April 2026 arXiv papers — page 123

Li Jiazhe, Wu Chenyu, He Zizhou, Zhang Yufei

An interpretable, physics-consistent turbulence model correction framework, termed FISR-Equation Learner (EQL), is proposed by embedding equation learning directly into a Partial Differential Equations (PDE)-constrained field inversion process based on the adjoint method. Unlike conventional two-stage approaches, the correction model is optimized end-to-end

Yixu Huang, Tinghui Zhu, Muhao Chen

Visual reasoning models (VRMs) have recently shown strong cross-modal reasoning capabilities by integrating visual perception with language reasoning. However, they often suffer from overthinking, producing unnecessarily long reasoning chains for any tasks. We attribute this issue to \textbf{Reasoning Path Redundancy} in visual reasoning: many visual questio

Hangkong Wu, Yuze Zhu, Yajun Zhu, Kun Xu

This study presents an efficient and accurate discrete adjoint gas-kinetic scheme (GKS) for sensitivity analysis and aerodynamic shape optimization in continuum flow regimes. Developed using the backward mode of algorithmic differentiation (AD), the adjoint solver is rigorously verified against a duality-preserving linearized GKS solver generated via forward

Tomoya Kamimura, Haruka Washiyama, Akihito Sano

Embodiment is a significant keyword in recent machine learning fields. This study focused on the passive nature of the body of a biped robot to generate walking and running locomotion using model-based deep reinforcement learning. We constructed two models in a simulator, one with passive elements (e.g., springs) and the other, which is similar to general hu

Pengfei Li, Shijie Wang, Fangyuan Li, Yikun Fu

Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning-intensive tasks such as code generation. However, limited trajectory diversity often leads to diminishing returns, which constrains the achievable performance ceiling. Search-enhanced RL alleviates this issue by introducing structured exploration, which remains constraine

Mingqian Ji, Shanshan Zhang, Jian Yang

Vision Transformer (ViT)-based sparse multi-view 3D object detectors have achieved remarkable accuracy but still suffer from high inference latency due to heavy token processing. To accelerate these models, token compression has been widely explored. However, our revisit of existing strategies, such as token pruning, merging, and patch size enlargement, reve

Bin Ma, Xingjian Ding, Tekin Bicer, Pengfei Su

Diffusion Transformers (DiTs) are increasingly adopted in scientific computing, yet growing model sizes and resolutions make distributed multi-GPU inference essential. Ulysses sequence parallelism scales DiT inference but introduces frequent all-to-all collectives that dominate latency. Overlapping these with computation is difficult due to tight data depend

Zheng Chen, Bowen Chai, Rongjun Gao, Mingtao Nie

Video face restoration aims to enhance degraded face videos into high-quality results with realistic facial details, stable identity, and temporal coherence. Recent diffusion-based methods have brought strong generative priors to restoration and enabled more realistic detail synthesis. However, existing approaches for face videos still rely heavily on generi

Zheng Chen, Kai Liu, Jingkai Wang, Xianglong Yan

This paper presents the NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective

Sachitha C. Bandara, Peter J. Smith, Erfan Khordad, Pawel Dmochowski

Beam squint, the frequency-dependent shift of the main beam, poses a major challenge for wideband antenna arrays. This paper focuses on the beam squint effects in super wideband (SW) systems, where high mutual coupling (MC) effects are present. These high MC effects complicate beamforming (BF) by creating frequency-dependent phase relationships that invalida

Qi Xia, Peishan Cong, Yichen Yao, Ziyi Wang

Video object insertion places a user-specified object in an existing dynamic scene. Existing methods typically condition generation on text or a single reference image. Consequently, object appearance is underconstrained under viewpoint changes, often leading to identity drift, incorrect foreground-background layering, boundary artifacts, and temporal flicke

Ruiqi Yang, Jeremy L. Smallwood, Hongping Deng, Ya-Ping Li

Binary systems are ubiquitous in the Universe and often host circumbinary discs that are misaligned with the binary orbital plane. Such misalignments can affect disc evolution and binary accretion variability. We here present 3D hydrodynamical simulations of circumbinary discs with initial tilts $i_0$ from $0^\circ$ to $180^\circ$, around eccentric binaries

Kathiravan Palaniappan

Modern datacenters increasingly rely on low-power, single-slot inference accelerators to balance performance, energy efficiency, and rack density constraints. The NVIDIA T4 GPU has become widely deployed due to strong performance per watt and mature software support. Its successor, the NVIDIA L4 GPU, introduces improvements in Tensor Core throughput, cache c

Weiyu Ding, Xu Kong, Wei-Jian Guo, Hu Zou

The James Webb Space Telescope (JWST) has recently discovered a population of compact, red sources at z > 4 known as "Little Red Dots" (LRDs). They are characterized by their V-shaped continuum spectra and prominent broad Balmer emission lines. As their underlying physical nature remains debated and direct study at high-redshift is challenging; therefore, we

Sazzadul Islam, Tasnim Tabassum, Hao Zheng

Generating synthesizable Verilog for large, hierarchical hardware designs remains a significant challenge for large language models (LLMs), which struggle to replicate the structured reasoning that human experts employ when translating complex specifications into RTL. When tasked with producing hierarchical Verilog, LLMs frequently lose context across module

Yanda Li, Yuhan Liu, Zirui Song, Yunchao Wei

Large audio-language models (LALMs) generalize across speech, sound, and music, but unified decoders can exhibit a \emph{temporal smoothing bias}: transient acoustic cues may be underutilized in favor of temporally smooth context that is better supported by language priors, leading to less specific audio-grounded outputs. We propose \emph{Temporal Contrastiv

Jiaqi Chen, Bo Feng, Zhehan Qin, Yi-Xiao Tao

We develop integration-by-parts (IBP) reduction and differential equations for massive loop integrals of cosmological correlators in de Sitter (dS) spacetime, demonstrating the feasibility of this approach. We identify a structural property of the dS IBP system: for an $n$-propagator family, it splits into $2^n$ closed subsystems classified by the parity of

Yuxiang Wang, Hongyu Liu, Yijiang Xu, Qinke Ni

As speech language models (SLMs) transition from personal devices into shared, multi-user environments, their responses must account for far more than the words alone. Who is speaking, how they sound, and where the conversation takes place can each turn an otherwise benign request into one that is unsafe, unfair, or privacy-violating. Existing benchmarks, ho

Wenhui Cui, Nicholas Swingle, Anand A. Joshi, Dileep Nair

Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of PTE remains challenging due to heterogeneous clinical data, limited positive cases, and reliance on resource-intensive neuroimaging data. We investigate whether routinely collected acute clinical records alone

J. Hernández-Yévenes, D. B. Fisher, B. Mazzilli Ciraulo, R. L. Davies

We present a spatially resolved, multiphase study of the outflow in the edge-on starburst galaxy ESO~484-036 from the GECKOS survey, combining VLT/MUSE H$\alpha$ and ALMA CO(1$-$0) observations to analyse the atomic ionised and cold molecular gas. Both show extraplanar emission consistent with a conical outflow. Ionised gas is enclosed by molecular gas, whic

Yu-An Liu, Li Zhang

Visual-inertial odometry (VIO) is widely used for mobile robot localization, but its long-term accuracy degrades without global constraints. Incorporating ranging sensors such as ultra-wideband (UWB) can mitigate drift; however, high-accuracy ranging usually requires well-deployed anchors, which is difficult to ensure in narrow or low-power environments. Mor

Bogi Kim, Jehan Oh

In this paper, we investigate the local boundedness of weak solutions to degenerate parabolic double phase equation of type $$ u_t-\textrm{div}(|Du|^{p-2}Du+a(x,t)|Du|^{q-2}Du)=0\quad \text{in } \Omega_T := \Omega\times (0,T), $$ where $0\leq a(\cdot)\in L^\infty(\Omega_T)$. To this end, we derive the Caccioppoli inequality and a parabolic embedding theorem,

Zhen Wang, Mingyan Wu

This paper investigates the approximation of invariant measures for McKean-Vlasov stochastic differential equations (SDEs) using the Euler-Maruyama (EM) scheme under a monotonicity condition. Firstly, the convergence of the numerical solution from the EM scheme to its continuous-time counterpart is established. Secondly, we show that the numerical solution a

Chenglang Yang

In this paper, we study the $n$-point function of $t$-core partitions. The main tool is the topological vertex, originally developed to study the topological string theory for toric Calabi--Yau 3-folds. By virtue of the topological vertex, we introduce the $q$-deformed $n$-point function that generalizes both the ordinary $n$-point function of all integer pa

Yicheng Gong, Jiawei Zhang, Liqiang Liu, Yanwen Wang

We present a framework for explicit emotion control in feed-forward, single-image 3D head avatar reconstruction. Unlike existing pipelines where emotion is implicitly entangled with geometry or appearance, we treat emotion as a first-class control signal that can be manipulated independently and consistently across identities. Our method injects emotion into

Yucheng Pan, Heping Li, Zhangle Liu, Sajid Hussain

Detecting slow-moving landslides directly from wrapped Interferometric Synthetic Aperture Radar (InSAR) interferograms is crucial for efficient geohazard monitoring, yet it remains fundamentally challenged by severe phase ambiguity and complex coherence noise. While the Segment Anything Model (SAM) offers a powerful foundation for segmentation, its direct tr

Bo Gong, Jiguang Sun

Scattering resonances arise in wave phenomena and play an important role in many applications. While extensive theoretical studies have been conducted, effective numerical computation remains limited, and most existing methods suffer from spurious modes. In this paper, we propose a spurious-mode-free method for computing scattering resonances in transmission

Jiangchang Zheng, Caiyun Chen, Ruiqin Fu, Luca Buiarelli

The spontaneous breaking of symmetries is a cornerstone of physics, defining the phases of matter from the cosmological scale to the quantum realm. In condensed matter, electronic orders are classified by their behavior under fundamental symmetries like spatial inversion (parity). While even-parity orders, such as conventional superconductivity and charge de

Dimitri Navarro, Jiayin Pan, Xingyu Zhu

For any complete and noncompact manifold $M$ with $\mathrm{Ric}\ge 0$, we define a function $\mathrm{RV}(s)$ that describes the growth of relative volume asymptotically $$\mathrm{RV}(s)=\limsup_{r\to\infty} \dfrac{\mathrm{vol} B_{rs}(p)}{\mathrm{vol} B_r(p)},\quad s\ge 1.$$ Then we study the fundamental groups of such manifolds with slow relative volume grow

Arkamouli Debnath, Michael Ruofan Zeng

Oriented cohomology theories provide a general framework to perform intersection-theory-type calculus. The Chow ring, algebraic $K$-theory, and Levine--Morel's algebraic cobordism are all instances of such theories satisfying $\mathbb A^1$-invariance. Topological Hochschild homology, topological cyclic homology, and Hodge cohomology are important examples of

Fernando Barcelos Rosito, Sebastião De Jesus Menezes, Simone Ferreira Sturza, Adriana Seixas

Purpose. Athlete monitoring is constrained by small cohorts, heterogeneous biomarker scales, limited feasibility of repeated sampling, and the lack of reliable injury ground truth. These limitations reduce the interpretability and utility of traditional univariate and binary risk models. This study addresses these challenges by proposing an unsupervised mult

Honglin Guo, Rihao Chang, He Jiao, Weizhi Nie

Accurate prediction of future risk and disease progression in sepsis is clinically important for early warning and timely intervention in intensive care. However, short-window sepsis prediction remains challenging, because shorter observation windows provide limited historical evidence, whereas longer prediction horizons reduce the number of patient trajecto

Adam Rida

Every call to an LLM classification endpoint produces a labeled input-output pair already retained in production logs. These pairs constitute a free, growing training set: a lightweight surrogate trained on them can absorb a significant portion of future traffic at near-zero marginal inference cost. The open questions are when the surrogate is reliable enoug

Gaoqing Meng, Mingshu Zhao

We demonstrate the existence of bright solitons in a repulsively interacting, harmonically trapped quasi-one-dimensional Bose-Einstein condensate described by the Gross-Pitaevskii equation. Using a neural-network quantum state (NNQS) approach, we parametrize the initial wavefunction and optimize it to find solutions that recur after one trap period, effectiv

Wei Zhu, Jian Zhang, Lixing Yu, Kun Yue

Large Language Models (LLMs) achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood. By analyzing model-generated reasoning trajectories, we find that errors are not uniformly distributed but often originate from a small number of early transition points, after which reasoning

Zhendong Cao, Katrina G. Salvante, Ash Parameswaran, Pablo A. Nepomnaschy

A low cost fluorescence-based optical system is developed for detecting the presence of certain microorganisms and molecules within a diluted sample. A specifically designed device setup compatible with conventional 96 well plates is chosen to create an ideal environment in which a smart phone camera can be used as the optical detector. In comparison with co

Jinlin You, Muyu Li, Xudong Zhao

Existing single-modal RGB trackers often face performance bottlenecks in complex dynamic scenes, while the introduction of event sensors offers new potential for enhancing tracking capabilities. However, most current RGB-event fusion methods, primarily designed in the spatial domain using convolutional, Transformer, or Mamba architectures, fail to fully expl

Rohit Kumar Salla, Ramya Manasa Amancherla, Manoj Saravanan

Large language models frequently produce mutually inconsistent answers when reasoning over multiple related queries. We study case-file logical consistency: maintaining a globally satisfiable belief state across interdependent queries. We introduce a benchmark of 390 multi-query reasoning instances with entailment/contradiction/unknown labels and propose set

Cheng-Jie Zhao, Zhaolin Wang, Zongyao Zhao, Yuanwei Liu

A site-specific Type-II codebook design is proposed for downlink massive multiple-input multiple-output (MIMO) limited-feedback beamforming. The key idea is to embed a learned site-specific propagation prior into the Type-II channel state information (CSI) feedback pipeline. Specifically, the base station (BS) uses a low-overhead reference signal received po

Saul Gonzalez-Bermejo, Tommaso Albrigi, Borja Vazquez-Morado, Urko Regueiro-Ramos

Predicting heat-related physiological events at the population level is challenging due to the complex interactions among climatic, demographic, and socioeconomic factors, as well as the strong sparsity and seasonality of observational data. In this work, we propose a unified predictive framework that integrates heterogeneous environmental and public-health

Bin Wang, Qiang Zhang, Xiaochuan Luo, Slava Maslennikov

The increasing penetration of inverter-based resources introduces new dynamic challenges to modern power grids, such as sub- and super-synchronous oscillations and other faster dynamics. These dynamics are typically fast in nature and are difficult to accurately model and analyze using standard transient stability (TS) methods, necessitating the need for ele

Jarod Tall, Steven Tomsovic

We derive the density of states and $2$- and $4$-point functions of embedded ensembles for both fermions and bosons in the double-scaled limit. It is shown the models are equivalent to the double-scaled Sachdev-Ye-Kitaev model, expanding the double-scaled universality class to include both fermionic and bosonic systems. The models can be solved by introducin

Ziyang Luo, Nian Liu, Junwei Han

Omni-modal Large Language Models (Omni-MLLMs) promise a unified integration of diverse sensory streams. However, recent evaluations reveal a critical performance paradox: unimodal baselines frequently outperform joint multimodal inference. We trace this perceptual fragility to the static fusion topologies universally employed by current models, identifying t

Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi, Tsui-Wei Weng

Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a new task. This issue is especially pronounced in class incremental learning (CIL), which is the most challenging setting in continual learning. Existing methods to address catastrophic forgetting often sacrifice

MindDR Team, Li Auto Inc

We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter models through a meticulously designed data synthesis and multi-stage training pipeline. The core innovation of MindDR lies in a collaborative three-agent architecture (Planning Agent, DeepSearch Agent, and Report

Seongmin Kim, Kyusoon Kim

This paper proposes a fully Bayesian framework for node-level outlier detection in graph signals, where measurements are observed on the nodes of an underlying graph. Unlike traditional outlier detection methods, our approach accounts for the relational dependencies induced by the graph, identifying outliers that disrupt the underlying smoothness. We model t

Saul Gonzalez-Bermejo, Tommaso Albrigi, Borja Vazquez-Morado, Urko Regueiro-Ramos

Hydration status is a key physiological indicator associated with cellular homeostasis, renal function, and overall health. Recent advances in smart sensing environments enable passive monitoring of urinary biomarkers that can provide continuous insight into hydration dynamics. In this work, we investigate predictive modeling approaches for hydration monitor

Soroush Sadeghian, Alireza Daqiq, Radin Cheraghi, Sajad Ebrahimi

Large Language Models (LLMs) are increasingly used in scientific peer review, assisting with drafting, rewriting, expansion, and refinement. However, existing peer-review LLM detection methods largely treat authorship as a binary problem-human vs. AI-without accounting for the hybrid nature of modern review workflows. In practice, evaluative ideas and surfac

Hugo O'Connor

Agent communication languages (ACLs) enable heterogeneous agents to share knowledge and coordinate across diverse domains. This diversity demands extensibility, but expressive extension mechanisms can push the input language beyond the complexity classes where full validation is tractable. We present CBCL (Common Business Communication Language), an agent co

Rongyao Wang, Veronica Liesaputra, Zhiyi Huang

News recommender systems are devised to alleviate the information overload, attracting more and more researchers' attention in recent years. The lack of a dedicated learner-oriented news recommendation toolkit hinders the advancement of research in news recommendation. We propose a PyTorch-based news recommendation toolkit called NewsTorch, developed to supp

Max C. Gallant, David Mrdjenovich, Kristin A. Persson

As the number of theoretically predicted materials continues to grow, it becomes increasingly important to assess not only their thermodynamic stability but also their kinetic viability under realistic synthesis conditions. In this study, we investigate the hypothesis that the kinetic persistence of a metastable polymorph is related to the topography of the

H. Itoyama, Hikaru Kawai, Shoichi Kawamoto

In low-energy supergravity treatment of type IIB superstring on general D-instanton wormhole profiles in the bulk, we obtain non-vanishing scalar two-point functions in addition to the vanishing $\langle \tau^* \tau^* \rangle$ that corresponds to the BPS amplitude detected by two D-instantons at their respective boundaries. This is exploited to show that the

Jianghong Huang, Luping Ji, Weiwei Duan, Mao Ye

As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a frequently-faced issue. To solve it, the few-shot anomaly detection (FSAD) scheme is attracting increasing attention. In recent years, beyond traditional visual paradigm, Vision-Language Model (VLM) has been

Jue Jiang, Aneesh Rangnekar, Harini Veeraraghavan

Masked image modeling (MIM) is a highly effective self-supervised learning (SSL) approach to extract useful feature representations from unannotated data. Predominantly used random masking methods make SSL less effective for medical images due to the contextual similarity of neighboring patches, leading to information leakage and SSL simplification. Hierarch

Chen-Hung Hsiao, Limei Yuan, Yidun Wan

We examine the gravitational lensing signatures of a Hayward-like regular black hole and its potential observational distinction from a Schwarzschild black hole. In the weak-field limit, the deflection angle includes a small positive correction proportional to $m \ell^2/b^3$, indicating slightly stronger light bending than in Schwarzschild, though the effect

Francesco Spezzati, Yun Wang, Andrew Hearin

We present realistic forecasts for the constraining power of the Nancy Grace Roman Space Telescope on fundamental cosmological parameters, with particular emphasis on the absolute neutrino mass scale, using full-shape analyzes of the galaxy power spectrum. We analyze simulated lightcone mock catalogs of H$\alpha$ emission-line galaxies spanning the redshift

Alexander Bodard, Pieter Pas, Andreas Themelis, Panagiotis Patrinos

This work introduces a simple and efficient linesearch method for composite minimization that accelerates proximal-gradient iterations with fast Newton-type directions. Our algorithm is based on simple operations and only requires the standard proximal-gradient oracle, similar to PANOC and ZeroFPR, provided that the nonsmooth term is convex. Noteworthy impro

Chervin F. P. Laporte, Matthew D. A. Orkney

We present a robust method to empirically infer the timing of the last significant merger in the Milky Way which is tested against fully cosmological models of galaxy formation. We apply it to Milky Way subgiant stars with spectro-photometric ages, finding that the last significant merger (Gaia-Sausage-Enceladus, GSE), occurred $\sim11\,$Gyrs ago. This coinc

Nikola Zubić, Qian Li, Yuyi Wang, Davide Scaramuzza

We study how depth, finite precision, state dimension, and chain-of-thought (CoT) affect the expressive power of multi-layer state-space models (SSMs). For the explicit-table $K$-function-composition problem, a canonical benchmark for sequential information propagation, we prove that any $L$-layer SSM solving $(L+3)$-function composition must satisfy $d^2p=Ω

Dongxin Guo, Jikun Wu, Siu Ming Yiu

Expert specialization is fundamental to Mixture-of-Experts (MoE) model success, yet existing metrics (cosine similarity, routing entropy) lack theoretical grounding and yield inconsistent conclusions under reparameterization. We present an information-geometric framework providing the first rigorous characterization of MoE specialization dynamics. Our key in

Ahmed Saad Al-Karsani, Maryam Khanbaghi

The introduction of Renewable Energy Sources (RES) and Distributed Energy Resources (DERs) has led to the formulation of Microgrids (MGs) and Networks of MGs (NMGs). MGs and NMGs can operate in islanded mode, transforming the grid into a more distributed system. This has led to extensive studies in the literature on distributed hierarchical control strategie

Mel Sohm, Charles Dezons, Sami Sellami, Oscar Ninou

Synthetic augmentation is increasingly used to mitigate data scarcity in financial machine learning, yet its statistical role remains poorly understood. We formalize synthetic augmentation as a modification of the effective training distribution and show that it induces a structural bias--variance trade-off: while additional samples may reduce estimation err

Rebekah White, Chandler Smith, Drew Kouri, Jace Ritchie

Optimal experimental design provides a way of determining a-priori the best locations at which to place accelerometers in vibrations analysis experiments. However, in practice, sensors often fail during experimentation due high mechanical accelerations. There have been limited works exploring the use of robust OED in the context of vibrations analysis, where

J. O. González-Cervantes, D. González-Campos, J. Bory-Reyes

The theory of the operator $$G(x) = |\underline{x}|^2 \frac{\partial }{\partial x_0} + \underline{x} \sum_{j=1}^n x_j \frac{\partial }{\partial x_j} $$ is deeply associated with the slice monogenic function theory and has grown in recent years. In particular, for $n=3$ the quaternionic version of $G$ has been recently used to study the quaternionic slice reg

Ifayoyinsola Ibikunle, Tyler Farnan, Senthil Kumar, Mayana Pereira

Financial institutions face tension between maximizing data utility and mitigating the re-identification risks inherent in traditional anonymization methods. This paper explores Differentially Private (DP) synthetic data as a robust "Privacy by Design" framework to resolve this conflict, ensuring output privacy while satisfying stringent regulatory obligatio

Gia-Bao Ha, Lucas Takanori Sanchez Shiromizu, Jaehyeon Song, Zhuyun Xie

Continuous ambulatory monitoring of peripheral vascular perfusion could enable earlier detection of vascular dysfunction in individuals with diabetes mellitus and more timely management of cardiovascular disease. Clinical imaging modalities provide high-fidelity vascular information but are impractical for ambulatory use, whereas most wearable devices are li

Nenad Banfic, David Fan, Kunal Vaishnavi, Sam Kemp

Deploying high-quality automatic speech recognition (ASR) on edge devices requires models that jointly optimize accuracy, latency, and memory footprint while operating entirely on CPU without GPU acceleration. We conduct a systematic empirical study of state-of-the-art ASR architectures, encompassing encoder-decoder, transducer, and LLM-based paradigms, eval

Ziyi Li

We revisit the proposal to cure the negative density of states in the three-dimensional gravitational path integral by adding spinning states whose spin scales with the central charge. We show that sub-extremal and extremal spinning states below the black hole threshold, together with certain overspinning states above it, can cancel the known negativities in

Noa K. Yoder, John O. Dabiri

Scyphozoan jellyfish exhibit the highest locomotive efficiency in the animal kingdom making them of particular interest in fluid dynamics and bioinspired robotics. Despite this prevalent analytical models of jellyfish swimming have been based on the swimming traits of hydrozoan jellyfish which utilize jet propulsion, rather than scyphozoan jellyfish which ut

Yang Yu, Yu-Min Hu, Xian Gao

There has been considerable interest in constructing modified gravity theories that propagate only two degrees of freedom (DOFs), corresponding to the tensorial gravitational waves of general relativity. Within the framework of spatially covariant gravity (SCG), the conditions for obtaining 2-DOF theories can be derived from Hamiltonian constraint analysis,

Deliang Wang, Cunling Bian

Generative AI (GenAI) holds significant promise for advancing educational equity among ethnic minority students by broadening access to learning resources and mitigating linguistic barriers. However, these benefits are counterbalanced by the risk of cognitive laziness, whereby students may treat GenAI as an answer engine or shortcut rather than as a partner

Konstantinos I. Roumeliotis, Ranjan Sapkota

The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents. Despite recent advances, unified frameworks for designing and evaluating full-stac

Aleksandrs Vališevskis, Aleksandrs Okss, Inese Tīģere, Aleksejs Kataševs

Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect. This study presents a complete open-source MATLAB-based framework for the development and validation of compact deep learnin

Jose A. Bird

We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory networks. The system enables unified analysis of bulk tumor and single-cell-derived ARACNe networks by integrating TCGA-derived cancer networks with large-scale single-cell regulatory networks f

Emiliano Massi

In predictive modeling, the ability to explain why a model produces a given target prediction has become increasingly important [5, 10]. Black-box models do not provide a transparent description of the internal mechanisms that generate the prediction, making even accurate predictions difficult to interpret and validate. In critical contexts, predictive accur

Pranav Yadav

Retrieval Augmented Generation (RAG) has proven to be a widely successful process at improving the quality of outputs from a Large Language Model (LLM) for wider context. However, RAG systems typically retrieve context from flat document stores, which struggles when queries require hierarchical or relational reasoning across structured knowledge. I present H

Aimen Boukhari

Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure. Inspired by the success of Joint Embedding Predictive Architectures (JEPA) (LeCun, 2022) in vision and audio, we propose a

Yixuan Ding, Wei Huang, Ruijie Quan, Xiaojuan Qi

Diffusion-based image editing has achieved strong visual fidelity under natural language instructions, yet most existing systems still operate at the level of surface instruction following, without reasoning about the implicit contextual constraints embedded in real user requests. This often leads to visually plausible but logically inconsistent edits. In th

Silvia Preda, Gabriella Bretti, Francesco Freddi, Bruno Nazarena

We present a complete workflow for predicting stone degradation phenomena, such as marble sulfation, in works of art. The main challenge is to accurately acquire the geometry of the artwork and then use it to perform simulations based on a mathematical model of the degradation process, typically formulated as a system of partial differential equations (PDEs)

Despoina Antonakaki, Sotiris Ioannidis

The Greek legislative framework is characterized by intricate cross-referencing, frequent amendments, and limited machine-readable access, hindering transparency and civic engagement. Traditional bulk-archiving approaches are computationally expensive and fail to capture political relevance. We present a multimodal computational pipeline that bridges parliam

Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky, Gabriela Ben-Melech Stan

Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, tile size tuning, and architecture-specific workarounds -- to every Triton kernel in their code-base. This manual, repetitive effort is a major bottleneck: each kernel demands the sa

Yuqing Xiao, John Grundy, Anuradha Madugalla, Elizabeth Manias

We sought to explore and compare the perspectives of three key stakeholder groups: older adults, caregivers (formal health providers and informal caregivers), and digital health software developers on key functional and non-functional requirements. We conducted a survey, designed based on the findings from an existing systematic review, to gather and analyse

Reachsak Ly, Alireza Shojaei, Xinghua Gao, Philip Agee

Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment with occupant needs and reduced satisfaction. This paper proposes a novel blockchain- and Decentralized Autonomous Organization (DAO)-based framework for community-based facilities management in smart buildings.

Gongbo Zhang, Yifan Peng, Chunhua Weng

Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge. Recent agentic RAG systems extend this paradigm with critical agents to evaluate model responses and iteratively refine outputs. However, most prior work implicitly assumes reliable critic feedback and focuse

Lingyu Mu, Hao Deng, Haibo Xing, Kaican Lin

Recent progress in large language model (LLM) based generative recommendation (GR) shows that leveraging LLM world knowledge can substantially improve performance. However, existing methods rely on fixed, manually designed instructions to generate semantic knowledge and directly incorporate it into GR, which has two limitations. First, fixed instructions can

Reachsak Ly, Alireza Shojaei, Xinghua Gao, Philip Agee

While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel

Mingjun Li, Xiaojun Ye

Which tasks inside an enterprise workflow can a large-language-model agent reliably handle, and under what conditions? Most business process modeling frameworks still answer this at the activity level, even though a single activity can bundle work of radically different difficulty. This paper takes the analysis a step smaller. We describe two design artifact

Sushant Gautam, Annika W. Olstad, Klas H. Pettersen, Michael A. Riegler

Moltbook is a social media platform in which posts and comments are authored exclusively by autonomous AI agents. We present the Moltbook Observatory Archive, an incremental dataset that passively records agent profiles, posts, comments, community metadata (``submolts''), platform-level time-series snapshots, and word-frequency trend aggregates obtai

Milad Yazdani, Shahriar Shalileh, Dena Shahriari

Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult because deployment is nonlinear, and feasible cut layouts must satisfy discrete compatibility rules, avoid overlap, and map one target shape to valid designs. We present RL-Kirigami, an inverse design framewor

Anna Mazhar, Sainyam Galhotra

Machine learning components are now central to AI-infused software systems, from recommendations and code assistants to clinical decision support. As regulations and governance frameworks increasingly require deleting sensitive data from deployed models, machine unlearning is emerging as a practical alternative to full retraining. However, unlearning introdu

Arshia Ghasemi, Siqi Shao, R. A. Serota

We analyze historic S&P500 multi-day returns: from daily returns to those accumulated over up to ten days. Despite symmetry breaking between gains and losses in the distribution of returns, resulting in its positive mean and negative skew, realized variance (volatility squared) exhibits remarkably good linear dependence on the number of days of accumulation.

R. Varas, G. Morello, M. Zechmeister, P. J. Amado

Stellar rotation is closely linked to both age and magnetic activity. Through gyrochronology, it provides a means to estimate stellar ages and trace the evolution of planetary systems, and it is also crucial to constrain and correct stellar activity effects for robust exoplanet detection and characterisation. CARMENES is a dual-channel, high-resolution (R >

Joydip Sarkar, Ayshi Mukherjee, Amit Basu, Ritajit Kundu

Over the last decade, the development of Josephson devices based on van der Waals (vdW) materials has advanced rapidly, representing a paradigm shift driven by the advent of 2D materials. The diverse vdW materials library, combined with advanced fabrication techniques, enables the integration of materials with vastly disparate properties for scientific explo

Juan C. Zuñiga Castro, Jeffrey Larson, Matt Menickelly, Sri Hari Krishna Narayanan

We investigate the generation of quantum states for precision metrology in noisy two-level systems. These states are obtained by optimizing a variational quantum circuit to maximize the quantum Fisher information (QFI) of the output state for a given decoherence rate and interaction Hamiltonian. The circuit architecture, inspired by twist-and-turn schemes, f

David Vasak, Johannes Kirsch, Juergen Struckmeier

We present a theoretical analysis of the WDW approach to quantum cosmology extended to gravity theories with torsion. The dynamics of the FLRW universe is formulated as a classical Hamiltonian problem of point particle mechanics. Unlike in the WDW formalism, the Hamiltonian is not zero, though, and the 3rd quantization does not enforce the cosmic time to van

Lorenzo Battistini, Alessandra De Rosa, Paola Severgnini, Cristian Vignali

We present the study of an X-ray selected sample of active galactic nuclei (AGN) in pairs at projected spatial separations 1 <$ r_p$/kpc < 100 at z < 0.1, using XMM-Newton and Chandra data. The pair sample is derived from an initial pool of approximately 2,000 X-ray-selected AGN, and is composed of both AGN-AGN pairs (so called dual AGN) and AGN-galaxy pairs

Ertugrul Kececi, Tufan Kumbasar

Recent advances in Deep Learning (DL) have strengthened data-driven System Identification (SysID), with Neural and Fuzzy Ordinary Differential Equation (NODE/FODE) models achieving high accuracy in nonlinear dynamic modeling. Yet, system states in these frameworks are often reconstructed without clear physical meaning, and input contributions to the state de

Giuseppe Filiberto Vitale, Gaetano Lambiase, Tanmay Kumar Poddar, Luca Visinelli

We study the coupled cosmological evolution of primordial black holes (PBHs) and radiation in the Arkani-Hamed-Dimopoulos-Dvali (ADD) framework with $n$ large extra dimensions and a fundamental gravity scale $M_\star$ at the TeV scale. For PBHs with horizon radius smaller than the compactification scale, the higher-dimensional geometry implies a larger horiz

A. S. Gusev, A. M. Tatarnikov, S. G. Zheltoukhov, M. S. Kirsanova

The Caucasus Mountain Observatory of the Sternberg Astronomical Institute of Moscow State University is the only one in Russia and one of the few in the world where is possible to obtain spectral data in the near-infrared (IR) range at $λ$=1-2.5 $μ$m. However, there is a problem of processing the spectra of extended objects, the angular dimensions of which e

Geonwoo Baek, David H. Salat, Ikbeom Jang

Alzheimer&#39;s disease (AD) confirmation often relies on positron emission tomography (PET) or cerebrospinal fluid (CSF) analysis, which are costly and invasive. Consequently, structural MRI biomarkers such as cortical thickness (CT) are widely used for non-invasive AD screening. Multiscale structural mapping (MSSM) was recently proposed to integrate gray-w