October 2025 arXiv papers — page 41

Zhaoyang Yu, Jiayi Zhang, Huixue Su, Yufan Zhao

Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where planning is fundamentally understood as a high-level form of action. However, current Large Language Model (LLM)-based agents lack this crucial capability to operate fluidly across decision granularities. This limitatio

Philip A. Ernst, Frederi G. Viens, Shuo Yan

In the current work, we provide theoretical results for testing (in)dependence between pairs of paths of most commonly studied non-stationary Gaussian processes - standard Brownian motion and fractional Brownian motion (fBm). Please see the PDF version of the paper for a full abstract.

Roger J Smith

Fiber optic pulsed polarimetry is a LIDAR-like fiber sensing technique that uses a backscatter enhanced single mode backscatter-tailored optical fiber(BTOF) to measure the distributed B fields on all Magnetic Fusion Energy devices. The BTOF has a series of wavelength resonant reflection fiber Bragg gratings written uniformly along its length. The fiber's Ver

Konstantin Schmidt, Thomas Richter

First Order Motion Model is a generative model that animates human heads based on very little motion information derived from keypoints. It is a promising solution for video communication because first it operates at very low bitrate and second its computational complexity is moderate compared to other learning based video codecs. However, it has strong limi

Asena Cetinkaya, Sahsene Altinkaya

In this researh work, we establish a new subclass of analytic functions constructed by the Mittag-Leffler function that maps the open unit disc onto the region bounded by the Cardioid domain. Using a technique introduced by Miller and Mocanu, we investigate several Briot-Bouquet differential subordinations for this function class.

Jiaqi Lv, Esha Sadia Nasir, Kesi Xu, Mostafa Jahanifar

Accurate detection and classification of nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning architecture featuring a shared encoder and parallel, cell-type-specialised decoders. Through multi-task learning, each decoder jointly predicts nuclei centroids, segmentation masks,

Bohan Li, Wenbin Huang, Yuhang Qiu, Yiwei Guo

Large Audio Language Models (LALMs), which couple acoustic perception with large language models (LLMs) to extract and understand diverse information from audio, have attracted intense interest from both academic and industrial communities. However, existing LALMs are highly sensitive to how instructions are phrased, affecting both (i) instruction-following

William Réveillard, Vasileios Saketos, Alexandre Proutiere, Richard Combes

Training or fine-tuning large language model (LLM)-based systems often requires costly human feedback, yet there is limited understanding of how to minimize such intervention while maintaining strong error guarantees. We study this problem for LLM-based classification systems in an active learning framework: an agent sequentially labels $d$-dimensional query

Yury Solyaev

In this work, we develop a potential-based formalism for Maxwell's equations in isotropic media with weak spatial dispersion within the electric quadrupole-magnetic dipole approximation. We introduce an operator form of the constitutive relations along with a modified Lorenz gauge condition, which enables the derivation of decoupled generalized wave equation

Siddharth Sahay, Radhika Agarwal

This paper presents an end-to-end multilingual translation pipeline that integrates a custom U-Net for text detection, the Tesseract engine for text recognition, and a from-scratch sequence-to-sequence (Seq2Seq) Transformer for Neural Machine Translation (NMT). Our approach first utilizes a U-Net model, trained on a synthetic dataset , to accurately segment

Alexis Ellis, Stacie Severyn, Fjollë Novakazi, Hadi Banaee

As human machine teaming becomes central to paradigms like Industry 5.0, a critical need arises for machines to safely and effectively interpret complex human behaviors. A research gap currently exists between techno centric robotic frameworks, which often lack nuanced models of human behavior, and descriptive behavioral ontologies, which are not designed fo

Paul Wild, Lutz Schröder, Karla Messing, Barbara König

The classical Kantorovich-Rubinstein duality guarantees coincidence between metrics on the space of probability distributions defined on the one hand via transport plans (couplings) and on the other hand via price functions. Both constructions have been lifted to the level of generality of set functors, with the construction based on couplings referred to as

Oleksii Molodchyk, Hendrik Drögehorn, Martin Lindner, Mario Kendziorski

The intermittent nature of renewable power availability is one of the major sources of uncertainty in power systems. While markets can guarantee that the demand is covered by the available generation, transmission system operators have to often intervene via economic redispatch to ensure that the physical constraints of the network are satisfied. To account

Yumo Yang, Anass Ben Bouazza, Xuejun Dong, Quan Zhou

The estimation of unknown parameters in nonlinear partial differential equations (PDEs) offers valuable insights across a wide range of scientific domains. In this work, we focus on estimating plant root parameters in the Richards equation, which is essential for understanding the soil-plant system in agricultural studies. Since conventional methods are comp

Donatella Bongiornoa, Alireza Khalili Golmankhanehb

In this paper, the exact solutions of certain non-linear differential equations defined on a fractal subset of the real line are presented. Particular attention is paid to the Riccati-type fractal differential equation, for which a connection with the Schrodinger equation is also provided.

Arya Dutta, Farook Rahaman

The Kalb-Ramond field is a background tensor field that arises in string theory and violates local Lorentz symmetry of spacetime, upon acquiring the Vacuum Expectation Value. A non-minimal coupling between the Kalb-Ramond VEV and the Ricci tensor may give rise to a modified black hole solution. Considering two copies of such black holes, we construct a thin-

Nikolaj B. Sillassen, Shuowen Jin, Georgios E. Magdis, Francesco Valentino

The Cosmic Vine is a massive protocluster at z=3.44 in the JWST CEERS field, offering an ideal laboratory for studying the early phases of cluster formation. Using the data from the DAWN JWST Archive, we conduct a comprehensive study on the large-scale structure, stellar mass function (SMF), quiescent members, and dark matter halos in the Cosmic Vine. First,

Eun-jin Shin, Matthew C. Smith, Debora Sijacki, Martin A. Bourne

We present a suite of high-resolution simulations to study how different stellar feedback channels regulate the growth of central intermediate-mass black holes (IMBHs) in dwarf galaxies hosting nuclear star clusters (NSCs). We employ a super-Lagrangian refinement scheme to resolve the self-gravity radius of the $\alpha$-accretion disc ($<0.01$~pc) and follow

Paul Laubie

We show a variation of the usual homological freeness criterion for operadic modules over a Koszul operad. We then apply this result to decorated partition posets for some operads, showing that their augmentation is Cohen-Macaulay and computing its homology. This work answers several open questions asked by B\'er\'enice Delcroix-Oger and Cl\'ement Dupont in

Rui-Hao Li, Semeon Valgushev, Khadijeh Najafi

The preparation of quantum Gibbs states at finite temperatures is a cornerstone of quantum computation, enabling applications in quantum simulation of many-body systems, machine learning via quantum Boltzmann machines, and optimization through thermal sampling techniques. In this work, we introduce a variational framework that leverages matrix product states

Alice Bellettini, Enrico Ortu, Vittorio Penna

In binary mixtures of Bose-Einstein condensates, massive-vortex dipoles can arise, and undergo scattering processes against obstacles. These show an intriguing dynamics, governed by the strongly nonlinear character of the quantum vortex motion, where we are able to highlight the effects of the boundaries. We first characterize such scattering dynamics via so

Tingyu Song, Yilun Zhao, Siyue Zhang, Chen Zhao

Existing approaches typically rely on large-scale fine-tuning to adapt LLMs for information reranking tasks, which is computationally expensive. In this work, we demonstrate that modern LLMs can be effectively adapted using only minimal, high-quality supervision. To enable this, we design LIMRANK-SYNTHESIZER, a reusable and open-source pipeline for generatin

Benjamin Girard, Sofia Zotova

Adapting Reiher's proof of Kemnitz's conjecture, we obtain two refinements of a theorem of Schmid and Zhuang. Our main results provide improved upper bounds for the Erd\H{o}s-Ginzburg-Ziv constant of rank-two-like $p$-groups, and their direct products with cyclic groups of order coprime to $p$. In particular, we determine the exact value of this constant, an

Tongxin Chen, Yinyu Nie, Yafei Hao, Shengchun Shen

Memristors are emerging as key electronic components that retain resistance states without power. Their non-volatile nature and ability to mimic synaptic behavior make them ideal for next-generation memory technologies and neuromorphic computing systems inspired by the human brain. In this study, we present a novel organic spintronic memristor based on a La0

Hanke Xie, Haopeng Lin, Wenxiao Cao, Dake Guo

Recent advances in text-to-speech (TTS) synthesis have significantly improved speech expressiveness and naturalness. However, most existing systems are tailored for single-speaker synthesis and fall short in generating coherent multi-speaker conversational speech. This technical report presents SoulX-Podcast, a system designed for podcast-style multi-turn, m

Raimondo Pala

This paper discusses the different contemporaneous causal interpretations of Panel Vector Autoregressions (PVAR). I show that the interpretation of PVARs depends on the distribution of the causing variable, and can range from average treatment effects, to average causal responses, to a combination of the two. If the researcher is willing to postulate a no re

Tabish Qureshi

The delayed-choice quantum eraser represents an interesting experiment that exemplifies Bohr's principle of complementarity in a beautiful way. According to the complementarity principle, in a two-path interference experiment, the knowledge of which path was taken by the particle and the appearance of interference are mutually exclusive. Even when the which-

Jyotiranjan Beuria

Collective motion in active matter is usually modelled through instantaneous local alignment, where each agent updates its heading from the current configuration of its neighbours. Many biological and engineered agents, however, possess internal regulatory variables that evolve more slowly than alignment itself and can store information about past alignment

Elise Devey

This paper investigates large-population stochastic control problems in which agents share their state information and cooperate to minimize a convex cost functional. The latter is decomposed into individual and coupling costs, with the distinctive feature that the coupling term is a pairwise interaction function between the controls. To address this setting

Jieyong Kim, Maryam Amirizaniani, Soojin Yoon, Dongha Lee

Intent identification serves as the foundation for generating appropriate responses in personalized question answering (PQA). However, existing benchmarks evaluate only response quality or retrieval performance without directly measuring intent identification capabilities. This gap is critical because without understanding which intents users prioritize, sys

Chen Lu, Ke Xue, Lei Yuan, Yao Wang

Dynamic algorithm configuration (DAC) is a recent trend in automated machine learning, which can dynamically adjust the algorithm's configuration during the execution process and relieve users from tedious trial-and-error tuning tasks. Recently, multi-agent reinforcement learning (MARL) approaches have improved the configuration of multiple heterogeneous hyp

Masahiro Kato

We develop a direct debiased machine learning framework comprising Neyman targeted estimation and generalized Riesz regression. Our framework unifies Riesz regression for automatic debiased machine learning, covariate balancing, targeted maximum likelihood estimation (TMLE), and density-ratio estimation. In many problems involving causal effects or structura

Joel Johansson, Daniel A. Perley, Ariel Goobar, Jacob L. Wise

We present the discovery of SN 2025wny (ZTF25abnjznp/GOTO25gtq) and spectroscopic classification of this event as the first gravitationally lensed Type I superluminous supernovae (SLSN-I). Deep ground-based follow-up observations resolves four images of the supernova with ~1.7" angular separation from the main lens galaxy, each coincident with the lensed ima

Anirban Das, Irtaza Khalid, Rafael Peñaloza, Steven Schockaert

Designing models that can learn to reason in a systematic way is an important and long-standing challenge. In recent years, a wide range of solutions have been proposed for the specific case of systematic relational reasoning, including Neuro-Symbolic approaches, variants of the Transformer architecture, and specialised Graph Neural Networks. However, existi

Matteo Capucci, David Jaz Myers

We study discrete opfibration classifiers in enhanced 2-categories and show how, under suitable hypotheses, such classifiers can be endowed with the structure of a (lax or pseudo-)T-algebra and classify strict discrete opfibrations in 2-categories of (lax or pseudo-)T-algebras and lax morphisms. This leads to a notion of discrete opfibration classifier in th

Bernardo Torres, Manuel Moussallam, Gabriel Meseguer-Brocal

Audio autoencoders learn useful, compressed audio representations, but their non-linear latent spaces prevent intuitive algebraic manipulation such as mixing or scaling. We introduce a simple training methodology to induce linearity in a high-compression Consistency Autoencoder (CAE) by using data augmentation, thereby inducing homogeneity (equivariance to s

Cláudia M. Araújo, Faustino A. Maciala, Pedro Patrício

We investigate generalized inverses of matrices associated with two classes of digraphs: double star digraphs and D-linked stars digraphs. For double star digraphs, we determine the Drazin index and derive explicit formulas for the Drazin inverse. We also provide necessary and sufficient conditions for the existence of the Moore-Penrose inverse and give its

Joran Leest, Ilias Gerostathopoulos, Patricia Lago, Claudia Raibulet

Monitoring machine learning (ML) systems is hard, with standard practice focusing on detecting distribution shifts rather than their causes. Root-cause analysis often relies on manual tracing to determine whether a shift is caused by software faults, data-quality issues, or natural change. We propose ML System Maps -- causal maps that, through layered views,

Hardy Chan, Serena Dipierro, Mattia Freguglia, Marco Inversi

For every $0 < s <3/4$, we study the asymptotic behavior of the $\varepsilon$-rescaled sum of the $s$-fractional Allen-Cahn energy and the squared $L^2$-norm of its first variation. We prove that the contribution of the first variation vanishes as $\varepsilon \to 0$. This implies the Gamma-convergence of the initial sum to either the classical perimeter or

E. Cohen Arazi, P. F. Velázquez, M. E. Ortega, A. Rodríguez-González

We present an unexplored scenario for interpreting the outflows in the EGO G338.92+0.55 (b) region (hereafter, EGO G338). Within this framework, we investigate the hypothesis that the interaction between two outflows is responsible for the observed morphology and kinematics of this astrophysical object. To explore this possibility, we reanalyse the region us

Wanmeng Li, Simone Mosco, Daniel Fusaro, Alberto Pretto

Annotating real-world LiDAR point clouds for use in intelligent autonomous systems is costly. To overcome this limitation, self-training-based Unsupervised Domain Adaptation (UDA) has been widely used to improve point cloud semantic segmentation by leveraging synthetic point cloud data. However, we argue that existing methods do not effectively utilize unlab

Anna Hinterberger, Jonas Bohn, Dasha Trofimova, Nicolas Knabe

Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential of the gut-liver axis for predicting colorectal neoplasia through liver-derived radiomic features extracted from routine CT images as a novel opportunistic screening approach. In th

KC Santosh, Rodrigue Rizk, Longwei Wang

The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques the prevailing reliance on large-scale, static datasets and monolithic training paradigms, advocating for a shift toward human-inspired, sustainable AI solutions. We introduce a nov

Svetlana Kiritchenko, Anna Kerkhof, Isar Nejadgholi, Kathleen C. Fraser

We investigate the effect of automatically generated counter-stereotypes on gender bias held by users of various demographics on social media. Building on recent NLP advancements and social psychology literature, we evaluate two counter-stereotype strategies -- counter-facts and broadening universals (i.e., stating that anyone can have a trait regardless of

A. La Marca, M. T. Nardone, L. Wang, B. Margalef-Bentabol

We investigate the role of galactic bars in fuelling and triggering Active Galactic Nucleus (AGN) in disc galaxies up to $z\sim 0.8$. We utilise a Deep Learning model, fine-tuned on Galaxy Zoo volunteer classifications, to identify (strongly and weakly) barred and unbarred disc galaxies in Hyper Suprime-Cam Subaru Strategic Program $i$-band images. We select

Anthony Opipari, Aravindhan K Krishnan, Shreekant Gayaka, Min Sun

Remembering where object segments were predicted in the past is useful for improving the accuracy and consistency of class-agnostic video segmentation algorithms. Existing video segmentation algorithms typically use either no object-level memory (e.g. FastSAM) or they use implicit memories in the form of recurrent neural network features (e.g. SAM2). In this

Charlotte Dietze

We consider a compact Riemannian manifold with boundary with a certain class of critical singular Riemannian metrics that are singular at the boundary. The corresponding Laplace-Beltrami operator can be seen as a Grushin-type operator plus a potential. We show in the critical case that the average density of eigenfunctions for the Laplace-Beltrami operator w

Scott Jones, Prakash Murali

Trapped ion (TI) qubits are a leading quantum computing platform. Current TI systems have less than 60 qubits, but a modular architecture known as the Quantum Charge-Coupled Device (QCCD) is a promising path to scale up devices. There is a large gap between the error rates of near-term systems ($10^{-3}$ to $10^{-4}$) and the requirements of practical applic

Andrei Jaikin-Zapirain, Marco Linton, Pablo Sánchez-Peralta

A group pair $(G, X)$ consists of a group $G$ together with a $G$-set $X$. Such a pair encodes properties of $G$ relative to the stabilisers of points in $X$. In this paper, we show how to combine properties of group pairs and their stabilisers to prove coherence results for $G$ and its group algebra, as well as to study the quotient of $G$ obtained by killi

Sidney Congard, Guillaume Munch-Maccagnoni, Rémi Douence

We analyse the problem of combining linearity, effects, and exceptions, in abstract models of programming languages, as the issue of providing some kind of strength for a monad $T(- \oplus E)$ in a linear setting. We consider in particular for $T$ the allocation monad, which we introduce to model and study resource-safety properties. We apply these results t

Shubham S. Ganar, Deepak J., Arindam Das

This study investigates the impact of surface functionalization, oil coating, and oil absorption on droplet impact behavior on textured polydimethylsiloxane(PDMS) substrates. The textured surfaces were fabricated with square micro-posts having spacings of 5 and 20 microns. The PDMS samples were functionalized with octadecyltrichlorosilane (OTS) to improve wa

Amanda Ricketts, Benjamin A. Clouter-Gergen, Anastasis Georgiou, Deborah Berhanu

Despite the well-established presence of amorphous compounds on planetary bodies such as the Moon and Mercury due to space weathering, the direct effect of atomic arrangement on the surface binding energies (SBEs) of elements on these bodies remains largely unexplored. Accurate SBE values are essential for reliably predicting sputtering yields and the energy

Yaoli Liu, Yao-Xiang Ding, Kun Zhou

This paper proposes FreeFuse, a training-free framework for multi-subject text-to-image generation through automatic fusion of multiple subject LoRAs. In contrast to prior studies that focus on retraining LoRAs to alleviate feature conflicts, our analysis shows that spatially routing LoRA residuals to their intended semantic regions provides an effective mec

Uijeong Jang, Ernest K. Ryu

The Nesterov accelerated gradient method, introduced in 1983, has been a cornerstone of optimization theory and practice. Yet the question of its point convergence had remained open. In this work, we resolve this longstanding open problem in the affirmative. The discovery of the proof was heavily assisted by ChatGPT, a proprietary large language model, and w

Martin Huber, Nicola A. Cavalcanti, Ayoob Davoodi, Ruixuan Li

Despite their mechanical sophistication, surgical robots remain blind to their surroundings. This lack of spatial awareness causes collisions, system recoveries, and workflow disruptions, issues that will intensify with the introduction of distributed robots with independent interacting arms. Existing tracking systems rely on bulky infrared cameras and refle

Bin Xie, Erjin Zhou, Fan Jia, Hao Shi

In this paper, we present Dexbotic, an open-source Vision-Language-Action (VLA) model toolbox based on PyTorch. It aims to provide a one-stop VLA research service for professionals in the field of embodied intelligence. It offers a codebase that supports multiple mainstream VLA policies simultaneously, allowing users to reproduce various VLA methods with jus

Shakthivelu Janardhanan, Ritanshi Agarwal, Wolfgang Kellerer, Carmen Mas-Machuca

Network sovereignty is a network operator's ability to reduce the dependency on component manufacturers to minimize the impact of manufacturer failures. Network operators now face new design challenges to increase network sovereignty and avoid vendor lock-in problems because a high dependency on a manufacturer corresponds to low survivability if that manufac

Hyeongseop Rha, Jeong Hun Yeo, Yeonju Kim, Yong Man Ro

The recent advancement of Multimodal Large Language Models (MLLMs) is transforming human-computer interaction (HCI) from surface-level exchanges into more nuanced and emotionally intelligent communication. To realize this shift, emotion understanding becomes essential allowing systems to capture subtle cues underlying user intent. Furthermore, providing fait

Arthur Le Saux, Isabelle Baraffe, Thomas Guillet, Jane Pratt

Multidimensional hydrodynamical simulations have transformed the study of stellar interiors over the past few decades. Most codes developed during that time use the anelastic approximation, which fixes the thermal structure of simulations and filters out sound waves. Many of them also use explicit time integration, which imposes severe constraints on the tim

Usama Zidan, Mohamed Gaber, Mohammed M. Abdelsamea

Graph neural networks have emerged as a promising paradigm for image processing, yet their performance in image classification tasks is hindered by a limited consideration of the underlying structure and relationships among visual entities. This work presents iPac, a novel approach to introduce a new graph representation of images to enhance graph neural net

Fatemeh Zahra Safaeipour, Jacob Chakareski, Morteza Hashemi

Mobile edge devices (e.g., AR/VR headsets) typically need to complete timely inference tasks while operating with limited on-board computing and energy resources. In this paper, we investigate the problem of collaborative inference in wireless edge networks, where energy-constrained edge devices aim to complete inference tasks within given deadlines. These t

J. G. S. Lunde, P. C. Breysse, D. T. Chung, K. A. Cleary

We describe a time-domain technique for separating $1/f$ gain fluctuations and continuum signal for a total power spectrometer, such as the CO Mapping Array Project (COMAP) Pathfinder instrument. The $1/f$ gain fluctuations of such a system are expected to be common-mode across frequency channels. If the instrument's system temperature is not constant across

Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis

Since their introduction, Kolmogorov-Arnold Networks (KANs) have been successfully applied across several domains, with physics-informed machine learning (PIML) emerging as one of the areas where they have thrived. In the PIML setting, Chebyshev-based physics-informed KANs (cPIKANs) have become the standard due to their computational efficiency. However, lik

Oscar Thees, Roman Müller, Matthias Templ

Anonymizing microdata requires balancing the reduction of disclosure risk with the preservation of data utility. Traditional evaluations often rely on single measures or two-dimensional risk-utility (R-U) maps, but real-world assessments involve multiple, often correlated, indicators of both risk and utility. Pairwise comparisons of these measures can be ine

Yiqi Huang, Xinrui Zhao

We prove that if a rescaled mean curvature flow is a global graph over the round cylinder with small gradient and converges super-exponentially fast, then it must coincide with the cylinder itself. We also show that the result is sharp with counter-examples of local graphs at arbitrarily super-exponential convergence rate with the domain expanding arbitraril

Elena Celledoni, Brynjulf Owren, Lars Ruthotto, Tianjiao Nicole Yang

Exploiting low-precision computations has become a standard strategy in deep learning to address the growing computational costs imposed by ever larger models and datasets. However, naively performing all computations in low precision can lead to roundoff errors and instabilities. Therefore, mixed precision training schemes usually store the weights in high

Cesar Cuenca, Maciej Dołęga

This is the second paper in a series studying the global asymptotics of discrete $N$-particle systems with inverse temperature parameter $\theta$ in the high temperature regime. In the first paper, we established necessary and sufficient conditions for the Law of Large Numbers at high temperature in terms of Jack generating functions. In this paper, we deriv

Chenyang Ma, Kai Lu, Ruta Desai, Xavier Puig

To understand and collaborate with humans, robots must account for individual human traits, habits, and activities over time. However, most robotic assistants lack these abilities, as they primarily focus on predefined tasks in structured environments and lack a human model to learn from. This work introduces COOPERA, a novel framework for COntinual, OPen-En

Elisabeth Jüttner, Janelle Pfeifer, Leona Krath, Stefan Korfhage

Volumetric video relighting is essential for bringing captured performances into virtual worlds, but current approaches struggle to deliver temporally stable, production-ready results. Diffusion-based intrinsic decomposition methods show promise for single frames, yet suffer from stochastic noise and instability when extended to sequences, while video diffus

Simone Martino, Matteo Becchi, Andrew Tarzia, Daniele Rapetti

The study of complex many-body systems via analysis of the trajectories of the units that dynamically move and interact within them is a non-trivial task. The workflow for extracting meaningful information from the raw trajectory data is often composed of a series of interconnected steps, such as, (i) identifying and tracking the constitutive objects/particl

Jingjie Zhang, Hanqun Cao, Zijun Gao, Yu Wang

Post-translational modifications (PTMs) form a combinatorial "code" that regulates protein function, yet deciphering this code - linking modified sites to their catalytic enzymes - remains a central unsolved problem in understanding cellular signaling and disease. We introduce COMPASS-PTM, a mechanism-aware, coarse-to-fine learning framework that unifies res

Peter A. Fisher, Johannes Autenrieb, Anuradha M. Annaswamy

We consider the problem of adaptive control of a class of feedback linearizable plants with matched parametric uncertainties whose states are accessible, subject to state constraints, which often arise due to safety considerations. In this paper, we combine adaptation and control barrier functions into a real-time control architecture that guarantees stabili

Jerzy Marcinkowski, Piotr Ostropolski-Nalewaja

We show that the entailment problem, for a given entailment problem for DL-Lite$_{core}$ ontology, and given conjunctive query with inequalities, is undecidable. We also show that this problem remains undecidable if conjunctive queries with safe negation are considered instead of conjunctive queries with inequalities.

Hemish Ahuja, Samradh Bhardwaj, Kirti Dhir, Roman Bagdasarian

Quantum phase transitions in Rydberg atom arrays present significant opportunities for studying many-body physics, yet distinguishing between different ordered phases without explicit order parameters remains challenging. We present a resource-efficient quantum machine learning approach combining classical shadow tomography with variational quantum circuits

Luiz Vale Silva

The FCC-ee phase of a Future Circular Collider is generating great interest due to its versatility, allowing the study of various electroweak thresholds, $Z$, $WW$, $ZH$, and $t \bar{t}$. Electroweak precision physics is complemented by flavour physics measurements based on the unprecedented statistics attainable at the $Z$ pole, and benefiting from the low-

Roham Koohestani, Ziyou Li, Anton Podkopaev, Maliheh Izadi

This paper studies standard controller architectures for agentic AI and derives automata-theoretic models of their interaction behavior via trace semantics and abstraction. We model an agent implementation as a finite control program augmented with explicit memory primitives (bounded buffers, a call stack, or read/write external memory) and a stochastic poli

Junwen Ma, Mingyu Ge, Yisen Wang, Yong Zhang

The nonlinear nature of chaotic systems results in extreme sensitivity to initial conditions and highly intricate dynamical behaviors, posing fundamental challenges for accurately predicting their evolution. To overcome the limitation that conventional approaches fail to capture both local features and global dependencies in chaotic time series simultaneousl

Milad Sefidgaran, Kimia Nadjahi, Abdellatif Zaidi

In this paper, we leverage stochastic projection and lossy compression to establish new conditional mutual information (CMI) bounds on the generalization error of statistical learning algorithms. It is shown that these bounds are generally tighter than the existing ones. In particular, we prove that for certain problem instances for which existing MI and CMI

Julie Mordacq, David Loiseaux, Vicky Kalogeiton, Steve Oudot

Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations without labeled data, often by enforcing invariance to input transformations such as rotations or blurring. Recent studies have highlighted two pivotal properties for effective representations: (i) avoiding dimensional collapse-where the learned features occupy onl

Valentin Reyes Häusler, Gabriel Ott, Aruna Jayasena, Andreas Peter

Fully homomorphic encryption allows the evaluation of arbitrary functions on encrypted data. It can be leveraged to secure outsourced and multiparty computation. TFHE is a fast torus-based fully homomorphic encryption scheme that allows both linear operations, as well as the evaluation of arbitrary non-linear functions. It currently provides the fastest boot

Zujing Liu, Junwen Pan, Qi She, Yuan Gao

Recent large vision-language models (LVLMs) can generate vision-text multimodal chain-of-thought (MCoT) traces after reinforcement fine-tuning (RFT). However, we observe that the visual information incorporated in MCoT is often inaccurate, though still yield correct answers, indicating a lack of faithfulness in the MCoT reasoning process. We attribute this u

A. Del Popolo, Saeed Fakhry, Maryam Shiravand, Morgan Le Delliou

In this study, we build upon the findings of Del Popolo et al. (2013) by further analyzing the influence of dynamical friction on the evolution of cosmological perturbations within the framework of the spherical collapse model (SCM) in a Universe dominated by generalized Chaplygin gas (GCG). Specifically, we investigate how dynamical friction alters the grow

Xin Jin, Siyuan Li, Siyong Jian, Kai Yu

Vision-language alignment in multi-modal large language models (MLLMs) relies on supervised fine-tuning (SFT) or reinforcement learning (RL). To align multi-modal large language models (MLLMs) in the post-training stage, supervised fine-tuning (SFT) is a stable choice but requires human annotations and lacks task generalizations, while Reinforcement Learning

Karthikeyan Chandra Sekaran, Markus Geisler, Dominik Rößle, Adithya Mohan

Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real-world datasets incorporate both vehicle-to-vehicle and vehicle-to-infrastr

Sima Noorani, Shayan Kiyani, George Pappas, Hamed Hassani

AI predictive systems are increasingly embedded in decision making pipelines, shaping high stakes choices once made solely by humans. Yet robust decisions under uncertainty still rely on capabilities that current AI lacks: domain knowledge not captured by data, long horizon context, and reasoning grounded in the physical world. This gap has motivated growing

Suyash Fulay, Sercan Demir, Galen Hines-Pierce, Hélène Landemore

A large share of retail investors hold public equities through mutual funds, yet lack adequate control over these investments. Indeed, mutual funds concentrate voting power in the hands of a few asset managers. These managers vote on behalf of shareholders despite having limited insight into their individual preferences, leaving them exposed to growing polit

Shames Al Mandalawi, Muzakkiruddin Ahmed Mohammed, Hendrika Maclean, Mert Can Cakmak

Enterprises need access decisions that satisfy least privilege, comply with regulations, and remain auditable. We present a policy aware controller that uses a large language model (LLM) to interpret natural language requests against written policies and metadata, not raw data. The system, implemented with Google Gemini~2.0 Flash, executes a six-stage reason

Shijian Wang, Jiarui Jin, Xingjian Wang, Linxin Song

Recent advances in image reasoning methods, particularly "Thinking with Images", have demonstrated remarkable success in Multimodal Large Language Models (MLLMs); however, this dynamic reasoning paradigm has not yet been extended to video reasoning tasks. In this paper, we propose Video-Thinker, which empowers MLLMs to think with videos by autonomously lever

Shayan Kiyani, Hamed Hassani, George Pappas, Aaron Roth

Calibration has emerged as a foundational goal in ``trustworthy machine learning'', in part because of its strong decision theoretic semantics. Independent of the underlying distribution, and independent of the decision maker's utility function, calibration promises that amongst all policies mapping predictions to actions, the uniformly best policy is the on

M. Moutzouri, J. Mackey, N. Castro, Y. Gong

Bow shocks around massive stars have primarily been detected in IR emission, but radio detections are becoming more frequent with the commissioning of sensitive and large field-of-view interferometers. Radio data probes both thermal and non-thermal emission, thereby constraining the relativistic electron population. We undertook a radio survey for bow shocks

Shreya Khisa, Ali Amhaz, Mohamed Elhattab, Chadi Assi

In this paper, we explore a novel joint uplink and downlink framework utilizing a pinching antenna system (PASS). We consider two waveguides, one dedicated to transmission and one to reception, and both of them are connected to a base station (BS). Each type of waveguide consists of several pinching antennas (PAs) in some preconfigured positions. In this fra

Bhawna Sahni, Yao Zhao, Zhen Li, Rajeev Dutt

We perform an ab initio computational investigation of the electronic and thermoelectric transport properties of one of the best performance half-Heusler (HH) alloys, NbFeSb. We use Boltzmann Transport equation while taking into account the full energy/momentum/band dependence of all relevant electronic scattering rates, i.e. with acoustic phonons, non-polar

Abdul Saboor, Zhuangzhuang Cui, Achiel Colpaert, Evgenii Vinogradov

This paper presents a comprehensive measurement-based trajectory-aware characterization of low-altitude Air-to-Ground (A2G) channels in a suburban environment. A 64-element Massive Multi-Input Multi-Output (MaMIMO) array was used to capture channels for three trajectories of an Uncrewed Aerial Vehicle (UAV), including two horizontal zig-zag flights at fixed

Nikesh Gyawali, Doina Caragea, Alex Vasenkov, Cornelia Caragea

Financial narratives from U.S. Securities and Exchange Commission (SEC) filing reports and quarterly earnings call transcripts (ECTs) are very important for investors, auditors, and regulators. However, their length, financial jargon, and nuanced language make fine-grained analysis difficult. Prior sentiment analysis in the financial domain required a large,

Hao Liang, Haifeng Wen, Kaishun Wu, Hong Xing

Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In its prototypical edge instantiation with underlying wireless transmissions enabled by analog over-the-air computing (AirComp), referred to as \emph{over-the-air FL (AirFL)}, the inherent channel noise plays a uniq

Riccardo Gozzo

This work investigates the computational burden of pricing binary options in rare event regimes and introduces an adaptation of the adaptive multilevel splitting (AMS) method for financial derivatives. Standard Monte Carlo becomes inefficient for deep out-of-the-money binaries due to discontinuous payoffs and extremely small exercise probabilities, requiring

Kinkar Chandra Das, Sultan Ahmad

The Hyperbolic Sombor index $HSO(G)$ of a graph $G$ is defined as \begin{align*} HSO(G) = \sum_{v_iv_j \in E(G)} \frac{\sqrt{d_i^{2}+d_j^{2}}}{\min\{d_i,d_j\}}, \end{align*} where $d_i$ and $d_j$ denote the degrees of the vertices $v_i$ and $v_j$, respectively. This index was recently introduced by Barman et al. [Geometric approach to degree-based topologica

Alessandro Contri, André Massing, Padmini Rangamani

Cellular morphodynamics requires solving systems of coupled partial differential equations on moving bulk and surface domains, where advection-dominant transport, structure preservation, and severe mesh distortions make robust simulation difficult. We present a holistic finite element framework that jointly addresses these obstacles for biophysical applicati

Litu Ou, Kuan Li, Huifeng Yin, Liwen Zhang

Confidence in LLMs is a useful indicator of model uncertainty and answer reliability. Existing work mainly focused on single-turn scenarios, while research on confidence in complex multi-turn interactions is limited. In this paper, we investigate whether LLM-based search agents have the ability to communicate their own confidence through verbalized confidenc

Georg Velev, Stefan Lessmann

The dynamic energy sector requires both predictive accuracy and runtime efficiency for short-term forecasting of energy generation under operational constraints, where timely and precise predictions are crucial. The manual configuration of complex methods, which can generate accurate global multi-step predictions without suffering from a computational bottle