May 2025 arXiv papers — page 84

Bosung Kim, Prithviraj Ammanabrolu

We introduce $\infty$-THOR, a new framework for long-horizon embodied tasks that advances long-context understanding in embodied AI. $\infty$-THOR provides: (1) a generation framework for synthesizing scalable, reproducible, and unlimited long-horizon trajectories; (2) a novel embodied QA task, Needle(s) in the Embodied Haystack, where multiple scattered clu

Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo

When language model (LM) users aim to improve the quality of its generations, it is crucial to specify concrete behavioral attributes that the model should strive to reflect. However, curating such principles across many domains, even non-exhaustively, requires a labor-intensive annotation process. To automate this process, we propose eliciting these latent

Rasool Ahmad, Jonathan C. Crowhurst, Stanimir A. Bonev

Under pressure, carbon monoxide (CO) transforms into a polymer that can be recovered to ambient conditions. While this transformation can occur without additional stimuli, experimental observations have shown that laser irradiation can induce a similar transformation at reduced pressure. The resulting polymeric phase, which is metastable under ambient condit

Yu-Ang Cheng, Leyang Hu, Hai Huang, Randall Balestriero

Autoregressive pretraining has become the de facto paradigm for learning general-purpose representations in large language models (LLMs). However, linear probe performance across downstream perception tasks shows substantial variability, suggesting that features optimized for next-token prediction do not consistently transfer well to downstream perception ta

Igor Udovichenko, Olivier Croissant, Anita Toleutaeva, Evgeny Burnaev

Risk-averse reinforcement learning finds application in various high-stakes fields. Unlike classical reinforcement learning, which aims to maximize expected returns, risk-averse agents choose policies that minimize risk, occasionally sacrificing expected value. These preferences can be framed through utility theory. We focus on the specific case of the expon

Pembe Ipek Al, Zameddin I. Ismailov, Fuad Kittaneh, Satyajit Sahoo

In this study, some estimates are given for the $ (A,q)$-numerical radius and $ (A,q)$-Crawford number via the $ A$-numerical radius and $ A$-Crawford number for the $ A $-bounded linear operators in any complex semi-Hilbert space, respectively. Then, some evolutions are studied for the tensor product of two operators. Lastly, some convergence properties of

Yuhui Zhang, Dongshen Wu, Yuichiro Wada, Takafumi Kanamori

A reliable uncertainty estimation method is the foundation of many modern out-of-distribution (OOD) detectors, which are critical for safe deployments of deep learning models in the open world. In this work, we propose TULiP, a theoretically-driven post-hoc uncertainty estimator for OOD detection. Our approach considers a hypothetical perturbation applied to

Ruihan Yang, Caiqi Zhang, Zhisong Zhang, Xinting Huang

Large Language Models (LLMs) are prone to hallucination, particularly in long-form generations. A promising direction to mitigate hallucination is to teach LLMs to express uncertainty explicitly when they lack sufficient knowledge. However, existing work lacks direct and fair evaluation of LLMs' ability to express uncertainty effectively in long-form generat

Hamza El Byad, Matteo Bachetti, Silvia Columbu, Giuseppe Rodriguez

The study of quasi-periodic oscillations in X-ray binaries provides valuable insights into the physics of accretion around compact objects. The M82 galaxy hosts two ultraluminous X-ray sources (ULXs), one of which is suspected to harbor an intermediate-mass black hole. Using 39 NuSTAR observations acquired between 2014--2024, we investigate the aperiodic X-r

Giorgio Orlando, Shingo Akama, Chunshan Lin

In this paper, we explore parity violation in a scalar trispectrum from a dynamical Chern-Simons gravity theory. So far, a graviton-mediated diagram with two vertexes being of general relativity has been studied in this theory by taking into account the impact of a modified dispersion relation of gravitons on graviton's bulk propagators. We instead study a p

Soham Mukherjee, Manfred Claassen, Paul-Christian Bürkner

Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approximate methods are required, for example, the recently developed class of Hilbert space Gaussian processes. They have been shown to significantly reduce computation time while retaini

Nikola Tankovic, Robert Sajina

This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers. The approach models context at the product category level, allowing offers to span multiple categories and enabling knowledge transfer acro

Manuel Hohmann, Ulbossyn Ualikhanova

We study the cosmological dynamics of a class of symmetric teleparallel gravity theories known as ``newer general relativity'' using the methods of dynamical systems, restricted to the case of vacuum solutions with a spatially flat Friedmann-Lema\^itre-Robertson-Walker metric. For the most general class of theories, we study generic properties of the solutio

Xuankun Rong, Wenke Huang, Jian Liang, Jinhe Bi

Multimodal Large Language Models (MLLMs) are increasingly deployed in fine-tuning-as-a-service (FTaaS) settings, where user-submitted datasets adapt general-purpose models to downstream tasks. This flexibility, however, introduces serious security risks, as malicious fine-tuning can implant backdoors into MLLMs with minimal effort. In this paper, we observe

Ce Yang, Ning Zhang, Jiaxuan Li, Unnati V. Mehta

Environmental epidemiologists are often interested in estimating the effect of time-varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effect

Srikanth Srinivas, Lagan Goel

Modern software development demands rapid, reliable testing methods to maintain high quality in increasingly complex systems. This paper details a comprehensive approach to designing and implementing robust test automation frameworks by leveraging Cucumber BDD with Java. By utilizing Cucumber BDD natural language syntax, the framework enables clear communica

Fabio Deelan Cunden, Giovanni Gramegna, Marilena Ligabò

We consider a free quantum particle in one dimension whose mass profile exhibits jump discontinuities. The corresponding Hamiltonian is a self-adjoint realisation of the kinetic-energy operator, with the specific realisation determined by the boundary conditions at the points of mass discontinuity. For a family of scale-free boundary conditions, we analyse t

Desiree Fisker, Alexander Krawciw, Sven Lilge, Melissa Greeff

This paper presents Virtual Teach and Repeat (VirT&R): an extension of the Teach and Repeat (T&R) framework that enables GPS-denied, zero-shot autonomous ground vehicle navigation in untraversed environments. VirT&R leverages aerial imagery captured for a target environment to train a Neural Radiance Field (NeRF) model so that dense point clouds and photo-te

Ofir Yaish, Yehuda Mishaly, Eliya Nachmani

We introduce a new paradigm for active sound modification: Active Speech Enhancement (ASE). While Active Noise Cancellation (ANC) algorithms focus on suppressing external interference, ASE goes further by actively shaping the speech signal -- both attenuating unwanted noise components and amplifying speech-relevant frequencies -- to improve intelligibility a

Peter Koymans, Carlo Pagano

We prove that for every number field $K$, there exist infinitely many elliptic curves $E$ over $K$ with rank exactly equal to 1.

Francesco Costantino, Matthieu Faitg

For each braided category $\mathcal{C}$ we show that, under mild hypotheses, there is an associated category of "half braided algebras" and their bimodules internal to $\mathcal{C}$ which is not only monoidal but even braided and balanced. We use this in the case where $\mathcal{C}$ is the category of modules over a ribbon Hopf algebra to interpret stated sk

Matthew Tremba, Paul Hovland, Ji Liu

Although quantum circuit depth is commonly used to approximate circuit runtimes, it overlooks a prevailing trait of current hardware implementation: different gates have different execution times. Recognizing the potential for discrepancies, we investigate depth's accuracy for comparing runtimes between compiled versions of the same circuit. In particular, w

Jonathan Block, Fedor Manin, Shmuel Weinberger

We can view the Lipschitz constant as a height function on the space of maps between two manifolds and ask (as Gromov did nearly 30 years ago) what its ``Morse landscape'' looks like: are there high peaks, deep valleys and mountain passes? A simple and relatively well-studied version of this question: given two points in the same component (homotopic maps),

Nithin Kumar Goona, Lama Tarsissi

This work proposes a higher-order iterative framework for solving matrix equations, inspired by the structure and functionality of neural networks. A modification of the classical Jacobi iterative method is introduced to compute higher-order coefficient matrices through matrix-matrix multiplications. The resulting method, termed the higher order Jacobi metho

Linqing Peng, Shuanglong Liu, Xing Zhang, Xiao Chen

The effective crystal field Hamiltonian provides the key description of the electronic properties of single-ion magnets, but obtaining its parameters from ab initio computation is challenging. We introduce a simple approach to derive the effective crystal field Hamiltonian through density functional calculations of randomly rotated mean-field states within t

Louis Shuo Wang, Jiguang Yu

In this work, we propose a stochastic version of the Rosenzweig-MacArthur model solely driven by internal demographic noise, extending classical Lotka-Volterra-type systems focused on external noise. We give a criterion for the existence and uniqueness of autonomous stochastic differential equations (SDEs) on an open submanifold of $\mathbb{R}^{n}$, and the

Shreyansh Padarha

Post-World War II armed conflicts have often been viewed with higher scrutiny in order to avoid a full-scale global war. This scrutiny has led to the establishment of determinants of war such as poverty, inequalities, literacy, and many more. There is a gap that exists in probing countries in the Global South for political party fragmentation and examining i

Ibrahim Ethem Hamamci, Sezgin Er, Suprosanna Shit, Hadrien Reynaud

Evaluating long-context radiology report generation is challenging. NLG metrics fail to capture clinical correctness, while LLM-based metrics often lack generalizability. Clinical accuracy metrics are more relevant but are sensitive to class imbalance, frequently favoring trivial predictions. We propose the CRG Score, a distribution-aware and adaptable metri

Junzhe Jiang, Nan Song, Jingyu Li, Xiatian Zhu

Driving simulation plays a crucial role in developing reliable driving agents by providing controlled, evaluative environments. To enable meaningful assessments, a high-quality driving simulator must satisfy several key requirements: multi-modal sensing capabilities (e.g., camera and LiDAR) with realistic scene rendering to minimize observational discrepanci

Hongyuan Tao, Ying Zhang, Zhenhao Tang, Hongen Peng

Recent advances in Large Language Models (LLMs) have shown promise in function-level code generation, yet repository-level software engineering tasks remain challenging. Current solutions predominantly rely on proprietary LLM agents, which introduce unpredictability and limit accessibility, raising concerns about data privacy and model customization. This pa

Jintian Shao

During the finetuning stage of text generation tasks, standard cross-entropy loss treats all tokens equally. This can lead models to overemphasize high-frequency, low-information tokens, neglecting lower-frequency tokens crucial for specificity and informativeness in generated content. This paper introduces a novel loss function, Power-Law Decay Loss (PDL),

Kerem Oktar, Katherine M. Collins, Jose Hernandez-Orallo, Diane Coyle

Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class of models that genuinely collaborate with humans in complex reasoning, from conceptualizing problems to brainstorming solutions. Such AI thought partners enable novel forms of col

Ashmita Das, Anjana Krishnan, Soham Sen, Sunandan Gangopadhyay

Horizon Brightened Acceleration Radiation (HBAR) signifies a unique radiation process and provides a promising framework in exploring acceleration radiation in flat/ curved spacetime. Its construction primarily relies on the transition probability of an atom falling through a high-Q cavity while interacting with a quantum field. The HBAR effect has typically

Can Chen, David Heurtel-Depeiges, Robert M. Vernon, Christopher James Langmead

Protein language models (pLMs) pre-trained on vast protein sequence databases excel at various downstream tasks but often lack the structural knowledge essential for some biological applications. To address this, we introduce a method to enrich pLMs with structural knowledge by leveraging pre-trained protein graph neural networks (pGNNs). First, a latent-lev

Aline B. Trench, Nihal Oturan, Aydeniz Demir, João P. C. Moura

In this study, the comparative efficiency of different electrochemical advanced oxidation processes, such as anodic oxidation with electrogenerated H2O2 (AO- H2O2), electro-Fenton (EF), and its combination with UV irradiation (photoelectron-Fenton (PEF)), was investigated for the removal of methylparaben (MP) using a carbon felt cathode and a boron-doped dia

Michael Lubasch, Yuta Kikuchi, Lewis Wright, Conor Mc Keever

For the solution of partial differential equations (PDEs), we show that the quantum Fourier transform (QFT) can enable the design of quantum circuits that are particularly simple, both conceptually and with regard to hardware requirements. This is shown by explicit circuit constructions for the incompressible advection, heat, isotropic acoustic wave, and Poi

Zeyu Wei, Shuo Wang, Xiaohui Rong, Xuemin Liu

Hallucinations -- plausible yet erroneous outputs -- remain a critical barrier to reliable deployment of large language models (LLMs). We present the first systematic study linking hallucination incidence to internal-state drift induced by incremental context injection. Using TruthfulQA, we construct two 16-round "titration" tracks per question: one appends

Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka, Ichiro Takeuchi

Graph Neural Networks (GNNs) have gained prominence for their ability to process graph-structured data across various domains. However, interpreting GNN decisions remains a significant challenge, leading to the adoption of saliency maps for identifying salient subgraphs composed of influential nodes and edges. Despite their utility, the reliability of GNN sa

Luzhe Sun, Jingtian Ji, Xiangshan Tan, Matthew R. Walter

Shared autonomy is an enabling technology that provides users with control authority over robots that would otherwise be difficult if not impossible to directly control. Yet, standard methods make assumptions that limit their adoption in practice-for example, prior knowledge of the user's goals or the objective (i.e., reward) function that they wish to optim

Chenghong Zhu, Xian Wu, Zhaohui Yang, Jingbo Wang

Quantum hardware development is progressing rapidly with substantial advancements achieved across leading platforms, including superconducting circuits, trapped-ion systems, and neutral atom arrays. As the pursuit of practical quantum advantage continues, efficient quantum program compilation becomes essential for transforming high-level representations of q

Sarah Lang, A'nya Buckner, Solomon Jones, Gabrielle Erwin

As humanity prepares for sustained off-world habitation, the development of regolith-based agriculture (RBA) is essential for achieving self-sufficiency in space crop production. However, lunar regolith's alkaline pH, poor water retention, and high metal content pose severe physiological and biochemical challenges to plant growth. This study evaluates th

Viet Pham, Thai Le

Large Language Models (LLMs) are increasingly deployed via third-party system prompts downloaded from public marketplaces. We identify a critical supply-chain vulnerability: conditional system prompt poisoning, where an adversary injects a ``sleeper agent'' into a benign-looking prompt. Unlike traditional jailbreaks that aim for broad refusal-breaking, our p

Aline B. Trench, Caio Machado Fernandes, João Paulo C. Moura, Lanna E. B. Lucchetti

Hydrogen peroxide (H2O2) stands as one of the foremost utilized oxidizing agents in modern times. The established method for its production involves the intricate and costly anthraquinone process. However, a promising alternative pathway is the electrochemical hydrogen peroxide production, accomplished through the oxygen reduction reaction via a 2-electron p

Nour Jedidi, Yung-Sung Chuang, James Glass, Jimmy Lin

With the growing success of reasoning models across complex natural language tasks, researchers in the Information Retrieval (IR) community have begun exploring how similar reasoning capabilities can be integrated into passage rerankers built on Large Language Models (LLMs). These methods typically employ an LLM to produce an explicit, step-by-step reasoning

Kamil Dutkiewicz, Marcin Płodzień, Abel Rojo-Francàs, Bruno Juliá-Díaz

Modern quantum engineering techniques allow for synthesizing quantum systems in exotic lattice geometries, from self-similar fractal networks to negatively curved hyperbolic graphs. We demonstrate that these structures profoundly reshape Bose-Einstein condensation. Fractal lattices dramatically lower the condensation temperature and enhance condensation fluc

Pablo Huijse, Jordy Davelaar, Joris De Ridder, Nicholas Jannsen

Supermassive black hole binaries (SMBHB) are expected to produce periodic modulations in active galactic nuclei (AGN) light curves, but distinguishing such signals from stochastic red-noise variability remains a major challenge. We present the first systematic search for statistically significant AGN periodicities using the optical photometry from the Gaia s

Alexandre Arbey, Etera R. Livine, Clara Montagnon

In the context of the general effort to model black hole dynamics, and in particular their return-to-equilibrium through quasi-normal modes, it is crucial to understand how much test-field perturbations deviate from physical perturbations in modified gravity scenarios. On the one hand, physical perturbations follow the modified Einstein equations of the cons

Isla Duporge, Sofia Minano, Nikoloz Sirmpilatze, Igor Tatarnikov

Ethological research increasingly benefits from the growing affordability and accessibility of drones, which enable the capture of high-resolution footage of animal movement at fine spatial and temporal scales. However, analyzing such footage presents the technical challenge of separating animal movement from drone motion. While non-trivial, computer vision

Daniel F. Perez-Ramirez, Dejan Kostic, Magnus Boman

Efficiently managing compute resources for Large Language Model (LLM) inference remains challenging due to the inherently stochastic and variable lengths of autoregressive text generation. Accurately estimating response lengths in advance enables proactive resource allocation, yet existing approaches either bias text generation towards certain lengths or rel

Saša Ceci, Hedim Osmanović, Branimir Zauner

The resonant properties of excited hadrons are commonly identified with the complex pole positions and residues of the scattering amplitude. The mass and total decay width are given by position, whereas the partial width is given by the magnitude of the residue. If this identification was correct, the partial width of famous $\Delta(1232)$ would be larger th

Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy

We present a generalised Hanson-Wright inequality and use it to establish new statistical insights into the geometry of data point-clouds. In the setting of a general random function model of data, we clarify the roles played by three notions of dimensionality: ambient intrinsic dimension $p_{\mathrm{int}}$, which measures total variability across orthogonal

Yuqicheng Zhu, Daniel Hernández, Yuan He, Zifeng Ding

Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction to KGE methods, providing uncertainty estimates by generating a set of answers that is guaranteed to include the true answer with a predefined confidence level. However, existing

Brendan L. King, Soumi De, Oleg Korobkin, Michael W. Coughlin

In this study we incorporate a new grid of kilonova simulations produced by the Monte Carlo radiative transfer code SuperNu in an inference pipeline for astrophysical transients, and evaluate their performance. These simulations contain four different two-component ejecta morphology classes. We analyze follow-up observational strategies by Vera Rubin Observa

Zhehao Huang, Yuhang Liu, Yixin Lou, Zhengbao He

Continual post-training adapts a single text-to-image diffusion model to learn new tasks without incurring the cost of separate models, but naive post-training causes forgetting of pretrained knowledge and undermines zero-shot compositionality. We observe that the absence of a standardized evaluation protocol hampers related research for continual post-train

Holly Holder, Jack C. Gartside, Alex Vanstone, Troy Dion

Artificial spin-vortex ice ('ASVI') is a reconfigurable nanomagnetic metamaterial consisting of magnetic nanoislands tailored to support both Ising macrospin and vortex textures. ASVI has recently shown functional applications including reconfigurable magnonics and neuromorphic computing, where the introduction of vortex textures broadens functionality beyon

J-M. Maillard

Landen transformation, and more generally modular correspondences, can be seen to be exact symmetries of some integrable lattice models, like the square Ising model, or the Baxter model. They are solutions of remarkable Schwarzian equations and have some compositional properties. Most of the known examples correspond, in an elliptic curves framework, to an a

Mohammed Al-Qudah, Fadi AlMahamid

The rapid expansion of Internet of Things (IoT) devices has introduced critical security challenges, underscoring the need for accurate anomaly detection. Although numerous studies have proposed machine learning (ML) methods for this purpose, limited research systematically examines how different preprocessing steps--normalization, transformation, and featur

Aline B. Trench, João Paulo C. Moura, Vanessa S. Antonin, Caio Machado Fernandes

The use of the oxygen reduction reaction (ORR) for in-situ production of H$_2$O$_2$ is an attractive alternative to replace the methods based on anthraquinone oxidation. This study investigates the modification of Vulcan XC72 carbon with Ce-doped Nb$_2$O$_5$ in different molar proportions and its application as electrocatalysts in the ORR. One performed the

Maicon P. Lebre, Dominike Pacine, Erika N. Lima, Alexandre A. C. Cotta

We employ first-principles density functional theory (DFT) calculations to investigate the structural, electronic, and catalytic properties of biphenylene supported on various metal substrates. The substrates considered are the (111) surfaces of Ag, Au, Ni, Pd, Pt, Cu, Al, and the Cu$_3$Au alloy. Our results reveal how the interaction between biphenylene and

Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu

Large language models (LLMs) have become increasingly central to AI applications worldwide, necessitating robust multilingual safety alignment to ensure secure deployment across diverse linguistic contexts. Existing preference learning methods for safety alignment, such as RLHF and DPO, are primarily monolingual and struggle with noisy multilingual data. To

Sudhansu Bala Das, Samujjal Choudhury, Tapas Kumar Mishra, Bidyut Kr. Patra

Tokenization is the act of breaking down text into smaller parts, or tokens, that are easier for machines to process. This is a key phase in machine translation (MT) models. Subword tokenization enhances this process by breaking down words into smaller subword units, which is especially beneficial in languages with complicated morphology or a vast vocabulary

Qiangqiang Liu, Qian Huang, Frank Fan, Haishan Wu

Meme tokens represent a distinctive asset class within the cryptocurrency ecosystem, characterized by high community engagement, significant market volatility, and heightened vulnerability to market manipulation. This paper introduces an innovative approach to assessing liquidity risk in meme token markets using entity-linked address identification technique

Lydia Makrygianni, Iair Arcavi, Megan Newsome, Ananya Bandopadhyay

Flares produced following the tidal disruption of stars by supermassive black holes can reveal the properties of the otherwise dormant majority of black holes and the physics of accretion. In the past decade, a class of optical-ultraviolet tidal disruption flares has been discovered whose emission properties do not match theoretical predictions. This has led

Melvyn Tyloo, Joaquín González, Nicolás Rubido

One of the most popular and innovative methods to analyse signals is by using Ordinal Patterns (OPs). The OP encoding is based on transforming a (univariate) signal into a symbolic sequence of OPs, where each OP represents the number of permutations needed to order a small subset of the signal's magnitudes. This implies that OPs are conceptually clear, metho

Enze Liu, Bowen Zheng, Xiaolei Wang, Wayne Xin Zhao

Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequential recommendation methods predominantly rely on non-reasoning paradigms, which may limit the model's computational capacity and result in suboptimal recommendation performance.

Yuechen Zhang, Jinbo Xing, Bin Xia, Shaoteng Liu

Despite the remarkable generation quality of video Diffusion Transformer (DiT) models, their practical deployment is severely hindered by extensive computational requirements. This inefficiency stems from two key challenges: the quadratic complexity of self-attention with respect to token length and the multi-step nature of diffusion models. To address these

Chaoyang Wang, Xiangtai Li, Lu Qi, Xiaofan Lin

Recent progress in panoramic image generation has underscored two critical limitations in existing approaches. First, most methods are built upon diffusion models, which are inherently ill-suited for equirectangular projection (ERP) panoramas due to the violation of the identically and independently distributed (i.i.d.) Gaussian noise assumption caused by th

Thorsten Hater, Juliette Courson, Han Lu, Sandra Diaz-Pier

Computational neuroscience has traditionally focused on isolated scales, limiting understanding of brain function across multiple levels. While microscopic models capture biophysical details of neurons, macroscopic models describe large-scale network dynamics. Integrating these scales, however, remains a significant challenge. In this study, we present a nov

Ziyue Qiao, Qianyi Cai, Hao Dong, Jiawei Gu

This paper addresses the challenge of graph domain adaptation on evolving, multiple out-of-distribution (OOD) graphs. Conventional graph domain adaptation methods are confined to single-step adaptation, making them ineffective in handling continuous domain shifts and prone to catastrophic forgetting. This paper introduces the Graph Continual Adaptive Learnin

Alex Krasnok

Precise optical phase control is crucial for innovations in telecommunications, optical computing, quantum information processing, and advanced sensing. However, conventional phase modulators often introduce parasitic amplitude modulation and struggle to provide a full 2{\pi} phase shift efficiently. This work introduces a novel paradigm for complete and rob

Jiafeng Lu, Xi Kang, Shiyin Shen, Qi Zeng

We introduce a non-parametric approach, the Stellar Population Synthesis with Equivalent Widths (SEW) method, to reconstruct spectrally-resolved attenuation curves for 169,568 star-forming galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Composite attenuation curves, stacked by stellar mass and inclination, reveal systematic trends: a hi

Ertuğrul Keçeci, Müjde Güzelkaya, Tufan Kumbasar

This paper addresses the System Identification (SYSID) problem within the framework of federated learning. We introduce a novel algorithm, Incremental Clustering-based federated learning method for SYSID (IC-SYSID), designed to tackle SYSID challenges across multiple data sources without prior knowledge. IC-SYSID utilizes an incremental clustering method, Cl

Wei Xiao, Jiacheng Liu, Zifeng Zhuang, Runze Suo

Improving the performance of pre-trained policies through online reinforcement learning (RL) is a critical yet challenging topic. Existing online RL fine-tuning methods require continued training with offline pretrained Q-functions for stability and performance. However, these offline pretrained Q-functions commonly underestimate state-action pairs beyond th

Alberto Muñoz-Ortiz, David Vilares, Caio Corro, Carlos Gómez-Rodríguez

We cast nested named entity recognition (NNER) as a sequence labeling task by leveraging prior work that linearizes constituency structures, effectively reducing the complexity of this structured prediction problem to straightforward token classification. By combining these constituency linearizations with pretrained encoders, our method captures nested enti

Jiaqi Wang, Kevin Qinghong Lin, James Cheng, Mike Zheng Shou

Reinforcement Learning (RL) has proven to be an effective post-training strategy for enhancing reasoning in vision-language models (VLMs). Group Relative Policy Optimization (GRPO) is a recent prominent method that encourages models to generate complete reasoning traces before answering, leading to increased token usage and computational cost. Inspired by th

Saisandri Saini, Akhilesh Nautiyal

In this work we consider a generalization of Starobinsky inflation obtained by combining power law ($R^\beta$), and $\alpha$-Starobinsky inflation ($E$-model). The Einstein frame potential for this model is that of power law Starobinsky inflation modified by a parameter $\alpha$ in the exponential. After computing power spectra for scalar and tensor perturba

Anton Ilderton, William Lindved

We show that scattering amplitudes on any gauge theory background admitting a coherent state description double copy to amplitudes in a curved spacetime. The metric of the spacetime is built from the gauge background using a notion of classical double copy which emerges naturally at the amplitude level. In the self-dual sector this map relates backgrounds wh

Quentin Macé, António Loison, Manuel Faysse

The ViDoRe Benchmark V1 was approaching saturation with top models exceeding 90% nDCG@5, limiting its ability to discern improvements. ViDoRe Benchmark V2 introduces realistic, challenging retrieval scenarios via blind contextual querying, long and cross-document queries, and a hybrid synthetic and human-in-the-loop query generation process. It comprises fou

Tomasz Hollanek, Yulu Pi, Cosimo Fiorini, Virginia Vignali

The introduction of the AI Act in the European Union presents the AI research and practice community with a set of new challenges related to compliance. While it is certain that AI practitioners will require additional guidance and tools to meet these requirements, previous research on toolkits that aim to translate the theory of AI ethics into development a

Michael Buzzy, Andreas Robertson, Peng Chen, Surya Kalidindi

Recent advances in Foundation Models for Materials Science are poised to revolutionize the discovery, manufacture, and design of novel materials with tailored properties and responses. Although great strides have been made, successes have been restricted to materials classes where multi-million sample data repositories can be readily curated (e.g., atomistic

Anindita Sarkar, Paranjoy Chaki, Priya Ghosh, Ujjwal Sen

We ask whether there exists a relation between controllability of the fluctuations in extractable energy of a quantum battery and (a) how open an arbitrary but fixed battery system is and (b) how large the battery is. We examine three classes of quantum processes for the energy extraction: unitary operations, completely positive trace-preserving (CPTP) maps,

Tajamul Ashraf, Mohammed Mohsen Peerzada, Moloud Abdar, Yutong Xie

Federated Learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy across decentralized participants. As FL adoption grows, numerous techniques have been proposed to tackle its practical challenges. However, the lack of standardized evaluation across key dimensions hampers systematic progress and fair c

Martin Böckling, Heiko Paulheim, Andreea Iana

Large Language Models (LLMs) have showcased impressive reasoning abilities, but often suffer from hallucinations or outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) remedies these shortcomings by grounding LLM responses in structured external information from a knowledge base. However, many KG-based RAG approaches struggle

Jaewon Lee, Charlie Stalker, Loris Colicchio, Fernando Redivo Cardoso

The visibility of two-photon interference is limited by the indistinguishability of the photons. In the cascaded emission of a three-level system, such as a single quantum dot, the indistinguishability of each photon in the pair is primarily affected by two main factors: the temporal correlation between paired photons and dephasing. Investigating the individ

Baran Barbarestani, Isa Maks, Piek Vossen

This paper introduces a method for detecting inappropriately targeting language in online conversations by integrating crowd and expert annotations with ChatGPT. We focus on English conversation threads from Reddit, examining comments that target individuals or groups. Our approach involves a comprehensive annotation framework that labels a diverse data set

Bidisha Bandyopadhyay, Christian Fendt, Dominik R. G. Schleicher, Neil M. Nagar

AGNs exhibit a wide range of black hole masses and inflow/outflow properties. It is now possible to probe regions close to the event horizons of nearby SMBHs using VLBI with earth-sized baselines, as performed by the EHT. This study explores the emission properties of accretion and outflows near the event horizon of both low-mass and high-mass SMBHs. Using r

Hanglei Zhang, Yiwei Guo, Zhihan Li, Xiang Hao

Most neural speech codecs achieve bitrate adjustment through intra-frame mechanisms, such as codebook dropout, at a Constant Frame Rate (CFR). However, speech segments inherently have time-varying information density (e.g., silent intervals versus voiced regions). This property makes CFR not optimal in terms of bitrate and token sequence length, hindering ef

Kaito Yura, Shohei Saga, Maresuke Shiraishi, Shuichiro Yokoyama

Some recent observations of the cosmic microwave background (CMB) anisotropies and the large-scale structure of the Universe imply cosmic parity violation. Among possible parity-violating sources, helical primordial magnetic fields (PMFs) are of particular interest, as they inherently violate parity symmetry and can explain the observed magnetic fields, espe

Kalle Koskinen, Christof Külske

We study the large-volume behavior of the spherical model for $d$-dimensional local spins, in the presence of $d$-dimensional random fields, for $d\geq 2$. We compare two models, one with volume-scaled random fields, and another one with non-scaled random fields, on the level of Aizenman-Wehr metastates, Newman-Stein metastates, as well as overlap distributi

Andrew Flatz, Michael C. Loper, Lezlie Weyer

The game of Knockout is a classic playground game played with two basketballs. This paper uses a Markov process to analyze each player's probability of winning the game given their starting position in line and shooting percentages, assuming all players are equally skilled. The two-player case is solved in general for any probability of a long shot and short

Anupam Mondal, Priyadarshi Mukherjee, Sasthi C. Ghosh

Reconfigurable intelligent surfaces (RIS) enable smart wireless environments by dynamically controlling signal propagation to enhance communication and localization. Unmanned aerial vehicles (UAVs) can act as flying base stations and thus, improve system performance by avoiding signal blockages. In this paper, we propose a gradient ascent and coordinate sear

Shriyank Somvanshi, Anannya Ghosh Tusti, Mahmuda Sultana Mimi, Md Monzurul Islam

The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved in each crash is essential to understanding crash dynamics and system accountability. However, existing approaches often overlook automation-specific factors and lack model sophisti

Guilherme V. Toledo, Yoni Zohar

The study of theory combination in Satisfiability Modulo Theories (SMT) involves various model theoretic properties (e.g., stable infiniteness, smoothness, etc.). We show that such properties can be partly captured by the natural density of the spectrum of the studied theories, which is the set of sizes of their finite models. This enriches the toolbox of th

Yibo Wang, Haotian Luo, Huanjin Yao, Tiansheng Huang

Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by enabling step-by-step problem-solving, yet its extension to Long-CoT introduces substantial computational overhead due to increased token length. Existing compression approaches -- instance-level and token-level -- either sacrifice essential local reasoning signals like reflection or y

Fanrui Zhang, Dian Li, Qiang Zhang, Jun Chen

The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large-scale, diverse datasets. Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content. To address these challenges, we introduce FakeVV, a larg

Iain R. Timmins, Fatemeh Torabi, Christopher H. Jackson, Paul C. Lambert

Background: Assessment of long-term survival for health technology assessment often necessitates extrapolation beyond the duration of a clinical trial. Without robust methods and external data, extrapolations are unreliable. Flexible Bayesian survival models that incorporate longer-term data sources, including registry data and population mortality, have bee

Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren

Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information retrieval. However, existing approaches face critical limitations that lack high-quality training trajectories or suffer from the distributional mismatches in simulated environments

Alihan Hüyük, Finale Doshi-Velez

Long-term planning, as in reinforcement learning (RL), involves finding strategies: actions that collectively work toward a goal rather than individually optimizing their immediate outcomes. As part of a strategy, some actions are taken at the expense of short-term benefit to enable future actions with even greater returns. These actions are only advantageou

Haonian Ji, Shi Qiu, Siyang Xin, Siwei Han

While foundation models (FMs), such as diffusion models and large vision-language models (LVLMs), have been widely applied in educational contexts, their ability to generate pedagogically effective visual explanations remains limited. Most existing approaches focus primarily on textual reasoning, overlooking the critical role of structured and interpretable

Jia-Hao Du, Shuai-Xia Xu, Yu-Qiu Zhao

In this paper, we study the asymptotic behavior of a family of pole-free solutions to the noncommutative Painlev\'e II equation. These particular solutions can be expressed in terms of the Fredholm determinant of the matrix version of the classical Airy operator, which are analogous to the Hastings-McLeod solution and the Ablowitz-Segur solution of the class