April 2026 arXiv papers — page 79

Ying Zeng, Miaosen Luo, Guangyuan Li, Yang Yang

Traditional photographic image editing typically requires users to possess sufficient aesthetic understanding to provide appropriate instructions for adjusting image quality and camera parameters. However, this paradigm relies on explicit human instruction of aesthetic intent, which is often ambiguous, incomplete, or inaccessible to non-expert users. In this

Rashi Gupta, Ankhi Roy

In this paper, we study the balance functions for pions, kaons, and protons in Pb--Pb collisions at $\sqrt{s_{\mathrm{NN}}} = 2.76$ TeV using the PYTHIA 8.3 + Angantyr model. The balance function is evaluated through two-particle azimuthal angular correlations $(\Delta \phi, \Delta \eta)$ between particle and antiparticle. Correlations are constructed for $\

Silvio Calderaro, Johanna Monti

The growing interest of Natural Language Processing (NLP) in minority languages has not yet bridged the gap in the preservation of oral linguistic heritage. In particular, extemporaneous poetry - a performative genre based on real-time improvisation, metrical-rhetorical competence - remains a largely unexplored area of computational linguistics. This methodo

Yue Xin, Yan Li, Bingzhe Hou

In this paper, we study the right invariant metric $d_{H^{\infty}}$ on the analytic automorphism group $\rm{Aut}(\mathbb{D})$ of the unit open disk $\mathbb{D}$ induced by maximal modulus, that is, $d_{H^{\infty}}(\varphi, \psi)=\sup_{z\in\mathbb{D}}|\varphi(z)-\psi(z)|$ for any $\varphi, \psi\in \rm{Aut}(\mathbb{D})$. We give the explicit formula of the rig

Aaron Chan, Osamu Iyama, Rene Marczinzik

We show that the class of twisted fractionally Calabi-Yau algebras of finite global dimension coincides with the stable endomorphism algebras of $d$-cluster tilting modules over $d$-representation-finite algebras. This is an application of our main result stating that an algebra $A$ of finite global dimension is twisted fractionally Calabi-Yau if and only if

J. Luna-Ramos, A. Martín-Ruiz

We study charge transport through a trivial-topological-trivial junction described by the continuous Qi-Wu-Zhang model, which realizes a two-dimensional Chern-insulating phase. The central region is tuned into the topological regime, while the adjoining leads remain trivial, and an electrostatic barrier of tunable height and width is applied exclusively to t

Simon Hirsch, Florian Ziel

Electricity price forecasting supports decision-making in energy markets and asset operation. Probabilistic forecasts are increasingly adopted to explicitly quantify uncertainty, typically issued as quantile predictions or ensembles of the full predictive distribution. However, how improvements in statistical forecast quality translate into economic value re

Monika Kish, Suchitra Pradhan, Jessica L. Ramsay, Paloma Munguía Salazar

The light-dependent magnetic compass of night-migratory songbirds is widely hypothesized to rely on the radical pair mechanism within retinal cryptochrome. However, bridging the mechanistic gap between microsecond quantum spin dynamics and the long-lived, global protein conformational changes required for cellular signalling remains a formidable challenge. H

Wenqing Wu, Chengzhi Zhang, Yi Zhao, Tong Bao

With the rapid advancement of Large Language Models (LLMs), the academic community has faced unprecedented disruptions, particularly in the realm of academic communication. The primary function of peer review is improving the quality of academic manuscripts, such as clarity, originality and other evaluation aspects. Although prior studies suggest that LLMs a

Zhongzhinan Dong, Jinsong Yang

In this paper, we investigate the quasinormal modes of two covariant effective black holes characterized by the quantum parameter $\zeta$, charge $Q$, and cosmological constant $\Lambda$, under the scalar perturbation. By employing the pseudo-spectral method, we numerically calculate the quasinormal frequencies and analyze the influence of $\zeta$ on the spe

F. A. Ivanyuk, S. V. Radionov, C. Ishizuka, S. Chiba

We solve the Langevin equations for the time evolution of parameters that describe the shape of fissioning system. On each integration step, we calculate the probability of neutron emission and estimate whether a neutron would be emitted or not. If yes, we decrease the excitation energy of the nucleus by the neutron separation energy plus the average energy

Jingze Ren, Yifan Wang, Hehu Xie, Qilong Zhai

This paper investigates quasiperiodic elliptic equations using a well-established projection method, by which the original problem is reformulated as a degenerate periodic variational problem defined on a higher-dimensional torus. The main analytical difficulty lies in the degeneracy of the projected torus problem: the variational formulation is coercive in

John Carlo Dimaculangan, Cameron A. Smith, Christian A. Yates

Cell proliferation and cell movement are fundamentally stochastic processes which lead to variability in the growth and spatial structure of cell populations in many biological settings, such as cell invasion, wound healing, and tumour growth. We develop stochastic, on-lattice agent-based models (ABMs) which incorporate volume exclusion, random movement, and

Joy Lai, Alex Mihailidis

Digital reminder systems are widely used in dementia care to support everyday tasks, but they are typically designed for one-way prompting rather than helping caregivers interpret engagement over time. We present Remindful, a caregiver-informed reminder platform that extends task prompting with caregiver-facing alerts, summaries, and review features to suppo

An-Yi Huang, Ya-Zhong Luo

This study presents a fast nonlinear programming algorithm for low-thrust multi-asteroid rendezvous missions. The core contribution is the derivation of analytical formulations for both first- and second-order gradients of low-thrust rendezvous $\Delta v$ through an iterative Lambert-based $\Delta v$ estimator and their application to derive the Hessian matr

Yanhui Chen, Jiahong Li, Jingchao Wang, Junyi Lin

Language-driven 3D Gaussian Splatting (3DGS) editing provides a more convenient approach for modifying complex scenes in VR/AR. Standard pipelines typically adopt a two-stage strategy: first editing multiple 2D views, and then optimizing the 3D representation to match these edited observations. Existing methods mainly improve view consistency through multi-v

Itai Zilberstein, Ratip Emin Berker, George Li, Ruben Martins

In an election where $n$ voters rank $m$ candidates, a Condorcet winning set is a committee of $k$ candidates such that for any outside candidate, a majority of voters prefer some committee member. Condorcet's paradox shows that some elections admit no Condorcet winning sets with a single candidate (i.e., $k=1$), and the same can be shown for $k=2$. On the o

Ningna Wang, Xiana Carrera, Christopher Batty, Oded Stein

Unsigned distance functions offer a powerful and flexible implicit surface representation that, unlike their signed counterparts, allow for surfaces that are open, non-orientable, or non-manifold. We consider the problem of reconstructing arbitrary surfaces from a finite set of samples of unsigned distance data. Existing methods for mesh reconstruction from

Chuou Xu, Liya Ji, Qifeng Chen

Reinforcement learning (RL) as post-training is crucial for enhancing the reasoning ability of large language models (LLMs) in coding and math. However, their capacity for visual semantic arithmetic, inferring relationships from images, remains underexplored. The classic text analogy "king"-"man"+"woman" = "queen" illustrates relational reasoning, yet replac

François Remy

Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failures and curate the training evidence needed to correct them. Late-interaction models such as ColBERT provide a first solution thanks to the interpretable token-level interaction scores they expose between documen

Jonas Waldendorf, Bashar Awwad Shiekh Hasan, Evgenii Tsymbalov

Hallucinations in Speech Large Language Models (SpeechLLMs) pose significant risks, yet existing detection methods typically rely on gold-standard outputs that are costly or impractical to obtain. Moreover, hallucination detection methods developed for text-based LLMs do not directly capture audio-specific signals. We investigate four attention-derived metri

Yanshuo Wang, Yuan Xu, Xuesong Li, Jie Hong

Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhibit distinct habits, preferences, and routines, such personalization is essential for truly effective assistance. However, effectively integrating long-term user data for personalization remains a key challenge.

Damián G. Hernández

We analyze information transmission in a recently proposed coarse-grained model of polymer replication by framing it as a communication channel between templates and copies. By calculating the mutual information in the steady-state limit of long chains, we recover the accurate-random phase diagram and establish that the information per-monomer depends solely

Carles Navarro, Philipp Tholke, Gianni de Fabritiis

Structure-based drug discovery faces the dual challenge of accurately capturing 3D protein-ligand interactions while navigating ultra-large chemical spaces to identify synthetically accessible candidates. In this work, we present a unified framework that addresses these challenges by combining contrastive 3D structure encoding with autoregressive molecular g

Juliusz Janicki, Savvas Chamezopoulos, Evangelos Kanoulas, Georgios Tsatsaronis

Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources. Membership Inference Attacks (MIA) aim to detect those documents and whether they have been included in the training corpora of the LLMs. The black-box MIAs require a significant amount of data manipulation; therefore, their compari

Maxim Raginsky, Benjamin Recht

The goal of machine learning is to find models that minimize prediction error on data that has not yet been seen. Its operational paradigm assumes access to a dataset $S$ and articulates a scheme for evaluating how well a given model performs on an arbitrary sample. The sample can be $S$ (in which case we speak of ``in-sample'' performance) or some entirely

Syed Sajid Ullah, Amir Khan

Construction workers are highly vulnerable to heat stress, yet tools that translate real-time physiological data into actionable safety intelligence remain scarce. This study addresses this gap by developing and evaluating deep learning models, specifically a baseline Long Short-Term Memory (LSTM) network and an attention-based LSTM, to predict heat stress a

Yican Sun, Chengwei Shi, Hangzhou Lyu, Yingfei Xiong

Automated theorem proving is fundamental to formal methods, and the recent trend is to integrate large language models (LLMs) and proof assistants to form effective proof agents. While existing proof agents show promising performance, they inadequately leverage reasoning capabilities of modern LLMs in high-level planning and self-critique. We argue that proo

Hajime Sotani

Supernovae are one of the most promising gravitational wave sources. But, since the system of the supernovae is nearly spherically symmetric, the expected gravitational waves from them are relatively weak, compared to the case of the compact binary mergers. Thus, at least using the current gravitational wave detectors, only the gravitational waves from a sup

Davide Allegro, Shiyao Li, Stefano Ghidoni, Vincent Lepetit

Current 3D mapping pipelines generally assume static environments, which limits their ability to accurately capture and reconstruct moving objects. To address this limitation, we introduce the novel task of active mapping of moving objects, in which a mapping agent must plan its trajectory while compensating for the object's motion. Our approach, Paparazzo,

Gustavo A. Fernandez Lezcano, Eduardo M. Garau, Bárbara Ivaniszyn

We study local approximation properties in hierarchical spline spaces through a twofold approach. First, we design and analyze a robust adaptive refinement algorithm to construct locally graded meshes. Second, we establish rigorous stability and approximation results using computationally efficient quasi-interpolation operators. The primary contribution is t

Jack A. Cook

The goal of this article is to give a proof of a result seemingly absent from the literature characterizing global sections of standard $\mathcal{D}$-modules on the flag variety. This characterization yields a mixture of the Langlands Classification of admissible representations with the Knapp-Zuckerman classification of tempered representations of a real re

J. Daniel E. Yeo, Christopher E. Thomas, David J. Wilson

Motivated by recent experimental observations of the flavour-exotic $T^*_{cs0}(2870)^0$ and $T^*_{c\bar{s}0}(2900)$, we present the first lattice QCD study of coupled-channel scattering of a charm meson with a light meson in the flavour-exotic sectors at the $SU(3)_f$ flavour symmetric point. Utilising five volumes with $m_\pi \approx 700$ MeV and employing

Subhasish Guchhait, Harshita Tiwari, Sumesh P. Thampi, Ranabir Dey

Biological microswimmers alter their motility in complex corner geometries, facilitating their survival. However, the dynamical features of low-Reynolds-number swimming at corners remain undefined. Here, we use active droplet microswimmers near a confined meniscus in a microchannel as a model system to study how microswimmer-corner interactions determine swi

Jiakai Tang, Runfeng Zhang, Weiqiu Wang, Yifei Liu

Scaling Transformer-based click-through rate (CTR) models by stacking more parameters brings growing computational and storage overhead, creating a widening gap between scaling ambitions and the stringent industrial deployment constraints. We propose LoopCTR, which introduces a loop scaling paradigm that increases training-time computation through recursive

John W. Barrett, Joseph Burridge

The product of a non-commutative matrix spectral triple with a simple two-dimensional internal space is considered. This is interpreted as a non-commutative spacetime that contains one charged Dirac fermion and its antiparticle. The inner fluctuations of a vacuum Dirac operator are calculated, using the standard technique of Connes' one-forms. This results i

Bobo Li, Rui Wu, Zibo Ji, Meishan Zhang

Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. To enhance reliability, multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing. While such role-playing effectively leverages domain expert know

Tianxiang Ma, Weijie Feng, Xinyu Wang, Zhiyong Cheng

Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue. Most existing approaches formulate ECPEC as an independent pairwise classification task, overlooking the distinct semantics of emotion diffusion and cause explanation, and failing to cap

Kajal Phukan, Rajdeep Mazumdar, Kalyan Malakar, Kalyan Bhuyan

In this work, we investigate the viability of some cosmological models derived from generalized horizon entropies, using Big Bang Nucleosynthesis (BBN) constraints. By analyzing the deviations in the expansion rate, we derive bounds on the model parameters from freeze-out temperature, helium, and deuterium abundances. Our results show that the freeze-out con

Zhihong Zhang, Jie Zhao, Xiaojian Huang, Jin Xu

Multimodal reward models (MRMs) play a crucial role in aligning Multimodal Large Language Models (MLLMs) with human preferences. Training a good MRM requires high-quality multimodal preference data. However, existing preference datasets face three key challenges: lack of granularity in preference strength, textual style bias, and unreliable preference signal

Yi He, Qi Guo, Filippo Fraternali, Hang Yang

Observations of highly rotationally supported gas discs in high redshift ($z$ > 3) star-forming galaxies challenge our understanding of galaxy formation, as the prevailing view holds that galaxies in the early universe are dynamically hot due to frequent mergers, gas accretion, and strong stellar feedback. We examined the kinematic properties of massive ($M_

Huy The Nguyen, Shengwen Wang

We study solutions to the self-dual Abelian Yang--Mills--Higgs (YMH) equations in the singular limit $\e \to 0 $, where the associated self-dual Ginzburg--Landau type energy \begin{align*} E_\e\begin{pmatrix}u\\ A\end{pmatrix} = \int_M \left( |\nabla^A u|^2 + \e^2 |F_A|^2 + \frac{(1 - |u|^2)^2}{4\e^2} \right) \mathrm{dvol}_g \end{align*} exhibits concentrati

Hongwei Xu

Teams of LLM agents increasingly collaborate on tasks spanning days or weeks: multi-day data-generation sprints where generator, reviewer, and auditor agents coordinate in real time on overlapping batches; specialists carrying findings forward across session restarts; product decisions compounding over many review rounds. This requires agents to share, evalu

Farbod Zorriassatine, Ahmad Lotfi

Agentic AI, with goal-directed, proactive, and autonomous decision-making capabilities, offers a compelling opportunity to address movement-related risks in human activity, including the persistent hazard of falls among elderly populations. Despite numerous approaches to fall mitigation through fall prediction and detection, existing systems have not yet fun

Sarah Lykke Tost, Adson Lucas de Paiva Sales, Henrik Østergaard, Vaishali Dhanoa

We designed and implemented InvestChat, a multimodal tablet-based application that supports stock market exploration with multiple coordinated views and an LLM-powered chat. We evaluated the application with 12 novice investors. Our findings suggest that combining natural language, touch, and pen input during stock market exploration facilitates user engagem

Xiangchen Wang, Weiye Zhu, Teng Wang, TianTian Geng

Recent navigation systems achieve strong benchmark results, yet real-world deployment often remains visibly stop-and-go. This bottleneck arises because the sense-inference-execution loop is still blocking: after each new observation, the controller must wait for sensing, transmission, and inference before motion can continue. Reducing action-generation cost

Anu Kundu, Constantinos Kalapotharakos, Zorawar Wadiasingh, Greg Olmschenk

NICER observations of millisecond pulsars (MSPs) suggest that non-dipolar magnetic fields are required to explain their surface X-ray hotspots. C. Kalapotharakos et al. (2021) modeled the NICER light curve of MSP J0030+0451 (J0030) using a static vacuum offset dipole-plus-quadrupole field and corresponding force-free (FF) solutions to jointly reproduce the X

Alankrit Chona, Igor Kozlov, Ambuj Kumar

We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of threat hunting: given a database of raw Windows event logs with no guided questions or hints, identify the exact timestamps of malicious events. The benchmark wraps 106 real attack procedures from the OTRF Securi

Junhao Bian, Yilin Bi, Tao Zhou

Hypergraphs serve as an effective tool widely adopted to characterize higher-order interactions in complex systems. The most intuitive and commonly used mathematical instrument for representing a hypergraph is the incidence matrix, in which each entry is binary, indicating whether the corresponding node belongs to the corresponding hyperedge. Although the in

Akash Yadav, Taiwo A. Adebiyi, Ruda Zhang

Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs and provide limited support for calibrated predictive uncertainty. We propose Stochastic Attention, a sample average lightweight inference-time modification that randomizes attention by replacing softmax weigh

Cédric Accard, Florent Renaud, Katarina Kraljic, Diana Ismail

The [CII] 158 $\mu$m line is widely used to trace star formation and the gas contents of high-redshift galaxies. However, it remains unclear under which physical conditions it reliably traces the molecular reservoir, and whether a unique conversion factor $\alpha_{\rm [CII]}$ can be applied across cosmic time. We investigate the evolution of the relation bet

Jianyang Gao, Yutong Gou, Yuexuan Xu, Jifan Shi

This technical note revisits the relationship between RaBitQ and TurboQuant under a unified comparison framework. We compare the two methods in terms of methodology, theoretical guarantees, and empirical performance, using a reproducible, transparent, and symmetric setup. Our results show that, despite the claimed advantage of TurboQuant, TurboQuant performs

Daniel K. J. Boneß, Gabriel Margiani, Wolfgang Belzig, Alexander Eichler

Activation processes govern noise-induced switching between long-lived states. In an equilibrium double well, the thermally activated switching rate exhibits a prefactor with a nonmonotonic dependence on environmental coupling, a foundational crossover known as Kramers turnover. Here, we demonstrate a Kramers turnover analogue in a Kerr parametric oscillator

Divyesh Gabbireddy, Suman Saha

Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious payload while preserving its behavior. These transformations make it difficult for traditional and machine learning-based detection systems to reliably identify attacks. Existing approaches for generating obfus

Wei Gao, Kaixuan Fan, Menghan Li, Jinhao Cheng

The superconducting diode effect (SDE) enables nonreciprocal supercurrent flow, holding immense potential for ultra-low-power quantum electronics. Intrinsic SDE typically requires materials with inherent symmetry breakings. Here, we report the discovery of SDE in chemical vapor deposition-grown molybdenum carbide ($\mathrm{Mo}_2\mathrm{C}$) nanoflakes, a mat

Mihir Shriniwas Arya, Avinash Anish, Aditya Ranjan

Social deduction games such as Mafia present a unique AI challenge: players must reason under uncertainty, interpret incomplete and intentionally misleading information, evaluate human-like communication, and make strategic elimination decisions. Unlike deterministic board games, success in Mafia depends not on perfect information or brute-force search, but

Miao Xie, Xiao Zhang, Yi Li, Chunli Lv

Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be factually incorrect. However, existing RAG approaches predominantly rely on vector similarity for retrieval, which is prone to semantic noise and fails to ensure that generated responses fully satisfy the complex c

Andrés Miniguano-Trujillo, Andrea Poiatti, Maurizio Grasselli, Benjamin Goddard

The nonlocal Cahn-Hilliard equation provides a natural extension of the classical model for phase separation by incorporating long-range interactions through a singular convolution kernel. While this formulation admits a rich existence and regularity theory, its numerical approximation remains challenging: discretisation of the nonlocal term leads to dense o

Christian P. Fries

We study cash-flow forecasting for derivatives used in liquidity management and clarify its relation to risk-neutral valuation and replication. While it is well known that expectations under different measures (e.g., $\mathbb{P}$ vs. $\mathbb{Q}$) can yield different undiscounted cash-flows, further inconsistencies arise when payment times are stochastic. We

Yuli Chen, Shuhao Zhang, Fanshen Meng, Bo Cheng

Depth pruning improves the deployment efficiency of large language models (LLMs) by identifying and removing redundant layers. A widely accepted standard for this identification process is to measure the similarity between layers using cosine distance. However, we find that methods relying solely on this one-dimensional heuristic can exhibit unpredictable pe

Erwin Hueger, Daniel Uxa, Lars Doerrer, Jochen Stahn

This study reports on advancements in operando characterization of volume changes in lithium-ion battery (LIB) electrode materials during electrochemical cycling. Volume changes are crucial for LIB operation because they are related to the amount of stored energy as well as LIB integrity, performance, and safety. The study introduces a method based on isotop

Ziqin Chen, Zuang Wang, Yongqiang Wang

Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local dataset. By avoiding the need for training data to leave individual users' devices, it enhances privacy and scalability compared to conventional centralized learning, where all data has to be aggregated to a c

Yuanchuan Guo, Buyu Lin, Jun S. Liu

In the FDR-controlling literature, mirror statistics offer a flexible alternative to $p$-value based procedures. When prior information is available, however, it is unclear how to incorporate mirror statistics in a principled way, and the standard equal split used by data-splitting methods can be inefficient. In this paper, we characterize a broader class of

Beining Wu, Fuyou Mao, Jiong Lin, Cheng Yang

Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers, yet current Generative Engine Optimization (GEO) methods optimize each instance in isolation, unable to accumulate or transfer effective strategies across tasks and engines. We reframe GEO as a strategy learning problem and propose MAGEO, a mul

Ali Hussein, Jun Chen, Chao Tian, S. Sandeep Pradhan

Perception-aware lossy source coding has attracted significant recent interest. It augments the classical distortion criterion with an explicit perception constraint, thereby enabling more refined control over fidelity and perceptual quality. Despite rapid progress, the diversity of rate-distortion-perception formulations and their underlying assumptions rem

Saket Maganti

The consensus that GCN, GraphSAGE, GAT, and EvolveGCN outperform feature-only baselines on the Elliptic Bitcoin Dataset is widely cited but has not been rigorously stress-tested under a leakage-free evaluation protocol. We perform a seed-matched inductive-versus-transductive comparison and find that this consensus does not hold. Under a strictly inductive pr

Hassen Souissi, Valentin Develay, Maksym Gromovyi, Edmond Cambril

Exciton-polariton lasers are coherent light sources which do not require the population inversion (transparency) condition to be fulfilled. They have been conceptualized at the end of the XXth century but until now they operate almost exclusively under optical injection, which severely limits the widespread integration of the polariton-based devices implemen

Bintao Cao, Ye Huang

We define the Verma vector system for each finite dimensional irreducible representation of the orthosymplectic Lie superalgebra $\mathfrak{spo}(4|1)$ with the highest weight $\lambda,$ via the conditions that making a tableau with shape $\lambda$ to be a Kashiwara-Nakashima tableau. We then show the linearly independence of this vector system. It turns out

Ruben Pascual, Inés Hernández, Salvador Gutiérrez, Javier Tardaguila

Variability in illumination is a primary factor limiting deep learning robustness for field-based plant disease detection. This study evaluates Histogram Matching (HM), a technique that transforms the pixel intensity distribution of an image to match a reference profile, to mitigate this in grapevine classification, distinguishing among healthy leaves, downy

Jess Jones, Raul Santos-Rodriguez, Sabine Hauert

Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding human-object interactions, but their application to robotic systems with non-humanoid morphologies remains largely unexplored. This work investigates whether VLMs can effectively infer affordances for robots with fundamentally different embodiments than humans, addressin

Tonmoy Talukder, G M Shahariar

This paper introduces \textit{Bangla Key2Text}, a large-scale dataset of $2.6$ million Bangla keyword--text pairs designed for keyword-driven text generation in a low-resource language. The dataset is constructed using a BERT-based keyword extraction pipeline applied to millions of Bangla news texts, transforming raw articles into structured keyword--text pa

Daniel W. Hook

The debate about scholarly knowledge infrastructure has long been framed as a contest between openness and commercial enclosure. This framing distorts both policy and practice. The real tension lies between the persistent cost of producing and refining structured metadata under deep technological friction, and the differentiated demands distinct communities

Ruihong ma, Engui Fan

We investigate the Cauchy problem for the focusing nonlinear Schr\"odinger (NLS) equation \begin{equation} iq_t(x,t)+q_{xx}(x,t)+2|q(x,t)|^2q(x,t)=0,\quad x\in\mathbb{R},\quad t\ge0,\nonumber \end{equation} subject to initial data $ q(x,0)$ satisfying the asymptotic boundary conditions \begin{equation}\label{eq:boundary} q(x,0) \sim q^{alg}(x,0) \quad \text{

Yi Xiang, Chengzhi Zhang

Automatic keyword extraction from academic papers is a key area of interest in natural language processing and information retrieval. Although previous research has mainly focused on utilizing abstract and references for keyword extraction, this paper focuses on the highlights section - a summary describing the key findings and contributions, offering reader

Sarunyu Thongjarast, Sarit Pasiphol, Suthee Ruangwises

Cyclic equalizability is a notion introduced by Shinagawa and Nuida in 2025, in the study of card-based cryptography. Informally, a collection of words is cyclically equalizable if, by inserting the same letters at the same positions in all words, they can be transformed into words that are cyclic shifts of one another. Shinagawa and Nuida showed that two bi

Yingping Wang, Yi Wu, Xiangyu Wu, Junwei Cui

Mixture-of-Experts (MoE) architectures are widely used in modern large language models and multimodal models. However, inference efficiency is often limited by highly dynamic and skewed expert workloads across different modalities. During the prefill stage with large batch sizes, vision tokens frequently dominate the input sequences. Under expert parallelism

Bowen Li, Haochen Ma, Yuxin Wang, Jie Yang

The rapid adoption of Large Language Models (LLMs) has spurred interest in automated peer review; however, progress is currently stifled by benchmarks that treat reviewing primarily as a rating prediction task. We argue that the utility of a review lies in its textual justification--its arguments, questions, and critique--rather than a scalar score. To addre

Rachel Yovel, Eran Treister

We present a convergent and scalable multigrid solver for high-frequency Helmholtz equations. Standard multigrid methods do not converge for high-frequency Helmholtz problems, and a common cure is adding a complex shift and using the shifted operator as a preconditioner. Nevertheless, the complex shift prevents scalability. In this work we present a new meth

Ethan YoungIn Shin, Baris Kale, Michael F. Howland

Quantifying and reducing uncertainty in Earth system model parameterizations is essential to improving their reliability in decision-making. Forward uncertainty propagation is used to derive parameter sensitivity but requires physically plausible parameter distributions first be learned from observations. Bayesian inference offers a principled approach but c

Khalid N. Anindya, Hong Guo

A single organic spin platform hosts both dimerized $S=\tfrac{1}{2}$ and effective Haldane $S=1$ sectors, linked by bond-texture inversion. At the junction, the fractional mode is controlled by termination parity: quenched by local fusion at one termination and released as an uncompensated spin-$\tfrac{1}{2}$-like degree of freedom at the parity-shifted one.

Jian-Chao Sun, Jiang He, Shuang-Nan Zhang, Shao-Lin Xiong

POLAR-2, the successor of the POLAR experiment aboard China's Tiangong-2 space lab, is set to be deployed on the China Space Station. The POLAR-2 mission aims to conducting high-precision polarization measurements of high-energy transients with a primary focus on Gamma-Ray Bursts (GRBs), following POLAR's pioneering accurate polarization measurements of GRB

Yinhao Xiao, Huixi Li, Yongluo Shen

BusyBox is one of the most widely reused userland components in Linux-based Internet-of-Things (IoT) firmware, yet its security assessment remains difficult because firmware images are frequently stripped, vendor patch practices are inconsistent, and the same source component is compiled for heterogeneous architectures. We propose EvoPatch-IoT, an evolution-

ATLAS Collaboration

A search for quantum black holes in electron+jet or muon+jet final states with high invariant mass is performed. The analysis uses data from $\sqrt{s}=13.6~\textrm{TeV}$ $pp$ collisions recorded by the ATLAS detector between 2022 and 2024 during Run~3 of the Large Hadron Collider, corresponding to an integrated luminosity of $164~\mathrm{fb}^{-1}$. This sear

Juwairiya S. Khan, Mostafa Mohammadi, Alexander L. Ammitzbøll, Ellen-Merete Hagen

Wrist function is essential in performing activities of daily living (ADLs). However, there is limited experimental evidence on the functional impact of wrist Abduction-Adduction (Ab-Ad) joint assistance in upper limb exoskeletons (ULEs) for rehabilitation. This study evaluates the effect of implementing an active wrist Ab-Ad joint in a five degree of freedo

Shai Bergman, Zhe Yang, Julien Eudine, Giorgio Negro

Modern distributed file systems rely on uncoordinated, per node page caches that replicate hot data locally across the cluster. While ensuring fast local access, this architecture underutilizes aggregate cluster DRAM capacity through massive data redundancy and incurs prohibitive coherence overhead via heavyweight, lock-based protocols. In this paper, we foc

Mehmet Sıddık Çadırcı, Yener Ünal

The multivariate generalised Gaussian distribution (MGGD) is commonly used to model high-dimensional vectors with non-Gaussian radial behaviour, ranging from sharp-peaked to heavy-tailed profiles. However, because many classical multivariate tests are based on covariance inversion or high-dimensional density estimation, formal goodness-of-fit assessment for

Rachel Hill, Tom Owen, Julian Hough

In 2025 one million Anti-Social Behaviour (ASB) cases were recorded in England & Wales, impacting community cohesion. Statutory guidance presents punitive interventions that lack technological input and does not often root ethical frameworks within government system design. This work takes a novel approach in framing ASB intervention as a human-computer inte

Leon Huet, Vittorio Del Tatto, Debarshi Banerjee, Alessandro Laio

In molecular liquids such as water, time-delayed influences between microscopic or mesoscopic variables are typically probed using time-correlation functions, which are symmetric under detailed balance and therefore blind to dynamical asymmetries. Here, we characterize waters dynamics using a causal inference metric that captures asymmetric couplings between

Bintao Cao, Ye Huang

We construct a one-to-one correspondence between the Verma basis vectors of a finite dimensional irreducible representation $L(\lambda)$ of the symplectic Lie algebra $\mathfrak{sp}_4$ and the Kashiwara-Nakashima tableaux of $\mathfrak{sp}_4$ with shape $\lambda $ naturally. We also give a proof of the linear independence of the Verma vector system directly.

Michael Achmann-Denkler, Mario Haim, Christian Wolff

This paper presents a computational case study that evaluates the capabilities of specialized machine learning models and emerging multimodal large language models for Visual Political Communication (VPC) analysis. Focusing on concentrated visibility in Instagram stories and posts during the 2021 German federal election campaign, we compare the performance o

Jianzhi Yan, Le Liu, Buzhou Tang, Yang Xiang

Large language models (LLMs) have achieved substantial advances in logical reasoning, yet they continue to lag behind human-level performance. In-context learning provides a viable solution that boosts the model's performance via prompting its input with expert-curated, in-domain exemplars. However, in many real-world, expertise-scarce domains, such as low-r

Jonathan M. Fraser, Thang Pham

We study the Falconer distance set problem in Euclidean space and obtain improved dimensional estimates under natural Fourier analytic assumptions cast in terms of the Fourier dimension and spectrum. Interestingly, under reasonably mild assumptions, we are able to beat the $d/2$ dimension threshold in dimensions $d \geq 5$. For example, we show that (in any

Chengjun Pan, Shichun Liu, Jiahang Lin, Dingwei Zhu

Reinforcement learning (RL) for LLM post-training faces a fundamental design choice: whether to use a learned critic as a baseline for policy optimization. Classical theory favors critic-based methods such as PPO for variance reduction, yet critic-free alternatives like GRPO have gained widespread adoption due to their simplicity and competitive performance.

Michael T. M. Emmerich, Ksenia Pereverdieva, André H. Deutz

We prove that, for every fixed $\theta_0>0$, selecting a subset of prescribed cardinality that maximizes the Solow--Polasky diversity indicator is NP-hard for finite point sets in $\mathbb{R}^2$ with the Euclidean metric, and therefore also for finite point sets in $\mathbb{R}^d$ for every fixed dimension $d \ge 2$. This strictly strengthens our earlier NP-h

Sonia Isabel Renteria Alva, Pedro Iván Suárez Navarro

Discontinuous piecewise differential systems exhibit dynamical behaviors with no counterpart in smooth systems, particularly in the presence of nonsmooth switching structures. In this work, we extend previous results for systems separated by a straight line to the case where the switching manifold is a nonregular curve, showing that the loss of regularity si

Neil D. Lawrence

The inaccessible game (Lawrence, 2025, 2026) is an information-theoretic dynamical system governed by three information loss axioms, a marginal entropy conservation constraint and maximum entropy dynamics. In this paper we look at selection in the game. Our aim is to develop a selection policy for the game rules based on a minimal set of assumptions. We seek

Felix Tripier, Woo Chang Chung, Jacob Young, Safwan Alam

We propose a fault-tolerant quantum computer architecture for trapped-ion devices, which we call the walking cat architecture. Our blueprint includes a compiler, a detailed description of all the quantum error-correction protocols, a micro-architecture, a sufficiently fast decoder, and thorough simulations. The backbone of the architecture is a cat factory,

Eliana Duarte, Nidhi Kaihnsa, Julia Lindberg, Angélica Torres

We consider the distance minimization problem to a real algebraic variety $X \subseteq \RR^n$ when the metric is induced by a polyhedral norm. Each point in the variety has a Voronoi cell whose geometry depends on the normal space at the point and the inner normal fan of the polyhedral ball. For codimension-one varieties, we decompose $X$ into sets of points

G. E. Volovik

In Ref. [1] the statistical structure of the turbulent cascade in the context of non-additive entropy was considered. Here we suggest that the vortex line ensemble in the Vinen quantum turbulence in superfluids is described by the non-extensive Tsallis-Cirto statistics with $\delta=3$. This in particular leads to the temperature, which describes the thermody

Hyunjung Joo, GyeongTaek Lee

The intonational structure of Seoul Korean has been defined with discrete tonal categories within the Autosegmental-Metrical model of intonational phonology. However, it is challenging to map continuous $F_0$ contours to these invariant categories due to variable $F_0$ realizations in real-world speech. Our paper proposes Dual-Glob, a deep supervised contras