April 2026 arXiv papers — page 140

Ruifeng Wang, Chi Fang, Ilya Kostanovski, Ke Xiao

An anomalous Hall effect (AHE) in antiferromagnetic (AF) systems with no net magnetization is of considerable interest for both fundamental physics and spintronic applications. Of particular interest is the two-dimensional van der Waals antiferromagnet FeTe that has an unusual fully magnetically compensated bicollinear AF structure and exhibits pronounced Ko

Dylan R. Ashley, Gaël Le Lan, Changsheng Zhao, Naina Dhingra

Large language models (LLMs) face a fundamental trade-off between computational efficiency (e.g., number of parameters) and output quality, especially when deployed on computationally limited devices such as phones or laptops. One way to address this challenge is by following the example of humans and have models ask for help when they believe they are incap

Vadim Borisov

Emotion classification in multilingual settings remains constrained by the scarcity of annotated data: existing corpora are predominantly English, single-label, and cover few languages. We address this gap by constructing a large-scale synthetic training corpus of over 1M multi-label samples (50k per language) across 23 languages: Arabic, Bengali, Dutch, Eng

Ziqi Wang, Xingzhou Lou, Meiqi Wu, Zhengqi Wen

Group Relative Policy Optimization (GRPO) enhances LLM reasoning but often induces overconfidence, where incorrect responses yield lower perplexity than correct ones, degrading relative calibration as described by the Area Under the Curve (AUC). Existing approaches either yield limited improvements in calibration or sacrifice gains in reasoning accuracy. We

Chenming Gao, Hongwei Lin, Gengchen Li

In the realm of computer-aided design (CAD) software, the intersection of B-spline surfaces stands as a fundamental operation. Despite the extensive history of surface intersection algorithms, the challenge of handling complex intersection topologies persists. While subdivision algorithms have demonstrated strong robustness in computing surface/surface inter

Zhaochen Liu, Limeng Qiao, Guanglu Wan, Tingting Jiang

Multimodal large language models (MLLMs) have exhibited remarkable performance in various visual tasks, yet still struggle with spatial reasoning. Recent efforts mitigate this by injecting geometric features from 3D foundation models, but rely on static single-layer extractions. We identify that such an approach induces a task misalignment bias: the geometri

Mingkun Quan, Runhu Li, Zixu Zhao

We investigate the mutual information harvesting of two circularly accelerated detectors that interact with the massless scalar fields near a reflecting boundary. We consider that the two detectors share a common rotational axis with the same acceleration and trajectory radius. As the interdetector separation increases, the mutual information may exhibit osc

Qiang Le, Yaguang Yang, Isaac E. Weintraub

Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge. While traditional optimal control methods can find ideal paths, the computational time is often too slow for real-time decision-making. To solve this challenge, we propose a method based on Deep Deterministic Policy Gradient (DDPG) and model the threat as a simple,

Linhao Yu, Tianmeng Yang, Siyu Ding, Renren Jin

RLVR improves reasoning in large language models, but its effectiveness is often limited by severe reward sparsity on hard problems. Recent hint-based RL methods mitigate sparsity by injecting partial solutions or abstract templates, yet they typically scale guidance by adding more tokens, which introduce redundancy, inconsistency, and extra training overhea

Ziyuan Xia, Jingyi Xu, Chong Cui, Yuanhong Yu

Training embodied AI agents depends critically on the visual fidelity of simulation environments and the ability to model dynamic humans. Current simulators rely on mesh-based rasterization with limited visual realism, and their support for dynamic human avatars, where available, is constrained to mesh representations, hindering agent generalization to human

Jianhui Wu, Jian Zhou, Zhi Zhou, Zhangjin Huang

High-quality global illumination (GI) in real-time rendering is commonly achieved using precomputed lighting techniques, with lightmap as the standard choice. To support GI for static objects in dynamic lighting environments, multiple lightmaps at different lighting conditions need to be precomputed, which incurs substantial storage and memory overhead. To o

Xin Qian, Dazhen Deng, Zhaoping He, Xingbo Wang

Knowledge-intensive text usually contains fruitful entities and complex relationships, such as academic articles and scientific exposition. Reading and comprehending such texts often demands considerable time and mental effort to track the relationships between entities. To reduce the burden, we present GraphTide, a visualization technique that progressively

Xihe Li, Runshan Wang

For positive integers $n$, $d$, $k$ and $h$, let $[n]^d$ be the $d$-dimensional grid of order $n$, and we refer to the equation $\sum_{i=1}^{h}x_{1,i}=\cdots =\sum_{i=1}^{h}x_{k,i}$ as the {\it $B_{k,h}$-equation}, where $x_{1,1}, \ldots, x_{1,h}, \ldots, x_{k,1}, \ldots, x_{k,h}$ are $kh$ points in $[n]^d$. In this paper, we study the rainbow Cameron-Erd\H{

Damir Assylbek, Nurmukhammed Aitymbetov, Marko Ristin, Dimitrios Zorbas

Many visual monitoring systems operate under strict communication constraints, where transmitting full-resolution images is impractical and often unnecessary. In such settings, visual data is often used for object presence, spatial relationships, and scene context rather than exact pixel fidelity. This paper presents two semantic image communication pipeline

Jinyuan Liu, Yong Liang Guan, Hong Niu, Qian Zhang

Future sixth-generation (6G) networks require high spectral efficiency (SE), massive connectivity, and stringent reliability under imperfect channel state information at the transmitter. Rate-splitting multiple access (RSMA) addresses part of this challenge by flexibly managing interference through common and private message streams, while fluid antenna syst

Darij Grinberg

We generalize the Abel--Hurwitz identities to an almost entirely noncommutative setting. Namely, let $V$ be a finite set of size $n$, and let $\mathbb{L}$ be any noncommutative ring. For each $s\in V$, let $x_{s}\in\mathbb{L}$. Set $x\left( S\right) :=\sum_{s\in S}x_{s}$ for any $S\subseteq V$. Let $X$ and $Y$ be two elements of $\mathbb{L}$ such that $X+Y$

Weichuang Zhang, Yiquan Wang, Xinzhou Zhang, Chi Zhang

FPGAs are well-suited for dataflow architectures that process data in a streaming or pipelined manner, thus satisfying the high computational and communication demands of emerging applications. However, manually implementing an efficient dataflow architecture for large-scale applications is still challenging, even for specialists who use high-level synthesis

You Qin, Linqing Wang, Hao Fei, Roger Zimmermann

The post-training pipeline for diffusion models currently has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL) with reward models. A fundamental gap separates them. SFT optimizes the denoiser only on ground-truth states sampled from the forward noising process; once inference deviates from these ideal states, subsequen

Jianhao Chen, Shiqin Wang, Haoyang Chen, Hanjie Zhao

Vision-Language Models (VLMs) expand the attack surface of safety-aligned systems by coupling visual perception with text generation. Existing multimodal jailbreak attacks primarily rely on crafted visual content, adversarial perturbations, or image-specific attack strategies, leaving the potential of reusable visual anchors in benign natural images largely

Lev Sorokin, Ivan Vasilev, Samuele Pasini

This report summarizes the results of the first edition of the Large Language Model (LLM) Testing competition, held as part of the DeepTest workshop at ICSE 2026. Four tools competed in benchmarking an LLM-based car manual information retrieval application, with the objective of identifying user inputs for which the system fails to appropriately mention warn

Yang Liu, Qianjie Lei, Xiaolong He, Yizhe Xue

Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental platform designed specifically for applied physics education. Utilizing an Arduino

Dohun Kim, Junsik Choe, Takashi Taniguchi, Kenji Watanabe

Moir\'e superlattices are generally assumed to act only at the interface where lattice mismatch or twist occurs. Here, we study charge transport in large-angle helical twisted trilayer graphene, where interlayer tunneling is strongly reduced. When only the top monolayer graphene is aligned with hBN, the electronic response reorganizes into a moir\'e-modulate

Michal Czakon, Felix Eschment, Terry Generet, Rene Poncelet

The production of two isolated photons in high-energy hadron collisions poses a challenge to perturbative QCD because of large corrections through next-to-next-to-leading order (NNLO). We present novel next-to-next-to-next-to-leading order ($\text{N}^3$LO) predictions and finally demonstrate perturbative convergence for this process. We discuss the considera

Mikhail Zibrov, Thomas Schwarz-Selinger, Michael Klimenkov, Ute Jäntsch

Recrystallized tungsten (W) samples were irradiated by 20 MeV self-ions at 1350 K to peak damage doses in the range of 0.001-2.3 dpa. The irradiation-induced defects were then decorated with deuterium (D) by a gentle D plasma exposure ($<5$ eV/D, $5.6 \times 10^{19}$ $\text{D} / (\text{m}^2 \text{s})$) at 370 K. The D depth profiles in the samples were measu

Rami V. Tabri

Repeated cross-sections reveal changes in ordinal distributions but not the transitions that produce those changes. This paper identifies which transitions are unavoidable under least-displacement reconciliation of observed marginals. I develop a probability metric for ordinal change based on threshold geometry and aggregation, and show that its optimal-tran

Xudong Wang, Chaoning Zhang, Qigan Sun, Zhenzhen Huang

Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. However, the effectiveness of RAG depends not only on the design of the retriever and the capacity of the underlying model, but also on how retrieved evidence is structured and aligned with the query. Existing RA

Sondos R. Khalil, Ahmad Y. Al-Dweik, Marwan Aloqeili, F. M. Mahomed

Four coframes of invariant 1-forms are explicitly constructed using the Inductive Cartan equivalence method with rank zero corresponding to four distinct branches. These coframes are employed to characterize non-linearizable fourth-order ODEs under point transformation with a five-point symmetry Lie subalgebra. Moreover, we propose a procedure for obtaining

Klichchupong Dabsamut, Adisak Boonchun, Walter R. L. Lambrecht

LiGa$_5$O$_8$ in a spinel type structure has recently been claimed to be an unintentional p-type ultra-wide-band-gap oxide semiconductor. While previous computational work did not yet identify the origin of p-type doping and in fact predicted insulating behavior by compensation of deep acceptors by shallow donors, defect characterization in terms of its opti

Rhyan Barrett, Robin Curth, Julia Westermayr

The functionality of catalysts, enzymes, and supramolecular assemblies emerges not from individual molecules alone, but from the subtle interplay between multiple components arranged in complex systems. Designing such systems is a grand challenge, the combinatorial explosion of possible chemical compositions and spatial arrangements makes brute-force explora

Sucharita Barik, Anupam Mondal, Sajal Mukherjee

We provide a recursive construction of an acyclic matching (also known as a gradient vector field, an equivalent notion to a discrete Morse function) on the independence complex of a graph with a simplicial vertex using given acyclic matchings on the independence complexes of specific subgraphs. As an application, we determine the homotopy type of the indepe

Loc X. Nguyen, Phuong-Nam Tran, Trung Thanh Pham, Avi Deb Raha

Semantic communication (SemCom) is accelerating its momentum to catch up with the massive increase in users' demands in both quantity and quality, with the assistance of advanced deep learning (DL) techniques. Specifically, SemCom can actively embed the semantic meaning of the data into the transmission process, while eliminating statistical redundancy to pr

Xusheng Xia, Zhilin Xia

Scaling laws are increasingly used as fast surrogate models for high energy laser atmospheric propagation, yet their calibration and comparison still depend on large collections of high-fidelity wave-optics simulations. Existing studies usually rely on privately organized simulation outputs, which makes it difficult to reproduce published fits or evaluate ne

Nora Brambilla, Abhishek Mohapatra, Tommaso Scirpa, Antonio Vairo

The impact of open-flavor thresholds on the quarkonium spectrum has been a subject of study since the introduction of the Cornell potential and has been quantified through various phenomenological approaches, most notably the $^3P_0$ model. We revisit this problem using the Born--Oppenheimer effective field theory (BOEFT), an effective field theory systemati

Sajjad Vardast, Alexander Muschet, N. Smijesh, Mohammad Rezaei-Pandari

The field of attosecond physics has expanded significantly in recent years, yet experimental facilities supporting attosecond pump attosecond probe spectroscopy remain rare. Here, we present a newly constructed beamline for the generation and application of energetic, isolated extreme ultraviolet (XUV) and soft X-ray attosecond pulses via upscaling of high-h

Vladimir A. Mazin, Mikhail A. Zorin, Dmitrii S. Korzh, Elvir Z. Karimov

Passwords still remain a dominant authentication method, yet their security is routinely subverted by predictable user choices and large-scale credential leaks. Automated password guessing is a key tool for stress-testing password policies and modeling attacker behavior. This paper applies LLM-driven evolutionary computation to automatically optimize prompts

Xuelin Xie, Xiliang Lu, Zhengshan Wang, Yang Zhang

The core challenge of hyperspectral image denoising is striking the right balance between data fidelity and noise prior modeling. Most existing methods place too much emphasis on the intrinsic priors of the image while overlooking diverse noise assumptions and the dynamic trade-off between fidelity and priors. To address these issues, we propose a denoising

Dino Conciatore, Elia Oggian, Federico Da Forno, Stefano Schuppli

Large-scale pre-training of Foundational Models (FM) constitutes a computationally intensive first phase for enabling AI across diverse scientific and societal applications. This first phase has positioned High-Performance Computing (HPC) facilities as indispensable backbones of "Sovereign AI" initiatives. While the massive throughput requirements of FM pre-

Valter Hudovernik, Federico López, Vid Kocijan, Akihiro Nitta

We introduce KumoRFM-2, the next iteration of a pre-trained foundation model for relational data. KumoRFM-2 supports in-context learning as well as fine-tuning and is applicable to a wide range of predictive tasks. In contrast to tabular foundation models, KumoRFM-2 natively operates on relational data, processing one or more connected tables simultaneously

Shiyu Li, Yicheng Feng, Weiwei Cui, Zhixiong Gong

High-frequency focused ultrasound is widely used in biomedical applications such as high-resolution imaging, neuromodulation, particle manipulation, and so on. However, dynamic tuning of the focal plane in conventional systems often relies on mechanically adjustable components or array-based control with complex system and high cost. In this work, an optical

LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb

A precision measurement of the muon charge asymmetry from $W$-boson decays in proton-proton collisions at $\sqrt{s}$ = 13 TeV is presented. The analysis utilizes data corresponding to an integrated luminosity of 5.1 $fb^{-1}$, recorded by the LHCb detector during 2016, 2017 and 2018. The asymmetry is measured for muons with transverse momentum between 25 and

Yuhao Liu, Dingju Wang, Ziyang Zheng

This paper presents our approach to the NTIRE 2026 3D Restoration and Reconstruction Challenge (Track 1), which focuses on reconstructing high-quality 3D representations from degraded multi-view inputs. The challenge involves recovering geometrically consistent and photorealistic 3D scenes in extreme low-light environments. To address this task, we propose E

Jannis Gabler, Clément Lhoste, Max Quast, Laura Mayrhuber

Compensatory trunk movements (CTMs) are commonly observed after stroke and can lead to maladaptive movement patterns, limiting targeted training of affected structures. Objective, continuous detection of CTMs during therapy and activities of daily living remains challenging due to the typically complex measurements setups required, as well as limited applica

J. M. Mazón, J. Toledo

In this paper we study existence and uniqueness of solutions for a very general class of doubly nonlinear diffusion equations on metric graphs, which provide the appropriate mathematical framework to describe complex tubular networks in which axial diffusion is the main focus. Some important particular cases covered in our study are the Porous Medium Equatio

Leandros Perivolaropoulos, Chrisostomos-Panagiotis Stamou

We investigate the impact of a late-time transition in the standardized absolute magnitude $M$ on the best-fit values of cosmological parameters using the Pantheon+ dataset. Extending previous analyses which focused on flat $\Lambda$CDM, we examine this transition within flat $\Lambda$CDM, wCDM, and CPL cosmologies, as well as a model-independent cosmographi

Sebastian Chenery, Stephen Theriault

We prove conditions under which the total space of the pullback of a sphere fibration over a connected sum is homotopy equivalent to a connected sum with a gyration. Existing results of this type often depend on geometric methods. We develop new methods based only on homotopy theory, allowing for generalisations from manifolds to Poincar\'e Duality complexes

M. Tóth, J. H. Pixley, G. Takács, M. Kormos

The sine-Gordon model serves as a foundational $1+1$-dimensional quantum field theory with numerous applications in condensed matter physics. Despite its integrability, characterizing its finite-temperature behavior remains a significant theoretical challenge. Here we use the previously developed Method of Random Surfaces (MRS) to evaluate two-point and high

Anatole Dahan, Martin Grohe, Daniel Neuen, Tomáš Novotný

We present an additive $\varepsilon n^{2}$-approximation algorithm for the Graph Edit Distance problem (GED) on graphs of VC dimension $d$ running in time $n^{O(d/\varepsilon^{2})}$. In particular, this recovers a previous result by Arora, Frieze, and Kaplan [Math. Program. 2002] who gave an $\varepsilon n^{2}$-approximation running in time $n^{O(\log n/\var

Ajay Kumar, Pritam Samanta, Prakash Parida

Being motivated by recent synthesis of a monolayer of gold, named goldene, from the nano-laminated ternary ceramic phase of Ti3AuC2, we are proposing two phases of goldene viz. goldene-I and goldene-II as anode material for Lithium-Ion batteries using first principles study. This innovative goldene-I monolayer, composed of triangular motifs of gold atoms, ex

Zijian Liu, Sihan Cao, Pengcheng Zheng, Kuien Liu

Recent Video Large Language Models (Video-LLMs) have demonstrated strong capability in video understanding, yet they still suffer from hallucinations. Existing mitigation methods typically rely on training, input modification, auxiliary guidance, or additional decoding procedures, while largely overlooking a more fundamental challenge. During generation, Vid

Kangmin Seo, MinKyu Lee, Tae-Young Kim, ByeongCheol Lee

Recent advances in 3D Gaussian Splatting (3DGS) have enabled impressive real-time photorealistic rendering. However, conventional training pipelines inherently assume full multi-view consistency among input images, which makes them sensitive to distractors that violate this assumption and cause visual artifacts. In this work, we revisit an underexplored aspe

Xudong You, Kai Wan, Xiang Zhang, Wenbo Huang

This paper considers a new secure gradient coding problem with uncoded groupwise keys, formalized as a (K, N, N_r, M, S) secure gradient coding model, where a user aims to compute the sum of the gradients from K datasets with the assistance of N distributed servers. We consider arbitrary heterogeneous data assignment, where each dataset is assigned to at lea

Ahmed Halawani, Yahya Meshalwi Khabrani, Abdulaziz Al-Mogheeth, Zheng-Hong Li

Quantum key distribution protocols based on the quantum eraser phenomenon offer an operational advantage: automatic identification of matching and mismatching encoding choices through interference, eliminating basis reconciliation. However, binary quantum eraser implementations permit an eavesdropper to recover Alice's encoded bit with $85\%$ probability. To

Yinxi He, Kang Liao, Chunyu Lin, Tianyi Wei

This paper introduces StructDiff, a generative framework based on a single-scale diffusion model for single-image generation. Single-image generation aims to synthesize diverse samples with similar visual content to the source image by capturing its internal statistics, without relying on external data. However, existing methods often struggle to preserve th

Francesco Chiumento, Julia Dietlmeier, Ronan P. Killeen, Kathleen M. Curran

Detecting amyloid-$\beta$ (A$\beta$) positivity is crucial for early diagnosis of Alzheimer's disease but typically requires PET imaging, which is costly, invasive, and not widely accessible, limiting its use for population-level screening. We address this gap by proposing a PET-guided knowledge distillation framework that enables A$\beta$ prediction from MR

Yanji He, Yuxin Jiang, Yiwen Wu, Bo Huang

Large Language Models are increasingly deployed for decision-making, yet their adoption in high-stakes domains remains limited by miscalibrated probabilities, unfaithful explanations, and inability to incorporate expert knowledge precisely. We propose IDEA, a framework that extracts LLM decision knowledge into an interpretable parametric model over semantica

Jiachen Yu, Kaihang Li, Jingfan Gu, Chang Cai

The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment employs a dual-phase liquid xenon time projection chamber to search for Coherent Elastic Neutrino-Nucleus Scattering (CE$\nu$NS) induced by reactor neutrinos. To detect these sub-keV nuclear recoils and minimize signal attenuation, it is critical to maintain a sufficiently low

Kacho Imtiyaz Ali Khan, Tauqir Shinwari, Soheil Ershadrad, Majid Ahmadi

Two-dimensional ferromagnets with strong perpendicular magnetic anisotropy exhibit magnetic order down to the monolayer thickness, beneficial for energy-efficient spintronic devices. In this work, molecular beam epitaxy has been employed to realize controlled Ni-doping in Fe$_{3}$GeTe$_{2}$ (FGT) epitaxial films. MBE not only enables a large-scale growth of

E. Masa, J. Alcolea, M. Santander-García, V. Bujarrabal

The shaping of planetary nebulae on their evolution from asymptotic giant branch circumstellar envelopes to their final, most often axisymmetrical, form is still a process with many unknown details. The key to understanding the whole shaping process is the study of the transition objects called pre-planetary nebulae (pPNe). In this context, modelling tools m

Afshan Hashmi

Retrieval-Augmented Generation (RAG) has become the standard paradigm for grounding Large Language Model outputs in external knowledge. Lumer et al. [1] presented the first systematic evaluation comparing vector-based agentic RAG against hierarchical node-based reasoning systems for financial document QA across 1,200 SEC filings, finding vector-based systems

Ying Zheng, Yiyi Zhang, Yi Wang, Lap-Pui Chau

Conventional deep network training generally optimizes all samples under a largely uniform learning paradigm, without explicitly modeling the heterogeneous competition among them. Such an oversimplified treatment can lead to several well-known issues, including bias under class imbalance, insufficient learning of hard samples, and the erroneous reinforcement

Shaiq e Mustafa, Salman Liaquat, Imran Hafeez Abbasi, Azhar Hasan

Micro-Doppler signatures are a proven modality for discriminating between drones and birds, but their reliability degrades in low-SNR, data-constrained settings where deep learning models often fail. This paper presents a systematic study of ten statistical and physics-motivated handcrafted features for micro-Doppler classification under controlled signal de

Yu Xia, Yuhao Huang, Yuemin Li, Jie Wang

Modern computing workloads demand energy-efficient, high-bandwidth interconnects, motivating photonic interposers as an alternative to electrical links. Here we demonstrate a compact 3D silicon nitride (SiN) photonic interposer prototype comprising two routing layers, with the 3D routing scheme optimized by a global optimization algorithm. The 3D interposer

Yida Niu, Xinhai Chang, Xin Liu, Ziyuan Jiao

Robots deployed in unstructured environments must coordinate whole-body motion -- simultaneously moving a mobile base and arm -- to interact with the physical world. This coupled mobility and dexterity yields a state space that grows combinatorially with scene and object diversity, demanding datasets far larger than those sufficient for fixed-base manipulati

Zhaoting Geng, Yitian Tong, Chuchen Zhang, Huajun Tang

Precise on-chip dispersion control is essential for advanced integrated photonic technologies, enabling applications ranging from high-speed communications and sensing to signal processing and biomedical imaging. However, existing on-chip dispersion control methods still suffer from substantial loss and a limited dispersion-bandwidth product (DBP) far from a

Younghoon Kim, Changryong Baek

This paper proposes a dynamic network framework for uncovering latent community paths in high-dimensional VAR-type models. By embedding a degree-corrected stochastic co-blockmodel (ScBM) into the transition matrices of VAR-type systems, we separate sending and receiving roles at the node level and summarize complex directional dependence in an interpretable

Massimo Villata

In 2006 and 2014, the Antarctic Impulsive Transient Antenna (ANITA), a balloon-borne radio observatory flying over Antarctica, detected two strange upward-going radio pulse events that have not yet been explained by our current understanding of physics. These were not signals reflected by the ice and therefore it must have been an air shower originating from

Henri Lahdelma, Kim Myyryläinen, Antti V. Vähäkangas

We develop the parabolic weak porosity to characterize the parabolic Muckenhoupt $A_1$ weights with time-lag. Our main result shows that a nonempty closed set is parabolic weakly porous if and only if the parabolic distance function of the set to a negative power is in the parabolic Muckenhoupt $A_1$ class. We apply a novel stopping time argument in combinat

Wang Liao, Rei Tokami, Yasunari Suzuki

Fault-tolerant quantum computers (FTQCs) based on surface codes and lattice surgery have been widely studied, and there is strong demand for a framework that can identify logical operations with low space-time cost, verify their functionality and fault tolerance, and demonstrate their optimality within a given search space, much like electronic design automa

Peng Wang, Biyu Zhou, Xuehai Tang, Jizhong Han

Unstructured model editing aims to update models with real-world text, yet existing methods often memorize text holistically without reliable fine-grained fact access. To address this, we propose FABLE, a hierarchical framework that decouples fine-grained fact injection from holistic text generation. FABLE follows a two-stage, fact-first strategy: discrete f

Yuqing Hou

Nash equilibrium is a fundamental solution concept in extensive-form games, while its efficient computation is still far from straightforward. This paper considers finite $n$-player extensive-form games with perfect recall under the sequence-form representation. Unlike existing approaches, which mainly treat the sequence form as a compact computational refor

Subhash Chand Bhoria, Pramod Eyyunni, Subhrangsu Santra

In the preceding decade, Andrews and Newman resurrected the concept of a `minimal excludant' of a partition ($mex$, for short), namely, the least positive missing integer in a partition. Subsequently, several authors have not only studied its generalizations, analogues and the like but also connected the mex to several important partition statistics. In the

Lang Qin, Mandong Zhang, Wenting Song, Zhiqiang Huang

Continuous contactless respiration monitoring of co-sleeping subjects faces a dilemma: conventional single-site multiple-input multiple-output (MIMO) radars struggle with limited angular resolution for closely spaced individuals, while distributed radar networks typically require complex hardware synchronization. To address these limitations, this paper prop

Prakul Sunil Hiremath

Edge intelligence is constrained by the energy and latency costs of shuttling data through electronic memory hierarchies. Optical systems offer a fundamentally different computational regime: once an input wavefront is launched into a structured medium, propagation, diffraction, and interference jointly enact a linear transformation whose cost is determined

Xu Chen, Lorenz Dörschel

This study addresses the stochastic Model Predictive Control (MPC) problem for linear time-invariant systems subjected to unknown disturbance distributions. By leveraging the most recent disturbance data, we construct a set of distributions with similar statistical properties contained within a Wasserstein ball, thereby accounting for the worst-case impacts

Yohei Ota

For a finite-dimensional Hopf algebra $H$, the canonical elements of the Heisenberg doubles $\mathcal{H}(H^\ast)$ and $\mathcal{H}(H)$ satisfy the pentagon and Hopf equations, respectively. In this paper we construct quasi-Hopf analogues of these structures. For a finite-dimensional quasi-Hopf algebra $H$, we consider natural quasi-Hopf analogues $\mathcal{H

Junyao Pan

Let $n$ be an integer with $n > 1$. For every $r$ satisfying the inequalities $0 \leq r < n$, the residue class modulo $n$ is defined as $r(n)=\{r + kn | k \in Z\}$, where $Z$ is the set of all integers. Then for $0 \leq r_1\neq r_2 < n$, the horizontal class transposition $\tau_{r_1(n), r_2(n)}$ is an involution that interchanges $r_1 + kn$ and $r_2 + kn$ f

Xiaoran Yan, Patrick Michel, Ruichen Ni, Yifei Jiao

Shock-physics numerical codes are essential tools for describing the short but extreme fragmentation stage of the hypervelocity impact process on asteroids. However, accurately representing complex interior structures, surfaces, and contact mechanics in these events remains a significant challenge for traditional hydrocodes. This study introduces and validat

Tomas Berriel Martins, Martin R. Oswald, Javier Civera

Vision-language models have been key to the development of open-vocabulary 2D semantic segmentation. Lifting these models from 2D images to 3D scenes, however, remains a challenging problem. Existing approaches typically back-project and average 2D descriptors across views, or heuristically select a single representative one, often resulting in suboptimal 3D

Mohamad Maassarani

We consider irreducible representations of finite quandles over $\mathbb{C}$. For $Q$ a finite quandle whose inner automorphism group $Inn(Q)$ have trivial Schur multipliers, we prove that the irreducible representations of $Q$ can be constructed out of what we call characters of $Q$ and irreducible linear represenations of the group $Inn(Q)$. For $G$ a fini

Takumi Kanezashi, Daisuke Tsukayama, Jun-ichi Shirakashi, Tetsuo Shibuya

Feedback-controlled electromigration (FCE) enables precise regulation of atomic migration by carefully optimizing multiple experimental parameters. However, manually fine-tuning these parameters poses significant challenges. This study investigated the feasibility of autonomously fabricating Au atomic junctions through gate-based quantum computing using a no

Junyu Ren, Xingjian Pan, Wensheng Gan, Philip S. Yu

Prompt injection has emerged as a critical security threat to large language models (LLMs), yet existing studies predominantly focus on single-dimensional attack strategies, such as semantic rewriting or character-level obfuscation, which fail to capture the combined effects of multi-space perturbations in realistic scenarios. In addition, systematic black-b

Flavien Mabilat

The study of the combinatorics of the modular group and of Coxeter's friezes naturally leads to the investigation of a matrix equation, sometimes referred to as the Conway-Coxeter equation. The solutions of size $n$ of this equation, called $\lambda$-quiddities, are $n$-tuples of elements of a given ring $B$. A detailled understanding of these objects relies

Amir Kargaran, Kamran Kaveh, Krishnendu Chatterjee

Cooperation is central to the organization of complex biological and social systems. Most theoretical models assume homogeneous environments; in reality, populations inhabit spatially varying landscapes in which the payoffs of cooperation differ across space. Here, we introduce a general framework for the evolution of cooperation in complex, heterogeneous en

Wanchun Ni, Jiugeng Sun, Yixian Liu, Mennatallah El-Assady

Improving policymaking is a central concern in public administration. Prior human subject studies reveal substantial cross-cultural differences in citizens' emotional responses to red tape during policy implementation. While LLM agents offer opportunities to simulate human-like responses and reduce experimental costs, their ability to generate culturally app

Adel Ali, Alexey Belyanin

We investigate electron paring in two-dimensional electron systems mediated by the vacuum fluctuations of a quantized magnetic flux generated by the inductor of an LC resonator. The interaction induces long-range attractive interactions between angular momentum states which lead to pairing in a broad class of materials with critical temperatures of few Kelvi

Georgios Mermigkis, Dimitris Metaxakis, Marios Tyrovolas, Argiris Sofotasios

Large Language Models (LLMs) are increasingly used to translate the technical outputs of eXplainable Artificial Intelligence (XAI) methods into accessible natural-language explanations. However, existing approaches often lack guarantees of accuracy, faithfulness, and completeness. At the same time, current efforts to evaluate such narratives remain largely s

Laura Boca de Giuli, Alessio La Bella, Manish Prajapat, Johannes Köhler

A key challenge in learning-based model predictive control (MPC) is to collect informative data online for model adaptation while ensuring safety and without penalising control performance. In this paper, we propose an online model adaptation scheme embedded within an MPC framework in which the last-layer parameters of a recurrent neural network are recursiv

Jean Fasel, William Hornslien

Using motivic homotopy theory we produce several explicit polynomial representatives of the suspension of the Hopf map defined over the integers. We derive from this computation an explicit rank 2 vector bundle on the Jouanolou device of the projective space of dimension 3 over the integers.

Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit Mitra

Data scarcity limits NLP development for low-resource African languages. We evaluate two data augmentation methods -- LLM-based generation (Gemini 2.5 Flash) and back-translation (NLLB-200) -- for Hausa and Fongbe, two West African languages that differ substantially in LLM generation quality. We assess augmentation on named entity recognition (NER) and part

Mingzhen Zhang, Puqing Jiang, Ronggui Yang

Accurate evaluation of buried thermal interfaces is vital for understanding and optimizing heat dissipation in wide- and ultra-wide-bandgap (WBG/UWBG) semiconductor devices. Conventional time-domain thermoreflectance (TDTR) typically probes only near-surface transport due to its restricted modulation frequency range. Here, we employ a frequency-tunable perio

Giulia Dileo, Deniz Poyraz, Bayram Şahin

We study transversely K\"ahler almost contact metric Lie algebras $(\mathfrak{g},\varphi,\xi,\eta,g)$ such that the structure $1$-form $\eta$ is a contact form. They include both quasi Sasakian and anti-quasi-Sasakian Lie algebras of maximal rank. In the case where the center of the Lie algebra is nontrivial, they are $1$-dimensional central extensions of K\

Ruoxiang Huang, Zhen Yuan

Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet their positional encoding mechanisms remain suboptimal. Existing approaches uniformly assign positional indices to all tokens, overlooking variations in information density within and across modalities, which leads to inefficient attention allocation where redund

Kyra Delray, Glyn Lewis, Bola Grace, Joseph Hayes

The menstrual cycle influences numerous physiological and psychological outcomes, yet standardised, open-source statistical methods for quantifying these cyclic effects remain lacking. We developed mcanalysis, an open-source package in R and Python implementing a Fourier-basis generalised additive model (GAM) for menstrual cycle research. The package provide

Li-Ping He, Feng-Kun Guo, Ulf-G. Meißner, De-Liang Yao

The subprocess $nn\to ppe^-e^-$ is a key ingredient in the interpretation of nuclear neutrinoless double-beta decay. Intermediate $\Delta$ resonances may provide additional enhancements to this transition. We take a first step toward a $\Delta$-full description of $nn\to ppe^-e^-$ by investigating the neutrinoless double-beta decay $\Delta^- \to p e^-e^-$ in

Victor David, Jérôme Delobelle, Jean-Guy Mailly

Similarity in formal argumentation has recently gained attention due to its significance in problems such as argument aggregation in semantics and enthymeme decoding. While existing approaches focus on propositional logic, we address the richer setting of First-Order Logic (FOL), where similarity must account for structured content. We introduce a comprehens

Bingjiang Zhang, Dihui Wang, Tao Chen, Heng Ban

Polyimide (PI) thin films are widely used in advanced technologies, yet accurate characterization of their thermal properties remains challenging, as evidenced by significant inconsistencies in reported data and an incomplete understanding of heat transfer mechanisms. In this study, we employ an optical Square-Pulsed Source (SPS) technique to simultaneously

Sindhunil Barman Roy

We report a phenomenological theory for the kinetic arrest (KA) of a first-order phase transition, taking the Mott metal-insulator transition in $V_2O_3$ as a test case. By defining a order parameter $\phi$ related to the monoclinic distortion of the high temperature metallic and mapping its Time-Dependent Ginzburg-Landau (TDGL) dynamics onto a disorder-infl

Remke Kloosterman

In 1981 Katsura presented a classification of non-rational Jacobian elliptic surfaces which admit a base change which is rational. In 2004 we presented a classification of Jacobian regular elliptic surfaces which do not satisfy infinitesimal Torelli. These classifications of quite different properties turn out to be very similar. In this paper we use an argu

D. Garrido, M. M. Vasconcelos, B. Peleato

Successive Interference Cancellation (SIC) is a powerful technique for managing interference in wireless networks, yet its optimal deployment in decentralized environments remains a challenge. This study investigates joint power and rate allocation in a two-user Gaussian interference channel incorporating SIC at the receivers. We characterize the global opti

Yogachandran Rahulamathavan, Nasir Iqbal, Juncheng Hu, Sangarapillai Lambotharan

Continual machine unlearning aims to remove the influence of data that should no longer be retained, while preserving the usefulness of the model on everything else. This setting becomes especially difficult when deletion requests arrive sequentially, because the model must repeatedly adapt without erasing previously retained knowledge. Low-Rank Adaptation (