Tootfinder

Optimal Evacuation Control in Large Urban Networks With Stochastic Demand
Alexander Hammerl, Wenlong Jin, Ravi Seshadri, Thomas Kj{\ae}r Rasmusssen, Otoo Anker Nielsen
https://arxiv.org/abs/2508.06797 …

Optimal Evacuation Control in Large Urban Networks With Stochastic Demand
We develop a risk-aware Model Predictive Control (MPC) framework for large-scale vehicular evacuations. Traffic dynamics are captured by the Generalized Bathtub Model, which describes the network-wide trip completion rate by tracking the time evolution of the distribution of remaining trip distances. We model evacuation inflow as a stochastic inflow process, and employ origin gating as the control policy, implemented through staged departure orders or adaptive ramp metering. A convex objective …

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…

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.

Designing de novo TIM Barrels: Insights into Stabilization, Diversification, and Functionalization Strategies
Julian Beck, Sergio Romero-Romero
https://arxiv.org/abs/2508.05699 …

Designing de novo TIM Barrels: Insights into Stabilization, Diversification, and Functionalization Strategies
The TIM-barrel fold is one of the most versatile and ubiquitous protein folds in nature, hosting a wide variety of catalytic activities and functions while serving as a model system in protein biochemistry and engineering. This review explores its role as a key fold model in protein design, particularly in addressing challenges in stabilization and functionalization. We discuss historical and recent advances in de novo TIM barrel design from the landmark creation of sTIM11 to the development of…

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…

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 …

Parametrizing the Hubble function instead of dark energy: Many possibilities
Sivasish Paul, Raj Kumar Das, Supriya Pan
https://arxiv.org/abs/2508.08072 https://

Parametrizing the Hubble function instead of dark energy: Many possibilities
In the present article, we propose a very simple parametrization of the Hubble function without parametrizing the dark components of the Universe. One of the novelties of the parametrization is that it may include a wide variety of the cosmological models, such as dark energy (both noninteracting and interacting fluids), modified gravity, cosmological matter creation and other known scenarios. The model is constrained with the latest astronomical probes from Hubble parameter measurements, three…

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…

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…

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…

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…

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…

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…

Fantasy Football WR draft strategy: Ja'Marr Chase, CeeDee Lamb, or Justin Jefferson No. 1?

https://www.cbssports.com/fantasy/football

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.

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…

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…

Vision Language Model-based Testing of Industrial Autonomous Mobile Robots
Jiahui Wu, Chengjie Lu, Aitor Arrieta, Shaukat Ali, Thomas Peyrucain
https://arxiv.org/abs/2508.02338 …

Vision Language Model-based Testing of Industrial Autonomous Mobile Robots
Autonomous Mobile Robots (AMRs) are deployed in diverse environments (e.g., warehouses, retail spaces, and offices), where they work alongside humans. Given that human behavior can be unpredictable and that AMRs may not have been trained to handle all possible unknown and uncertain behaviors, it is important to test AMRs under a wide range of human interactions to ensure their safe behavior. Moreover, testing in real environments with actual AMRs and humans is often costly, impractical, and pot…

Monte Carlo studies of the emergent spacetime in the polarized IKKT model
Chien-Yu Chou, Jun Nishimura, Cheng-Tsung Wang
https://arxiv.org/abs/2507.18472 https://

Monte Carlo studies of the emergent spacetime in the polarized IKKT model
The IKKT matrix model has been investigated as a promising nonperturbative formulation of superstring theory. One of the recent developments concerning this model is the discovery of the dual supergravity solution corresponding to the model obtained after supersymmetry-preserving mass deformation, which is dubbed the polarized IKKT model. Here we perform Monte Carlo simulations of this model in the case of matrix size N = 2 for a wide range of the deformation parameter Omega. While we reproduce…

A near-exact linear mixed model for genome-wide association studies
Zhibin Pu, Shufei Ge, Shijia Wang
https://arxiv.org/abs/2508.05278 https://arxiv.org/pd…

A near-exact linear mixed model for genome-wide association studies
Linear mixed models (LMM) are widely adopted in genome-wide association studies (GWAS) to account for population stratification and cryptic relatedness. However, the parameter estimation of LMMs imposes substantial computational burdens due to large-scale operations on genetic similarity matrices (GSM). We introduced the near-exact linear mixed model (NExt-LMM), a novel LMM framework that overcomes critical computational bottlenecks in GWAS through the following key innovations. Firstly, we exp…

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/

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…

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…

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 …

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 …

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…

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…

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 …

A robust intermittency equation formulation for transition modeling in Spalart-Allmaras RANS simulations of airfoil flows across a wide range of Reynolds numbers
Valerio D'Alessandro, Matteo Falone, Luca Giammichele, Renato Ricci
https://arxiv.org/abs/2508.02547

A robust intermittency equation formulation for transition modeling in Spalart-Allmaras RANS simulations of airfoil flows across a wide range of Reynolds numbers
This paper introduces a new robust formulation for local correlation-based laminar-to-turbulent transition models. This mechanism is incorporated into Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with the Spalart-Allmaras (SA) turbulence model, considering both $γ$ and $γ$-${\widetilde{\mathrm{Re}}_{θ,t}}$ transition frameworks. In this context, special attention is placed on numerical stabilization of the $γ$ transport equation, which is identified as the root cause of instabi…

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…

I'm definitely going to miss Mona's column customizability, as I was set up with notifications in one column of the left and home timeline twice as wide on the right. @… has Model-T style column support: any layout you want as long as the columns are all the same size and the home timeline is the leftmost.

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),…

Partial Floquet Transformation and Model Order Reduction of Linear Time-Periodic Systems
Sam Bender, Christopher Beattie
https://arxiv.org/abs/2508.00221 https://

Partial Floquet Transformation and Model Order Reduction of Linear Time-Periodic Systems
Time-periodic dynamical systems occur commonly both in nature and as engineered systems. Large-scale linear time-periodic dynamical systems, for example, may arise through linearization of a nonlinear system about a given periodic solution (possibly as a consequence of a baseline periodic forcing) with subsequent spatial discretization. The potential need to simulate responses to a wide variety of input profiles (viewed as perturbations off a baseline periodic forcing) creates a potent incentiv…

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…

A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
Mohammed A. Ramadhan, Abdulhakeem O. Mohammed
https://arxiv.org/abs/2507.19702 https://…

A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains. However, existing approaches often face trade-offs between accuracy and computational efficiency. To address these challenges, we propose 1D-CGS, a lightweight and effective hybrid model that integrates the speed of one-dimensional convolutional neural networks (1D-CNN) with the topological representation power of GraphSAGE for efficient node ranking. The model uses a…

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…

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…

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…

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…

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
Dai Li, Kevin Course, Wei Li, Hongwei Li, Jie Hua, Yiqi Chen, Zhao Zhu, Rui Jian, Xuan Cao, Bi Xue, Yu Shi, Jing Qian, Kai Ren, Matt Ma, Qunshu Zhang, Rui Li
https://arxiv.org/abs/2508.02929 …

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
While scaling laws promise significant performance gains for recommender systems, efficiently deploying hyperscale models remains a major unsolved challenge. In contrast to fields where FMs are already widely adopted such as natural language processing and computer vision, progress in recommender systems is hindered by unique challenges including the need to learn from online streaming data under shifting data distributions, the need to adapt to different recommendation surfaces with a wide div…

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…

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…

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…

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…

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…

aLLoyM: A large language model for alloy phase diagram prediction
Yuna Oikawa, Guillaume Deffrennes, Taichi Abe, Ryo Tamura, Koji Tsuda
https://arxiv.org/abs/2507.22558 https://…

aLLoyM: A large language model for alloy phase diagram prediction
Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifically trained on alloy compositions, temperatures, and their corresponding phase information. To develop aLLoyM, we curated question-and-answer (Q&A) pairs for binary and ternary phase diagrams using the open-source Computational Phase Diagram Database (CPDDB) and assessments based on CALPHAD (CALculation of PHAse Diagr…

Vanilla-Converter: A Tool for Converting Camunda 7 BPMN Models into Camunda 8 Models
Dragana Sunaric, Charlotte Verbruggen, Dominik Bork
https://arxiv.org/abs/2508.04352 https:/…

Vanilla-Converter: A Tool for Converting Camunda 7 BPMN Models into Camunda 8 Models
As organizations prepare for the end-of-life of Camunda 7, manual migration remains complex due to fundamental differences between the two platforms. We present Vanilla-Converter, a command-line tool that facilitates the migration of BPMN models from Camunda 7 to Camunda 8. Vanilla-Converter automates the transformation process, supports a wide range of BPMN elements, and produces a transformed model and a detailed transformation log indicating automatic changes and remaining manual conversion …

Placing the Near-Earth Object Impact Probability in Context
C. R. Nugent, K. P. Andersen, James M. Bauer, C. T. Jensen, L. K. Kristiansen, C. P. Hansen, M. M. Nielsen, C. F. Vesterg{\aa}rd
https://arxiv.org/abs/2508.02418

Placing the Near-Earth Object Impact Probability in Context
Near-Earth objects (NEOs) have the potential to cause extensive damage and loss of life on Earth. Advancements in NEO discovery, trajectory prediction, and deflection technology indicate that an impact could be prevented, with sufficient warning time. We derive an impact frequency of NEOs 140m and larger, using the NEOMOD2 NEO population model and JPL Horizons. We then place that frequency in context with other preventable causes of death; allowing for comparison between a planet-wide event and…

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…

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/

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…

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement
Hong Su
https://arxiv.org/abs/2508.04289 https://arxiv.org/…

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement
Large language models (LLMs) have shown impressive capabilities across a wide range of language tasks. However, their reasoning process is primarily guided by statistical patterns in training data, which limits their ability to handle novel problems and perform consistent logical reasoning. In this paper, we propose a method-based model that enhances LLMs with explicit, reusable procedures extracted from training content, generated responses, and user interactions. Each method is represented as…

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…

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…

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 …

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…

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…

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…

Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need
Michael Davies, Neal Crago, Karthikeyan Sankaralingam, Christos Kozyrakis
https://arxiv.org/abs/2507.14397

Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need
This paper presents a limit study of transformer-based large language model (LLM) inference, focusing on the fundamental performance bottlenecks imposed by memory bandwidth, memory capacity, and synchronization overhead in distributed inference systems. We develop a hardware-agnostic performance model that abstracts away implementation details, enabling the analysis of a wide range of current and near-future hardware technologies. Our analysis spans from current HBM3 memory technology used in A…

Analytical Modeling of Batteryless IoT Sensors Powered by Ambient Energy Harvesting
Jimmy Fernandez Landivar, Andrea Zanella, Ihsane Gryech, Sofie Pollin, Hazem Sallouha
https://arxiv.org/abs/2507.20952

Analytical Modeling of Batteryless IoT Sensors Powered by Ambient Energy Harvesting
This paper presents a comprehensive mathematical model to characterize the energy dynamics of batteryless IoT sensor nodes powered entirely by ambient energy harvesting. The model captures both the energy harvesting and consumption phases, explicitly incorporating power management tasks to enable precise estimation of device behavior across diverse environmental conditions. The proposed model is applicable to a wide range of IoT devices and supports intelligent power management units designed t…

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Mohammad Aflah Khan, Ameya Godbole, Johnny Tian-Zheng Wei, Ryan Wang, James Flemings, Krishna Gummadi, Willie Neiswanger, Robin Jia
https://arxiv.org/abs/2507.19419

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often inaccessible to researchers. We present TokenSmith, an open-source library for interactive editing, inspection, and analysis of datasets used in Megatron-style pretraining frameworks such as GPT-NeoX, Megatron, and NVIDIA NeMo. TokenSmith supports a wide range of operations including searching, viewing, ingesting, expor…

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 …

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…

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…

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-

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…

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…

AudioGen-Omni: A Unified Multimodal Diffusion Transformer for Video-Synchronized Audio, Speech, and Song Generation
Le Wang, Jun Wang, Feng Deng, Chen Zhang, Kun Gai, Di Zhang
https://arxiv.org/abs/2508.00733

AudioGen-Omni: A Unified Multimodal Diffusion Transformer for Video-Synchronized Audio, Speech, and Song Generation
We present AudioGen-Omni - a unified approach based on multimodal diffusion transformers (MMDit), capable of generating high-fidelity audio, speech, and songs coherently synchronized with the input video. AudioGen-Omni introduces a novel joint training paradigm that seamlessly integrates large-scale video-text-audio corpora, enabling a model capable of generating semantically rich, acoustically diverse audio conditioned on multimodal inputs and adaptable to a wide range of audio generation task…

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…

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…

BEV-LLM: Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous Driving
Felix Brandstaetter, Erik Schuetz, Katharina Winter, Fabian Flohr
https://arxiv.org/abs/2507.19370

BEV-LLM: Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous Driving
Autonomous driving technology has the potential to transform transportation, but its wide adoption depends on the development of interpretable and transparent decision-making systems. Scene captioning, which generates natural language descriptions of the driving environment, plays a crucial role in enhancing transparency, safety, and human-AI interaction. We introduce BEV-LLM, a lightweight model for 3D captioning of autonomous driving scenes. BEV-LLM leverages BEVFusion to combine 3D LiDAR poi…

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…

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 …

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…

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 …

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…

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…

Effects of strong turbulence for water waves
E. A. Kochurin, E. A. Kuznetsov
https://arxiv.org/abs/2507.22557 https://arxiv.org/pdf/2507.22557

Effects of strong turbulence for water waves
The results of direct numerical simulation of plane-symmetric turbulence of water waves for potential flows within the framework of conformal variables taking into account low-frequency pumping and high-frequency viscous dissipation are presented. In this model, for a wide range of pumping amplitudes, the weak turbulence regime was not detected. It is shown that for typical turbulence parameters, the main effects are the processes of wave breaking, the formation of cusps on wave crests, which m…

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…

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…

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…

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…

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 …

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…

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…

Testing Homogeneity in a heteroscedastic contaminated normal mixture
Xiaoqing Niu, Pengfei Li, Yuejiao Fu
https://arxiv.org/abs/2507.15630 https://

Testing Homogeneity in a heteroscedastic contaminated normal mixture
Large-scale simultaneous hypothesis testing appears in many areas such as microarray studies, genome-wide association studies, brain imaging, disease mapping and astronomical surveys. A well-known inference method is to control the false discovery rate. One popular approach is to model the $z$-scores derived from the individual $t$-tests and then use this model to control the false discovery rate. We propose a new class of contaminated normal mixtures for modelling $z$-scores. We further design…

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 …

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Kai Liu, Zixiong Wei, Wei Gao, Poulumi Dey, Marcel H. F. Sluiter, Fei Shuang
https://arxiv.org/abs/2507.21297

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Universal machine learning interatomic potentials (uMLIPs) are reshaping atomistic simulation as foundation models, delivering near \textit{ab initio} accuracy at a fraction of the cost. Yet the lack of reliable, general uncertainty quantification limits their safe, wide-scale use. Here we introduce a unified, scalable uncertainty metric \(U\) based on a heterogeneous model ensemble with reuse of pretrained uMLIPs. Across chemically and structurally diverse datasets, \(U\) shows a strong correl…

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…

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…

Retention analysis of edited knowledge after fine-tuning
Fufang Wen, Shichang Zhang
https://arxiv.org/abs/2507.14198 https://arxiv.or…

Retention analysis of edited knowledge after fine-tuning
Large language models (LLMs) store vast amounts of knowledge, which often requires updates to correct factual errors, incorporate newly acquired information, or adapt model behavior. Model editing methods have emerged as efficient solutions for such updates, offering localized and precise knowledge modification at significantly lower computational cost than continual training. In parallel, LLMs are frequently fine-tuned for a wide range of downstream tasks. However, the effect of fine-tuning on…

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…