May 2025 arXiv papers

Ivan Novak

We determine all groups which occur as torsion subgroups of $\mathbb Q$-curves defined over number fields of degrees $3$, $5$ and $7$. In particular, we prove that every torsion subgroup of a $\mathbb Q$-curve defined over a number field of degree $3,5$ or $7$ already occurs as a torsion subgroup of an elliptic curve with rational $j$-invariant. As the quadr

Dongzhe Zheng

We study two metric constructions for Hodge theory on bundles, together with the response of harmonic representatives and magnetic energies to degeneration. Our first result is an $L^1$ coercivity estimate for the covariant derivative on functions of bounded variation: the coercivity constant is positive exactly when the connection has no nonzero parallel se

Monoshi Kumar Roy, Simin Chen, Benjamin Steenhoek, Jinjun Peng

Understanding and reasoning about code semantics is essential for enhancing code LLMs' abilities to solve real-world software engineering (SE) tasks. Although several code reasoning benchmarks exist, most rely on synthetic datasets or educational coding problems and focus on coarse-grained reasoning tasks such as input/output prediction, limiting their e

Kazuki Irie, Morris Yau, Samuel J. Gershman

We develop hybrid memory architectures for general-purpose sequence processing neural networks, that combine key-value memory using softmax attention (KV-memory) with fast weight memory through dynamic synaptic modulation (FW-memory) -- the core principles of quadratic and linear transformers, respectively. These two memory systems have complementary but ind

Shangbin Feng, Yike Wang, Weijia Shi, Yulia Tsvetkov

We propose Data Swarms, an algorithm to optimize the generation of synthetic evaluation data and advance quantitative desiderata of LLM evaluation. We first train a swarm of initial data generators using existing data, and define various evaluation objectives to reflect the desired properties of evaluation (e.g., generate more difficult problems for the eval

Chiyu Zhang, Marc-Alexandre Cote, Michael Albada, Anush Sankaran

Large language model (LLM) agents have shown impressive capabilities in human language comprehension and reasoning, yet their potential in cybersecurity remains underexplored. We introduce DefenderBench, a practical, open-source toolkit for evaluating language agents across offense, defense, and cybersecurity knowledge-based tasks. DefenderBench includes env

Rynaa Grover, Jayant Sravan Tamarapalli, Sahiti Yerramilli, Nilay Pande

Recent Multimodal Large Language Models (MLLMs) demonstrate strong high-level visual reasoning on tasks such as visual question answering and image captioning. Yet existing benchmarks largely overlook their ability to capture fine-grained perceptual details. As MLLMs are increasingly deployed in safety and reliability critical settings, perceptual acuity bec

Binghang Lu, Changhong Mou, Guang Lin

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving forward and inverse problems involving partial differential equations (PDEs) by incorporating physical laws into the training process. However, the performance of PINNs is often hindered in real-world scenarios involving noisy observational data and missing physics, particul

Patrick Nyadjo Fonga

Let $E$ be a subset of the projective line over a commutative field $\mathbb{K}$. When $\mathbb{K}$ has infinite cardinality, it is well known that if $E$ contains at most three elements, then the group of linear fractional transformations preserving $E$ is either infinite or isomorphic to the symmetric group on three elements. In this work, we investigate t

Dongzhe Zheng

A connection and a nonzero parallel adjoint field determine an invariant hyperplane constraint on a principal bundle. Its sign mirror preserves the hyperplane and reverses its coorientation; global gauge realization is controlled by a twisted stabilizer reduction. For regular fields we identify the normalizing gauge extension as a pushout of the torus-normal

Tianze Yang, Tyson Jordan, Ruitong Sun, Ninghao Liu

We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722 unique images featuring both contextually coherent and inconsistent scenes, enabling effective

Siddharth Prabhu, Srinivas Rangarajan, Mayuresh Kothare

Inverse problem or parameter estimation of ordinary differential equations (ODEs), the iterative process of minimizing the mismatch between model-predicted and experimental states by tuning the parameter values within an optimization formulation, is commonplace in chemical engineering applications. A popular method for parameter estimation is sequential opti

Mina Huh, Zihui Xue, Ujjaini Das, Kumar Ashutosh

People use videos to learn new recipes, exercises, and crafts. Such videos remain difficult for blind and low vision (BLV) people to follow as they rely on visual comparison. Our observations of visual rehabilitation therapists (VRTs) guiding BLV people to follow how-to videos revealed that VRTs provide both proactive and responsive support including detaile

Shi Mao, Yogeshwar Nath Mishra, Wolfgang Heidrich

The desire for cameras with smaller form factors has recently lead to a push for exploring computational imaging systems with reduced optical complexity such as a smaller number of lens elements. Unfortunately such simplified optical systems usually suffer from severe aberrations, especially in off-axis regions, which can be difficult to correct purely in so

Mingwei Zheng, Chengpeng Wang, Xuwei Liu, Jinyao Guo

Functional correctness is critical for ensuring the reliability and security of network protocol implementations. Functional bugs, instances where implementations diverge from behaviors specified in RFC documents, can lead to severe consequences, including faulty routing, authentication bypasses, and service disruptions. Detecting these bugs requires deep se

Debarati Bhattacharjee, Ashish Anand

This paper presents a framework to convert argumentative texts into argument knowledge graphs (AKG). The proposed argumentative knowledge representation framework (AKReF) extends the theoretical foundation and enables the AKG to provide a graphical view of the argumentative structure that is easier to understand. Starting with basic annotations of argumentat

Joan Hernández

We characterize the s-parabolic Lipschitz caloric capacity of corner-like $s$-parabolic Cantor sets in $\mathbb{R}^{n+1}$ for $1/2<s\leq 1$. Despite the spatial gradient of the s-heat kernel lacking temporal anti-symmetry, we obtain analogous results to those known for analytic and Riesz capacities.

Wei Dai, Peilin Chen, Chanakya Ekbote, Paul Pu Liang

Clinical decision-making routinely demands reasoning over heterogeneous data, yet existing multimodal language models (MLLMs) remain largely vision-centric and fail to generalize across clinical specialties. To bridge this gap, we introduce QoQ-Med-7B/32B, the first open generalist clinical foundation model that jointly reasons across medical images, time-se

Yongkang Xiao, Sinian Zhang, Yi Dai, Huixue Zhou

Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information. Recently, generative large language models (LLMs) have been increasingly employed for graph tasks. However, current approaches typically encode graph context in textual form, which fails to fully exploit the poten

Xinxu Wei, Kanhao Zhao, Yong Jiao, Lifang He

As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While most existing brain foundation models are pre-trained on time-series signals or connectome features, we propose a novel graph-based pre-training paradigm for constructing a brain gra

Daozhe Lin, Qiang Du

This paper investigates solution strategies for nonlinear problems in Hilbert spaces, such as nonlinear partial differential equations (PDEs) in Sobolev spaces, when only finite measurements are available. We formulate this as a nonlinear optimal recovery problem, establishing its well-posedness and proving its convergence to the true solution as the number

Nikola Milosevic, Johannes Müller, Nico Scherf

In constrained Markov decision processes, enforcing constraints during training is often thought of as decreasing the final return. Recently, it was shown that constraints can be incorporated directly into the policy geometry, yielding an optimization trajectory close to the central path of a barrier method, which does not compromise final return. Building o

Megan S. Harris, Mohammad Mahdi Naderi, Ehsanoddin Ghorbanichemazkati, John C. Little

This introductory overview introduces a unified modeling framework for representing interconnected environmental systems using Model-Based Systems Engineering and Hetero-functional Graph Theory. Existing environmental models encode rich structural and process-level information, yet much of this knowledge remains implicit within domain-specific formulations.

Ben Zindorf, Sougato Bose

Multi-controlled single-target (MC) gates are some of the most crucial building blocks for varied quantum algorithms. How to implement them optimally is thus a pivotal question. To answer this question in an architecture-independent manner, and to get a worst-case estimate, we should look at a linear nearest-neighbor (LNN) architecture, as this can be embedd

Li Zhang, Morgan Gray, Jaromir Savelka, Kevin D. Ashley

Large Language Models (LLMs) demonstrate potential in complex legal tasks like argument generation, yet their reliability remains a concern. Building upon pilot work assessing LLM generation of 3-ply legal arguments using human evaluation, this paper introduces an automated pipeline to evaluate LLM performance on this task, specifically focusing on faithfuln

Junaid Muzaffar, Khubaib Ahmed, Ingo Frommholz, Zeeshan Pervez

Training reinforcement learning (RL) agents often requires significant computational resources and prolonged training durations. To address this challenge, we build upon prior work that introduced a neural architecture with permutation-invariant sensory processing. We propose a modified attention mechanism that applies a non-linear transformation to the key

J. Leaños, M. Lomelí-Haro, Christophe Ndjatchi, L. M. Ríos-Castro

Let $G=(V(G),E(G))$ be a simple graph, and let $U\subseteq V(G)$. Two distinct vertices $x,y\in U$ are $U$-mutually visible if $G$ contains a shortest $x$-$y$ path that is internally disjoint from $U$. $U$ is called a mutual-visibility set of $G$ if any two vertices of $U$ are $U$-mutually visible. The mutual-visibility number $μ(G)$ of $G$ is the size of a

Frank Göhmann, Andreas Klümper, Karol K. Kozlowski

Owing to the fact that the particle current operator in non-relativistic gases is proportional to the total momentum operator, the particle transport in such systems is always ballistic and fully characterized by a Drude weight $Δ$. The Drude weight can be calculated within linear response theory. It is given by the formula $Δ= 2 πD$, where $D$ is the densit

Max Chicky Fang

The Kruithof iterative scaling process, which adjusts matrices to meet target row and column sums, is a longstanding problem that lacks a general closed form for its limit. While Nathanson derived the closed form for the Sinkhorn limit of $2\times 2$ matrices when target row and column sums are 1, and recent work by Rowland and Wu has advanced understanding

Alessandro Ferreri, Vincenzo Macrì, Yoshihiko Hasegawa, David Edward Bruschi

Quantum optomechanics describes the interaction between a confined field and a fluctuating wall due to radiation pressure. The dynamics of this system is typically understood using perturbation theory up to second order in the small coupling. Improving beyond this regime can shed light onto new phenomena. In this work we study high-order resonant wall-field

Kausthubh Chandramouli, Kelly Mae Allen, Christopher Mori, Dror Baron

In the noisy intermediate-scale quantum (NISQ) era, quantum error mitigation (QEM) is essential for producing reliable outputs from quantum circuits. We present a statistical signal processing approach to QEM that estimates the most likely noiseless outputs from noisy quantum measurements. Our model assumes that circuit depth is sufficient for depolarizing n

Vahid Garousi, Zafar Jafarov, Aytan Movsumova, Atif Namazov

Context: As generative AI (GenAI) tools such as ChatGPT and GitHub Copilot become pervasive in education, concerns are rising about students using them to complete rather than learn from coursework-risking overreliance, reduced critical thinking, and long-term skill deficits. Objective: This paper proposes and empirically applies a causal model to help educa

Sarah Schreyer, Anton Tausendfreund, Florian Immig, Ulrich Oberhofer

Natural gas supplies in Europe were disrupted and energy prices soared in the context of Russia&#39;s invasion of Ukraine. Electricity prices in France experienced the largest relative increase among European countries, even though the share of natural gas in the electricity mix is small compared to its neighbours. In this article, we demonstrate the importa

Yunguan Fu, Wenjia Bai, Weixi Yi, Charlotte Manisty

Here we present a versatile foundation model that can perform a range of clinically-relevant image analysis tasks, including segmentation, landmark localisation, diagnosis, and prognostication. A multi-view convolution-transformer masked autoencoder, named as CineMA, was trained on 15 million cine images from 74,916 subjects. The model was validated on multi

B. Barsbay

The full widths of the vector charmonium and bottomonium hybrid mesons $H_{ \mathrm{c}}$ and $H_{\mathrm{b}}$, characterized by the quantum numbers $1^{ \mathrm{--}}$, are determined by analyzing their dominant strong decay modes: $H_{\mathrm{c}} \to D^{+}D^{-}$, $D_{0}\overline{D}_{0}$, $ D_{s}^{+}D_{s}^{-} $ and $H_{\mathrm{b}} \to B^{+}B^{-}$, $B_{0}\over

Richard E. Neddo, Emmanuel Atindama, Zander W. Blasingame, Chen Liu

Adversarial attacks have emerged as a critical threat to autonomous driving systems. These attacks exploit the underlying neural network, allowing small, almost invisible, perturbations to alter the behavior of such systems in potentially malicious ways, e.g., causing a traffic sign classification network to misclassify a stop sign as a speed limit sign. Pri

Marziyeh Mohammadi, Mohsen Vejdanihemmat, Mahshad Lotfinia, Mirabela Rusu

Differential privacy (DP) is a key technique for protecting sensitive patient data in medical deep learning (DL). As clinical models grow more data-dependent, balancing privacy with utility and fairness has become a critical challenge. This scoping review synthesizes recent developments in applying DP to medical DL, with a particular focus on DP-SGD and alte

Pinjun Zheng, Ruiqi Wang, Yuchen Zhang, Md. Jahangir Hossain

The transition to sixth-generation (6G) networks calls for wireless transceivers with enhanced adaptability and efficiency. Reconfigurable antennas (RAs) have emerged as a promising solution, enabling dynamic control over the electromagnetic properties of individual antenna elements. Their integration into antenna arrays is particularly attractive due to the

Femi Bello, Anubrata Das, Fanzhi Zeng, Fangcong Yin

It has been hypothesized that neural networks with similar architectures trained on similar data learn shared representations relevant to the learning task. We build on this idea by extending the conceptual framework where representations learned across models trained on the same data can be expressed as linear combinations of a \emph{universal} set of basis

Yu Huang, Junhao Chen, Shuliang Liu, Hanqian Li

The rapid development of Artificial Intelligence Generated Content (AIGC) has led to significant progress in video generation, but also raises serious concerns about intellectual property protection and reliable content tracing. Watermarking is a widely adopted solution to this issue, yet existing methods for video generation mainly follow a post-generation

Kym Derriman

The Quantum Skip Gate (QSG) is a unitary circuit primitive that coherently superposes the execution and omission of an expensive quantum subroutine based on the outcome of a cheaper preceding subroutine, without mid-circuit measurement or loss of coherence. By using a control qubit and an internal flag, QSG enables conditional quantum logic entirely within a

Alessandro De Stefani, Thomas Polstra, Austyn Simpson

We construct examples of noetherian three-dimensional local geometrically normal domains of prime characteristic which are $F$-injective but not $F$-full. Along the way, we find examples of two-dimensional local geometrically normal domains which are $F$-injective but not $F$-anti-nilpotent. A crucial theme of our constructions is the behavior of $F$-injecti

Weijie Xu, Shixian Cui, Xi Fang, Chi Xue

Large language models (LLMs) are increasingly evaluated on single-answer multiple-choice tasks, yet many real-world problems require identifying all correct answers from a set of options. This capability remains underexplored. We introduce SATA-BENCH, the first dedicated benchmark for evaluating LLMs on Select All That Apply (SATA) questions across diverse d

Hanjun Luo, Shenyu Dai, Chiming Ni, Xinfeng Li

Despite the rapid advancement of LLM-based agents, the reliable evaluation of their safety and security remains a significant challenge. Existing rule-based or LLM-based evaluators often miss dangers in agents&#39; step-by-step actions, overlook subtle meanings, fail to see how small issues compound, and get confused by unclear safety or security rules. To o

Alan G. Cesar, Mario Novello, Eduardo Bittencourt, Fernando A. Franco

We investigate the cosmological dynamics induced by nonlinear electrodynamics in a homogeneous and isotropic universe, focusing on the role of primordial electromagnetic fields with random spatial orientations. Building upon a generalization of the Tolman-Ehrenfest averaging procedure, we derive a modified energy-momentum tensor consistent with the spacetime

Yao Ji, Zhuoyi Pang, Fei Yao, Jian-Hui Zhang

It is well-known that in the study of mixing between nonlocal gluon and quark bilinear operators there exists an ambiguity when relating coordinate space and momentum space results, which can be conveniently resolved through Mellin moments matching in both spaces. In this work, we show that this ambiguity is due to the lack of a proper regularization prescri

Lorenzo Jaime Yu Flores, Ori Ernst, Jackie Chi Kit Cheung

Well-calibrated model confidence scores can improve the usefulness of text generation models. For example, users can be prompted to review predictions with low confidence scores, to prevent models from returning bad or potentially dangerous predictions. However, confidence metrics are not always well calibrated in text generation. One reason is that in gener

Wei Chen, Yuxuan Liang

Spatio-temporal forecasting is crucial in many domains, such as transportation, meteorology, and energy. However, real-world scenarios frequently present challenges such as signal anomalies, noise, and distributional shifts. Existing solutions primarily enhance robustness by modifying network architectures or training procedures. Nevertheless, these approach

Adam Visokay, Ruth Bagley, Ian Kennedy, Chris Hess

Rental listings offer a window into how urban space is socially constructed through language. We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods, identifying mismatches between institutional boundaries and neighborhood claims. Through manual and large language model annotation, we cl

Daniele Molino, Camillo Maria Caruso, Filippo Ruffini, Paolo Soda

Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language alignment grounded in volumetric space. Existing Text-to-CT approaches condition generation on encoders pretrained with language only or 2D vision-language objectives, providing conditioning signals that are linguistic

Viktor D. Zozulia, Natalia Ya. Sotnikova, Anton A. Smirnov

For the first time, we investigate the resonant structure of $N$-body galactic bar at the stage of buckling using action-angle variables. We studied the evolution of vertical actions ($J_z$) and angles associated with vertical resonance ($θ_\mathrm{res}=θ_z - θ_R$) for all orbits in the bar. For this purpose, we divide the orbits into types according to the

Guy Ohad, María Camarasa-Gómez, Jeffrey B. Neaton, Ashwin Ramasubramaniam

Optimal tuning of functional parameters in density functional theory approximations, based on enforcing the ionization potential theorem, has emerged as the method of choice for the non-empirical prediction of the electronic structure of finite systems. This method has recently been extended to the bulk limit, based on an ansatz that generalizes the ionizati

Niyati Bafna, Matthew Wiesner

Prior research indicates that LID model performance significantly declines on accented speech; however, the specific causes, extent, and characterization of these errors remain under-explored. (i) We identify a common failure mode on accented speech whereby LID systems often misclassify L2 accented speech as the speaker&#39;s native language or a related lan

Julia Beuster, Carsten Andrich, Sebastian Giehl, Marc Miranda

As integrated sensing and communication (ISAC) capabilities become more prevalent in the mobile 6G radio landscape, there is a substantial opportunity to enhance situational awareness across diverse applications through multi-static radar sensing within meshed ISAC networks. To facilitate the development and testing of detection and localization algorithms a

Andrzej A. Zdziarski, Gregoire Marcel, Alexandra Veledina, Aleksandra Olejak

We review current challenges in understanding the values and origin of the spins of black holes in binaries. Thanks to recent advances in astrophysical instrumentation, the spins can now be measured using both gravitational waves emitted by merging black holes and electromagnetic radiation from accreting X-ray binaries containing black holes. A key finding o

Haesung Pyun, Yoonah Park, Yohan Jo

In dialogue state tracking (DST), in-context learning comprises a retriever that selects labeled dialogues as in-context examples and a DST model that uses these examples to infer the dialogue state of the query dialogue. Existing methods for constructing training data for retrievers suffer from three key limitations: (1) the synergistic effect of examples i

Jingyi Yang, Shuai Shao, Dongrui Liu, Jing Shao

With the rapid development of multimodal large language models (MLLMs), they are increasingly deployed as autonomous computer-use agents capable of accomplishing complex computer tasks. However, a pressing issue arises: Can the safety risk principles designed and aligned for general MLLMs in dialogue scenarios be effectively transferred to real-world compute

Andreu Ballus Santacana

We present a tractable, incremental framework for topological dialogue semantics based on finite, discrete semantic spaces. Building on the intuition that utterances correspond to open sets and their combinatorial relations form a simplicial complex (the dialogue nerve), we give a rigorous foundation, a provably correct incremental algorithm for nerve update

Ziqi Liu, Pei Zeng, Yi Ding

Real-world multichannel time series prediction faces growing demands for efficiency across edge and cloud environments, making channel compression a timely and essential problem. Motivated by the success of Multiple-Input Multiple-Output (MIMO) methods in signal processing, we propose a predictability-aware compression-decompression framework to reduce runti

Julian Quevedo, Ansh Kumar Sharma, Yixiang Sun, Varad Suryavanshi

Evaluating robot control policies is difficult: real-world testing is costly, and handcrafted simulators require manual effort to improve in realism and generality. We propose a world-model-based policy evaluation environment (WorldGym), an autoregressive, action-conditioned video generation model which serves as a proxy to real world environments. Policies

Running Yang, Wenlong Deng, Minghui Chen, Yuyin Zhou

Clinical tasks such as diagnosis and treatment require strong decision-making abilities, highlighting the importance of rigorous evaluation benchmarks to assess the reliability of large language models (LLMs). In this work, we introduce a knowledge-guided data augmentation framework that enhances the difficulty of clinical multiple-choice question (MCQ) data

Adam R. Vernon, Mitch Peaks

A scheme for excitation and individual addressing using Rydberg states of trapped Barium ions is presented for the purpose of fast gates and entanglement. Dipole matrix elements, dynamic polarizabilities, and one- and two-photon transition strengths are computed with a Supersymmetric Wentzel-Kramers-Brillouin (SWKB) method. A favorable two-photon excitation

Mikhail V. Kochetov, Serhii D. Koval

We study generic graded contractions of Lie algebras from the perspectives of group cohomology, affine algebraic geometry and monoidal categories. We show that generic graded contractions with a fixed support are classified by a certain abelian group, which we explicitly describe. Analyzing the variety of generic graded contractions as an affine algebraic va

Petros Raptopoulos, Giorgos Filandrianos, Maria Lymperaiou, Giorgos Stamou

Contract review is a complex and time-intensive task that typically demands specialized legal expertise, rendering it largely inaccessible to non-experts. Moreover, legal interpretation is rarely straightforward-ambiguity is pervasive, and judgments often hinge on subjective assessments. Compounding these challenges, contracts are usually confidential, restr

Ruiming Min, Minghao Liu

With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy conce

Samuel D. Tomlinson, Aliki M. Tsopelakou, Tzia M. Onn, Steven R. H. Barrett

Atmospheric pollution from particulate matter, volatile organic compounds and greenhouse gases is a critical environmental and public health issue, leading to respiratory diseases and climate change. A potential mitigation strategy involves utilising ventilation systems, which process large volumes of indoor and outdoor air and remove particulate pollutants

Xianghui Ze, Beiyi Zhu, Zhenbo Song, Jianfeng Lu

Generating multiview-consistent $360^\circ$ ground-level scenes from satellite imagery is a challenging task with broad applications in simulation, autonomous navigation, and digital twin cities. Existing approaches primarily focus on synthesizing individual ground-view panoramas, often relying on auxiliary inputs like height maps or handcrafted projections,

William Andrew Simon, Leonid Yavits, Konstantina Koliogeorgi, Yann Falevoz

Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the backbone of the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized, and Participatory (termed &#39;P4&#39;) medicine. All genomic data are currently processed in energy-hungry computer clusters and centers, necessitating data transfer, consuming su

Danfeng Li, Hui Zhang, Sheng Wang, Jiacheng Li

Despite recent advances in diffusion models, top-tier text-to-image (T2I) models still struggle to achieve precise spatial layout control, i.e. accurately generating entities with specified attributes and locations. Segmentation-mask-to-image (S2I) generation has emerged as a promising solution by incorporating pixel-level spatial guidance and regional text

Stepan Bosak, Miloslav Capek, Jiri Matas

This paper presents a novel bi-level topology optimization strategy within the method-of-moments paradigm. The proposed approach utilizes an auxiliary variables called edge resistivities related to the Rao-Wilton-Glisson method-of-moments basis functions, for a definition of a fast local optimization algorithm. The local algorithm combines automatic differen

Michael Brocidiacono, James Wellnitz, Konstantin I. Popov, Alexander Tropsha

Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for novel protein targets dissimilar from the training set. In contrast, physics-based binding free energy calculations offer high accuracy across chemical space but are computationally p

Lukas Silvester Barth, Parvaneh Joharinad, Jürgen Jost, Walter Wenzel

A metric relation by definition is symmetric. Since many data sets are non-symmetric, in this paper we develop a systematic theory of non-symmetric cost functions. Betweenness relations play an important role. We also introduce the notion of a Dress group in the non-symmetric setting and indicate a notion of curvature.

Jayanta Dey, Nicholas Soures, Miranda Gonzales, Itamar Lerner

In this pilot study, we propose a neuro-inspired approach that compresses temporal sequences into context-tagged chunks, where each tag represents a recurring structural unit or``community&#39;&#39; in the sequence. These tags are generated during an offline sleep phase and serve as compact references to past experience, allowing the learner to incorporate i

Carlos A. Gomez-Uribe

We derive the Planck law from a classical variational principle over probability densities, without invoking quantum states, quantized oscillator energies, or ensemble averages. We construct a generalized free energy functional involving entropy and Fisher information, with weights determined by the dimensionless ratio of quantum to thermal energy. When extr

Chenjun Xu, Bingbing Wen, Bin Han, Robert Wolfe

Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We examine three LLMs, Llama-3-70B-instruct, Claude-3-Sonnet, and GPT-4o, on a range of QA tasks of varying difficulty, and show that models exhibit subtle differences from human pat

Chang Cai, Wenjun Jiang, Xiaojun Yuan, Ying-Jun Angela Zhang

Massive connectivity supports the sporadic access of a vast number of devices without requiring prior permission from the base station (BS). In such scenarios, the BS must perform joint activity detection and channel estimation (JADCE) prior to data reception. Message-passing algorithms have emerged as a prominent solution for JADCE under a Bayesian inferenc

Grace M. Sommers, Michael Foss-Feig, David Hayes, David A. Huse

We introduce a fault-tolerant protocol for code concatenation of a generalized Shor code using a butterfly network architecture with high noise thresholds and low ancilla overhead to allow implementation on current devices. We develop a probability passing decoder using tensor networks that applies Bayesian updates to the marginal error probabilities after e

Samantha Sandberg-Clark, Krystal Taylor

This article focuses on the occurrence of 3-point configurations in subsets of $\mathbb{R}^d$ of sufficient thickness. We prove that a compact set $A\subset \mathbb{R}^d$ contains a similar copy of any linear $3$-point configuration (such as a $3$-point arithmetic progression) provided $A$ satisfies a mild Yavicoli-thickness condition and an $r$-uniformity c

Xuan Xu, Fufang Wen, Beilin Chu, Zhibing Fu

In natural language processing (NLP), the focus has shifted from encoder-only tiny language models like BERT to decoder-only large language models(LLMs) such as GPT-3. However, LLMs&#39; practical application in the financial sector has revealed three limitations: (1) LLMs often perform worse than fine-tuned BERT on discriminative tasks despite costing much

Ke Niu, Zhuofan Chen, Haiyang Yu, Yuwen Chen

Computer-Aided Design (CAD) plays a pivotal role in industrial manufacturing. Orthographic projection reasoning underpins the entire CAD workflow, encompassing design, manufacturing, and simulation. However, prevailing deep-learning approaches employ standard 3D reconstruction pipelines as an alternative, which often introduce imprecise dimensions and limit

Zhenan Sui

We prove the existence of a smooth complete $3$-convex hypersurface which satisfies prescribed curvature equation $\prod\limits_{i = 1}^n (H - κ_i) = \big( (n - 1) σ\big)^n$ for $n = 4$ and has prescribed asymptotic boundary $Γ$ at the infinity of hyperbolic space of dimension 5, where $σ\in (0, 1)$ is a constant and $Γ$ is assumed to have nonnegative mean c

Ziyan Luo, Tianwei Ni, Pierre-Luc Bacon, Doina Precup

A key approach to state abstraction is approximating behavioral metrics (notably, bisimulation metrics) in the observation space and embedding these learned distances in the representation space. While promising for robustness to task-irrelevant noise, as shown in prior work, accurately estimating these metrics remains challenging, requiring various design c

Fumika Suzuki, Nikolai A. Sinitsyn

We discuss a classical anisotropic oscillator and the Foucault pendulum as examples illustrating non-conservation of action variables in integrable classical mechanical systems with adiabatically slow evolution. We also emphasize the importance of the mass parameter of a harmonic oscillator, alongside its frequency, in explicitly time-dependent situations.

Adrian Azzarelli, Ge Gao, Ho Man Kwan, Fan Zhang

As research on neural volumetric video reconstruction and compression flourishes, there is a need for diverse and realistic datasets, which can be used to develop and validate reconstruction and compression models. However, existing volumetric video datasets lack diverse content in terms of both semantic and low-level features that are commonly present in re

Peng Xia, Jinglu Wang, Yibo Peng, Kaide Zeng

Medical Large Vision-Language Models (Med-LVLMs) have shown strong potential in multimodal diagnostic tasks. However, existing single-agent models struggle to generalize across diverse medical specialties, limiting their performance. Recent efforts introduce multi-agent collaboration frameworks inspired by clinical workflows, where general practitioners (GPs

Xintong Jiang

In this paper, we prove the canonical bundle formula for Fano type fibrations and Shokurov&#39;s conjecture on boundedness of complements for Fano type threefold pairs $(X,B)$ with fibration structures in large characteristics. In particular, we prove the conjecture when $-(K_X+B)\not\equiv 0$ is nef and not big in large characteristics.

Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang

Constrained by the cost and ethical concerns of involving real seekers in AI-driven mental health, researchers develop LLM-based conversational agents (CAs) with tailored configurations, such as profiles, symptoms, and scenarios, to simulate seekers. While these efforts advance AI in mental health, achieving more realistic seeker simulation remains hindered

Márcio Ferreira, Constança Providência

The slope of the neutron star mass-radius curve, $dM/dR$, is studied to understand the information it may carry about the composition of neutron stars, particularly with regard to the presence of non-nucleonic degrees of freedom. This study uses two large sets of relativistic mean-field equations of state with either nucleonic or nucleonic and hyperonic degr

Levin Maier

In this article, we combine V. Arnold&#39;s celebrated approach via the Euler-Arnold equation -- describing the geodesic flow on a Lie group equipped with a right-invariant metric \cite{Arnold66} -- with his formulation of the motion of a charged particle in a magnetic field \cite{ar61}. We introduce the \emph{magnetic Euler-Arnold equation}, which is the Eu

Kaustabh Mondal, Gunja Sachdeva

Let $G$ be a connected semisimple simply connected Lie group with a compact Cartan subgroup and let $Γ$ be a uniform lattice in $G$. Let $\widehat{G}_d$ denote the set of equivalence classes of unitary discrete series representations of $G$. We prove that for any finite subset of $\widehat{G}_d$ satisfying a certain condition, the associated finite set of di

Huayu Huang, Banglei Guan, Yang Shang, Qifeng Yu

Photomechanics is a crucial branch of solid mechanics. The localization of point targets constitutes a fundamental problem in optical experimental mechanics, with extensive applications in various missions of UAVs. Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties. Reconstructing the trajectories of points

Wenzhang Liu, Muqaddar Abbas, Pei Zhang, Jiawei Lai

We present a theoretical study demonstrating enhanced tunability of the photonic spin Hall effect (PSHE) using a strongly interacting Rydberg atomic medium under electromagnetically induced transparency (EIT) conditions. In contrast to conventional approaches that rely on static refractiveindex profiles or metamaterials, here the PSHE is controlled via a non

Ruihan Yang, Yikai Zhang, Aili Chen, Xintao Wang

Large language models (LLMs) have enabled agents to perform complex reasoning and decision-making through free-form language interactions. However, in open-ended language action environments (e.g., negotiation or question-asking games), the action space can be formulated as a joint distribution over tokens, resulting in an exponentially large action space. S

L. Nađđerđ, J. Milošević, D. Devetak, F. Wang

The method of Q-cumulants has been shown as a powerful tool to study the fine details of the azimuthal anisotropies in high-energy nucleus-nucleus collisions. A new method for the fast calculation of arbitrary order Q-cumulant $v_{n}\{2k\}$ values, based on the partition of a non-negative integer l $\le$ m for calculation of the 2m-particle azimuthal correla

Yudong Zhang, Ruobing Xie, Xingwu Sun, Jiansheng Chen

The choice of a suitable visual language projector (VLP) is critical to the successful training of large visual language models (LVLMs). Mainstream VLPs can be broadly categorized into compressed and uncompressed projectors, and each offers distinct advantages in performance and computational efficiency. However, their security implications have not been tho

Junquan Huang, Zong-Gan Chen, Yuncheng Jiang, Zhi-Hui Zhan

GCN-based traveling salesman problem (TSP) solvers face two critical challenges: poor cross-scale generalization for TSPs and high training costs. To address these challenges, we propose a Subgraph-Based Rescaling Graph Convolutional Network (RsGCN). Focusing on the scale-dependent features (i.e., features varied with problem scales) related to nodes and edg

Fasheng Xu, Jing Hou, Wei Chen, Karen Xie

Generative AI (GenAI) is rapidly transforming knowledge work, yet its implications for organizational hierarchies remain poorly understood. Unlike earlier automation technologies, GenAI can both perform tasks autonomously and assist human workers, while its intrinsic fallibility, the tendency to produce confident but incorrect outputs, demands continuous hum

Tianhui Liu, Hetian Pang, Xin Zhang, Tianjian Ouyang

Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce \textit{CityLens}, a comprehensive benchmark designed to evaluate the capabilities of Large Vision-Language Models (LVLMs) in predicting socioeconomic indicators from satellite

Mehran Z-Abyaneh, Mehrdad Farhoudi

We reduce the Taub-NUT metric dimensionally to three spatial dimensions by treating time as an extra curled dimension, and end up with the 3-dimensional Einstein field equations plus a corresponding Maxwell type equations for a gravitomagnetic field, associated with the NUT charge, which also acts as a source for the Einstein field equations. In this approac

Xingtong Ge, Xin Zhang, Tongda Xu, Yi Zhang

The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale