May 2025 arXiv papers — page 104

Ela Celikbas, Olgur Celikbas, Jürgen Herzog, Shinya Kumashiro

Motivated by the definition of nearly Gorenstein rings, we introduce the notion of full-trace modules over commutative Noetherian local rings--namely, finitely generated modules whose trace equals the maximal ideal. We investigate the existence of such modules and prove that, over rings that are neither regular nor principal ideal rings, every positive syzyg

Nolan R Wallach

In paper I of his masterpiece Harmonic Analysis on Real Reductive Groups, Harish-Chandra included an important inequality that is useful in proving that certain key integrals depending on a parameter converge for large values of the parameter. His proof involved the Tarski-Seidenberg Theorem. The purpose of this note is an elementary proof of the inequality

William Alberto Cruz-Castañeda, Marcellus Amadeus

This report introduces the experience of developing Amadeus Verbo, a family of large language models for Brazilian Portuguese. To handle diverse use cases, Amadeus Verbo includes base-tuned, merged, and instruction-tuned models in sizes of 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameters. Thus, the main objective is to show how easy it is to fine-tune founda

Kungang Li, Xiangyi Chen, Ling Leng, Jiajing Xu

In the realm of online advertising, accurately predicting the conversion rate (CVR) is crucial for enhancing advertising efficiency and user satisfaction. This paper addresses the challenge of CVR prediction while adhering to user privacy preferences and advertiser requirements. Traditional methods face obstacles such as the reluctance of advertisers to shar

Bingying Dai, Yinan Lin, Kejue Jia, Zhao Ren

Quantifying the effects of amino acid mutations in proteins presents a significant challenge due to the vast combinations of residue sites and amino acid types, making experimental approaches costly and time-consuming. The Potts model has been used to address this challenge, with parameters capturing evolutionary dependency between residue sites within a pro

Yongchun Li

We study the Regularized A-optimal Design (RAOD) problem, which selects a subset of $k$ experiments to minimize the inverse of the Fisher information matrix, regularized with a scaled identity matrix. RAOD has broad applications in Bayesian experimental design, sensor placement, and cold-start recommendation. We prove its NP-hardness via a reduction from the

Takuma Nakamura, Yifan Liu, Naijun Jin, Haotian Cheng

We demonstrate thermal-noise-limited direct locking of a semiconductor distributed feedback (DFB) laser to a sub-1 mL volume, ultrastable optical cavity, enabling extremely compact and simple ultrastable laser systems. Using the optoelectronic laser locking method, we realize over 140 dB suppression of the DFB free-running laser noise at 10 Hz offset, a leve

Luiz F. V. Figueiredo, Viviana G. R. Lobo, Mariane B. Alves, Thais C. O. Fonseca

This work introduces a Bayesian smoothing approach for the joint graduation of mortality rates across multiple populations. In particular, dynamical linear models are used to induce smoothness across ages through structured dependence, analogously to how temporal correlation is accommodated in state-space time-indexed models. An essential issue in subnationa

Paul M. Goldbart

A rich variety of amorphous solids are found in nature and technology, including ones formed via the vulcanization of long, flexible molecules. A special class -- those featuring a wide gap between the long timescales over which constraints in them release and the much shorter timescales over which their unconstrained freedoms relax -- exhibit states of ther

Zahra Honjani, Mohsen Heidari

Classical shadow tomography (CST) involves obtaining enough classical descriptions of an unknown state via quantum measurements to predict the outcome of a set of quantum observables. CST has numerous applications, particularly in algorithms that utilize quantum data for tasks such as learning, detection, and optimization. This paper introduces a new CST pro

Pierre Albin, Markus Banagl, Paolo Piazza

We introduce smooth atlas stratified spaces. We show that this class is closed under cartesian products; consequently, it is possible to define fiber bundles of smooth atlas stratified spaces. We describe the resolution of such a space to a manifold with fibered corners and use this result in order to prove that the class of smooth atlas stratified spaces co

Jose Sosa, Danila Rukhovich, Anis Kacem, Djamila Aouada

Multi-modal data in Earth Observation (EO) presents a huge opportunity for improving transfer learning capabilities when pre-training deep learning models. Unlike prior work that often overlooks multi-modal EO data, recent methods have started to include it, resulting in more effective pre-training strategies. However, existing approaches commonly face chall

Luca Capogna, Ryan Gibara, Riikka Korte, Nageswari Shanmugalingam

We prove global H\"older regularity result for weak solutions $u\in N^{1,p}(\Omega, \mu)$ to a PDE of $p$-Laplacian type with a measure as non-homogeneous term: \[ -\text{div}\!\left( |\nabla u|^{p-2}\nabla u \right)=\overline\nu, \] where $1<p<\infty$ and $\overline\nu \in (N^{1,p}(\Omega,\mu))^*$ is a signed Radon measure supported in $\overline \Omega$. H

Zach Yarbrough, Andre Guimaraes, Prathamesh Joshi, Gabriela González

We present a method to identify and categorize gravitational wave candidate triggers identified by matched filtering gravitational wave searches (pipelines) caused by transient noise (glitches) in gravitational wave detectors using Support Vector Machine (SVM) classifiers. Our approach involves training SVM models on pipeline triggers which occur outside per

Prateek Verma, Mert Pilanci

This paper presents a fascinating find: By training an auto-regressive LLM model on text tokens, the text model inherently develops internally an ability to understand images and audio, thereby developing the ability to see and hear just by reading. Popular audio and visual LLM models fine-tune text LLM models to give text output conditioned on images and au

Hao Tang, Kevin Ellis, Suhas Lohit, Michael J. Jones

The task of estimating the world model describing the dynamics of a real world process assumes immense importance for anticipating and preparing for future outcomes. For applications such as video surveillance, robotics applications, autonomous driving, etc. this objective entails synthesizing plausible visual futures, given a few frames of a video to set th

Tianhong Zhao

We take the trace of Von-Neumann's ergodic theorem and get a trace formula of a unitary matrix family. It is an extension of Poisson summation formula in higher dimension. We also construct a family of crystalline measure with complex coefficient.

Eric Han, Jun Chen, Karthik Abinav Sankararaman, Xiaoliang Peng

As large language models (LLMs) are increasingly deployed in diverse user facing applications, aligning them with real user preferences becomes essential. Existing methods like Reinforcement Learning from Human Feedback (RLHF) rely on expert annotators trained on manually defined guidelines, whose judgments may not reflect the priorities of everyday users. W

Phoebe Chua, Cathy Mengying Fang, Takehiko Ohkawa, Raja Kushalnagar

Unlike spoken languages where the use of prosodic features to convey emotion is well studied, indicators of emotion in sign language remain poorly understood, creating communication barriers in critical settings. Sign languages present unique challenges as facial expressions and hand movements simultaneously serve both grammatical and emotional functions. To

O. Deniz Kose, Gonzalo Mateos, Yanning Shen

The growing enforcement of the right to be forgotten regulations has propelled recent advances in certified (graph) unlearning strategies to comply with data removal requests from deployed machine learning (ML) models. Motivated by the well-documented bias amplification predicament inherent to graph data, here we take a fresh look at graph unlearning and lev

Kamal Giri, Amit Garu

Distributed systems frequently encounter consistency violation faults (cvfs), where nodes operate on outdated or inaccurate data, adversely affecting convergence and overall system performance. This study presents a machine learning-based approach for analyzing the impact of CVFs, using Dijkstra's Token Ring problem as a case study. By computing program tran

Rodolfo E. Maza

This paper addresses the periodic homogenization of quasilinear elliptic PDEs in a two-component domain with an interfacial thermal barrier. It introduces a periodic extension operator that ensures strong convergence of function sequences in the Sobolev space. Moreover, two families of quasilinear elliptic problems in two-component domains with interfacial r

Ross Nordby

To help evaluate and understand the latent capabilities of language models, this paper introduces an approach using optimized input embeddings, or 'soft prompts,' as a metric of conditional distance between a model and a target behavior. The technique aims to facilitate latent capability discovery as a part of automated red teaming/evaluation suites and to p

Han Liu, Ruoyao Wen, Srijith Nair, Jia Liu

To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on various downstream tasks without requiring aggregated data. However, the repeated exchange of model updates in FL can result in prohibitively high communication costs, hindering the d

Florian Girelli, Christopher Pollack, Aldo Riello

We explore global Poisson-Lie (PL) symmetries using a Lagrangian, or "covariant phase space" approach, that manifestly preserves spacetime covariance. PL symmetries are the classical analog of quantum-group symmetries. In the Noetherian framework symmetries leave the Lagrangian invariant up to boundary terms and necessarily yield (on closed manifolds) $\math

Kevin Angers, Kourosh Darvish, Naruki Yoshikawa, Sargol Okhovatian

Automating biological experimentation remains challenging due to the need for millimeter-scale precision, long and multi-step experiments, and the dynamic nature of living systems. Current liquid handlers only partially automate workflows, requiring human intervention for plate loading, tip replacement, and calibration. Industrial solutions offer more automa

Kaspar Rothenfusser

Since Edmund Husserl coined the term "Formal Ontologies" in the early 20th century, a field that identifies itself with this particular branch of sciences has gained increasing attention. Many authors, and even Husserl himself have developed what they claim to be formal ontologies. I argue that under close inspection, none of these so claimed formal ontologi

Gábor Székelyhidi

We study Calabi-Yau metrics on a projective manifold in K\"ahler classes converging to a semiample class given by a fibration. We show that the Gromov-Hausdorff limit of the metrics is homeomorphic to the base of the fibration and in addition the discriminant locus has Hausdorff codimension at least 2. This resolves conjectures of Tosatti.

Amine Elhafsi, Daniel Morton, Marco Pavone

Autonomous robots must reason about the physical consequences of their actions to operate effectively in unstructured, real-world environments. We present Scan, Materialize, Simulate (SMS), a unified framework that combines 3D Gaussian Splatting for accurate scene reconstruction, visual foundation models for semantic segmentation, vision-language models for

Mateo Neira, Valentina Marin, Elsa Arcaute

We present a novel analytical framework to examine socio-spatial segregation across multiple spatial scales, explicitly leveraging information theory and percolation theory. This framework emphasizes the interplay between regional connectivity and population distribution, which are critical for understanding how spatial inequalities arise and persist in urba

Salman Habib, Remi Chou, Taejoon Kim

The reconstruction of sparse signals from a limited set of measurements poses a significant challenge as it necessitates a solution to an underdetermined system of linear equations. Compressed sensing (CS) deals with sparse signal reconstruction using techniques such as linear programming (LP) and iterative message passing schemes. The interval passing algor

Navid Hashemi, Lars Lindemann, Jyotirmoy Deshmukh

This study presents a scalable data-driven algorithm designed to efficiently address the challenging problem of reachability analysis. Analysis of cyber-physical systems (CPS) relies typically on parametric physical models of dynamical systems. However, identifying parametric physical models for complex CPS is challenging due to their complexity, uncertainty

Xuefeng Du

Ensuring the reliability and safety of machine learning models in open-world deployment is a central challenge in AI safety. This thesis develops both algorithmic and theoretical foundations to address key reliability issues arising from distributional uncertainty and unknown classes, from standard neural networks to modern foundation models like large langu

Isabelle Lee, Sarah Liaw, Dani Yogatama

Reasoning in language models is difficult to evaluate: natural-language traces are unverifiable, symbolic datasets are too small, and most benchmarks conflate heuristics with inference. We present FOL-Traces, the first large-scale dataset of programmatically verified reasoning traces, enabling rigorous evaluation of structured logical inference. We also prop

Rama Alyoubi, Taif Alharbi, Albatul Alghamdi, Yara Alshehri

This study presents a robust framework that leverages advanced imaging techniques and machine learning for feature extraction and classification of key human attributes-namely skin tone, hair color, iris color, and vein-based undertones. The system employs a multi-stage pipeline involving face detection, region segmentation, and dominant color extraction to

Giulio Burgio, Guillaume St-Onge, Laurent Hébert-Dufresne

People organize in groups and contagions spread across them. A simple stochastic process, yet complex to model due to dynamical correlations within and between groups. Moreover, groups can evolve if agents join or leave in response to contagions. To address the lack of analytical models that account for dynamical correlations and adaptation in groups, we int

Yicheng Qian, Joshua Clune, Clark Barrett, Jeremy Avigad

Proof automation is crucial to large-scale formal mathematics and software/hardware verification projects in ITPs. Sophisticated tools called hammers have been developed to provide general-purpose proof automation in ITPs such as Coq and Isabelle, leveraging the power of ATPs. An important component of a hammer is the translation algorithm from the ITP's log

Arjun Sengupta, Rainer J. Fries

The evolution of jets showers in high energy nuclear collisions is influenced in various ways by the presence of a surrounding medium. The interaction of jet constituents with the medium can happen during the partonic stage of the jet, during hadronization, and even during its hadronic stage. We demonstrate how flow of the ambient medium in a direction trans

Manuel Servin

This paper examines the historical development that led to error-free phase demodulation of pixelated spatial-carrier interferograms between 2004 and 2010. It also evaluates evidence suggesting that 4D Technology Corporation (4DTC), in their SPIE 7790 publication [18], may have adopted key concepts from a manuscript submitted by Servin and collaborators, whi

Mohammad R. Salmanpour, Seyed Mohammad Piri, Somayeh Sadat Mehrnia, Ahmad Shariftabrizi

Artificial intelligence (AI) holds strong potential for medical diagnostics, yet its clinical adoption is limited by a lack of interpretability and generalizability. This study introduces the Pathobiological Dictionary for Liver Cancer (LCP1.0), a practical framework designed to translate complex Pathomics and Radiomics Features (PF and RF) into clinically m

Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Ye Wang, Gang Tan

Large Language Models (LLMs) exhibit impressive capabilities, but remain susceptible to a growing spectrum of safety risks, including jailbreaks, toxic content, hallucinations, and bias. Existing defenses often address only a single threat type or resort to rigid outright rejection, sacrificing user experience and failing to generalize across diverse and nov

Sil Hamilton, Rebecca M. M. Hicke, Matthew Wilkens, David Mimno

Although the context length of large language models (LLMs) has increased to millions of tokens, evaluating their effectiveness beyond needle-in-a-haystack approaches has proven difficult. We argue that novels provide a case study of subtle, complicated structure and long-range semantic dependencies often over 128k tokens in length. Inspired by work on compu

Shashwat Khandelwal, Shreejith Shanker

Recent research has highlighted the vulnerability of in-vehicle network protocols such as controller area networks (CAN) and proposed machine learning-based intrusion detection systems (IDSs) as an effective mitigation technique. However, their efficient integration into vehicular architecture is non-trivial, with existing methods relying on electronic contr

Jacques Demongeot, Eric Goles, Houssem ben Khalfallah, Marco Montalva-Medel

Many familial diseases are caused by genetic accidents, which affect both the genome and its epigenetic environment, expressed as an interaction graph between the genes as that involved in one familial disease we shall study, the hereditary angioedema. The update of the gene states at the vertices of this graph (1 if a gene is activated, 0 if it is inhibited

Daniel Alpay, Paula Cerejeiras, Uwe Kaehler

We are studying the fundamental tools for a quantum calculus based on the Tsallis $q$-exponential In particular we are looking at $q$-Fock spaces, structural identities, as well as rational functions in this context.

Mateus Pelicer, Veronica Dexheimer, Joaquin Grefa

For densities beyond nuclear saturation, there is still a large uncertainty in the equations of state (EoS) of dense matter that translate into uncertainties in the internal structure of neutron stars. The MUSES Calculation Engine provides a free and open-source composable workflow management system, which allows users to calculate the EoS of dense and hot m

Tyler Arant

This paper studies when an arithmetical equivalence relation $E$ can be realized as the connectedness relation of a graph $G$ which is simpler to define than $E$. Several examples of such equivalence relations are established. In particular, it is proved that the $\Sigma^0_3$ relation of computable isomorphism of structures on $\N$ in a computable first-orde

Frederik Wenkel, Wilson Tu, Cassandra Masschelein, Hamed Shirzad

Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet exhaustively exploring the space of possible perturbations (e.g., multi-gene perturbations or across tissues and cell types) is prohibitively expensive, motivating methods that can generalize to unseen con

Fadel M. Megahed, Ying-Ju Chen, L. Allision Jones-Farmer, Younghwa Lee

This study introduces a framework for evaluating consistency in large language model (LLM) binary text classification, addressing the lack of established reliability assessment methods. Adapting psychometric principles, we determine sample size requirements, develop metrics for invalid responses, and evaluate intra- and inter-rater reliability. Our case stud

Zhiwei Liu, Paul Thompson, Jiaqi Rong, Sophia Ananiadou

Despite the many benefits of large language models (LLMs), they can also cause harm, e.g., through automatic generation of misinformation, including conspiracy theories. Moreover, LLMs can also ''disguise'' conspiracy theories by altering characteristic textual features, e.g., by transforming their typically strong negative emotions into a more positive tone

Yaning Wang, Jinglun Yu, Wenhan Guo, Yu Sun

We propose an OCT super-resolution framework based on a plug-and-play diffusion model (PnP-DM) to reconstruct high-quality images from sparse measurements (OCT B-mode corneal images). Our method formulates reconstruction as an inverse problem, combining a diffusion prior with Markov chain Monte Carlo sampling for efficient posterior inference. We collect hig

P. H. M. Barros, P. R. S. Carvalho, H. A. S. Costa

In this work, we propose to investigate the information behavior of quantum systems through accelerated detectors quadratically coupled with a massless scalar field. In addition, we made detailed comparisons with the case of linear coupling. The perturbative method was used to evolve the density matrix that describes the interaction of the detector-field sys

Xinyu Dai

In this study, I investigate the dynamic decision problem with a finite parameter space when the functional form of conditional expected rewards is misspecified. Traditional algorithms, such as Thompson Sampling, guarantee neither an $O(e^{-T})$ rate of posterior parameter concentration nor an $O(T^{-1})$ rate of average regret. However, under mild condition

Debargha Sarkar, Jishnu N. Nampoothiri, Muhittin Mungan, Jack T. Parley

Recent computer simulations reveal several intriguing features in the evolution of properties of amorphous solids subjected to repeated cyclic shear deformation. These include the divergence of the number of cycles to reach steady states as the yielding point is approached, a non-monotonic change of properties with cycles, and the possibility of a spectrum o

Francesco Giancaterini, Alain Hecq, Joann Jasiak, Aryan Manafi Neyazi

This paper introduces a new approach for bubble detection based on mixed causal and noncausal autoregressive processes and their tail process representation during an explosive episode. Departing from traditional definitions of bubbles as nonstationary and temporarily explosive processes, we adopt a perspective in which prices are assumed to follow a strictl

Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao

Customizable multilingual zero-shot singing voice synthesis (SVS) has various potential applications in music composition and short video dubbing. However, existing SVS models overly depend on phoneme and note boundary annotations, limiting their robustness in zero-shot scenarios and producing poor transitions between phonemes and notes. Moreover, they also

Henry Fontana

A trigonal canonical curve lies on a rational normal surface scroll $Q \subset \mathbb{P}^{g-1}$. In this note we use this fact to compute the Harder-Narasimhan filtration of the normal bundle of a general such curve $C$ in $\mathbb{P}^{g-1}$. We also compute the Harder-Narasimhan filtration of the Normal bundle of a general canonical curve of genus $6$.

Ye Yuan, Haolun Wu, Hao Zhou, Xue Liu

Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extraction plays a pivotal role in transforming fragmented telecom knowledge into well-structured formats, empowering diverse AI models to better understand network terminologies. This wor

Xiaoyan Bai, Ike Peng, Aditya Singh, Chenhao Tan

Consider this prompt "Draw a unicorn with two horns". Should large language models (LLMs) recognize that a unicorn has only one horn by definition and ask users for clarifications, or proceed to generate something anyway? We introduce concept incongruence to capture such phenomena where concept boundaries clash with each other, either in user prompts or in m

Ming Zeng, Ji Wang, Gui Zhou, Fang Fang

Pinching antennas have recently garnered significant attention due to their ability to dynamically reconfigure wireless propagation environments. Despite notable advancements in this area, the exploration of energy efficiency (EE) maximization in pinching-antenna systems remains relatively underdeveloped. In this paper, we address the EE maximization problem

Regol Florence, Schwinn Leo, Sprague Kyle, Coates Mark

A significant challenge in maintaining real-world machine learning models is responding to the continuous and unpredictable evolution of data. Most practitioners are faced with the difficult question: when should I retrain or update my machine learning model? This seemingly straightforward problem is particularly challenging for three reasons: 1) decisions m

Abhay Gupta, Michael Lu, Kevin Zhu, Sean O'Brien

Current large language models (LLMs) struggle to answer questions that span tens of thousands of tokens, especially when multi-hop reasoning is involved. While prior benchmarks explore long-context comprehension or multi-hop reasoning in isolation, none jointly vary context length and reasoning depth in natural narrative settings. We introduce NovelHopQA, th

Junyi Liu, Yi Lee, Haowei Deng, Connor Clayton

Quantum computing imposes stringent requirements for the precise control of large-scale qubit systems, including, for example, microsecond-latency feedback and nanosecond-precision timing of gigahertz signals -- demands that far exceed the capabilities of conventional real-time systems. The rapidly evolving and highly diverse nature of quantum control necess

Tuan-Nghia Bui, Huy-Son Nguyen, Cam-Van Thi Nguyen, Hoang-Quynh Le

Bundle recommendation aims to recommend a set of items to each user. However, the sparser interactions between users and bundles raise a big challenge, especially in cold-start scenarios. Traditional collaborative filtering methods do not work well for this kind of problem because these models rely on interactions to update the latent embedding, which is har

Josh Rowe, Mikael Horal, Hari Sudan Sundar, Muthukumaran Arumugam

Azure Cosmos DB is a cloud-native distributed database, operating at a massive scale, powering Microsoft Cloud. Think 10s of millions of database partitions (replica-sets), 100+ PBs of data under management, 20M+ vCores. Failovers are an integral part of distributed databases to provide data availability during outages (partial or full regional outages). Whi

Wenjie Lin, Jin Wei-Kocsis, Jiansong Zhang, Byung-Cheol Min

While large language models (LLMs) have shown great potential across various domains, their applications in robotics remain largely limited to static prompt-based behaviors and still face challenges in complex tasks under zero-shot or few-shot settings. Inspired by human metacognitive learning and creative problem-solving, we address this limitation by explo

Ahmed Adel Attia, Dorottya Demszky, Jing Liu, Carol Espy-Wilson

Recent progress in speech recognition has relied on models trained on vast amounts of labeled data. However, classroom Automatic Speech Recognition (ASR) faces the real-world challenge of abundant weak transcripts paired with only a small amount of accurate, gold-standard data. In such low-resource settings, high transcription costs make re-transcription imp

Hansika Weerasena, Xiaoguo Jia, Prabhat Mishra

Network-on-Chip (NoC) enables on-chip communication between diverse cores in modern System-on-Chip (SoC) designs. With its shared communication fabric, NoC has become a focal point for various security threats, especially in heterogeneous and high-performance computing platforms. Among these attacks, Distributed Denial of Service (DDoS) attacks occur when mu

Ali Mohajerzarrinkelk, Maryam Ahang, Mehran Zoravar, Mostafa Abbasi

Precise estimation of the Remaining Useful Life (RUL) of rolling bearings is an important consideration to avoid unexpected failures, reduce downtime, and promote safety and efficiency in industrial systems. Complications in degradation trends, noise presence, and the necessity to detect faults in advance make estimation of RUL a challenging task. This paper

Hootan Mahmoodiyan, Maryam Ahang, Mostafa Abbasi, Homayoun Najjaran

Ensuring the reliable operation of power transformers is critical to grid stability. Dissolved Gas Analysis (DGA) is widely used for fault diagnosis, but traditional methods rely on heuristic rules, which may lead to inconsistent results. Machine learning (ML)-based approaches have improved diagnostic accuracy; however, power transformers operate under varyi

Gordana Ispirova, Michael Sebek, Giulia Menichetti

This chapter explores the evolution, classification, and health implications of food processing, while emphasizing the transformative role of machine learning, artificial intelligence (AI), and data science in advancing food informatics. It begins with a historical overview and a critical review of traditional classification frameworks such as NOVA, Nutri-Sc

Maribel Fernández, Daniele Nantes-Sobrinho, Daniella Santaguida

Narrowing extends term rewriting with the ability to search for solutions to equational problems. While first-order rewriting and narrowing are well studied, significant challenges arise in the presence of binders, freshness conditions and equational axioms such as commutativity. This is problematic for applications in programming languages and theorem provi

Ofer Naaman

This note is a follow up to arXiv:2408.07861, describing how to construct Josephson junction, inductor, and mutual inductance models using components that are available in the Keysight ADS core library.

Botao Amber Hu, Helena Rong

Drawing on Andrew Parker's "Light Switch" theory-which posits that the emergence of vision ignited a Cambrian explosion of life by driving the evolution of hard parts necessary for survival and fueling an evolutionary arms race between predators and prey-this essay speculates on an analogous explosion within Decentralized AI (DeAI) agent societies. Currently

Jacob X Li, Shreyas S Raman, Jessica Wan, Fahad Samman

Large Language Models (LLMs) are increasingly used in tasks requiring internal state tracking, yet their ability to model state transition dynamics remains poorly understood. We evaluate how well LLMs capture deterministic state dynamics across 3 domains: Box Tracking, Abstract DFA Sequences, and Complex Text Games, each formalizable as a finite-state system

Mirza Ahad Baig, Krzysztof Pietrzak

The Nakamoto consensus protocol underlying the Bitcoin blockchain uses proof of work as a voting mechanism. Honest miners who contribute hashing power towards securing the chain try to extend the longest chain they are aware of. Despite its simplicity, Nakamoto consensus achieves meaningful security guarantees assuming that at any point in time, a majority o

Melanie Walsh, Connor Franklin Rey, Chang Ge, Tina Nowak

Algorithmic systems are increasingly being adopted by cultural heritage institutions like libraries. In this study, we investigate U.S. public libraries' adoption of one specific automated tool -- automated collection diversity audits -- which we see as an illuminating case study for broader trends. Typically developed and sold by commercial book distributor

Marcel R. R. Hughes, Masaki Shigemori

BPS states in holographic CFTs naturally split into those describing black holes in the bulk and those that do not, with black hole states only existing above a certain energy threshold. In the context of the AdS$_3$/CFT$_2$ duality this can be seen from the agreement of the CFT and supergraviton supersymmetric indices up to a certain central charge-scaling

Nathan Roll, Calbert Graham, Yuka Tatsumi, Kim Tien Nguyen

Human listeners readily adjust to unfamiliar speakers and language varieties through exposure, but do these adaptation benefits extend to state-of-the-art spoken language models? We introduce a scalable framework that allows for in-context learning (ICL) in Phi-4 Multimodal using interleaved task prompts and audio-text pairs, and find that as few as 12 examp

Danqing Wang, Zhuorui Ye, Xinran Zhao, Fei Fang

Winning competitive debates requires sophisticated reasoning and argument skills. There are unique challenges in the competitive debate: (1) The time constraints force debaters to make strategic choices about which points to pursue rather than covering all possible arguments; (2) The persuasiveness of the debate relies on the back-and-forth interaction betwe

Susav Shrestha, Brad Settlemyer, Nikoli Dryden, Narasimha Reddy

Accelerating large language model (LLM) inference is critical for real-world deployments requiring high throughput and low latency. Contextual sparsity, where each token dynamically activates only a small subset of the model parameters, shows promise but does not scale to large batch sizes due to union of active neurons quickly approaching dense computation.

Volodymyr Derkach

Let A be a closed symmetric operator with the deficiency index (p,p), $p<\infty$, acting in a Hilbert space H and let L be a subspace of H. The set of L-resolvents of a densely defined symmetric operator in a Hilbert space with a proper gauge L was described by Krein and Saakyan. The Krein--Saakyan theory of L-resolvent matrix was extended by Shmul'yan and T

Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub

We study a fundamental learning problem over multiple groups with unknown data distributions, where an analyst would like to learn the mean of each group. Moreover, we want to ensure that this data is collected in a relatively fair manner such that the noise of the estimate of each group is reasonable. In particular, we focus on settings where data are colle

Zhi Tu, Liangkun Niu, Wei Fan, Tianyi Zhang

Autonomous driving systems (ADS) require extensive testing and validation before deployment. However, it is tedious and time-consuming to construct traffic scenarios for ADS testing. In this paper, we propose TrafficComposer, a multi-modal traffic scenario construction approach for ADS testing. TrafficComposer takes as input a natural language (NL) descripti

Chris Sypherd, Sergei Petrov, Sonny George, Vaishak Belle

In recent years, large language models have demonstrated remarkable performance across diverse tasks. However, their task effectiveness is heavily dependent on the prompting strategy used to elicit output, which can vary widely in both performance and token usage. While task performance is often used to determine prompting strategy success, we argue that eff

Kai Yin, Xiangjue Dong, Chengkai Liu, Lipai Huang

Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios.

Ali Devran Kara

We study reinforcement learning with linear function approximation and finite-memory approximations for partially observed Markov decision processes (POMDPs). We first present an algorithm for the value evaluation of finite-memory feedback policies. We provide error bounds derived from filter stability and projection errors. We then study the learning of fin

Tatsuo Kobayashi, Hiroshi Okada, Hajime Otsuka

We apply non-invertible selection rules coming from a fusion algebra to radiative neutrino mass models where fields are labeled by the elements in the algebra. Since non-invertible selection rules only hold at tree level, radiative corrections naturally explain the origin of tiny neutrino masses. Furthermore, a remnant symmetry of the fusion algebra protects

Francisco Pérez-Galarce, Jorge Martínez-Palomera, Karim Pichara, Pablo Huijse

Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks, recurrent neural networks, and transformer models. While these models have achieved high accuracy, they require high-quality,

Minxuan Wu, Joseph Antonelli, Zhihua Su

In this article, we extend predictor envelope models to settings with multivariate outcomes and multiple, functional predictors. We propose a two-step estimation strategy, which first projects the function onto a finite-dimensional Euclidean space before fitting the model using existing approaches to envelope models. We first develop an estimator under a lin

Salvatore Vitale, Matthew Mould

Measuring the distribution of spin tilts-the angles between the spin vectors and the binary orbital angular momentum-in stellar-mass binary black holes detected by LIGO-Virgo-KAGRA would provide valuable insight into their astrophysical origins. Analyses of the 69 binary black holes detected through LIGO-Virgo-KAGRA's third observing run yielded model-depend

Chin-Jou Li, Eunjung Yeo, Kwanghee Choi, Paula Andrea Pérez-Toro

Automatic speech recognition (ASR) for dysarthric speech remains challenging due to data scarcity, particularly in non-English languages. To address this, we fine-tune a voice conversion model on English dysarthric speech (UASpeech) to encode both speaker characteristics and prosodic distortions, then apply it to convert healthy non-English speech (FLEURS) i

Gabriel Santos Barbosa, Jorge Vitório Pereira

We study families of singular holomorphic foliations on complex projective manifolds whose total intersection defines a foliation of unexpectedly low codimension.

Mai Lee Chang, Samantha Reig, Alicia, Lee

As the population of older adults increases, there is a growing need for support for them to age in place. This is exacerbated by the growing number of individuals struggling with cognitive decline and shrinking number of youth who provide care for them. Artificially intelligent agents could provide cognitive support to older adults experiencing memory probl

Ryan Solgi, Kai Zhen, Rupak Vignesh Swaminathan, Nathan Susanj

The efficient implementation of large language models (LLMs) is crucial for deployment on resource-constrained devices. Low-rank tensor compression techniques, such as tensor-train (TT) networks, have been widely studied for over-parameterized neural networks. However, their applications to compress pre-trained large language models (LLMs) for downstream tas

Jonas F. G. Santos

Leveraging the unique quantum properties of non-Gaussian states is crucial for advancing continuous variable quantum technologies. Recent experimental advancements in generating non-Gaussian states, coupled with theoretical findings of their superior performance in quantum information protocols compared to Gaussian states, motivate this investigation. This w

Poetri Sonya Tarabunga, Yi-Ming Ding

Quantum Monte Carlo (QMC) methods are essential for the numerical study of large-scale quantum many-body systems, yet their utility has been significantly hampered by the difficulty in computing key quantities such as off-diagonal operators and entanglement. This work introduces Bell-QMC, a novel QMC framework leveraging Bell sampling, a two-copy measurement

Ayse D Lokmanoglu, Dror Walter

Understanding visual narratives is crucial for examining the evolving dynamics of media representation. This study introduces VisTopics, a computational framework designed to analyze large-scale visual datasets through an end-to-end pipeline encompassing frame extraction, deduplication, and semantic clustering. Applying VisTopics to a dataset of 452 NBC News

So Won Jeong, Claire Donnat

Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by their high sensitivity to hyperparameter tuning and the absence of established methodologies for selecting the optimal models. To address these challenges, we propose LOBSTUR-GNN (

Nisarga Nilavadi, Andrey Rudenko, Timm Linder

We introduce a unified approach to forecast the dynamics of human keypoints along with the motion trajectory based on a short sequence of input poses. While many studies address either full-body pose prediction or motion trajectory prediction, only a few attempt to merge them. We propose a motion transformation technique to simultaneously predict full-body p