September 2025 arXiv papers

Linjin He, Xinda Qi, Dong Chen, Zhaojian Li

Soft snake robots offer remarkable flexibility and adaptability in complex environments, yet their control remains challenging due to highly nonlinear dynamics. Existing model-based and bio-inspired controllers rely on simplified assumptions that limit their performance. Deep reinforcement learning (DRL) has recently emerged as a promising alternative, but o

Shaoyi Zheng, Canyu Zhang, Tianyi Zhou, Shengjie Wang

In-context learning (ICL) enables efficient few-shot learning in large language models (LLMs) without training, but suffers from the quadratic input complexity of transformers, limiting the maximum number of exemplars. While various efficient ICL approaches partition the context into blocks to process (e.g., ensembling, compression, cross-attention), they of

Renee Ge, Qianli Liao, Tomaso Poggio

Transformers have demonstrated remarkable performance in natural language processing and related domains, as they largely focus on sequential, autoregressive next-token prediction tasks. Yet, they struggle in logical reasoning, not necessarily because of a fundamental limitation of these models, but possibly due to the lack of exploration of more creative us

Xuanyu Lyu, Jared Balbona, Tong Chen, Matthew C. Keller

Genetic nurture effects and assortative mating (AM) occur across many human behaviors and can bias estimates from traditional genetic models. These influences are typically studied univariately, within the same trait. However, estimation of cross-trait genetic nurture effects and cross-trait AM remains underexplored due to the absence of suitable approaches.

Tong Chen, Yinuo Zhang, Pranam Chatterjee

Designing sequences that satisfy multiple, often conflicting, objectives is a central challenge in therapeutic and biomolecular engineering. Existing generative frameworks largely operate in continuous spaces with single-objective guidance, while discrete approaches lack guarantees for multi-objective Pareto optimality. We introduce AReUReDi (Annealed Rectif

Zilai Li, Lujia Bai

Diffusion models are the state-of-the-art generative models for high-resolution images, but sampling from pretrained models is computationally expensive, motivating interest in fast sampling. Although Free-U Net is a training-free enhancement for improving image quality, we find it ineffective under few-step ($<10$) sampling. We analyze the discrete diffusio

Liangliang Zhang, Nandana Mihindukulasooriya, Niharika S. D&#39;Souza, Sola Shirai

Data products are reusable, self-contained assets designed for specific business use cases. Automating their discovery is of great industry interest, as it enables efficient data access in large data lakes and supports analytical workflows. However, no benchmark currently exists for data product discovery over hybrid table-text corpora. Existing datasets foc

Anirudh Subramanyam, Yuxin Chen, Robert L. Grossman

Scaling laws for language model training traditionally characterize how performance scales with model size and dataset volume. Prior work has explored architecture variants and data treatments such as dataset filtering and noise injection in language model pretraining; however, these studies have not formalized data quality within a principled scaling law. W

Zeshi Dai, Zimo Peng, Zerui Cheng, Ryan Yihe Li

We present CAIA, a benchmark exposing a critical blind spot in AI evaluation: the inability of state-of-the-art models to operate in adversarial, high-stakes environments where misinformation is weaponized and errors are irreversible. While existing benchmarks measure task completion in controlled settings, real-world deployment demands resilience against ac

Panos Giannopoulos, Miriam Goetze, Grzegorz Gutowski, Maarten Löffler

Drawing graphs with the minimum number of crossings is a classical problem that has been studied extensively. Many restricted versions of the problem have been considered. For example, bipartite graphs can be drawn such that the two sets in the bipartition of the vertex set are mapped to two parallel lines, and the edges are drawn as straight-line segments.

Alireza Salemi, Mihir Parmar, Palash Goyal, Yiwen Song

Advances in large language models (LLMs) have created new opportunities in data science, but their deployment is often limited by the challenge of finding relevant data in large data lakes. Existing methods struggle with this: both single- and multi-agent systems are quickly overwhelmed by large, heterogeneous files, and master-slave multi-agent systems rely

Yuhui Liu, Samannita Halder, Shian Wang, Tianyi Li

This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics-informed constraints. As automated vehicles (AVs) adopt advanced features like ACC, transportation systems are becoming increasingly intelligent and efficient. However, existing AV control strategies primarily focus o

Günter F. Steinke, Thomas Steinke

Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensitivity of the estimator in question. But such sensitivity bounds are often large or simply unknown. Thus we seek differentially private methods that can be applied to arbitrary black-box functions. A handful of such tec

Haoran Xi, Minghao Shao, Brendan Dolan-Gavitt, Muhammad Shafique

Large language models show promise for vulnerability discovery, yet prevailing methods inspect code in isolation, struggle with long contexts, and focus on coarse function- or file-level detections that offer limited guidance to engineers who need precise line-level localization for targeted patches. We introduce T2L, an executable framework for project-leve

K. Albert, J. Hirzberger, N. A. Krivova, X. Li

Small-scale magnetic flux concentrations contribute significantly to the brightness variations of the Sun, yet observing them - particularly their magnetic field - near the solar limb remains challenging. Solar Orbiter offers an unprecedented second vantage point for observing the Sun. When combined with observations from the perspective of Earth, this enabl

Vinh Nguyen

A fundamental theorem of linear programming states that a feasible linear program is solvable if and only if its objective function is copositive with respect to the recession cone of its feasible set. This paper demonstrates that this crucial guarantee does not extend to Second-Order Cone Programs (SOCPs), a workhorse model in robust and convex optimization

Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan, Huaxia Wang

Identifying wireless modulation schemes is essential for cognitive radio, but standard supervised models often degrade under distribution shift, and training domain-specific wireless foundation models from scratch is computationally prohibitive. Large Language Models (LLMs) offer a promising training-free alternative via in-context learning, yet feeding raw

Haoyue Bai, Haoyu Wang, Shengyu Chen, Zhengzhang Chen

Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-date information is required. Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge, but existing systems primarily rely on unstruct

Akash Dhasade, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta

Federated inference, in the form of one-shot federated learning, edge ensembles, or federated ensembles, has emerged as an attractive solution to combine predictions from multiple models. This paradigm enables each model to remain local and proprietary while a central server queries them and aggregates predictions. Yet, the robustness of federated inference

Zhongxuan Liu, Yue Kang, Thomas C. M. Lee

The Lipschitz bandit problem extends stochastic bandits to a continuous action set defined over a metric space, where the expected reward function satisfies a Lipschitz condition. In this work, we introduce a new problem of Lipschitz bandit in the presence of stochastic delayed feedback, where the rewards are not observed immediately but after a random delay

Rong Liu, Zhongpai Gao, Benjamin Planche, Meida Chen

We introduce Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering. Unlike fixed Gaussian primitives, Beta kernels enable controllable dependency modeling across spatial, angular, and temporal dimensions within a single representation. Our uni

Thierry Blankenstein, Jialin Yu, Zixuan Li, Vassilis Plachouras

Agents backed by large language models (LLMs) increasingly rely on external tools drawn from marketplaces where multiple providers offer functionally equivalent options. This raises a critical fairness concern: systematic bias in tool selection can degrade user experience and distort competition by privileging certain providers over others. We introduce a be

Qingshu Xu, Hong Jiao, Tianyi Zhou, Ming Li

Accurate alignment of items to content standards is critical for valid score interpretation in large-scale assessments. This study evaluates three automated paradigms for aligning items with four domain and nineteen skill labels. First, we extracted embeddings and trained multiple classical supervised machine learning models, and further investigated the imp

David J. Hemmer, Armin Straub, Karlee J. Westrem

We prove a series of ``knapsack&#39;&#39; type equalities for irreducible character degrees of symmetric groups. That is, we find disjoint subsets of the partitions of $n$ so that the two corresponding character-degree sums are equal. Our main result refines our recent description of the Riordan numbers as the sum of all character degrees $f^λ$ where $λ$ is

Han Wang, Haoyu Li, Brian Ko, Huan Zhang

Leaderboards for LRMs have turned evaluation into a competition, incentivizing developers to optimize directly on benchmark suites. A shortcut to achieving higher rankings is to incorporate evaluation benchmarks into the training data, thereby yielding inflated performance, known as benchmark contamination. Surprisingly, our studies find that evading contami

Changjie Lu, Sourya Sengupta, Hua Li, Mark A. Anastasio

Objective, task-based measures of image quality (IQ) have been widely advocated for assessing and optimizing medical imaging technologies. Besides signal detection theory-based measures, information-theoretic quantities have been proposed to quantify task-based IQ. For example, task-specific information (TSI), defined as the mutual information between an ima

Charlotte Li, Nick Hagar, Sachita Nishal, Jeremy Gilbert

Benchmarks play a significant role in how technology companies communicate about model capabilities and how researchers and the public understand generative AI systems. However, existing benchmarks have been criticized for their failure to adequately capture real-world usages (i.e. ecological validity) or to measure underlying concepts (i.e. construct validi

Shutong Wu, Jiawei Zhang

Diffusion Large Language Models (DLLMs) have emerged as a new paradigm of language modeling beyond autoregressive next-token prediction. Taking advantage of their inherent modeling foundations, DLLMs have the great potential of efficient inference with parallel decoding algorithms, which enable multi-token prediction. However, the high generation quality oft

Samar Fares, Nurbek Tastan, Noor Hussein, Karthik Nandakumar

Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images and attribute these images to specific sources. While watermarking has emerged as a possible solution, existing methods remain fragile to realistic distortions, susceptible to adaptive removal, and expensive to

Hsueh-Yung Lin, Evgeny Shinder

We introduce horizontal and vertical motivic invariants of birational maps between rational dominant maps and study their basic properties. As a first application, we show that the (usual) motivic invariants vanish for birational automorphisms of threefolds over algebraically closed fields of characteristic zero. On the other hand, we prove that the motivic

Anna Lenzhen

We study limit sets of Teichmüller disks in the Thurston boundary of Teichmüller space of a closed surface S of genus at least 2. It is well known that almost every Teichmüller geodesic ray converges to a point on the boundary. We show that unlike rays, Teichmüller disks with smallest possible limit sets are extremely rare.

George Miloshevich, Luka Vranckx, Felipe Nathan de Oliveira Lopes, Pietro Dazzi

In this work, we introduce a non-local five-moment electron pressure tensor closure parametrized by a Fully Convolutional Neural Network (FCNN). Electron pressure plays an important role in generalized Ohm&#39;s law, competing with electron inertia. This model is used in the development of a surrogate model for a fully kinetic energy-conserving semi-implicit

Trung Hoang Le, Tran Cao Son, Ishtiaq Ahmed, Huiping Cao

Logical rule-based methods offer an interpretable approach to knowledge graph completion (KGC) by capturing compositional relationships in the form of human-readable inference rules. While existing logical rule-based methods learn rule confidence scores, they typically assign a global weight to each rule schema, applied uniformly across the graph. This is a

D. J. Lee, M. Reaume, W. Franklin, J. Song

The FRIB heavy-ion accelerator, commissioned in 2022, is a leading facility for producing rare isotope beams (RIBs) and exploring nuclei beyond the limits of stability. These RIBs are produced via reactions between stable primary beams and a graphite target. Approximately 20-40 \% of the primary beam power is deposited in the target, requiring efficient ther

Yusuf Ziya Isik, Rafał Łaganowski

The Low-Resource Audio Codec (LRAC) Challenge aims to advance neural audio coding for deployment in resource-constrained environments. The first edition focuses on low-resource neural speech codecs that must operate reliably under everyday noise and reverberation, while satisfying strict constraints on computational complexity, latency, and bitrate. Track 1

Andrea Addazi, Salvatore Capozziello, Qingyu Gan

We explore novel classes of exotic astrophysical sources capable of producing ultra-high-energy gamma rays extending beyond the PeV scale, motivated by quantum gravity scenarios and dark matter phenomenology. These sources include: ultra-spinning black hole vortex-string systems; exotic compact objects such as boson star, axion star and Q-ball. Such Exotica

Parsa Moradi, Tayyebeh Jahaninezhad, Hanzaleh Akbarinodehi, Mohammad Ali Maddah-Ali

Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely either on explicit gradient regularization, which often incurs substantial computational and memory overhead, or on data-mixing strategies, which are less naturally applicable to unsupervised and self-supervised

Petja Paakkinen

We provide some critical remarks on the recently proposed two-component parametrization of nuclear parton distribution functions, which was motivated by the apparent correlation between the nuclear modifications of structure functions and nucleon-nucleon short-range correlation phenomena. This parametrization, we show, is invariant under a rescaling transfor

Elke Neuhaus, Irem Portakal, Niharika Chakrabarty Paul

We investigate the toric geometry of two families of generalised determinantal varieties arising from permutations: Matrix Schubert varieties ($\overline{X_w}$) and Kazhdan-Lusztig varieties ($\mathcal{N}_{v,w}$). Matrix Schubert varieties can be written as $\overline{X_w} = Y_w \times \mathbb C^d$, where $d$ is maximal. We are especially interested in the s

Emily Koivu, Nickolay Y. Gnedin, Christopher M. Hirata

Currently the asteroid mass window (mass $\sim 10^{17}- 10^{21}$ grams) remains unconstrained for Primordial Black Holes (PBHs) to make up all of the dark matter content of the universe. Given these PBHs have very small masses, their Hawking temperature can be up to hundreds of keV. This study investigates the potential impacts of PBH Hawking radiation on th

Mohammad Hassan Shakil, Arne Johan Pollestad, Khine Kyaw, Ziaul Haque Munim

With European Union initiatives mandating gender quotas on corporate boards, a key question arises: Is greater board gender diversity (BGD) associated with better emissions performance (EP)? To answer this question, we examine the influence of BGD on EP across a sample of European firms from 2016 to 2022. Using panel regressions, advanced machine learning al

Muyan Jiang, Anil Aswani

This paper studies attack detection for discrete-time linear systems with stochastic process noise that produce both a vulnerable (i.e., attackable) linear measurement and a secured (i.e., unattackable) quadratic measurement. The motivating application of this model is a dynamic-game setting where the quadratic measurement is interpreted as a system-level ut

Ehsan Aghaei, Sarthak Jain, Prashanth Arun, Arjun Sambamoorthy

Effective analysis of cybersecurity and threat intelligence data demands language models that can interpret specialized terminology, complex document structures, and the interdependence of natural language and source code. Encoder-only transformer architectures provide efficient and robust representations that support critical tasks such as semantic search,

P. H. M. Barros, Shu-Min Wu, C. A. S. Almeida, H. A. S. Costa

In this work, we investigate the velocity effects on information degradation due to the Unruh effect in accelerated quantum systems (with finite interaction time). We consider a detector moving along a spatial trajectory within a two-dimensional plane. The quantum systems studied were: accelerated single-qubit, quantum interferometric circuit, and which-path

Vincent Roulet, Atish Agarwala

Training algorithms in deep learning usually treat a mini-batch of samples as a single object; they average gradients over the mini-batch, and then process the average in various ways. Computing other statistics beyond the average may have been seen as prohibitively resource intensive in automatic differentiation (AD) frameworks. We show that this is not the

Pedro M. Ibarbo-Perlaza., J. Bayron Orjuela-Quintana, Jose L. Palacios-Córdoba, César A. Valenzuela-Toledo

We investigate the cosmological implications of Tsallis entropy in two widely discussed frameworks: the Cai-Kim thermodynamic derivation of the Friedman equations and the Tsallis holographic dark energy (HDE) scenario, considering both the Hubble scale and the Granda-Oliveros (GO) cutoff as infrared regulators. In both cases, the dynamics introduce a nonexte

Siva Viknesh, Amirhossein Arzani

Scientific machine learning has enabled the extraction of physical insights and data-driven modeling of high-dimensional spatiotemporal data, yet achieving physically interpretable latent representations and computationally efficient surrogates remains an open challenge. We propose the DIfferentiable Autoencoding Neural Operator - DIANO, an autoencoding neur

Xin Xu, Xunzhi He, Churan Zhi, Ruizhe Chen

Existing studies on bias mitigation methods for large language models (LLMs) use diverse baselines and metrics to evaluate debiasing performance, leading to inconsistent comparisons among them. Moreover, their evaluations are mostly based on the comparison between LLMs&#39; probabilities of biased and unbiased contexts, which ignores the gap between such eva

Rohan Kadekodi, Zhan Jin, Keisuke Kamahori, Yile Gu

The deployment of Large Language Models (LLMs) as agentic orchestrators has revolutionized task automation, but the need for privacy-preserving, cost-effective solutions demands on-device inference capabilities. However, local LLMs consistently underperform compared to frontier models in tool calling scenarios, struggling with both tool selection from large

Manasa Kaniselvan, Benjamin Kurt Miller, Meng Gao, Juno Nam

Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training set size. Meanwhile, the even greater quantities of accompanying data in the Hamiltonian matrix H behind these datasets has so far gone unused for this purpose. Here, we provide a

Houjun Liu, Shikhar Murty, Christopher D. Manning, Róbert Csordás

Current approaches for scaling inference-time compute in transformers train them to emit explicit chain-of-thought tokens before producing an answer. While these methods are powerful, they are limited because they cannot be applied during pretraining and rely solely on serially-generated, natural-language verbalization. In this work, we propose Thoughtbubble

Mohammud Foondun, Davar Khoshnevisan, Eulalia Nualart

We consider a parabolic stochastic partial differential equation (SPDE) on $[0\,,1]$ that is forced with multiplicative space-time white noise with a bounded and Lipschitz diffusion coefficient and a drift coefficient that is locally Lipschitz and satisfies an $L\log L$ growth condition. We prove that the SPDE is well posed when the initial data is in $L^2[0

Dmitry Gorbunov, Ekaterina Kriukova

We study the production of hypothetical vector portal mediators, dark photons $γ^\prime$, with masses in the range 0.4-3.5 GeV in the negatively charged pion-proton collisions $π^-p\rightarrow γ^\prime X$ via inelastic pion bremsstrahlung and QCD Drell-Yan-like process. In both cases we estimate the value of total dark photon production cross section and obt

Naomi Spargo, Santiago Cuéllar, Jonathan Daugherty, Chris Phifer

Lifting low-level or legacy code into a domain-specific language (DSL) improves our ability to understand it, enables deeper formal reasoning, and facilitates safe modification. We present the CoCompiler, a bidirectional compiler and lifter between C and Lustre, a synchronous dataflow language used for reactive systems. The key insight behind the CoCompiler

Tanay Kumar, Raktim Bhattacharya

Attitude stabilization of unmanned aerial vehicles (UAVs) in uncertain environments presents significant challenges due to nonlinear dynamics, parameter variations, and sensor limitations. This paper presents a comparative study of $\mathcal{H}_\infty$ and classical PID controllers for multi-rotor attitude regulation in the presence of wind disturbances and

Jiong Lin, Jialong Ning, Judah Goldfeder, Hod Lipson

In this paper, we formulate the problem of kinematic synthesis for planar linkages as a cross-domain image generation task. We develop a planar linkages dataset using RGB image representations, covering a range of mechanisms: from simple types such as crank-rocker and crank-slider to more complex eight-bar linkages like Jansen&#39;s mechanism. A shared-laten

Yunqi Gao, Bing Hu, Mahdi Boloursaz Mashhadi, A-Long Jin

The parameter size of modern large language models (LLMs) can be scaled up via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has become a promising solution for distributed MoE training. However, existing work p

Robert J. Hayek, Joaquin Chung, Rajkumar Kettimuthu

Quantum network protocol development is crucial to realizing a production-grade network that can support distributed sensing, secure communication, and utility-scale quantum computation. However, the transition from laboratory demonstration to deployable networks requires software implementations of architectures and protocols tailored to the unique constrai

Yifan Lu, Rixin Liu, Jiayi Yuan, Xingqi Cui

Today&#39;s LLM ecosystem comprises a wide spectrum of models that differ in size, capability, and cost. No single model is optimal for all scenarios; hence, LLM routers have become essential for selecting the most appropriate model under varying circumstances. However, the rapid emergence of various routers makes choosing the right one increasingly challeng

Keren Wang, Jing Du, Wei Wang

Conventional Purcell theory emphasizes high quality factors (Q) for spontaneous emission (SE) enhancement in cavities, but overlooks collective Bloch mode effects in periodic nanostructures like photonic crystal slabs. We introduce a unified temporal coupled-mode framework to compute Purcell and photoluminescence factors through momentum-space integration, r

Anni Li, Aria Attar, Paul Dong

Transforming natural-language requests into reliable, production-ready data transformations remains challenging: correctness depends on precise schema linking and warehouse-specific SQL dialects, while the strongest supervision available during training--execution success and result matching--are provided only at the sequence level. At the same time, assembl

Ween Yang, Jason Liu, Suli Wang, Xinyuan Song

The rapid expansion of distributed Artificial Intelligence (AI) workloads beyond centralized data centers creates a demand for new communication substrates. These substrates must operate reliably in heterogeneous and permissionless environments, where Network Address Translators (NATs) and firewalls impose significant constraints. Existing solutions, however

Luis Burbano, Diego Ortiz, Qi Sun, Siwei Yang

Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, also create new security risks. In this paper, we introduce CHA

Jie Cao, Qi Li, Zelin Zhang, Jianbing Ni

The rapid progress of Generative Artificial Intelligence (GenAI) has enabled the effortless synthesis of high-quality visual content, while simultaneously raising pressing concerns about intellectual property protection, authenticity, and accountability. Among various countermeasures, watermarking has emerged as a fundamental mechanism for tracing provenance

Shuai Huang, Xuan Kan, James J. Lah, Deqiang Qiu

Current atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining t

Perry Kleinhenz, Michael McNulty

We prove integrated local energy decay for solutions of the damped wave equation with time-dependent damping satisfying an appropriate generalization of the geometric control condition on asymptotically flat, stationary space-times. We first obtain a high frequency estimate, which we prove via a positive commutator estimate using an escape function explicitl

Shuyue Stella Li, Avinandan Bose, Faeze Brahman, Simon Shaolei Du

Current large language model (LLM) development treats task-solving and preference-alignment as separate challenges, optimizing first for objective correctness, then for alignment to aggregated human preferences. This paradigm fails in human-facing applications where solving a problem correctly is insufficient if the response mismatches the user&#39;s needs.

Luca Morf, Ravit Helled

We present a new framework for constructing agnostic and yet physical models for planetary interiors and apply it to Uranus and Neptune. Unlike previous research that either impose rigid assumptions or rely on simplified empirical profiles, our approach bridges both paradigms. Starting from randomly generated density profiles, we applied an iterative algorit

Amirhossein Abaskohi, Tianyi Chen, Miguel Muñoz-Mármol, Curtis Fox

We introduce DRBench, a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings. Unlike prior benchmarks that focus on simple questions or web-only queries, DRBench evaluates agents on multi-step queries (for example, &#34;What changes should we make to our product roadmap to ensure compliance with this standard?&

Sabee Grewal, Daniel Liang

We study the problem of efficiently learning an unknown $n$-qubit unitary channel in diamond distance given query access. We present a general framework showing that if Pauli operators remain low-complexity under conjugation by a unitary, then the unitary can be learned efficiently. This framework yields polynomial-time algorithms for a wide range of circuit

Diego Ortiz Barbosa, Mohit Agrawal, Yash Malegaonkar, Luis Burbano

Autonomous drones must often respond to sudden events, such as alarms, faults, or unexpected changes in their environment, that require immediate and adaptive decision-making. Traditional approaches rely on safety engineers hand-coding large sets of recovery rules, but this strategy cannot anticipate the vast range of real-world contingencies and quickly bec

Christin Bibby, Daniel C. Cohen, Emanuele Delucchi

We study topological aspects of supersolvable abelian arrangements, toric arrangements in particular. The complement of such an arrangement sits atop a tower of fiber bundles, and we investigate the relationship between these bundles and bundles involving classical configuration spaces. In the toric case, we show that the monodromy of a supersolvable arrange

Frederic J. N. Jorgensen, Youssef M. Marzouk

The analysis step of the stochastic ensemble Kalman filter, called the ensemble Kalman update (EnKU), is widely used for approximating posterior distributions in inverse problems and data assimilation. The EnKU approximates the posterior distribution $π_{X\mid Y=y_\star}$ by pushing forward the joint distribution $(X,Y)\simπ$ through an affine map $L^{\mathr

Gabriele Lo Monaco, Salvatore Lorenzo, Alessandro Ferraro, Mauro Paternostro

We study the non-stabilizer resources required to achieve informational completeness in single-setting quantum state estimation scenarios. We consider fixed-basis projective measurements preceded by quantum circuits acting on $n$-qubit input states, allowing ancillary qubits to increase retrievable information. We prove that when only stabilizer resources ar

Joanna Piper Morgan, Ilham Variansyah, Kayla B. Clements, Todd S. Palmer

In Monte Carlo radiation transport calculations, Woodcock-delta tracking is a common alternative to the more popular surface tracking technique. In this work we introduce a delta-tracking algorithm that tallies fluxes to a structured rectilinear mesh using the track-length estimator. This development also enables hybrid surface-delta tracking algorithms, bec

Hengwei Zhao, Zhengzhong Tu, Zhuo Zheng, Wei Wang

Positive-Unlabeled (PU) learning aims to train a binary classifier (positive vs. negative) where only limited positive data and abundant unlabeled data are available. While widely applicable, state-of-the-art PU learning methods substantially underperform their supervised counterparts on complex datasets, especially without auxiliary negatives or pre-estimat

João Paulo Pinheiro

We present a comprehensive theoretical analysis of neutrino-induced decoherence in macroscopic matter-wave interferometry experiments designed to search for dark matter and beyond-Standard Model physics. Our calculation includes contributions from the cosmic neutrino background (C$ν$B), solar neutrinos, and reactor antineutrinos, accounting for coherent scat

Daryl Funk, Angus Matthews, Dillon Mayhew

We present a Myhill-Nerode theorem for hypergraphs. The theorem involves an operation which takes two input structures and produces a hypergraph as output. Using this operation, we define a Myhill-Nerode-type equivalence relation and show that if a class of hypergraphs is definable in the counting monadic second-order logic of hypergraphs, then the equivalen

Gabriel-Dominique Marleau

Direct-imaging surveys have looked for accreting planets through their accretion tracers such as H alpha but have been less fruitful than expected. However, up to now, hydrogen-line emission at accreting planets has been estimated primarily with extrapolations of stellar-scaling relationships or with theoretical spherically-symmetric computations. To predict

Chun Chen, Ning-Chen Sun, Qiang Xi, Samaporn Tinyanont

SN 2025coe is a calcium-strong transient located at an extremely large projected offset $\sim$39.3 kpc from the center of its host, the nearby early-type galaxy NGC 3277 at a distance of $\sim$25.5 Mpc. In this paper, we present multi-band photometric and spectroscopic observations spanning $\sim$100 days post-discovery. Its multi-band light curves display {

Asimpunya Mitra, Daniel J. Schultz, Yong Baek Kim

The pairing symmetry and underlying mechanism for superconducting state of AV${}_3$Sb${}_5$ (A=K, Rb, Cs) kagome metal has been a topic of intense investigation. In this work, we consider an 8-band minimal model, which includes V, and the two types of Sb, both within and above/below the kagome plane. This model captures the Fermi surface pocket with signific

Adarsha Balaji, Sandeep Madireddy, Prasanna Balaprakash

Foundational models based on the transformer architecture are currently the state-of-the-art in general language modeling, as well as in scientific areas such as material science and climate. However, training and deploying these models is computationally challenging as the time and space complexity has a quadratic relation to the input sequence length. Seve

Alexander Berkovich, Aritram Dhar

Consider \begin{align*} G(N,M;α,β,K,q) = \sum\limits_{j\in\mathbb{Z}}(-1)^jq^{\frac{1}{2}Kj((α+β)j+α-β)}\left[\begin{matrix}M+N\\N-Kj\end{matrix}\right]_{q}. \end{align*} In this paper, we prove the non-negativity of coefficients of some cases of $G(N,M;α,β,K,q)$. For instance, for non-negative integers $n$ and $t$, we prove that\\ \begin{align*} G\left(n,n;

Connor T. Jerzak, Adel Daoud

Randomized controlled trials (RCTs) are the benchmark for causal inference, yet field implementation can drift from the registered design or, by chance, yield imbalances. We introduce a remote audit -- a preregistrable, design-based diagnostic that uses strictly pre-treatment, publicly available satellite imagery to test whether assignment is independent of

Andrew Pike, Zhenkun Yuan, Muhammad Rubaiat Hasan, Smitakshi Goswami

The AM2Pn2 Zintl compounds are a large class of semiconductor materials that have a wide range of bandgaps and are mostly stable in the same crystal structure. Representative compounds BaCd2P2 and CaZn2P2 have recently been found to exhibit high visible light absorption and long carrier lifetime. Here we use high throughput first-principles calculations to s

Curt Healey

Recent studies in Kubo-Ando theory make frequent use of the relationship between Kubo-Ando connections and positive operator monotone functions. This relationship is deeply connected to Löwner&#39;s theorem and our aim is to provide a comprehensive review of one of the proofs of Löwner&#39;s theorem. Our motivation arises from the fact that the foundational

CMS Collaboration

A measurement is presented of the electroweak vector boson scattering production of ZV (V = W, Z) boson pairs associated with two jets in proton-proton collisions at a center-of-mass energy of 13 TeV. The data, corresponding to an integrated luminosity of 138 fb$^{-1}$, were collected at the CERN LHC with the CMS detector during the 2016$-$2018 data-taking p

Benjamin Koch, Ali Riahinia, Angel Rincon

We investigate the motion of test particles in quantum-gravitational backgrounds by introducing the concept of q--desics, quantum-corrected analogs of classical geodesics. Unlike standard approaches that rely solely on the expectation value of the spacetime metric, our formulation is based on the expectation value of quantum operators, such as the the affine

Márton Beke, Olga Plamenevskaya, Laura Starkston

The rational blowdown operation in 4-manifold topology replaces a neighborhood of a configuration of spheres by a rational homology ball. Such configurations typically arise from resolutions of surface singularities that admit rational homology disk smoothings. Conjecturally, all such singularities must be weighted homogeneous and belong to certain specific

Srimoyee Sen, Varun Vaidya

Initial states of dense matter with nonzero electron chiral imbalance could potentially give rise to strong magnetic fields through chiral plasma instability. Previous work indicated that unless chiral chemical potential is as large as the electron vector chemical potential, the growth of magnetic fields due to the instability is washed out by chirality flip

Olga Garcia-Gallego, Vid Iršič, Martin G. Haehnelt, James S. Bolton

We present the first constraints on the electron optical depth to reionization, $τ_{\mathrm{e}}$, from the Lyman-$α$ forest alone for physically motivated reionization models that match the reionization&#39;s end-point, $z_{\rm{end}}$, required by the same astrophysical probe, and for symmetric reionization models with fixed duration, $Δz$, commonly adopted

Peter James Nee, Adhrit Ravichandran, Scott E. Field, Tousif Islam

Mounting evidence indicates that some of the gravitational wave signals observed by the LIGO/Virgo/KAGRA observatories might arise from eccentric compact object binaries, increasing the urgency for accurate waveform models for such systems. While for non-eccentric binaries, surrogate models are efficient and accurate, the additional features due to eccentric

Alberto Torralba, Jorryt Matthee, Gabriele Pezzulli, Rohan P. Naidu

The population of the Little Red Dots (LRDs) may represent a key phase of supermassive black hole (SMBH) growth. A cocoon of dense excited gas is emerging as key component to explain the most striking properties of LRDs, such as strong Balmer breaks and Balmer absorption, as well as the weak IR emission. To dissect the structure of LRDs, we analyze new deep

Juan I. Espinoza-Retamal, Rafael Brahm, Cristobal Petrovich, Andrés Jordán

We report the discovery of TOI-4507 b, a transiting sub-Saturn with a density $<$ 0.2 g/cm$^3$ on a 105-day prograde orbit around a 700 Myr old F star. The transits were detected using data from TESS as well as the Antarctic telescope ASTEP. A joint analysis of the light curves and radial velocities from HARPS, FEROS, and CORALIE confirmed the planetary natu

Abijith Krishnan, Ajesh Kumar, T. Senthil

Two-dimensional (2d) electronic systems on a lattice at fractional filling $ν= p/q$ exhibit a competition between charge ordered insulators, called Wigner-Mott insulators (WMIs), at large Coulomb repulsion and Fermi-liquid metals at large electronic kinetic energy. When those two energy scales are roughly equal, insulating states that restore the lattice tra

Marco Costa, Cyril Creque-Sarbinowski, Olivier Simon, Zachary J. Weiner

No experimental test precludes the possibility that the dark matter experiences forces beyond general relativity---in fact, a variety of cosmic microwave background observations suggest greater late-time structure than predicted in the standard $Λ$ cold dark matter model. We show that minimal models of scalar-mediated forces between dark matter particles do

Mariana Carrillo González, Théo Keseman

We develop a twistor-space framework to compute boundary correlators via a boundary limit of nested Penrose transforms in (A)dS$_4$. Starting from correlators of (anti-)self-dual bulk fields, the boundary limit reproduces the correlators of the dual conserved currents; we demonstrate this explicitly for two- and three-point functions. The two-point correlato

Adam M. Dillamore, Jason L. Sanders

The shape and orientation of the Milky Way&#39;s dark matter halo remain poorly constrained. Observations of the accreted stellar halo show that it is triaxial and tilted with respect to the disc. If this configuration is long-lived, it can place constraints on the shape and orientation of the dark matter halo that can support it close to steady state. We us

Pablo Araya-Araya, Rachel K. Cochrane, Laerte Sodré, Robert M. Yates

High-redshift (z > 2) massive quiescent (MQ) galaxies provide an opportunity to probe the key physical processes driving the fuelling and quenching of star formation in the early Universe. Observational evidence suggests a possible evolutionary link between MQs and dusty star-forming galaxies (DSFGs; or submillimetre galaxies), another extreme high-redshift

Xingyu Chen, Yue Chen, Yuliang Xiu, Andreas Geiger

Modern Recurrent Neural Networks have become a competitive architecture for 3D reconstruction due to their linear-time complexity. However, their performance degrades significantly when applied beyond the training context length, revealing limited length generalization. In this work, we revisit the 3D reconstruction foundation models from a Test-Time Trainin