April 2026 arXiv papers — page 20

Sangjin Lee, Sangkook Choi

Quantum simulation is a cornerstone application for quantum computing, yet standard methods face a trade-off between circuit depth and accuracy: Trotterization depth scales with the number of Hamiltonian terms $L$, while sampling-based qDRIFT is restricted to $O(t^2)$ error scaling. Here, We introduce qSHIFT, an adaptive sampling protocol that overcomes thes

Yufei Yin, Jie Zheng, Qianke Meng, Zhou Yu

Zero-shot 3D Visual Grounding (3DVG) is a critical capability for open-world embodied AI. However, existing methods are fundamentally bottlenecked by the poor quality of open-vocabulary 3D proposals, suffering from inaccurate categories and imprecise geometries, as well as the spatial redundancy of exhaustive multi-view reasoning. To address these challenges

Daeho Noh, Jaehyeong Ock, Sergey G. Menabde, Min Seok Jang

The biaxial van der Waals crystal {\alpha}-phase molybdenum trioxide ({\alpha}-MoO3) supports hyperbolic phonon-polaritons with anomalous dispersion in the Type-I Reststrahlen band (RB-I). Despite the low loss and long lifetime of these polaritons, dispersion engineering in this regime has remained largely unexplored. In this work, we show that when two {\al

Douglas S. Bridges

This note deals with the question: If T is a linear mapping between Banach spaces X and Y, and x belongs to X and has small norm, is x close to the kernel of T? It draws on notions of Z-stability and provides an affirmative constructive answer when T is onto Y, sequentially continuous, and has located kernel.

Ao Nakagawa, Ryo Toyama, Keisuke Masuda, Weinan Zhou

The anomalous Hall effect (AHE) and anomalous Nernst effect (ANE) are the transverse transport phenomena in magnetic materials, which reflect the Berry curvature arising from the electronic structure near the Fermi level. Lattice strain provides a direct means to tune these effects by modifying the electronic structure; however, disentangling the strain-indu

Michael Wienczkowski

Modern software systems are increasingly developed within rapid continuous integration and deployment (CI/CD) pipelines, where ensuring security prior to release presents significant technical and organizational challenges. Traditional static and dynamic analysis tools provide valuable structural and behavioral insights, yet they often operate in non-adaptiv

Yuqi Li, Qian Zhou, Huiran Duan, Jingjie Wang

Gait recognition is an attractive biometric modality for long-range and contact-free identification, but high-performing gait models often rely on deep and computationally expensive architectures that are difficult to deploy in practice. Knowledge distillation (KD) offers a natural way to transfer knowledge from a powerful teacher to an efficient student; ho

Daniela Calvetti, Erkki Somersalo

A way to lower computational cost in large scale inverse problems and problems depending on poorly known model parameters is to replace the detailed model by an approximate one. Inverse problems are typically ill-posed, and the model discrepancy introduced by using approximate models often shows up in the computed solutions as disturbing artifacts or blurrin

Hengrui Zhu, Frans Pretorius, James M. Stone

We demonstrate that ultra-relativistic black hole encounters reveal a new regime of the two-body interaction in general relativity. Evolving equal-mass, nonspinning black holes with initial center-of-mass Lorentz factors up to $\gamma\approx 5.1$ using numerical relativity, we find that the resulting waveforms defy the standard expectation of a post-Newtonia

Liliang Ye, Guiyi Zeng, Yunyao Zhang, Yi-Ping Phoebe Chen

Predicting social media popularity requires understanding both the intrinsic appeal of content and the external context that determines how it is exposed to users. Existing methods focus on content signals but do not separate them from exposure-related patterns, which causes the learned representations to absorb platform-specific visibility effects and weake

Hao Wen, Jingsu Kang

We report our multi-stage framework designed for the problem of multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) MRI of the human heart. The pipeline consists of a preprocessing step using multidimensional contrast limited adaptive histogram equalization (MCLAHE); coarse region segmentation from MCLAHE-enhanced and down-sampled MRI u

Hao Guo, Fei Wang, Junjie Chen, Yiqi Nie

While Vision-Language Models (VLMs) have achieved state-of-the-art performance in general visual tasks, their perceptual robustness remains remarkably brittle when confronted with optical illusions. These failures are often attributed to shortcut heuristics, where models prioritize linguistic priors and memorized prototypes over direct visual evidence. In th

Ran Huang

The original SCR theory proposed that inflation has two distinct expressions: circulation inflation, measured by rising transaction prices, and reservation inflation, measured by the rising real weight of monetary symbols, debt contracts, reserve claims, and other nominal stores of value relative to physical goods. A companion Japan paper tested one side of

Beomchul Park, Minsu Koh, Heejo Kong, Seong-Whan Lee

Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss function. In parameterized PDE families, variations in coefficients or boundary/initial conditions define distinct tasks. This makes training individual PINNs for each task computationally prohibitive, while cross-t

Wei Yang, Rui Zhong, Zihan Lin, Xiaodan Wang

Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user interests evolve over time and exhibit nonstationary dynamics, where different preference factors change at different rates. This challenge is amplified in multimodal settings because visual and textual cues can do

Andrew Yen Chang

This paper studies how online discussion shapes and assesses political violence across different settings, particularly how moral evaluation, as a social perception, varies across institutional contexts. We take France and the United States as case studies, both democracies, and three incidents of political violence: the 2020 killing of Samuel Paty in France

Jiaqi Guo, Mingzhen Li, Haohong Wang, Aggelos K. Katsaggelos

We study generative super-resolution (SR) in real-world scenarios where content and degradations vary across domains, genres, and segments. For example, images and videos may alternate between text overlays, fast motion, smooth cartoons, and low-light faces, each benefiting from different forms of side information. Existing metadata-guided SR methods typical

Yerong Wu, Tianxing Wu, Minghao Zhu, Hangyu Sha

Achieving realistic human-like conversation for virtual characters requires not only a simple memorization and recall of past events, but also the strategic utilization of memory to meet factual needs and social engagement. Current memory utilization relevant (e.g., memory-augmented generation, long-term dialogue, and etc.) benchmarks overlook this nuance, t

Hongjun Wang, Po Hu, Kai Han

Generalized Category Discovery (GCD) aims to categorize unlabelled instances from both known and unknown classes by transferring knowledge from labelled data of known classes. Existing methods assume all data comes from a single domain, yet real-world unlabelled data often exhibits domain shifts alongside semantic shifts. We study GCD under domain shifts and

Himadri S Samanta

Digital biomarkers for depression have largely relied on static acoustic descriptors, pooled summary statistics, or conventional machine learning representations. Such approaches may miss nonlinear temporal organization embedded in conversational vocal dynamics. We hypothesized that depression is associated with altered recurrence structure in vocal state tr

John Hateley, Sriram Narasimhan, Omid Abari

Passive RFID tags offer a cost-effective and scalable solution for tracking numerous deployed assets. However, in forested environments, signal attenuation and multipath effects generally limit RFID spatial accuracy to the meter level. Conversely, while cameras employing stereo vision can achieve centimeter-level precision, relying solely on computer vision

Christian Amor, Adrián Corrochano, Marco Edoardo Rosti, Soledad Le Clainche

Adding flexible polymers to a Newtonian solvent confers complex properties to the resulting solution. The additional complexity substantially increases the computational cost of numerical simulations, which often makes them prohibitively expensive. Here, we propose hybrid reduced-order models to accelerate simulations of viscoelastic turbulent jets. The mode

Himadri S Samanta

Automated depression detection often relies on static aggregation of conversational signals, potentially obscuring clinically meaningful behavioral dynamics. We investigated whether entropy-driven temporal biomarkers improve depression detection beyond standard pooled features using the DAIC-WOZ corpus. Using 142 labeled participants, we reconstructed uttera

Alvaro Gonzalez-Garcia, William Lopez-Perez, Paola Pacheco, Luz Ramirez-Montes

The interplay between unconventional magnetism and band topology in two-dimensional materials has emerged as an important theme in condensed matter physics. Here, we present first-principles calculations that reveal the coexistence of d-wave altermagnetism and topological behavior in Janus FeSeX (X = S, Te) monolayers. The chemical asymmetry of the Janus str

Rui Song, Tianhui Cai, Markus Gross, Yun Zhang

3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent works commonly incorporate geometric priors, such as LiDAR measurements, either for initialization or as training constraints, with the goal of improving photometric reconstruction q

John Paul P. Miranda

This study applied the Apriori algorithm to analyze behavioral interaction patterns associated with learned helplessness (LH) in mathematics tutoring system logs. Interaction data were examined across three dimensions: LH level (low vs. high), system-based intervention (with vs. without), and problem-solving outcomes (solved vs. unsolved). The analysis of th

Seongjin Choi, Sunbin Yoo, Sugie Lee

Do e-scooter speed governance policies yield behavioral safety gains beyond the mechanical cap they impose? A firmware ceiling mechanically prevents speeding, but whether the same riders also generate fewer harsh accelerations and harsh decelerations when the ungoverned mode is withdrawn remains open. We analyze 19.5 million GPS-instrumented trips from 52 So

Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Manan Suri, Raviteja Bommireddy

Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive the answer, rather than only the region containing the final response. Creating such structured evidence across diagrams, charts, maps, circuits, and infographics is time-consuming, and existing annotation t

Aaron Chan, Tengfei Li, Tianyi Xiao, Angela Chen

We introduce LATTICE, a benchmark for evaluating the decision support utility of crypto agents in realistic user-facing scenarios. Prior crypto agent benchmarks mainly focus on reasoning-based or outcome-based evaluation, but do not assess agents' ability to assist user decision-making. LATTICE addresses this gap by: (1) defining six evaluation dimensions th

Antonine Phigareau

We define the notion of couple density $(D, \mathbf b)$ where $D$ is a non-empty subset of $\mathbb Z^{m}$ and $ \mathbf b$ a fixed element in $\{0, \cdots, q-2\}^{m};$ We determine a minimum in terms of the density of the couple $(D,\mathbf b)$ for the $q$-adic valuation of the sum $ S_{\ell}(F,\mathbf b)$ with $F$ a Laurent polynomial. And we show that thi

Junhu Fu, Ke Chen, Weidong Guo, Shuyu Liang

Controllable medical video generation has achieved remarkable progress, but it still lacks interpretability, which requires the alignment of generated contents with physical priors and faithful clinical manifestations. To push the boundaries from mere controllability to interpretability, we propose DepthPilot, the first interpretable framework for colonoscop

Jiamian Li, Bing-Zhao Li

Point clouds can be regarded as discrete samples of smooth manifolds and are typically analyzed via the eigenfunctions of the Laplace-Beltrami operator. This paper extends manifold spectral analysis to the fractional domain, enabling continuous interpolation between the spatial and spectral domains for point cloud data. First, a point cloud manifold fraction

Yi Zhang, Yiwen Zhang, Kai Zheng, Tong Chen

The remarkable text understanding and generation capabilities of large language models (LLMs) have revitalized the field of general recommendation based on implicit user feedback. Rather than deploying LLMs directly as recommendation models, a more flexible paradigm leverages their ability to interpret users' historical interactions and semantic contexts to

Kohei Watanabe

I developed a new version of Latent Semantic Scaling (LSS) employing word2vec as a masked language model. Unlike original spatial models, it assigns polarity scores to words and documents as predicted probabilities of seed words to occur in given contexts. These probabilistic polarity scores are more accurate, interpretable and consistent than those spatial

Raihana Adelia Putri, Aisyah Musfirah, Anggi Puspita Ningrum, Luluk Muthoharoh

This study compares machine learning and deep learning approaches for cyberbullying detection in Indonesian-language Instagram comments. Using a balanced dataset of 650 comments labeled as Bullying and Non-Bullying, the study evaluates Naive Bayes, Logistic Regression, and Support Vector Machine with TF-IDF features, as well as BiLSTM and BiLSTM with Bahdana

Yunier Bello-Cruz

Behling, Bello-Cruz, Lara-Urdaneta, Oviedo, and Santos showed that the circumcentric direction $d$ of a finitely generated polyhedral cone $\KK\subset\RR^n$ admits an inscribed Euclidean ball of radius $\norm{d}^2$ inside the polar cone $\Kpolar$. We sharpen this result in several ways. The exact set of admissible perturbations is a polyhedron, strictly larg

Runzhong Zhang, Suchen Wang, Yueqi Duan, Yansong Tang

In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed network to predict the action of each frame with the neighboring frames. However, this would result in ambiguity when estimating similar actions, such as pouring juice and pouring coffee. To address this, we aim

Qing-Hong Cao, Ying-Ying Li, Xiaohui Liu, Liang-Qi Zhang

Quantum simulations of scalar quantum field theories (QFT) provide important benchmarks for demonstrating quantum advantage. We revisit digitization in the occupation basis, which is typically hindered by unfavorable circuit depth scaling. We present an approach that achieves exponential reductions in circuit depth and significantly mitigates Trotter errors

Weitao Bi

A century after Prandtl's mixing length hypothesis, full-profile scaling of the mixing length in pressure-gradient turbulent boundary layers (PG TBLs) remains debated, especially for adverse pressure gradients (APGs). This work presents a symmetry-based analytical model for the mixing length in equilibrium APG TBLs by extending the structural ensemble dynami

Morgan O'Brien

In this article, we study the bilaterally almost uniform (b.a.u.) convergence of weighted averages of a positive Dunford-Schwartz operator on the noncommutative $L_p$-spaces associated to a semifinite von Neumann algebra by a large number of weighting sequences. We do this by extending the classical "subsequence argument" to the noncommutative setting. This

William R. T. Tait, Sriram Murali, Chao-Hua Hsu, Jantakan Nedsaengtip

This work realizes a three-dimensionally (3D) nanoarchitected, all organic, "self-separating" lithium-ion electrochemical energy storage (EES) device that is cycled as a solid-state full cell. The device is enabled by a monolithic carbon anode with a co-continuous pore network, derived from the structure direction of resols by an ultra-large molar mass block

Guanchun Wang, Chenxiao Wu, Xiangrong Zhang, Zelin Peng

Open-vocabulary semantic segmentation (OVSS) in remote sensing images is a promising task that employs textual descriptions for identifying undefined land cover categories. Despite notable advances, existing methods typically employ a static inference paradigm, overlooking the distinct distribution of each scene, resulting in semantic ambiguity in diverse la

Soogand Alavi, Salar Nozari

Consumers are increasingly delegating purchase decisions to AI agents, providing natural-language descriptions of their preferences and identity. We argue that these representations constitute an information channel, role coherence, through which sellers can infer willingness to pay without explicit disclosure by the buyer agent, leading to preference leakag

Sk Tanzir Mehedi, Raja Jurdak, Chadni Islam, Abu Bakar Siddique Mahi

The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning (ML) detectors struggle to detect these attacks due to the high-dimensional and sparse nature of dyna

Ganxi Xu, Zhao-Rong Lai, Yuting Tang, Yonghao Song

Brain encoding models not only serve to decipher how visual stimuli are transformed into neural responses, but also represent a critical step toward visual prostheses that restore vision for patients with severe vision disorders. Brain encoding involves two fundamental steps: achieving faithful reconstruction of neural responses and establishing cross-modal

Chaitanya Vilas Garware, Sharif Noor Zisad

Small and medium sized businesses (SMBs) face an escalating cybersecurity threat landscape, yet most lack the resources to staff full Security Operations Centers (SOCs) or deploy enterprise grade detection platforms. This paper presents OpenSOC-AI, a lightweight log analysis framework that uses parameter efficient fine tuning of a 1.1-billion parameter langu

Yuhan Wang, Ruobing Yan, Zhe Su, Hejing Chen

This paper addresses the problem of anomaly detection in accounting subject association structures, proposing a structured modeling and unsupervised discriminant framework based on graph neural networks. This framework is used to mine stable correspondences between subjects and identify structural deviations from general ledger details and voucher entries. T

Oscar Ivan Torres Mena, Francisco J Sevilla

The transport properties of discrete-time random walks on ring networks with deterministic shortcuts are investigated through analytical and numerical methods. The network consists of a periodic chain where each node is connected to its nearest neighbors and to nodes located at a fixed distance $r$. Using the spectral properties of the transition matrix, we

Hasibur Rahman, Kenji Numata, Evelyn T Lai, Maria Cheriyan

Serious illness conversations (SICs) align care with patients' values, goals, and preferences, yet they rarely occur in emergency departments (EDs), where time constraints and emotional burden often leave clinicians making high-stakes decisions without documented insight into what matters most to patients. We present a case study of ED GOAL-AI, a voice-based

David Quiroga, Hannes Leipold, Bibhas Adhikari

Loading high dimensional distributions is an important task for utilizing quantum computers on applications ranging from machine learning to finance. The high dimensionality leads to a curse of dimensionality, representing a d-dimensional distribution with k resolution requires dk qubits and an unstructured parameterized circuit would express a unitary in an

Andrew Goldberg, Ethan Ransing, Anton Kourakin, Cael Magner

In this paper, we introduce GET-2D-1.0, a fast grasp planner for the GET asymmetrical gripper that operates from a single-view RGB-D image, using the Ferrari-Canny metric and a novel sampling strategy, and GET-3D-1.0, a mesh-based method using a 3D gripper model and ray-tracing. We evaluate both grasp planners against baselines with physical experiments, whi

Jeremy Nixon, Annika Singh

In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that starts at idea generation and ends with executable code. Our system combines structured meta-prompt engineering with executable code generation to create new ML classifiers. The OMEGA framework has been utili

Miguel Castillón, Biswajit Khara, Jørgen S. Dokken, Thomas M. Surowiec

The phase-field method has emerged as a powerful tool for simulating fracture mechanics, yet it presents significant numerical challenges, particularly regarding the enforcement of physical constraints such as irreversibility and boundedness of the phase-field variable. This work proposes the proximal Galerkin (PG) methodology as a robust and efficient frame

Theodore Glavas, Nikhita Vedula, Dushyanta Dhyani, Yilun Zhu

Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context. While standard autoregressive decoding is slow due to its sequential nature, the independence between output sequences offers an opportunity for parallelism. We present Hyper-Parallel Decoding, a novel decoding

Kota Yoshioka

We study the non-emptyness of moduli of stable sheaves on an elliptic ruled surface with a nef. anticanonical bundle.

P. Beretta, A. Codello

The novel functional dimensional regularization (FDR) scheme has proven capable of yielding results that are competitive with the state-of-the-art in the computation of critical exponents in $d=3$, while also reproducing those from the $\varepsilon$-expansion for the Ising and other universality classes. In this work, we show that this is not a mere coincide

Akshay Mittal, Elyson De La Cruz

Autonomous AI agent ecosystems require stronger mechanisms for secure discovery, identity verification, capability attestation, and policy governance. Current deployments frequently lack (1) uniform agent discovery, (2) cryptographic agent authentication, (3) capability proofs that protect secrets, and (4) enforceable policy controls. This paper presents an

Jon-Paul Cacioli

A predecessor pilot (Cacioli, 2026) found that Llama-3-8B implements prompted sandbagging as positional collapse rather than answer avoidance. However, fixed option ordering in MMLU-Pro left open whether this reflected a model-level position-dominant policy or dataset-level distractor structure. This pre-registered follow-up (3 models, 2,000 MMLU-Pro items,

Ertian Chen, Hiroyuki Kasahara, Katsumi Shimotsu

Estimating dynamic discrete choice models with unobserved heterogeneity is computationally costly because it requires repeatedly solving fixed-point equations for all unobserved types. We develop the EM-NPL(q) framework that combines the Expectation-Maximization (EM) algorithm with an inner fixed-point solver truncated to q iterations. For the workhorse clas

Kota Yoshioka

We shall introduce a stability condition for a coherent sheaf associated to an elliptic surface. Then we study the behavior under relative Fourier-Mukai transforms.

Yuejian Wang, Zhongyan Wu, K C Bhupendra, Dongzhou Zhang

Understanding how electronic transport evolves from localized to itinerant regimes in correlated cluster solids remains an important challenge in condensed-matter physics. Here we investigate the pressure-dependent transport properties of the lacunar spinel GaNb4Se8, a cluster Mott insulator at ambient conditions. At low pressures, the resistivity follows Ef

Ryan Allen, Melissa Greeff

Reliable backup localization for unmanned aerial vehicles (UAVs) operating in GNSS-denied nighttime conditions remains an open challenge due to the severe modality gap between daytime RGB maps and nighttime thermal imagery. This work presents a semantic reprojection framework for map-relative nighttime UAV localization by aligning segmented thermal observati

Henglin Pu, Ahmad Musallam, Husheng Li, Lajos Hanzo

Integrated sensing and communication (ISAC) is increasingly expected to operate under aggressive spectrum reuse, where co-channel orthogonal frequency division multiplexing (OFDM) interference can be catastrophic for data recovery on the time-frequency (TF) grid. We show that supporting blind ISAC is feasible by exploiting a fundamental asymmetry in the impa

Wei Hu, Ziheng Liu

This paper explores the restriction behavior of silting-induced $t$-structures and co-$t$-structures on triangulated categories endowed with metrics. For compactly generated triangulated categories admitting small coproducts, silting subcategories of compact objects give rise to canonical $t$-structures. We establish that a silting subcategory being contrava

Sita Dugu, Shaham Quadir, Christopher P. Muzzillo, Zhenkun Yuan

Ternary Zintl phosphides are promising light-absorbing semiconductors for thin-film optoelectronic applications, but strategies for controlling their microstructure and optoelectronic quality remain underexplored. Here, we report the synthesis of phase-pure SrZn2P2 thin films using radio-frequency co-sputtering in a PH3 + Ar atmosphere and investigate the im

Vivek Mishra

This study investigates whether international equity markets systematically price global macroeconomic risks. The empirical analysis is conducted using monthly excess returns for ten G20 countries over the period 2000-2024. A Dynamic Factor Model (DFM) is employed to extract latent global factors from a set of macroeconomic variables capturing global inflati

Dan Aguero, Alessandro Arsie, Pedro Frejlich, Igor Mencattini

The notion of \emph{concurrence} was recently proposed as the natural compatibility relation between Dirac structures, generalizing the commutativity of two Poisson structures. We address the question of when a reduction scheme -- that is, a way to induce a Dirac structure on a quotient of a submanifold -- respects this relation. After characterizing the min

Sai Gangula, Andrew B. Newman, Meng Gu, Sirio Belli

Quiescent galaxies in the distant universe exhibit a range of gas content that may indicate a variety of quenching processes are at play. Mapping the distribution and kinematics of the gas can illuminate its origins, but nearly all such observations have been unresolved. We present JWST/NIRSpec IFU observations of MRG-M0138, a gravitationally lensed, massive

Shi-Min Liang, Jian-Fu Zhang, Nian-Yu Yi

We perform numerical simulations of particle acceleration in relativistic, self-driven turbulent magnetic reconnection using the MHD-PIC method. We systematically investigate the dependence of the non-thermal particle spectral exponent on the plasma $\beta$. We find that particle acceleration proceeds in two stages: an initial, efficient first-order Fermi ph

Daksh Aggarwal

Given a curve $C$ that is a degree $k$ cover $C \to \mathbb{P}^1$ totally ramified at two points $p$ and $q$, we can seek to understand the space of degree $d$ line bundles on $C$ with prescribed ramification at $p$ and $q$. The corresponding subschemes of $\text{Pic}^d(C)$ are called transmission loci and are parameterized via elements of the (extended) $k$

Victoria Gomes, Delaney Selb, Fabio Palomba, Rodrigo Spinola

Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) have emerged as promising tools to support empirical workflows, yet their use remains fragmented, with no comprehensive synthesis to guide responsible adoption. Aims: This study ana

Thomas Konstantinovsky, Gur Yaari

We introduce Flashback, a reversible string decomposition that repeatedly peels the maximal leading and trailing character runs from a sentinel-wrapped input, recording each pair as one bilateral token. Decomposition and reconstruction both run in O(n) time and space. Our central result is a run-pairing theorem: Flashback is equivalent to pairing the first r

Sebastian Bürger, Harshit Joshi, S Ganga Prasath, Rahul Chajwa

We study experimentally and theoretically the Stokesian settling of a well-known class of porous shapes: Bravais lattice unit-cells, whose porosity we vary controllably by changing their lattice spacing. In our experiments, conducted in a square cuboidal container with its long-axis aligned along gravity, we find that the settling speed U and the solid fract

Panyi Dong, Zhiyu Quan

The growing reliance of machine learning models in high-stakes, highly regulated domains such as finance and insurance has created a growing tension between predictive performance, interpretability, and regulatory fairness requirements. In these settings, models are expected not only to deliver reliable predictions but also to provide transparent decision ra

James Freitag, Léo Jimenez, Joel Nagloo

Using model theory and differential algebra, we give necessary conditions for algebraic ordinary differential equations to have a complex Pfaffian solution on some complex domain. These tools also allow us to give many examples of algebraic ordinary differential equations that do not have real Pfaffian solution on any open interval. We also give a sufficient

Morayo Danielle Adeyemi, Ryan A. Rossi, Franck Dernoncourt

Fashion AI systems routinely encode the aesthetic logic of specific houses, editors, and historical moments without disclosing it. We present FASH-iCNN, a multimodal system trained on 87,547 Vogue runway images across 15 fashion houses spanning 1991-2024 that makes this cultural logic inspectable. Given a photograph of a garment, the system recovers which ho

Yehan Xu, Zanbin Xing, Khépani Raya, Lei Chang

The BESIII Collaboration has recently reported two measurements of the two-photon decay width of the $\eta_c$ meson. The 2024 result is significantly larger than most theoretical and empirical expectations, while a subsequent measurement published in early 2026 shows better agreement with the world average and conventional theoretical estimates. In this work

Tatsuya Chuman, Yousuke Udagawa, Hitoshi Kiya

A privacy-preserving clothing classification scheme is presented to enable secure occupant-centric control (OCC) systems. Although the utilization of camera images for HVAC control has been widely studied to optimize thermal comfort, privacy protection of occupant images has not been considered in prior works. While various privacy-preserving methods have be

Shamik Das, Sudipa Mondal

In this article, we produce infinite families of non-congruent numbers in the residue class of $1,2,$ and $3$ modulo $8$ with arbitrarily many triples or quadruples prime factors. In short, we use Monsky matrix to show that the $2$-Selmer rank of the corresponding congruent number elliptic curve is zero. We also establish some quantitative results to conclud

Chris Percy, Artur d'Avila Garcez

This article reviews and updates the framework for accountability in AI based on account- ability ecosystems. We update the framework in light of the latest developments since the release of Large Language Models for general public use. We propose three interlinked updates to the original AI accountability ecosystem: (i) reorienting the accountability ecosys

Severin Kohler, Diego Boscá, Falk Meyer-Eschenbach, Prabash Galgane Banduge

Background: Interoperability between clinical and research data systems is essential for enabling secondary use of EHR data. The openEHR standard provides structured, model-driven clinical information, while the OMOP Common Data Model (CDM) supports large-scale observational analytics. The Eos engine and OMOP Conversion Language (OMOCL) previously introduced

Tianji He, Yulin Shao, Fen Hou

Deploying large language models (LLMs) at the network edge is hindered by their enormous cost, yet the reasoning quality they provide remains indispensable. Heterogeneous collaboration between edge small models and a server LLM has emerged as a promising direction, but existing methods fail under the dynamic conditions of multi-user contention, autoregressiv

Chengcheng Sun, Yajie Song, Cheng Zhai, Jiayun Tian

Graph Neural Networks (GNNs) have emerged as the leading paradigm for link prediction, enabling the inference of missing connections and the anticipation of potential future links. However, existing reviews lack systematic exploration specifically targeting underlying GNN architectures and diverse graph structures. To address this critical gap, this paper pr

Chukwuemeka Ugwu, Oluwafemi Richard Oyeleke

Speech recruits the same executive, attentional, and working memory processes underlying instrumental activities of daily living, or IADLs, providing a non-invasive proxy for cognitive assessment. Yet most speech-based dementia detection systems depend on transcription, discard within-recording temporal structure, and are validated on a single English corpus

Kaining Li, Ruichen Yan, Yuxin Dong

Media bias detection is a critical task for ensuring fair and balanced information dissemination, yet existing sentence-level approaches classify each sentence independently, ignoring inter-sentence contextual signals that human annotators naturally exploit. We present \textbf{HierBias}, a hierarchical context-conditioned media bias detector that formally mo

Pratik Rakesh Singh, Mohammadi Zaki, Aneesh Mukkamala, Pankaj Wasnik

Automatic speech recognition (ASR) systems, despite low overall word error rates, produce residual lexical errors that disproportionately affect semantically critical tokens such as named entities, negations, and sentiment-bearing words. These errors are often structured, arising from phonetic similarity rather than random noise, making naive token-level cor

Barak Or

Retrieval-Augmented Generation (RAG) grounds Large Language Models in external knowledge, yet current evaluations rely on discrete heuristics that suffer from ''epistemic blindness'' - failing to distinguish genuine contextual information extraction from parametric memory recall. To address this, we introduce the Normalized Context Utilizatio

Rajarshi Misra, Aditya Sharma, Vinti Agarwal, Hari Om Aggrawal

Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic document graphs exhibiting strong class-consistent clustering. Ignoring this can blur class boundaries and lead to over-smoothing. We propose ModTGCN, a modularity-aware graph neural network for text classification

Jaehoon Lee, CheolWon Na, Suyoung Bae, Jin-Seop Lee

Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct

Anna Neumann, Holli Sargeant, Jatinder Singh

Generative artificial intelligence (GenAI) is increasingly operated by natural language instructions (prompts). Across the pipeline, stakeholders designate various forms, e.g. end-user guidelines, developer specifications, or system prompts, as prompt governance instruments. These textual artifacts are intended to shape model behaviour by specifying constrai

Zeyuan Wang, Dongyang Hou, Cheng Yang, Xuezhi Cui

Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS tasks rely on extensive specialized tool libraries. However, tool documentation often exceeds the context window limits of LLMs, making precise tool retrieval essential for agentic workflows. Existing tool retr

Md. Rejaul Korim Sadi, Toufiqur Rahman Tasin, Golam Mostofa Naeem

Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scales. Existing taxonomies classify hallucination by output type, distinguishing intrinsic from extrinsic failures and faithfulness from factuality divergence. These frameworks are descriptively rigorous but do not

Aizierjiang Aiersilan

The integration of artificial intelligence into clinical medicine creates a fundamental tension between algorithmic probabilistic reasoning and the experiential intuition of expert physicians; applying Lawrence Lessig's \enquote{Code is Law} framework, I argue that the architecture of clinical AI systems already functions as de facto medical regulation,

Carlo Cattani, Dioneia Motta Monte-Serrat

This paper proposes a structural and dynamical framework for modeling cognitive processes within a cybernetic perspective. Cognitive states are represented as elements of a state space evolving through an iterative update rule of the form \[ X_{t+1} = π\big(F(f(X_t))\big), \] where $f$ describes internal transformations, $F$ represents interpretative mapping

Youness Bouchari, Matteo Boffa, Marco Mellia, Idilio Drago

Large Language Model (LLM) agents are increasingly proposed to automate offensive security tasks, with recent studies reporting near human-level success rates in Capture-the-Flag (CTF) challenges. We here revisit these results, providing a second look at these claims. We engineer different agent architectures of increasing complexity and modularity on 30 web

Aditya Tiwari, Konduri Naga Lakshmi Rekha, Rajesh Kumar Mundotiya

Reasoning-based Large Language Models (LLMs) like PO4ISR have set new benchmarks in session-based recommendation. However, the reproducibility of their reasoning capabilities across diverse semantic domains remains unexplored. In this work, we conduct a rigorous reproducibility study of PO4ISR to assess its generalization limits. Our analysis reveals a criti

Abdul Joseph Fofanah, Lian Wen, David Chen, Tsungcheng Yao

Anomaly detection in dynamic networks is critical for applications from cybersecurity to industrial monitoring, yet existing methods face challenges in energy efficiency, temporal precision, and adaptability. This paper introduces ASTDP-GAD, a novel Adaptive Spiking Temporal Dynamics Plasticity framework for Graph Anomaly Detection that integrates spiking gr

Davood Marripour, Jahanfar Abouie

We study prethermal time quasicrystalline (TQC) order in a quasiperiodically driven chain of non-interacting spin-1/2 particles. The drive consists of two parts, switched on and off periodically with frequency $ω_d$: (i) disordered Ising interactions, with exchange couplings chosen from a symmetric interval $[-J/2, J/2]$, allowing random antiferromagnetic or

Daiana Rinja, Eduardo Araujo Oliveira, Sonsoles López-Pernas, Mohammed Saqr

Generative AI is reshaping higher education programming through vibe coding, where students collaborate with AI via natural language rather than writing code line-by-line. We conceptualize this practice as help-seeking, analyzing 19,418 interaction turns from 110 undergraduate students. Using inductive coding and Heterogeneous Transition Network Analysis, we

Pierpaolo Condò, Francesco Tombesi, Marco Laurenti, Alfredo Luminari

IRAS 13224-3809 is one of the most intensively studied narrow-line Seyfert 1 galaxies, with a rich literature reporting diverse and sometimes contrasting interpretations of its complex X-ray spectra and variability. Notably, a fast and variable ultra-fast outflow (UFO) was discovered in this source, sparking debate over its nature and driving mechanisms. Mot