April 2026 arXiv papers — page 80

Yikuan Huang, Zheqi Fan, Kaiqi Hu, Yifan Ye

Text-based financial networks are increasingly used to study cross-stock return predictability. A common approach constructs links from similarities in firms' disclosure embeddings, but such networks often contain spurious edges because textual proximity does not necessarily imply economic connection. We propose a two-stage framework that first builds a spar

Mateo Sanabria, Carlos Varela, Camilo Rocha, Nicolas Cardozo

In the rewriting logic framework, equational-based specifications are used to define deterministic functional behavior, abstract data types, and canonical representations of data. These specifications include a (possibly order-sorted) signature and equations interpreted modulo structural axioms, such as associativity, commutativity, and identity. While equat

Hongyu Zhang, Yufan Deng, Zilin Pan, Peng-Tao Jiang

Generating high-quality videos from complex temporal descriptions that contain multiple sequential actions is a key unsolved problem. Existing methods are constrained by an inherent trade-off: using multiple short prompts fed sequentially into the model improves action fidelity but compromises temporal consistency, while a single complex prompt preserves con

Xia Zhang, Xiaohu Li, Zhiping Kou

Acetone (CH3COCH3) is a ubiquitous interstellar molecule, and serves as an important tracer of hot core chemistry. We conducted a line survey of acetone and its precursor acetaldehyde (CH3CHO) towards 60 hot cores by using the ALMA 3 mm lines observations. We calculated the rotational temperatures and column densities of acetone using the XCLASS software. Ac

Yarin Levi, Ran Dubin

This paper presents Map Reduce Graph (MRG), a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and parameter formats. MRG enables real-time updates, explainable visu

Dibyendu Mondal, Ashish Kumar Patra, Rahul Maitra

Determining the exponentially scaled ground state wavefunction and the associated molecular properties remains one of the central challenges in quantum chemistry. Hybrid quantum-classical algorithms implemented on quantum computers offer a promising route toward addressing this problem. However, despite several successful demonstrations on small molecular sy

Hossein Gholampour, Logan E. Beaver

Unknown payloads can strongly affect compliant robotic manipulation, especially when the payload center of mass is not aligned with the tool center point. In this case, the payload generates an offset wrench at the robot wrist. During motion, this wrench is not only related to payload weight, but also to payload inertia. If it is not modeled, the compliant c

Kelly McConvey, Dipto Das, Maya Ghai, Angelina Zhai

Fairness audits of institutional risk models are critical for understanding how deployed machine learning pipelines allocate resources. Drawing on multi-year collaboration with Centennial College, where our prior ethnographic work introduced the ASP-HEI Cycle, we present a replica-based audit of a deployed Early Warning System (EWS), replicating its model us

Sharadh Jois, Gregory M. Stephen, Samuel W. LaGasse, Genda Gu

We measure magneto-transport and critical currents in Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ Hall bar devices. Above critical current in an applied magnetic field, we observe longitudinal differential voltage along one edge comparable in magnitude but opposite in sign to the other edge. This phenomenon is unaffected by reversal of the applied field, and seems unique

Changjiang Bu, Haotian Zeng, Qingying Zhang

A hypergraph is called uniform when every hyperedge contains the same number of vertices, otherwise, it is called non-uniform. In the real world, many systems give rise to non-uniform hypergraphs, such as email networks and co-authorship networks. A uniform hypergraph has a natural one-to-one correspondence with its adjacency tensor. In 2019, Benson proposed

Sanjeeda Sultana, Chayan Ranjit, Surajit Chattopadhyay, Ertan Güdekli

In this work, we have analyzed the cosmology of the Tsallis holographic dark energy (THDE), a particular case of Nojiri-Odintsov HDE proposed in [S. Nojiri and S. D. Odintsov, \textit{Gen. Relativ. Gravit.} \textbf{38} (2006), 1285; \textit{Eur. Phys. J. C} \textbf{77} (2017) 528], using Hubble's horizon cutoff in $f(R,T)=\mu R+\nu T$ model considering press

Fanyu Wang, Xiaoxi Kang, Paul Burgess, Aashish Srivastava

More than half of the global population struggles to meet their civil justice needs due to limited legal resources. While Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, significant challenges remain even at the foundational step of legal issue identification. To investigate LLMs' capabilities in this task, we constructed a

Raf Cluckers, Immanuel Halupczok

We correct the statements and proofs of the (auxiliary) Propositions 4.1 and 4.2 of our paper `Evaluation of motivic functions, non-nullity, and integrability in fibers' in Advances in Mathematics, Vol. 409, Part A, Paper No. 108635, 29 pages (2022), and we explain how the proofs of the main results can be adapted to work with those corrected propositions.

Lesi Chen, Xinliang Zhang, Chengchang Liu, Junru Li

When the objective has Lipschitz continuous $p$th-order derivatives, it is known that convex-concave minimax problems can be solved with $\mathcal{O}(\epsilon^{-2/(p+1)})$ $p$th-order oracle calls. This complexity upper bound was speculated to be optimal as it is achieved by a natural generalization of the optimal first-order method. In this work, we show an

Tomislav Matulić, Ivan Škrabo, Dubravko Babić, Damir Seršić

We present a physics-driven framework for accurate evaluation of discrete spectral bands using a low-cost multispectral setup built from off-the-shelf RGB cameras and narrow multi-band optical filters. The approach starts by explicitly formulating a linear measurement model. The camera responses are expressed as linear mixtures of unknown spectral components

Kyuhee Kim, Auguste Poiroux, Antoine Bosselut

Formal verification guarantees proof validity but not formalization faithfulness. For natural-language logical reasoning, where models construct axiom systems from scratch without library constraints, this gap between valid proofs and faithful translations is especially acute. We investigate whether frontier models exploit this gap when generating Lean 4 pro

Ahmed Farooq

This article establishes a first-principles statistical field theory of fully developed isotropic turbulence. Applying an exact Helmholtz decomposition to the local angular momentum field ($\Lvec = \rvec \times \uvec$) reveals a segregation into two orthogonally distinct topological phases: a longitudinal condensate of macroscopic coherent structures ($\PhiL

Vasundra Srininvasan

Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy memory, multi-step reasoning, and binding regulatory constraints. Current evaluation reports a single task-success scalar that conflates distinct failure modes and hides whether an agent is aligned with the stan

Netzer Moriya

We propose a cavity-based scheme that uses photonic chirality to control braiding and read out non-Abelian anyons in a fractional quantum Hall platform. Counter-propagating cavity modes interfere with a classical reference tone to create a rotating pinning landscape whose direction is set by photon circulation, so that opposite photonic branches drive opposi

Venkatesh Seenuvasan Revathi, Jeyapradhap Thirisangu, Karthick Subramani

Rayleigh-Taylor Instability (RTI) typically arises when a dense fluid is superimposed on a lighter fluid, where the desta- bilizing gravitational force acting on miscible fluids drives chaotic mixing. We theoretically present an acoustofluidic method utilizing standing bulk acoustic waves (BAW) to counteract RTI and suppress the mixing of fluids. To success-

Ken Kennedy, Iman Evazzade

In this paper, we examine the use of self-stabilizing algorithms, operating in a hierarchical manner, to determine intellectual property risks at a macro level. We are both interested in finding a solution that will support all defined intellectual property dimensions as well as suboptimal solutions in order to minimize risk.

Suvinava Basak

Batch Normalization (BN) is a cornerstone of deep learning, yet it fundamentally breaks down in micro-batch regimes (e.g., 3D medical imaging) and non-IID Federated Learning. Removing BN from deep architectures, however, often leads to catastrophic training failures such as vanishing gradients and dying channels. We identify that standard activation function

Yuhan Hu, Xiaolei Fang

Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industri

Yifan Zhang

We study a family of local depth-based corrections to maxmin landmark selection for lazy witness persistence. Starting from maxmin seeds, we partition the cloud into nearest-seed cells and replace or move each seed toward a deep representative of its cell. The principal implemented variant, \emph{support-weighted partial recentering}, scales the amount of mo

Sara Ortega-Martinez, Raul E. Angulo, Sergio Contreras, Jonás Chaves-Montero

Spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and Euclid are mapping the spatial distribution of millions of galaxies, with Emission Line Galaxies (ELGs) serving as the dominant tracer in the redshift range $0.8<z<1.6$. Standard approaches for extracting cosmological information from galaxy clustering, however, typically disca

Wander Nauta, Marcus Gerhold, Marieke Huisman

As deductive verifiers mature, their potential user base is growing from the initial core developers to other users. To convince external users of the suitability of verifiers, these tools must run reliably out of the box, give meaningful error messages and display correct results. Yet deductive verifiers are large and complex software systems and their own

Ewan Cameron

We present a dual-track pipeline for detecting biblical allusions in literary fiction and apply it to the novels of Cormac McCarthy. A bottom-up embedding track uses inverse document frequency to identify rare vocabulary shared with the King James Bible, embeds occurrences in their local context for sense disambiguation, and passes candidate passage pairs th

C. Han, R. F. Wimmer-Schweingruber, P. Kühl, L. Berger

Solar eruptive events such as flares and coronal mass ejection (CME)-driven shocks can release solar energetic particles (SEPs) into the heliosphere. The heliospheric current sheet (HCS) is a large-scale structure that separates regions of opposite magnetic polarity, and its influence on SEP propagation remains poorly understood. We classify SEE events into

Xiang Chen, Hao Li, Jiangxin Dong, Jinshan Pan

This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provided a unified benchmark to evaluate the robustness and generalization abil

Thomas Zollo, Jimmy Wang, Richard Zemel

Reasoning language models can solve increasingly complex tasks, but struggle to produce the calibrated confidence estimates necessary for reliable deployment. Existing calibration methods usually depend on labels or repeated sampling at inference time, making them impractical in many settings. We introduce a method for unsupervised confidence calibration of

Ya-Xun He, Xing-Jian Liu, Qun Wang, Ting Chen

Superconducting films emerge from the complex interplay of multiple growth parameters, making their optimization challenging. In iron-based superconductors, compressive strain is known to enhance the transition temperature (Tc) of FeSe films, yet reported Tc values vary widely even on identical substrates, indicating factors beyond strain are critical. Here,

Sriram Narayanan, Mohamed Naveed Gul Mohamed, Ishan Paranjape, Indranil Nayak

In recent years, computational power and data availability breakthroughs have revolutionized our ability to analyze complex physical systems through the inverse problem approach. Data-driven techniques like system identification and machine learning play an important role in this field, allowing us to gain insights into previously inaccessible phenomena. How

Abdul Rahman

Let $\pi:X\to \Delta$ be a one-parameter degeneration whose central fiber $X_0$ is a complex threefold with finitely many ordinary double points $\Sigma=\{p_1,\dots,p_r\}\subset X_0$. Associated with this degeneration is the corrected finite-node perverse extension, together with its mixed-Hodge-module refinement and a finite-node schober datum whose pervers

Xinhao Zhang, Xi Chen, François Portet, Maxime Peyrard

Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, the mechanisms driving these optimization gains remain poorly understood. In this work, we present a large-scale study of LLM-guided evolutionary search, collecting optimization trajectories for 15 LLMs across

S. Rahimipour, B. Rafiei, E. Salahinejad

This work focuses on the structure, wettability and corrosion behaviors of Ti-6Al-4V alloy after roughening treatments in different concentrations of NaOH aqueous solutions followed by low surface energy hexadecyltrimethoxysilane (HDTMS) coating. In this regard, scanning electron microscopy, contact angle measurements, potentiodynamic polarization and electr

Sarang Nambiar, Dhruv Pradhan, Ezekiel Soremekun

Pre-trained machine learning models (PTMs) are commonly provided via Model Hubs (e.g., Hugging Face) in standard formats like Pickles to facilitate accessibility and reuse. However, this ML supply chain setting is susceptible to malicious attacks that are capable of executing arbitrary code on trusted user environments, e.g., during model loading. To detect

Wooyeon Kim, Andreas Wieser, Pengyu Yang

We prove a local-global principle for primitive representations of binary quadratic forms by quaternary quadratic forms. Our method is a variant of Linnik's ergodic method showing density for certain homogenous toral sets. The central ingredient is a measure classification result of Einsiedler and Lindenstrauss for actions of rank two diagonalizable groups o

Xiaochen Wang

Heterogeneity in individual characteristics and behaviour is a fundamental property of complex dynamical systems. While previous studies on evolutionary dynamics of strategies evolution in various systems have predominantly focused on the structural heterogeneity, dynamical heterogeneity in individuals' strategy update has been largely neglected. Here, we in

S. Komossa, D. Grupe, A. Kraus, P. G. Edwards

SDSSJ110546.07+145202.4 stands out as a unique radio changing-look Narrow-line Seyfert 1 (NLS1) galaxy that has brightened dramatically and shows an exceptionally long duration of its "on" phase. We present the first high-frequency radio observations, the first simultaneous radio spectral energy distributions (SEDs), the first optical--UV--X-ray SEDs, and th

Pasquale Di Bari

In the first part of the talk, I review general properties of $SO(10)$-inspired leptogenesis. This high-scale leptogenesis scenario is based on the simple assumption that the neutrino Dirac mass matrix is not too different from the up quark mass matrix. After showing how this necessarily implies a production of the asymmetry from the next-to-lightest right h

Emma Herbst, Sebastian Westrich, Alena Erlenbach, Jonas Gutsche

In many chemical reactions, short-lived radical intermediates play a crucial role, while detecting such short-lived species in-situ remains challenging. The optically readable electronic spin of nitrogen-vacancy (NV) centers in diamond is a nanoscale sensor for such radical species: its longitudinal spin relaxation time (T$_{1}$) reacts to magnetic fluctuati

Xinwei He, Yansong Zheng, Qianru Han, Zhichuan Wang

Vision foundation models have shown great promise for open-set 3D object retrieval (3DOR) through efficient adaptation to multi-view images. Leveraging semantically aligned latent space, previous work typically adapts the CLIP encoder to build view-based 3D descriptors. Despite CLIP's strong generalization ability, its lack of fine-grainedness prompted us to

Guray Ozgur, Tahar Chettaoui, Eduarda Caldeira, Jan Niklas Kolf

Face Image Quality Assessment is crucial for reliable face recognition systems, yet existing Vision Transformer-based approaches rely exclusively on final-layer representations, ignoring quality-relevant information captured at intermediate network depths. This paper presents the first comprehensive investigation of how intermediate representations within Vi

Alessandro G. Buda, Giuseppe Primiero, Leonardo Ceragioli, Melissa Antonelli

Generative AI systems are known to amplify biases present in their training data. While several inference-time mitigation strategies have been proposed, they remain largely empirical and lack formal guarantees. In this paper we introduce CTLF, a branching-time logic designed to reason about bias in series of generative AI outputs. CTLF adopts a counting worl

Alessandro Cotellucci, Davide Giusti

We present an update on the ongoing computation of the isospin-breaking effects in the Pion Decay Constant from the BMW Collaboration. The calculation is carried out with N$_f$=2+1+1 staggered quarks with a near-physical pion mass and QED$_{\text{L}}$. We give an update on the isosymmetric value and the current determination used to compute the gradient-flow

Yefim Shulman, Agnieszka Kitkowska, Mark Warner

For online health communities, community trust is paramount. Yet, advances in Large Language Models (LLMs) generating advice may erode this trust, especially if users cannot identify whether LLMs have been used. We investigate the feasibility of community-based detection of health advice authorship and how self-moderation of LLMs could help enhance advice ut

Mohammad Rowhani Sistani, Katarzyna Kosek-Szott, Pierluigi Gallo

Wireless links deployed in orchards often exhibit significant variability in the strength of the received signal that is not adequately captured by classical distance-based propagation models. In row-structured olive groves, signal attenuation differs markedly between along-row and cross-row propagation directions, leading to discrepancies when using omnidir

Dikran S Meliksetian

We introduce and empirically validate Landscape Span Compression (LSC), a device-agnostic metric for quantifying how hardware noise distorts the variational energy landscape of the Quantum Approximate Optimization Algorithm (QAOA). Intuitively, LSC measures how much noise flattens the energy landscape, approaching 1 as the landscape collapses toward a barren

Simon Bohnen, Gabriel Garbers, Lukas Ellinger, Georg Groh

Knowledge work demands sustained self-regulation, prioritization, and reflection-yet existing planning tools only partially support these needs. Digital to-do list applications feature task persistence but lack goal representation. Paper-based planning frameworks offer effective planning strategies but cannot adapt to individual users. Conversational AI syst

Kyungjin Kim, Minjeong Kim, Soobeen Jeong, Jiyeon So

The widespread, addictive consumption of short-form videos, which allegedly causes "brain rot," has become an urgent public concern. This study proposes that self-related cues serve as an intrinsic, self-reflective strategy that enhances self-control over media overuse. We developed an app that de-immerses users by periodically displaying different self-rela

Botao Amber Hu, Yilan Elan Tao, Bernhard Riecke, Yue Li

Mixed reality systems support shared anchors and co-located interaction, yet they lack a socially legible protocol for entering another person's mixed reality in public settings. We frame this as a protocol problem: co-located MR sharing requires a staged sequence -- Discover, Consent, Confirm, Allow, Spatial Colocation, Sync Objects, Permission Management -

Suleyman Ozdel, Amr Nader, Yasmeen Abdrabou, Enkelejda Kasneci

With the growing use of eye tracking on VR and mobile platforms, gaze data is increasing. While scanpath comparison is important to gaze behavior analysis, existing methods lack privacy-preserving capabilities for real-world use. We present a garbled-circuit (GC)-based approach enabling secure storage and privacy-preserving scanpath comparison under the semi

Robbert Decruyenaere, Clara Tanghe, Senne Van Wellen, Karel Van Acoleyen

Precise and flexible control of structured light fields is essential for applications ranging from optical trapping and quantum simulation to microscopy and materials processing. Acousto-optical deflectors (AODs) are widely used in these settings due to their high speed, large damage threshold, and ability to generate steerable optical tweezers. Multi-tone d

Jaroslav Moravec, Radim Šára, Akihiro Sugimoto

Maintaining long-term accuracy of stereo camera calibration parameters is important for autonomous systems' perception. This work proposes Online Tracking of Essential Matrix by Stochastic Optimization (TESO). The core mechanisms of TESO are: 1) a robust loss function based on kernel correlation over tentative correspondences, 2) an adaptive online stochasti

Andreas Mueller

Many mechanical systems exhibit changes in their kinematic topology altering the mobility. Ideal contact is the best known cause, but also stiction and controlled locking of parts of a mechanism lead to topology changes. The latter is becoming an important issue in human-machine interaction. Anticipating the dynamic behavior of variable topology mechanisms r

Tristan Hübsch, Djordje Minic

General relativity is a background-independent theory of a dynamical classical spacetime geometry. Quantum theory is formulated in a classical spacetime, as an intrinsically probabilistic, contextual theory of non-classical, interfering probabilities, with a fixed Born rule for computing those probabilities. We argue that the quantum nature of spacetime, whi

Zheng Lian, Xiaojiang Peng, Kele Xu, Ziyu Jia

MER2026 marks the fourth edition of the MER series of challenges. The MER series provides valuable data resources to the research community and offers tasks centered on recent research trends, establishing itself as one of the largest challenges in the field. Throughout its history, the focus of MER has shifted from discriminative emotion recognition to gene

Xiaodong Guo, Xiaokang Li, Benoît Fauqué, Alaska Subedi

Thanks to electron-electron ($e$-$e$) collisions conserving momentum, metallic electron fluids are viscous. Yet, this viscosity is rarely detectable in bulk transport. Here, we report on the canonical realization of the Gurzhi effect in an elemental three-dimensional metal: cadmium. Using focused ion beam microstructuring to tune the effective thickness, we

Guray Ozgur, Tahar Chettaoui, Eduarda Caldeira, Jan Niklas Kolf

Face Image Quality Assessment (FIQA) aims to assess the recognition utility of face samples and is essential for reliable face recognition (FR) systems. Existing approaches require computationally expensive procedures such as multiple forward passes, backpropagation, or additional training, and only recent work has focused on the use of Vision Transformers.

Ren Ito, Akio Nago, Shou Tanigawa

In the standard formulation of relativistic quantum field theory, a $\mathbb{Z}_2$-graded structure is assumed to realize locality and the boson-fermion dichotomy. While $\mathbb{Z}_2^n$-graded extensions are known to be allowed at the level of symmetry, their realization in interacting quantum field theories remains unclear. In this paper, we construct a cl

Qian Zhang, Lech Szymanski, Haibo Zhang, Jeremiah D. Deng

Sequential Recommendation (SR) aims to predict the next interaction of a user based on their behavior sequence, where complementary relations often provide essential signals for predicting the next item. However, mainstream models relying on sparse co-purchase statistics often mistake spurious correlations (e.g., due to popularity bias) for true complementar

Jon-Paul Cacioli

Aggregate metacognitive quality scores mask within-model variation across MMLU benchmark domains. We administered 1,500 MMLU items (250 per domain, under an a priori six-domain grouping) to 33 frontier LLMs from eight model families and computed Type-2 AUROC per model-domain cell using verbalized confidence (0-100). Total observations: 47,151. Every model wi

Joshua Niemeijer, Alaa Eddine Ben Zekri, Reza Bahmanyar, Philipp M. Schmälzle

Understanding road scenes in a geometrically consistent, scene-centric representation is crucial for planning and mapping. We present GOLD-BEV, a framework that learns dense bird's-eye-view (BEV) semantic environment maps-including dynamic agents-from ego-centric sensors, using time-synchronized aerial imagery as supervision only during training. BEV-aligned

Duru Paker, Suleyman Ozdel, Enkelejda Kasneci

Passwords remain the primary authentication method, yet user-created passwords are often the weakest due to the security-usability trade-off. Although AI-based password generators are emerging, little is known about their effectiveness and user perceptions. This eye-tracking study examined how behavior during password creation, selection, and memorization re

Changjiang Bu, Yifan Sun, Haotian Zeng

In this paper, we determine the graphs with maximum value of the sum number from $k$-clique spectral radius to $(2r-1)$-clique spectral radius among all $2K_{r}$-free graphs on $n$ vertices for $ r\le k$ and large $n$. We also determine the graphs with maximum $3$-clique spectral radius among all $2K_{3}$-free graphs on $n$ vertices. Our results are spectral

Fabian Bschorr, Pia Gebhard, Tobias Speidel, Volker Rasche

Purpose: Quasi-random Sobol-based sampling schemes exhibit deterministic structural artifacts when aggressively undersampled, particularly at low encoding densities required for accelerated 2D SPI/CSI. To address these limitations, two advanced undersampling strategies are investigated to mitigate deterministic behavior, improving image quality for time-cons

Fan Li, Chonghuinan Wang, Lina Lei, Yuping Qiu

Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforcement Learning from Human Feedback (RLHF) to diffusion-based

Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Mir Tafseer Nayeem, Amran Bhuiyan

Automatic evaluators such as reward models play a central role in the alignment and evaluation of large vision-language models (LVLMs). Despite their growing importance, these evaluators are almost exclusively assessed on English-centric benchmarks, leaving open the question of how well these evaluators generalize across languages. To answer this question, w

Yukai Feng, Zhiheng Wu, Zhengxing Wu, Junwen Gu

Traditional policy learning methods in cooperative pursuit face fundamental challenges in biomimetic underwater robots, where long-horizon decision making, partial observability, and inter-robot coordination require both expressiveness and stability. To address these issues, a novel framework called Mamba-based multi-agent group relative policy optimization

R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar

IceCube is a cubic-kilometer-scale neutrino detector located at the geographic South Pole. A precise directional reconstruction of IceCube neutrinos is vital for associations with astronomical objects. In this context, we discuss neural posterior estimation of the neutrino direction via a transformer encoder that maps to a normalizing flow on the 2-sphere. I

Gerard Pons, Carlos Escolano, Besim Bilalli, Anna Queralt

Knowledge Graph Embeddings (KGEs) support a wide range of downstream tasks over Knowledge Graphs (KGs). In practice, KGs evolve as new entities and facts are added, motivating Continual Knowledge Graph Embedding (CKGE) methods that update embeddings over time. Current CKGE approaches address catastrophic forgetting (i.e., the performance degradation on previ

Tobias Kiecker, Jan Arne Sparka, Martin Reuter, Albert Ziegler

Maintaining consistency between code and documentation is a crucial yet frequently overlooked aspect of software development. Even minor mismatches can confuse API users, introduce new bugs, and increase overall maintenance effort. This creates demand for automated solutions that can assist developers in identifying code-documentation inconsistencies. Howeve

Yi Zhao, Di Yuan, Tao Deng, Suzhi Cao

Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically consists of two phases: (i) distributing the global model from a server to clients, and (ii) collecting updated local models from clients to the server for aggregation. This paper focuses on a type of FL where c

Ziyang Wang, Jiangfeng Xiao, Chuan Xiao, Ruoxiang Li

Large language models (LLMs) are expensive to serve because model parameters, attention computation, and KV caches impose substantial memory and latency costs. We present GRASPrune, a structured pruning framework applied after pretraining that jointly prunes FFN channels and KV head groups under a single global budget. Instead of learning importance scores w

Suleyman Ozdel, Virmarie Maquiling, Kadir Burak Buldu, Yasmeen Abdrabou

Reproducibility in eye-tracking research is increasingly important as researchers conduct diverse experiments and seek to validate or replicate findings. However, exact replication remains challenging due to differences in laboratory practices and experimental setups. Inconsistent stimulus presentation can yield divergent metrics from identical oculomotor be

Mingze Zhang, Yizhan Li, Yutong Li, Zexia Li

Scientific tools dictate the boundaries of human knowledge, serving as the foundation for perceptions and explorations. In the era of Big Science, science are increasingly dependent on advanced analytical technologies and experimental platforms. Over the past decades, national and supranational entities have invested massive financial resources, collaborativ

Sho Hoshino, Ukyo Honda, Peinan Zhang

While self-consistency is known to improve performance on symbolic reasoning, its effect on the recall of encyclopedic knowledge is unclear due to a lack of targeted evaluation grounds. To address this, we establish such a knowledge recall split for the popular MMLU benchmark by applying a data-driven heuristic from prior work. We validate this split by show

Niclas Doll, Jasper Schulze Buschhoff, Shalaka Satheesh, Hammam Abdelwahab

This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via continual pre-training and merging. We address the scarcity of specialized non-English data by constructing a high-quality German medical corpus (FineMed-de) from FineWeb2. This corpus is used to continually p

Hyeok Woo, William Luke Matthewson, Arman Shafieloo

Cosmic distances can be measured using two complementary probes: Type Ia supernovae (SN Ia), serving as standard candles, and baryon acoustic oscillations (BAO), serving as standard rulers. The luminosity distance derived from supernovae and the angular diameter distance obtained from BAO must be mutually consistent if these data are to be combined for cosmo

Xiaoqi Zhuang, Jefersson A. Dos Santos, Jungong Han

Satellite image composition plays a critical role in remote sensing applications such as data augmentation, disaste simulation, and urban planning. We propose HarmoniDiff-RS, a training-free diffusion-based framework for harmonizing composite satellite images under diverse domain conditions. Our method aligns the source and target domains through a Latent Me

Matthew Harrison, John Carlin, Chengyuan Liu, Sarah Dunnett

This paper presents a framework to bridge the gap between subjective stakeholder context and formal system architecture. This is achieved using Soft Systems Methodology (SSM) and Systems Modelling Language version 2 (SysML v2). The methodology utilises the precision of Kernel Modelling Language (KerML) and the alignment of SysML v2 with ISO 42010 to define a

Irfan Glogić, Sarah Kistner, Birgit Schörkhuber

We study the focusing semilinear heat equation with an additional defocusing Hénon-type nonlinearity, the coupling of which is measured by a constant $c >0$. For $c \in (0,c^*)$, the model admits a closed-form self-similar blowup solution in every space dimension $d \geq 1$. Restricting ourselves to the three-dimensional case, we study the stability of this

Muhammad Khalil, Ke Wang, Jinho Choi

Reconfigurable intelligent surfaces (RISs) have recently attracted interest for non-terrestrial networks (NTNs), especially for improving satellite communication performance. However, RIS-assisted urban NTN designs that jointly support reliable communication and user positioning under blockage, while maintaining low online complexity, remain limited. This pa

F. Clozza, S. Manti, F. Sgaramella, L. Abbene

Kaonic atoms, formed when a negatively charged kaon replaces an electron in an atomic orbit, provide access to bound-state quantum electrodynamics (BSQED) in electromagnetic fields far stronger than in ordinary atoms. Here, we report an experimental test of BSQED in a regime where the mean Coulomb field exceeds the Schwinger limit. Using high-precision x-ray

Zhiheng Fu, Yupeng Hu, Qianyun Yang, Shiqi Zhang

Composed Image Retrieval (CIR) has attracted significant attention due to its flexible multimodal query method, yet its development is severely constrained by the Noisy Triplet Correspondence (NTC) problem. Most existing robust learning methods rely on the "small loss hypothesis", but the unique semantic ambiguity in NTC, such as "partial matching", invalida

Ardamon Sten, Sudeep Kumar Ghosh

The Josephson diode effect (JDE) has attracted significant attention for enabling directional, dissipationless supercurrents, positioning Josephson junctions as promising building blocks for next-generation quantum devices. Hybrid semiconductor-superconductor nanowires provide an experimentally accessible platform for realizing the JDE and hosting Majorana b

Seunghee Han, Jaewoong Lee, Jihan Kim

Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework representation maps to a single property value. This assumption becomes problematic for experimental MOFs, where samples reported as the same framework can exhibit different properties because of differences in crys

Robbe Van den Eede, Marc Denecker

Inductive definitions are an important form of knowledge. The logic FO(ID) is an extension of classical first-order logic FO with general non-monotone inductive definitions. Most existing proof systems for inductive definitions impose syntactic constraints on their definitions, thereby excluding many useful and natural definitions. We extend an existing sequ

Andrew D. McRae, Richard Y. Zhang

Low-rank matrix recovery can be solved to statistical optimality by convex matrix optimization under the classical assumption of restricted isometry property (RIP). However, for large problems, the convex formulation is commonly replaced by a smooth rank-constrained factored nonconvex problem for which algorithmic theory typically only guarantees convergence

Yuanpeng Tu

In this paper, we consider 2D incompressible Euler equations in an unbounded domain with a free surface and a fixed bottom at finite depth. The fluid motion is under the influence of gravity and surface tension. We construct initial data with a flat free surface and small velocity, such that the $L^\infty$ norm of the vorticity gradient has at least a double

Yining Pan, Shijie Li, Yuchen Wu, Xulei Yang

This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve generalization under domain shifts commonly encountered in real-world autonomous driving. A straightforward solution is to employ a pseudo-labeling strategy, which is widely used in UDA to generate supervision for u

Simon Pauli, Andreas Futschik

This paper proposes an extension to discrete Phase-Type distributions (DPH) by introducing random rewards. These allow for modeling a system in which a visit to a certain state does not emit a deterministic reward. Instead, the rewards follow either a Bernoulli or a geometric distribution. Utilizing this increased flexibility, we further sketch a possible us

Anjie Qiu, Donglin Wang, Sanket Partani, Andreas Weinand

The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet of Things (IoT) networks, particularly concerning energy efficiency. As model training and data transmission contribute significantly to energy consumption, optimizing these processes has become critical for s

Yiming Pan, Chengwei Hu, Xuancheng Huang, Can Huang

Large language models (LLMs) have demonstrated strong potential in agentic tasks, particularly in slide generation. However, slide generation poses a fundamental challenge: the generation process is text-centric, whereas its quality is governed by visual aesthetics. This modality gap leads current models to frequently produce slides with aesthetically subopt

Y. Tokunaga, M. -T. Suzuki, S. Krämer, H. Sakai

We report low-temperature $^{115}$In nuclear magnetic resonance (NMR) study of the prototypical heavy-fermion compound CeIrIn$_5$ in high magnetic fields applied close to the crystallographic $c$ axis. For this orientation, a field-induced transition was previously reported to take place at about 28 T. Although we do not observe any change of the NMR spectru

N. Kh. Bastykova, T. S. Ramazanov, S. K. Kodanova

We investigate the interplay between shear viscosity and diffusion in a 2D Yukawa liquid subjected to an external magnetic field.

Carlos Baptista De Lima, Julian Hough, Frank Förster, Patrick Holthaus

Achieving truly fluid interaction with robots with speech interfaces remains a hard problem, and the experience of current Human-Robot Interaction (HRI) remains laboured and frustrating. Some of the barriers to fluid interaction stem from a lack of a suitable development platform for HRI for improving interaction, even in robotic Wizard-of-Oz (WoZ) modes of

François Bechet, Jérôme Maquoi, Luís Cruz, Benoît Vanderose

Green software engineering is emerging as a crucial response to information technology's rising energy impact, especially in continuous development. However, there remain challenges in devising automated methods for identifying energy regressions across commits and their associated code change patterns. In particular, little effort has been put into automati

Vasiliki Papanikou, Evaggelia Pitoura

Graph representation learning has achieved notable success in encoding graph-structured data into latent vector spaces, enabling a wide range of downstream tasks. However, these node representations remain opaque and difficult to interpret. Existing explainability methods primarily focus on supervised settings or on explaining individual representation dimen

Panagiotis Charalampopoulos, Taha El Ghazi, Jonas Ellert, Paweł Gawrychowski

Many string processing problems can be phrased in the streaming setting, where the input arrives symbol by symbol and we have sublinear working space. The area of streaming algorithms for string processing has flourished since the seminal work of Porat and Porat [FOCS 2009]. Unfortunately, problems with efficient solutions in the classical setting often do n