces hard-to-specify rewards with pairwise trajectory preferences, yet regret-oriented theory often assumes that preference labels are generated consistently from a single ground-truth objective. In practical RLHF systems, however, feedback is typically \emph{multi-source} (annotators, experts, reward mod

Cailin Winston, Claris Winston, René Just

Tool-augmented Large Language Models (TaLLMs) extend LLMs with the ability to invoke external tools, enabling them to interact with real-world environments. However, a major limitation in deploying TaLLMs in sensitive applications such as customer service and business process automation is a lack of reliable compliance with domain-specific operational polici

M. Kerem Aydin, Vishwanath Saragadam, Emma Alexander

Thermal cameras provide reliable visibility in darkness and adverse conditions, but thermal imagery remains significantly harder to use for novel view synthesis (NVS) than visible-light images. This difficulty stems primarily from two characteristics of affordable thermal sensors. First, thermal images have extremely low dynamic range, which weakens appearan

Islam M. Tanash, Nuria Gonzalez-Prelcic, Risto Wichman

In this paper, we present a novel stochastic geometry-based approach to analyze the effect of residual Doppler shift on orthogonal frequency-division multiple access (OFDMA) systems in low earth orbit (LEO) satellite-terrestrial networks. Focusing on multiuser systems employing common Doppler compensation, we analytically formulate the coverage probability b

Nathan X. Roth, Martin Cordiner, Stefanie Milam, Geronimo Villanueva

Interstellar objects are interlopers from other planetary systems, and their volatile compositions provide a glimpse into planet formation around their host star. We present near-infrared spectra of the coma of interstellar object 3I/ATLAS measured with the James Webb Space Telescope. Our results demonstrate an unexpectedly high D/H = $(3.33\pm0.31)\%$ for m

Chiara Spiniello

Ultra-compact massive galaxies (UCMGs) exhibit elevated stellar-to-dynamical mass ratios when dynamical masses are estimated using standard virial prescriptions. This discrepancy has been interpreted as non-homology driven by their compactness. This study investigates how the stellar-to-dynamical mass ratio depends on compactness (C), velocity dispersion ($\

Spencer Carmichael, Katherine A. Skinner

Thermal cameras offer several advantages for simultaneous localization and mapping (SLAM) with mobile robots: they provide a passive, low-power solution to operating in darkness, are invariant to rapidly changing or high dynamic range illumination, and can see through fog, dust, and smoke. However, uncooled microbolometer thermal cameras, the only practical

Truong Quynh Hoa, Hoang Dinh Cuong, Truong Xuan Khanh

We propose Melaguard, a multimodal ML framework (Transformer-lite, 1.2M parameters, 4-head self-attention) for detecting neurovascular instability (NVI) from wearable-compatible physiological signals prior to structural stroke pathology. The model fuses heart rate variability (HRV), peripheral perfusion index, SpO2, and bilateral phase coherence into a compo

Yuran Li, Di Wu, Benoit Boulet

Verification-guided self-improvement has recently emerged as a promising approach to improving the accuracy of large language model (LLM) outputs. However, existing approaches face a trade-off between inference efficiency and accuracy: iterative verification-rectification is computationally expensive and prone to being trapped in faulty reasoning, while best

Eduard Feireisl

We consider a continuous data assimilation method for the barotropic Navier--Stokes system. The observed solution is supposed to be bounded on the whole time period of observation, while the synchronized solution, usually provided by a numerical method, belongs to the class of dissipative solutions that is considerably larger than the class of conventional w

Hong Jeong

Decoder-only language models are stateless: hidden representations are discarded after every forward pass and nothing persists across sessions. Jeong (2026a) showed that trained memory adapters give a frozen encoder-decoder backbone persistent latent-space memory, building on the lateral-memory framework of Jeong (2026b,c). Here we ask whether the same princ

I. N. Mosaki, A. V. Turlapov

A long chain of Bose condensates freely expands and interferes after being released from an optical lattice. The interference fringes are well resolved both in the case of equal phases of the condensates and in the case of fluctuating phases. In the second case the positions of the fringes also fluctuate. The spectrum of the spatial density distribution, how

Gabriele Padovani, Sandro Fiore

While results visualization is a critical phase to the communication of new academic results, plots are frequently shared without the complete combination of code, input data, execution context and outputs required to independently reproduce the resulting figures. Existing reproducibility solutions tend to focus on computational pipelines or workflow managem

Vasiliy Makhalov, Andrey Turlapov

For a long periodic chain of Bose condensates prepared in the free space, the subsequent evolution and interference dramatically depend on the difference between the phases of the adjacent and more distant condensates. If the phases are equal, the initial periodic density distribution reappears at later times, which is known as the Talbot effect. For randoml

Jingwei Huang, Kuroush Nezafati, Zhikai Chi, Ruichen Rong

Extracting structured information from clinical notes requires navigating a dense web of interdependent variables where the value of one attribute logically constrains others. Existing Large Language Model (LLM)-based extraction pipelines often struggle to capture these dependencies, leading to clinically inconsistent outputs. We propose deep reflective reas

Hannah Berin-Costain, Harry Wang, Kirsten Morris, Jun Liu

This paper proposes a computable state-estimation error bound for learning-based Kazantzis--Kravaris/Luenberger (KKL) observers. Recent work learns the KKL transformation map with a physics-informed neural network (PINN) and a corresponding left-inverse map with a conventional neural network. However, no computable state-estimation error bounds are currently

Yen-Ting Piao, Jay Chiehen Liao, Wei-Tang Chien, Toshiki Ogimoto

While Large Audio-Language Models (LALMs) have been shown to exhibit degraded instruction-following capabilities, their ability to infer task patterns from in-context examples under audio conditioning remains unstudied. To address this gap, we present ALICE, a three-stage framework that progressively reduces textual guidance to systematically evaluate LALMs'

Weili Cao, Xunjian Yin, Bhuwan Dhingra, Shuyan Zhou

Large Language Models (LLMs) have demonstrated remarkable progress in scaling to access massive contexts. However, the access is via the latent and uninterpretable attention mechanisms, and LLMs fail to effective process long context, exhibiting significant performance degradation as context length increases. In this work, we study whether long-context proce

Manuel Arca Sedda, Lavinia Paiella, Cristiano Ugolini, Filippo Santoliquido

The population of binary black hole (BBH) mergers observed by the LIGO-Virgo-KAGRA (LVK) collaboration offers a window into the cosmic evolution of compact binaries and their formation. We employ the semi-analytic population-synthesis code B-POP to model BBHs assembled through isolated binary evolution and dynamical interactions in young, globular, and nucle

Jhacson Meza, Martin R. Oswald, Torsten Sattler

Novel view synthesis (NVS) approaches such as NeRFs or 3DGS can produce photo-realistic 3D scene representation from a set of images with known extrinsic and intrinsic parameters. The necessary camera poses and calibrations are typically obtained from the images via Structure-from-Motion (SfM). Classical SfM approaches rely on local feature matches between t

Salvador Villegas

We consider stable solutions of semilinear elliptic equations of the form $-\Delta u=f(u)$ in a bounded domain $\Omega\subset\mathbb{R}^N$. In a well-known paper \cite{cfrs}, Cabr\'e, Figalli, Ros-Oton and Serra obtained interior estimates for the $W^{1,2}$-norm of $u$ in terms of the $L^1$-norm of $u$ and proved interior H\"older regularity for dimensions $

Muriel Médard, Tarun Chitra, Moritz Grundei, Sajida Zouarhi

We study pricing mechanisms for low-latency payload delivery in settings where participant rewards depend on the time required to reconstruct a payload. In such environments, the decoding time distribution determines deadline-meeting probabilities and therefore bounds a participant's willingness to pay for additional delivery rate. Using a mean-field formula

Karan Kumar Singh, Nikita Gajbhiye

Food security policy formulation in data-scarce regions remains a critical challenge due to limited structured datasets, fragmented textual reports, and demographic bias in decision-making systems. This study proposes ZeroHungerAI, an integrated Natural Language Processing (NLP) and Machine Learning (ML) framework designed for evidence-based food security po

Matthew Haulmark, Jason Fox Manning

In this paper, we obtain an action on a cube complex from an action on a path-connected topological space with a system of divisions. In the settings of hyperbolic groups or relatively hyperbolic groups with no peripheral splittings, our result provides an alternate route to Sageev's construction of a cube complex action from a collection of (relatively) qua

Carolina Garcia, Lucía Perea Durán, Agnese Venezia, Alex Conradie

A generalisation of Takens' delay-coordinate embedding theorem to stochastic systems, the Stochastic Embedding Sufficiency Theorem, is an inverse methodology enabling non-parametric recovery of both drift and diffusion fields from scalar time series without prior assumptions about the governing physics. A blind protocol using only time series data is applied

Yuanhong Zheng, Ruichuan An, Xiaopeng Lin, Yuxing Liu

Human cognition of new concepts is inherently a streaming process: we continuously recognize new objects or identities and update our memories over time. However, current multimodal personalization methods are largely limited to static images or offline videos. This disconnects continuous visual input from instant real-world feedback, limiting their ability

Sebastian Rodriguez, Mikhael Tannous, Jad Mounayer, Camilo Cruz

Unidirectional tapes surface roughness determines the evolution of the degree of intimate contact required for ensuring the thermoplastic molecular diffusion and the associated inter-tapes consolidation during manufacturing of composite structures. However, usual characterization of rough surfaces relies on statistical descriptors that even if they are able

Abolfazl Mohammadi-Seif, Carlos Soares, Rita P. Ribeiro, Ricardo Baeza-Yates

Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates of predictive confidence remains a critical challenge. This issue arises in scenarios where the likelihood of error inferred from learned representations follows a bimodal distribution, resu

Yin Chen, Shan Ren, Runxuan Zhang

Using group actions and orbit-stabilizer methods, we study the geometry of isomorphism classes of finite-dimensional $\omega$-Lie algebras over a field $\mathbb{K}$ of characteristic $\neq 2$ and establish a one-to-one correspondence between the set of isomorphism classes and the orbit space of a stabilizer of $\omega$. We also apply techniques from computat

Yuhang Yao, Syed A. Jafar

For classical point-to-point channels, it has been shown by Bennett et al. that quantum entanglement assistance cannot improve their capacity, and by Cubitt et al. that entanglement assistance cannot activate (increase from zero to non-zero) their zero-error capacity. In contrast, we show that for classical point-to-point channels with causal CSIT (channel s

Neil A. Ernst, Ahmed Musa Awon, Swapnil Hingmire, Ze Shi Li

Research software (also called scientific software) is essential for advancing scientific endeavours. Research software encapsulates complex algorithms and domain-specific knowledge and is a fundamental component of all science. A pervasive challenge in developing research software is technical debt, which can adversely affect reliability, maintainability, a

R. E. Mennickent, G. Djurašević, J. A. Rosales, J. Garcés

Many intermediate-mass close binaries exhibit photometric cycles longer than their orbital periods, likely related to accretion-disk variability. Previous studies indicate that historical light curves (LC) provide key constraints on disk evolution and may help trace mass-transfer changes in these systems. We investigate the short- and long-term variability o

Mahmoud Elhadidy, Roshan M. D'Souza, Amirhossein Arzani

Scientific machine learning is increasingly used to build surrogate models, yet most models are trained under a restrictive assumption in which future data follow the same distribution as the training set. In practice, new experimental conditions or simulation regimes may differ significantly, requiring extrapolation and model updates without re-access to pr

Ignacio Martínez López, Rafael Alves Batista, Miguel A. Sánchez-Conde, Antonio Juan Rubio-Montero

In this work, we investigate dark matter (DM) detection in the context of weakly interacting massive particles (WIMPs). Upon annihilation, WIMPs generate cascades of secondary particles through various channels, many of which culminate in the production of gamma rays. As these gamma rays travel toward Earth, their spectra are reshaped by interactions with th

Abien Fred Agarap, Arnulfo P. Azcarraga

The Mixture-of-Experts (MoE) model uses a set of expert networks that specialize on subsets of a dataset under the supervision of a gating network. A common issue in MoE architectures is ``expert collapse'' where overlapping class boundaries in the raw input feature space cause multiple experts to learn redundant representations, thus forcing the gating netw

Ata Poyraz Turna, Asrin Efe Yorulmaz, Tamer Başar

Classical Bayesian persuasion studies how a sender influences receivers through carefully designed signaling policies within a single strategic interaction. In many real-world environments, such interactions are repeated across multiple games, creating opportunities to exploit structural similarity across tasks. In this work, we introduce Meta-Persuasion alg

Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee

We investigate whether independently trained language models converge to geometrically compatible latent representations, and whether this compatibility can be exploited to correct model behavior at inference time without any weight updates. We learn a linear projection matrix that maps activation vectors from a large teacher model into the coordinate system

Maxime Fontana, Michael Spratling, Miaojing Shi

Adapting models pre-trained on large-scale datasets is a proven way to reach strong performance quickly for down-stream tasks. However, the growth of state-of-the-art mod-els makes traditional full fine-tuning unsuitable and difficult, especially for multi-task learning (MTL) where cost scales with the number of tasks. As a result, recent studies investigate

Leonardo Pedroso, W. P. M. H. Heemels, Pedro Batista

Covariance intersection (CI) methods provide a principled approach to fusing estimates with unknown cross-correlations by minimizing a worst-case measure of uncertainty that is consistent with the available information. This paper introduces a generalized CI framework, called overlapping covariance intersection (OCI), which unifies several existing CI formul

Hendrik Ellenberg, René Sondenheimer

We analyze the impact of photon loss on the photon-number statistics of Gaussian states. Specifically, we propose and carefully evaluate several methods to mitigate deviations in the photon-number distributions of lossy (displaced) squeezed vacuum states from those of their lossless counterparts. These methods rely on appropriately redefining the parameters

Zhi-Yuan Wei, Joel Rajakumar, Jon Nelson, Daniel Malz

We study how matrix-product-operator (MPO) truncation errors evolve when simulating two setups: (1) 1D Haar-random circuits under either depolarizing noise or amplitude-damping noise, and (2) 1D Lindbladian dynamics of a non-integrable quantum Ising model under either depolarizing or amplitude-damping noise. We first show that the average purity of the syste

Simon Friederich, Mritunjay Tyagi

We explore whether quantum field theory can be understood as the statistical mechanics of a time-reversal-invariant stochastic generalization of Hamiltonian dynamics. The motivation for this project, started with this paper, is to assign sharp values to all observables and thereby avoid the quantum measurement problem. In classical mechanics, motion is deter

Orna Kupferman, Noam Shenwald

We introduce and study coverage games - a novel framework for multi-agent planning in settings in which a system operates several agents but does not have full control on them, or interacts with an environment that consists of several agents. The game is played between a coverer, who has a set of objectives, and a disruptor. The coverer operates several agen

Yichun Xu, Navjot K. Khaira, Tejinder Singh

The key-value (KV) cache is a foundational optimization in Transformer-based large language models (LLMs), eliminating redundant recomputation of past token representations during autoregressive generation. However, its memory footprint scales linearly with context length, imposing critical bottlenecks on GPU memory capacity, memory bandwidth, and inference

Vitaly Aksenov, Eve Bodnia, Michael H. Freedman, Michael Mulligan

Human mathematics (HM), the mathematics humans discover and value, is a vanishingly small subset of formal mathematics (FM), the totality of all valid deductions. We argue that HM is distinguished by its compressibility through hierarchically nested definitions, lemmas, and theorems. We model this with monoids. A mathematical deduction is a string of primiti

Karol Łukanowski, Michał Wójcik, Stefano Olivares, Konrad Banaszek

Optical key distribution (OKD) protects the physical layer of communication links by taking advantage of the inherent noise present in the photodetection process. It allows for efficient generation of a shared random key between two distant users that is secure against passive eavesdropping and can be subsequently used for cryptographic purposes. Moreover, i

Jacob Carlson, Neil Shephard

The potential system is a nonparametric time series model for assessing the causal impact of moving an assignment at time $t$ on an outcome at future time $t+h$, accounting for the presence of features. The potential system provides nonparametric content for, e.g., time series experiments, time series regression, local projection, impulse response functions

Kohei Kawabata, Shinsei Ryu

Non-Hermitian disordered systems have emerged as a central arena in modern physics, with ramifications spanning condensed matter, quantum, statistical, and high energy contexts. The same principles also underlie phenomena beyond physics, such as network science, complex systems, and biophysics, where dissipation, nonreciprocity, and stochasticity are ubiquit

Y. Sungtaek Ju

Probabilistic circuit (PC) structure learning is hampered by greedy algorithms that make irreversible, locally optimal decisions. We propose SymCircuit, which replaces greedy search with a learned generative policy trained via entropy-regularized reinforcement learning. Instantiating the RL-as-inference framework in the PC domain, we show the optimal policy

Haoyu Xie, Shengkai Xu, Cheng Guo, Muhammad Usama Saleem

Multi-view human mesh recovery (HMR) is broadly deployed in diverse domains where high accuracy and strong generalization are essential. Existing approaches can be broadly grouped into geometry-based and learning-based methods. However, geometry-based methods (e.g., triangulation) rely on cumbersome camera calibration, while learning-based approaches often g

Men Niu, Xinxin Fan, Quanliang Jing, Shaoye Luo

Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, etc. Nevertheless, many adversarial attacks still exist to threaten various c-MARL systems. At present, the studies mainly focus on single-adversary perturbation attacks and white-box adversari

Dhruv Menon, Vivek Singh, Xu Chen, Mohammad Reza Alizadeh Kiapi

Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the system

Philippe Gonzalez, Vera Margrethe Frederiksen, Torsten Dau, Tobias May

A multi-task learning framework is proposed for optimizing a single deep neural network (DNN) for joint noise reduction (NR) and hearing loss compensation (HLC). A distinct training objective is defined for each task, and the DNN predicts two time-frequency masks. During inference, the amounts of NR and HLC can be adjusted independently by exponentiating eac

So Won Jeong, Veronika Ročková

Computational pathology involves the digitization of stained tissues into whole-slide images (WSIs) that contain billions of pixels arranged as contiguous patches. Statistical analysis of WSIs largely focuses on classification via multiple instance learning (MIL), in which slide-level labels are inferred from unlabeled patches. Most MIL methods treat patches

Talha Akyildiz, Hessam Mahdavifar

Local constraint ordered statistics decoding (LC-OSD) provides strong soft decision performance for short block length linear codes, but its practical cost is dominated by the number of tested error patterns (TEPs). This paper proposes a neural early stopping (NES) protocol for LC-OSD with explicit cost control through one trade-off parameter balancing frame

Klaudiusz Czudek, Tomasz Szarek

We construct an e-chain on a locally compact space with the unique stationary distribution such that the strong law of large numbers does not hold. This answers negatively the question asked by \"O. Stenflo.

Antony Gitau, Martin Paulson, Bjørn-Jostein Singstad, Karl Thomas Hjelmervik

The classification of white blood cells (WBCs) from peripheral blood smears is critical for the diagnosis of leukemia. However, automated approaches still struggle due to challenges including class imbalance, domain shift, and morphological continuum confusion, where adjacent maturation stages exhibit subtle, overlapping features. We present a multi-stage fi

Yujie Zhou, Pengyang Ling, Jiazi Bu, Bingjie Gao

In practical AI workflows, complex tasks often involve chaining multiple generative models, such as using a video or 3D generation model after a 2D image generator. However, distributional mismatches between the output of upstream models and the expected input of downstream models frequently degrade overall generation quality. To address this issue, we propo

Siyang Ling, Sam S. C. Wong

Power law tails induced by nonlinearities of General Relativity (``sourced'' or ``nonlinear'' tails) were recently shown to dominate the late time waveform of Schwarzschild black hole ringdowns. We extend the analytical results regarding such nonlinear tails from Schwarzschild to Kerr black holes by studying the Teukolsky equation. Using a far field approxim

D. A. Langis, I. Liodakis, K. I. I. Koljonen, P. M. Kouch

High-energy (HE) neutrinos have been observed by the IceCube (IC) Neutrino observatory for over a decade. Nevertheless, the astrophysical origin and the responsible mechanisms producing these HE neutrinos are still a mystery, with many astrophysical phenomena as potential emitters. A plethora of previous studies have attempted to study the correlation betwee

Avery Bailey, Kaitlin Kratter, Andrew Youdin

In the core accretion model of giant planet formation, the late stages of runaway growth are regulated by the hydrodynamic infall of gas from the protoplanetary disk. For a subset of planet-disk pairings, this scenario is analogous to the classical Bondi problem, which has motivated a Bondi-like parameterization of accretion in some population synthesis mode

Korbinian Kottmann, David Wierichs, Guillermo Alonso-Linaje, Nathan Killoran

We introduce the flag decomposition as a central tool for unitary synthesis. It lets us carve out a diagonal unitary with $2^n$ degrees of freedom in such a way that the remaining flag circuit is parametrized by the optimal number of $4^n-2^n$ rotations. This enables us to produce parameter-optimal quantum circuits for generic unitaries and matrix product st

Rujula Yete, Wynn Jacobson-Galan, Ferdinand Ferdinand, Luc Dessart

We present multi-wavelength observations and a comprehensive analysis of the nearby (D$\sim$14 Mpc) Type IIb supernova (SN IIb) 2024iss. Observations of SN2024iss include an early ZTF detection at $\sim$40 minutes after first light and the earliest Hubble Space Telescope UV spectrum for a SN IIb to date at 7 days after first light. With the bolometric light

Tobias Géron, Maria R. Drout, W. V. Jacobson-Galán, C. D. Kilpatrick

The final stages of stellar evolution can be constrained by studying pre-SN variability. The incredible amount of data coming from the upcoming Rubin Legacy Survey of Space and Time (LSST) will be fundamental to this type of work. However, robustly measuring pre-SN variability can be hard, as even state-of-the-art image subtraction pipelines struggle when th

Avery Bailey, Andrew Youdin, Kaitlin Kratter

In this paper, we extend the foundational work of Bondi (1952) to include the effects of radiative feedback in gas-pressure-dominated environments. We construct steady-state spherically symmetric accretion solutions including radiative heating and cooling. Under the simplifying assumption of a constant opacity, the solutions are controlled by four dimensionl

Lucas Leclerc, Sergi Julià-Farré, Gabriel Silva Freitas, Guillaume Villaret

Analog quantum simulators offer a powerful microscopic probe of quantum many-body systems, yet have largely been benchmarked against model Hamiltonians rather than real materials. Here, we use a 256-qubit Rydberg simulator to implement the effective Hamiltonian of the frustrated triangular-lattice magnet TmMgGaO$_4$. Simulated magnetization curves agree quan

Ming Xie, Sankar Das Sarma

Moir\'e-induced narrow electronic bands in transition metal dichalcogenide superlattices support many correlated quantum phases characterized by novel charge, flavor, and topological orders. Among these, magnetic ordering emerges as the most ubiquitous, often serving as the parent state for other correlated phases, including quantum anomalous Hall states, as

Arman Sauliere, Guglielmo Lami, Pedro Ribeiro, Andrea De Luca

We study error correction type protocols in which a quantum channel encodes logical information into an enlarged Hilbert space. Specifically, we consider channels realized by one dimensional random noisy quantum circuits with spatially local interaction gates. We analyze both noise acting after the encoding and noise affecting the encoding circuit itself. Us

Aritra Sinha, Hannes Karlsson, Martin Ulaga, Alexander Wietek

Competition and coexistence of charge orders and superconductivity are hallmarks in many strongly correlated electron systems. Here, we unravel the precise role of charge fluctuations on the superconducting state in the $t$-$t'$-$J$ model of the high-temperature cuprate superconductors. Using finite-temperature tensor network simulations, we investigate ther

Lucas S. Mandacarú Guerra, Stephanie O'Neil, Mariangela Lisanti, Sandip Roy

We present the first detailed analysis of the effects of dissipative dark matter on stellar streams. As a concrete example, we generate a cosmological hydrodynamic zoom-in simulation of a Milky Way-mass galaxy, assuming that the dark matter consists of Cold Dark Matter (CDM) with a sub-component ($\sim6\%$) of Atomic Dark Matter (ADM). The ADM subcomponent b

Nhat-Minh Nguyen, Kazuyuki Akitsu, Atsushi Taruya

The scale-dependent bias in halo and galaxy power spectra is a key signature of local primordial non-Gaussianity (local PNG), with PNG sensitivity scaling as $b_\phi/b_1$ -- the ratio of their responses to long-wavelength primordial potential $b_\phi$ and late-time density fluctuations $b_1$. For number density fluctuations, these responses are closely tied

V. Alfradique, C. R. Bom, G. Teixeira, A. Santos

A new measurement of the Hubble constant $H_0$ is presented using the statistical dark siren method applied to a sample of seven well-localized gravitational-wave (GW) events from the fourth LIGO-Virgo-KAGRA (LVK) observing run and ten additional events from the first three runs. Galaxy catalogs from the DESI Legacy Imaging Survey (LS) are combined with a de

Chang Nie, Chaoyou Fu, Yifan Zhang, Haihua Yang

Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality

Yu Qi, Xinyi Xu, Ziyu Guo, Siyuan Ma

Video generative models show emerging reasoning behaviors. It is essential to ensure that generated events remain causally consistent across frames for reliable deployment, a property we define as reasoning coherence. To bridge the gap in literature for missing reasoning coherence evaluation, we propose MME-CoF-Pro, a comprehensive video reasoning benchmark

Xinyi Shang, Yi Tang, Jiacheng Cui, Ahmed Elhagry

Existing tampering detection benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are untouched or only trivially modified, while subtle yet consequential edits outside the mask are treated as natural. We reformulate VLM image tampering from coarse region labels to a pixel-grounded, meaning and

Jiazheng Xing, Fei Du, Hangjie Yuan, Pengwei Liu

Recent advances in diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained control over both foreground and background elements. However, precise face-attribute alignment across subjects remains challenging, as existing methods lack explicit mechanisms to ensure intra-group consistency.

Sebastian Gerard, Josephine Sullivan

Many segmentation tasks, such as medical image segmentation or future state prediction, are inherently ambiguous, meaning that multiple predictions are equally correct. Current methods typically rely on generative models to capture this uncertainty. However, identifying the underlying modes of the distribution with these methods is computationally expensive,

Anqi Dong, Yongxin Chen, Karl H. Johansson, Johan Karlsson

Steering large-scale swarms with only limited control updates is often needed due to communication or computational constraints, yet most learning-based approaches do not account for this and instead model instantaneous velocity fields. As a result, the natural object for decision making is a finite-window control quantity rather than an infinitesimal one. T

Sebastian Gerard, Josephine Sullivan

Predicting future states in uncertain environments, such as wildfire spread, medical diagnosis, or autonomous driving, requires models that can consider multiple plausible outcomes. While diffusion models can effectively learn such multi-modal distributions, naively sampling from these models is computationally inefficient, potentially requiring hundreds of

Yuan Zhou, Luanyuan Dai, Yongzhi Li, Shijie Hao

Video-driven 3D human reaction generation aims to synthesize 3D human motion in response to the action observed in a video, playing an important role in interactive multimedia systems and embodied agents. Yet reaction motions generated by current methods often fail to match what the observed video calls for. We observe that one factor behind this failure is

Satoshi Iizuka, Shun Okamoto, Kazuhiro Fukui

In this work, we propose Image-to-Image Rectified Flow Reformulation (I2I-RFR), a practical plug-in reformulation that recasts standard I2I regression networks as continuous-time transport models. While pixel-wise I2I regression is simple, stable, and easy to adapt across tasks, it often over-smooths ill-posed and multimodal targets, whereas generative alter

Jingyang Lin, Jialian Wu, Jiang Liu, Ximeng Sun

Video agentic models have advanced challenging video-language tasks. However, most agentic approaches still heavily rely on greedy parsing over densely sampled video frames, resulting in high computational cost. We present VideoSeek, a long-horizon video agent that leverages video logic flow to actively seek answer-critical evidence instead of exhaustively p

Alejandro Almodóvar, Mar Elizo, Patricia A. Apellániz, Santiago Zazo

Causal generative models provide a principled framework for answering observational, interventional, and counterfactual queries from observational data. However, many deep causal models rely on highly expressive architectures with opaque mechanisms, limiting auditability in high-stakes domains. We propose KaCGM, a causal generative model for mixed-type tabul