April 2026 arXiv papers — page 153

Mohak Goyal, Nishka Arora, Ashish Goel

Community Notes is X's crowdsourced fact-checking program: contributors write short notes that add context to potentially misleading posts, and other contributors rate whether those notes are helpful. Its algorithm uses a matrix factorization model to separate ideology from note quality, so notes are surfaced only when they receive support across ideological

Salim I. Amoukou, Nicolas J-B. Brunel

We analyze two widely used local attribution methods, Local Shapley Values and LIME, which aim to quantify the contribution of a feature value $x_i$ to a specific prediction $f(x_1, \dots, x_p)$. Despite their widespread use, we identify fundamental limitations in their ability to reliably detect locally important features, even under ideal conditions with e

Ovaisa Jan, Idrees Qasim, Nusrat Ahmed Dar

In this paper, we derive new bounds for the zeros of quaternionic polynomials by applying localization theorems, which includes Gershgorin-type theorems for the left eigenvalues of matrices of left monic quaternionic polynomials. These results yield sharper estimates compared to existing bounds, including improvements upon Cauchy, Fujiwara and Opfer's classi

Maksim A Kazanskii

Rotating space tethers have long been proposed as momentum-exchange devices capable of transporting payloads between orbital regimes without continuous propellant expenditure, offering a potential alternative to conventional propulsion for transfers from low Earth orbit to higher orbits. In this work, we numerically investigate a system of multiple rotating

Ved Datar, Vamsi Pritham Pingali, Harish Seshadri

We prove that any complete non-compact K\"ahler surface with positive sectional curvature is biholomorphic to $\mathbb{C}^2$, establishing the two dimensional case of the weaker form of Yau's uniformisation conjecture. In contrast to all previous results, no assumptions are made on the geometry at infinity. The proof introduces a new approach towards Yau-typ

Julien Walther, Rémi Giraud, Michaël Clément

Superpixels offer a compact image representation by grouping pixels into coherent regions. Recent methods have reached a plateau in terms of segmentation accuracy by generating noisy superpixel shapes. Moreover, most existing approaches produce a single fixed-scale partition that limits their use in vision pipelines that would benefit multi-scale representat

Hai Huang, Huiming Zhang, Biye Xie, Wengang Bi

Tunable bound states in the continuum (BICs) in photonic crystal slabs are highly sensitive to substrate-induced mirror-symmetry breaking and typically degrade into finite-$Q$ quasi-BICs in realistic integrated platforms. Here we show that such degradation can be deterministically reversed. Using temporal coupled-mode theory and full-wave simulations, we dem

Seulki Lee

What values, evidence preferences, and source trust hierarchies do AI systems actually exhibit when facing structured dilemmas? We present the first large-scale empirical mapping of AI decision-making across all three layers of the Authority Stack framework (S. Lee, 2026a): value priorities (L4), evidence-type preferences (L3), and source trust hierarchies (

Ovaisa Jan, Idrees Qasim

Locating the zeros of quaternionic polynomials is a fundamental problem with significant implications across scientific and engineering disciplines, yet the noncommutative nature of quaternion multiplication makes it fundamentally more complex than the classical complex case. In this paper, we develop new bounds for the zeros of polynomials with quaternionic

Yangfan Wang, Tianyang Sun, Chen Tang, Jie Liu

Lifelong model editing (LME) aims to sequentially rectify outdated or inaccurate knowledge in deployed LLMs while minimizing side effects on unrelated inputs. However, existing approaches typically apply parameter perturbations to a static and dense set of LLM layers for all editing instances. This practice is counter-intuitive, as we hypothesize that differ

Pascal Bégout, Jérôme Bolte, Thomas Mariotti, Francisco Silva

Gradient extremals are loci along which the gradient is an eigenvector of the Hessian. These objects provide a natural geometric framework connecting several notions, notably valleys and talwegs, which we analyze from a variational viewpoint in the generic case. We then show that trajectories of the gradient flow and of its discrete counterpart exhibit direc

Marie-Pierre Béal, Vincent Jugé, Jean Mairesse, Dominique Perrin

Sofic measures, also known as hidden Markov measures, have been extensively studied. In this paper, we survey some equivalent definitions of this notion and improve a bound for deciding whether a sofic measure is~$k$-step Markov. We prove that if an invariant sofic measure with a linear representation of dimension~$n$ is a~$k$-step Markov chain, then~$k$ can

Stefan Schulz, Fernando Edelstein, Hannah Dröge, Matthias B. Hullin

Real-time free-viewpoint rendering requires balancing multi-camera redundancy with the latency constraints of interactive applications. We address this challenge by combining lightweight geometry with learning and propose 3DTV, a feedforward network for real-time sparse-view interpolation. A Delaunay-based triplet selection ensures angular coverage for each

Mostapha Benhenda

Let M be a smooth compact connected manifold, on which there exists an effective smooth circle action preserving a positive smooth volume. We show that on M, the smooth closure of the smooth volume-preserving conjugation class of some Liouville rotations of angle alpha contains a smooth volume-preserving diffeomorphism T that is metrically isomorphic to an i

Tianzhe Zhao, Jiaoyan Chen, Shuxiu Zhang, Haiping Zhu

Large language models (LLMs) have achieved remarkable success across a wide range of applications especially when augmented by external knowledge through retrieval-augmented generation (RAG). Despite their widespread adoption, recent studies have shown that LLMs often struggle to perform faithful reasoning when conflicting knowledge is retrieved. However, ex

Ana Defendini Cortes, Livia Tomova, Leonie Koban

Humans are inherently social and seek connection with others for survival. Recent studies suggest that acute social isolation leads to craving for social interactions, but the brain mechanisms of social craving and their relationship to brain networks underlying drug and food craving remain incompletely understood. Here we harnessed an existing dataset and t

Tiziano Santilli, Mina Alipour, Mahyar Tourchi Moghaddam

Digital nudging systems lack architectural guidance for translating behavioral science into software design. While research identifies nudge strategies and quality attributes, existing architectures fail to integrate multi-dimensional user modeling with ethical compliance as architectural concerns. We present an architecture that uses behavioral theory throu

András Biró

Let $\Gamma\subseteq PSL(2, \mathbb R)$ be a finite volume Fuchsian group. The hyperbolic circle problem is the estimation of the number of elements of the $\Gamma$-orbit of $z$ in a hyperbolic circle around $w$ of radius $R$, where $z$ and $w$ are given points of the upper half plane and $R$ is a large number. An estimate with error term $e^{\frac 23R}$ is

Jianfeng Xu

Shannon's rate-distortion theory treats source symbols as unstructured labels. When the source is a knowledge base equipped with a logical proof system, a natural fidelity criterion is closure fidelity: a reconstruction is acceptable if it preserves the deductive closure of the original. This paper develops a rate-distortion theory under this criterion. Cent

Aleksandr Azatov, Bruno Missoni

We review the constraints on baryon inhomogeneities derived from measurements of the deuterium abundance, $D/H$, and apply them to a range of baryogenesis models. In particular, we derive bounds on electroweak baryogenesis as well as on more exotic scenarios. Our results show that, across most of the relevant parameter space, electroweak baryogenesis remains

Hector R. Rodriguez, Jiechen Huang, Wenjian Yu

We present CapBench, a fully reproducible, multi-PDK dataset for capacitance extraction. The dataset is derived from open-source designs, including single-core CPUs, systems-on-chip, and media accelerators. All designs are fully placed and routed using 14 independent OpenROAD flow runs spanning three technology nodes: ASAP7, NanGate45, and Sky130HD. From the

CocoaBench Team, Shibo Hao, Zhining Zhang, Zhiqi Liang

LLM agents now perform strongly in software engineering, deep research, GUI automation, and various other applications, while recent agent scaffolds and models are increasingly integrating these capabilities into unified systems. Yet, most evaluations still test these capabilities in isolation, which leaves a gap for more diverse use cases that require agent

Tom Bewley, Salim I. Amoukou, Emanuele Albini, Saumitra Mishra

Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream business outcomes (e.g. a bank's loan approval rate), so understanding their causes can be crucial. We propose \ours{}: a Shapley value method for attributing prediction shifts to changes in the conditional proba

Dylan Greaves

We present an explicit deterministic transformation of a fixed number of i.i.d. uniform random variables with exact Beta$(a,1-a)$ law for $0<a<1$, using only elementary operations (an ``extended one-liner'', see \cite{devroye1996oneline}). As corollaries, the families Beta$(a,b)$ with $\min(a,b)<1$, Gamma$(c)$ with $c<1$, and Dirichlet$(\alpha_1,\dots,\alpha

Anqi Liu, Jiangtao Zhao, Guiyuan Jiang, Feng Hong

Accurate air traffic prediction in the terminal airspace (TA) is pivotal for proactive air traffic management (ATM). However, existing data-driven approaches predominantly rely on time series-based forecasting paradigms, which inherently overlook critical aircraft state information, such as real-time kinematics and proximity to airspace boundaries. To addres

Yajing Gui, Benling Li

This paper studies spherically symmetric sprays, i.e., sprays that are invariant under orthogonal transformations. We first establish a canonical form for such sprays, showing that their geodesic coefficients can be expressed as \(G^i = |y|\alpha(r,s) y^i + |y|^2\beta(r,s) x^i\), where \(r = |x|^2\) and \(s = \langle x,y\rangle/|y|\). For projectively flat s

Yuqi Ji, Junjie Ke, Lihuo He, Lizhi Wang

Existing object detectors often struggle to generalize across domains while adapting to emerging novel categories. Adaptive open-set object detection (AOOD) addresses this challenge by training on base categories in the source domain and adapting to both base and novel categories in the target domain without target annotations. However, current AOOD methods

Sergio Ferrari, Daniel Errandonea

Using density functional theory, we study the influence of hydrostatic pressure on the crystal structure of lanthanide monoxides, focusing on the monoxides formed by the fifteen elements of the lanthanide series, from La to Lu. Calculations are performed using two methods for the ambient pressure B1 (NaCl type) structure, the general gradient approximation (

Yingxu Wang, Jiaxin Huang, Mengzhu Wang, Nan Yin

Multi-hop Knowledge Graph Question Answering (KGQA) requires coherent reasoning across relational paths, yet existing methods often treat each reasoning step independently and fail to effectively leverage experience from prior explorations, leading to fragmented reasoning and redundant exploration. To address these challenges, we propose Trajectoryaware Reas

Jinjian Sheng, Kazumune Hashimoto, Shuang Zhao, Mahdieh S. Sadabadi

Long-horizon finite-control-set model predictive control (FCS-MPC) can improve transient regulation and flying-capacitor balancing in flying-capacitor three-level boost converters (FC-TLBCs). However, searching over switching sequences becomes computationally expensive at high switching frequencies. We train a feedforward neural network to imitate an $N$-ste

Manas Pandey

Stationary multi-magnetometer arrays are routinely deployed in geomagnetic observatories, laboratory shielded rooms, and ground-based monitoring stations. The standard analysis pipeline reduces each sensor to an independent power spectrum, discarding any inter-sensor structure that is itself diagnostic of measurement health and of localised magnetic activity

Zixiong Yu, Jun Rao, Guhan Chen, Songtao Tian

Synthesizing high-quality mathematical reasoning data without human priors remains a significant challenge. Current approaches typically rely on seed data mutation or simple prompt engineering, often suffering from mode collapse and limited logical complexity. This paper proposes a hierarchical synthesis framework that formulates data synthesis as an unsuper

Han Feng, Yan Ge

In this paper, we investigate the relationship between positive definite functions on the unit sphere $\sph$ and on the Euclidean space $\RR^d$. For the dimension $d$ to be odd, a new technique is developed to establish the inheritance of positive (semi-)definite property from $\RR^d$ to $\sph$ and the converse. For $d=2$, it is proved that a function define

Peijie Zhang, Pablo Botella, Neha Bura, Xiao Dong

Silicate frameworks exhibit diverse structural responses under extreme conditions, which are strongly influenced by hydration. Here, we present a comparative high-pressure synchrotron X-ray diffraction study of Na2ZrSi2O7 and its hydrated analogue Na2ZrSi2O7.H2O up to 30 GPa, combined with electronic structure calculations. At ambient conditions, both phases

Vardan Elbakyan, Rolf Kuiper, André Oliva, Verena Wolf

We investigate how resolving the inner few astronomical units of a massive protostellar disk affects the migration, disruption, and accretion signatures of an inward-moving fragment. In particular, we aim to determine whether the predicted burst strength and duration depend on the adopted sink cell size. We present a new three-dimensional radiation-hydrodyna

Bianca Trinkenreich, Fabio Calefato, Kelly Blincoe, Viggo Tellefsen Wivestad

Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices. Despite rapid adoption, there is limited empirical evidence on how GenAI is used in SE research and its implications for research practices and governance. Aims: We conduct a large-scale survey of 457 SE resea

Jonas Schießl, Ruchuan Ou, Michael H. Baumann, Timm Faulwasser

Chance constraints are widely used in stochastic model predictive control (MPC) to enforce probabilistic state and input constraints in the presence of unbounded disturbances. However, they only restrict violation probabilities and do not account for the magnitude of rare but severe constraint violations. In this paper, we extend the indirect feedback approa

Pietro Grassi, Edoardo Paperi, Chiara Seghieri, Daniele Vignoli

The transition of end-of-life care to palliative care (PC) sparks intense debate: does it provide economic relief or shift unremunerated labor costs onto families? Evaluating this is hindered by causal inference challenges and skewed healthcare costs. To overcome these limitations, we introduce a Synthetic Data Generation framework. Using pan-European SHARE

Jianjie Zheng, Zhichen Liu, Zhanyu Shen, Jingxiang Qu

Nowadays, wearable devices can continuously lifelog ambient conversations, creating substantial opportunities for memory systems. However, existing benchmarks primarily focus on online one-on-one chatting or human-AI interactions, thus neglecting the unique demands of real-world scenarios. Given the scarcity of public lifelogging audio datasets, we propose a

Anik Ghosh, Matteo Ziliani, Marco Astarita, Samuele Trezzi

Binary amplitude spatial light modulators, such as digital micromirror devices (DMDs), are increasingly relevant for computer generated holography due to their high refresh rates, low cost, and due to the emergence of subwavelength pixel architectures. However, the binary constraint limits the reconstruction quality, as conventional approaches rely on a bina

Abbas Arghavani, Subhrakanti Dey, Anders Ahlen

This paper investigates covert wireless communication with a Fusion Center (FC) that aggregates raw energy measurements from multiple Wardens via soft fusion. Extending our prior work on power-threshold randomization, we consider a stronger adversarial model in which FC randomizes both the number of active Wardens W and the detection threshold t, while Alice

Renjie Zou, Sheng Xiao, Zhi Qin, Zhigang Xiao

We have developed a systematic approach to calculate the correlation function for spin-1/2 particles, incorporating both central and noncentral components of the interparticle interaction. This is achieved by extending the variable phase method to accommodate noncentral potentials and numerically solving the Schr\"odinger equation. Within this framework, the

Thomas Deppisch

Multichannel speech enhancement is widely used as a front-end in microphone array processing systems. While most existing approaches produce a single enhanced signal, direction-preserving multiple-input multiple-output (MIMO) methods instead aim to provide enhanced multichannel signals that retain directional properties, enabling downstream applications such

Nicole Rogalla, Yuzhen Qin, Mario Senden, Ahmed El-Gazzar

Forecasting neural activity in response to naturalistic stimuli remains a key challenge for understanding brain dynamics and enabling downstream neurotechnological applications. Here, we introduce a generative forecasting framework for modeling neural dynamics based on autoregressive flow matching (AFM). Building on recent advances in transport-based generat

Abbas Arghavani, Alessandro V. Papadopoulos, Vahid Azimi Mousolou, Giuseppe Nebbione

Covert quantum communication is usually analyzed under idealized assumptions that channel parameters, such as transmissivity and background noise, are perfectly known and constant. In realistic optical links, including satellite, fiber, and free-space systems, these parameters vary because of environmental fluctuations, calibration noise, and estimation erro

Shivam Sharma, Sankalp Nagaonkar, Ashish Choithani, Ashutosh Trivedi

We benchmark how internal reasoning traces, which we call thought streams, affect video scene understanding in vision-language models. Using four configurations of Google's Gemini 2.5 Flash and Flash Lite across scenes extracted from 100 hours of video, we ask three questions: does more thinking lead to better outputs, where do the gains stop, and what do th

Tuo Liu, Shuijin Lin, Shaozhen Yan, Haifeng Wang

The biological definition of Alzheimer's disease (AD) relies on multi-modal neuroimaging, yet the clinical utility of positron emission tomography (PET) is limited by cost and radiation exposure, hindering early screening at preclinical or prodromal stages. While generative models offer a promising alternative by synthesizing PET from magnetic resonance imag

Ke-Yi Wang, Qiong Liu

Infrared (IR) excess emission around white dwarfs (WDs) is commonly attributed to circumstellar debris disks and/or low-mass companions, providing a unique window into the evolution of planetary systems and binary evolution after the main-sequence stage. Based on a spectroscopically confirmed WD sample from the DESI Early Data Release, we performed a systema

Xue Qin, Simin Luan, John See, Cong Yang

Recent progress in embodied AI has produced a growing ecosystem of robot policies, foundation models, and modular runtimes. However, current evaluation remains dominated by task success metrics such as completion rate or manipulation accuracy. These metrics leave a critical gap: they do not measure whether embodied systems are governable -- whether they resp

Giuseppe Del Vecchio Del Vecchio, Manas Kulkarni, Satya N. Majumdar, Sanjib Sabhapandit

We analyze a class of one-dimensional quantum systems characterized by a position-dependent kinetic term arising as the continuum limit of an inhomogeneous tight-binding model with spatially varying hopping amplitudes. In this limit, the Schrodinger equation takes the so-called BenDaniel-Duke form with an effective mass, scaling as $m_{eff}(x) = m_{eff}|x|^{

Haill An, Suhyeon Kim, Donghyuk Choo, Younhyun Jung

Direct volume rendering (DVR) aims to help users identify and examine regions of interest (ROIs) within volumetric data, and feature representations that support effective ROI classification and clustering play a fundamental role in volume exploration. Existing approaches typically rely on either explicit local feature representations or implicit convolution

Tim J. M. Jaspers, Francisco Caetano, Cris H. B. Claessens, Carolus H. J. Kusters

Computer-aided detection (CADe) of early neoplasia in Barrett's esophagus is a low-prevalence surveillance problem in which clinically relevant findings are rare. Although many CADe systems report strong performance on balanced or enriched datasets, their behavior under realistic prevalence remains insufficiently characterized. The RARE25 challenge addresses

Anurag Das, Anna Kukleva, Xinting Hu, Yuki M. Asano

Semantic segmentation requires dense pixel-level annotations, which are costly and time-consuming to acquire. To address this, we present SeSAM, a framework that uses a foundational segmentation model, i.e. Segment Anything Model (SAM), with weak labels, including coarse masks, scribbles, and points. SAM, originally designed for instance-based segmentation,

Takayuki Yokoyama, Yasuhiro Tada

We investigate hard-core bosons at half filling on a three-leg ladder under the uniform artificial gauge field. By analyzing current patterns and correlation functions, we uncover a rich quantum phase diagram containing multiple superfluid and insulating phases. In bosonic ladder systems, increasing the gauge flux typically destabilizes the Meissner phase an

Olivier Gasquet

Exact tight bounds of the complexity of the satisfiability problem for dense modal logics is a difficult question, likely somewhere between $\PSPACE$ and $\EXPSPACE$ depending of the logic under question. For a class of them, called here $n$-dense logics (characterized by axioms $\Box^n p\rightarrow \Box p$), we refine the known results -- membership in $\NE

Ofir Reich

There is rising interest in using Machine Learning (ML) model predictions as outcomes in causal analysis. However, these methods have faced challenges in finding the true treatment effects. It is also challenging to make choices about which prediction models to choose, since we are interested not only in the accuracy of the prediction but in its ability to p

Yuki Inoue, Hsiang-Yu Huang, Vivek Kumar, Daiki Tanabe

We investigate the sensitivity of a torsion-bar gravitational-wave detector to Yukawa-type deviations from Newtonian gravity using a differential gravitational calibrator (GCal), where two rotating mass systems cancel the leading Newtonian torque. We derive an exact expression for the residual torque and map the Yukawa signal into a strain-equivalent respons

Nirmalendu Prakash, Narmeen Fatimah Oozeer, Xin Su, Phillip Howard

CLIP retrieval is typically framed as a pointwise similarity problem in a shared embedding space. While CLIP achieves strong global cross-modal alignment, many retrieval failures arise from local geometric inconsistencies: nearby items are incorrectly ordered, leading to systematic confusions (e.g., pentagon vs. hexagon) and produces diffuse, weakly controll

Hikaru Wakaura

Near-term quantum computers must protect fragile coherence against decoherence to deliver useful results. Catalytic quantum error correction (CQEC) addresses this challenge by amplifying residual coherence with a reusable catalyst, achieving threshold-free recovery whenever the target coherent modes survive in the noisy state. However, the original protocol

Doudou Zhou, Yiran Zhang, Dian Jin, Yingye Zheng

Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing

Shuang Zeng, Boxu Xie, Lei Zhu, Xinliang Zhang

Deep learning has greatly advanced medical image segmentation, but its success relies heavily on fully supervised learning, which requires dense annotations that are costly and time-consuming for 3D volumetric scans. Barely-supervised learning reduces annotation burden by using only a few labeled slices per volume. Existing methods typically propagate sparse

Alexander Rothkopf, W. A. Horowitz, Jan Nordström

In this contribution we present recent developments in the formulation and solution of Initial Boundary Value Problems (IBVPs). Building upon a modern variational action formulation of classical dynamics, we treat Initial Boundary Value Problems directly on the action level, bypassing governing equations. We show that by including coordinate maps as dynamica

Camile Lendering, Erkut Akdag, Egor Bondarev

Accurate defect segmentation is critical for industrial inspection, yet dense pixel-level annotations are rarely available. A common workaround is to convert inexpensive bounding boxes into pseudo-masks using foundation segmentation models such as the Segment Anything Model (SAM). However, these pseudo-labels are systematically noisy on industrial surfaces,

Han Wua, Lishan Zhang, Chunming Lu

Background: Traditional research on collaborative learning scaffolding is often time-consuming and resource-heavy, which hinders the rapid iteration and optimization of instructional strategies. LLM-based multi-agent systems have recently emerged as a powerful tool to simulate complex social interactions and provide a novel paradigm for educational research.

Gilles Callebaut, Jarne Van Mulders

This paper experimentally investigates geometry-based multi-antenna RF wireless power transfer (WPT) using a large-scale distributed indoor transmit array measuring 8 m by 4 m. Geometry-based beamforming uses known transmitter and receiver positions to perform phase-only precoding, avoiding the need for explicit channel estimation or feedback. The experiment

Gaurav Katoch, Debajyoti Sarkar, Bhim Sen

This paper extends our previous work (arXiv:2504.14313) of a single timelike subregion to two, in the framework of AdS$_3$-Vaidya holography. We confirm the positivity of timelike mutual information and the statement of weak monotonicity when the subregions are non-overlapping. We also study entanglement inequalities such as Araki-Lieb inequality and strong

Qiling Gu, Wenlong Zhang, Zhidong Zhang

This paper considers the inverse problem of identifying the source term of parabolic equations from sparse boundary measurements. We used data from moving sensors to locate the unknown source term. This work first proves the uniqueness of the inverse problem under such measurements. Then the movement strategy of the sensor is given, from which the authors bu

Tianyang Dai, Ming Chang, Yan Chen, Yang Hu

Unsupervised remote photoplethysmography (rPPG) promises to leverage unlabeled video data, but its potential is hindered by a critical challenge: training on low-quality "in-the-wild" videos severely degrades model performance. An essential step missing here is to assess the suitability of the videos for rPPG model learning before using them for the task. Ex

Takumi Oshima, Yuma Ichikawa, Koji Hukushima

We propose a hybrid sampling method, tensor-network population annealing (TNPA), which combines tensor-network (TN) initialization with population annealing (PA). We apply this method to the two-dimensional Edwards-Anderson Ising spin glass. The approach is motivated by the limitations of existing methods: TN-based samplers can become numerically unstable in

Marta López-Rauhut, Loic Landrieu, Mathieu Aubry, Anne-Laure Ligozat

New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy is driving steady increases in energy consumption, greenhouse gas emissions, and a plethora of other environmental impacts linked to datacenter construction and hardware manufacturing. Mitigating the environ

Devendra Ghori

State-of-the-art deepfake detectors achieve near-perfect in-domain accuracy yet degrade under cross-generator shifts, heavy compression, and adversarial perturbations. The core limitation remains the decoupling of semantic artifact learning from physical invariants: optical-flow discontinuities, specular-reflection inconsistencies, and cardiac-modulated refl

Kai D. Röseler, Felix Eder, Fabian O. von Rohr

The discovery and control of intergrowth structures represent an important avenue for the targeted synthesis of new, more complex structure types. When including magnetic framework metal atoms, this enhanced complexity can transfer to rich magnetic ground states. Here, we show that the subtle adjustment of the composition of alkali-tellurium fluxes enables t

João Gonçalves, Sonia de Jager, Petr Knoth, David Pride

This intermediate technical report introduces the SHARE family of base models and the MIRROR user interface. The SHARE models are the first causal language models fully pretrained by and for the social sciences and humanities (SSH). Their performance in modelling SSH texts is close to that of general purpose models (Phi-4) which use 100 times more tokens, as

Shodai Hamana, Yasushi Narushima

The nonlinear conjugate gradient methods are known to be an effective approach for standard unconstrained optimization problems especially for large-scale problems.This paper proposes a proximal nonlinear conjugate gradient method, which extends the nonlinear conjugate gradient methods to composite objective functions, namely, the sum of a smooth nonconvex f

R. Asanovski, P. Rinaudo, A. Chasin, Y. Zhao

We investigate the origin of negative threshold voltage shifts in back-gated amorphous IGZO TFTs under positive bias and high temperature stress. Combined DC and 1/f noise measurements reveal that the stress does not generate new dielectric traps but instead broadens the IGZO conduction band tail states. A recovery experiment confirms that the associated thr

Levent Aksoy, Muhammad Sohaib Munir, Sedat Akleylek

Over the years, many techniques have been introduced to protect integrated circuits (ICs) from hardware security threats that emerged in the globalized IC manufacturing supply chain, such as overproduction and piracy. However, most of these techniques have been rendered inefficient since they do not rely on provably secure algorithms. Moreover, the previousl

Yi Shi

Let $(V, G)$ be an orthogonal representation of a compact Lie group $G$ with nontrivial copolarity, and $\Sigma$ a fat section of $(V, G)$. If $E$ is a $G$-invariant compact convex set in $V$, then $P=E\cap\Sigma$ is a convex set in $\Sigma$. We prove that up to conjugacy the face structure of $E$ is completely determined by that of $P$ and that a face of $E

Elouan Colybes, Shirin Salehi, Anke Schmeink

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often suffers from significant communication and computational overhead, limiting its scalability and sustainability. In this work, we introduce a Full Compression Pipeline (FCP) for FL in communication-con

Yijie Zhong, Yunfan Gao, Weipeng Jiang, Haofen Wang

Image clustering aims to group images in an unsupervised fashion. Traditional methods focus on knowledge from visual space, making it difficult to distinguish between visually similar but semantically different classes. Recent advances in vision-language models enable the use of textual knowledge to enhance image clustering. However, most existing methods re

Michele Azzone, Carlo Bechi, Gabriele Sbaiz

Driven by the increasing frequency and intensity of natural disasters and chronic climate threats, we investigate the impact of physical climate risk on global equity portfolios. By employing a panel regression analysis on sectoral returns, we provide statistical evidence that extreme temperature events exert a negative effect on most sectors. We introduce t

Yuzhe Zhang, Qizhen Hong, Xiaoyong Wang, Quanhua Sun

A new radiation solver, RAPRAL (RAdiation Prediction based on RAy tracing and Line-by-line) implemented in C++, is developed for simulating high-temperature thermochemical nonequilibrium radiative processes. RAPRAL integrates detailed line-by-line spectral modeling with a ray-tracing solution of the radiative transfer equation, enabling accurate resolution o

Runyu Zhu, SiXun Dong, Zhiqiang Zhang, Qingxia Ye

Low-light conditions severely hinder 3D restoration and reconstruction by degrading image visibility, introducing color distortions, and contaminating geometric priors for downstream optimization. We present NAKA-GS, a bionics-inspired framework for low-light 3D Gaussian Splatting that jointly improves photometric restoration and geometric initialization. Ou

Chenhao Fang, Jordi Mola, Mark Harman, Jason Nawrocki

Although LLMs drive automation, it is critical to ensure immense consideration for high-stakes enterprise workflows such as those involving legal matters, risk management, and privacy compliance. For Meta, and other organizations like ours, a single hallucinated clause in such high stakes workflows risks material consequences. We show that by framing halluci

Arjun Bhardwaj, Maximum Wilder-Smith, Mayank Mittal, Vaishakh Patil

In-hand object reorientation requires precise estimation of the object pose to handle complex task dynamics. While RGB sensing offers rich semantic cues for pose tracking, existing solutions rely on multi-camera setups or costly ray tracing. We present a sim-to-real framework for monocular RGB in-hand reorientation that integrates 3D Gaussian Splatting (3DGS

Chen Zhan, Xiaoyu Tan, Gengchen Ma, Yu-Jie Xiong

The integration of Large Language Models (LLMs) into clinical decision support is critically obstructed by their opaque and often unreliable reasoning. In the high-stakes domain of healthcare, correct answers alone are insufficient; clinical practice demands full transparency to ensure patient safety and enable professional accountability. A pervasive and da

Zekun Qian, Ruize Han, Wei Feng

Object-level spatial-temporal understanding is essential for video question answering, yet existing multimodal large language models (MLLMs) encode frames holistically and lack explicit mechanisms for fine-grained object grounding. Recent work addresses this by serializing bounding box coordinates as text tokens, but this text-coordinate paradigm suffers fro

Liaoyuan Fan, Zetian Xu, Chen Cao, Wenyao Zhang

Pretrained video generation models provide strong priors for robot control, but existing unified world action models still struggle to decode reliable actions without substantial robot-specific training. We attribute this limitation to a structural mismatch: while video models capture how scenes evolve, action generation requires explicit reasoning about whe

Jean-Christophe Mourrat, Loucas Pillaud-Vivien

We present a counterexample to the statement of convergence of the mean-field Langevin descent-ascent flow on $\mathbb{R}^2$. We consider payoff functions that are shaped as a double well in each coordinate, and for which the deterministic dynamics admits a limit cycle. When the coupling between the two coordinates is sufficiently strong and the entropic reg

Minh-Vuong Nguyen, Fatemeh Shiri, Zhuang Li, Karin Verspoor

Large Language Models (LLMs) are increasingly being explored for clinical question answering and decision support, yet safe deployment critically requires reliable handling of patient measurements in heterogeneous clinical notes. Existing evaluations of LLMs for clinical numerical reasoning provide limited operation-level coverage, restricted primarily to ar

Zoran M. Cenev, Ville S. I. Havu, Jaakko V. I. Timonen

We report the motion of individual magnetic Quincke rollers composed of silica particles doped with superparamagnetic iron oxide nanoparticles, whose activity arises from the coupling between Quincke rolling and an externally applied rotating magnetic field. We applied a clockwise (CW) rotating magnetic field of magnitude approximately 11 mT and rotational f

Mert Baştuğ

We establish an asymptotic rigidity result for isometric immersions of codimension-1. Specifically, we consider a sequence of immersions from a compact $d$-dimensional manifold into a complete $(d+1)$-dimensional manifold whose elastic energies vanish asymptotically, where the elastic energy quantifies both stretching and bending. We show that such a sequenc

Feiyang Li, Yile Wang

Task vectors, representing directions in model or activation spaces that encode task-specific behaviors, have emerged as a promising tool for steering large language models (LLMs). However, existing approaches typically require fine-tuning or invasive manipulation of internal states, limiting their flexibility and scalability. We propose \textsc{DeCoVec} (De

Joyjit Kundu, Joshua Klein, Aakash Patel, Dwaipayan Biswas

The rise of generative AI workloads, particularly language model inference, is intensifying on/off-chip memory pressure. Multimodal inputs such as video streams or images and downstream applications like Question Answering (QA) and analysis over large documents incur long context lengths, requiring caching of massive Key and Value states of the previous toke

Tadeusz Inglot

We study an empirical interpretation of the Pitman efficiency in testing for uniformity in the two-parametric family of the beta distributions. We show that for contamination models the Pitman efficiency approximates relative efficiency very well.

Nikhil Mehta, Sachin Gupta, Gouri RP Anand

India generates vast biomedical data through postgraduate research, government hospital services and audits, government schemes, private hospitals and their electronic medical record (EMR) systems, insurance programs and standalone clinics. Unfortunately, these resources remain fragmented across institutional silos and vendor-locked EMR systems. The fundamen

Giovanni Fantuzzi, Didier Henrion, Martin Kru{ž}ík, Ajay Murali

A function of a matrix is polyconvex when it can be expressed as a convex function of the matrix minors. Polyconvexity is a regularity condition ensuring existence of minimizers in nonlinear elasticity and, more broadly, in vectorial problems of the calculus of variations, when minimizing integral gradient functionals. The polyconvex envelope of a function i

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The inclusive production of the $\eta_c(1S)$, $\eta_c(2S)$ and $\chi_{c}$ charmonium states in $b$-hadron decays is studied with LHCb Run~2 data, corresponding to an integrated luminosity of $5.9~\text{fb}^{-1}$, using charmonia decays to $\phi\phi$ pairs. The production branching fractions of the $\chi_{c}(1P)$ states in $b$-hadron decays are measured, usin

Samir Wagle, Reewaj Khanal, Abiral Adhikari

Hate speech detection in Devanagari-scripted social media memes presents compounded challenges: multimodal content structure, script-specific linguistic complexity, and extreme data scarcity in low-resource settings. This paper presents our system for the CHiPSAL 2026 shared task, addressing both Subtask A (binary hate speech detection) and Subtask B (three-

Yueying Li, Fengxiang Wang, Yan Li, Mingshuo Chen

Multimodal Large Language Models (MLLMs) have demonstrated immense potential in Earth observation. However, the massive visual tokens generated when processing Ultra-High-Resolution (UHR) imagery introduce prohibitive computational overhead, severely bottlenecking their inference efficiency. Existing visual token compression methods predominantly adopt stati

Atharva Gupta, Dhruv Kumar, Yash Sinha

The POLAR SemEval-2026 Shared Task aims to detect online polarization and focuses on the classification and identification of multilingual, multicultural, and multi-event polarization. Accurate computational detection of online polarization is challenging due to nuanced rhetoric, implicit framing, and the high cost of human-in-the-loop annotation. Building o