March 2026 arXiv papers — page 107

Amirhossein Roknilamouki, Arnob Ghosh, Eylem Ekici, Ness B. Shroff

While offline reinforcement learning provides reliable policies for real-world deployment, its inherent pessimism severely restricts an agent's ability to explore and collect novel data online. Drawing inspiration from safe reinforcement learning, exploring near the boundary of regions well covered by the offline dataset and reliably modeled by the simulator

Nived Rajaraman, Audrey Huang, Miro Dudik, Robert Schapire

Chain-of-thought reasoning, where language models expend additional computation by producing thinking tokens prior to final responses, has driven significant advances in model capabilities. However, training these reasoning models is extremely costly in terms of both data and compute, as it involves collecting long traces of reasoning behavior from humans or

Flávio B. Gonçalves, Marcos O. Prates, Gareth O. Roberts

Geostatistics is a branch of statistics concerned with stochastic processes over continuous domains, with Gaussian processes (GPs) providing a flexible and principled modelling framework. However, the high computational cost of simulating or computing likelihoods with GPs limits their scalability to large datasets. This paper introduces the piecewise continu

Anton T. Than, Jim Furches, Debopriyo Biswas, Sarah Chehade

Nonlocal games provide application-level benchmarks for quantum hardware whose classical performance bounds are information-theoretic, holding against all classical strategies regardless of computational resources. We implement a 14-vertex graph coloring game, the smallest graph exhibiting a quantum-classical separation for this game type, on four trapped-io

Avraham Kreindel, Isaac Barouch Essayag, Aryeh Lev Zabokritskiy

We study error-correcting codes in the space $\mathcal{S}_{n,q}$ of length-$n$ multisets over a $q$-ary alphabet under the deletion metric, motivated by permutation channels in which ordering is completely lost and errors act only on symbol multiplicities. We develop two complementary directions. First, we present polynomial Sidon-type constructions over fin

Chad Nester, Niels Voorneveld

We present a calculus that models a simple sort of process interaction. Our calculus consists of a collection of terms together with a rewrite relation, parameterised by an arbitrary multicategory whose morphisms we understand as non-interactive processes. We show that our calculus is confluent and terminating, and that terms modulo the induced convertibilit

Taeyoung Lee, Gregory S. Chirikjian

This paper presents a global, coordinate-free formulation of the Fokker-Planck equation on Riemannian manifolds. In the Stratonovich formulation, the infinitesimal generator is expressed intrinsically through Lie derivatives, and its adjoint is derived via the divergence theorem, yielding a concise geometric form of the Fokker-Planck equation. In the Ito for

D. L. Suhas, Weiqing Han, Toshiaki Shinoda, Rui Sun

Marine heatwaves (MHWs) threaten marine ecosystems and significantly impact weather patterns. In the Arabian Sea, summer MHWs are of particular concern due to their potential impacts on the Indian summer monsoon, a lifeline for nearly a billion people. However, the drivers of these MHWs and their influence on atmospheric circulation and monsoon rainfall rema

Lukas Rapp, Muriel Médard, Eugene Tang, Ken R. Duffy

We introduce a generalized low-density parity-check decoding framework for quantum Tanner codes utilizing soft-output guessing random additive noise decoding (SOGRAND). By soft-output decoding entire component codes rather than individual parity checks, we mitigate the effects of trapping sets and cycles, resulting in improved convergence. Because our decode

Ajay Annamareddy, Bu Wang, Paul M. Voyles, Izabela Szlufarska

While diffusion in crystalline solids is quantitatively understood through defect-mediated atomic hops, no comparable quantitative framework exists for glasses. In these systems, the origin of large diffusion activation energies remains puzzling, despite local rearrangements involving low barriers. Using molecular dynamics simulations of metallic glasses, we

Xiaoli Yan, Millicent A. Firestone, Murat Keceli, Santanu Chaudhuri

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the HPHT environment required for nanodiamond formation, subsequ

Kaiyang Li, Shihao Ji, Zhipeng Cai, Wei Li

Approximate subgraph matching (ASM) is a task that determines the approximate presence of a given query graph in a large target graph. Being an NP-hard problem, ASM is critical in graph analysis with a myriad of applications ranging from database systems and network science to biochemistry and privacy. Existing techniques often employ heuristic search strate

A. V. Lipatov, G. I. Lykasov, M. A. Malyshev

We report the results of calculations of inclusive soft hadron production in proton-lead collisions at the LHC in the framework of modified quark-gluon string model (QGSM) extended to $pA$ interactions. Our consideration involves the nuclear modification of previously proposed transverse momentum dependent (TMD, or unintegrated) gluon density in a proton, wh

Naveen Gupta, Bharath K Sriperumbudur

Estimation of the mean and covariance functions is a fundamental problem in functional data analysis, particularly for discretely observed functional data. In this work, we study a regularization-based framework for estimating the mean and the covariance functions within a reproducing kernel Hilbert space (RKHS) setting. Our approach utilizes a spectral regu

Zachary Lee, Nataša Pavlović, Gigliola Staffilani, Nicola Visciglia

In this paper we consider the twice-renormalized, complex-valued modified KdV (mKdV) on the one-dimensional torus introduced by Chapouto. Our main result is the construction of an invariant measure supported at low-regularity. This work complements the work of Kenig et al., which constructed invariant measures supported in higher-regularity spaces for the no

Md Hasibul Husain Hisham, Shireen Elhabian, Ganesh Adluru, Jason Mendes

Accelerated 3D late gadolinium enhancement (LGE) MRI requires robust reconstruction methods to recover thin atrial structures from undersampled k-space data. While unrolled model-based networks effectively integrate physics-driven data consistency with learned priors, they operate at the acquired resolution and may fail to fully recover high-frequency detail

Abhijeet M. Kulkarni, Ioannis Poulakakis, Guoquan Huang

Proprioceptive-only state estimation is attractive for legged robots since it is computationally cheaper and is unaffected by perceptually degraded conditions. The history of joint-level measurements contains rich information that can be used to infer the dynamics of the system and subsequently produce navigational measurements. Recent approaches produce the

James Usevitch

This paper presents novel theoretical results to guarantee multi-agent set invariance using Matrix Control Barrier Functions in sampled-data systems. More specifically, the paper presents conditions under which heterogeneous control-affine agents applying zero-order-hold control inputs can compute control inputs to render safe sets defined by matrix inequali

Devjyoti Chakraborty, Zaki Sukma, Rakandhiya D. Rachmanto, Kriti Ghosh

Neural Radiance Fields (NeRF) have emerged as a powerful approach for photorealistic 3D reconstruction from multi-view images. However, deploying NeRF for satellite imagery remains challenging. Each scene requires individual training, and optimizing architectures via Neural Architecture Search (NAS) demands hours to days of GPU time. While existing approache

Geetha Ramasubbu, Andrè Kaup, Christian Herglotz

The main contributions of this paper are twofold: First, we present an in-depth analysis of the impact of frame rate reductions on the visual quality of the video and the encoding as well as decoding energy. Second, we propose a lightweight frame rate selection method for energy- and quality-aware encoding. Concerning the first contribution, this paper perfo

Yuxiang Guan, Iman Shames, Tyler Summers

We formulate and study a class of two-player zero-sum stochastic dynamic games with partial and asymmetric information. Information asymmetry introduces fundamental challenges involving \emph{belief representation} and \emph{theory of mind} issues, where agents must impute belief states and estimates of other agents to inform their own strategy. To avoid an

S. Ismailzadeh, B. Abedi Ravan

Photonic quantum computing has gained significant interest in recent years due to its potential for scaling to large numbers of qubits. A critical requirement for fault-tolerant quantum computation is the reliable generation of non-Gaussian quantum states, typically achieved using Gaussian operations and photon-number-resolving detectors. However, the probab

Alice Woodbridge, Kasra Amini, Fredrik Lundell, Outi Tammisola

The mechanical response of yield-stress materials below the yield point remains a subject of debate. Two of the most widely used constitutive models for these materials offer fundamentally conflicting views: one permits plastic flow at all stress levels, the other assumes entirely recoverable viscoelasticity below yield. Using parallel superposition rheometr

Jasmine Rienecker, Katarina Mpofu, Naman Goel, Siddhartha Datta

Large language models (LLMs) based AI systems increasingly mediate what billions of people see, choose and buy. This creates an urgent need to quantify the systemic risks of LLM-driven market intermediation, including its implications for market fairness, competition, and the diversity of information exposure. This paper introduces ChoiceEval, a reproducible

Zhanqi Zhang, Shun Li, Bernardo L. Sabatini, Mikio Aoi

Intracortical brain-computer interfaces (BCIs) can decode speech from neural activity with high accuracy when trained on data pooled across recording sessions. In realistic deployment, however, models must generalize to new sessions without labeled data, and performance often degrades due to cross-session nonstationarities (e.g., electrode shifts, neural tur

Nikhil Gosala, B. Ravi Kiran, Senthil Yogamani, Abhinav Valada

Monocular 3D object tracking aims to estimate temporally consistent 3D object poses across video frames, enabling autonomous agents to reason about scene dynamics. However, existing state-of-the-art approaches are fully supervised and rely on dense 3D annotations over long video sequences, which are expensive to obtain and difficult to scale. In this work, w

Zijin Gu, Tatiana Likhomanenko, Vimal Thilak, Jason Ramapuram

Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of \emph{expert paths} -- the sequence of expert selections a token makes across all layers. This perspective reveals that, despite $N^L$ possible paths for $N$ experts across $L$ layers,

Shawn Samuel Carl McAdam, Samuel Opoku Agyemang, Alexei Cheviakov

General equations describing shear displacements in incompressible hyperelastic materials, holding for an arbitrary form of strain energy density function, are presented and applied to the description of nonlinear Love-type waves propagating on an interface between materials with different mechanical properties. The model is valid for a broad class of hyper-

Piotr A. Kowalski, Szymon Kucharczyk, Jacek Mańdziuk

This paper presents the constrained Hybrid Metaheuristic (cHM) algorithm as a general framework for continuous optimisation. Unlike many existing metaheuristics that are tailored to specific function classes or problem domains, cHM is designed to operate across a broad spectrum of objective functions, including those with unknown, heterogeneous, or complex p

Alvin Rajkomar, Pavan Sudarshan, Angela Lai, Lily Peng

Background: Clinical trials rely on transparent inclusion criteria to ensure generalizability. In contrast, benchmarks validating health-related large language models (LLMs) rarely characterize the "patient" or "query" populations they contain. Without defined composition, aggregate performance metrics may misrepresent model readiness for clinical use. Metho

Jaime Cofre

In this article, I advance the idea that physics plays a central role in cell differentiation and makes fundamental contributions to morphogenesis, revealing the totipotent nature of the zygote. Totipotency is a persistent mechanical memory that preserves the biomechanical records of animal morphogenesis. I examine the mechanical and biophysical pathways und

Shuang Zhou, Angela Iovino, Marcella Longhetti, Francesco La Barbera

We investigate the evolutionary pathways of green valley (GV) galaxies drawn from the SDSS-IV/MaNGA survey. The GV sample is divided into fast- and slow-rotating galaxies based on stellar spin, and their stellar and gas-phase metallicities are compared. Fast-rotating galaxies exhibit systematically higher metallicities than slow-rotating galaxies in both gas

Saba Mahmoodpour, Andrew M. Moran

Condensed-phase spectral line shapes encode the strength and timescale of interactions between molecules and their environments, yet these ideas are often difficult to introduce at the undergraduate level due to their reliance on formal theoretical treatments. We present a visualization-based approach that combines analytic results with numerical simulations

Jin Mo Yang, Hyung-Sin Kim, Saewoong Bahk

Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -- a scorer that leads on one benchmark often falters on another. We attribute this inconsistency to a shared structural limitation: logit-based methods see only the classifier's confidence

Matthew I. Jones, Zachary Winkeler

Graph colorings have been of interest to mathematicians for a long time, but relatively recently, social scientists have also found them to be interesting tools for studying group behavior. In the last 20 years, scientists have begun to study how coloring problems can be solved by groups of individuals on a graph, which has led to new insights into network s

Luigi Caputi, Sabino Di Trani

The aim of this work is to explicitly compute the K-theory of the category of matroids with respect to the covering family of Tutte coverings. In particular, we show that this is equivalent to the K-theory spectrum of the category of graphic matroids on looped forests, with the covering family generated by isomorphisms. Further, we show that this yields an e

Silvio Barandun

We illuminate the fundamental mechanism responsible for the transition between the non-Hermitian skin effect and defect-induced Anderson localization in the bulk via the study of Lyapunov exponents. We obtain a proof that the change of the topological invariant associated with an eigenvalue coincides with the eigenvector crossover from non-Hermitian skin eff

Cheng-Hao He, Yi-Zhuang You, Xiao Yan Xu

Symmetry plays a central role in modern physics, from classifying quantum states to characterizing phases of matter through spontaneous symmetry breaking. In interacting fermionic systems with multiple internal degrees of freedom, however, determining the full continuous symmetry group and classifying possible order parameters remain challenging. In this wor

Sara Pohland, Xenofon Foukas, Ganesh Ananthanarayanan, Andrey Kolobov

Mobile robotic manipulation--the ability of robots to navigate spaces and interact with objects--is a core capability of physical AI. Foundation models have led to breakthroughs in their performance, but at a significant computational cost. We present the first measurement study of mobile robotic manipulation workloads across onboard, edge, and cloud GPU pla

C. S. Elder, Guillaume Marçais, Carl Kingsford

We study Turnpike with uncertain measurements: reconstructing a one-dimensional point set from an unlabeled multiset of pairwise distances under bounded noise and rounding. We give a combinatorial characterization of realizability via a multi-matching that labels interval indices by distinct distance values while satisfying all triangle equalities. This yiel

Marios Krestenitis, Christos Tzelepis, Konstantinos Ioannidis, Stefanos Vrochidis

Visual-Language Models (VLMs) have achieved remarkable progress in image captioning, visual question answering, and visual reasoning. Yet they remain prone to vision-language misalignment, often producing overly generic or hallucinated descriptions. Existing approaches address this via instruction tuning-requiring costly, large-scale annotated datasets or vi

Simon M. Brealy, Lawrence A. Bull, Daniel S. Brennan, Pauline Beltrando

Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring the use of additive Gaussian processes to reveal variations in turbine-specific and farm-level power models over a collected wind farm dataset. The predictions illustrate patterns i

Gregory N. Frank

Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the layer where alignment often operates: routing from concept detection to behavioral policy. We study political censorship in Chinese-origin language models as a natural experiment, using probes, surgical ablations, and

Philipp Wittenberg, Lizzie Neumann, Kristof Maes, Jan Gertheiss

In Structural Health Monitoring (SHM), sensor measurements and derived features such as eigenfrequencies often exhibit systematic daily patterns and can therefore be naturally represented as functional data. Furthermore, these patterns are typically influenced by environmental factors, particularly temperature, which can substantially affect the observed sys

Cheng-Hao He, Yi-Zhuang You, Xiao Yan Xu

A central question beyond the Landau paradigm is the non-perturbative critical theory of the symmetric mass generation (SMG) transition, where strong interactions gap Dirac fermions in (2+1) dimensions without triggering spontaneous symmetry breaking or topological order. While previous studies have already provided evidence for direct SMG transitions in (2+

Eric Steinbring, Y. Jack Ng

Spacetime is foamy due to quantum fluctuations. Various gedanken experiments show that distances fluctuate by amounts consistent with the holographic principle, hence the name "holographic quantum foam" (HQF). One important prediction of HQF is that necessarily there exists a dark sector in the universe. The resulting cosmology is found (at least qualitative

Kwanghee Park, Ryotaro Hoshino, Yoshitaka Tanimura

We model, simulate, and analyze the intramolecular modes of liquid H2O and D2O to elucidate how energy excitation, relaxation, and vibrational dephasing interplay through anharmonic mode-mode coupling. Our analysis employs two-dimensional (2D) correlation spectra, a representative observable in nonlinear infrared vibrational spectroscopy. Accurate reproducti

Diego Hernández-Juárez, Mónica Rodríguez, Miriam Peña

We identify different dust features in our compilation of infrared spectra for 267 planetary nebulae (PNe) from the Spitzer, ISO, and IRAS telescopes. We classify 209 objects according to their dust type: mixed dust (MD), oxygen-rich dust (ORD), carbon-rich dust (CRD), PNe with only polycyclic aromatic hydrocarbons (PAHs) in their spectra (oPAH), and feature

Kwanghee Park, Seiji Ueno, Yoshitaka Tanimura

We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian (MAB) models from molecular dynamics (MD) trajectories for simulating nonlinear vibrational spectra of intramolecular modes of molecular liquids. By leveraging machine learning (ML) techniques to capture vibrational anhar

Chenguang Pan, Zhou Zhang, Weixuan Xiao, Chengyuan Yao

In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educational data mining (EDM) research. We conceptualize EDM-ARS as a general framework for domain-aware automated research pipelines, where educational expertise is embedded into each stage of

Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina, Hervé Déjean

While large language models (LLMs) have advanced the development of general-purpose agents, robust generalization to unseen tasks remains challenging. Two common approaches are supervised fine-tuning and training-free memory-augmented generation using retrieved experience; yet both have limitations: fine-tuning often fails to extrapolate to new tasks, while

Koudzo Togbévi Selom Sobah, Amah Séna D'Almeida

Since the pioneering work of James E. Broadwell, discrete velocity models (DVMs) have played a fundamental role in approximating the Boltzmann equation and in the analysis of non-equilibrium gas dynamics. Despite their apparent simplicity, many fundamental analytical questions remain open, in particular the global existence and uniqueness of classical soluti

Florian Grundbacher, Tomasz Kobos

We determine certain Banach-Mazur distances involving $\ell_p$-direct sums of finite-dimensional real normed spaces and related cone constructions of convex bodies. Using a recent characterization of the optimal Banach-Mazur position with respect to the Euclidean ball, we derive a closed formula for the distance from $X_1 \oplus_p \cdots \oplus_p X_k$ to Euc

Matthew Pharr, Nikolas Logan, Carlos Paz-Soldan, Jong-Kyu Park

Resonant drive in tokamaks is routinely quantified using a variety of different metrics that target different aspects of a resonant response to an external perturbation. Two of the most direct metrics, $\Delta_{mn}$ and $b_{pen}$, are widely used but their relative behavior was previously uncharacterized. This work examines how these metrics representing the

Yash Ranjan, Rahul Sengupta, Anand Rangarajan, Sanjay Ranka

Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the behavior models controlling the actions of the actors are overly simplistic and fails to capture realistic actor-actor interactions. Deep learning-based methods have been applied to model vehicles and pedestrians as ``agents" respond

Georg Friedrich Harrer, Andrew Giuliani, Misha Padidar, Robert Davies

The non-resonant divertor (NRD) offers a promising exhaust solution for stellarators, combining topological simplicity with resilience to magnetic field perturbations. To experimentally validate the robustness of non-resonant divertors in a quasi-axisymmetric (QA) configuration, we introduce STAR_Lite, a new stellarator experiment at Hampton University. This

Hadi Salmasian, Alistair Savage, Yaolong Shen

We study the classification of submodules of module categories over monoidal categories, extending ideas of Coulembier on the classification of tensor ideals in monoidal categories. We develop a framework that applies to module categories equipped with a twisted cylinder twist, a structure closely related to the twisted reflection equation and quantum symmet

Zhenkun Yuan, Genevieve Amobi, Shaham Quadir, Smitakshi Goswami

Halide perovskites have revolutionized optoelectronics by demonstrating that long carrier lifetime can be achieved in materials processed in relatively uncontrolled environments, whereas conventional inorganic semiconductors typically suffer from short carrier lifetime unless very carefully prepared and postprocessed. Here, we report the discovery of excepti

S. Mohammad Mousavi, Sarvesh Joshi, Franck Vernerey, Nikolaos Bouklas

Cavitation in soft elastomers and adhesives is often viewed as an elastic instability, commonly tied to the study of incompressible solids. It is the first step prior to fibrillation and ultimate failure in adhesives. Building on the work of Lamont et al. (2025), elastomeric materials are treated as a crosslinked van der Waals fluid. The van der Waals contri

Tamer Shanableh

Neural Representations for Videos (NeRV) encode entire video sequences within neural network parameters, offering an alternative paradigm to conventional video codecs. However, the convolutional decoder of NeRV remains computationally expensive and memory intensive, limiting its deployment in resource-constrained environments. This paper proposes LRConv-NeRV

Annalisa T. Taylor, Malachi Landis, Ping Guo, Todd D. Murphey

Applying micro-patterns to surfaces has been shown to impart useful physical properties such as drag reduction and hydrophobicity. However, current manufacturing techniques cannot produce micro-patterned surfaces at scale due to high-cost machinery and inefficient coverage techniques such as raster-scanning. In this work, we use multiple robots, each equippe

Jessica Birky, Rory K. Barnes

We present Active Learning for Accelerated Bayesian Inference (\texttt{alabi}): an open-source Python package for performing Bayesian inference with computationally expensive models. Given a forward model and observational data to construct a likelihood and priors, \texttt{alabi}\ uses a Gaussian Process (GP) surrogate model trained to predict posterior prob

Haocheng Luo, Zehang Deng, Thanh-Toan Do, Mehrtash Harandi

Direct Preference Optimization (DPO) has emerged as a popular algorithm for aligning pretrained large language models with human preferences, owing to its simplicity and training stability. However, DPO suffers from the recently identified squeezing effect (also known as likelihood displacement), where the probability of preferred responses decreases uninten

Jiaxin Liu, Anzhe Cheng, Paul Bogdan

When an RL agent's observations contain distractors driven by the same confounders as its true state, observational data alone cannot identify which dimensions the agent controls. In our benchmarks, even state-conditioned observational selectors can collapse when distractors mimic controllable state variables. We propose Interventional Boundary Discovery (IB

Tomek Kaszyński

Can multi-agent communication pressure extract discrete, compositional representations of invisible physical properties from frozen video features? We show that agents communicating through a Gumbel-Softmax bottleneck with iterated learning develop positionally disentangled protocols for latent properties (elasticity, friction, mass ratio) without property l

Philippe Formont, Maxime Darrin, Ismail Ben Ayed, Pablo Piantanida

Recent reasoning-based large language models have shown strong performance on tasks with verifiable outcomes, but their use in de novo molecular generation remains limited by the lack of training environments where rewards can be computed without reference molecules. We introduce MolRGen, a benchmark and molecular verifier for training and evaluating reasoni

Sitan Chen, Jingqiu Ding, Mahbod Majid, Walter McKelvie

Bayesian methods lie at the heart of modern data science and provide a powerful scaffolding for estimation in data-constrained settings and principled quantification and propagation of uncertainty. Yet in many real-world use cases where these methods are deployed, there is a natural need to preserve the privacy of the individuals whose data is being scrutini

Ilya I. Bogdanov, Fedor Petrov, Anton Sadovnichiy, Fedor Ushakov

In 2019, P. Higgins formulated [1] a question about bipartite graphs (see Conjecture 1 below); this question arises in the study of regular finite semigroups. F. V. Petrov formulated [2] another combinatorial conjecture (Conjecture 3); Conjecture 3 implies Conjecture 1 and seems simple itself. However, both conjectures remain unproven in the general case. In

Adam Samorzewski, Adrian Kliks

In this paper, we examine the distribution of radio signal propagation within the city of Poznan (Poland) to determine optimal locations for deploying Reconfigurable Intelligent Surfaces (RIS). The study focuses on designing a 5G/6G Radio Access Network (RAN), incorporating eight Base Stations (BSs) that utilize either Single Input Single Output (SISO), or M

Ben Adcock, Khiem Can, Xuemeng Wang

Random Feature Models (RFMs) have become a powerful tool for approximating multivariate functions and solving partial differential equations efficiently. Sparse Random Feature Expansions (SRFE) improve traditional RFMs by incorporating sparsity, making it particularly effective in data-scarce settings. In this work, we integrate active learning with sparse r

Matan Sade, Aviv Tsarfati, Ofek Birnholtz

High-energy astrophysical events, particularly Gamma Ray Bursts (GRBs), have been proposed as significant contributors to mass extinction events on Earth-like planets in most of the galaxy, internal to our radius in it. This paper examines the extent to which GRBs may reset the evolutionary progress of complex life through repeated extinction-level disruptio

Yaqi Wu, Jingfeng Wang, Xin Maizie Zhou, Yanxiang Zhao

Spatial transcriptomics (ST) profiles gene expression across a tissue section while preserving the spatial coordinates. Because current ST technologies typically profile two-dimensional tissue slices, integrating and aligning slices from different regions of the same three-dimensional tissue or from samples under different conditions enables analyses that re

Sindhuja Madabushi, Arda Dogan, Jonathan Liu, Dian Chen

Existing XAI metrics measure faithfulness for a single model, ignoring model multiplicity where near-optimal classifiers rely on different or spurious acoustic cues. In noisy farm environments, stationary artifacts such as ventilation noise can produce explanations that are faithful yet unreliable, as masking-based metrics fail to penalize redundant shortcut

Bohan Wu, Roberto Martín-Martín, Li Fei-Fei

We address the challenge of learning to manipulate deformable objects with unknown dynamics. In non-rigid objects, the dynamics parameters define how they react to interactions -- how they stretch, bend, compress, and move -- and they are critical to determining the optimal actions to perform a manipulation task successfully. In other robotic domains, such a

Xuan Chen, Lu Yan, Ruqi Zhang, Xiangyu Zhang

Large Language Model (LLM) agents increasingly act through external tools, making their safety contingent on tool-call workflows rather than text generation alone. While recent benchmarks evaluate agents across diverse environments and risk categories, a fundamental question remains unanswered: how complete are existing test suites, and what unsafe interacti

Simon Rubinstein-Salzedo, Stephen Zhou

We find that partisan mis\`ere quotients can have any finite cardinality other than 3, answering a question of Allen. This contrasts with impartial mis\`ere quotients, which must have even cardinality.

James M. Hyman

We study multi-digit correlations in Benford sequences b^n for integer bases 2 <= b <= 1000, measuring dependence via conditional mutual information (CMI). A resonance ratio derived from the continued fraction expansion of log_10(b) classifies bases into convergent and persistent regimes (Theorem 3.13): among 996 bases surveyed, 84 (8.4%) exhibit persistent

Yongseok Jo, Minyong Jung, Greg L. Bryan, Seoyoung Kim

Nuclear Star Clusters (NSCs) are dense stellar systems located at the centers of galaxies. Employing Enzo-Abyss, which integrates hydrodynamics with a direct N-body solver, we introduce a simulation capable of resolving the evolution of NSCs within a live galaxy. This includes live dark matter, gaseous dynamics, star formation and feedback, collisional dynam

Vasil A. Saroka

The graph-theoretic topological frustration is a peculiar situation on a finite piece of the honeycomb lattice that prevents a full pairwise coupling of the lattice sites via nearest neighbor links, even when the total number of sites is an even number. This type of frustration is inherent for organic molecules that are classified as concealed non-Kekulean h

Mohammad NaseriTehrani, MohammadJavad Salehi, Antti Tölli

Integrating coded caching (CC) into multiple-input multiple-output (MIMO) communications significantly enhances the achievable degrees of freedom (DoF). This paper investigates a practical cache-aided asymmetric MIMO configuration with cache ratio $γ$, where a server with $L$ transmit antennas communicates with $K$ users. The users are partitioned into $J$ g

Himadri S Samanta

Early detection of Alzheimer's disease from spontaneous speech has emerged as a promising non-invasive screening approach. However, the influence of automatic speech recognition (ASR) quality on downstream clinical language modeling remains insufficiently understood. In this study, we investigate Alzheimer's disease detection using lexical features derived f

Md. Mehedi Hasan, Rafid Mostafiz, Bikash Kumar Paul, Md. Abir Hossain

ROS 2 has become a dominant middleware for robotic systems, where perception, estimation, planning, and control pipelines are structured as directed acyclic graphs of callbacks executed under a shared executor. However, default ROS 2 executors use best-effort dispatch without cross-DAG priority enforcement, leading to callback contention, structural priority

Wenshuo Wang, Fan Zhang

Researchers train neural simulators on uniformly sampled numerical simulation data. But under the same budget, does systematically sampled data provide the most effective information? A fundamental yet unformalized problem is how to sample training data for neural simulators so as to maximize rollout accuracy. Existing data sampling methods either tend to co

Chloe H. Choi, Alison L. Marsden, Daniele E. Schiavazzi

Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational inference can efficiently estimate the joint posterior distribution of boundary conditions, with amortization of training efforts over clinical targets. However, even the most mod

Gökçen Devlet Şen, Juan E. Machado, Gülay Öke Günel, Johannes Schiffer

Continuous-time primal-dual gradient dynamics (PDGD) is an ubiquitous approach for dynamically solving constrained distributed optimization problems. Yet, the distributed nature of the dynamics makes it prone to communication uncertainties, especially time delays. To mitigate this effect, we propose a delay-robust continuous-time PDGD. The dynamics is obtain

Saket Sanjeev Chaturvedi, Joshua Bergerson, Tanwi Mallick

As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities, from potential "biosafety risks" to "dangerous explosions." Ensuring trustworthy deployment in science requires a new paradigm centered on reliability (ensuring factual accuracy and reproducibility), safety (preven

Pratiti Paul, Christo K. Thomas, Walid Saad, Eric W. Burger

In this paper, the challenge of resilient vehicular communications is investigated for a vehicle-to-vehicle (V2V) wireless link subject to timing jitter induced by the stochastic evolution of interference and inter-vehicular separation dynamics. To characterize the dynamic behavior of V2V systems under the influence of multiple stochastic deterioration proce

Kyle K. Boone, Daniel J. Eisenstein

We study the non-Gaussianity of the large-scale clustering of high-redshift halos, seeking to assess which terms of standard bias expansions are needed to understand these highly biased populations. We find that the clustering can be well modeled with only linear and quadratic bias parameters while assuming a Gaussian underlying matter field. Our analysis fo

Avidaan Srivastava, René Doyon, François Bouchy, Étienne Artigau

A particularly intriguing subclass of rocky exoplanets are the ultra-short period (USP) worlds that orbit their host stars in less than a day. These planets are particularly rare around M dwarf stars, with so far only ten that have a constrained mass and radius. We present the validation and characterization of the ultra-short period (0.3-days), Earth-sized

Martin Nägele, Christian Nöbel, Rico Zenklusen

The odd-red bipartite perfect matching problem asks to find a perfect matching containing an odd number of red edges in a given red-blue edge-colored bipartite graph. While this problem lies in $\mathsf{P}$, its polyhedral structure remains elusive, despite renewed attention to achieving better polyhedral understanding, nurtured by recent advances from two c

Avery Johnson, Mohammad Majharul Islam, Riad Akram, Abdullah Muzahid

The exponential increase in complex IPs within modern SoCs, driven by Moore's Law, has created a pressing need for fast and accurate hardware-software power-performance analysis. Traditional performance simulators (such as cycle accurate simulators) are often too slow to simulate full benchmarks within a reasonable timeframe; require considerable effort for

Anuj K. Nayak, Paul G. Baity, Peter J. Love, Nicholas Jeon

Fault-tolerant quantum computation demands extremely low logical error rates, yet superconducting qubit arrays are subject to radiation-induced correlated noise arising from cosmic-ray muon-generated quasiparticles. The quasiparticle density is unknown and time-varying, resulting in a mismatch between the true noise statistics and the priors assumed by stand

Tae Gwan Park, Xufan Li, Kyungnam Kang, Austin Houston

Defects in two-dimensional transition metal dichalcogenides (TMDCs) broadly affect their optical and electronic properties. Directly capturing the ultrafast processes of exciton trapping and defect-bound exciton formation is crucial for understanding and advancing defect-mediated optoelectronics and quantum technologies. However, the weak transient optical a

Utsab Gangopadhyaya, Suman Pal, Gargi Chaudhuri

In this work, we have calculated the transport coefficients: shear viscosity and thermal conductivity inside the neutron star core. Our calculation is based on the relativistic kinetic theory approach using a modified BUU equation for quasi-particles whose mass and the chemical-potential and thus in turn the Fermi surface varies with the baryonic density $\r

Braden M. Weight, Zheng Pei, Sergei Tretiak

In computational chemistry of molecular materials, strong static correlation effects appear when electronic states, often involving the ground state, become quasi-degenerate, as occurs, for example, in bond-breaking processes. Such situations present significant challenges for accurate theoretical treatment. In these regimes, many-body methods involving a si

Marcel Cech, Johan du Buisson, Cecilia De Fazio, Federico Carollo

Kinetically constrained models have been widely studied in the context of glass formers and non-equilibrium statistical mechanics. Although their simple local rules often result in structureless static properties, their dynamics exhibit intricate emergent phenomena. In this work, we investigate monitored quantum circuit models that interpolate between classi

Jose M. Folgueiras, Lucas G. Chej, Luis L. Zurdo, Alejandro G. Monastra

Phase-sensitive optical coherence tomography (PhS-OCT) enables precise, contactless measurements of temperature-dependent changes in transparent solids. In this work, we used a common-path spectral-domain OCT system to measure optical path differences (OPD) in a 1-mm-thick soda-lime glass slide immersed in a thermal bath. The OPD variation showed a strong li

Purna Vindhya Kota, Meer Mehran Rashid, Somdatta Goswami, Lori Graham-Brady

Predicting stress fields in hyperelastic materials with complex microstructures remains challenging for traditional deep learning surrogates, which struggle to capture both sharp stress concentrations and the wide dynamic range of stress magnitudes. Convolutional architectures such as UNet tend to oversmooth high-frequency gradients, while neural operators l

Ulrich Bauer, Fabian Lenzen, Michael Lesnick

In the theory of persistent homology, a well known duality relates the barcodes of the absolute homology and relative cohomology of a one-parameter simplicial filtration. Motivated by the problem of computing free presentations of the (co)homology of multiparameter Rips filtrations, we give a multiparameter generalization of this duality. Considering two dua

Maria Giovanna Dainotti, Aleksander Łukasz Lenart, Biagio De Simone, William Giarè

Testing the $\Lambda$CDM model requires cosmological probes spanning the wide redshift interval between Type Ia Supernovae (SNe Ia, $z\lesssim2.9$) and the Cosmic Microwave Background (CMB, $z\approx1100$). Gamma-Ray Bursts (GRBs), observed up to redshift $z=9.2$, offer the opportunity to explore this regime. Here, we investigate how many GRBs are needed to