March 2026 arXiv papers — page 97

Irene Hou, Alexander Qin, Lauren Cheng, Philip J. Guo

More scientists are now using AI, but prior studies have examined only how they use it 'at the desk' for computer-based work. However, given that scientific work often happens 'beyond the desk' at lab and field sites, we conducted the first study of how scientific practitioners use AI for embodied physical tasks. We interviewed 12 scientific practitioners do

Gerardo Valente Vazquez-Garcia, Carmina Perez Guerrero, Eduardo Garduño, Miguel Gonzalez-Mendoza

In this work we present a novel framework for fire safety management in industrial settings through the implementation of a smart camera platform for jet flames characterization. The approach seeks to alleviate the lack of real-time solutions for industrial early fire segmentation and characterization. As a case study, we demonstrate how a SoC FPGA, running

Ange-Clément Akazan, Abdoulaye Koroko, Verlon Roel Mbingui, Choukouriyah Arinloye

The success of deep learning in computer vision has been driven by models of increasing scale, from deep Convolutional Neural Networks (CNN) to large Vision Transformers (ViT). While effective, these architectures are parameter-intensive and demand significant computational resources, limiting deployment in resource-constrained environments. Inspired by Tiny

Tsuri Farhana, Omrit Filtser, Shalev Goldshtein

We study unlabeled multi-robot motion planning for unit-disk robots in a polygonal environment. Although the problem is hard in general, polynomial-time solutions exist under appropriate separation assumptions on start and target positions. Banyassady et al. (SoCG'22) guarantee feasibility in simple polygons under start--start and target--target distances of

Zhan Gao, Bishwadeep Das, Elvin Isufi

Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often expand as nodes continually attach with an unknown pattern. The latter requires developing filter-based decision-making paradigms that take evolution and uncertainty into account. Existing approaches rely on

Xiaodan Du, Ruize Xu, David Yunis, Yael Vinker

We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward reinforcement learning following supervised fine-tuning. Our approach is enabled by a new dataset we call ControlSketch-Part, containing rich part-level annotations for sketches, obtained

Sasan Mansouri, Edoardo Pilla, Mark Wahrenburg, Fabian Woebbeking

Answering financial questions is often treated as an information retrieval problem. In practice, however, much of the relevant information is already available in curated vendor systems, especially for quantitative analysis. We study whether, and under which conditions, Model Context Protocol (MCP) offers a more reliable alternative to standard retrieval-aug

Sarah L. V. Zahn, Luna Zamok, Sonia Coriani, Stephan P. A. Sauer

The analysis of nuclear magnetic resonance parameters, such as the indirect nuclear spin-spin coupling constants, in terms of contributions from localised molecular orbitals is a commonly used approach for gaining a deeper understanding of experimentally observed trends in these parameters. In the vast majority of these studies, contributions from pairs of o

Jack Yi Wei, Narges Armanfard

Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free training samples, fully unsupervised settings with unlabeled and potentially contaminated training data, and semi-supervised settings with limited anomaly labels. Existing deep learning approaches typically tr

Md Meftahul Ferdaus, Elias Ioup, Mahdi Abdelguerfi, Anton Netchaev

Deploying deep learning models on embedded devices for tasks such as aerial disaster monitoring and infrastructure inspection requires architectures that balance accuracy with strict constraints on model size, memory, and latency. This paper introduces VeloxNet, a lightweight CNN architecture that replaces SqueezeNet's fire modules with gated multi-layer per

Samuel Wilson

In 1919, Ramanujan discovered his famous congruences for the partition function. Not too long after, Freeman Dyson conjectured a combinatorial statistic existed that explained the three congruences, which he dubbed the \textit{crank}. A crank generating function for the partition function was discovered in 1988 by George Andrews and Frank Garvan. Since then

Aaron Lauve, Anthony Lazzeroni

In this manuscript we lift the theory of r-quasisymmetric functions to the theory of Hopf monoids. We provide a general method of interpolating between two Hopf monoids, one being the free monoid on a positive comonoid and the other being the free commutative monoid on a positive comonoid.

Richard Coll Josifov, Luc Devroye, Gabor Lugosi

We investigate the behaviour of five classical centrality measures--Jordan, rumor, betweenness, degree, and closeness centralities--in the setting of uniform random recursive trees. Motivated by applications in network archaeology, we focus on two fundamental questions: (i) the birth index (time of arrival) of the most central vertex, and (ii) the relative c

Richard Schubert, Marvin Loba, Alexander Blödel, David Klüner

This paper presents a process for coordinating stakeholders in their consideration of performance indicators and respective interface requirements for automated vehicles. These performance indicators are obtained and processed based on the system's self-perception and enable the realization of self-aware and self-adaptive vehicles. This is necessary to allow

Samuel Wilson

The study of integer partitions and their congruences dates back to 1919 when Ramanujan discovered his famous congruences for the partition function, $p(n)$. Since then, many other kinds of partition functions have been discovered, as well as their respective congruences. Recently, Hirschorn and Sellers have consider partitions in which the odd parts may app

Zach Hunter, Aleksa Milojević, Benny Sudakov, Istvan Tomon

Determining the randomized (or distributional) communication complexity of disjointness is a central problem in communication complexity, having roots in the foundational work of Babai, Frankl, and Simon in the 1980s and culminating in the famous works of Kalyanasundaram-Schnitger and Razborov in 1992. However, the question of obtaining tight bounds for prod

Yousef K. Chahine, J. Gabriel Richardson, Evan J. Katz, Adam J. Fallon

A double-heralding technique is presented for producing heralded entangled photon pairs from spontaneous parametric down-conversion (SPDC). Compared to the swap-heralded schemes studied in previous cascaded SPDC and zero-added-loss multiplexing (ZALM) proposals, this double-heralding technique is found to yield the most resource-efficient implementation in t

Yushan Han, Calen Randall

Heat stress has harmful effects that impact communities across the Unitedt States, particularly when high temperatures are accompanied by high humidity. The combined impact of temperature and humidity can be summarized by the heat index (HI). Current state-of-the-art numerical weather prediction models are often biased when forecasting temperature and humidi

Carles Cardó

The equational probabilistic spectrum of a finite algebra is the set of probabilities with which equations are satisfied in the algebra. We study algebras with minimal spectrum, that is, spectra consisting only of the values $1$ and $1/|\A|$. We show that, apart from trivial cases, groupoids with minimal spectrum are quasigroups. We further prove that severa

Abhinav Goel, Derek Lim, Hannah Lawrence, Stefanie Jegelka

The inclusion of symmetries as an inductive bias, known as equivariance, often improves generalization on geometric data (e.g. grids, sets, and graphs). However, equivariant architectures are usually highly constrained, designed for symmetries chosen a priori, and not applicable to datasets with other symmetries. This precludes the development of flexible, m

Eva-Maria Hainzl

In a recent work, a central limit theorem for pattern counts in random planar maps was proven by reducing the problem to a face count problem. We provide a shorter proof by circumventing this reduction through the computation of bivariate coefficient asymptotics from a functional equation with one catalytic variable and extend the result to pattern counts wi

Michael Drmota, Eva-Maria Hainzl

Discrete differential equations appear most prominently in planar map and lattice path enumeration. In this work we consider discrete differential equations with an additional parameter $x$, where the order of the equation is $1$ for $x=0$ but $k> 1$ for $x\ne 0$. We call such equations singularly perturbed. The main contribution of this work is to show that

J. Montaño-Domínguez, F. Ramírez-Zavaleta, E. S. Tututi, O. Vázquez-Hernández

Tree-level lepton-flavor-violating decays induced by Lorentz-violating effects within the Yukawa sector of the Standard Model Extension are studied. These new physics effects are parameterized by the $(Y_{f})_{\mu\nu}^{AB}$ tensor, with $\mu$ and $\nu$ denoting Lorentz indices and $A$, $B$ being indices in the flavor space. Since this tensor is antisymmetric

Myeongkyun Kang, Soopil Kim, Xiaoxiao Li, Sang Hyun Park

Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from visual instruction tuning, which fine-tunes models on datasets consisting of curated image-instruction-output triplets. However, in the medical domain, constructing large-scale, high-quality instruction datasets is

Rahul Jain, Keval Doshi, Burak Uzkent, Garin Kessler

Recent progress in multimodal large language models (MLLMs) has led to a surge of benchmarks for long-video reasoning. However, most existing benchmarks rely on localized cues and fail to capture narrative reasoning, the ability to track intentions, connect distant events, and reconstruct causal chains across an entire movie. We introduce NA-VQA, a benchmark

Aziz Kharoof, Cihan Okay

We develop tools for characterizing vertices of fiber products of polytopes and apply them to simplicial distribution polytopes, a class of probability polytopes arising in quantum foundations and quantum information. In the theory of simplicial distributions, a pair of simplicial sets encoding measurement and outcome spaces determines a convex polytope of c

Ichiro Oda

We consider a problem whether bound states are made in a scalar theory with a fourth-order derivative term or not. After rewriting the theory to a standard scalar theory with second-order derivative terms, we calculate a correlation function of the composite operators made out of massive ghosts with negative norm and massive normal fields with positive norm

Harmeet Dhillon, Pranay Katyal, Brendan Long, Rohan Walia

In multi-robot systems, traditional radio frequency (RF) communication struggles with contention and jamming. Optical communication offers a strong alternative. However, conventional frame-based cameras suffer from limited frame rates, motion blur, and reduced robustness under high dynamic range lighting. Event cameras support microsecond temporal resolution

Matthew Simon Tan, Davit Aghamalyan, Varun Narasimhachar

The no-broadcasting theorem, a fundamental limitation on the communication of quantum information, holds that a physical process cannot broadcast copies of an unknown quantum state to two or more receivers. Recent work has explored ways of circumventing this limitation using "virtual" implementations of non-physical processes using measurement and data-proce

Leonardo G. J. M. Voltarelli, Natalia Osiecka-Drewniak, Marcin Piwowarczyk, Ewa Juszynska-Galazka

Liquid crystal textures encode rich structural information, yet mapping these images to mesophase identity remains challenging because visually similar patterns can arise from distinct structures. Here we present a simple, interpretable representation that maps textures to a 75-dimensional frequency vector of two-by-two ordinal patterns, grouped into eleven

Eric B. Roon, Jeffrey H. Schenker

In this letter, we fill a hole in the existing literature about disordered quantum spin systems generated by a random local interaction $\{\mathfrak{h}(Z)\}_{Z\Subset \mathbb{Z}^\nu}$ satisfying a statistical version of translation invariance. We show such systems always have disordered ground states in the thermodynamic limit with the same symmetry. A key t

Shree Harsha Bokkahalli Satish, Maria Teleki, Christoph Minixhofer, Ondrej Klejch

SpeechLLMs process spoken language directly from audio, but accent and vocal identity cues can lead to biased behaviour. Current bias evaluations often miss how such bias manifests in end-to-end speech interactions and how users experience it. We distinguish quality-of-service disparities (e.g., off-topic or low-effort responses) from content-level bias in c

Jinming Wang, Hai Wang, Hongkai Wen, Geyong Min

High-quality GPS trajectories are essential for location-based web services and smart city applications, including navigation, ride-sharing and delivery. However, due to low sampling rates and limited infrastructure coverage during data collection, real-world trajectories are often sparse and feature unevenly distributed location points. Recovering these tra

Lucas Ferraz, Ana F. Rodrigues, Pedro Giesteira Cotovio, Mafalda Ventura

Adeno-associated viral (AAV) vectors are widely used delivery platforms in gene therapy, and the design of improved capsids is key to expanding their therapeutic potential. A central challenge in AAV bioengineering, as in protein design more broadly, is the vast sequence design space relative to the scale of feasible experimental screening. Machine-guided ge

Pedro Paulo Balbi, Kévin Perrot, Marius Rolland, Eurico Ruivo

The global majority problem, often referred to as the Density Classification Task, is a classical benchmark in the context of probing the computational capabilities of automata networks. It poses the simple yet challenging problem of determining, by totally local means, whether an arbitrary initial configuration of binary states can evolve to a final, homoge

Dinh Van Tuan, Junghwan Kim, Hanan Dery

Energy renormalizations of resident carriers and excitons are studied theoretically, and compared with recent experiments of electrostatically-doped WSe$_2$ monolayers. The calculated energy renormalization of resident carriers, subjected to strong out-of-plane magnetic field, reveals the importance of dynamical screening in transition metal dichalcogenides.

Andresa Rodrigues de Campos, David Lee, Imry Kissos, Piyush Paritosh

In-context learning has established itself as an important learning paradigm for Large Language Models (LLMs). In this paper, we demonstrate that LLMs can learn encoding keys in-context and perform analysis directly on encoded representations. This finding enables lossless prompt compression via dictionary encoding without model fine-tuning: frequently occur

Vincent Siu, Jingxuan He, Kyle Montgomery, Zhun Wang

Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instruction led to the action, what objective is being pursued, and whether the action serves that objective. However, existing definitions of security attacks against LLM agents often fa

Jihoon Jeong, Pooneh Mousavi, Mirco Ravanelli, Cem Subakan

Large audio-language models (LALMs) can generate reasoning chains for their predictions, but it remains unclear whether these reasoning chains remain grounded in the input audio. In this paper, we propose an RL-based strategy that grounds the reasoning outputs of LALMs with explicit timestamp annotations referring to relevant segments of the audio signal. Ou

Mehmet Demirci, M. Fauzi Mustamin

The recent low-energy electron recoil (ER) results reported by the LUX-ZEPLIN (LZ) experiment have established the most stringent constraints to date on new physics scenarios, specifically for solar axion-like particles with keV-scale masses and mirror dark matter. Motivated by this enhanced sensitivity and the resulting restrictive limits, our present work

Keith Rush

We analyze a fixed-point iteration $v \leftarrow \phi(v)$ arising in the optimization of a regularized nuclear norm objective involving the Hadamard product structure, posed in DMR+22 in the context of an optimization problem over the space of algorithms in private machine learning. We prove that the iteration $v^{(k+1)} = \text{diag}((D_{v^{(k)}}^{1/2} M D_

Samuel N. Cohen, Filippo de Feo, Jackson Hebner, Justin Sirignano

We develop deep learning-based approximation methods for fully nonlinear second-order PDEs on separable Hilbert spaces, such as HJB equations for infinite-dimensional control, by parameterizing solutions via Hilbert--Galerkin Neural Operators (HGNOs). We prove the first Universal Approximation Theorems (UATs) which are sufficiently powerful to address these

Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob Foerster

Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin G\"odel Machine (DGM) demonstrates open-ended self-improve

Tianyu Bell Pan, Damon L. Woodard

Large language models (LLMs) demonstrate strong performance, but they often lack transparency. We introduce GeoLAN, a training framework that treats token representations as geometric trajectories and applies stickiness conditions inspired by recent developments related to the Kakeya Conjecture. We have developed two differentiable regularizers, Katz-Tao Con

A. V. Moiseev, A. Arshinova, A. A. Smirnova

Considerable observational evidence suggests that the activity of supermassive black holes in galactic nuclei is transient. The term ``active galactic nuclei archaeology'' has even been coined. This implies the possibility of reconstructing the history of activity, such as changes in the nuclear luminosity over time across various regions of the electromagne

Faizuddin Ahmed, Mohsen Fathi, Ahmad Al-Badawi

In this work, we investigate observable signatures of a magnetically charged Anti-de Sitter black hole in string-inspired Euler-Heisenberg theory. We analyze photon trajectories, the photon sphere, and the resulting black hole shadow. We derive the photon sphere and shadow radii and show that both deviate from the Schwarzschild and Schwarzschild-AdS cases. I

Jibu Tom Jose, Gal Friedmann, Omri Ram

Turbulent separated flows in axisymmetric expansions can sustain fundamentally different transport organization despite nearly identical mean topology. Using stereo-PIV and time-resolved planar PIV, we compare abrupt $90^\circ$ (step) and gradual $45^\circ$ (wedge) axisymmetric expansions at step height Reynolds numbers of 25000 and 35000. Despite similar re

Xiao Fang, Yiming Gong, Stanislav Panev, Celso de Melo

Deep neural networks (DNNs) have achieved remarkable success in computer vision but remain highly vulnerable to adversarial attacks. Among them, camouflage attacks manipulate an object's visible appearance to deceive detectors while remaining stealthy to humans. In this paper, we propose a new framework that formulates vehicle camouflage attacks as a conditi

Mandana Mohammadi Looey, Amrita Basak, Satadru Dey

Direct Ink Writing (DIW) has gained attention for its potential to reduce printing time and material waste. However, maintaining precise geometry and consistent print quality remains challenging under dynamically varying operating conditions. This paper presents a control-focused approach using a model reference adaptive control (MRAC) strategy based on a re

Tanmay Dokania, Yashwanth Kumar Nakka

In this paper, we present an equivalent convex optimization formulation for discrete-time stochastic linear systems subject to linear chance constraints, alongside a tight convex relaxation for quadratic chance constraints. By lifting the state vector to encode moment information explicitly, the formulation captures linear chance constraints on states and co

Volodymyr Seliuchenko

We introduce TrustFlow, a reputation propagation algorithm that assigns each software agent a multi-dimensional reputation vector rather than a scalar score. Reputation is propagated through an interaction graph via topic-gated transfer operators that modulate each edge by its content embedding, with convergence to a unique fixed point guaranteed by the cont

Myeongkyun Kang, Yanting Yang, Xiaoxiao Li

Fine-grained representation learning is crucial for retrieval and phrase grounding in chest X-rays, where clinically relevant findings are often spatially confined. However, the lack of region-level supervision in contrastive models and the limited ability of large vision language models to capture fine-grained representations in external validation lead to

Jihoon Suh, Yeongjun Jang, Junsoo Kim, Takashi Tanaka

We develop a variational encrypted model predictive control (VEMPC) protocol whose online execution relies only on encrypted polynomial operations. The proposed approach reformulates the MPC problem into a sampling-based estimator, in which the computation of the quadratic cost is naturally handled by tilting the sampling distribution, thus reducing online e

Lucas Souza e Silva, Luis Rodrigues

This paper formulates an optimal control framework for computing cruise airspeeds in predecessor-follower platoons of all-electric aircraft that balance operational cost and airspace complexity. To quantify controller workload and coordination effort, a novel pairwise dynamic workload (PDW) function is developed. Within this framework, the optimal airspeed s

F. Rivero, D. Guillet, M. Brodeur, J. A. Clark

Precision measurements of $\beta$ decay transitions offer a promising channel through which the Standard Model (SM) can be probed. There is currently an ongoing effort to increase the precision on measurements of $\mathcal{F}t$-values for superallowed $\beta$ decay transitions between mirror nuclides. These allow for a determination of $V_{ud}$ which is comp

Filip V. Jensen, José P. Carvalho, Nino Wili, Asbjorn Holk Thomsen

Pulsed Dynamic Nuclear Polarization (DNP) is currently receiving substantial interest as a means to enhance the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) by orders of magnitude. It has also received much attention as a central ingredient in many modalities of electron spin-involved quantum sensing. Relative to spin

Fedor V. Fomin, Petr A. Golovach, M. S. Ramanujan, Saket Saurabh

In the d-Euclidean Distance Matrix Completion (d-EDMC) problem, one aims to determine whether a given partial matrix of pairwise distances can be extended to a full Euclidean distance matrix in d dimensions. This problem is a cornerstone of computational geometry with numerous applications. While classical work on this problem often focuses on exploiting con

Alessandro Fortunati, Filomena Pacella

In this paper we study an overdetermined problem which is directly related to the well known torsion problem studied by J. Serrin. A perturbed version of the latter is tackled by using asymptotic series as well as tools borrowed from the celebrated Nekhoroshev Theorem. In a similar fashion to this class of results, we establish the existence of infinitely ma

Eleni Zapridou, Michael Koepf, Panagiotis Sioulas, Ioannis Mytilinis

Concurrent workloads often extract insights from high-throughput, real-time data streams. Existing stream processing engines isolate each query's resources, ensuring robust performance but incurring high infrastructure costs. In contrast, sharing work reduces the amount of necessary resources but introduces inter-query interference, leading to performance de

Raj Dahya

We establish explicit means via which natural dilations of completely positive (CP) maps can be constructed \`a la Kraus's IInd representation theorem. To obtain this, we rely on the Choi-Jamio{\l}kowski correspondence and develop a Cholesky algorithm for bi-partite systems. This enables a canonical construction of adjoint actions which recover the behaviour

Zhao Song

In this paper, we show how to generalize the lazy update regime from dynamic matrix product [Cohen, Lee, Song STOC 2019, JACM 2021] to dynamic kronecker product. We provide an algorithm that uses $n^{\omega( \lceil k/2 \rceil, \lfloor k/2 \rfloor, a )-a}$ amortized update time and $ n^{\omega( \lceil(k-s)/2 \rceil, \lfloor (k-s)/2 \rfloor,a )}$ worst case qu

Habib Ammari, Jiayu Qiu

We rigorously prove the existence of interface modes in a sharp interface model, which bifurcate from the double Dirac cone as a consequence of the band inversion induced by super-symmetry breaking. The exact number of interface modes are determined. The proof is based on a discrete version of the layer-potential framework. Moreover, we prove that such inter

Sophia Yazzourh, Erica E. M. Moodie

Precision medicine aims to tailor therapeutic decisions to individual patient characteristics. This objective is commonly formalized through dynamic treatment regimes, which use statistical and machine learning methods to derive sequential decision rules adapted to evolving clinical information. In most existing formulations, these approaches produce a singl

Mohammad Eini, Abdullah Karaaslanli, Vassilis Kalantzis, Panagiotis A. Traganitis

Several graph data mining, signal processing, and machine learning downstream tasks rely on information related to the eigenvectors of the associated adjacency or Laplacian matrix. Classical eigendecomposition methods are powerful when the matrix remains static but cannot be applied to problems where the matrix entries are updated or the number of rows and c

I. Casal Iglesias, F. J. Matute-Cañadas, G. O. Steffensen, A. Ibabe

Ultrastrong light-matter coupling (USC) gives access to exotic quantum phenomena and promises faster quantum gates, yet coherent time-domain control in this regime remains largely unexplored. Here, we realize USC in a hybrid system consisting of an InAs nanowire-based gatemon qubit coupled to a superconducting resonator. Spectroscopy reveals an avoided cross

Joachim Kock, Jesper Michael Møller

We develop objective linear algebra in a new setting with a cardinality functor that can take negative values. The signs arise as little homotopies, as ratios between orientations. To illustrate the workings of the theory we give an objective treatment of exterior powers and determinants.

Hossein Darban

Recent advances in nanoscale fabrication enable atomic-scale manipulation of two-dimensional (2D) materials by introducing engineered pores and perforations. This provides new opportunities to tailor functional properties of 2D materials for applications such as selective ion transport, desalination membranes, and molecular filtration. Despite this progress,

Julián A. Zúñiga, Arles V. Gil Rebaza, Diego F. Coral

In this work, we present a theoretical study of spin transport in a trilayer pseudospin-valve (PSV) heterostructure composed of electrode (L_l)/insulator/electrode (L_r). The insulating layer corrresponds to a semiconductor (SC) with a zinc-blende crystal structure from the III-V (GaSb, InSb, InAs, and GaAs) or the II-VI (ZnSe), while the electrodes are ferr

Zeyuan Hu

Equipping query processing systems with provable theoretical guarantees has been a central focus at the intersection of database theory and systems in recent years. However, the divergence between theoretical abstractions and system assumptions creates a gap between an algorithm's high-level logical specification and its low-level physical implementation. En

Damian Brzozowski, Yu Liu, Øyvind Finnseth, Egil Y. Tokle

We report emergent magnetic behavior in heterostructures composed of (111)-oriented La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) and (00$l$)-oriented Bi$_2$Te$_3$ (BT), controlled by interfacial reconstructions. When BT is deposited directly onto LSMO, an intermediate interfacial layer forms between the two materials. Polarized Neutron Reflectometry modeling reveals t

Bart Cleuren, Ralf Eichhorn

A novel paradigm for sorting is introduced, based upon resetting. Using simple examples, we demonstrate that sorting is achieved by resetting the velocity component(s) or orientation of the particles, rather than position. The objects to be sorted are microparticles, modeled as suspended and spatially extended Brownian particles. This sorting-by-resetting sc

João Filipe, Gregor Behnke

Classical planning problems are typically defined using lifted first-order representations, which offer compactness and generality. While most planners ground these representations to simplify reasoning, this can cause an exponential blowup in size. Recent approaches instead operate directly on the lifted level to avoid full grounding. We explore a middle gr

Kéva Djambaé

We develop an explicit geometric construction of automorphisms of finite fields arising from isogeny cycles. Let $k$ be a finite field, $E/k$ an elliptic curve, and $\ell$ an integer coprime to $\mathrm{char}(k)$. Let $\mathfrak{h}$ be an ideal of $\mathrm{End}(E)$ dividing $\ell$, and consider the corresponding torsion subgroup $E[\mathfrak{h}]\subseteq E[\

Jonas Mayer Martins, Jaap Jumelet, Viola Priesemann, Lisa Beinborn

Why do some languages like Czech permit free word order, while others like English do not? We address this question by pretraining transformer language models on a spectrum of synthetic word-order variants of natural languages. We observe that greater word-order irregularity consistently raises model surprisal, indicating reduced learnability. Sentence rever

Viliana Devbunova

Prior work uses linear probes on benchmark prompts as evidence of evaluation awareness in large language models. Because evaluation context is typically entangled with benchmark format and genre, it is unclear whether probe-based signals reflect context or surface structure. We test whether these signals persist under partial control of prompt format using a

Giovanna Citti, Vasiliki Liontou

We propose a model of the functional architecture of curvature-sensitive cells in the primary visual cortex. The model accounts for the modular and hierarchical organization of the cortex, the horizontal connectivity, and the shape of receptive profiles of these cells as Gabor-type filters. We construct a canonical affine subbundle of the cotangent bundle of

Kiwan Wong, Maximillian Stölzle, Wei Xiao, Daniela Rus

Safe operation is essential for deploying robots in human-centered 3D environments. Soft continuum manipulators provide passive safety through mechanical compliance, but still require active control to achieve reliable collision avoidance. Existing approaches, such as sampling-based planning, are often computationally expensive and lack formal safety guarant

Shu Yang, Shenzhe Zhu, Hao Zhu, José Ramón Enríquez

Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are implicitly optimized for a single-principal interaction paradigm, in which the model is designed to satisfy the objectives of one dominant user whose instructions are treated as the sole source of authority

Nathan Weill, Kaizheng Wang

We propose a principled framework for unsupervised domain adaptation under covariate shift in kernel Generalized Linear Models (GLMs), encompassing kernelized linear, logistic, and Poisson regression with ridge regularization. Our goal is to minimize prediction error in the target domain by leveraging labeled source data and unlabeled target data, despite di

Peyman Parsa, Waleed El-Sayed, Parisa Behjat, Shabir Barzanjeh

Nonlinear dynamics clamp the amplitude of mechanical resonators driven into self-oscillation by optomechanical backaction. Here we overcome the conventional limits of self-oscillation amplitude by navigating the nonlinear dynamical landscape of a diamond optomechanical cavity supporting coherent optomechanics at room temperature. By exploiting the bistable p

Veronica Sanz

We study when a network of coupled oscillators with inertia ceases to follow a time-dependent driving protocol coherently, using a simplified graph-based model motivated by inverter-dominated energy systems. We show that this loss of tracking is diagnosed most clearly in the frequency dynamics, rather than in phase-based observables. Concretely, a tracking r

Paolo Mantero, Vinh Nguyen

In this paper we prove that the Stanley--Reisner ideal or cover ideal $I$ of a matroid is minimally resolvable by iterated mapping cones. As a technical tool for this purpose, we introduce and study focal matroids, which are submatroids of a matroid $\mathcal{M}$ that are constructed relative to minimal $\ell$-covers of $\mathcal{M}$. Our second main result

Chanh Nguyen, Shutong Jin, Florian T. Pokorny, Erik Elmroth

Cloud robotics enables robots to offload high-dimensional motion planning and reasoning to remote servers. However, for continuous manipulation tasks requiring high-frequency control, network latency and jitter can severely destabilize the system, causing command starvation and unsafe physical execution. To address this, we propose Speculative Policy Orchest

Kaizhao Sun, Baihao Wu, Kun Yuan, Wotao Yin

Many real-world optimization models contain exploitable sparsity and block structure, but this structure is often obscured in algebraic form, limiting the effectiveness of modern parallel algorithms. We propose an automatic pipeline that converts a generic multiblock problem into a canonical two-block formulation suitable for parallel Alternating Direction M

Mohammad Hadi Nezhad, Francisco Enrique Vicente Castro, Ivon Arroyo

Supporting users in protecting sensitive information when using conversational agents (CAs) is crucial, as users may undervalue privacy protection due to outdated, partial, or inaccurate knowledge about privacy in CAs. Although privacy knowledge can be developed through standalone resources, it may not readily translate into practice and may remain detached

Yunyi Zhang, Soji Adeshina, Sheng Guan, Ashwin Ganesh

Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance while managing costs. As model pools scale to include dozens of frontier models with narrow performance gaps, existing approaches face significant challenges: manually defined task taxonomies cannot capture fine-graine

Brandon Tam, Mario Ghossoub, Silvana M. Pesenti

We study a problem of optimal allocation in a discrete-time multi-period pure-exchange economy, where agents have preferences over stochastic endowment processes that are represented by strongly time-consistent dynamic risk measures. We introduce the notion of dynamic Pareto-optimal allocation processes and show that such processes can be constructed recursi

Simone Albanesi, Sebastiano Bernuzzi, Alessandro Nagar

We study the plunge and merger of a non-spinning particle falling into a Kerr black hole following an eccentric planar inspiral. The dynamics is driven by an effective-one-body radiation reaction, and the corresponding numerical inspiral-merger-ringdown waveforms are obtained by solving the Teukolsky equation with the 2+1 time-domain code Teukode. We then an

Lawrence W. Abrams

The Federal Trade Commission has recently filed an administrative complaint against the Big 3 pharmacy benefit managers claiming they engaged in unfair conduct in violation of Section 5 of the FTC Act. They never used the word collusion in the complaint and chose not to sue under The Sherman Act, Section 1. We view this as a novel case of market design collu

Federico Garcia Crespi, Eduardo Yubero Funes, Marina Alfosea Simon

(a) Many air quality forecasting studies report gains from machine learning, but evaluations often use static chronological splits and omit persistence baselines, so the operational added value under routine updating is unclear. (b) Using 2,350 daily PM10 observations from 2017 to 2024 at an urban background monitoring station in southern Europe, we compare

Francesco Coghi, Juan P. Garrahan

Self-interacting jump processes (SIJPs) describe systems with non-Markovian stochastic dynamics in which transition rates depend on empirical observables of the process, which gives rise to long-range memory and feedback. We derive the ``level-2.5'' large deviation (LD) principle governing the joint fluctuations of empirical occupation measure and the flux m

Jinxin Yang, Mohammad Moltafet, Hamid R. Sadjadpour

We consider a status update system consisting of a sampler, a sink, and a controller located at the sink. The controller sends requests to the sampler to generate and transmit status updates. Packet transmissions from the controller to the sampler (reverse link) and from the sampler to the sink (forward link) experience random delays. The reverse and forward

Ziqian Yao, Clarissa Daniel, Kaicheng Pan, Tzu-Hsuan Hsu

In this work, we demonstrate the first single-crystal aluminum nitride (AlN) wafer-based thickness-extensional (TE) mode bulk acoustic resonator for piezoelectric power conversion. The device exhibits a high series resonance 3-dB quality factor ($Q$) of 1677 and an electromechanical coupling coefficient ($k^2$) of 6.1%, highlighting the strong potential of A

Abinav Rao, Sujan Rachuri, Nikhil Vemuri

LLMs can execute every step of chain-of-thought reasoning correctly and still produce wrong final answers. We introduce the Novel Operator Test, a benchmark that separates operator logic from operator name, enabling rigorous distinction between genuine reasoning and pattern retrieval. By evaluating Boolean operators under unfamiliar names across depths 1-10

G. Arcadi, J. P. Garcés, M. Lindner

We propose a simple and flexible mechanism by which sterile neutrinos with masses below the electroweak scale can simultaneously account for the observed baryon asymmetry of the Universe and the dark matter abundance. Crucially, neutrinos in this mass range behave as Dirac particles at high temperatures, allowing connections to Dirac leptogenesis, while at l

Jingrui Cheng, Junhao Tian

In this paper, we observe that if the initial data of pseudo Calabi flow has volume form $C^0$ close to a smooth one, then the flow is immediately smooth for $t>0$. As an application, we show that if the initial data has volume form $C^0$ close to that of a cscK metric, then the pseudo Calabi flow exists for $t\in (0,+\infty)$. We also prove similar improvem

Jian Tang, Thomas Siyuan Ding, Shuhan Ding, Jiangxu Li

Memory is a foundational concept across disciplines, from neurobiology and electronics to artificial intelligence and quantum gravity. In materials, memory effects typically arise from ferroic orders, such as ferroelectricity and ferromagnetism, where information is stored in charge or spin degrees of freedom. Here, we report a surprising discovery of a nonv

Renee Y. Ge, Azadeh Shohoudi, Malini Iyengar, Quefeng Li

Candidate binary endpoints are often considered as surrogates for time-to-event (TTE) clinical endpoints, primarily because they can be assessed at earlier time points. To be submitted for regulatory approval candidate binary endpoints need to validated. The most well-known method for performing such validation employs a meta-analytic framework to estimate i

Mauro Artigiani, Artur Avila, Sébastien Ferenczi, Pascal Hubert

We investigate weak mixing for some classes of interval translation mappings. We give two distinct proofs that a typical Bruin-Troubetzkoy interval translation mapping is weakly mixing. Moreover, we show that the second approach extends to other classes of interval translation mappings. In particular, we show that Bruin interval translation mappings on any n

Shixian Xie, Motahhare Eslami, John Zimmerman

Despite significant advances in responsible AI research, industry adoption remains limited, leaving many HCI contributions underutilized in practice. This position paper argues that current research often fails to account for the fundamental need for capitalist enterprises to create value. To achieve immediate real-world impact, responsible AI research must