March 2026 arXiv papers — page 87

Ling Xiao, Daeun Song, Xuesu Xiao, Toshihiko Yamasaki

Language models (LMs) are increasingly applied to robotic navigation; however, existing benchmarks primarily emphasize navigation success rates while paying limited attention to social compliance. Moreover, relying on large-scale LMs can raise efficiency concerns, as their heavy computational overhead leads to slower response times and higher energy consumpt

Tian-Cong Wang, Jun-Shuo Zhang, Xiao-Hui Liu, Wei-Yang Wang

Polarization measurements of fast radio bursts (FRBs) probe the magnetized plasma surrounding their central engines. FRB~20240114A is an exceptionally active repeating source, with 17,356 bursts detected between 2024 January 28 and 2025 May 30 by FAST, enabling time-resolved polarimetric studies. In this work, we present a polarimetric catalog of 6,131 brigh

Xueqi Ma, Shuo Yang, Yanbei Jiang, Shu Liu

Despite remarkable advances in large Vision-Language Models (VLMs), spatial reasoning remains a persistent challenge. In this work, we investigate how attention heads within VLMs contribute to spatial reasoning by analyzing their functional roles through a mechanistic interpretability lens. We introduce CogVSR, a dataset that decomposes complex spatial reaso

Huanyu Wang, Ziyu Xia, Zhuoming Chen, Beidi Chen

Large language model (LLM) services are mostly centralized, leading to scalability bottlenecks and underutilization of substantial scattered GPU resources. While decentralization offers a promising alternative, existing frameworks primarily focus on cooperation among GPU providers while overlooking their inherent competitive dynamics, imposing substantial co

Dongwook Choa, Jeongseok Oh

For a $(-1)$-shifted Lagrangian in a critical locus, we construct a homomorphism from the $K$-group of matrix factorisations of the critical locus to the $K$-group of the Lagrangian, partially answering the Joyce-Safronov conjecture. The key step is the construction of a specialisation functor for categories of matrix factorisations along the deformation to

Zhichao Wu, Junyin Ye, Zhilong Zhang, Yihao Sun

While current embodied policies exhibit remarkable manipulation skills, their execution remains unsatisfactorily slow as they inherit the tardy pacing of human demonstrations. Existing acceleration methods typically require policy retraining or costly online interactions, limiting their scalability for large-scale foundation models. In this paper, we propose

Hoang Van Quyet

In this paper, we establish a generalized geometric framework based on the Gauss-Bonnet theorem and the Jacobi metric to investigate the gravitational deflection of massive spinning particles up to the quadrupole order $\mathcal{O}(s^2)$. Deviating from conventional geodesic approaches that are strictly limited to the pole-dipole approximation, we incorporat

Aratrika Mustafi, Soumya Mukherjee

We propose a dense associative memory for empirical measures (weighted point clouds). Stored patterns and queries are finitely supported probability measures, and retrieval is defined by minimizing a Hopfield-style log-sum-exp energy built from the debiased Sinkhorn divergence. We derive retrieval dynamics as a spherical Hellinger Kantorovich (SHK) gradient

Anish Lakkapragada

We introduce Exponential Family Discriminant Analysis (EFDA), a unified generative framework that extends classical Linear Discriminant Analysis (LDA) beyond the Gaussian setting to any member of the exponential family. Under the assumption that each class-conditional density belongs to a common exponential family, EFDA derives closed-form maximum-likelihood

Chien-Ping Lu

Classical Amdahl's Law conceptualized the limit of speedup for an era of fixed serial-parallel decomposition and homogeneous replication. Modern heterogeneous systems need a different conceptual framework: constrained resources must be allocated across heterogeneous hardware while workloads themselves change, with some stages becoming effectively bounded and

Yoshiyuki Endo

We study polynomial random dynamical systems with complete connections on the Riemann sphere. In this framework, the choice of the next polynomial map is governed by a state-dependent rule with memory, extending both i.i.d. random dynamics and non-i.i.d. Markovian models. For each initial state, we define the probability that the random orbit tends to infini

Linshan Sun, Sergio Carbajo

Indirect control of ultraviolet (UV) pulse phase through nonlinear frequency conversion is attractive when direct UV pulse shaping is limited by material loss, dispersion, and damage threshold. Here we cast dispersive four-wave mixing (DFWM) as a pump-conditioned spectral kernel and show that, in a locally one-to-one mapping regime, the signal-to-idler conve

Weijie Gao, Nguyen H. Ngo, Daiki Ichikawa, Mingxiang Li

Terahertz bands enable ultra-broadband wireless communications but require compact, low-cost, and efficient transceiver modules. Conventional implementations based on metallic waveguides or silicon lenses suffer from high loss, bulkiness, and fabrication complexity. Here, we present a compact terahertz transceiver module enabled by a resonant tunneling diode

Zonglin Yang, J. -H. Xie, Lining Zhang, Jiyou Jia

Deploying high-fidelity AI tutors in schools is often blocked by the Resource Curse -- the need for expensive cloud GPUs and massive data engineering. In this practitioner report, we present a replicable Standard Operating Procedure that breaks this barrier. Using a Vision-Language Model data cleaning strategy and a novel Shadow-RAG architecture, we localize

Yiyao Lian, Zhenyu Wan, Zhaoyang Yin

In this paper, we study the global existence of solutions of the Cauchy problem for a class of weakly dissipative nonlinear dispersive wave equations $u_t-u_{xxt}+(f\left(u\right))_x-(f\left(u\right))_{xxx}+\left(g\left(u\right)+\frac{f^{\prime\prime}\left(u\right)}{2}u_x^2\right)_x+\lambda\left(u-u_{xx}\right)=0$. This includes the weakly dissipative Camass

Ling Xiao, Toshihiko Yamasaki

Most fine-grained fashion image retrieval (FIR) methods assume a static setting, requiring full retraining when new attributes appear, which is costly and impractical for dynamic scenarios. Although pretrained models support zero-shot inference, their accuracy drops without supervision, and no prior work explores class-incremental learning (CIL) for fine-gra

Ziru Chen, Salvatore Talarico, Qing Xia, Xihan Peng

In next generation of Wi-Fi networks Multiple Access Point Coordination (MAPC) is poised to significantly enhance the network performance by enabling a set of Access Points (APs) to coordinate with each other through advanced coordinating schemes so that to reduce inter-AP contention and congestion. This paper focuses on defining a framework to facilitate th

Sibasish Mishra, Aritra Sarkar, Sebastian Feld

The scalability of quantum computing in supporting sophisticated algorithms critically depends not only on qubit quality and error handling, but also on the efficiency of classical control, constrained by the cryogenic control bandwidth and energy budget. In this work, we address this challenge by investigating the algorithmic complexity of quantum circuits

Zixuan Zhang, Kaixuan Huang, Tuo Zhao, Mengdi Wang

Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data concentrated on low-dimensional structures -- remains incomplete. This paper investigates how diffusion models learn such structured data, focusing on two key aspects: statistical complexity and influence of dat

Guanzhou Chen, Erfei Cui, Changyao Tian, Danni Yang

Instruction-based image editing has emerged as a key capability for unified multimodal models (UMMs), yet constructing large-scale, diverse, and high-quality editing datasets without costly proprietary APIs remains challenging. Previous image editing datasets either rely on closed-source models for annotation, which prevents cost-effective scaling, or employ

Jon-Paul Cacioli

How do transformer language models represent magnitude? Recent work disagrees: some find logarithmic spacing, others linear encoding, others per-digit circular representations. We apply the formal tools of psychophysics to resolve this. Using four converging paradigms (representational similarity analysis, behavioural discrimination, precision gradients, cau

Xinyu Liu, Dongbin Xiu

We study the evolution of observables of dynamical systems. For linear systems, we show that observables satisfy a closed differential equation whose minimal order is determined by the dynamical system and observation operator. This yields a minimal order closure and an equivalent discrete delay representation of the observable dynamics. For nonlinear system

Manh Nguyen, Anh Nguyen, Dung Nguyen, Svetha Venkatesh

Multi-Agent Debate has emerged as a promising framework for improving the reasoning quality of large language models through iterative inter-agent communication. However, broadcasting all agent messages at every round introduces noise and redundancy that can degrade debate quality and waste computational resources. Current approaches rely on uncertainty esti

James Evans, Benjamin Bratton, Blaise Agüera y Arcas

The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is fundamentally plural, social, and relational. Recent advances in agentic AI reveal that frontier reasoning models, such as DeepSeek-R1, do not improve simply by "thinking longer". Instead, they simulate internal "societies of thought," sp

Jingbin Hu, Haoyu Zhang, Dake Guo, Qirui Zhan

Large Language Models (LLMs) have advanced audio generation through discrete representation learning. However, most existing neural codecs focus on speech and emphasize reconstruction fidelity, overlooking unified low frame rate modeling across diverse audio domains, including speech, music, and general sound. Moreover, high reconstruction quality does not n

Eliseo Sarmiento Rosales, Egor Maximenko, Dionisio Manuel Tun Molina, Juan Carlos Jimenez Cervantes

We establish an exact noise-model-derived characterization of quantum error correction under diagonal local phase noise. Under uniform locality, the maximal logical dimension under t-local phase errors equals Aq(n,2t+1), the classical q-ary packing function. Because no affine or stabilizer structure is imposed, nonlinear spectral supports achieve this bound

Sen Fang, Weiyuan Ding, Zhezhen Cao, Zhou Yang

Large Language Models (LLMs) are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound. We identify a root cause shared by both major mitigation paradigms (agent-based debate and retrieval augmentation): reasoning in an ungrounded deliberative space that lacks a bounded, hypothesis-specific evidence base. Without

Shadi Sartipi, John Wu, Sina Ghotbi, Nikhita Vedula

Detecting product price outliers is important for retail and e-commerce stores as erroneous or unexpectedly high prices adversely affect competitiveness, revenue, and consumer trust. Classical techniques offer simple thresholds while ignoring the rich semantic relationships among product attributes. We propose an agentic Large Language Model (LLM) framework

Dmitri N. Zakharov, Xiaohui Qu, Hong Wang, Yuewei Lin

We use in situ transmission electron microscopy with automated tracking to study supported gold nanoparticles (NPs) during high-temperature vacuum annealing. \rev{The average mass loss per NP is governed by a flat, nearly size-independent substrate-mediated evaporation profile.} On top of \rev{this mean shrinkage}, individual NPs show significant fluctuation

Chao Wang, Xuancheng Zhou, Ruilin Hou, Xiaoyu Cheng

Accurately characterizing non-linear functional manifolds with singularities is a fundamental challenge in scientific computing. While Multi-Layer Perceptrons (MLPs) dominate, their spectral bias hinders resolving high-curvature features without excessive parameters. We introduce Continued Fraction Neural Networks (CFNNs), integrating continued fractions wit

Bytedance Seed

We present Seed1.8, a foundation model aimed at generalized real-world agency: going beyond single-turn prediction to multi-turn interaction, tool use, and multi-step execution. Seed1.8 keeps strong LLM and vision-language performance while supporting a unified agentic interface-search, code generation and execution, and GUI interaction. For deployment, it o

Zehao Li, Tao Ren, Zishi Zhang, Xi Chen

Large language model (LLM) training is often bottlenecked by memory constraints and stochastic gradient noise in extremely high-dimensional parameter spaces. Motivated by empirical evidence that many LLM gradient matrices are effectively low-rank during training, we present an unbiased, memory-efficient, low-rank matrix estimator with the lowest variance tha

Kry Yik Chau Lui, Cheng Chi, Kishore Basu, Yanshuai Cao

Despite their dominance in vision and language, deep neural networks often underperform relative to tree-based models on tabular data. To bridge this gap, we incorporate five key inductive biases into deep learning: robustness to irrelevant features, axis alignment, localized irregularities, feature heterogeneity, and training stability. We propose \emph{Las

Cheng Yang, Dong Li

Flexible antenna technology has recently emerged as a key enabler for next-generation wireless communications, which can effectively exploit the spatial degrees of freedom (DoF). However, existing conventional metrics (e.g., spectral and energy efficiency) cannot directly measure the variability for different flexible antenna structures for the spatial DoF.

George M. Bergman

Let $P,$ $S,$ and $T$ be semigroups, $f:P\to S$ and $g:P\to T$ semigroup homomorphisms, and $X$ a generating set for $S$ (possibly infinite). Clearly, a <i>necessary</i> condition for there to exist a homomorphism $S\to T$ making a commuting triangle with $f$ and $g$ is that for every relation $f(p) = w(x_1,\,\dots\,,\,x_n)$ holding in $S$, with $p\in P,$ $w

Hanzhang Hu, Zetao Ma, Lei Zhang

In this paper, we develop a Localized Orthogonal Decomposition (LOD) method for the two-dimensional time-dependent nonlinear Schr\"{o}dinger equation with a wave operator. We prove that our method preserves conservation laws and admits a unique numerical solution; furthermore, we obtain unconditional (i.e., time-step restriction-free) optimal-order superconv

Yangyi Wu, Tianqi Wang, Xilin Liu

With the development of Large Language Models (LLMs) in consulting, their role in moral decision-making has become prominent. However, existing research predominantly consider AI as an independent "moral agent" adhering to the "Human-AI Alignment" paradigm. In this study, we propose that AI should serve as a "moral assistant", facilitating users' moral growt

Yusheng Zheng, Yiwei Yang, Wei Zhang, Andi Quinn

LLM agent frameworks increasingly offer checkpoint-restore for error recovery and exploration, advising developers to make external tool calls safe to retry. This advice assumes that a retried call will be identical to the original, an assumption that holds for traditional programs but fails for LLM agents, which re-synthesize subtly different requests after

Mark Magsino

We present a statistical analysis of a variant of the periodogram method that forms power spectral density estimates by cross-correlating the discrete Fourier transforms of adjacent time windows. The proposed estimator is closely related to cross-power spectral methods and to a technique introduced by Nelson, which has been observed empirically to improve de

Jianming Tong, Devansh Jain, Yujie Li, Charith Mendis

Modern reconfigurable AI accelerators rely on rich mapping and data-layout flexibility to sustain high utilization across matrix multiplication, convolution, and emerging applications beyond AI. However, exposing this flexibility through fine-grained micro-control results in prohibitive control overhead of fetching configuration bits from off-chip memory. Th

Qiange Wang, Haoran Lv, Yanfeng Zhang, Weng-Fai Wong

Graph Neural Network (GNN) on streaming graphs has gained increasing popularity. However, its practical deployment remains challenging, as the inference process relies on Runtime Embedding Computation (RTEC) to capture recent graph changes. This process incurs heavyweight multi-hop graph traversal overhead, which significantly undermines computation efficien

Jiayang Wan, Hongwei Hou, Jiawei Zhuang, Wenjin Wang

This paper investigates narrowband coordinated user scheduling in multi-cell massive multiple-input multiple-output (MIMO) systems. We formulate the problem under a spectral-efficiency maximization criterion, revealing inherent challenges in computational complexity and signaling overhead. To address these, we develop a user-scheduling-oriented CKM (US-CKM)

Yijie Hao, Lingjie Chen, Ali Emami, Joyce Ho

Can we trust the reasoning traces that large reasoning models (LRMs) produce? We investigate whether these traces faithfully reflect what drives model outputs, and whether models will honestly report their influence. We introduce Thought Injection, a method that injects synthetic reasoning snippets into a model's <think> trace, then measures whether the mode

Alice Cai, Iman YeckehZaare, Shuo Sun, Vasiliki Charisi

Artificial intelligence (AI) is poised to profoundly reshape how work is executed and organized, but we do not yet have deep frameworks for understanding where AI can be used. Here we provide a comprehensive ontology of work activities that can help systematically analyze and predict uses of AI. To do this, we disaggregate and then substantially reorganize t

Shiwen Shan, Yintong Huo, Hongzhan Zhong, Zhining Wang

Logs are essential for diagnosing failures and conducting retrospective studies, leading many software organizations to retain log messages for a long time. Nevertheless, the volume of generated log data grows rapidly as software systems grow, necessitating an effective compression method. Apart from general-purpose compressors (e.g., Gzip, Bzip2), many rece

Ruijie Miao, Zhiming Wang, Wang Li, Shiwei Wu

Key-value (KV) caching is widely used to accelerate transformer inference, but its memory cost grows linearly with input length, limiting long-context deployment. Existing token eviction methods reduce memory by discarding less important tokens, which can be viewed as a coarse form of dimensionality reduction that assigns each token either zero or full dimen

Jiahao Chen, Zhiming Zhao, Yuwen Pu, Chunyi Zhou

Federated learning (FL) has attracted substantial attention in both academia and industry, yet its practical security posture remains poorly understood. In particular, a large body of poisoning research is evaluated under idealized assumptions about attacker participation, client homogeneity, and success metrics, which can substantially distort how security

Shogo Shimada, Akira Saito

In various engineering fields including mechanical, aerospace, and civil engineering, the identification of modal parameters, including natural frequencies, damping ratios, and mode shapes, is crucial for determining the vibration characteristics of engineered structures. A common method for identifying the modal parameters of structures involves experimenta

Ke Ma, Francesca Valsecchi, Yuchen Tan, Mingjia Ji

Temporary luxury branded events run on short cycles and bespoke builds that accelerate material churn. We present a circular phygital product-service system that operationalises the circular economy (CE) through a 4R frame (Refuse, Reduce, Reuse, and Recycling) across warehouse-to-event journeys. Developed via a multi-method design inquiry with a tier-1 cont

Di Kong, Yikai Wang, Wenjie Guo, Yifan Bu

Slice-based volumetric imaging is widely applied and it demands representations that compress aggressively while preserving internal structure for analysis. We introduce GaussianPile, unifying 3D Gaussian splatting with an imaging system-aware focus model to address this challenge. Our proposed method introduces three key innovations: (i) a slice-aware pilin

Tsung-Han Yang, Satoshi Okamoto, D. Alan Tennant, Michael A. McGuire

Topological phase transitions provide a unique window into the interplay between structure, magnetism, and Weyl physics in magnetic Weyl semimetals. However, realizing an intrinsic Weyl phase transition between two distinct Weyl states near room temperature remains challenging. Here, we demonstrate that a magnetostructural transition effectively induces such

Yongqiang Li, Feng Shu, Shaofan Chen, Yuanyuan Wu

This paper investigates secure Directional Modulation (DM) design enhanced by a rotatable active Reconfigurable Intelligent Surface (RIS). In conventional RIS-assisted DM networks, the security performance gain is limited due to the multiplicative path loss introduced by the RIS reflection path. To address this challenge, a Secrecy Rate (SR) maximization pro

Zhilong Zhang, Haoxiang Ren, Yihao Sun, Yifei Sheng

Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety risks of real-world interaction. Training VLA models in interactive world models avoids these issues but introduces several challenges, including pixel-level world modeling, multi-

Paul H. Frampton, Thomas W. Kephart

The LHC has failed to discover any new elementary particle since the Higgs boson completed the standard model in 2012, Here we adopt the attractive method of quiver gauge field theories to make predictions of additional particles which might be found at Run 4 of the upgraded LH-LHC scheduled to begin in 2030. We use an $SU(3)^4$ quiver gauge theory and exhau

Zhi-Hao Cui, Hao Wu

Finite excursions away from zero of a spectrally positive compound Poisson process with a negative drift can always be decomposed into two parts lying above and below zero, respectively. This paper is concerned with the asymptotic relationships among the lengths and heights of these two parts. Our results state that both their lengths and heights are asympto

Chang-Wei Yueh, Andy Zhao, Ashutosh Nayyar, Rahul Jain

We study Bayesian learning in episodic, finite-horizon zero-sum Markov games with unknown transition and reward models. We investigate a posterior algorithm in which each player maintains a Bayesian posterior over the game model, independently samples a game model at the beginning of each episode, and computes an equilibrium policy for the sampled model. We

Hang-Cheng Dong, Pengcheng Cheng, Shuhuan Li

Solving ill-posed inverse problems necessitates effective regularization strategies to stabilize the inversion process against measurement noise. While classical methods like Tikhonov regularization require heuristic parameter tuning, and standard deep learning approaches often lack interpretability and generalization across resolutions, we propose SC-Net (S

Anibal Mantilla-Guerra, Christian Mejia-Escobar, Jorge Azorin-Lopez, Jose Garcia-Rodriguez

The use of clean energy is a global trend, with solar photovoltaic plants serving as a cornerstone of this energy transition. To support this rapid growth, optimize energy utilization, and enable a wide range of applications and services, it is essential to have access to more sophisticated and detailed solar data. Specifically, existing datasets lack integr

Hao Xu, Yuntian Chen, Chongqing Kang, Dongxiao Zhang

The global shift towards renewable energy necessitates the development of ultrahigh-voltage (UHV) AC transmission to bridge the gap between remote energy sources and urban demand. While UHV grids offer superior capacity and efficiency, their implementation is often hindered by corona-induced audible noise (AN) and radio interference (RI). Since these emissio

Franco Dassi, Andres E Rubiano, Iván Velásquez

We introduce a novel residual-based a posteriori error estimator for the conforming $C^1$ Virtual Element Method (VEM) applied to the buckling eigenvalue problem, incorporating nonlinear plane stress effects in both two and three dimensions. The estimator is fully computable on general polyhedral meshes and implemented within the open-source \texttt{vem++} l

Elizabeth Xiao

We equip the graded polynomial algebra generated by nonplanar rooted binary trees with a Hopf algebra structure by defining a coproduct which disallows cutting both children of any given vertex, refining Connes-Kreimer's notion of admissible cuts. We show that the terms in this coproduct have an additional combinatorial interpretation in terms of subsets of

C. Biliotti, M. Riccaboni, J. W. Lockhart, J. A. Evans

Women and men pursue different but complementary forms of scientific innovation. Analyzing 261,452 solo-authored papers by U.S. scholars, with patterns confirmed by millions of multi-authored articles, we show that women more often bridge distant disciplines through novel reference combinations, while men more often recombine concepts within fields. Women's

Robin Bloomfield, John Rushby

Confidence is central to safety and assurance cases: how much confidence a decision requires and how much the argument actually provides are both important questions. We present a new method for assessing probabilistic confidence in assurance case arguments that is simple, systematic and sound. It exploits the ways claims are decomposed in a structured argum

Hong Liu, Xiaolong Liao, Yi Zhang

We examine parameter degeneracies in Culetu, Bardeen and Hayward regular black holes across lensing, shadow and quasinormal mode regimes. Our analysis reveals that while Einstein ring data yield extremely loose constraints, with the regularization parameter $q$ exceeding $\mathcal{O}(10^3)$, they fail to improve the parameter estimation when combined with st

Truong Thanh Hung Nguyen, Hélène Fournier, Piper Jackson, Makoto Itoh

Multi-agent systems (MAS) are increasingly used in healthcare to support complex decision-making through collaboration among specialized agents. Because these systems act as collective decision-makers, they raise challenges for trust, accountability, and human oversight. Existing approaches to trustworthy AI largely rely on explainability, but explainability

Hao Yang, Chen Lan

Regular black holes and black-bounce spacetimes frequently emerge in theoretical frameworks beyond general relativity, as well as in general relativity coupled to non-linear sources. A profound complication in these frameworks is source ambiguity: a single spacetime metric can often be supported by multiple, inequivalent matter-source interpretations, such a

Jiawei Zhang, Xiaolu Jia, Sakurako Tanida, Claudio Feliciani

Pedestrian congestion at corridor intersections often originates from localized fluctuations in motion rather than from a macroscopic collapse of flow. Understanding pedestrian instability at corridor intersections remains challenging because existing studies mainly rely on density, average speed, or flow-based measures and limited datasets, making it diffic

Simon J Haward, Amy Q Shen

Recent works reveal the importance of chaotic flow fluctuations as a mechanism for the enhanced resistance observed in viscoelastic porous media flows, and also show how chaotic fluctuations are affected by the structural disorder of porous media. We seek further insight by performing pressure drop measurements and flow velocimetry on two viscoelastic fluids

Sofia Morgado, Filipa Valdeira, Niklas Sander, Diogo Ferreira

Coronary angiography (CAG) reports contain clinically relevant physiological measurements, yet this information is typically in the form of unstructured natural language, limiting its use in research. We investigate the use of Large Language Models (LLMs) to automatically extract these values, along with their anatomical locations, from Portuguese CAG report

Jasmine Brazilek, Miles Tidmarsh

We investigate the robustness of value alignment via midtraining with synthetic documents, using animal compassion as a value that is both important in its own right and orthogonal to existing alignment efforts. To evaluate compassionate reasoning, we develop and publicly release Animal Norms In Moral Assessment (ANIMA), a 26-question evaluation spanning 13

Santiago Ampudia Castelazo, Umar Sohail Qureshi, Tim Barklow, Ariel Schwartzman

We present a study probing the Higgs self-coupling with the X-ray free-electron laser Compton $\gamma\gamma$ Collider (XCC) concept. The analysis is performed considering the $\gamma\gamma \to HH \to bb\overline{bb}$ channel, and results are then extrapolated to obtain a projection on the Higgs self-coupling sensitivity that ranges between 7% and 12%. An ens

Michelle Baker, Sara Pollock

In this paper, we apply acceleration to the inverse-free preconditioned Krylov subspace method introduced by Golub and Ye, which solves the symmetric generalized eigenvalue problem for the algebraically smallest eigenvalue. As the method is an improvement on steepest descent, we consider acceleration based on Nesterov accelerated steepest descent and Polyak'

Alankrita Bhatt, Mukur Gupta, Germain Kolossov, Andrea Montanari

Generating data from discrete distributions is important for a number of application domains including text, tabular data, and genomic data. Several groups have recently used random $k$-satisfiability ($k$-SAT) as a synthetic benchmark for new generative techniques. In this paper, we show that fundamental insights from the theory of random constraint satisfa

M. L. Arias, A. F. Torres, M. Kraus, L. S. Cidale

The post-main-sequence evolution of massive stars involves phases of intense, often eruptive mass loss, including the B[e] supergiant phase. These hot stars are surrounded by cool, dense circumstellar disks that host complex chemistry, producing both molecules and dust. Understanding the mass-loss history of B[e] supergiants is essential for constraining ste

Feiran Wang, Zezhou Shang, Gaowen Liu, Yan Yan

Streaming feed-forward 3D reconstruction enables real-time joint estimation of scene geometry and camera poses from RGB images. However, without explicit dynamic reasoning, streaming models can be affected by moving objects, causing artifacts and drift. In this work, we propose RayMap3R, a training-free streaming framework for dynamic scene reconstruction. W

James Alcala, Rayna Andreeva, Vladimir A. Kobzar, Dustin G. Mixon

When training a neural network for classification, the feature vectors of the training set are known to collapse to the vertices of a regular simplex, provided the dimension $d$ of the feature space and the number $n$ of classes satisfies $n\leq d+1$. This phenomenon is known as neural collapse. For other applications like language models, one instead takes

Dong Liu, Yanxuan Yu, Ben Lengerich, Ying Nian Wu

As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference. While prior works such as Multi-Query Attention (MQA) and Multi-Latent Attention (MLA) reduce memory by sharing or compressing KV features, they often

Muralikrishnna G. Sethuraman, Faramarz Fekri

Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the system variables -- assumptions that fail to hold in many real-world settings. For instance, in genomics, cyclic regulatory networks are common, and measurements are often corrupte

Liangyu Yuan, Yufei Huang, Mingkun Lei, Tong Zhao

Diffusion models generate synthetic images through an iterative refinement process. However, the misalignment between the simulation-free objective and the iterative process often causes accumulated gradient error along the sampling trajectory, which leads to unsatisfactory results and a failure to generalize. Guidance techniques like Classifier Free Guidanc

Tomasz Frelek, Rohan Patil, Akshar Tumu, Henrik I. Christensen

We present a scalable self-supervised approach for segmenting feasible vehicle trajectories from monocular images for autonomous driving in complex urban environments. Leveraging large-scale dashcam videos, we treat recorded ego-vehicle motion as implicit supervision and recover camera trajectories via monocular structure-from-motion, projecting them onto th

Nilay Tiwari

Denoising diffusion probabilistic models (DDPMs) have emerged as powerful generative models for complex distributions, yet their use in arbitrage-free derivative pricing remains largely unexplored. Financial asset prices are naturally modeled by stochastic differential equations (SDEs), whose forward and reverse density evolution closely parallels the forwar

Taihei Shiotani, Masahiro Kaneko, Ayana Niwa, Yuki Maruyama

Social biases reflected in language are inherently shaped by cultural norms, which vary significantly across regions and lead to diverse manifestations of stereotypes. Existing evaluations of social bias in large language models (LLMs) for non-English contexts, however, often rely on translations of English benchmarks. Such benchmarks fail to reflect local c

Silvana M. Pesenti, Thai Nguyen

We consider the problem of active portfolio management, where an investor seeks the portfolio with maximal expected utility of the difference between the terminal wealth of their strategy and a proportion of the benchmark's, subject to a budget and a deviation constraint from the benchmark portfolio. As the investor aims at outperforming the benchmark, they

Smriti Nandan Paul, Siwei Fan

The cislunar regime departs from near-Earth orbital behavior through strongly non-linear, non-Keplerian dynamics, which adversely affect the accuracy of uncertainty propagation and state estimation. Additional challenges arise from long-range observation requirements, restrictive sensor-target geometry and illumination conditions, the need to monitor an expa

Zihua Wu, Georg Gartner

The prevailing approach to improving large language model (LLM) reasoning has centered on expanding context windows, implicitly assuming that more tokens yield better performance. However, empirical evidence - including the "lost in the middle" effect and long-distance relational degradation - demonstrates that contextual space exhibits structural gradients,

Ruiying Ma, Shreya Shankar, Ruiqi Chen, Yiming Lin

Users across enterprises increasingly rely on AI agents to query their data through natural language. However, building reliable data agents remains difficult because real-world data is often fragmented across multiple heterogeneous database systems, with inconsistent references and information buried in unstructured text. Existing benchmarks only tackle ind

Logan Banker, Michael Wozniak, Mohanad Alameer, Smriti Nandan Paul

As near-Earth resident space objects proliferate, there is an increasing demand for reliable technologies in applications of on-orbit servicing, debris removal, and orbit modification. Rendezvous and docking are critical mission phases for such applications and can benefit from greater autonomy to reduce operational complexity and human workload. Machine lea

Jared Sisler, Claudio U. Hail, Zoey S. Davidson, Austin M. K. Fehr

Active wavefront control in high-power laser illumination systems is important for technologies such as additive manufacturing, free-space laser communication, and power transmission. Conventional spatial light modulators (SLMs) and mechanical beam-steering devices are unsuitable for such applications as they rely on metal mirrors and electrical contacts whi

Cuidi Wei, Shaoyu Tu, Daiki Hata, Toru Hasegawa

Our analysis of recent Internet traces shows that up to 71% of flows contain suspicious behaviors indicative of low-volume network attacks such as port scans. However, distinguishing anomalous traffic in real time is challenging as each attack flow may comprise only a few packets. We extend prior work that tracks heavy hitter flows to also detect low-volume

Hung Yun Tseng, Wuzhen Li, Blerina Gkotse, Grigorios Chrysos

The potential of Large Language Models (LLMs) to provide harmful information remains a significant concern due to the vast breadth of illegal queries they may encounter. Unfortunately, existing benchmarks only focus on a handful types of illegal activities, and are not grounded in legal works. In this work, we introduce an ontology of crime-related concepts

Wen-Xiang Chen

This paper develops a gravitational-thermodynamic interpretation of two ensemble structures with singular behavior, denoted as canonical ensemble A and grand canonical ensemble B. Ensemble A is modeled as a stellar-type system in which energy plays the dominant thermodynamic role under an effectively fixed particle-number condition, whereas ensemble B is mod

Kamilė Lukošiūtė, John Halstead, Luca Righetti

AI companies and governments are increasingly concerned about frontier AI systems enabling cybercrime, yet defining meaningful capability thresholds requires knowing the scale of cybercrime today. Current estimates of global cybercrime damages vary from tens of billions to tens of trillions of dollars, with little systematic evaluation of their reliability.

Krati Saxena, Tomohiro Shibata

Recommending safe and effective medication combinations from electronic health records (EHRs) is a core clinical AI problem, yet it remains difficult because patient trajectories are long, noisy, and clinically heterogeneous. Existing methods typically excel at either temporal modeling across visits or pharmacological knowledge integration (e.g., drug-drug i

Artur Morys-Magiera, Marek Długosz, Paweł Skruch

We present a comparative study of methods for generating realistic, constrained small- to medium-scale road networks with built-in redundancy. In this research, we evaluate the proposed Evolutionary Algorithm (EA) with connectivity and redundancy constraints against the Wave Function Collapse (WFC) method - commonly used in procedural terrain generation for

J. Redeker, C. Lin, Y. W. Wah, J. K. Ahn

We performed searches for $K_L\rightarrow π^0π^0X$ where $X$ may be an axion-like particle which promptly decays to two photons, and the first search for $K_L \rightarrow π^0π^0γγ$ at the KOTO experiment using data taken in 2021. The search is performed for $X$ mass in the range of 160$\unicode{x2013}$220 MeV/$c^2$. Three events were observed in the signal r

K. B. Hidalgo-Castro, J. A. Méndez-Bermúdez, Edson D. Leonel

We study transport and escape in the Stochastic Web Map (SWM), an area-preserving system with phase-space structure controlled by a symmetry parameter $q$ and nonlinearity $K$. By analyzing the survival probability $P_{\text{S}}(n)$ and escape frequency $P_{\text{E}}(\ln n)$, we show that in the chaotic regime escape dynamics is governed by a single time sca

Milos Gravara, Juan Luis Herrera, Stefan Nastic

Compound AI is a distributed intelligence approach that represents a unified system orchestrating specialized AI/ML models with engineered software components into AI workflows. Compound AI production deployments must satisfy accuracy, latency, and cost objectives under varying loads. However, many deployments operate on fixed infrastructure where horizontal

Nicolas Moulonguet, Eloi Descamps, José Lorgeré, Astghik Saharyan

The quantum theory of the electromagnetic field enables the description of multiphoton states exhibiting nonclassical statistical properties, often reflected in non-Gaussian phase-space distributions. While non-Gaussianity alone does not fully characterize quantum states, several classifications have been proposed to hierarchize non-Gaussian states according

Giorgio Micali, David Garnés-Galindo, Mariano Matilla-García, Manuel Ruiz-Marín

We propose a nonparametric test of spatial independence for data observed on irregular, non-lattice point clouds $\mathcal{V}_{n}\subset\mathbb{R}^{2}$. For each location $v\in\mathcal{V}_{n}$, we encode the local spatial configuration through the ordinal pattern of the $m$ nearest-neighbour observations, obtaining a symbolic representation that is invariant

Gauthier Bertrand, Ramiro Godoy-Diana, Benjamin Thiria, Marc Fermigier

We study experimentally a a three-dimensional reduced model of a sail shape performing pitching oscillations around a mean incidence angle ($α_{m}$) with respect to an incoming flow in a hydrodynamic channel at a constant velocity where the Reynolds number based on the mean chord of the sail is Re$_{c} = ρU_{\infty} c / μ= 11900$. The problem is inspired by