Tootfinder

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan
https://arxiv.org/abs/2510.09316 https://

Large Language Model Prompt Datasets: An In-depth Analysis and Insights
A prompt is a natural language instruction that defines a specific task for a large language model (LLM) and serves as the primary interface for human-LLM interaction. With the growing deployment of LLMs, diverse prompt datasets are emerging from platforms such as GitHub and social media. These datasets span a wide array of applications and content types, facilitating both broader LLM utilization and improved prompt engineering. In this work, we--for the first time--have compiled an extensive l…

Modular Counting over 3-Element and Conservative Domains
Andrei A. Bulatov, Amirhossein Kazeminia
https://arxiv.org/abs/2510.09950 https://arxiv.org/pdf/25…

Modular Counting over 3-Element and Conservative Domains
In the Constraint Satisfaction Problem (CSP for short) the goal is to decide the existence of a homomorphism from a given relational structure $G$ to a given relational structure $H$. If the structure $H$ is fixed and $G$ is the only input, the problem is denoted $CSP(H)$. In its counting version, $\#CSP(H)$, the task is to find the number of such homomorphisms. The CSP and #CSP have been used to model a wide variety of combinatorial problems and have received a tremendous amount of attention f…

Scaling behaviour of rotating convection in a spherical shell with different Prandtl numbers
Wei Fan, Qi Wang, Yufeng Lin
https://arxiv.org/abs/2508.09416 https://

Scaling behaviour of rotating convection in a spherical shell with different Prandtl numbers
Rayleigh-Benard convection in a rotating spherical shell provides a simplified model for convective dynamics of planetary and stellar interiors. In this study, we build more than 200 numerical models of rotating convection in a spherical shell over a wide range of $\Pran$ ($10^{-2}\le\Pran\le10^2$). As increasing the Rayleigh number $Ra$, we characterise four different flow regimes, starting from the linear onset to multiple modes, then transiting to the geostrophic turbulence and eventually ap…

Classic - Breakfast in America (1979) von
#Supertramp #Model Kate Murtagh im Jahr 2016
https://www.instagram.com/p/DJquMx6Oany/

COVER ART MATTERS on Instagram: "In 2016, Kate Murtagh, the actress and model who famously portrayed the waitress on Supertramp’s iconic Breakfast in America album cover, was 95 years old. That year, a touching photo surfaced online showing her recreating the playful expression she made in 1979, a wide, cheeky smile and raised eyebrows that had become part of rock history. The original album cover featured Murtagh dressed as a diner waitress styled after the Statue of Liberty, holding a glass of orange juice in place of a torch and a menu instead of a tablet. Behind her was a whimsical model of the New York City skyline made entirely from breakfast items like cereal boxes, egg cartons, and cutlery, perfectly matching the album’s satirical take on American culture. Kate Murtagh passed away in October 2017 at the age of 96, leaving behind one of the most memorable images in rock music history. Art Direction by Mike Doud Design by Mick Haggerty Photography by Aaron Rapoport"
151K likes, 570 comments - coverartmatters on May 15, 2025: "In 2016, Kate Murtagh, the actress and model who famously portrayed the waitress on Supertramp’s iconic Breakfast in America album cover, was 95 years old. That year, a touching photo surfaced online showing her recreating the playful expression she made in 1979, a wide, cheeky smile and raised eyebrows that had become part of rock history. The original album cover featured Murtagh dressed as a diner waitress styled after the Statu…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Vu Duc Anh Nguyen, Ziyue Li
https://arxiv.org/abs/2510.09350 https://…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while sim…

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models
Taha Binhuraib, Ruimin Gao, Anna A. Ivanova
https://arxiv.org/abs/2509.09152 https://

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models
We introduce LITcoder, an open-source library for building and benchmarking neural encoding models. Designed as a flexible backend, LITcoder provides standardized tools for aligning continuous stimuli (e.g., text and speech) with brain data, transforming stimuli into representational features, mapping those features onto brain data, and evaluating the predictive performance of the resulting model on held-out data. The library implements a modular pipeline covering a wide array of methodological…

Towards Confidential and Efficient LLM Inference with Dual Privacy Protection
Honglan Yu, Yibin Wang, Feifei Dai, Dong Liu, Haihui Fan, Xiaoyan Gu
https://arxiv.org/abs/2509.09091

Towards Confidential and Efficient LLM Inference with Dual Privacy Protection
CPU-based trusted execution environments (TEEs) and differential privacy (DP) have gained wide applications for private inference. Due to high inference latency in TEEs, researchers use partition-based approaches that offload linear model components to GPUs. However, dense nonlinear layers of large language models (LLMs) result in significant communication overhead between TEEs and GPUs. DP-based approaches apply random noise to protect data privacy, but this compromises LLM performance and sem…

NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice
Yuqi Zhou, Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Shenhao Wang
https://arxiv.org/abs/2509.07123

NestGNN: A Graph Neural Network Framework Generalizing the Nested Logit Model for Travel Mode Choice
Nested logit (NL) has been commonly used for discrete choice analysis, including a wide range of applications such as travel mode choice, automobile ownership, or location decisions. However, the classical NL models are restricted by their limited representation capability and handcrafted utility specification. While researchers introduced deep neural networks (DNNs) to tackle such challenges, the existing DNNs cannot explicitly capture inter-alternative correlations in the discrete choice cont…

On Sampling of Multiple Correlated Stochastic Signals
Lin Jin, Hang Sheng, Hui Feng, Bo Hu
https://arxiv.org/abs/2509.09225 https://arxiv.org/pdf/2509.0922…

On Sampling of Multiple Correlated Stochastic Signals
Multiple stochastic signals possess inherent statistical correlations, yet conventional sampling methods that process each channel independently result in data redundancy. To leverage this correlation for efficient sampling, we model correlated channels as a linear combination of a smaller set of uncorrelated, wide-sense stationary latent sources. We establish a theoretical lower bound on the total sampling density for zero mean-square error reconstruction, proving it equals the ratio of the jo…

Voltage Synchronization and Proportional Current Sharing of Grid-Forming Inverters
Qianxi Tang, Li Peng
https://arxiv.org/abs/2509.09277 https://arxiv.org/…

Voltage Synchronization and Proportional Current Sharing of Grid-Forming Inverters
Most previously proposed controllers are analyzed in the small-signal/quasi-steady regime rather than large-signal or transient stability for grid-forming inverters (GFMI). Additionally, methods that presume system-wide data--global measurements and complete grid-model knowledge--are challenging to realize in practice and unsuitable for large-scale operation. Moreover, proportional current sharing is rarely embedded into them. The whole system is a high-order, nonlinear differential system, mak…

Hysteresis and the Barkhausen noise in magnets
Deepak Dhar, Sanjib Sabhapandit
https://arxiv.org/abs/2509.08536 https://arxiv.org/pdf/2509.08536

Hysteresis and the Barkhausen noise in magnets
We provide an overview of studies of hysteresis in models of magnets. We discuss the shape of the hysteresis loop, dynamical symmetry breaking, and the dependence of the area of the loop on the amplitude and frequency of the driving field. The Barkhausen noise in the hysteresis loop may be modelled by the wide distribution of sizes of magnetization jumps in the random-field Ising model. We discuss the distribution of sizes of these jumps in the random field Ising model on the Bethe lattice.

ISMIE: A Framework to Characterize Information Seeking in Modern Information Environments
Shuoqi Sun, Danula Hettiachchi, Damiano Spina
https://arxiv.org/abs/2510.07644 https://…

ISMIE: A Framework to Characterize Information Seeking in Modern Information Environments
The modern information environment (MIE) is increasingly complex, shaped by a wide range of techniques designed to satisfy users' information needs. Information seeking (IS) models are effective mechanisms for characterizing user-system interactions. However, conceptualizing a model that fully captures the MIE landscape poses a challenge. We argue: Does such a model exist? To address this, we propose the Information Seeking in Modern Information Environments (ISMIE) framework as a fundamental s…

Scaling Performance of Large Language Model Pretraining
Alexander Interrante-Grant, Carla Varela-Rosa, Suhaas Narayan, Chris Connelly, Albert Reuther
https://arxiv.org/abs/2509.05258

Scaling Performance of Large Language Model Pretraining
Large language models (LLMs) show best-in-class performance across a wide range of natural language processing applications. Training these models is an extremely computationally expensive task; frontier Artificial Intelligence (AI) research companies are investing billions of dollars into supercomputing infrastructure to train progressively larger models on increasingly massive datasets. Unfortunately, information about the scaling performance and training considerations of these large trainin…

OpenAI releases GDPval, a benchmark to test AI performance on "economically valuable, real-world tasks", and says Claude Opus 4.1 was the best performing model (Maxwell Zeff/TechCrunch)
https://techcrunch.com/2025/09/25/openai-says-g…

OpenAI says GPT-5 stacks up to humans in a wide range of jobs | TechCrunch
A new test from OpenAI aims to understand how close AI is to outperforming humans at economically valuable work.

Search for a photon peak from keV-scale dark matter annihilation with NuSTAR: Constraints on $\langle \sigma v \rangle$ after 11 years of observations
E. I. Zakharov, V. V. Barinov, D. S. Gorbunov, R. A. Krivonos, A. A. Mukhin
https://arxiv.org/abs/2509.08506

Search for a photon peak from keV-scale dark matter annihilation with NuSTAR: Constraints on $\langle σv \rangle$ after 11 years of observations
We report new constraints on the velocity-independent annihilation cross section $\langle σv \rangle$ of keV-scale dark matter particles based on 11 years of observations with the NuSTAR X-ray telescope. Using the unfocused stray light mode of the instrument, which provides a wide field-of-view and a stable instrumental background, we perform a sensitive search for photon signatures from dark matter annihilation in the Galactic halo. We model the resulting diffuse X-ray spectrum over the 3-20 …

NFL Projection Model, NFC win totals: How wide is gap between Eagles and everyone else? https://www.nytimes.com/athletic/6565019/2025/08/21/nfl-projection-model-nfc-win-totals-2025/

Capillary rise on hydrogel surfaces
Anagha Datar, Joonas Ryssy, Aku O. Toivonen, Matilda Backholm
https://arxiv.org/abs/2509.08331 https://arxiv.org/pdf/25…

Capillary rise on hydrogel surfaces
Capillary rise occurs when a thin tube contacts a liquid, which rises against gravity due to the capillary force. This phenomenon is present in a wide range of everyday and industrial settings and provides the means to measure the physical properties of liquids. Here, we report on the unusual ultra-slow capillary rise on a solid-like material of agarose hydrogels. The observed meniscus motion cannot be described with classical capillary rise models, and we develop a new model based on the fluid…

NFL Projection Model, NFC win totals: How wide is gap between Eagles and everyone else? https://www.nytimes.com/athletic/6565019/2025/08/21/nfl-projection-model-nfc-win-totals-2025/

Empirical Validation: Investigating the $\Lambda_s $CDM Model with new DESI BAO Observations
Manish Yadav, Archana Dixit, Anirudh Pradhan, M S Barak
https://arxiv.org/abs/2509.26049

Empirical Validation: Investigating the $Λ_s $CDM Model with new DESI BAO Observations
The $Λ$CDM model has long served as the cornerstone of modern cosmology, offering an elegant and successful framework for interpreting a wide range of cosmological observations. However, the rise of high-precision datasets has revealed statistically significant tensions, most notably the Hubble tension and the $S_8$ discrepancy, which challenge the completeness of this standard model. In this context, we explore the $Λ_{\rm s}$CDM model-an extension of $Λ$CDM featuring a single additional pa…

Making congestion control robust to per-packet load balancing in datacenters
Barak Gerstein, Mark Silberstein, Isaac Keslassy
https://arxiv.org/abs/2509.07907 https://

Making congestion control robust to per-packet load balancing in datacenters
Per-packet load-balancing approaches are increasingly deployed in datacenter networks. However, their combination with existing congestion control algorithms (CCAs) may lead to poor performance, and even state-of-the-art CCAs can collapse due to duplicate ACKs. A typical approach to handle this collapse is to make CCAs resilient to duplicate ACKs. In this paper, we first model the throughput collapse of a wide array of CCAs when some of the paths are congested. We show that addressing duplica…

Spectrum of slip dynamics, scaling & statistical laws emerge from simplified model of fault and damage zone architecture
M. Almakari, N. Kheirdast, C. Villafuerte, M. Y. Thomas, P. Dubernet, J. Cheng, A. Gupta, P. Romanet, S. Chaillat, H. S. Bhat
https://arxiv.org/abs/2509.04909

Spectrum of slip dynamics, scaling & statistical laws emerge from simplified model of fault and damage zone architecture
Seismological and geodetic observations of a fault zone reveal a wide range of slip dynamics, scaling, and statistical laws. However, the underlying physical mechanisms remain unclear. In this study, we show that incorporating an off-fault damage zone-characterized by distributed fractures surrounding a main fault-can reproduce many key features observed in seismic and geodetic data. We model a 2D shear fault zone in which off-fault cracks follow power-law size and density distributions, and ar…

Bayesian Analysis of the Neutron Star Equation of State and Model Comparison: Insights from PSR J0437 4715, PSR J0614 3329, and Other Multi-Physics Data
Sk Md Adil Imam, N. K. Patra
https://arxiv.org/abs/2509.07109

Bayesian Analysis of the Neutron Star Equation of State and Model Comparison: Insights from PSR J0437+4715, PSR J0614+3329, and Other Multi-Physics Data
We perform a comprehensive Bayesian analysis to constrain the neutron star (NS) equation of state (EoS) using a wide range of terrestrial and astrophysical data. The terrestrial inputs include quantities related to symmetric nuclear matter (SNM) and symmetry energy up to two times saturation density ($ρ_0\sim$0.16 fm$^{-3}$), derived from finite nuclei and heavy-ion collisions (HICs). The astrophysical constraints incorporate NS radii and tidal deformabilities from recent NICER observations an…

Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model
J. B. Lestz, G. Avdeeva, S. M. Kaye, M. V. Gorelenkova, F. D. Halpern, J. McClenaghan, A. Y. Pankin, K. E. Thome
https://arxiv.org/abs/2509.04360

Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model
The Multi-Mode Model (MMM) for turbulent transport was applied to a large set of well-analyzed discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate its sensitivities to a wide range of plasma conditions. MMM calculations were performed for hundreds of milliseconds in each discharge by performing time-dependent predictive simulations with the 1.5D tokamak integrated modeling code TRANSP. A closely related study concluded that MMM predicted electron ($T_e$) and ion …

PAC Learnability in the Presence of Performativity
Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
https://arxiv.org/abs/2510.08335 https://arxiv.org/p…

PAC Learnability in the Presence of Performativity
Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead to decreased test-time performance. In this paper, we study the question of whether and when performative binary classification problems are…

WaveLLDM: Design and Development of a Lightweight Latent Diffusion Model for Speech Enhancement and Restoration
Kevin Putra Santoso, Rizka Wakhidatus Sholikah, Raden Venantius Hari Ginardi
https://arxiv.org/abs/2508.21153

WaveLLDM: Design and Development of a Lightweight Latent Diffusion Model for Speech Enhancement and Restoration
High-quality audio is essential in a wide range of applications, including online communication, virtual assistants, and the multimedia industry. However, degradation caused by noise, compression, and transmission artifacts remains a major challenge. While diffusion models have proven effective for audio restoration, they typically require significant computational resources and struggle to handle longer missing segments. This study introduces WaveLLDM (Wave Lightweight Latent Diffusion Model),…

A comprehensive comparison of neural operators for 3D industry-scale engineering designs
Weiheng Zhong, Qibang Liu, Diab Abueidda, Seid Koric, Hadi Meidani
https://arxiv.org/abs/2510.05995

A comprehensive comparison of neural operators for 3D industry-scale engineering designs
Neural operators have emerged as powerful tools for learning nonlinear mappings between function spaces, enabling real-time prediction of complex dynamics in diverse scientific and engineering applications. With their growing adoption in engineering design evaluation, a wide range of neural operator architectures have been proposed for various problem settings. However, model selection remains challenging due to the absence of fair and comprehensive comparisons. To address this, we propose and …

Robust superconductivity upon doping chiral spin liquid and Chern insulators in a Hubbard-Hofstadter model
Clemens Kuhlenkamp, Stefan Divic, Michael P. Zaletel, Tomohiro Soejima, Ashvin Vishwanath
https://arxiv.org/abs/2509.02675

Robust superconductivity upon doping chiral spin liquid and Chern insulators in a Hubbard-Hofstadter model
Demonstrating superconductivity in purely repulsive Hubbard models is a compelling goal which underscores the counter-intuitive ability of Coulomb interactions to mediate superconductivity. Here, we present numerical evidence for robust superconductivity in a triangular Hubbard-Hofstadter model at $π/2$ flux per plaquette. Employing infinite density matrix renormalization group calculations on infinite cylinders of finite circumference, we observe superconducting ground states for a wide range…

DuoServe-MoE: Dual-Phase Expert Prefetch and Cache Scheduling for Efficient MoE LLM Inference
Yuning Zhang, Grant Pinkert, Nan Yang, Yanli Li, Dong Yuan
https://arxiv.org/abs/2509.07379

DuoServe-MoE: Dual-Phase Expert Prefetch and Cache Scheduling for Efficient MoE LLM Inference
Large Language Models (LLMs) have demonstrated impressive performance across a wide range of deep learning tasks. Mixture of Experts (MoE) further enhances their capabilities by increasing model width through sparsely activated expert branches, which keeps inference computation efficient. However, the large number of expert weights introduces significant GPU memory pressure, especially in resource-constrained environments such as single-GPU servers. More importantly, MoE inference consists of t…

Path integral approach to quantum thermalization
Alexander Altland, Kun Woo Kim, Tobias Micklitz
https://arxiv.org/abs/2509.06028 https://arxiv.org/pdf/250…

Path integral approach to quantum thermalization
We introduce a quasiclassical Green function approach describing the unitary yet irreversible dynamics of quantum systems effectively acting as their own environment. Combining a variety of concepts of quantum many-body theory, notably the nonlinear $σ$-model of disordered systems, the $G Σ$-formalism for strong correlations, and real time path integration, the theory is capable of describing a wide range of system classes and disorder models. It extends previous work beyond perturbation theo…

Moments of density-dependent branching processes and their genealogy
Mathilde Andr\'e, F\'elix Foutel-Rodier, Emmanuel Schertzer
https://arxiv.org/abs/2509.05231 https:/…

Moments of density-dependent branching processes and their genealogy
A density-dependent branching process is a particle system in which individuals reproduce independently, but in a way that depends on the current population size. This feature can model a wide range of ecological interactions at the cost of breaking the branching property. We propose a general approach for studying the genealogy of these models based on moments. Building on a recent work of Bansaye, we show how to compute recursively these moments in a similar spirit to the many-to-few formula …

Low and high frequency noise in LEDs
Danylo Bohomolov (Chemnitz University of Technology, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"), Vita Ivanova (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"), Ulrich T. Schwarz (Chemnitz University of Technology)
https://

Low and high frequency noise in LEDs
LED degradation is usually associated with defects in the active region. Whereby the noise analysis can be a strong instrument to reveal them. The results of optical noise measurements for commercially available blue LED samples in a wide frequency range from kHz to MHz are reported. Noise spectra were decomposed into components according to the presented theoretical model which includes 1/f-type noise, generation-recombination noise, and white noise. The 1/f$^γ$-type noise was modeled as a su…

NFL anytime touchdown scorer picks, player props, Week 5: This wide receiver scores for a plus-money payout

https://www.cbssports.com/nfl/news/nfl-anytime-touchdown-scorer-pic…

OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!
Jingdi Lei, Varun Gumma, Rishabh Bhardwaj, Seok Min Lim, Chuan Li, Amir Zadeh, Soujanya Poria
https://arxiv.org/abs/2509.26495

OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!
Large Language Model (LLM) safety is one of the most pressing challenges for enabling wide-scale deployment. While most studies and global discussions focus on generic harms, such as models assisting users in harming themselves or others, enterprises face a more fundamental concern: whether LLM-based agents are safe for their intended use case. To address this, we introduce operational safety, defined as an LLM's ability to appropriately accept or refuse user queries when tasked with a specific…

gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity
Hugh Blayney, \'Alvaro Arroyo, Xiaowen Dong, Michael M. Bronstein
https://arxiv.org/abs/2510.08450 https://…

gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity
Graph Neural Networks (GNNs) leverage the graph structure to transmit information between nodes, typically through the message-passing mechanism. While these models have found a wide variety of applications, they are known to suffer from over-squashing, where information from a large receptive field of node representations is collapsed into a single fixed sized vector, resulting in an information bottleneck. In this paper, we re-examine the over-squashing phenomenon through the lens of model st…

TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model
Xiyan Huang, Zhe Xu, Chenxi Xiao
https://arxiv.org/abs/2509.10063 https://

TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model
Robot skill acquisition processes driven by reinforcement learning often rely on simulations to efficiently generate large-scale interaction data. However, the absence of simulation models for tactile sensors has hindered the use of tactile sensing in such skill learning processes, limiting the development of effective policies driven by tactile perception. To bridge this gap, we present TwinTac, a system that combines the design of a physical tactile sensor with its digital twin model. Our har…

MedFormer: a data-driven model for forecasting the Mediterranean Sea
Italo Epicoco, Davide Donno, Gabriele Accarino, Simone Norberti, Alessandro Grandi, Michele Giurato, Ronan McAdam, Donatello Elia, Emanuela Clementi, Paola Nassisi, Enrico Scoccimarro, Giovanni Coppini, Silvio Gualdi, Giovanni Aloisio, Simona Masina, Giulio Boccaletti, Antonio Navarra
https://…

MedFormer: a data-driven model for forecasting the Mediterranean Sea
Accurate ocean forecasting is essential for supporting a wide range of marine applications. Recent advances in artificial intelligence have highlighted the potential of data-driven models to outperform traditional numerical approaches, particularly in atmospheric weather forecasting. However, extending these methods to ocean systems remains challenging due to their inherently slower dynamics and complex boundary conditions. In this work, we present MedFormer, a fully data-driven deep learning m…

Unfolding Framework with Complex-Valued Deformable Attention for High-Quality Computer-Generated Hologram Generation
Haomiao Zhang, Zhangyuan Li, Yanling Piao, Zhi Li, Xiaodong Wang, Miao Cao, Xiongfei Su, Qiang Song, Xin Yuan
https://arxiv.org/abs/2508.21657

Unfolding Framework with Complex-Valued Deformable Attention for High-Quality Computer-Generated Hologram Generation
Computer-generated holography (CGH) has gained wide attention with deep learning-based algorithms. However, due to its nonlinear and ill-posed nature, challenges remain in achieving accurate and stable reconstruction. Specifically, ($i$) the widely used end-to-end networks treat the reconstruction model as a black box, ignoring underlying physical relationships, which reduces interpretability and flexibility. ($ii$) CNN-based CGH algorithms have limited receptive fields, hindering their ability…

Modi linear failure rate distribution with application to survival time data
Lazhar Benkhelifa
https://arxiv.org/abs/2509.20831 https://arxiv.org/pdf/2509.…

Modi linear failure rate distribution with application to survival time data
A new lifetime model, named the Modi linear failure rate distribution, is suggested. This flexible model is capable of accommodating a wide range of hazard rate shapes, including decreasing, increasing, bathtub, upside-down bathtub, and modified bathtub forms, making it particularly suitable for modeling diverse survival and reliability data. Our proposed model contains the Modi exponential distribution and the Modi Rayleigh distribution as sub-models. Numerous mathematical and reliability prop…

Scintillator-integrated microchannel plate photomultiplier tubes for ultrafast timing over keV-GeV energy scales
Ryosuke Ota, Yuya Onishi, Daehee Lee, Yuki Ichikawa, Koji Kuramoto, Kenshi Shimano, Yutaka Hasegawa, Eric Berg, Takahiro Moriya, Simon R. Cherry, Sun Il Kwon
https://arxiv.org/abs/2510.03488…

Scintillator-integrated microchannel plate photomultiplier tubes for ultrafast timing over keV-GeV energy scales
Precise measurement of radiation has long played a vital role in a wide range of research and industrial fields, from fundamental physics beyond the Standard Model to medical imaging such as time-of-flight positron emission tomography. Developing radiation detectors that achieve high timing precision-on the order of a few tens of picoseconds-and energy measurement capabilities remains indispensable yet challenging. In this study, we developed two types of scintillator-integrated microchannel pl…

Anomalous proteinaceous shells with octagonal local order
Sergei B. Rochal, Aleksey S. Roshal, Olga V. Konevtsova, Rudolf Podgornik
https://arxiv.org/abs/2510.07555 https://

Anomalous proteinaceous shells with octagonal local order
Proteinaceous shells useful for various biomedical applications exhibit a wide range of anomalous structures that are fundamentally different from icosahedral viral capsids described by the Caspar-Klug paradigmatic model. Exploring the Protein Data Bank, we have identified nine different types of anomalous shells structurally close to flat octagonal quasicrystals. As we show, these numerous shells have cubic nets cut from short-period approximants of an octagonal tiling composed of square and r…

Bilevel Mixed-Integer Linear Program with Binary Tender
Bo Zhou, Ruiwei Jiang, Siqian Shen
https://arxiv.org/abs/2509.02370 https://arxiv.org/pdf/2509.0237…

Bilevel Mixed-Integer Linear Program with Binary Tender
Bilevel programs model sequential decision interactions between two sets of players and find wide applications in real-world complex systems. In this paper, we consider a bilevel mixed-integer linear program with binary tender, wherein the upper and lower levels are linked via binary decision variables and both levels may involve additional mixed-integer decisions. We recast this bilevel program as a single-level formulation through a value function for the lower-level problem and then propose …

Eigenvalue distribution from bootstrap estimates
Samuel Kov\'a\v{c}ik, Katar\'ina Magdolenov\'a
https://arxiv.org/abs/2509.16005 https://arxiv.…

Eigenvalue distribution from bootstrap estimates
The bootstrap method has proven useful for a wide range of matrix models. Here, we show that the computed momenta can be used to reconstruct the underlying eigenvalue probability distribution, which in turn allows us to compute the free energy of the model, a necessary quantity for identifying the thermodynamically preferred solution. We verify the method on the well-studied quartic potential model and then apply it to a recently analysed asymmetric multi-trace model. We consider an extended cl…

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
Sara Makenali, Babak Rokh, Ali Azarpeyvand
https://arxiv.org/abs/2509.04244 https://

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
Deep Neural Networks (DNNs) have achieved significant advances in a wide range of applications. However, their deployment on resource-constrained devices remains a challenge due to the large number of layers and parameters, which result in considerable computational and memory demands. To address this issue, pruning and quantization are two widely used compression techniques, commonly applied individually in most studies to reduce model size and enhance processing speed. Nevertheless, combining…

Development of an Optimized Parameter Set for Monovalent Ions in the Reference Interaction Site Model of Solvation
Felipe Silva Carvalho, Alexander McMahon, David A. Case, Tyler Luchko
https://arxiv.org/abs/2509.22430

Development of an Optimized Parameter Set for Monovalent Ions in the Reference Interaction Site Model of Solvation
Accurate modeling of aqueous monovalent ions is essential for understanding the function of biomolecules, such as nucleic acid stability and binding of charged drugs to protein targets. The 1D and 3D reference interaction site models (1D- and 3D-RISM) of molecular solvation, as implemented in the AmberTools molecular modeling suite, are well suited for modeling mixtures of ionic species around biomolecules across a wide range of concentrations. However, the available ion model parameters were o…

Optimal $(\alpha,\beta)$-Dense Subgraph Search in Bipartite Graphs
Yalong Zhang, Rong-Hua Li, Qi Zhang, Guoren Wang
https://arxiv.org/abs/2508.18616 https://

Optimal $(α,β)$-Dense Subgraph Search in Bipartite Graphs
Dense subgraph search in bipartite graphs is a fundamental problem in graph analysis, with wide-ranging applications in fraud detection, recommendation systems, and social network analysis. The recently proposed $(α, β)$-dense subgraph model has demonstrated superior capability in capturing the intrinsic density structure of bipartite graphs compared to existing alternatives. However, despite its modeling advantages, the $(α, β)$-dense subgraph model lacks efficient support for query proces…

Research on the Integration of Embodied Intelligence and Reinforcement Learning in Textual Domains
Haonan Wang, Junfeng Sun, Mingjia Zhao, Wei Liu
https://arxiv.org/abs/2510.01076

Research on the Integration of Embodied Intelligence and Reinforcement Learning in Textual Domains
This article addresses embodied intelligence and reinforcement learning integration in the field of text processing, aiming to enhance text handling with more intelligence on the basis of embodied intelligence's perception and action superiority and reinforcement learning's decision optimization capability. Through detailed theoretical explanation and experimental exploration, a novel integration model is introduced. This model has been demonstrated to be very effective in a wide range oftext p…

Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools
Ping He, Changjiang Li, Binbin Zhao, Tianyu Du, Shouling Ji
https://arxiv.org/abs/2509.21011 https://

Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools
The remarkable capability of large language models (LLMs) has led to the wide application of LLM-based agents in various domains. To standardize interactions between LLM-based agents and their environments, model context protocol (MCP) tools have become the de facto standard and are now widely integrated into these agents. However, the incorporation of MCP tools introduces the risk of tool poisoning attacks, which can manipulate the behavior of LLM-based agents. Although previous studies have i…

Named Entity Recognition of Historical Text via Large Language Model
Shibingfeng Zhang, Giovanni Colavizza
https://arxiv.org/abs/2508.18090 https://arxiv.o…

Named Entity Recognition of Historical Text via Large Language Model
Large language models have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. One such task is Named Entity Recognition (NER), which involves identifying and classifying proper names in text, such as people, organizations, locations, dates, and other specific entities. NER plays a crucial role in extracting information from unstructured textual data, enabling downstream applications such as information retrieval from unstructured text. Tr…

Rethinking Layer-wise Model Merging through Chain of Merges
Pietro Buzzega, Riccardo Salami, Angelo Porrello, Simone Calderara
https://arxiv.org/abs/2508.21421 https://

Rethinking Layer-wise Model Merging through Chain of Merges
Fine-tuning pretrained models has become a standard pathway to achieve state-of-the-art performance across a wide range of domains, leading to a proliferation of task-specific model variants. As the number of such specialized modules in-creases, merging them into a unified model without retraining has become a critical challenge. Existing merging techniques often rely on interference heuristics,importance weighting, or activation matching while treating each layer independently, thereby failing…

Probing the coupling of axions to tops and gluons with LHC measurements
Jon Butterworth, Matthew Cullingworth, Joseph Egan, Fabian Esser, Veronica Sanz, Maria Ubiali
https://arxiv.org/abs/2508.21660

Probing the coupling of axions to tops and gluons with LHC measurements
We study axion-like particles (ALPs) whose dominant interactions are with gluons and third-generation quarks, and whose couplings to light Standard Model (SM) particles arise at one loop. These loop-induced effects lead to ALP decays and production channels that can be probed at the LHC, even when tree-level couplings are absent. Using an effective field theory (EFT) description that includes momentum-dependent corrections from radiative effects, we reinterpret a wide range of LHC measurements …

PeV particle acceleration and non-thermal emission in the `minimalist' model of the extended jets in W50/SS433
A. M. Bykov, S. M. Osipov, V. I. Romansky, Y. A. Uvarov, E. Churazov, I. Khabibullin
https://arxiv.org/abs/2509.09883

PeV particle acceleration and non-thermal emission in the `minimalist' model of the extended jets in W50/SS433
The W50 nebula around microquasar SS~433, powered by supercritical accretion, features two `extended jets' (tens of pc long and a few pc wide) from which polarized X-ray and very high energy radiation above 100 TeV is detected. Here we present a model of very high energy particle acceleration in these extended jets. In the `minimalist' model (discussed in Churazov, Khabibullin, and Bykov, 2024), a collimated outflow aligned with the rotation axis is propagating through a more isotropic wind pro…

RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimation
Xiucheng Wang, Qiming Zhang, Nan Cheng
https://arxiv.org/abs/2509.01875

RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimation
Accurate localization of non-cooperative signal sources in non-line-of-sight (NLoS) environments remains a critical challenge with a wide range of applications, including autonomous navigation, industrial automation, and emergency response. In such settings, traditional positioning techniques relying on line-of-sight (LoS) or cooperative signaling fail due to severe multipath propagation and unknown transmit power. This paper proposes a novel generative inference framework for NLoS localization…

Commutative algebra neural network reveals genetic origins of diseases
JunJie Wee, Faisal Suwayyid, Mushal Zia, Hongsong Feng, Yuta Hozumi, Guo-Wei Wei
https://arxiv.org/abs/2509.26566

Commutative algebra neural network reveals genetic origins of diseases
Genetic mutations can disrupt protein structure, stability, and solubility, contributing to a wide range of diseases. Existing predictive models often lack interpretability and fail to integrate physical and chemical interactions critical to molecular mechanisms. Moreover, current approaches treat disease association, stability changes, and solubility alterations as separate tasks, limiting model generalizability. In this study, we introduce a unified framework based on multiscale commutative a…

Characterizing Agent-Based Model Dynamics via $\epsilon$-Machines and Kolmogorov-Style Complexity
Roberto Garrone (University of Milano-Bicocca)
https://arxiv.org/abs/2510.12729

Characterizing Agent-Based Model Dynamics via $ε$-Machines and Kolmogorov-Style Complexity
We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $ε$-machine is reconstructed as a reference model that summarizes the system-wide informational regime. At the micro level, $ε$-machines are reconstructed for each caregiver-elder dyad and variable, and are complemented with algorithm-agnostic Kolmogorov-style mea…

Coupled Planetary Interior and Tidal Evolution
Tim Hallatt, Sarah Millholland
https://arxiv.org/abs/2509.22923 https://arxiv.org/pdf/2509.22923

Coupled Planetary Interior and Tidal Evolution
We present a new planetary structure/thermal evolution model, designed for use in problems that couple orbital dynamics with planetary structure. We first benchmark our structural/thermal evolution calculations against the \texttt{MESA} stellar evolution code, finding excellent agreement across a wide range of planet mass, equilibrium temperature, entropy, and extra heating deposited at various depths in the planet. We then apply our method to study the tidal migration histories of Neptunes in …

Accelerated Discovery of High-\k{appa} Oxides with Physics-Based Factorized Machine Learning
Atsushi Takigawa, Shin Kiyohara, Yu Kumagai
https://arxiv.org/abs/2509.26022 https:/…

Accelerated Discovery of High-\k{appa} Oxides with Physics-Based Factorized Machine Learning
Considerable effort continues to be devoted to the exploration of next-generation high-\k{appa} materials that combine a high dielectric constant with a wide band gap. However, machine learning (ML)-based virtual screening has remained challenging, primarily due to the low accuracy in predicting the ionic contribution to the dielectric tensor, which dominates the dielectric performance of high-\k{appa} materials. We here propose a joint ML model that predicts Born effective charges using an equ…

Week 1 NFL player props, odds, predictions: Back Ja'Marr Chase over 83.5 yards for Week 1 NFL WR prop picks

https://www.cbssports.com/nfl/news/week-1-

Wireless Channel Foundation Model with Embedded Noise-Plus-Interference Suppression Structure
Yuwei Wang, Li Sun, Tingting Yang
https://arxiv.org/abs/2509.15993 https://

Wireless Channel Foundation Model with Embedded Noise-Plus-Interference Suppression Structure
Wireless channel foundation model (WCFM) is a task-agnostic AI model that is pretrained on large-scale wireless channel datasets to learn a universal channel feature representation that can be used for a wide range of downstream tasks related to communications and sensing. While existing works on WCFM have demonstrated its great potentials in various tasks including beam prediction, channel prediction, localization, etc, the models are all trained using perfect (i.e., error-free and complete) c…

A Stochastic Model for Illiquid Stock Prices and its Conclusion about Correlation Measurement
Erina Nanyonga, Juma Kasozi, Fred Mayambala, Hassan W. Kayondo, Matt Davison
https://arxiv.org/abs/2509.10553

A Stochastic Model for Illiquid Stock Prices and its Conclusion about Correlation Measurement
This study explores the behavioral dynamics of illiquid stock prices in a listed stock market. Illiquidity, characterized by wide bid and ask spreads affects price formation by decoupling prices from standard risk and return relationships and increasing sensitivity to market sentiment. We model the prices at the Uganda Securities Exchange (USE) which is illiquid in that the prices remain constant much of the time thus complicating price modelling. We circumvent this challenge by combining the M…

A comprehensive equivalent circuit model for high overtone bulk acoustic resonators (HBARs)
Vikrant J. Gokhale, Brian P. Downey
https://arxiv.org/abs/2509.21640 https://

A comprehensive equivalent circuit model for high overtone bulk acoustic resonators (HBARs)
This paper presents a new and comprehensive equivalent circuit model for high overtone bulk acoustic resonators (HBARs). HBARs demonstrate several very sharp resonance modes distributed nearly periodically over a very wide frequency range. This spectrum response of HBARs offers unique advantages but poses significant modeling challenges. The proposed circuit incorporates and models the unique physical components of the HBAR: piezoelectric transducer, substrate (a perfectly periodic multimode ca…

InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios
Chenglin Yu, Yang Yu, Songmiao Wang, Yucheng Wang, Yifan Yang, Jinjia Li, Ming Li, Hongxia Yang
https://arxiv.org/abs/2509.22502 …

InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios
Large Language Model (LLM) agents have demonstrated remarkable capabilities in organizing and executing complex tasks, and many such agents are now widely used in various application scenarios. However, developing these agents requires carefully designed workflows, carefully crafted prompts, and iterative tuning, which requires LLM techniques and domain-specific expertise. These hand-crafted limitations hinder the scalability and cost-effectiveness of LLM agents across a wide range of industrie…

Reconstruction of Transient Anisotropic Diffusion in Transient Diffusion Models
Vanda Maria Luchesi
https://arxiv.org/abs/2509.19338 https://arxiv.org/pdf/…

Reconstruction of Transient Anisotropic Diffusion in Transient Diffusion Models
This presentation abstract, presented at 12th Applied Inverse Problems Conference, to be held at FGV EMAp, Rio de Janeiro, Brazil, in 2025, describe the theory results of applying the Chebyshev pseudospectral method (CPM) to reconstruct the anisotropic transient diffusion in a transiente two-dimensional diffusion model with boundary conditions. This model can be easily applied to a wide range of diffusion phenomena across various science domains.

A hybrid dynamical-stochastic model of maximum temperature time series of Imphal, Northeast India incorporating nonlinear feedback and noise diagnostics
Mairembam Kelvin Singh, Athokpam Langlen Chanu, R. K. Brojen Singh, Moirangthem Shubhakanta Singh
https://arxiv.org/abs/2509.01269

A hybrid dynamical-stochastic model of maximum temperature time series of Imphal, Northeast India incorporating nonlinear feedback and noise diagnostics
Climate variability is a complex phenomenon resulting from numerous interacting components of a climate system across a wide range of temporal and spatial scales. Although significant advances have been made in understanding global climate variability, there are relatively less studies on regional climate modeling, particularly in developing countries. In this work, we propose a framework of data driven hybrid dynamical stochastic modeling to investigate the variability of maximum temperature r…

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Hinrich Sch\"utze, Volker Tresp, Yunpu Ma
https://arxiv.org/abs/2508.19828

Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless, constrained by limited context windows that hinder long-horizon reasoning. Recent efforts to address this limitation often augment LLMs with an external memory bank, yet most existing pipelines are static and heuristic-driven, lacking any learned mechanism for deciding what to store, update, or retrieve. We present Memory-R1, a reinforcement learning …

Blow-Up Results of Time Fractional Heat Equation With Nonlinear Neumann Boundary Conditions
Hind Ghazi Hameed, Burhan Selcuk, Maan A. Rasheed
https://arxiv.org/abs/2509.18927 ht…

The Hubble Tension resolved by the DESI Baryon Acoustic Oscillations Measurements
X. D. Jia, J. P. Hu, D. H. Gao, S. X. Yi, F. Y. Wang
https://arxiv.org/abs/2509.17454 https://

The Hubble Tension resolved by the DESI Baryon Acoustic Oscillations Measurements
The $Λ$ cold dark matter ($Λ$CDM) cosmological model provides a good description of a wide range of astrophysical and cosmological observations. However, severe challenges to the phenomenological $Λ$CDM model have emerged recently, including the Hubble constant tension and the significant deviation from the $Λ$CDM model reported by the Dark Energy Spectroscopic Instrument (DESI) collaboration. Despite many explanations for the two challenges have been proposed, the origins of them are still…

UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
Roman Jacome, Romario Gualdr\'on-Hurtado, Leon Suarez-Rodriguez, Henry Arguello
https://arxiv.org/abs/2509.14394

UTOPY: Unrolling Algorithm Learning via Fidelity Homotopy for Inverse Problems
Imaging Inverse problems aim to reconstruct an underlying image from undersampled, coded, and noisy observations. Within the wide range of reconstruction frameworks, the unrolling algorithm is one of the most popular due to the synergistic integration of traditional model-based reconstruction methods and modern neural networks, providing an interpretable and highly accurate reconstruction. However, when the sensing operator is highly ill-posed, gradient steps on the data-fidelity term can hinde…

On Modeling and Solving the Boltzmann Equation
Liliane Basso Barichello
https://arxiv.org/abs/2508.13232 https://arxiv.org/pdf/2508.13232

On Modeling and Solving the Boltzmann Equation
The Boltzmann equation has been a driving force behind significant mathematical research over the years. Its challenging theoretical complexity, combined with a wide variety of current scientific and technological problems that require numerical simulations based on this model, justifies such interest. This work provides a brief overview of studies and advances related to the solution of the linear Boltzmann equation in one- and two-dimensional spatial dimensions. In particular, relevant aspect…

Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Shunlin Lu, Kangning Yin, Xiaojie Niu, Minyue Dai, Jingbo Wang, Jiangmiao Pang
https://arxiv.org/abs/2509.13780 http…

Behavior Foundation Model for Humanoid Robots
Whole-body control (WBC) of humanoid robots has witnessed remarkable progress in skill versatility, enabling a wide range of applications such as locomotion, teleoperation, and motion tracking. Despite these achievements, existing WBC frameworks remain largely task-specific, relying heavily on labor-intensive reward engineering and demonstrating limited generalization across tasks and skills. These limitations hinder their response to arbitrary control modes and restrict their deployment in com…

On the Fitness Landscape in the $NK$ Model
Wei-Kuo Chen, Si Tang
https://arxiv.org/abs/2508.12464 https://arxiv.org/pdf/2508.12464

On the Fitness Landscape in the $NK$ Model
The $NK$ model, introduced by Kauffman, Levin, and Weinberger, is a random field used to describe the fitness landscape of certain species with $N$ genetic loci, each interacting with $K$ others. The model has wide applications in understanding evolutionary and natural selection as it captures ruggedness feature of the fitness landscape. Earlier literature has been focused on the case $K$ being a fixed positive integer and used tools from Ergodic and Markov theory. In this paper, by viewing it …

Towards Understanding Visual Grounding in Visual Language Models
Georgios Pantazopoulos, Eda B. \"Ozyi\u{g}it
https://arxiv.org/abs/2509.10345 https://

Towards Understanding Visual Grounding in Visual Language Models
Visual grounding refers to the ability of a model to identify a region within some visual input that matches a textual description. Consequently, a model equipped with visual grounding capabilities can target a wide range of applications in various domains, including referring expression comprehension, answering questions pertinent to fine-grained details in images or videos, caption visual context by explicitly referring to entities, as well as low and high-level control in simulated and real …

Estimation and inference in generalised linear models with constrained iteratively-reweighted least squares
Pierre Masselot, Devon Nenon, Jacopo Vanoli, Zaid Chalabi, Antonio Gasparrini
https://arxiv.org/abs/2509.18406

Estimation and inference in generalised linear models with constrained iteratively-reweighted least squares
We propose a simple and flexible framework for generalised linear models (GLM) with linear constraints on the coefficients. Linear constraints are useful in a wide range of applications, allowing the fitting of model with high-dimensional or highly collinear predictors, as well as encoding assumptions on the association between some or all predictors and the response. We propose the constrained iteratively-reweighted least squares (CIRLS) to fit the model, iterating quadratic programs to ensure…

Linear Power System Modeling and Analysis Across Wide Operating Ranges: A Hierarchical Neural State-Space Equation Approach
Weicheng Liu, Di Liu, Songyan Zhang, Chao Lu
https://arxiv.org/abs/2508.17774

Linear Power System Modeling and Analysis Across Wide Operating Ranges: A Hierarchical Neural State-Space Equation Approach
Developing a unified small-signal model for modern, large-scale power systems that remains accurate across a wide range of operating ranges presents a formidable challenge. Traditional methods, spanning mechanistic modeling, modal identification, and deep learning, have yet to fully overcome persistent limitations in accuracy, universal applicability, and interpretability. In this paper, a novel hierarchical neural state-space equation approach is proposed to overcome these obstacles, achieving…

Survey of Specialized Large Language Model
Chenghan Yang, Ruiyu Zhao, Yang Liu, Ling Jiang
https://arxiv.org/abs/2508.19667 https://arxiv.org/pdf/2508.1966…

Survey of Specialized Large Language Model
The rapid evolution of specialized large language models (LLMs) has transitioned from simple domain adaptation to sophisticated native architectures, marking a paradigm shift in AI development. This survey systematically examines this progression across healthcare, finance, legal, and technical domains. Besides the wide use of specialized LLMs, technical breakthrough such as the emergence of domain-native designs beyond fine-tuning, growing emphasis on parameter efficiency through sparse comput…

Preparation Methods and Applications of Biomimetic Membranes
Ajit Seth, Sajal K. Ghosh, Veerendra K. Sharma
https://arxiv.org/abs/2509.25796 https://arxiv.…

Preparation Methods and Applications of Biomimetic Membranes
Model biomembrane systems play a crucial role in advancing biomedical research by providing simplified yet effective platforms for exploring complex biological mechanisms. These systems span a wide range of scales, from single-molecule-thick lipid monolayers to micron-sized giant unilamellar vesicles. Their efficacy and applicability largely depend on selecting an optimal model and an appropriate synthesis process. This chapter offers a comprehensive description of conventional synthesis techni…

Autoregressive-Gaussian Mixture Models: Efficient Generative Modeling of WSS Signals
Kathrin Klein, Benedikt B\"ock, Nurettin Turan, Wolfgang Utschick
https://arxiv.org/abs/2509.17953

Autoregressive-Gaussian Mixture Models: Efficient Generative Modeling of WSS Signals
This work addresses the challenge of making generative models suitable for resource-constrained environments like mobile wireless communication systems. We propose a generative model that integrates Autoregressive (AR) parameterization into a Gaussian Mixture Model (GMM) for modeling Wide-Sense Stationary (WSS) processes. By exploiting model-based insights allowing for structural constraints, the approach significantly reduces parameters while maintaining high modeling accuracy. Channel estimat…

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Kaixiang Zhao, Lincan Li, Kaize Ding, Neil Zhenqiang Gong, Yue Zhao, Yushun Dong
https://arxiv.org/abs/2508.15031

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
Machine learning (ML) models have significantly grown in complexity and utility, driving advances across multiple domains. However, substantial computational resources and specialized expertise have historically restricted their wide adoption. Machine-Learning-as-a-Service (MLaaS) platforms have addressed these barriers by providing scalable, convenient, and affordable access to sophisticated ML models through user-friendly APIs. While this accessibility promotes widespread use of advanced ML c…

Overdispersed radio source counts and excess radio dipole detection
Lukas B\"ohme, Dominik J. Schwarz, Prabhakar Tiwari, Morteza Pashapour-Ahmadabadi, Benedict Bahr-Kalus, Maciej Bilicki, Catherine L. Hale, Caroline S. Heneka, Thilo M. Siewert
https://arxiv.org/abs/2509.16732

Overdispersed radio source counts and excess radio dipole detection
The source count dipole from wide-area radio continuum surveys allows us to test the cosmological standard model. Many radio sources have multiple components, which can cause an overdispersion of the source counts distribution. We account for this effect via a new Bayesian estimator, based on the negative binomial distribution. Combining the two best understood wide-area surveys, NVSS and RACS-low, and the deepest wide-area survey, LoTSS-DR2, we find that the source count dipole exceeds its exp…

Interplay between Hubbard interaction and charge transfer energy in three-orbital Emery model
Yan Peng, Mi Jiang
https://arxiv.org/abs/2509.11028 https://a…

Interplay between Hubbard interaction and charge transfer energy in three-orbital Emery model
We use the numerically unbiased determinant quantum Monte Carlo (DQMC) method to systematically investigate the three-orbital Emery model in the normal state in a wide range of local interactions, charge transfer energy, and doping levels. We focus on the influence of the onsite Hubbard $U_{dd}$ and charge transfer energy scale $ε_p$ on the electronic properties via the orbital occupancies, local moments, spin correlations, and spectral properties. Rich features of the orbital-resolved local a…

LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
Xingxuan Zhang, Gang Ren, Han Yu, Hao Yuan, Hui Wang, Jiansheng Li, Jiayun Wu, Lang Mo, Li Mao, Mingchao Hao, Ningbo Dai, Renzhe Xu, Shuyang Li, Tianyang Zhang, Yue He, Yuanrui Wang, Yunjia Zhang, Zijing Xu, Dongzhe Li, Fang Gao, Hao Zou, Jiandong Liu, Jiashuo Liu, Jiawei Xu, Kaijie Cheng, Kehan Li, Linjun Zhou, Qing Li, Shaohua Fan, Xiaoyu Lin, Xinyan Han, Xuanyue Li, Yan Lu, Yuan Xue, Yuanyuan Jiang, Zi…

LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
We argue that progress toward general intelligence requires complementary foundation models grounded in language, the physical world, and structured data. This report presents LimiX, the first installment of our large structured-data models (LDMs). LimiX treats structured data as a joint distribution over variables and missingness, thus capable of addressing a wide range of tabular tasks through query-based conditional prediction via a single model. LimiX is pretrained using masked joint-distri…

Situational Awareness for Safe and Robust Multi-Agent Interactions Under Uncertainty
Benjamin Alcorn, Eman Hammad
https://arxiv.org/abs/2509.23425 https://…

Situational Awareness for Safe and Robust Multi-Agent Interactions Under Uncertainty
Multi-agent systems are prevalent in a wide range of domains including power systems, vehicular networks, and robotics. Two important problems to solve in these types of systems are how the intentions of non-coordinating agents can be determined to predict future behavior and how the agents can achieve their objectives under resource constraints without significantly sacrificing performance. To study this, we develop a model where an autonomous agent observes the environment within a safety rad…

Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
Lingyu Kong, Jaeheon Shim, Guoxiang Hu, Victor Fung
https://arxiv.org/abs/2509.21694

Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
Atomistic foundation models (AFMs) have great promise as accurate interatomic potentials, and have enabled data-efficient molecular dynamics simulations with near quantum mechanical accuracy. However, AFMs remain markedly slower at inference and are far more memory-intensive than conventional interatomic potentials, due to the need to capture a wide range of chemical and structural motifs in pre-training datasets requiring deep, parameter-rich model architectures. These deficiencies currently l…

Identifying birdsong syllables without labelled data
M\'elisande Teng, Julien Boussard, David Rolnick, Hugo Larochelle
https://arxiv.org/abs/2509.18412 https://

Unified Gas Heating Constraints on Extended Dark Matter Compact Objects
TaeHun Kim, Philip Lu, Volodymyr Takhistov
https://arxiv.org/abs/2508.18344 https://

Unified Gas Heating Constraints on Extended Dark Matter Compact Objects
We present the first unified constraints on a broad class of extended dark matter compact objects (EDCOs) from interstellar gas heating. These include axion stars, Q-balls, axion miniclusters, dark fermion stars and primordial black holes surrounded by dark matter halos, which arise in a wide range of theories beyond the Standard Model. As such massive objects traverse the interstellar medium, their gravitational influence generates wakes and, if sufficiently compact, drives accretion flows tha…

Hamiltonian Truncation Framework for Gauge Theories on the Interval
Rachel Houtz, James Ingoldby
https://arxiv.org/abs/2509.17890 https://arxiv.org/pdf/250…

Hamiltonian Truncation Framework for Gauge Theories on the Interval
In this work, we investigate gauge theories in two dimensions nonperturbatively using the Hamiltonian truncation approach. Working on a spatial interval and adopting the axial gauge, we remove all gauge field degrees of freedom and express the interacting Hamiltonian in the eigenbasis of the free Dirac theory, truncated at a finite energy. As a benchmark we analyse the Schwinger model, where our numerical spectra agree closely with the exact results from bosonization across a wide range of coup…

Fast-dLLM v2: Efficient Block-Diffusion LLM
Chengyue Wu, Hao Zhang, Shuchen Xue, Shizhe Diao, Yonggan Fu, Zhijian Liu, Pavlo Molchanov, Ping Luo, Song Han, Enze Xie
https://arxiv.org/abs/2509.26328

Fast-dLLM v2: Efficient Block-Diffusion LLM
Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks, yet their inherent sequential decoding limits inference efficiency. In this work, we propose Fast-dLLM v2, a carefully designed block diffusion language model (dLLM) that efficiently adapts pretrained AR models into dLLMs for parallel text generation, requiring only approximately 1B tokens of fine-tuning. This represents a 500x reduction in training data compared …

A Deep Learning Model of Lightning Stroke Density
Randall Jones II, Joel A. Thornton, Chris J. Wright, Robert Holzworth
https://arxiv.org/abs/2509.10399 https://

A Deep Learning Model of Lightning Stroke Density
Lightning plays a crucial role in the Earth's climate system, yet existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study develops deep learning-based parameterizations of lightning stroke density using meteorological variables from the ERA and IMERG datasets. Convolutional neural networks (CNNs) with U-Net architectures are trained using World Wide Lightning Location Network (W…

A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases
Sayantan Banerjee, Agnieszka Wylomanska, Sundar S
https://arxiv.org/abs/2508.12970

A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases
The autoregressive time series model is a popular second-order stationary process, modeling a wide range of real phenomena. However, in applications, autoregressive signals are often corrupted by additive noise. Further, the autoregressive process and the corruptive noise may be highly impulsive, stemming from an infinite-variance distribution. The model estimation techniques that account for additional noise tend to show reduced efficacy when there is very strong noise present in the data, esp…

Real-time bubble nucleation and growth for false vacuum decay on the lattice
Daan Maertens, Jutho Haegeman, Karel Van Acoleyen
https://arxiv.org/abs/2508.13645 https://

Real-time bubble nucleation and growth for false vacuum decay on the lattice
We revisit quantum false vacuum decay for the one-dimensional Ising model, focusing on the real-time nucleation and growth of true vacuum bubbles. Via matrix product state simulations, we demonstrate that for a wide range of parameters, the full time-dependent quantum state is well described by a Gaussian ansatz in terms of domain wall operators, with the associated vacuum bubble wave function evolving according to the linearized time-dependent variational principle. The emerging picture shows …

Adaptive Heavy-Tailed Stochastic Gradient Descent
Bodu Gong, Gustavo Enrique Batista, Pierre Lafaye de Micheaux
https://arxiv.org/abs/2508.21353 https://ar…

Adaptive Heavy-Tailed Stochastic Gradient Descent
In the era of large-scale neural network models, optimization algorithms often struggle with generalization due to an overreliance on training loss. One key insight widely accepted in the machine learning community is the idea that wide basins (regions around a local minimum where the loss increases gradually) promote better generalization by offering greater stability to small changes in input data or model parameters. In contrast, sharp minima are typically more sensitive and less stable. Mot…

Mind the Gap: Time-of-Check to Time-of-Use Vulnerabilities in LLM-Enabled Agents
Derek Lilienthal, Sanghyun Hong
https://arxiv.org/abs/2508.17155 https://a…

Mind the Gap: Time-of-Check to Time-of-Use Vulnerabilities in LLM-Enabled Agents
Large Language Model (LLM)-enabled agents are rapidly emerging across a wide range of applications, but their deployment introduces vulnerabilities with security implications. While prior work has examined prompt-based attacks (e.g., prompt injection) and data-oriented threats (e.g., data exfiltration), time-of-check to time-of-use (TOCTOU) remain largely unexplored in this context. TOCTOU arises when an agent validates external state (e.g., a file or API response) that is later modified before…

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics
Yu Bao, Keke Song, Jiahui Liu, Yanzhou Wang, Yifei Ning, Penghua Ying, Ping Qian
https://arxiv.org/abs/2508.20350

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics
Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we present an accurate and transferable machine-learned potential (MLP) for the tungsten-hydrogen binary system within the neuroevolution potential (NEP) framework, trained through active learning on extensive density functional theory data. The developed NEP-WH model reproduces a wide range of lattice and defect properties…

MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance
Yi Dong, Yusuke Muraoka, Scott Shi, Yi Zhang
https://arxiv.org/abs/2508.10429 http…

MM-Food-100K: A 100,000-Sample Multimodal Food Intelligence Dataset with Verifiable Provenance
We present MM-Food-100K, a public 100,000-sample multimodal food intelligence dataset with verifiable provenance. It is a curated approximately 10% open subset of an original 1.2 million, quality-accepted corpus of food images annotated for a wide range of information (such as dish name, region of creation). The corpus was collected over six weeks from over 87,000 contributors using the Codatta contribution model, which combines community sourcing with configurable AI-assisted quality checks; e…

Systolic Array-based Architecture for Low-Bit Integerized Vision Transformers
Ching-Yi Lin, Sahil Shah
https://arxiv.org/abs/2508.20334 https://arxiv.org/p…

Systolic Array-based Architecture for Low-Bit Integerized Vision Transformers
Transformer-based models are becoming more and more intelligent and are revolutionizing a wide range of human tasks. To support their deployment, AI labs offer inference services that consume hundreds of GWh of energy annually and charge users based on the number of tokens processed. Under this cost model, minimizing power consumption and maximizing throughput have become key design goals for the inference hardware. While graphics processing units (GPUs) are commonly used, their flexibility com…

Hot but Coherent: Doublons at Infinite Temperature in the Hubbard chain
C\u{a}t\u{a}lin Pa\c{s}cu Moca, Bal\'azs D\'ora, Gergely Zar\'and
https://arxiv.org/abs/2509.20504

Hot but Coherent: Doublons at Infinite Temperature in the Hubbard chain
We investigate the spectral properties and the dynamics of doublons in the one-dimensional Hubbard model at infinite temperature. Using a Chebyshev expansion approach formulated in the superfermionic representation, we compute the momentum- and frequency-resolved doublon spectral function across a wide range of interaction strengths $U$ and in the presence of an external electric field. Increasing the interaction, the spectrum gradually splits into separate bands associated with two-hole and tw…

Assessment of Power System Stability Considering Multiple Time-Scale Dynamics: Insights into Hopf Bifurcations in Presence of GFL and GFM IBRs
Luis David Pabon Ospina
https://arxiv.org/abs/2508.14677

Assessment of Power System Stability Considering Multiple Time-Scale Dynamics: Insights into Hopf Bifurcations in Presence of GFL and GFM IBRs
Real power systems exhibit dynamics that evolve across a wide range of time scales, from very fast to very slow phenomena. Historically, incorporating these wide-ranging dynamics into a single model has been impractical. As a result, power engineers rely on time-scale decomposition to simplify models. When fast phenomena are evaluated, slow dynamics are neglected (assumed stable), and vice versa. This paper challenges this paradigm by showing the importance of assessing power system stability w…

Subjective Behaviors and Preferences in LLM: Language of Browsing
Sai Sundaresan, Harshita Chopra, Atanu R. Sinha, Koustava Goswami, Nagasai Saketh Naidu, Raghav Karan, N Anushka
https://arxiv.org/abs/2508.15474

Subjective Behaviors and Preferences in LLM: Language of Browsing
A Large Language Model (LLM) offers versatility across domains and tasks, purportedly benefiting users with a wide variety of behaviors and preferences. We question this perception about an LLM when users have inherently subjective behaviors and preferences, as seen in their ubiquitous and idiosyncratic browsing of websites or apps. The sequential behavior logs of pages, thus generated, form something akin to each user's self-constructed "language", albeit without the structure and grammar imbu…

Modelling peaks over thresholds in panel data: a two-level grouped panel generalized Pareto regression
Zefan Liu, Natalia Nolde
https://arxiv.org/abs/2509.15023 https://

Modelling peaks over thresholds in panel data: a two-level grouped panel generalized Pareto regression
Panel data arise in a wide range of application areas, and developing modelling methods for extreme values under such a setup is essential for reliable risk assessment and management. When choosing to model the marginal distributions of univariate extremes, one may wish to balance the flexibility in capturing the heterogeneity among margins and the efficiency of estimation. This can be achieved through a combination of regression techniques and assuming a latent group structure based on paramet…

One Equation to Rule Them All -- Part II: Direct Data-Driven Reduction and Regulation
Junyu Mao, Emyr Williams, Thulasi Mylvaganam, Giordano Scarciotti
https://arxiv.org/abs/2508.17251

One Equation to Rule Them All -- Part II: Direct Data-Driven Reduction and Regulation
The Sylvester equation underpins a wide spectrum of control synthesis and systems analysis tools associated with cascade interconnections. In the preceding Part I [1] of this article, it was shown that such an equation can be reformulated using data, enabling the production of a collection of data-driven stabilisation procedures. In this second part of the article, we continue to develop the framework established in Part I to solve two important control-theoretic problems: model order reduction…

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Madhushan Ramalingam
https://arxiv.org/abs/2509.10048 https://

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
Predictive models are being increasingly used across a wide range of domains, including safety-critical applications such as medical diagnosis and criminal justice. Reliable uncertainty estimation is a crucial task in such settings. Tabular Prior-data Fitted Network (TabPFN) is a recently proposed machine learning foundation model for tabular dataset, which uses a generative transformer architecture. Variational Bayesian Last Layers (VBLL) is a state-of-the-art lightweight variational formulati…