October 2025 arXiv papers — page 47

Shunsuke Matsuno

This paper studies how experts with veto power -- gatekeeping experts -- influence agents through communication. Their expertise informs agents' decisions, while veto power provides discipline. Gatekeepers face a dilemma: transparent communication can invite gaming, while opacity wastes expertise. How can gatekeeping experts guide behavior without being game

Liwei Jiang, Yuanjun Chai, Margaret Li, Mickel Liu

Language models (LMs) often struggle to generate diverse, human-like creative content, raising concerns about the long-term homogenization of human thought through repeated exposure to similar outputs. Yet scalable methods for evaluating LM output diversity remain limited, especially beyond narrow tasks such as random number or name generation, or beyond rep

Mohammad Tariqul Islam, Du Liu, Deblina Sarkar

Centered kernel alignment (CKA) is a popular metric for comparing representations, determining equivalence of networks, and neuroscience research. However, CKA does not account for the underlying manifold and relies on numerous heuristics that cause it to behave differently at different scales of data. In this work, we propose Manifold approximated Kernel Al

Yao Bai, Cheng-Yang Lee, Ruifeng Leng, Siyi Zhou

We establish a new spin-statistics theorem for a class of free pseudo-Hermitian quantum field theories whose particles furnish unitary irreducible representations of the Poincar\'{e} group. In this framework, free pseudo-Hermitian fields with integer spin exhibit fermionic statistics, whereas those with half-integer spin exhibit bosonic statistics, opposite

Myeongseob Ko, Hoang Anh Just, Charles Fleming, Ming Jin

Machine unlearning has emerged as a prevalent technical solution for selectively removing unwanted knowledge absorbed during pre-training, without requiring full retraining. While recent unlearning techniques can effectively remove undesirable content without severely compromising performance on standard benchmarks, we find that they may inadvertently create

Paul Schwerdtner, Jules Berman, Benjamin Peherstorfer

Deep neural networks using state space models as layers are well suited for long-range sequence tasks but can be challenging to compress after training. We use that regularizing the sum of Hankel singular values of state space models leads to a fast decay of these singular values and thus to compressible models. To make the proposed Hankel singular value reg

Yuepeng Jiang, Huakang Chen, Ziqian Ning, Jixun Yao

Generating full-length, high-quality songs is challenging, as it requires maintaining long-term coherence both across text and music modalities and within the music modality itself. Existing non-autoregressive (NAR) frameworks, while capable of producing high-quality songs, often struggle with the alignment between lyrics and vocal. Concurrently, catering to

Benedictus C. G. Cinun, Tua A. Tamba, Immanuel R. Santjoko, Xiaofeng Wang

This paper presents the complete design, control, and experimental validation of a low-cost Stewart platform prototype developed as an affordable yet capable robotic testbed for research and education. The platform combines off the shelf components with 3D printed and custom fabricated parts to deliver full six degrees of freedom motions using six linear act

Zhaoming Hu, Ruikang Zhong, Xidong Mu, Dengao Li

A pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitiga

Yi Tao, Zhen Gao, Fangquan Ye, Jingbo Xu

The development of the low-altitude economy has led to a growing prominence of uncrewed aerial vehicle (UAV) safety management issues. Therefore, accurate identification, real-time localization, and effective countermeasures have become core challenges in airspace security assurance. This paper introduces an integrated UAV management and control system based

Zeyu Wang, Zilong Chen, Chenhui Gou, Feng Li

Unified multimodal models have recently shown remarkable gains in both capability and versatility, yet most leading systems are still trained from scratch and require substantial computational resources. In this paper, we show that competitive performance can be obtained far more efficiently by strategically fusing publicly available models specialized for e

Dev Gurung, Shiva Raj Pokhrel

In this paper, we propose a groundbreaking quantum-secure federated learning (QFL) framework designed to safeguard distributed learning systems against the emerging threat of quantum-enabled adversaries. As classical cryptographic methods become increasingly vulnerable to quantum attacks, our framework establishes a resilient security architecture that remai

Bin Wang, YiLu Zhong, MiDi Wan, WenJie Yu

Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical concern. Existing studies predominantly concentrate on adversarial attacks or inherent flaws within the models. However, a more prevalent yet underexplored issue concerns how the quality of a benign but poorly

Yuzhou Wu, Mingyang Wu, Di Liu, Rong Yin

Serving as an emerging and powerful tool, Large Language Model (LLM)-driven Human Digital Twins are showing great potential in healthcare system research. However, its actual simulation ability for complex human psychological traits, such as distrust in the healthcare system, remains unclear. This research gap particularly impacts health professionals' trust

Yongbo Wang, Haonan Wang, Guodong Mu, Ruixin Zhang

With the ever-increasing volume of visual data, the efficient and lossless transmission, along with its subsequent interpretation and understanding, has become a critical bottleneck in modern information systems. The emerged codebook-based solution utilize a globally shared codebook to quantize and dequantize each token, controlling the bpp by adjusting the

Zhuoxuan Li, Jieyuan Pei, Tangwei Ye, Zhongyuan Lai

Next Point-of-Interest (POI) recommendation is a critical task in modern Location-Based Social Networks (LBSNs), aiming to model the complex decision-making process of human mobility to provide personalized recommendations for a user's next check-in location. Existing hyperbolic POI recommendation models, predominantly based on rotations and graph representa

Judah Goldfeder, Matthew So, Hod Lipson

Auxiliary Learning (AL) is a form of multi-task learning in which a model trains on auxiliary tasks to boost performance on a primary objective. While AL has improved generalization across domains such as navigation, image classification, and NLP, it often depends on human-labeled auxiliary tasks that are costly to design and require domain expertise. Meta-l

Tarannum Zaki, Michael L. Nelson, Michele C. Weigle

Screenshots of social media posts are a common approach for information sharing. Unfortunately, before sharing a screenshot, users rarely verify whether the attribution of the post is fake or real. There are numerous legitimate reasons to share screenshots. However, sharing screenshots of social media posts is also a vector for mis-/disinformation spread on

Omar Allam, Brook Wander, SungYeon Kim, Rudi Plesch

Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a complementary dataset o

Xiangfei Qiu, Xingjian Wu, Hanyin Cheng, Xvyuan Liu

Time series forecasting holds significant value in various domains such as economics, traffic, energy, and AIOps, as accurate predictions facilitate informed decision-making. However, the existing Mean Squared Error (MSE) loss function sometimes fails to accurately capture the seasonality or trend within the forecasting horizon, even when decomposition modul

Matthew So, Judah Goldfeder, Mark Lis, Hod Lipson

There has been a historic assumption that the biometrics of an individual are statistically uncorrelated. We test this assumption by training Bi-Encoder networks on three verification tasks, including fingerprint-to-fingerprint matching, iris-to-iris matching, and cross-modal fingerprint-to-iris matching using 274 subjects with $\sim$100k fingerprints and 7k

Mouxiao Huang, Borui Jiang, Dehua Zheng, Hailin Hu

Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks, yet often suffer from inefficiencies due to redundant visual tokens. Existing token merging methods reduce sequence length but frequently disrupt spatial layouts and temporal continuity by disregarding positional relationships. In this work, we propose a novel

Binhong Li, Benshuai Lyu

A semi-empirical model is developed in this paper to predict the broadband shock-associated noise (BBSAN) generated by shock-instability interaction (SII) in imperfectly expanded supersonic jets. The model makes use of a semi-empirically modified Pack's model that accounts for the decay in both shock amplitude and shock spacing and a Gaussian wave-packet mod

Mingzhi Zhu, Ding Shang, Sai Qian Zhang

Photorealistic Codec Avatars (PCA), which generate high-fidelity human face renderings, are increasingly being used in Virtual Reality (VR) environments to enable immersive communication and interaction through deep learning-based generative models. However, these models impose significant computational demands, making real-time inference challenging on reso

Xintong Lyu, Daniel D. B. Koll

An emerging goal of exoplanet science is to constrain the surface composition of airless exoplanets. Without the protection of an atmosphere, these planets are likely covered by a powder-like regolith, similar to the Moon. Laboratory studies show that, under vacuum conditions, such regoliths can develop subsurface temperature gradients, also known as the sol

Xiaofan Zhou, Lu Cheng

Continual Learning (CL) is essential for enabling self-evolving large language models (LLMs) to adapt and remain effective amid rapid knowledge growth. Yet, despite its importance, little attention has been given to establishing statistical reliability guarantees for LLMs under CL, particularly in the setting of continual domain pretraining (CDP). Conformal

Pranav Saxena

Modeling open-vocabulary language fields in 3D is essential for intuitive human-AI interaction and querying within physical environments. State-of-the-art approaches, such as LangSplat, leverage 3D Gaussian Splatting to efficiently construct these language fields, encoding features distilled from high-dimensional models like CLIP. However, this efficiency is

Avinay Bhat, Mun Jung Jung, Gray Putnam, Haiwang Yu

We investigate a deep learning-based signal processing for liquid argon time projection chambers (LArTPCs), a leading detector technology in neutrino physics. Identifying regions of interest (ROIs) in LArTPCs is challenging due to signal cancellation from bipolar responses and various detector effects observed in real data. We approach ROI identification as

Suzanne Boyd, Christian Wolf

Hyperbolic Julia sets of complex polynomials are known to be computable in polynomial time due to pioneering work of Braverman in 2005 (10.1016/j.entcs.2004.06.031). In this paper, we present an alternative method for establishing poly-time computability of hyperbolic Julia sets, which allows us to establish, via a new algorithm, lower computability of the h

Mingze Gong, Juan Du, Jianbang You

Anomaly detection in complex, high-dimensional data, such as UAV sensor readings, is essential for operational safety but challenging for existing methods due to their limited sensitivity, scalability, and inability to capture intricate dependencies. We propose the Diffuse to Detect (DTD) framework, a novel approach that innovatively adapts diffusion models

Jinshi Sai, Eduard I. Vorobyov, Alexandr Skliarevskii, Michihiro Takami

We investigated the possibility of determining the mechanism of the FU Orionis-type outburst based on molecular line observations of protoplanetary disks with synthetic observations of distinct numerical burst models. The morphology of the synthetic $\mathrm{C^{18}O}$ emission is sensitive to gas temperature and does not coincide with the actual gas disk str

Huaisheng Zhu, Zhengyu Chen, Shijie Zhou, Zhihui Xie

Recent Uniform State Diffusion Models (USDMs), initialized from a uniform prior, offer the promise of fast text generation due to their inherent self-correction ability compared to masked diffusion models. However, they still rely on complex loss formulations with additional computational overhead, which hinders scalability. In this work, we explore a simpli

Karthik Duraisamy

Following recent work, this manuscript clarifies what the Gauss-Appell principle determines in incompressible, inviscid flow and how it connects to classical projection methods. At a fixed time, freezing the velocity and varying only the material acceleration leads to minimization of a quadratic subject to acceleration-level constraints. First-order conditio

Aviral Pandey, Dhruv Vaish, I-Ting Lin, Rikky Muller

Neural recording implants are a crucial tool for both neuroscience research and enabling new clinical applications. The power consumption of high channel count implants is dominated by the circuits used to amplify and digitize neural signals. Since circuit designers have pushed the efficiency of these circuits close to the theoretical physical limits, reduci

Zhiting Ma, Weifeng Zhao

This work is concerned with relaxation models arising from numerical schemes for hyperbolic-parabolic systems. Such models are a hyperbolic system with both the hyperbolic part and the stiff source term involving a small positive parameter, and thus are endowed with complicated multiscale properties. Relaxation models are the basis of constructing correspond

Runtao Zhou, Giang Nguyen, Nikita Kharya, Anh Totti Nguyen

The reasoning capabilities of Large Language Models (LLMs) have led to their increasing employment in several critical applications, particularly education, where they support problem-solving, tutoring, and personalized study. Chain-of-thought (CoT) reasoning capabilities [1, 2] are well-known to help LLMs decompose a problem into steps and explore the solut

Chang-Zheng Yuan

Charmonium is a bound state of a charmed quark and a charmed antiquark, and a charmoniumlike state is a resonant structure that contains at least a charmed quark-antiquark pair but has properties that are incompatible with a conventional charmonium state. The charmoniumlike states are also called XYZ particles to indicate their underlying nature is still unc

Spencer H. Bryngelson

Ensemble-averaged polydisperse bubbly flow models require statistical moments of the evolving bubble size distribution. Under step forcing, these moments reach statistical equilibrium in finite time. However, the transitional phase before equilibrium and cases with time-dependent forcing are required to predict flow in engineering applications. Computing the

Alex Krotz, Ethan Byrd, Ken Miyazaki, Roel Tempelaar

QC Lab is an open-source Python package for QC dynamics simulations aimed to promote the development of QC algorithms, and their application to a wide variety of relevant model problems. It follows a modular design that facilitates cross-compatibility between algorithms and models. By decomposing algorithms and models into a series of tasks and ingredients t

Dian Wu, Fei Shi, Jia-Cheng Sun, Bo-Wen Wang

Multiparticle entanglement is a valuable resource for quantum technologies, including measurement based quantum computing, quantum secret sharing, and a variety of quantum sensing applications. The direct way to detect this resource is to observe correlations arising from local measurements performed simultaneously on all particles. However, this approach is

Zecheng Yin, Hao Zhao, Zhen Li

Objective-oriented navigation(ObjNav) enables robot to navigate to target object directly and autonomously in an unknown environment. Effective perception in navigation in unknown environment is critical for autonomous robots. While egocentric observations from RGB-D sensors provide abundant local information, real-time top-down maps offer valuable global co

Qiyu Liao, Dadong Wang, Rebecca Haling, Jiajun Liu

Accurate estimation of pasture biomass is important for decision-making in livestock production systems. Estimates of pasture biomass can be used to manage stocking rates to maximise pasture utilisation, while minimising the risk of overgrazing and promoting overall system health. We present a comprehensive dataset of 1,162 annotated top-view images of pastu

Peiwen Jiang, Jiajia Guo, Chao-Kai Wen, Shi Jin

Semantic communication is a promising technique for emerging wireless applications, which reduces transmission overhead by transmitting only task-relevant features instead of raw data. However, existing methods struggle under extremely low bandwidth and varying channel conditions, where corrupted or missing semantics lead to severe reconstruction errors. To

Martin Bizzarro, Anders Johansen, Caroline Dorn

Planets form and obtain their compositions from the leftover material present in protoplanetary disks of dust and gas surrounding young stars. The chemical make-up of a disk influences every aspect of planetary composition including their overall chemical properties, volatile content, atmospheric composition, and potential for habitability. This Review discu

Yang-Dong-Jun Ou, Hou-Jun Lü, Jia-Ming Chen, Ben-Yang Zhu

The physical origin of prompt emission in gamma-ray bursts (GRBs) remains an open question since it has been studied more than half a century. Three alternative models (i.e. dissipative photosphere, internal shock, and Internal-Collision-induced MAgnetic Reconnection and Turbulence, ICMART) have been proposed to interpret the observations of GRB prompt emiss

Thanyanee Srichaisak, Arissa Ieochai, Aueaphum Aueawattthanaphisut

Background: Upper-limb weakness and tremor (4--12 Hz) limit activities of daily living (ADL) and reduce adherence to home rehabilitation. Objective: To assess technical feasibility and clinician-relevant signals of a sensor-fused wearable targeting the triceps brachii and extensor pollicis brevis. Methods: A lightweight node integrates surface EMG (1 kHz), I

Daniel Sin, Milad Toutounchian

In our article, we describe a method for generating counterfactual explanations in high-dimensional spaces using four steps that involve fitting our dataset to a model, finding the decision boundary, determining constraints on the problem, and computing the closest point (counterfactual explanation) from that boundary. We propose a discretized approach where

Zongshun Zhang, Ibrahim Matta

Edge intelligent applications like VR/AR and language model based chatbots have become widespread with the rapid expansion of IoT and mobile devices. However, constrained edge devices often cannot serve the increasingly large and complex deep learning (DL) models. To mitigate these challenges, researchers have proposed optimizing and offloading partitions of

Zikai Li

To increase statistical efficiency in a randomized experiment, researchers often use stratification (i.e., blocking) in the design stage. However, conventional practices of stratification fail to exploit valuable information about the predictive relationship between covariates and potential outcomes. In this paper, I introduce an adaptive stratification proc

Yifan Zhang, Lanser Contributors

Coding agents fail when text-level guesses outrun program facts: they hallucinate APIs, drift to the wrong symbol, and apply edits without evidence that the workspace remains valid. Compilers, type checkers, and language servers already compute the missing supervision signal, in the form of diagnostics, symbol resolution, type information, references, and re

Pedro Bairrão

This dissertation consists of a comprehensive and pedagogical review of Maldacena's original derivation of the AdS/CFT correspondence and the main topics of string theory necessary to understand it. The large $N$ expansion of Yang-Mills theory is presented as the main motivation for seeking a string-theoretic language for gauge theories. The bosonic string i

Ke-Xiong Yan, Yang Liu, Yang Xiao, Jun-Hao Lin

Environmentally induced decoherence poses a fundamental challenge to quantum energy storage systems, causing irreversible energy dissipation and performance aging of quantum batteries (QBs). To address this issue, we propose a QB protocol utilizing the nonlocal coupling properties of giant atoms (GAs). In this architecture, both the QB and its charger are im

Margarida Mendonca, Alvaro Figueira

Social media has reshaped political discourse, offering politicians a platform for direct engagement while reinforcing polarization and ideological divides. This study introduces a novel topic evolution framework that integrates BERTopic-based topic modeling with Moral Foundations Theory (MFT) to analyze the longevity and moral dimensions of political topics

Diana Shvydka, Victor Karpov

Degradation of joint cartilages can result in osteoarthritis (OA) affecting about 10\% of the US population and responsible for significant hospitalization costs. While observations show that low dose radiation treatments (LDRT) bring improvements for a majority of OA patients, the underlying mechanism is not sufficiently understood. Here, we show how the ra

Shulan Yan, Andrew Ma, Qingzheng Yu, Taotao Fang

We investigate the atomic gas (HI) content of galaxies in groups using early data from the FAST All Sky HI survey (FASHI). Taking advantage of FAST's blind, wide-area coverage and uniform sensitivity, we assemble a sample of $230$ group galaxies belonging to $182$ groups at $z\leq0.03$. These groups were identified using a halo-based group finder, and they h

Noor Aftab, Xunhe, Zhang, Sean Walker

The Mittelman-di Cicco-Walker (MDW) H$\alpha$ Sky Survey is an autonomously-operated all-sky narrow-band (3nm) H$\alpha$ imaging survey. The survey was founded by amateur astronomers and the northern sky (Decl. $\geq$ 0$^\circ$) is presented here in its second stage of refinement for academic use. Each 3.6$\times$3.6 sq. deg MDW field has 12 20-minute indivi

Vishvesh Bhat, Omkar Ghugarkar, Julian McAuley

Generalization across Agentic tool-calling environments remains a key unsolved challenge in developing reliable agentic reasoning systems. While large language models (LLMs) demonstrate strong performance on isolated benchmarks, their ability to transfer reasoning strategies and co-ordinate tools across diverse domains is poorly understood. In this work, we

Vaibhav Raj, Indradyumna Roy, Ashwin Ramachandran, Soumen Chakrabarti

Subgraph matching is vital in knowledge graph (KG) question answering, molecule design, scene graph, code and circuit search, etc. Neural methods have shown promising results for subgraph matching. Our study of recent systems suggests refactoring them into a unified design space for graph matching networks. Existing methods occupy only a few isolated patches

Yadong Jiao, Xiaoyan Cheng, Yuansheng Tang, Ming Xu

The polar codes introduced by Arikan in 2009 achieve the capacity of binary-input discrete memoryless channels (BIDMCs) with low complexity encoding and decoding. Identifying the unreliable synthetic channels, generated by Arikan transformation during the construction of these polar codes, is crucial. Currently, because of the large size of the output alphab

Wang Hao, Kuang Zhang, Hou Chengyu, Yang Yifan

Modal decomposition techniques, such as Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Singular Spectrum Analysis (SSA), have advanced time-frequency signal analysis since the early 21st century. These methods are generally classified into two categories: numerical optimization-based methods (EMD, VMD) and spectral decompositio

Armin Gerami, Kazem Faghih, Ramani Duraiswami

Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by connecting them to external knowledge, improving accuracy and reducing outdated information. However, this introduces challenges such as factual inconsistencies, source conflicts, bias propagation, and security vulnerabilities, which undermine the trustworthiness of RAG systems. A

Jacob Migdail

In this dissertation, we extend the odd Khovanov bracket to link cobordisms and prove that our construction is functorial up to sign. We then build an odd Khovanov theory for dotted link cobordisms. Out of the dotted theory, a module structure on the odd Khovanov homology of a diagram over the exterior algebra of the diagram's coloring group arises. We finis

Jingzehua Xu, Yangyang Li, Yangfei Chen, Guanwen Xie

Robotic arms are increasingly deployed in uncertain environments, yet conventional control pipelines often become rigid and brittle when exposed to perturbations or incomplete information. Virtual Model Control (VMC) enables compliant behaviors by embedding virtual forces and mapping them into joint torques, but its reliance on fixed parameters and limited c

Jia Ren, Xiao-Yan Li, Yun Wang, Lu-Lu Zhang

We presented the multi-wavelength afterglow fitting results for three events that exhibit late afterglow rebrightening behavior: EP240414a ($z=0.401$), GRB 240529A ($z=2.695$), and GRB 240218A ($z=6.782$), which span a broad range of redshifts, from the local to the high-redshift universe.We prove that the peculiar afterglow light curves of three bursts can

Ryutaroh Matsumoto

As measurements are costly and prone to errors on certain quantum computing devices, we should reduce the number of measurements and the number of measured qudits as small as possible in quantum erasure correction. It is intuitively obvious that a decoder can omit measurements of stabilizers that are irrelevant to erased qudits, but this intuition has not be

Christofer Valencia, Alexis Llumigusín, Silvia Alvarez, Abrahan Arias

Geotechnical reports are crucial for assessing the stability of rock formations and ensuring safety in modern engineering. Traditionally, these reports are prepared manually based on field observations using compasses, magnifying glasses, and notebooks. This method is slow, prone to errors, and subjective in its interpretations. To overcome these limitations

Darshana Priyasad, Tharindu Fernando, Maryam Haghighat, Harshala Gammulle

Natural disasters, such as volcanic eruptions, pose significant challenges to daily life and incur considerable global economic losses. The emergence of next-generation small-satellites, capable of constellation-based operations, offers unparalleled opportunities for near-real-time monitoring and onboard processing of such events. However, a major bottleneck

Shihao Cai, Chongming Gao, Haoyan Liu, Wentao Shi

The powerful reasoning and generative capabilities of large language models (LLMs) have inspired researchers to apply them to reasoning-based recommendation tasks, which require in-depth reasoning about user interests and the generation of recommended items. However, previous reasoning-based recommendation methods have typically performed inference within th

Arunima Bhattacharya, Ravi Shankar, Jeremy Wall

In this paper, we establish interior Hessian and gradient estimates for the two-dimensional Lagrangian mean curvature equation when the phase changes signs, provided the gradient of the phase vanishes along its zero set. At the critical phase in two dimensions, the Jacobi inequality degenerates, preventing the use of higher-dimensional methods to obtain Hess

Ryuya Hora

In 1901, Bouton proved that a winning strategy of the game of Nim is given by the bitwise XOR, called the nim-sum. But, why does such a weird binary operation work? Led by this question, this paper introduces a categorical reinterpretation of combinatorial games and the nim-sum. The main categorical gadget used here is recursive coalgebras, which allow us to

Yuqin Jiang, Song Jiang, Jacob Algrim, Trevor Harms

Linguistic Landscape (LL) research traditionally relies on manual photography and annotation of public signages to examine distribution of languages in urban space. While such methods yield valuable findings, the process is time-consuming and difficult for large study areas. This study explores the use of AI powered language detection method to automate LL a

Giovanni Sileno, Jean-Louis Dessalles

Cognitive studies and artificial intelligence have developed distinct models for various inferential mechanisms (categorization, induction, abduction, causal inference, contrast, merge, ...). Yet, both natural and artificial views on cognition lack apparently a unifying framework. This paper formulates a speculative answer attempting to respond to this gap.

Amit Joshi

We present a merge-free algorithm for multi-way co-ranking, the problem of computing cut indices $i_1,\dots,i_m$ that partition each of the $m$ sorted sequences such that all prefix segments together contain exactly $K$ elements. Our method extends two-list co-ranking to arbitrary $m$, maintaining per-sequence bounds that converge to a consistent global fron

Saeed Najafi, Alona Fyshe

Aligning Large Language Models (LLMs) with human preferences is crucial, but standard methods like Reinforcement Learning from Human Feedback (RLHF) are often complex and unstable. In this work, we propose a new, simpler approach that recasts alignment through the lens of Maximum Marginal Likelihood (MML) estimation. Our new MML based Preference Optimization

Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh, Quoc Viet Hung Nguyen

Multimodal federated learning in real-world settings often encounters incomplete and heterogeneous data across clients. This results in misaligned local feature representations that limit the effectiveness of model aggregation. Unlike prior work that assumes either differing modality sets without missing input features or a shared modality set with missing f

Min-Seok Seo

Whereas local field operators play the crucial role in reconciling quantum mechanics and special relativity, they are not trivially compatible with the diffeomorphism invariance of gravity. In order to address this issue, we consider the background geometry which breaks the isometry spontaneously. Then the local gauge invariant operator can be constructed th

Nicholas I-Hsien Kuo, Blanca Gallego, Louisa Jorm

Foundation models refer to architectures trained on vast datasets using autoregressive pre-training from natural language processing to capture intricate patterns and motifs. They were originally developed to transfer such learned knowledge to downstream predictive tasks. Recently, however, some studies repurpose these learned representations for phenotype d

Ivan Dimitrov, Khoa Nguyen, Charles Paquette, David Wehlau

We study the category $\mathcal{M}_{\mathfrak{sl}(m|1)}(k|k)$ of $\mathcal U(\mathfrak h)\text{-free}$ $\mathcal U(\mathfrak{sl}(m|1))$-modules of rank $k$ in each parity (rank $(k|k)$), where $k\in\mathbb{Z}_{\geq1}$. We construct an explicit family of such modules, provide an isomorphism theorem, and establish an indecomposability criterion.

Ilias Magoulas, Francesco A. Evangelista

Clifford gates and transformations, which map products of elementary Pauli or Majorana operators to other such products, are foundational in quantum computing, underpinning the stabilizer formalism, error-correcting codes, magic state distillation, quantum communication and cryptography, and qubit tapering. Moreover, circuits composed entirely of Clifford ga

Filippo Pascucci, Stefania De Palo, Sara Conti, David Neilson

We derive the normal and anomalous proper polarization functions and the screened Coulomb interactions in a two-dimensional superfluid electron-hole bilayer, including all first-order corrections beyond the Random Phase Approximation (RPA). This requires a modification of the perturbation method as first noted by Nozières and Schrieffer [1, 2]. We discuss th

Yitao Feng, Yu-An Chen, Po-Shen Hsin, Ryohei Kobayashi

An internal symmetry in a lattice model is said to be onsiteable if it can be disentangled into an onsite action by introducing ancillas and conjugating with a finite-depth circuit. A standard lore holds that onsiteability is equivalent to being anomaly-free, which is indeed valid for finite 0-form symmetries in (1+1)D. However, for higher-form symmetries, t

F. Alejandro Padilla-Gomez, Sining Gong, Michael S. Murillo, F. R. Graziani

We report a fully kinetic, quantum study of Kinetic Electrostatic Electron Nonlinear (KEEN) waves, showing that quantum diffraction systematically erodes the classical trapping mechanism, narrow harmonic locking to the fundamental, and hasten post-drive decay. Electrons are evolved with a second-order Strang-split 1D1V Wigner-Poisson solver that couples cons

Roger de Belsunce, James M. Sullivan, Patrick McDonald

Cosmological studies of the Lyman-Alpha (Lya) forest typically constrain parameters using two-point statistics. However, higher-order statistics, such as the three-point function (or its Fourier counterpart, the bispectrum) offer additional information and help break the degeneracy between the mean flux and power spectrum amplitude, albeit at a significant c

Mohamed Moussadek Maiza, Maxence Mayrand

We introduce the Lax-Kirchhoff moduli space associated with a finite quiver $Γ$ and a compact connected Lie group $G$. On each oriented edge we consider the Lax equation $\dot{A}_1 + [A_0, A_1] = 0$ and impose a Kirchhoff-type matching condition for the fields $A_1$ at interior vertices. Modulo gauge transformations trivial on the boundary, this yields a mod

Siamak Ghodsi, Amjad Seyedi, Tai Le Quy, Fariba Karimi

Fair graph clustering seeks partitions that respect network structure while maintaining proportional representation across sensitive groups, with applications spanning community detection, team formation, resource allocation, and social network analysis. Many existing approaches enforce rigid constraints or rely on multi-stage pipelines (e.g., spectral embed

Jonathan Louvet, François Damanet, Thierry Bastin

Bound entanglement, a weak -- yet resourceful -- form of quantum entanglement, remains notoriously hard to detect and construct. We address this in this paper by leveraging symmetric random induced states, where positive partial transpose (PPT) bound entanglement arises naturally under partial tracing when proper parameters are selected. We investigate the p

Tobias Dannegger, Imre Hagymási, Levente Rózsa, Ulrich Nowak

Magnetic phase transitions between ordered phases are often understood on the basis of semi-classical spin models. Deviations from the classical description due to the quantum nature of the atomic spins as well as quantum fluctuations are usually treated as negligible if long-range order is preserved, and are rarely quantified for actual materials. Here, we

Aaron C. Trigg, Eric Burns, Michela Negro, Suman Bala

Magnetar giant flares (MGFs) are rare, extremely bright bursts of gamma-rays from highly magnetized neutron stars. These events are challenging to identify because, at extragalactic distances, they can appear similar to other astrophysical phenomena. Only a handful have been confidently identified to date, limiting our understanding of their origin and physi

Anwesha Das, John Duff, Jörg Hoffmann, Vera Demberg

Adaptive agent design offers a way to improve human-AI collaboration on time-sensitive tasks in rapidly changing environments. In such cases, to ensure the human maintains an accurate understanding of critical task elements, an assistive agent must not only identify the highest priority information but also estimate how and when this information can be commu

Marios-Petros Kitsaras, Johannes Tölle, Pierre-François Loos

The accurate prediction of ionization potentials (IPs) is central to understanding molecular reactivity, redox behavior, and spectroscopic properties. While vertical IPs can be accessed directly from electronic excitations at fixed nuclear geometries, the computation of adiabatic IPs requires nuclear gradients of the ionized states, posing a major theoretica

F. Guarneri, J. T. Schindler, R. A. Meyer, D. Yang

Quasars at the redshift frontier (z > 7.0) are fundamental probes of black hole (BH) growth and evolution but notoriously difficult to identify. At these redshifts, machine learning-based selection methods have proven to be efficient, but require appropriate training sets to express their full potential. Here, we present QUEST, a Variational Auto-Encoder cap

Joana Bustamante, Anupama Ghata, Aakash A. Naik, Christina Ertural

Argyrodite-type Ag-based sulfides combine exceptionally low lattice thermal and high ionic conductivity, making them promising candidates for thermoelectric and solid-state energy applications. In this work, we studied Ag8TS6 (T= Si, Ge, Sn) argyrodite family by combining chemical-bonding analysis, lattice vibrational properties simulation, and experimental

Liyang Chen, Xiaofeng Wang, Qinyu Wu, Moira Andrews

We present optical, ultraviolet, and X-ray observations of supernova (SN) 2024iss, a Type IIb SN that shows a prominent double-peaked light curve. We modeled the first peak with a semianalytical shock-cooling model and the X-ray emission with a free-free model. We compare the envelope radius and mass-loss rate with other Type IIb SNe to explore the relations

Zhenglai Shen, Hongyu Zhou

Compounding climate hazards, such as wildfire-induced outages and urban heatwaves, challenge the stability and equity of cities. We present a Hazard-Responsive Digital Twin (H-RDT) that combines physics-informed neural network modeling, multimodal data fusion, and equity-aware risk analytics for urban-scale response. In a synthetic district with diverse buil

Peilin He, James Joshi

Reconstructing high-quality images from low-resolution inputs using Residual Dense Spatial Networks (RDSNs) is crucial yet challenging. It is even more challenging in centralized training where multiple collaborating parties are involved, as it poses significant privacy risks, including data leakage and inference attacks, as well as high computational and co

M. D. Lepinzan, C. T. Davies, T. Castro, N. Schuster

Context. Cosmic voids are vast underdense regions in the cosmic web that encode crucial information about structure formation, the composition of the Universe, and its expansion history. Due to their lower density, these regions are less affected by non-linear gravitational dynamics, making them suitable candidates for analysis using semi-analytic methods. A

Oscar P. Bruno, Manuel A. Santana

Fourier transform-based methods enable accurate, dispersion-free simulations of time-domain scattering problems by evaluating solutions to the Helmholtz equation at a discrete set of frequencies sufficient to approximate the inverse Fourier transform. However, in the case of scattering by trapping obstacles, the Helmholtz solution exhibits nearly-real comple

Xin Wu, Fei Teng, Xingwang Li, Ji Zhang

Time series frequently manifest distribution shifts, diverse latent features, and non-stationary learning dynamics, particularly in open and evolving environments. These characteristics pose significant challenges for out-of-distribution (OOD) generalization. While substantial progress has been made, a systematic synthesis of advancements remains lacking. To

Andrej Leban

The Distributional Principal Autoencoder (DPA) combines distributionally correct reconstruction with principal-component-like interpretability of the encodings. In this work, we provide exact theoretical guarantees on both fronts. First, we derive a closed-form relation linking each optimal level-set geometry to the data-distribution score. This result expla

Mike Boyle

Shifts of finite type defined from shift equivalent matrices must be flow equivalent.