December 2025 arXiv papers — page 60

Han-Seul Jeong, Youngjoon Park, Hyungseok Song, Woohyung Lim

Vehicle Routing Problems (VRPs) with diverse real-world attributes have driven recent interest in cross-problem learning approaches that efficiently generalize across problem variants. We propose ARC (Attribute Representation via Compositional Learning), a cross-problem learning framework that learns disentangled attribute representations by decomposing them

Ni Ding, Songpei Lu, Wenjing Yang, Zijian Zhang

This paper studies how to achieve individual indistinguishability by pufferfish privacy in aggregated query to a multi-user system. It is assumed that each user reports realization of a random variable. We study how to calibrate Laplace noise, added to the query answer, to attain pufferfish privacy when user changes his/her reported data value, leaves the sy

Pouria M. Oqaz, Emanuele Crisostomi, Elena Dieckmann, Robert Shorten

In this paper, we formulate the design of efficient digitalised deposit return schemes as a control problem. We focus on the recycling of paper cups, though the proposed methodology applies more broadly to reverse logistics systems arising in circular economy R-strategies. Each item is assumed to carry a digital wallet through which monetary rewards are allo

Zhihao Cheng, Chi Zhang, Chuanfei Dong, Hongyang Zhou

Mars lacks a global intrinsic dipole magnetic field, but its interaction with the solar wind generates a global induced magnetosphere. Until now, most studies have relied on single-spacecraft measurements, which could not simultaneously capture upstream solar wind conditions and the induced magnetic fields, thereby limiting our understanding of the system. H

Masaoki Mori

In this paper, we construct a higher dimensional generalization of affine buildings and introduce a new structure, which we call Babel buildings. These buildings are non-connected, non-convex metric spaces of non-positive curvature. Despite their non-standard properties, Babel buildings provide an effective framework for studying the structure of groups acti

Dawan Sardar Hama Ali, Luqman Garib Karim Barznjy

The study was carried out to known the response of two industrial potato cultivars (Hermes, and Challenger) Netherlands origin, to chelated potassium fertilizer and humic acid due to growth, yield and quality in the fall season of 2024, planted in an open field of the educational field of Horticulture Department, College of Agricultural Engineering Sciences,

Shunsuke Imai

Uniform confidence bands for functions are widely used in empirical analysis. A variety of simple implementation methods (most notably multiplier bootstrap) have been proposed and theoretically justified. However, an implementation over a literally continuous index set is generally computationally infeasible, and practitioners therefore compute the critical

Jimmy Lamboley, Mickaël Nahon

We study a higher order analogue to the Alt-Caffarelli functional that arises in several shape optimization problems, among which the minimization of the critical buckling load of a clamped plate of fixed area. We obtain several regularity results up to the boundary in two dimensions, in particular we prove the full regularity of the boundary (analytic outsi

Evgeny A. Mityushov

We introduce a curvature atlas for left-invariant metrics on SU(2), based on the inertial curvature field derived from the Euler-Poincare equations. We prove that the classical integrable cases of the heavy top--spherical, Lagrange, Kovalevskaya, and Goryachev-Chaplygin--correspond precisely to degenerate curvature signatures of this field, namely isotropic,

Izumi Seno, Shu-ichiro Inutsuka, Jiro Shimoda

Thermal instability in the circum-galactic medium (CGM) can be responsible for the existence of cold clouds (e.g., high-velocity clouds) embedded in a hot diffuse medium (e.g., X-ray emitting gas). While many previous studies have analyzed thermal instability in uniform medium, the instability mechanism in gravitationally stratified medium like CGM has not b

Anandita Garg, Uma Gaba, Deepan Muthirayan, Anish Roy Chowdhury

The advent of Large Language Models (LLMs) has raised concerns about their enormous carbon footprint, starting with energy-intensive training and continuing through repeated inference. This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined tasks. Here, we present a comparative analys

Jensen Zhang, Ningyuan Liu, Yijia Fan, Zihao Huang

Large language models (LLMs) often generate hallucinated content that lacks factual or contextual grounding, limiting their reliability in critical applications. Existing approaches such as supervised fine-tuning and reinforcement learning from human feedback are data intensive and computationally expensive, while static parameter editing methods struggle wi

Thanh Dat Hoang, Thanh Trung Huynh, Matthias Weidlich, Thanh Tam Nguyen

Text2SQL, the task of generating SQL queries from natural language text, is a critical challenge in data engineering. Recently, Large Language Models (LLMs) have demonstrated superior performance for this task due to their advanced comprehension and generation capabilities. However, privacy and cost considerations prevent companies from using Text2SQL soluti

Hau Chan, Jianan Lin, Chenhao Wang

We study the problem of locating a single obnoxious facility on the normalized line segment $[0,1]$ with strategic agents from a mechanism design perspective. Each agent has a preference for the undesirable location of the facility and would prefer the facility to be far away from their location. We consider the utility of the agent, defined as the distance

Zhenhao Zhou, Dan Negrut

We present ChronoDreamer, an action-conditioned world model for contact-rich robotic manipulation. Given a history of egocentric RGB frames, contact maps, actions, and joint states, ChronoDreamer predicts future video frames, contact distributions, and joint angles via a spatial-temporal transformer trained with MaskGIT-style masked prediction. Contact is en

Yuan Qilong, Michal Pavelka

We study the Joint Routing-Assignment (JRA) problem in which items must be assigned one-to-one to placeholders while simultaneously determining a Hamiltonian cycle visiting all nodes exactly once. Extending previous exact MIP solvers with Gurobi and cutting-plane subtour elimination, we develop a solver tailored for practical packaging-planning scenarios wit

Hollis Williams

Semiclassical gravity, in which a classical spacetime is sourced by the quantum expectation value of the stress-energy tensor, is a standard framework for describing the gravitational interaction of quantum matter. In the nonrelativistic limit this approach leads to the Schr\"odinger-Newton equation, which is often assumed to be consistent at least in the we

Zelin Wan, Han Jun Yoon, Nithin Alluru, Terrence J. Moore

We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared Mental Models (SMM). Designed for mission-critical applications such as surveillance and rescue, DASH introduces "bait tasks" to detect insider threats, e.g., compromised Unmanned

Nayana Venkatareddy, Partha Sarathi Mondal, Shradha Mishra, Prabal K. Maiti

We study the kinetics of two-temperature induced phase separation (2-TIPS) in dilute binary mixtures of active ("hot") and passive ("cold") particles using molecular dynamics simulations and a coarse-grained hydrodynamic model. Following a temperature quench, cold particles nucleate into mobile clusters that move ballistically and merge through successive co

Ruiqi Chen, Kaitong Cai, Yijia Fan, Keze Wang

Traditional animation production involves complex pipelines and significant manual labor cost. While recent video generation models such as Sora, Kling, and CogVideoX achieve impressive results on natural video synthesis, they exhibit notable limitations when applied to animation generation. Recent efforts, such as AniSora, demonstrate promising performance

Saeideh Yousefzadeh, Hamidreza Pourreza

Visual Place Recognition (VPR) in long-term deployment requires reasoning beyond pixel similarity: systems must make transparent, interpretable decisions that remain robust under lighting, weather and seasonal change. We present Text2Graph VPR, an explainable semantic localization system that converts image sequences into textual scene descriptions, parses t

Soumyadip Das, Luke Antoncich, Jingbo B. Wang

Quantum Reservoir Computing (QRC) leverages the natural dynamics of quantum systems for information processing, without requiring a fault-tolerant quantum computer. In this work, we apply QRC within a hybrid quantum classical framework for image denoising. The quantum reservoir is implemented using a Rydberg atom array, while a classical neural network serve

M V Vismaya, M V Sangaranarayanan

The partition functions of ferromagnetic Ising models of square lattices in a finite magnetic field is deduced using topological considerations within a heuristic graph-theoretical approach. These equations are derived separately for low and high temperature regimes while the exact solution of Onsager is obtained therefrom when the magnetic field is zero. Th

Rongyao Cai, Yuxi Wan, Kexin Zhang, Ming Jin

Intuitively, a more deterministic time series should be easier to forecast. However, point-wise loss functions (e.g., MSE and MAE), serving as differentiable surrogates for the ideal optimization target, score each timestamp independently and therefore disregard temporal dependence. This mismatch induces a systematic optimization bias that cannot be eliminat

Hao-Chen Liu, Zifei Yin, Xin-Lei Zhang, Guowei He

This work proposes a data-driven explicit algebraic stress-based detached-eddy simulation (DES) method. Despite the widespread use of data-driven methods in model development for both Reynolds-averaged Navier-Stokes (RANS) and large-eddy simulations (LES), their applications to DES remain limited. The challenge mainly lies in the absence of modelled stress d

Prabigya Acharya, Liza Shrestha

Automated masking of Personally Identifiable Information (PII) is critical for privacy-preserving conversational systems. While current frontier large language models demonstrate strong PII masking capabilities, concerns about data handling and computational costs motivate exploration of whether lightweight models can achieve comparable performance. We compa

Huiqi Deng, Qihan Ren, Zhuofan Chen, Zhenyuan Cui

Understanding what kinds of cooperative structures deep neural networks (DNNs) can represent remains a fundamental yet insufficiently understood problem. In this work, we treat interactions as the fundamental units of such structure and investigate a largely unexplored question: how DNNs encode interactions under different levels of contextual complexity, an

Anwar Alajmi, Gabriele Pergola

Online sexism increasingly appears in subtle, context-dependent forms that evade traditional detection methods. Its interpretation often depends on overlapping linguistic, psychological, legal, and cultural dimensions, which produce mixed and sometimes contradictory signals in annotated datasets. These inconsistencies, combined with label scarcity and class

Sumin Park, Noseong Park

Finding the optimal configuration of Sparse Mixture-ofExperts (SMoE) that maximizes semantic differentiation among experts is essential for exploiting the full potential of MoE architectures. However, existing SMoE frameworks either heavily rely on hyperparameter tuning or overlook the importance of diversifying semantic roles across experts when adapting th

Ali Zeytoon-Nejad

Society as a whole faces a host of economic tradeoffs, many of which emerge around economic policies. An example of tradeoffs that any society faces in many economic realms is the tradeoff between economic efficiency and income equality (aka the efficiency-equality tradeoff). This tradeoff has been called "the Big Tradeoff" by the esteemed economist Arthur O

Qinglin Zeng, Jing Yang, Keze Wang

Large language models (LLMs) have achieved strong performance on complex reasoning tasks using techniques such as chain-of-thought and self-consistency. However, ensemble-based approaches, especially self-consistency which relies on multiple reasoning trajectories, often incur substantial computational overhead. To improve efficiency, prior work has leverage

Wencan Mao, Quanxi Zhou, Tomas Couso Coddou, Manabu Tsukada

Unmanned aerial vehicles (UAVs) have emerged as a promising auxiliary platform for smart agriculture, capable of simultaneously performing weed detection, recognition, and data collection from wireless sensors. However, trajectory planning for UAV-based smart agriculture is challenging due to the high uncertainty of the environment, partial observations, and

Joseph Samper Finberg

Locally covariant algebraic quantum field theory (LCQFT) satisfies Einstein causality through microcausality and operational no-signalling, yet Bell-type correlations persist in entangled field states across spacelike regions. We demonstrate that this apparent tension reflects a fundamental covariance obstruction: no assignment of classical past variables ca

Xianzhe Dai, Debin Liu

For a vector bundle $E^{n+k}$ over a closed manifold $M^n$ with $k$ even and $n$ odd, we equip the metric with an adiabatic parameter, and prove that the index of $E$ is the same as the index of $M$. We also introduce an analog of analytic torsion on $E$ using the Witten Laplacian. Moreover, we prove that the Quillen metric associated with this analytic tors

Haotian Deng, Chris Farber, Jiyoon Lee, David Tang

Automated short-answer grading (ASAG) remains a challenging task due to the linguistic variability of student responses and the need for nuanced, rubric-aligned partial credit. While Large Language Models (LLMs) offer a promising solution, their reliability as automated judges in rubric-based settings requires rigorous assessment. In this paper, we systemati

Charles J. Lovering, Seth Ebner, Brandon Smock, Michael Krumdick

Professionals in academia, law, and finance audit their documents because inconsistencies can result in monetary, reputational, and scientific costs. Language models (LMs) have the potential to dramatically speed up this auditing process. To understand their abilities, we introduce a benchmark, FIND (Finding INconsistencies in Documents), where each example

Juha Park, Seokho Kim, Wonjae Shin, H. Vincent Poor

Low Earth Orbit (LEO) satellite communications (SATCOM) offers high-throughput, low-latency global connectivity to a very large number of users. To accommodate this demand with limited hardware resources, beam hopping (BH) has emerged as a prominent approach in LEO SATCOM. However, its time-domain switching mechanism confines coverage to a small fraction of

Jianglin Lu, Yuanwei Wu, Ziyi Zhao, Hongcheng Wang

Complex image restoration aims to recover high-quality images from inputs affected by multiple degradations such as blur, noise, rain, and compression artifacts. Recent restoration agents, powered by vision-language models and large language models, offer promising restoration capabilities but suffer from significant efficiency bottlenecks due to reflection,

Zhe Li, Kun Cheng, Hanyue Mo, Jintao Lu

A vision-based trajectory analysis solution is proposed to address the "zero-speed braking" issue caused by inaccurate Controller Area Network (CAN) signals in commercial vehicle Automatic Emergency Braking (AEB) systems during low-speed operation. The algorithm utilizes the NVIDIA Jetson AGX Xavier platform to process sequential video frames from a blind sp

Quanxi Zhou, Wencan Mao, Yilei Liang, Manabu Tsukada

The widespread application of wireless communication technology has promoted the development of smart agriculture, where unmanned aerial vehicles (UAVs) play a multifunctional role. We target a multi-UAV smart agriculture system where UAVs cooperatively perform data collection, image acquisition, and communication tasks. In this context, we model a Markov de

Runze Mao, Rui Zhang, Xuan Bai, Tianhao Wu

Predicting multiphysics dynamics is computationally expensive and challenging due to the severe coupling of multi-scale, heterogeneous physical processes. While neural surrogates promise a paradigm shift, the field currently suffers from an "illusion of mastery", as repeatedly emphasized in top-tier commentaries: existing evaluations overly rely on simplifie

Sanjib Basu, Navdeep Tamang

This paper presents a generalized version of a theorem of Grzegorek and Labuda in category bases and also endeavours to establish a variant formulation of the same in Marczewski structures.

Xuanming Cui, Hong-You Chen, Hao Yu, Hao Yuan

Traditional multimodal retrieval systems rely primarily on bi-encoder architectures, where performance is closely tied to embedding dimensionality. Recent work, Think-Then-Embed (TTE), shows that incorporating multimodal reasoning to elicit additional informative tokens before embedding can further improve retrieval. In this paper, we extend this paradigm wi

Amit Barman, Atanu Mandal, Sudip Kumar Naskar

In multilingual nations like India, access to legal information is often hindered by language barriers, as much of the legal and judicial documentation remains in English. Legal Machine Translation (L-MT) offers a scalable solution to this challenge by enabling accurate and accessible translations of legal documents. This paper presents our work for the JUST

Marios Papamichalis, Regina Ruane

Many network datasets exhibit connectivity with variance by resolution and large-scale organization that coexists with localized departures. When vertices have observed ordering or embedding, such as geography in spatial and village networks, or anatomical coordinates in connectomes, learning where and at what resolution connectivity departs from a baseline

Chuyue Peng, Joshua Ginzburg, Uri Dickman, Jacob Bair

The 3$\omega$ method is widely used to measure the thermal conductivity and the specific heat of wires and thin films. These measurements are typically performed under high vacuum conditions, which justify the use of heat transfer models that exclude thermal losses to a surrounding fluid. Here, we study the effect of thermal conduction from a joule-heated wi

Yifan Zhao, Xinglong Yu, Yi Sun, Honglin Kuang

Fully homomorphic encryption (FHE) schemes like RNS-CKKS enable privacy-preserving outsourced computation (PPOC) but suffer from high computational latency and ciphertext expansion, especially on the resource-constrained edge side. Hybrid Homomorphic Encryption (HHE) mitigates these issues on the edge side by replacing HE with lightweight symmetric encryptio

Marios Papamichalis, Regina Ruane

Bayesian predictive synthesis provides a coherent Bayesian framework for combining multiple predictive distributions, or agents, into a single updated prediction, extending Bayesian model averaging to allow general pooling of full predictive densities. This paper develops a static, graphon level version of Bayesian predictive synthesis for random networks. A

Xiaodong Feng, Tao Tang, Xiaoliang Wan, Tao Zhou

Spectral bias implies an imbalance in training dynamics, whereby high-frequency components may converge substantially more slowly than low-frequency ones. To alleviate this issue, we propose a cross-attention-based architecture that adaptively reweights a scaled multiscale random Fourier feature bank with learnable scaling factors. The learnable scaling adju

Ahmad Sohrabi

Recent quantum models of cognition have successfully simulated several interesting effects in human experimental data, from vision to reasoning and recently even consciousness. The latter case, consciousness has been a quite challenging phenomenon to model, and most efforts have been through abstract mathematical quantum methods, mainly focused on conceptual

Marios Papamichalis, Regina Ruane, Theofanis Papamichalis

Crime counts in city neighbourhoods, disease counts in counties, and sales at firms joined by trade are naturally represented as counts on the nodes of a network. In each case a high count at one node can raise the counts at the nodes linked to it next period. The strength of that spillover changes over time, and standard network autoregressions hold it fixe

Pengcheng Li, Qiang Fang, Tong Zhao, Yixing Lan

Adversarial Imitation Learning (AIL) is a dominant framework in imitation learning that infers rewards from expert demonstrations to guide policy optimization. Although providing more expert demonstrations typically leads to improved performance and greater stability, collecting such demonstrations can be challenging in certain scenarios. Inspired by the suc

Liwen Gao, Xuejun Guo

In this paper, we investigate the determinants involving some trigonometric functions. We establish a connection between these determinants and the special values of Dirichlet L-functions, thereby extending Guo's results to arbitrary positive integers n. In addition, we also prove a conjecture raised by Zhi-Wei Sun. Our main tool is the spectral decompositio

Yuan Zhou, Qingzhi Zou, Ignacio Pagonabarraga, Kaihuan Zhang

The habitat of microorganisms is typically complex and viscoelastic. A natural question arises: Do polymers in a suspension of active swimmers enhance chaotic motion or promote orientational order? We address this issue by performing lattice Boltzmann simulations of squirmer suspensions in polymer solutions. At intermediate swimmer volume fractions, comparin

Yifei Jia, Yi Du, Lihui Guo

The Ekman boundary layer is a fundamental concept in fluid dynamics that describes fluid motion near boundaries affected by Earth's rotation. Most theoretical studies have simplified their analysis by assuming a planar boundary surface, resulting in limited exploration of structures with general smooth boundary conditions. Investigating the impact of boundar

Nitya Ravi, Kelly A. Douglass, Regina Demina

The baryonic Tully-Fisher relation (BTFR), a relationship between rotational velocity and baryonic mass in spiral galaxies, probes the relative content of baryonic and dark matter in galaxies and thus provides a good test of Lambda CDM. Using H-alpha kinematics to model the rotation curves of spiral galaxies, we construct the BTFR for 5743 SDSS MaNGA DR17 ga

Tiantian Zhou, Li Chen, Yutian Lei

In this paper, we investigate a multi-dimensional nonlocal degenerate diffusion-aggregation equation with a diffusion exponent $m$ in the intermediate range $\frac{2d}{2d-\gamma}<m<\frac{d+\gamma}{d}$, where the nonlocal aggregation term is given by singular potential $|x|^{-\gamma}$, $0<\gamma\leq d-2$. Under two different assumptions on the initial data, w

Effiong Blessing, Chiung-Yi Tseng, Somshubhra Roy, Junaid Rehman

Memory-augmented spiking neural networks (SNNs) promise energy-efficient neuromorphic computing, yet their generalization across sensory modalities remains unexplored. We present the first comprehensive cross-modal ablation study of memory mechanisms in SNNs, evaluating Hopfield networks, Hierarchical Gated Recurrent Networks (HGRNs), and supervised contrast

Tim Jeric, Tatsuo Kobayashi, Hajime Otsuka, Maki Takeuchi

We study the origin of modular weights of wave functions in magnetized $T^{2}$ models. It is explicitly demonstrated that the modular weights of the wave functions on magnetized $T^2$ is equivalent to their mass level. We further extend this result to magnetized $T^{2g}$ models. As a result, we construct the wave functions of excited states in magnetized $T^

Sumaiya Ali, Areej Alhothali, Ohoud Alzamzami, Sameera Albasri

Placenta Accreta Spectrum (PAS) is a serious obstetric condition that can be challenging to diagnose with Magnetic Resonance Imaging (MRI) due to variability in radiologists' interpretations. To overcome this challenge, a hybrid 3D deep learning model for automated PAS detection from volumetric MRI scans is proposed in this study. The model integrates a 3D D

Riki Shimizu, Xilin Jiang, Nima Mesgarani

Target speaker extraction (TSE) aims to isolate a desired speaker's voice from a multi-speaker mixture using auxiliary information such as a reference utterance. Although recent advances in diffusion and flow-matching models have improved TSE performance, these methods typically require multi-step sampling, which limits their practicality in low-latency sett

Weijie Zhou, Xuangtang Xiong, Ye Tian, Lijun Yue

Multimodal Large Language Models (MLLMs) have empowered embodied agents with remarkable capabilities in planning and reasoning. However, when facing ambiguous natural language instructions (e.g., "fetch the tool" in a cluttered room), current agents often fail to balance the high cost of physical exploration against the cognitive cost of human interaction. T

Yanan Wang, Min Xue

This work focuses on three-component defocusing Kundu-Eckhaus equation, which serves as a significant coupled model for describing complex wave propagation in nonlinear optical fibers. By employing binary Darboux transformation based on 4x4 matrix spectral problem, we derive vector dark soliton solutions, and meanwhile, the exact expressions of asymptotic da

Bin Wang, Wenjie Yu, Yilu Zhong, Hao Yu

Large language models (LLMs) for code generation are becoming integral to modern software development, but their real-world prevalence and security impact remain poorly understood. We present the first large-scale empirical study of AI-generated code (AIGCode) in the wild. We build a high-precision detection pipeline and a representative benchmark to disting

Zhenwei Li, Shi Jia, Dandan Wei, Hongwei Ge

Be stars are rapidly rotating main-sequence (MS) stars that play a crucial role in understanding stellar evolution and binary interactions. In this letter, we propose a new formation scenario for black hole (BH) + Be star binaries (hereafter BHBe binaries), where the Be star is produced through the Wind Roche Lobe Overflow (WRLOF) mechanism. Our analysis is

John Chen, Sihan Cheng, Can Gurkan, Ryan Lay

Large Language Models' capacity to reason in natural language makes them uniquely promising for 4X and grand strategy games, enabling more natural human-AI gameplay interactions such as collaboration and negotiation. However, these games present unique challenges due to their complexity and long-horizon nature, while latency and cost factors may hinder LLMs'

Qixiang Chen, Cheng Zhang, Chi-Wing Fu, Jingwen Ye

Recent multimodal large language models (MLLMs) show great potential in natural image understanding. Yet, they perform well, mainly on reasoning in-view contents within the image frame. This paper presents the first study on out-of-view (OOV) understanding, i.e., the ability to reason objects, activities, and scenes beyond the visible frame of a perspective

Philip Korman

We consider radially symmetric solutions for a class of resonant problems on a unit ball $B \subset R^n$ around the origin \[ \Delta u+\la _1 u +g(u)=f(r) \s \mbox{for $x \in B$}, \s u=0 \s \mbox{on $\partial B$} \,. \] Here the function $g(u)$ is periodic of mean zero, $x \in R^n$, $r=|x|$, $\la _1$ is the principal eigenvalue of $\Delta$ on $B$. The proble

Sumanth Bharadwaj Hachalli Karanam, Dhiwahar Adhithya Kennady

Manual software beta testing is costly and time-consuming, while single-agent large language model (LLM) approaches suffer from hallucinations and inconsistent behavior. We propose a multi-agent committee framework in which diverse vision-enabled LLMs collaborate through a three-round voting protocol to reach consensus on testing actions. The framework combi

Yi Wang, John Joon Young Chung, Melissa Roemmele, Yuqian Sun

Interactive narrative (IN) authors craft spaces of divergent narrative possibilities for players to explore, with the player's input determining which narrative possibilities they actually experience. Generative AI can enable new forms of IN by improvisationally expanding on pre-authored content in response to open-ended player input. However, this extrapola

Saad Alqithami

Large-scale networked multi-agent systems increasingly underpin critical infrastructure, yet their collective behavior can drift toward undesirable emergent norms such as collusion, resource hoarding, and implicit unfairness. We present the Adaptive Accountability Framework (AAF), an end-to-end runtime layer that (i) records cryptographically verifiable inte

Kenji Saito

Sensor data in IoT (Internet of Things) systems is vulnerable to tampering or falsification when transmitted through untrusted services. This is critical because such data increasingly underpins real-world decisions in domains such as logistics, healthcare, and other critical infrastructure. We propose a general method for secure sensor-data logging in which

Qiyuan Wang, Giovanni Motta, Genaro Sucarrat, Vinay L. Kashyap

We develop a new and powerful method to analyze time series to rigorously detect flares in the presence of an irregularly oscillatory baseline, and apply it to stellar light curves observed with TESS. First, we remove the underlying non-stochastic trend using a time-varying amplitude harmonic model. We then model the stochastic component of the light curves

Shuwei Pei, Joran Borger, Arda Kosay, Muhammed O. Sayin

Learning-based traffic signal control is typically optimized for average performance under a few nominal demand patterns, which can result in poor behavior under atypical traffic conditions. To address this, we develop a distributionally robust multi-agent reinforcement learning framework for signal control on a 3x3 urban grid calibrated from a contiguous 3x

Wejian Yan

Electrical tomography techniques have been widely employed for multiphase-flow monitoring owing to their non invasive nature, intrinsic safety, and low cost. Nevertheless, conventional reconstructions struggle to capture fine details, which hampers broader adoption. Motivated by recent advances in deep learning, this study introduces a Pix2Pix generative adv

Rishabh Mishra

This technical note addresses the challenge of accurate turbulence characterization using robust, bandwidth-limited sensors which fail to resolve the high-wavenumber dissipation range. To correct the resulting underestimation of turbulent kinetic energy (TKE), a novel analytical spectral model is derived from a variational principle governing cascade resista

Gentrit Zenuni, Ari Laaksonen, Robin H. A. Ras, Ali Afzalifar

Nucleation and film coalescence represent two fundamentally different pathways for capillary condensation. Yet, both have so far been proposed as the processes driving the condensation in nanometric confinements, leading to a long-standing and overlooked ambiguity. Here, we delineate the dichotomy between these mechanisms and test their validity using an exp

Anand Aruna Kumar, S. K. Srivatsa, Rajesh Tengli

We develop a variational regularisation framework that enables analytical solutions of the stationary de~Broglie--Bohm wave equation. The formulation begins with a Fisher-information-augmented action functional for the probability density and phase fields, yielding the Madelung (Hamilton--Jacobi and continuity) equations and, upon complex recombination, a Sc

Aofei Chang, Ting Wang, Fenglong Ma

Medical Large Vision-Language Models (Med-LVLMs) have shown promising results in clinical applications, but often suffer from hallucinated outputs due to misaligned visual understanding. In this work, we identify two fundamental limitations contributing to this issue: insufficient visual representation learning and poor visual attention alignment. To address

Alexander M. Glandon, Khan M. Iftekharuddin

Multisource domain adaptation (MDA) aims to use multiple source datasets with available labels to infer labels on a target dataset without available labels for target supervision. Prior works on MDA in the literature is ad-hoc as the pretraining of source models is either based on weight sharing or uses independently trained models. This work proposes a Baye

Sungjoon Park, Varun Ramamurthi, Owen Terry

In language modeling, neologisms are new tokens trained to represent a concept not already included in a given model's vocabulary. Neologisms can be used to encourage specific behavior in models, for example by appending prompts with "Give me a neologism answer." Behavioral steering can also be achieved through fine-tuning, albeit with more compute and less

Akihiro Sakurai, Naoya Kajio, Ko Yamamoto

This paper presents a learning-based model of pedestrian flows that integrates multi scale behaviors such as global route selection and local collision avoidance in urban spaces, particularly focusing on pedestrian movements at Shibuya scramble crossing. Since too much congestion of pedestrian flows can cause serious accidents, mathematically modeling and pr

Zikang Yuan, Guanjie Wang, Qifeng Liao

In this work, we present an adaptive adjoint-oriented neural network (adaptive AONN) for solving parametric optimal control problems governed by partial differential equations. The proposed method integrates deep adaptive sampling techniques with the adjoint-oriented neural network (AONN) framework. It alleviates the limitations of AONN in handling low-regul

Zcahary Slepian, Alessandro Greco

Previous work (Slepian 2024) showed that the Smith-Zaldarriaga (2011) algorithm to realize Cosmic Microwave Background (CMB) maps with any desired harmonic-space bispectrum could be generalized to produce a 3D density field with any desired N-Point Correlation Functions (NPCFs, N = $2, 3, \ldots$) about a particular, specified set of ``primary'' points. This

Alexander Doudkin

Large language models (LLMs) are sensitive to the personas imposed on them at inference time, yet prompt-level "drug" interventions have never been benchmarked rigorously. We present the first controlled study of psychoactive framings on GPT-5-mini using ARC-Challenge. Four single-sentence prompts -- LSD, cocaine, alcohol, and cannabis -- are compared agains

Gui-Qiang G. Chen, Jiawen Zhang, Shengguo Zhu

A fundamental open problem in the theory of the compressible Navier-Stokes equations is whether regular spherically symmetric flows can develop singularities, such as cavitation or implosion, in finite time. A formidable challenge lies in how the well-known coordinate singularity at the origin can be overcome to control the lower or upper bound of the densit

Chathuranga M. Wijerathna Basnayaka, Haeyoung Lee, Pandelis Kourtessis, John M. Senior

Non terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite systems, play a vital role in supporting future mission critical applications such as disaster relief. Recent advances in artificial intelligence (AI)-native communications enable LEO satellites to act as intelligent edge nodes capable of on board learning and task oriented inferen

Nikita Kosogorov, Gregg Hallinan, Casey Law, Jack Hickish

We report on a search for prompt, low-frequency radio emission from the gravitational-wave (GW) merger S250206dm using the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA). Early alerts favored a neutron-star-containing merger, making this a compelling target. Motivated by theoretical predictions of coherent radio bursts from mergers involving

Sergey Parnovsky, A. A. Varlamov

Olive oil is an integral part of Mediterranean culture, shaped by more than three thousand years of history, rich traditions, evolving technologies, and fundamental physical principles. This article explores the role of physics in the production of olive oil, highlighting how physical laws govern each stage of the process.

Arjun R, Pratyush Prakash Patra, A. V. Anil Kumar

We investigate the interplay between nonreciprocal interactions and chemical-potential imbalance in a two-species nonreciprocal Blume-Capel model. Combining a systematic mean-field bifurcation analysis with large-scale Monte Carlo simulations in two and three dimensions, we map the model&#39;s dynamical regimes and transitions. Mean-field theory predicts a r

Xiao Yang, Qiyao Peng, Sander C. Hille

The behaviour is investigated of solutions to a diffusion equation on the real line with nonlocal and singular reaction term, i.e., given by a Dirac source or sink at the origin. It gives a simplified representation of for example a control system that senses concentration at a distance, but &#34;intervenes&#34; at the origin. Positivity of solutions (for po

Zhongyuan Liu, Shuying Tian, Huafei Xie, Pingping Yang

In this paper, we are concerned with qualitative properties of multi-peak solutions of the following nonlinear Schrödinger equations \begin{equation*} -Δu+V(x)u= u^{p-\varepsilon},\,\,\,u>0,\,\,\,\text{in}\,\,\,\mathbb{R}^N, \end{equation*} where $V(x)$ is a nonnegative continuous function, $\varepsilon>0$, $p=\frac{N+2}{N-2}$, $N\geq6$. The existence of mul

Neelanjan Chakraborti, Snehasish Nandy, Sudeep Kumar Ghosh

Unambiguously identifying unconventional magnetic orders requires probes that are directly sensitive to their momentum-dependent spin-split band structures. Here, we employ a framework based on Zeeman quantum geometry to study magnetotransport at the interface between a magnetic topological insulator and an unconventional magnetic insulator. By choosing the

Ruiqi Chen, Giacomo Vedovati, Todd Braver, ShiNung Ching

Dynamical systems models such as recurrent neural networks (RNNs) are increasingly popular in theoretical neuroscience for hypothesis-generation and data analysis. Evaluating the dynamics in such models is key to understanding their learned generative mechanisms. However, such evaluation is impeded by two major challenges: First, comparison of learned dynami

Riju Pal, Md. Nur Hasan, Chumki Nayak, Mrinal Deka

Quasi-2D van der Waals ferromagnet Fe$_4$GeTe$_2$, featuring the simultaneous presence of high Curie temperature ($T_\mathrm{C}$ $\sim 270$ K) and a spin-reorientation transition at $T_\mathrm{SR}$ $\sim 110$ K, is a rare system where strong interplay of spin dynamics, lattice vibrations, and electronic structure leads to a wide range of interesting phenomen

Zhexi Lu, Hongliang Chi, Nathalie Baracaldo, Swanand Ravindra Kadhe

Membership inference attacks (MIAs) pose a critical privacy threat to fine-tuned large language models (LLMs), especially when models are adapted to domain-specific tasks using sensitive data. While prior black-box MIA techniques rely on confidence scores or token likelihoods, these signals are often entangled with a sample&#39;s intrinsic properties - such

Dorilson Silva Cambui

In earlier work, we introduced a discrete Fibonacci-based ontogenetic model in which the metabolic scaling exponent $b(n)$ is treated as a dynamic function of an organism&#39;s developmental stage, and we estimated $b(n)$ for selected mammalian species. In the present article, we revisit this framework with a complementary aim. Rather than proposing new para

Ming Zeng, Frederic Rendell-Bhatti, Eamonn T. Connolly, Yang Wang

Barocaloric (BC) effects at liquid-vapor transitions in hydrofluorocarbons drive most commercial technologies used for heating and cooling in the heating, ventilation and air-conditioning sector. However, these fluids suffer from huge global warming potential and alternative gases are less efficient, toxic or flammable. Solid-solid and solid-liquid BC materi

Daniel Schwabe

The MEVIR 2 framework innovates and improves how we understand trust decisions in our polarized information landscape. Unlike classical models assuming ideal rationality, MEVIR 2 recognizes that human trust emerges from three interacting foundations: how we process evidence procedurally, our character as epistemic agents virtue theory, and our moral intuitio

Takeshi Fukuyama

Recent ALICE results demonstrate that over 90\% of light nuclei and anti-nuclei ($d$, $\bar d$) observed in heavy-ion collisions originate from a non-equilibrium, multi-stage process: $\Delta$-resonance production, decay into correlated nucleons, and their subsequent coalescence in a cooler hadronic environment. Although the final particle yields appear ther