April 2026 arXiv papers — page 83

Yong He, Kangxiang Qin, Haoran Tang

Statistical analysis of network data has attracted considerable attention in recent years, due to the rapid advancement of well-trained network models and the accessibility of large public network datasets. In this article, we propose a transfer learning procedure for boosting estimation accuracy of a target network structure based on the well-known Degree-C

Cristiano Villa

Loss-based priors assign probability mass to parameter values according to the inferential loss incurred when they are excluded from the parameter space, and provide a general solution for discrete parameters. Extending this idea to continuous settings is challenging, as the exclusion of a single point induces no loss. We propose a neighbourhood-exclusion fr

Haoyang Chen, Yi Liu, Jianzhi Shao, Tao Zhang

Thinking LLMs produce reasoning traces before answering. Prior activation steering work mainly targets on shaping these traces. It remains less understood how answer tokens actually read and integrate the reasoning to produce reliable outcomes. Focusing on quantitative reasoning, we analyze the answer-to-reasoning attention and observe a benign self-reading

Alex Beaudin, Bjørn Andreas Kristiansen, Kristoffer Gryte, Corrado Chiatante

Efficient and robust path planning hinges on combining all accessible information sources. In particular, the task of path planning for robotic environmental exploration and monitoring depends highly on the current belief of the world. To capture the uncertainty in the belief, we present a Gaussian process based path planning method that adapts to multi-moda

Weijie Zhao, Mingquan Liu, Bolun Wang, Simo Wu

Scaling Transformers typically necessitates training larger models from scratch, as standard architectures struggle to expand without discarding learned representations. We identify the primary bottleneck in the attention mechanism's linear projections, which strictly confine feature extraction to fixed-dimensional subspaces, limiting both expressivity and i

Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin, Alexey Petrenko

Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLABC (Reinforcement Learning for Accelerator Beamline Control), an open-source Python framework that automatically transforms standard Elegant beamline configurations into reinforcement learning environments. R

Lin Sha, Haiyun Guo, Tao Wang, Cong Zhang

Vision-Language Models (VLMs) have become central to autonomous driving systems, yet their deployment is severely bottlenecked by the massive computational overhead of multi-view camera and multi-frame video input. Existing token pruning methods, primarily designed for single-image inputs, treat each frame or view in isolation and thus fail to exploit the in

Kunquan Li, Yingxue Zhang, Fandong Meng, Jinsong Su

Recent years have witnessed growing interest in applying Large Reasoning Models (LRMs) to Machine Translation (MT). Existing approaches predominantly adopt a "think-first-then-translate" paradigm. Although explicit reasoning trajectories significantly enhance translation quality, they incur prohibitive inference costs and latency. To address these limitation

Juan José Marín, José María Martell, Dorina Mitrea, Marius Mitrea

We prove several characterizations of $\mathscr{C}^{1,\omega}$-domains (aka Lyapunov domains), where $\omega$ is a growth function satisfying natural assumptions. For example, given an Ahlfors regular domain $\Omega\subseteq{\mathbb{R}}^n$, we show that the modulus of continuity of the geometric measure theoretic outward unit normal $\nu$ to $\Omega$ is domi

Justin Rainier Go, Kurt Christian Andaya, Roemer Gabriel Caliboso, Aaron Daniel Go

As generative AI becomes integral to software development, the risk of over-reliance and diminished critical thinking grows. This study introduces "Ceci," our Caring Empathic C IDE designed to support novice programmers by prioritizing learning and emotional support over direct code generation. The researchers conducted a comparative pilot study between Ceci

Johannes Schusterbauer, Ming Gui, Yusong Li, Pingchuan Ma

Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function evaluations. While convenient, this ignores the heterogeneity of natural images: some regions are easy to denoise, whereas others benefit from more refinement or additional context. Motivated by this, we explore

Alen Andrašek, Matija Kazalicki, Domagoj Vlah

We prove that there exist infinitely many quartic rational Diophantine quadruples, that is, sets of four pairwise distinct nonzero rational numbers whose pairwise products increased by 1 are fourth powers in Q. To the best of our knowledge, no examples of such quadruples were previously known. Our construction is motivated by computer experiments and leads n

Zhong Han Ervin Yeoh, Jiang Kan

Precise Event Spotting (PES) is essential in fast-paced sports such as tennis, where fine-grained events occur within very short temporal windows. Accurate frame-level localization is challenging because of motion blur, subtle action differences, and limited annotated data. We study two complementary distillation strategies for few-shot PES: Adaptive Weight

Édouard Bonnet, Yeonsu Chang, Julien Duron, Colin Geniet

Reduced parameters [BKW, JCTB '26; BKRT, SODA '22] are defined via contraction sequences. Based on this framework, we introduce the reduced component max-leaf, denoted by $\operatorname{cml}^\downarrow$, where component max-leaf is the maximum number of leaves in any spanning tree of any connected component. Reduced component max-leaf is strictly sandwiched

Qiubai Zhu, Qingwang Wang, Haibin Yuan, Wei Chen

Knowledge Graph (KG) can effectively integrate valuable information from massive data, and thus has been rapidly developed and widely used in many fields. Traditional KG construction methods rely on manual annotation, which often consumes a lot of time and manpower. And KG construction schemes based on deep learning tend to have weak generalization capabilit

Antos Cheeramban Varghese, Rajasekhar Anguluri, Anamitra Pal

The process of calibrating instrument transformers (ITs) has been greatly simplified by using phasor measurement unit (PMU) data since this process eliminates the need for (a) additional hardware, and (b) taking ITs offline. However, such simplification comes at the cost of knowing the line parameters, whose estimation using PMU data in turn requires calibra

Hang Cheng, Fanhe Dong, Long Zeng

This paper presents the first exploration of text-to-image diffusion models for zero-shot sketch-based 3D shape retrieval (ZS-SBSR). Existing sketch-based 3D shape retrieval methods struggle in zero-shot settings due to the absence of category supervision and the extreme sparsity of sketch inputs. Our key insight is that large-scale pretrained diffusion mode

Sho Kubota, Kiyoto Yoshino

This paper studies the search for a single arc in a graph using the Szegedy walk. Arc search can be interpreted as finding a quantum particle not only in its position but also with a specific internal state. The quantum walk employed in this study is essentially the model proposed by Segawa and Yoshie for the purpose of edge search. First, we investigate how

Michele Grimaldi, David Nakath, Oscar Pizarro, Jonatan Scharff Willners

Robust 3D reconstruction across varying environmental conditions remains a critical challenge for robotic perception, particularly when transitioning between air and water. To address this, we introduce BALTIC, a controlled benchmark designed to systematically evaluate modern 3D reconstruction methods under variations in medium and lighting. The benchmark co

Jun Gao, Senlin Zhu, Luca Brandt, Jianjun Tao

We investigate the Marangoni modulation of coupled Rayleigh-Taylor and Faraday instabilities in a vertically oscillated Newtonian liquid film carrying insoluble surfactants. Linear stability analysis using Floquet theory reveals that an increasing Marangoni number (Ma) selectively suppresses subharmonic modes, driving the system into a harmonic-dominated reg

Xin Ning, Qiankun Li, Xiaolong Huang, Qiupu Chen

With the accumulation of resources in the era of big data and the rise of pre-trained models in deep learning, optimizing neural networks for various tasks often involves different strategies for fine-tuning pre-trained models versus training from scratch. However, existing optimizers primarily focus on reducing the loss function by updating model parameters

Tomoaki Yoshizawa

We consider the Cauchy problem of the viscous $\beta$-plane equations. We first establish the global well-posedness of the system for the initial data sufficiently small compared to the Rossby parameter. The smoothing effect of the flow is then proved, and it is shown that the obtained global solution satisfies the equation in the classical sense. We also re

Chaonan Ji, Jinwei Qi, Sheng Xu, Peng Zhang

Existing facial reenactment methods struggle with a trade-off between expressiveness and fine-grained controllability. Holistic facial reenactment models often sacrifice granular control for expressiveness, while methods designed for control may struggle with fidelity and robust disentanglement. Instead of treating facial motion as a monolithic signal, we ex

Siqi Liang, Xiawei Wang, Yudi Zhang, Jiaying Zhou

Personalized recommendation requires models that capture sequential user preferences while remaining robust to sparse feedback and semantic ambiguity. Recent work has explored large language models (LLMs) as recommenders and re-rankers, but pure prompt-based ranking often suffers from poor calibration, sensitivity to candidate ordering, and popularity bias.

Yaping Mao

Corsten and Frankl conjectured that a simplex is diameter-Ramsey if and only if its circumcenter lies in its convex hull. We disprove this conjecture in every dimension $d\ge 3$. The main tool is a sufficient criterion based on a higher-order deficit decomposition: if the squared deficits $D^2-\|p_i-p_j\|^2$ admit a nonnegative decomposition over subsets of

Mohammad Saim, Tianyu Jiang

Large language models have been extensively studied for emotion recognition and moral reasoning as distinct capabilities, yet the extent to which emotions influence moral judgment remains underexplored. In this work, we develop an emotion-induction pipeline that infuses emotion into moral situations and evaluate shifts in moral acceptability across multiple

Wei Shao, Yihang Wang, Gaoyu Zhu, Ziqiang Cheng

Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. Such training-based or controllable decoding approaches cannot completely suppress the model's inherent toxicity, whereas detoxifying the pretraining dataset can fundamentally re

Sira Gratz, Greg Stevenson

We characterize those finite groups for which the bounded derived category of finite dimensional representations over an algebraically closed field of characteristic $p$ has distributive lattice of thick subcategories: they are precisely the $p$-nilpotent groups. Along the way we give necessary and sufficient criteria for the bounded derived category and per

Priya, Ranjeet Kumar, Labh Singh, Surender Verma

Motivated by the first oscillation results from JUNO, we study the phenomenological viability of texture zeros in the Dirac neutrino mass matrix. The improved precision on the solar mixing angle $\sin^2{\theta_{12}}$ and the solar mass-squared difference $\Delta m_{21}^2$ provide a stringent probe for scrutinizing predictive texture zero frameworks. We perfo

Pedro C. Ormonde, Yuanhang Zhu, Daniel Quinn, Keith W Moored

Three-dimensional experiments are presented on a school of three pitching hydrofoils. Two side-by-side leader foils maintain the same relative positions while the location of a third follower foil is varied. Force and flow measurements detail the mechanisms that drive the school to achieve collective thrust and efficiency that are 58% and 24% higher than iso

Peiliang Gong, Han Zhang, Zhen Jiang, Chenyu Liu

Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing source data. However, existing SFDA methods rely solely on the limited internal knowledge of source-pretrained models, leading to inferior cross-domain generalization and unreliabl

Sanjeet Swaroop Panda, Lorcan O. Conlon, Li Gong, Ping Koy Lam

Quantum sensing with undetected photons is a technique where photons of one wavelength probe a sample, but information is extracted by measuring photons of another wavelength that never interacts with the sample. This has seen significant experimental advances in applications such as spectroscopy, microscopy, and bio-sensing. However, a detailed theoretical

Jinming Zhang, Haibo Yuan, Zhijia Tian

Radial velocity (RV) is crucial for stellar kinematics and Galactic archaeology. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has obtained over ten million low-resolution spectra ($R \sim 1800$), yielding RVs for millions of stars, but these suffer from (1) wavelength-dependent inconsistencies (relative shifts between spectral segme

Isaiah Thompson, Tanmay Sen, Ritwik Bhattacharya

Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world settings, these logs are often distributed across multiple organizations and cannot be centralized due to privacy and security constraints. Existing log anomaly detection methods, including recent large language

Manav Pandey

When a language model agrees with a user's false belief, is it failing to detect the error, or noticing and agreeing anyway? We show the latter. Across twelve open-weight models from five labs, spanning small to frontier scale, the same small set of attention heads carries a "this statement is wrong" signal, whether the model is evaluating a claim on its own

Yutong Ye, Weilong Ren, Yang Liu, Mengyi Yan

Exact subgraph matching is a fundamental graph operator that supports many graph analytics tasks, yet it remains computationally challenging due to its NP-completeness. Recent learning-based approaches accelerate query processing via dominance-preserving vertex embeddings, but they suffer from expensive offline training, limited pruning effectiveness, and he

Shiyu Wang, Zhiguang Yan, Clemens Gneiting, Rui Li

Wave--particle duality demonstrates the peculiar nature of quantum mechanics. In which-way experiments, depending on the measurement scheme, a particle exhibits either wave-like or particle-like properties, as summarized by Bohr's principle of complementarity. In this work, we implement Mach-Zehnder (MZ) interferometry on a two-dimensional (2D) superconducti

Chaitanya Dwivedi, Binxuan Huang, Himanshu Gupta, Pratik Jayarao

Mixture-of-Experts (MoE) has become the dominant architecture for scaling large language models: frontier models routinely decouple total parameters from per-token computation through sparse expert routing. Scaling laws show that under fixed active computation, model quality scales predictably with total parameters, and MoEs realize this by increasing expert

Zikang Liu, Peilan Xu

Generative answer engines expose content through selective citation rather than ranked retrieval, fundamentally altering how visibility is determined. This shift calls for new optimization methods beyond traditional search engine optimization. Existing generative engine optimization (GEO) approaches primarily rely on token-level text rewriting, offering limi

Oleg Solozobov

When automated decision systems fail, organizations frequently discover that formally compliant governance infrastructure cannot reconstruct what happened or why. This paper synthesizes an operational governance evidence framework -- structural accountability collapse diagnostics, decision trace schemas, evidence sufficiency measurement, and label-free monit

Diego Gomez-Zara, Hernán Valdivieso, Jorge Pérez, Denis Parra

Codebooks are central to framing research, providing theoretically grounded criteria for analyzing news content. While traditionally codebooks are built from theoretical frameworks and researchers' knowledge, applying these codebooks to large news corpora often exposes ambiguities, borderline cases, and underspecified rules that are difficult to resolve thro

Omar Rodríguez-Tzompantzi

We develop a systematic Hamiltonian formulation for a gravitating topological matter system in three-dimensional spacetime, coupling a scalar matter field and a two-form gauge field to first-order Hilbert--Palatini gravity. We perform the Dirac--Bergmann analysis, obtaining the full structure of the constraints, classifying them into first- and second-class

Eun-Ju Park, Youjin Shin, Simon S. Woo

As a means to balance the growth of the AI industry with the need for privacy protection, machine unlearning plays a crucial role in realizing the ``right to be forgotten'' in artificial intelligence. This technique enables AI systems to remove the influence of specific data while preserving the rest of the learned knowledge. Although it has been actively st

Kundan Mukhia, Imran Ansari, Md. Nurujjaman

We identify a robust structural signature of stock markets during exogenous shock events by analyzing collective return dynamics across G5 countries. Using Random Matrix Theory, we introduce the complexity gap, defined as the difference between the normalized largest eigenvalue and the average pairwise correlation, to quantify changes in market structure. Th

Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos, Olivia Weng

Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and throughput requirements demand small batch sizes and require that model weights remain fully on-chip. Spatial dataflow implementations are common for extreme-edge applications. Spatial dataflow works well for small

Ruibing Hou, Mingyue Zhou, Yuwei Gui, Mingshuang Luo

Faithfully modeling human behavior in dynamic environments is a foundational challenge for embodied intelligence. While conditional motion synthesis has achieved significant advances, egocentric motion generation remains largely underexplored due to the inherent complexity of first-person perception. In this work, we investigate Egocentric Vision-Language (E

Yulai Zhang, Yinrong Zhang, Ting Wu, Linqi Ye

Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, as well ascontrol switching issues between different states such as up-right walking and fall recovery. To address these problems,this paper proposes a modular reinforcement learning (RL)framework for achieving

A. D. Brynes, J. K. Jones, M. King, M. A. Johnson

Standardization of data formats in a scientific discipline brings a range of benefits to researchers, as it enables the sharing of workflows and solutions to common problems, provides the foundation for generically useful tools that can be applied across the field, and gives a basis for cross-checking and validation that can be understood by all. Owing to th

Yuanye Wu, Keyi Wang, Linqi Ye, Boyang Xing

Learning diverse locomotion skills for humanoid robots in a unified reinforcement learning framework remains challenging due to the conflicting requirements of stability and dynamic expressiveness across different gaits. We present a multi-gait learning approach that enables a humanoid robot to master five distinct gaits -- walking, goose-stepping, running,

A. D. Brynes, M. King, K. R. L. Baker, R. Banerjee

The simulation of a physical system in a virtual replica, known as a digital twin, is a useful way to interrogate the system non-invasively, providing the ability to perform predictive maintenance and surveillance, and to investigate potential novel configurations without perturbing the system. This article presents the implementation of an auto-generating d

Sachin Khoja, Kamlesh Sawant, Palak Jain, Sairaj Dhople

This paper presents an automated software toolchain for synthesizing hardware-implementable analog circuits that solve constrained optimization problems. The proposed toolchain supports nonlinear objective functions with linear and quadratic constraints. It maps optimization variables to capacitor voltages, implementing dynamics that enforce Karush-Kuhn-Tuck

Roberto Martinez-Maldonado, Vanessa Echeverria, Jenna Hawes, YJ Kim

Education is not merely the transmission of information or the optimisation of individual performance; it is a fundamentally social, constructive, and relational practice. However, recent advances in generative artificial intelligence (GenAI) increasingly emphasise efficiency, automation, and individualised assistance, risking the weakening of relational lea

Rania Elbadry, Sarfraz Ahmad, Ahmed Heakl, Dani Bouch

English financial NLP has advanced rapidly through benchmarks targeting earnings analysis, market sentiment, tabular reasoning, and financial question answering, yet Arabic financial NLP remains virtually nonexistent, despite 422 million speakers, $4.9 trillion in Gulf sovereign wealth, and a $4-5 trillion Islamic finance industry requiring specialized Shari

O. R. Jadhav, L. K. Dewangan, A. K. Maity, Sanhueza Patricio

To investigate the role of magnetic fields toward the G35N and G35S sub-regions in the G35.20-0.74 star-forming complex, we utilized multi-wavelength polarimetric observations from the SOFIA/HAWC+ at 154 $\mu$m and ACT at 220 GHz/1.3 mm. The ACT 220 GHz polarization data (resolution $\sim$1$'$) show an hourglass-shaped plane-of-sky magnetic field morphologie

Manas Kumar Dalai, Ankita Mahakhuda, Abinash Prusty

Naturally produced stingless bee hive (NP-SBH) is an intricately produced material by the combination of waxes, resin and other biological materials that offers protection and structural stability to the bee colony. This study explores a detailed analysis of Indian stingless bee hive material using multi-characterization techniques approach to evaluate their

Bharathi Thiruvengadam, Jaikrishnan Janardhanan

We give two new elementary proofs of the complete Kobayashi hyperbolicity of the twice-punctured complex plane. We also present an extremely short proof that bounded domains are complete Kobayashi hyperbolic. Our proofs rely neither on the fact that the universal cover of the twice-punctured plane is the disk nor on the existence of negatively curved metrics

Swee Hong Chan, Steven Heilman, Greta Panova

Linek's 1989 problem asks whether the numbers of independent sets of trees avoid infinitely many positive integers. We show that the set of natural numbers realized as the number of independent sets of a tree has a lower growth exponent of $0.1966$. We further prove that the set of positive integers representable by connected planar graphs has asymptotic den

Jinglin Xu, Yi Li, Chuxiong Sun, Xiao Xu

Multi-modal test-time adaptation (TTA) enhances the resilience of benchmark multi-modal models against distribution shifts by leveraging the unlabeled target data during inference. Despite the documented success, the advancement of multi-modal TTA methodologies has been impeded by a persistent limitation, i.e., the lack of explicit modeling of category-condi

Huan Qing

Estimating the number of components is a fundamental challenge in unsupervised learning, particularly when dealing with high-dimensional data with many components or severely imbalanced component sizes. This paper addresses this challenge for classical Gaussian mixture models. The proposed estimator is simple: center the data, compute the singular values of

JinFeng Xie, Chengfu Ou, Peipeng Yu, Xiaoyu Zhou

The rapid adoption of diffusion-based generative models has intensified concerns over the attribution and integrity of AI-generated content (AIGC). Existing single-domain watermarking methods either fail under regeneration, remain vulnerable to black-box reprompting that enables adversarial framing, or provide no spatial evidence for tampered regions. We pro

Dahyun Jung, Jaewook Lee, Heuiseok Lim

Large language models (LLMs) require frequent knowledge updates to reflect changing facts and mitigate hallucinations. To meet this demand, lifelong knowledge editing has emerged as a continual approach to modify specific pieces of knowledge without retraining the entire model. Existing parameter editing methods struggle with stability during sequential edit

Ying Zhang, Daoxin Chen

Cultural newcomers (CNs), including new immigrants and international students, often encounter cognitive barriers and social anxiety, exacerbated by unfamiliar cultural terminology in daily interactions. This research examines these challenges in the context of ordering in foreign restaurants. Current translation tools have significant limitations in their i

Shashank Sharma, Janina Hoffmann, Vinay Namboodiri

We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a \textit{set of learned options} for the next token, indexed by a discrete latent variable. Instead of relying on temperature or sampling heuristics to induce diversity, OLLM models variation explicitly: a small latent space paramet

Chua Jin Chou, Khant That Lwin, Ezekiel Soremekun

Code LLMs often portray inconsistent program behaviors. Developers typically employ benchmarks to assess Code LLMs, but most benchmarks are hand-crafted, static and do not target consistency property. In this work, we pose the scientific question: how can we automatically discover inconsistent program behaviors in Code LLMs? To address this challenge, we pro

Dipayan Sarkar, Qifeng Li

The electric vehicle (EV) charging demands (CD) are jointly determined by the EV owners' behavior (i.e., human factor) and the electricity prices (i.e., decisions of distribution system operators (DSO)). However, most existing studies either neglect the decision-dependent nature of EVCD uncertainty or idealistically treat EV owners as perfect decision-makers

Kun Qian, Zhuge Xia, Qian Ma, Qi Zhang

Super-resolution SAR tomography (TomoSAR) is performed on a discretized elevation grid, leading to off-grid bias and spectral leakage. Classical Toeplitz-Vandermonde gridless formulations avoid elevation discretization but rely on uniform sampling, whereas covariance- or subspace-based estimation is difficult in single-look repeat-pass TomoSAR. We propose DU

Kun Wang, Cheng Qian, Miao Yu, Lilan Peng

Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is threatened by critical safety vulnerabilities. While prior works have demonstrated the feasibility of backdoors in MLLMs via fine-tuning data poisoning to manipulate inference, the underlying mechanisms of backdoor at

Xinyao Zhang, Rui Wang, Jinhao Cui, Haotian Huang

Multi-window mobile scenarios, such as split-screen and foldable modes, make GUI display defects more likely by forcing applications to adapt to changing window sizes and dynamic layout reflow. Existing detection techniques are limited in two ways: they are largely passive, analyzing screenshots only after problematic states have been reached, and they are m

Wenyi Zhou, Ravi Kumar Bandapelli, Hari Paudyal, Bangzheng Han

Atomic-scale heterostructures of van der Waals (vdW) magnets and semiconductors provide a unique environment for exploring magnetic dynamics. In contrast to typical photothermal excitation of precessional magnetization dynamics by a pump laser pulse, we find that ultrafast optical excitation of a WS$_2$/CrGeTe$_3$ (CGT) bilayer produces an opposite sign of m

Andrei Andrusenko, Vladimir Bataev, Lilit Grigoryan, Nune Tadevosyan

Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in both offline and low-latency streaming settings remains challenging. We present a Unified ASR framework for Transducer (RNNT) training that supports both offline and streaming decoding within a single model, usin

Kou Takubo, Shintaro Suzuki, Kohei Yamamoto, Kohei Yamagami

Correlations between the localized and conductive spins/charges have been the central issue of various fascinating quantum phenomena found on itinerant electron systems. Here, the obvious multiplet structures are presented on the Mn 2$p$ to 3$d$ x-ray absorption for a heavy fermion $\alpha$-(Yb,Lu)(Al$_{1-x}$Mn$_x$)B$_4$, indicating that the unoccupied elect

Hao Dong

This study proposes a high-order multi-scale method tailored for time-dependent nonlinear thermo-electro-mechanical coupling problems of composite structures with highly spatial heterogeneity, which incorporate temperature-dependent material properties and Joule heating effect. By employing the multi-scale asymptotic approach and the Taylor series technique,

Dao Duy Tung, Nguyen Quoc Chuong, Vu Tuan Hai, Le Bin Ho

In recent years, quantum kernel methods have shown promising applications on near-term quantum devices. However, selecting an appropriate encoding circuit for a given dataset requires costly evaluation of multiple candidates, formulated as a meta-learning problem. In this paper, we propose an automated recommender that utilizes the intrinsic characteristics

Grant Molnar

We give an exposition of the Newton-Leibniz calculus. We begin by defining the integral as a limit of Riemann sums, verify the integrals of the standard catalog of functions by direct manipulation, prove the substitution lemmas as theorems about Riemann sums, cross the Fundamental Theorem of Calculus, and harvest the differential calculus on the other side.

Małgorzata Curyło, Eric Thrane, Paul D. Lasky, Dawson S. Gaynor

We present a practical implementation of a phase-coherent mapping technique for pulsar timing arrays that resolves the full complex polarisation state of the gravitational-wave sky as a function of direction and frequency. Unlike standard cross-correlation methods, this approach preserves the amplitude, phase, and polarisation of the signal in every sky pixe

Andrew Zhuoer Feng, Cunxiang Wang, Yu Luo, Lin Fan

Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics. LLM's performance in thousand-words level and open-ended writing is inadequately assessed by traditional reference-based metrics or modern LLM-as-a-judge methods. We

Yilun Liu, Ruihong Qiu, Zi Huang

Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task-specific supervision. While graph neural networks rely on fixed label spaces and supervised objectives, recent large language model (LLM)-based approaches often overlook graph context or depend on

Aby Mammen Mathew

Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlations, and 38.6% of the baseline errors are the result of these artifacts. We propose Product-of-Experts (PoE) training, which downweights examples where biased models are overconfident. PoE nearly preserves accur

Bingwei Zhang, Chee Yap

We recently introduced a novel architecture for the design of validated IVP algorithms. This architecture forms the basis of our complete validated algorithm for IVP. A key subroutine in our algorithm is the \textbf{Euler Tube}: it gave a technique for refining end- and full-enclosures and is also key to deriving a complexity bound of our IVP solver. In this

Yuan Shan

Physical activity (PA) is widely recognized as protective against mental distress, yet whether this benefit varies systematically across population subgroups remains poorly understood. Using pooled data from ten consecutive annual waves of the U.S. Behavioral Risk Factor Surveillance System (2015-2024; n = 3,242,218), we investigate heterogeneity in the asso

Siddharth Chandak, Ramanan Tamizholi, Nicholas Bambos

We establish finite-time last-iterate guarantees for vanilla stochastic gradient descent in co-coercive games under noisy feedback. This is a broad class of games that is more general than strongly monotone games, allows for multiple Nash equilibria, and includes examples such as quadratic games with negative semidefinite interaction matrices and potential g

Zhen Liu, Yuhan Liu, Jinjun Wang, Jianyi Liu

In vision-and-language navigation (VLN), self-improvement from policy-induced experience, using only standard VLN action supervision, critically depends on balancing behavioral diversity and learning stability, which governs whether the agent can extract a reliable learning signal for improvement. Increasing behavioral diversity is necessary to expose altern

Xiao Wang

Chain-of-thought (CoT) reasoning and reasoning-tuned models such as DeepSeek-R1 are commonly assumed to reduce shallow heuristic biases by thinking carefully. We test this on position bias in multiple-choice QA and find a different story: within any reasoning-capable model, per-question position bias scales with the length of the reasoning trajectory. Across

Thomas M Henderson, Guo P. Chen, Gustavo E. Scuseria

Zero-seniority methods have shown great promise for the description of strongly-correlated electronic systems. Other seniority sectors have been much less explored, and in particular the maximal seniority sector and zero seniority have the same underlying algebraic structure. We introduce a seniority eigenstate configuration interaction in which the wave fun

Shreeyam Kacker, Kerri Cahoy

Satellite constellation design requires optimizing orbital parameters across multiple satellites to maximize mission specific metrics. For many types of mission, it is desirable to maximize coverage and minimize revisit gaps over ground targets. Existing approaches to constellation design either restrict the design space to symmetric parametric families such

Tadashi Wadayama, Takumi Takahashi

We propose a three-module extension of score-based VAMP (SC-VAMP) for signal recovery in nonlinear channels, where the received signal is obtained by applying a nonlinearity to a linear mixture of the transmitted signal, followed by additive Gaussian noise. The key idea is to introduce a latent variable representing the output of the linear mixing stage, whi

Yishu Wei, Yi Lin, Adam Flanders, George Shih

Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improves accuracy, it can degrade reasoning. We propose a two-stage approach: SFT on disease labels followed by Group Relative Policy Optimization (GRPO) to refine predictions by optimizing accuracy and format with

Lingxue Lyu

Language-guided unmanned aerial vehicles (UAVs) often fail not from bad reasoning or perception, but from execution mismatch: the gap between a planned trajectory and the controller's ability to track it when the real dynamics differ from training (mass changes, drag shifts, actuator delay, wind). We propose AeroBridge-TTA, a language-conditioned control pip

Ziqian Zhang, Ryan L. Russell, Choon Kong Lai, Benjamin J. Eggleton

Photonic stepped-frequency (SF) radar offers high range resolution and only requires low-speed driving electronics, but existing architectures face challenges in achieving low phase noise and uniform frequency steps simultaneously. Here, we demonstrate a photonic SF radar system that exploits dual Brillouin lasers in a shared fiber cavity to simultaneously s

Prashant Kumar Pathak, Chandra Biksheswaran Mouleeswaran, Rama Teja Repaka

Large-scale cloud security platforms must continuously query millions of structured cloud resource records distributed across thousands of tenant accounts. Broad, account-spanning queries saturate database infrastructure, producing P95 latencies exceeding 60 seconds. We identify buffer cache pressure as the dominant latency driver: in a controlled experiment

Tenghao Cai, Lei Li, Tsung-Hui Chang

In 5G and beyond networks, efficient scheduling is essential to exploit the gains of multi-user MIMO (MU-MIMO) equipped with carrier aggregation and joint transmission (JT). However, cross-cell and cross-carrier scheduling under QoS constraints is challenging due to the strong coupling across users, base stations, and carriers. In this work, we address this

Aoduo Li, Haoran Lv, Hongjian Xu, Shengmin Li

High-fidelity character voice synthesis is a cornerstone of immersive multimedia applications, particularly for interacting with anime avatars and digital humans. However, existing systems struggle to maintain consistent persona traits across diverse emotional contexts. To bridge this gap, we present ATRIE, a unified framework utilizing a Persona-Prosody Dua

Qin Dai, Benjamin Heinzerling, Kentaro Inui

Understanding a discourse requires tracking entities and the relations that hold between them. While Large Language Models (LLMs) perform well on relational reasoning, the mechanism by which they bind entities, relations, and attributes remains unclear. We study discourse-level relational binding and show that LLMs encode it via a Cell-based Binding Represen

Tomonori Ikeda, Tatsuya Sawano, Naomi Tsuji, Yoshitaka Mizumura

Accurate reconstruction of recoil-electron directions is critical for enhancing the point-spread function of electron-tracking Compton cameras (ETCCs) in gamma-ray imaging. Although full three-dimensional (3D) readout systems achieve high-precision reconstruction, they are impractical for large-area detectors because of the enormous data volume. This study p

Shiyu Li, Zhixiong Gong

The ability to trap a single cell or microparticle in three dimensions is important for biomedical and microfluidic applications. Single-beam acoustic tweezers based on focused waves provide a compact and biocompatible approach because of their high spatial resolution and strong intensity gradients. However, 3D trapping remains challenging, especially at hig

Abhinav Agarwal

LLM-assisted defect discovery has a precision crisis: plausible-but-wrong reports overwhelm maintainers and degrade credibility for real findings. We present Refute-or-Promote, an inference-time reliability pattern combining Stratified Context Hunting (SCH) for candidate generation, adversarial kill mandates, context asymmetry, and a Cross-Model Critic (CMC)

Boyan Shi, Wei Chen, Shuyuan Zhao, Junfeng Shen

The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capabilities of Large Language Models. However, existing methods face two primary challenges: (1)Imprecise Routing in the current MoE-LoRA method fails to explicitly match input semantics with expert capabilities, l

M. Salado-Mejía

In this work, we review the open quantum dynamics of the most known bipartite systems, such as the qubit-qubit system, the oscillator-oscillator system, and the qubit-oscillator system. First, we compare each system with and without rotating wave approximation. In this analysis, we observe the influence of the counter-rotating term in the system dynamics. Al

Tim Browning, Ritabrata Munshi, Victor Y. Wang

We show how the circle method can be used to study rational points on a certain cubic fourfold, going beyond the square-root barrier.

Kai Xi, Stephen Gould, Sylvie Thiébaux

Efficient construction of models capturing the preconditions and effects of actions is essential for applying AI planning in real-world domains. Extensive prior work has explored learning such models from high-level descriptions of state and/or action sequences. In this paper, we tackle a more challenging setting: learning lifted action models from sequences