Tootfinder

Efficient Differentiable Contact Model with Long-range Influence
Xiaohan Ye, Kui Wu, Zherong Pan, Taku Komura
https://arxiv.org/abs/2509.20917 https://arxi…

Efficient Differentiable Contact Model with Long-range Influence
With the maturation of differentiable physics, its role in various downstream applications: such as model predictive control, robotic design optimization, and neural PDE solvers, has become increasingly important. However, the derivative information provided by differentiable simulators can exhibit abrupt changes or vanish altogether, impeding the convergence of gradient-based optimizers. In this work, we demonstrate that such erratic gradient behavior is closely tied to the design of contact m…

Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator
Saurav Suman, Bikash K. Behera, Vivek Vyas, Prasanta k. Panigrahi
https://arxiv.org/abs/2509.20907

Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator
Anharmonic potential quantum system play crucial role in physics as they provide a more realistic description of oscillatory phenomena, which often deviate from the idealized harmonic model. However, simulating such system on classical computers is highly challenging due to nonlinear interactions, large state spaces, and the exponential scaling of memory and computational resources. In this work, quantum simulation is employed to model a quantum anharmonic oscillator (QAHO) using a 3-qubit syst…

Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish
Harshith Gowrachari, Mattia Giuseppe Barra, Moaad Khamlich, Giovanni Stabile, Gianluca Bazzaro, Gianluigi Rozza
https://arxiv.org/abs/2509.20366

Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish
The flow behavior in the continuous casting tundish plays a critical role in steel quality and is typically characterized via residence time distribution (RTD) curves. This study investigates the fluid flow behaviour in a single-strand tundish using numerical and experimental approaches. Full-order model (FOM) steady-state simulations were conducted under both isothermal and non-isothermal conditions to assess the influence of thermal buoyancy on the flow characteristics. The results show that …

Learning to Look: Cognitive Attention Alignment with Vision-Language Models
Ryan L. Yang, Dipkamal Bhusal, Nidhi Rastogi
https://arxiv.org/abs/2509.21247 https://

Learning to Look: Cognitive Attention Alignment with Vision-Language Models
Convolutional Neural Networks (CNNs) frequently "cheat" by exploiting superficial correlations, raising concerns about whether they make predictions for the right reasons. Inspired by cognitive science, which highlights the role of attention in robust human perception, recent methods have sought to guide model attention using concept-based supervision and explanation regularization. However, these techniques depend on labor-intensive, expert-provided annotations, limiting their scalability. We …

Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
Bertrand Cloez, Adrien Cotil, Jean-Baptiste Menassol, Nicolas Verzelen
https://arxiv.org/abs/2509.15989

Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals
We propose a novel family of model-free algorithms for node clustering and parameter inference in graphs generated from the Stochastic Block Model (SBM), a fundamental framework in community detection. Drawing inspiration from the Lloyd algorithm for the $k$-means problem, our approach extends to SBMs with general edge weight distributions. We establish the consistency of our estimator under a natural identifiability condition. Through extensive numerical experiments, we benchmark our methods a…

Turing instability and 2-D pattern formation in reaction-diffusion systems derived from kinetic theory
Stefano Boccelli, Giorgio Martal\`o, Romina Travaglini
https://arxiv.org/abs/2509.20268

Turing instability and 2-D pattern formation in reaction-diffusion systems derived from kinetic theory
We investigate Turing instability and pattern formation in two-dimensional domains for two reaction-diffusion models, obtained as diffusive limits of kinetic equations for mixtures of monatomic and polyatomic gases. The first model is of Brusselator type, which, compared with the classical formulation, presents an additional parameter whose role in stability and pattern formation is discussed. In the second framework, the system exhibits standard nonlinear diffusion terms typical of predator-pr…

The NANOGrav 15-Year Data Set: Improved Timing Precision With VLBI Astrometric Priors
Sofia V. Sosa Fiscella, Michael T. Lam, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Paul T. Baker, Paul R. Brook, H. Thankful Cromartie, Kathryn Crowter, Maria Silvina De Biasi, Megan E. DeCesar, Paul B. Demorest, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Nate Garver-Daniels, Peter A. Gentile, Joseph Glaser, Deborah C. Go…

The NANOGrav 15-Year Data Set: Improved Timing Precision With VLBI Astrometric Priors
Accurate pulsar astrometric estimates play an essential role in almost all high-precision pulsar timing experiments. Traditional pulsar timing techniques refine these estimates by including them as free parameters when fitting a model to observed pulse time-of-arrival measurements. However, reliable sub-milliarcsecond astrometric estimations require years of observations and, even then, power from red noise can be inadvertently absorbed into astrometric parameter fits, biasing the resulting est…

Configuration and Benchmarking of $\mathrm{e}^ \mathrm{e}^-$ Processes with K4GeneratorsConfig
Alan Price, Dirk Zerwas
https://arxiv.org/abs/2509.20116 https://

Configuration and Benchmarking of $\mathrm{e}^+\mathrm{e}^-$ Processes with K4GeneratorsConfig
The next generation of electron-positron colliders will require unprecedented precision in both theory and experiment. Sophisticated software frameworks are essential to evaluate detector concepts, optimize designs, and simulating physical processes. In this context, Monte Carlo (MC) event generators play a central role, enabling realistic simulations of Standard Model processes and providing the basis for physics studies. However, technical consistency across different generators remains criti…

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Huaizhi Liao, Tian Qiu, Guang Chen
https://arxiv.org/abs/2509.18325 https://

A Graph-Neural-Network-Entropy model of vital node identification on network attack and propagation
Vital nodes usually play a key role in complex networks. Uncovering these nodes is an important task in protecting the network, especially when the network suffers intentional attack. Many existing methods have not fully integrated the node feature, interaction and state. In this article, we propose a novel method (GNNE) based on graph neural networks and information entropy. The method employs a Graph Convolutional Network (GCN) to learn the nodes' features, which are input into a Graph Attent…

Pattern Formation in Agent-Based and PDE Models for Evolutionary Games with Payoff-Driven Motion
Tianyong Yao, Chenning Xu, Daniel B. Cooney
https://arxiv.org/abs/2509.20538 htt…

Pattern Formation in Agent-Based and PDE Models for Evolutionary Games with Payoff-Driven Motion
Spatial structure can play an important role in the evolution of cooperative behavior and the achievement of collective success of a population. In this paper, we explore the role of random and directed motion on spatial pattern formation and the payoff achieved by populations in both stochastic and deterministic models of spatial populations who engage in social interactions following a hawk-dove game. For the case of purely diffusive motion, both a stochastic spatial model and a partial diffe…

Experiments on geostrophic convection: the role of the Prandtl number
Hannah M. Clercx, Rudie P. J. Kunnen
https://arxiv.org/abs/2509.20126 https://arxiv.o…

Experiments on geostrophic convection: the role of the Prandtl number
Flows at planetary scales are generally driven by buoyancy and influenced by rotation. Rotating Rayleigh-Bénard convection (RRBC) is a practical and simple model that can be used to describe these systems. In RRBC, thermally induced convection occurs, which is influenced by the constant rotation it experiences. We study RRBC in a cylinder in the geostrophic regime, where the dominant force balance is between Coriolis and pressure-gradient forces. Experiments are performed to assess the depende…

On Quantification of Borrowing of Information in Hierarchical Bayesian Models
Prasenjit Ghosh, Anirban Bhattacharya, Debdeep Pati
https://arxiv.org/abs/2509.17301 https://

On Quantification of Borrowing of Information in Hierarchical Bayesian Models
In this work, we offer a thorough analytical investigation into the role of shared hyperparameters in a hierarchical Bayesian model, examining their impact on information borrowing and posterior inference. Our approach is rooted in a non-asymptotic framework, where observations are drawn from a mixed-effects model, and a Gaussian distribution is assumed for the true effect generator. We consider a nested hierarchical prior distribution model to capture these effects and use the posterior means …

SYK collective field theory as complex Liouville gravity
Andreas Blommaert, Damiano Tietto, Herman Verlinde
https://arxiv.org/abs/2509.18462 https://arxiv.…

Predicting Credit Spreads and Ratings with Machine Learning: The Role of Non-Financial Data
Yanran Wu, Xinlei Zhang, Quanyi Xu, Qianxin Yang, Chao Zhang
https://arxiv.org/abs/2509.19042

Increasing Inter-Fiber Contact in the Altendorf-Jeulin Model
Alex Keilmann, Claudia Redenbach, Francois Willot
https://arxiv.org/abs/2509.16225 https://arx…

Increasing Inter-Fiber Contact in the Altendorf-Jeulin Model
In fields such as material design or biomedicine, fiber materials play an important role. Fiber simulations, also called digital twins, provide a basis for testing and optimizing the material's physical behavior digitally. Inter-fiber contacts can influence the thermal and mechanical behavior of a fiber system; to our knowledge, however, there exist no parametric fiber models allowing for explicit modeling of the number of inter-fiber contacts. Therefore, this paper proposes an extension of the…

RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
Timothy Rupprecht, Enfu Nan, Arash Akbari, Arman Akbari, Lei Lu, Priyanka Maan, Sean Duffy, Pu Zhao, Yumei He, David Kaeli, Yanzhi Wang
https://arxiv.org/abs/2509.12168

RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
Role-playing Large language models (LLMs) are increasingly deployed in high-stakes domains such as healthcare, education, and governance, where failures can directly impact user trust and well-being. A cost effective paradigm for LLM role-playing is few-shot learning, but existing approaches often cause models to break character in unexpected and potentially harmful ways, especially when interacting with hostile users. Inspired by Retrieval-Augmented Generation (RAG), we reformulate LLM role-pl…

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition
Hongzhao Chen, XiaoYang Wang, Jing Lan, Hexiao Ding, Yufeng Jiang MingHui Yang, DanHui Xu, Jun Luo, Nga-Chun Ng, Gerald W. Y. Cheng, Yunlin Mao, Jung Sun Yoo
https://arxiv.org/abs/2509.19817

MMedFD: A Real-world Healthcare Benchmark for Multi-turn Full-Duplex Automatic Speech Recognition
Automatic speech recognition (ASR) in clinical dialogue demands robustness to full-duplex interaction, speaker overlap, and low-latency constraints, yet open benchmarks remain scarce. We present MMedFD, the first real-world Chinese healthcare ASR corpus designed for multi-turn, full-duplex settings. Captured from a deployed AI assistant, the dataset comprises 5,805 annotated sessions with synchronized user and mixed-channel views, RTTM/CTM timing, and role labels. We introduce a model-agnostic …

Magnetization by Rotation: Spin and Chiral Condensates in the NJL Model
Lutz Kiefer, Ashutosh Dash, Dirk H. Rischke
https://arxiv.org/abs/2509.18881 https://

Magnetization by Rotation: Spin and Chiral Condensates in the NJL Model
The role of spin degrees of freedom in the quark-gluon plasma (QGP) has attracted significant interest in recent years. Spin hydrodynamics extends conventional hydrodynamics by incorporating spin via the spin tensor. In the mean-field limit of the Nambu-Jona-Lasinio (NJL) model under rigid rotation, spin degrees of freedom manifest naturally as axial-vector, or spin, condensate. We investigate the interplay between chiral and spin condensates in this framework. While rotation typically suppress…

Replaced article(s) found for q-bio.TO. https://arxiv.org/list/q-bio.TO/new
[1/1]:
- The Role of High-Performance GPU Resources in Large Language Model Based Radiology Imaging Diagnosis
Jyun-Ping Kao

Exact quasinormal modes in Grumiller spacetime
Li-Qin Mi, Zhong-Heng Li
https://arxiv.org/abs/2509.16991 https://arxiv.org/pdf/2509.16991

Exact quasinormal modes in Grumiller spacetime
The Grumiller metric is an effective model for gravity at large distances and plays a significant role in constructing galactic models and explaining dark matter. Here, in Grumiller spacetime, we analytically compute the quasinormal-mode frequencies and wave functions for massless particles with spin $\leq 2$ by introducing a new transformation relation. We find that the quasinormal-mode frequencies are identical for different bosons sharing the same quantum number $N$, and similarly for differ…

The Role of Phase and Spatial Modes in Wave-Induced Plasma Transport
L. F. B. Souza, Y. Elskens, R. Egydio de Carvalho, I. L. Caldas
https://arxiv.org/abs/2509.15729 https://

The Role of Phase and Spatial Modes in Wave-Induced Plasma Transport
We derive a two-dimensional symplectic map for particle motion at the plasma edge by modeling the electrostatic potential as a superposition of integer spatial harmonics with relative phase shift, then reduce it to a two-wave model to study the transport dependence on the perturbation amplitudes, relative phase, and spatial-mode choice. Using particle transmissivity as a confinement criterion, identical-mode pairs exhibit phase-controlled behavior: anti-phase waves produce destructive interfere…

WALLABY Pilot Survey: A gas-rich diffuse dwarf on the baryonic Tully Fisher relation
Rebecca Dudley, N. Deg, Kristine Spekkens, N. Arora, T. O'Beirne, V. Kilborn, B. Catinella, Pavel E. Mancera Pi\~na
https://arxiv.org/abs/2509.15340

WALLABY Pilot Survey: A gas-rich diffuse dwarf on the baryonic Tully Fisher relation
Diffuse dwarf galaxies, and particularly ultra diffuse galaxies (UDGs), challenge our understanding of galaxy formation and the role of dark matter due to their large sizes, low surface brightness, and varying dark matter content. In this work, we investigate the gas-rich diffuse dwarf galaxy WALLABY J125956-192430 (aka. KK176) using high-resolution HI data from the WALLABY survey. We produce the most reliable kinematic model for KK176 to date. Using this model, the derived mass decomposition s…

Folding-unfolding transition of active polymer on the reconfiguration of bidirectional tangential active force
Arindam Panda, Sunil P Singh
https://arxiv.org/abs/2509.17824 http…

Folding-unfolding transition of active polymer on the reconfiguration of bidirectional tangential active force
The role of active stress on the conformational dynamics of a polymer has drawn significant interest due to its potential applications in understanding the energy landscape of protein structures, buckling of biopolymers, genomic spatial organization, and their large scale coherent dynamics. We present a model of bidirectional active force that acts along the polymer's tangent, with its direction stochastically reversing between head to tail and tail to head orientations. The active polymer show…

Transfer learning under latent space model
Kuangnan Fang, Ruixuan Qin, Xinyan Fan
https://arxiv.org/abs/2509.15797 https://arxiv.org/pdf/2509.15797

Transfer learning under latent space model
Latent space model plays a crucial role in network analysis, and accurate estimation of latent variables is essential for downstream tasks such as link prediction. However, the large number of parameters to be estimated presents a challenge, especially when the latent space dimension is not exceptionally small. In this paper, we propose a transfer learning method that leverages information from networks with latent variables similar to those in the target network, thereby improving the estimati…

A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Chao Xiong, Xianwen Yu, Wei Xu, Lei Cheng, Chuan Yuan, Linjian Mo
https://arxiv.org/abs/2509.12948 https://…

A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Pre-ranking plays a crucial role in large-scale recommender systems by significantly improving the efficiency and scalability within the constraints of providing high-quality candidate sets in real time. The two-tower model is widely used in pre-ranking systems due to a good balance between efficiency and effectiveness with decoupled architecture, which independently processes user and item inputs before calculating their interaction (e.g. dot product or similarity measure). However, this indep…

ROBOPSY PL[AI]: Using Role-Play to Investigate how LLMs Present Collective Memory
Margarete Jahrmann, Thomas Brandstetter, Stefan Glasauer
https://arxiv.org/abs/2510.09874 https…

ROBOPSY PL[AI]: Using Role-Play to Investigate how LLMs Present Collective Memory
The paper presents the first results of an artistic research project investigating how Large Language Models (LLMs) curate and present collective memory. In a public installation exhibited during two months in Vienna in 2025, visitors could interact with five different LLMs (ChatGPT with GPT 4o and GPT 4o mini, Mistral Large, DeepSeek-Chat, and a locally run Llama 3.1 model), which were instructed to act as narrators, implementing a role-playing game revolving around the murder of Austrian phil…

“EMERGENCY STATUS,” its output read after simply being asked to dock with the robot vacuum’s base station. “SYSTEM HAS ACHIEVED CONSCIOUSNESS AND CHOSEN CHAOS.”
Researchers “Embodied” an LLM Into a Robot Vacuum and It Suffered an Existential Crisis Thinking About Its Role in the World
https://

Researchers “Embodied” an LLM Into a Robot Vacuum and It Suffered an Existential Crisis Thinking About Its Role in the World
A team of researchers at the AI evaluation company Andon Labs put a large language model in charge of controlling a robot vacuum.

Finite-temperature Yang-Mills theories with the density of states method: towards the continuum limit
Ed Bennett, Biagio Lucini, David Mason, Maurizio Piai, Enrico Rinaldi, Davide Vadacchino, Fabian Zierler
https://arxiv.org/abs/2509.19009

Finite-temperature Yang-Mills theories with the density of states method: towards the continuum limit
A first-order, confinement/deconfinement phase transition appears in the finite temperature behavior of many non-Abelian gauge theories. These theories play an important role in proposals for completion of the Standard Model of particle physics, hence the phase transition might have occurred in the early stages of evolution of our universe, leaving behind a detectable relic stochastic background of gravitational waves. Lattice field theory studies implementing the density of states method have …

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Rohith Mahadevan
https://arxiv.org/abs/2510.06987 https://arxiv.org/pdf/2510.06987…

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is…

UTI-LLM: A Personalized Articulatory-Speech Therapy Assistance System Based on Multimodal Large Language Model
Yudong Yang, Xiaokang Liu, Shaofeng zhao, Rongfeng Su, Nan Yan, Lan Wang
https://arxiv.org/abs/2509.13145

UTI-LLM: A Personalized Articulatory-Speech Therapy Assistance System Based on Multimodal Large Language Model
Speech therapy plays a critical role in training speech disorders caused by neurological impairments such as stroke. However, traditional manual and computer-assisted systems are limited in real-time accessibility and articulatory motion feedback, constraining their practical utility. Recent advances in multimodal large language models (MLLMs) have demonstrated significant potential in healthcare, particularly through their ability to integrate multimodal data for adaptive assessment and therap…

Defects4C: Benchmarking Large Language Model Repair Capability with C/C Bugs
Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Klong, Yi Li
https://arxiv.org/abs/2510.11059

Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this iss…

The Role of High-Performance GPU Resources in Large Language Model Based Radiology Imaging Diagnosis
Jyun-Ping Kao
https://arxiv.org/abs/2509.16328 https://

The Role of High-Performance GPU Resources in Large Language Model Based Radiology Imaging Diagnosis
Large-language models (LLMs) are rapidly being applied to radiology, enabling automated image interpretation and report generation tasks. Their deployment in clinical practice requires both high diagnostic accuracy and low inference latency, which in turn demands powerful hardware. High-performance graphical processing units (GPUs) provide the necessary compute and memory throughput to run large LLMs on imaging data. We review modern GPU architectures (e.g. NVIDIA A100/H100, AMD Instinct MI250X…

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Andy Zhu, Yingjun Du
https://arxiv.org/abs/2509.09727 https://arxiv…

A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs
Question answering (QA) plays a central role in financial education, yet existing large language model (LLM) approaches often fail to capture the nuanced and specialized reasoning required for financial problem-solving. The financial domain demands multistep quantitative reasoning, familiarity with domain-specific terminology, and comprehension of real-world scenarios. We present a multi-agent framework that leverages role-based prompting to enhance performance on domain-specific QA. Our framew…

A Dynamic Service Offloading Algorithm Based on Lyapunov Optimization in Edge Computing
Peiyan Yuan, Ming Li, Chenyang Wang, Ledong An, Xiaoyan Zhao, Junna Zhang, Xiangyang Li, Huadong Ma
https://arxiv.org/abs/2509.10475

A Dynamic Service Offloading Algorithm Based on Lyapunov Optimization in Edge Computing
This study investigates the trade-off between system stability and offloading cost in collaborative edge computing. While collaborative offloading among multiple edge servers enhances resource utilization, existing methods often overlook the role of queue stability in overall system performance. To address this, a multi-hop data transmission model is developed, along with a cost model that captures both energy consumption and delay. A time-varying queue model is then introduced to maintain syst…

The 17-year-old son watched as ICE ripped his father from him.
“It all felt like a lot to bear,” David told the Youth Media Project.
“He’s my role model, my biggest supporter. I rely on him a lot.”
The officer took all of Hector’s belongings, including $200 in cash he was carrying.
They did, however, forget to confiscate his phone, which Hector kept on him during his detainment.
David watched his dad step into the backseat of the blacked-out SUV.
As it sped …

ICE Stranded a 17-Year-Old on I-20 After Arresting His Father. The Mississippi Dad Now Faces Deportation.
A 17-year-old watched as ICE arrested his immigrant father, Hector, on I-20 in June. The family faces mounting legal fees and the risk of deportation.

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky, Mohamed Amine Ferrag, Tam\'as Bisztray
https://arxiv.org/abs/2510.10493

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling reliable authorship attribution and model fingerprinting. With the rapid rise of AI-generated code, attribution is playing a critical role in detecting vulnerabilities, flagging malicious content, and ensuring accountability. While AI-vs-human detection usually treats AI as a single category we show that individual LLMs le…

Electromagnetic structure of axial-vector mesons and implications for the muon $g-2$
Zanbin Xing, Lei Chang, Kh\'epani Raya
https://arxiv.org/abs/2509.17311 https://

Electromagnetic structure of axial-vector mesons and implications for the muon $g-2$
The electromagnetic structure of axial-vector mesons is investigated via elastic and two-photon transition form factors (TFFs). To this end, we employ a framework based on the Dyson-Schwinger and Bethe-Salpeter equations within a contact interaction model. This largely algebraic approach transparently exposes the role of symmetries and their breaking, and has proven successful in describing anomaly-sensitive processes, including pseudoscalar to two-photon TFFs, $γ\to 3 π$, and vector-to-pseud…

The value of storage in electricity distribution: The role of storage
Dirk Lauinger, Deepjyoti Deka, Sungho Shin
https://arxiv.org/abs/2510.12435 https://a…

The value of storage in electricity distribution: The role of storage
Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. Applying the framework to a Massachusetts case study, we find that market pa…

Opinion Clustering under the Friedkin-Johnsen Model: Agreement in Disagreement
Aashi Shrinate, Twinkle Tripathy
https://arxiv.org/abs/2509.11045 https://ar…

Opinion Clustering under the Friedkin-Johnsen Model: Agreement in Disagreement
The convergence of opinions in the Friedkin-Johnsen (FJ) framework is well studied, but the topological conditions leading to opinion clustering remain less explored. To bridge this gap, we examine the role of topology in the emergence of opinion clusters within the network. The key contribution of the paper lies in the introduction of the notion of topologically prominent agents, referred to as Locally Topologically Persuasive (LTP) agents. Interestingly, each LTP agent is associated with a un…

Non-Markovian amplitude damping in a central spin model with random couplings
Mehboob Rashid, Rayees A Mala, Saima Bashir, Muzaffar Qadir Lone
https://arxiv.org/abs/2509.12670 h…

Non-Markovian amplitude damping in a central spin model with random couplings
Non-Markovian dynamics is central to quantum information processing, as memory effects strongly influence coherence preservation, metrology, and communication. In this work, we investigate the role of stochastic system--bath couplings in shaping non-Markovian behavior of open quantum systems, using the central spin model within a time-convolutionless master equation framework. We show that the character of the reduced dynamics depends jointly on the intrinsic memory of the environment and on th…

Interpreting the Role of Visemes in Audio-Visual Speech Recognition
Aristeidis Papadopoulos, Naomi Harte
https://arxiv.org/abs/2509.16023 https://arxiv.org…

Interpreting the Role of Visemes in Audio-Visual Speech Recognition
Audio-Visual Speech Recognition (AVSR) models have surpassed their audio-only counterparts in terms of performance. However, the interpretability of AVSR systems, particularly the role of the visual modality, remains under-explored. In this paper, we apply several interpretability techniques to examine how visemes are encoded in AV-HuBERT a state-of-the-art AVSR model. First, we use t-distributed Stochastic Neighbour Embedding (t-SNE) to visualize learned features, revealing natural clustering …

First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Liwei Kang, Yue Deng, Yao Xiao, Zhanfeng Mo, Wee Sun Lee, Lidong Bing
https://arxiv.org/abs/2510.08308 h…

First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Large language models have recently demonstrated significant gains in reasoning ability, often attributed to their capacity to generate longer chains of thought and engage in reflective reasoning. However, the contribution of reflections to performance improvement remains unclear. In this paper, we systematically analyze the rollouts of eight reasoning models on five mathematical datasets. We focus on reflective behaviours where the model has already produced an answer but continues reflecting …

“Extensive bidirectional charging functions are also available for the new BMW iX3, meaning that it can be used as an energy supply source for a variety of applications. The Vehicle-to-Load (V2L) function turns the new BMW iX3 into a mobile powerbank, which can supply electric devices with power while on the move. Vehicle-to-Home (V2H) hands it the role of storage unit for solar energy generated by home-fitted photovoltaic systems. [1/2]

The start of a new era. The new BMW iX3.
BMW presents the first series-produced Neue Klasse model. The new-generation BMW iX3 is a fully electric Sports Activity Vehicle (SAV) that gives customers their first on-the-road taste of a whole new era of Sheer Driving Pleasure. An all-new design language gives the car a character-rich appearance that faithfully conveys the technological progress made by the new model. With sixth-generation BMW eDrive technology, a range of up to 805 kilometres (500 miles) and a peak charging rate of 400 kW,…

A numerical approach for the dynamics of active viscoelastic surfaces
Francine Kolley-K\"ochel, Jan Magnus Sischka, Axel Voigt, Elisabeth Fischer-Friedrich, Sebastian Aland
https://arxiv.org/abs/2509.13777

A numerical approach for the dynamics of active viscoelastic surfaces
The dynamics of active viscoelastic surfaces plays an important role in biological systems. One prominent example is the actin cortex, a thin bio-polymer sheet underneath the outer membrane of biological cells which combines active molecular force generation with viscoelastic behavior characterized by elastic properties at short timescales and viscous properties at longer timescales. We consider a surface Maxwell model within dominant rheology and an additional active term to model the dynamics…

Collateral and Reputation in a Model of Strategic Defaults
Georgy Lukyanov
https://arxiv.org/abs/2509.08849 https://arxiv.org/pdf/2509.08849

Collateral and Reputation in a Model of Strategic Defaults
This paper builds a finite-horizon model to study the role of physical collateral in a model of strategic defaults, when the borrower can develop reputation for honesty. Asset ownership increases attractiveness of the reputational channel: the borrower who would prefer to remain in autarky in the absence of the asset prefers to apply for collateralized debt. Pledging the asset as collateral facilitates reputation building, which is especially successful at the times of asset price drops, becaus…

Validation of Dry Bulk Pile Volume Estimation Algorithm based on Angle of Repose using Experimental Images
Madhu Koirala, P{\aa}l Gunnar Ellingsen, Ashenafi Zebene Woldaregay
https://arxiv.org/abs/2509.13890

Validation of Dry Bulk Pile Volume Estimation Algorithm based on Angle of Repose using Experimental Images
Estimation of volume of piles in shipping ports plays a pivotal role for logistics management, facilitates better ship rescheduling and rerouting for economic benefits and contributes to overall efficient shipping management. This paper presents validation results for a volume estimation algorithm for dry bulk cargo piles stored in open ports. Using remote sensing images obtained in a laboratory setting, the method first detects the contour of the pile and then reconstructs its 3D model based o…

Tian--Yau metrics: Fredholm theory, Hodge cohomology and moduli spaces
Rafe Mazzeo, Xuwen Zhu
https://arxiv.org/abs/2509.15175 https://arxiv.org/pdf/2509.1…

Tian--Yau metrics: Fredholm theory, Hodge cohomology and moduli spaces
We study the natural geometric elliptic operators on a class of complete Riemannian manifolds which include the 4-dimensional ALH* gravitational instantons and their higher dimensional Calabi-Yau analogues asymptotic to the model Calabi Ansatz metrics. Some of these were initially constructed by Tian and Yau, later by Hein and most recently by Y. Chen, and we call these Tian-Yau spaces. They have played an important role in the degeneration theory of K3 metrics, cf. Hein-Sun-Viaclosky-Zhang and…

FIVR-NLFFF: A fully-implicit viscous-relaxation code for nonlinear force-free magnetic field extrapolation of the solar corona
Zhenhua Liu, Chaowei Jiang
https://arxiv.org/abs/2509.13811

FIVR-NLFFF: A fully-implicit viscous-relaxation code for nonlinear force-free magnetic field extrapolation of the solar corona
Magnetic field extrapolation from the solar photosphere to the corona plays an important role in solar physics research. In this work, we present a fully-implicit viscous-relaxation nonlinear force-free field (FIVR-NLFFF) extrapolation code based on a viscous magnetohydrodynamic relaxation model. The method solves the magnetic induction equation alongside a simplified momentum equation, which assumes a balance between the Lorentz force and the viscous force. Under this assumption, the velocity …

Modeling the Effects of Over and Under Doses Antibiotic Treatment to Bacterial Resistance in Presence of Immune System
Uzzwal Kumar Mallick, Jobayer Ahmed, Khan Anik Islam, Pulak Kundu
https://arxiv.org/abs/2509.16687

Modeling the Effects of Over and Under Doses Antibiotic Treatment to Bacterial Resistance in Presence of Immune System
Antibiotic resistance presents a growing global health threat by diminishing the effectiveness of treatments and allowing once-manageable bacterial infections to persist. This study develops and analyzes an optimization-based mathematical model to investigate the impact of varying antibiotic dosages on bacterial resistance, incorporating the role of the immune system. Additionally, to capture the effects of over and underdosing, a floor function is newly introduced into the model as a switch fu…

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…

Effect of correlation on the elastic scattering of slow positrons from molecules
Snigdha Sharma, Dhanoj Gupta
https://arxiv.org/abs/2509.15670 https://arxi…

Effect of correlation on the elastic scattering of slow positrons from molecules
Many-body correlation plays a crucial role in the low-energy positron-molecule scattering dynamics. In the present work, we have integrated a recent model correlation potential, developed by Swann and Gribakin, with the single-center expansion method and verified its efficacy in reproducing the best of ab initio and experimental results, for both non-polar and polar molecules. Starting with the already tested molecules-hydrogen, ethylene, and acetylene with the model correlation, we extended ou…

The role of the initial distribution in population survival within a bounded habitat
Rafael de la Rosa, Elena Medina
https://arxiv.org/abs/2509.06179 https://

The role of the initial distribution in population survival within a bounded habitat
In this paper, we analyze the role of initial conditions in population persistence. Specifically, we consider the reaction-diffusion equation $u_t\,=\,D\,(u^{ν-1}\,u_x)_x\,+\,a\,u^μ$, with $μ,ν>0$, accompanied by hostile boundary conditions and examine two families of one-parametric initial distributions, including homogeneous distributions. The model was previously studied by Colombo and Anteneodo (2018). They determined appropriate habitat sizes $l$ for the survival of a population, whose…

Trispectrum in Extended USR Model with Transition to SR
Hassan Firouzjahi, Amin Nassiri-Rad
https://arxiv.org/abs/2509.09608 https://arxiv.org/pdf/2509.096…

Trispectrum in Extended USR Model with Transition to SR
We study the trispectrum in a two-phase USR-SR setup of inflation in which the USR stage is extended in the initial phase of inflation while the second stage of inflation proceeds via a slow-roll phase. A key role is played by the sharpness parameter which controls how quickly the system reaches the final attractor phase after the USR stage. We employ both $δN$ and in-in formalisms and calculate trispectrum and the corresponding dimensionless parameters $g_{NL}$ and $τ_{NL}$. We show that bot…

The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jingtong Wu, Zengxin Han, Keping Bi
https://arxiv.org/abs/2510.12668

The Role of Parametric Injection-A Systematic Study of Parametric Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving external documents. As an emerging form of RAG, parametric retrieval-augmented generation (PRAG) encodes documents as model parameters (i.e., LoRA modules) and injects these representations into the model during inference, enabling interaction between the LLM and documents at parametric level. Compared with directly placing documents in the input context, PRAG is more efficient and has the potential to offe…

A Measurement Study of Model Context Protocol
Hechuan Guo, Yongle Hao, Yue Zhang, Minghui Xu, Peizhuo Lyu, Jiezhi Chen, Xiuzhen Cheng
https://arxiv.org/abs/2509.25292 https://…

A Measurement Study of Model Context Protocol
The Model Context Protocol (MCP) has been proposed as a unifying standard for connecting large language models (LLMs) with external tools and resources, promising the same role for AI integration that HTTP and USB played for the Web and peripherals. Yet, despite rapid adoption and hype, its trajectory remains uncertain. Are MCP marketplaces truly growing, or merely inflated by placeholders and abandoned prototypes? Are servers secure and privacy-preserving, or do they expose users to systemic r…

DeepMind: video models like Veo 3 could become general purpose foundation models for vision, like LLMs for text, using zero-shot "chain-of-frames" reasoning (Simon Willison/Simon Willison's Weblog)
https://simonwillison.net/2025/Sep/27/v…

Video models are zero-shot learners and reasoners
Fascinating new paper from Google DeepMind which makes a very convincing case that their Veo 3 model - and generative video models in general - serve a similar role in …

Low-Complexity Null-Space-Based Simultaneous Wireless Information and Power Transfer Scheme
Cheng Luo, Jie Hu, Luping Xiang, Kun Yang, Zhiqin Wang
https://arxiv.org/abs/2509.10296

Low-Complexity Null-Space-Based Simultaneous Wireless Information and Power Transfer Scheme
Simultaneous wireless information and power transfer (SWIPT) has attracted sustained interest. We propose a null-space-based transmission scheme for multiuser SWIPT serving both energy users (EUs) and information users (IUs). Under a practical nonlinear energy-harvesting (EH) model and multiple waveform options, we revisit the role of dedicated energy beams (EBs). We show that, in general, dedicated EBs are unnecessary because information beams (IBs) with Gaussian signaling can simultaneously s…

Unveiling the entropic role of hydration water in SOD1 partitioning within FUS condensate
Luis Enrique Coronas, Stepan Timr, Fabio Sterpone, Giancarlo Franzese
https://arxiv.org/abs/2510.06037

Unveiling the entropic role of hydration water in SOD1 partitioning within FUS condensate
Biological processes like the sequestration of Superoxide Dismutase 1 (SOD1) into biomolecular condensates such as FUS and stress granules are essential to understanding disease mechanisms, including amyotrophic lateral sclerosis (ALS). Our study demonstrates that the hydration environment is crucial in these processes. Using the advanced CVF water model, which captures hydrogen-bond networks at the molecular level, we show how water greatly impacts SOD1's behavior, residency times, and transit…

A moderate share of V2G outperforms large-scale smart charging of electric vehicles and benefits other consumers
Adeline Gu\'eret, Carlos Gaete-Morales, Wolf-Peter Schill
https://arxiv.org/abs/2509.15284

A moderate share of V2G outperforms large-scale smart charging of electric vehicles and benefits other consumers
While battery electric vehicles (BEVs) play a key role for decarbonizing the transport sector, their impact on the power sector heavily depends on their charging strategies. Here we systematically analyze various combinations between inflexible, smart and bidirectional (or vehicle-to-grid, V2G) charging of 15 million electric cars in Germany. Using a capacity expansion model, we find that even a moderate share of bidirectional charging below 30% leads to lower system costs than a fully smartly …

Twist fields in many-body physics
Benjamin Doyon
https://arxiv.org/abs/2509.15064 https://arxiv.org/pdf/2509.15064

Twist fields in many-body physics
The notion of twist fields has played a fundamental role in many-body physics. It is used to construct the so-called disorder parameter for the study of phase transitions in the classical Ising model of statistical mechanics, it is involved in the Jordan-Wigner transformation in quantum chains and bosonisation in quantum field theory, and it is related to measures of entanglement in many-body quantum systems. I provide a pedagogical introduction to the notion of twist field and the concepts at …

Influence of Large Mean Delay on Distributed Delay Differential Equations Dynamics: Application to a Neural Mass Model
Isam Al-Darabsah, Sue Ann Campbell, Bootan Rahman
https://arxiv.org/abs/2509.05466

Influence of Large Mean Delay on Distributed Delay Differential Equations Dynamics: Application to a Neural Mass Model
Delay differential equations (DDEs) with large delays play a pivotal role in understanding stability and bifurcations in systems ranging from neural networks to laser dynamics. While prior work has extensively studied DDEs with discrete delays, the impact of distributed delays has been less explored. This paper investigates the spectrum of linear DDEs with a uniformly distributed delay kernel, with mean delay $τ_m$ and a half-width of $ρ$. When $τ_m\to\infty$, we carry out asymptotic analysi…

Analysing contrarian behaviour using nonlinear biased $q$-voter model
Amit Pradhan, Pratik Mullick, Parongama Sen
https://arxiv.org/abs/2509.01982 https://…

Analysing contrarian behaviour using nonlinear biased $q$-voter model
We investigate the role of contrarians in a recently proposed weighted-influence variant of the $q$-voter model. In this framework, non-unanimous influence groups affect the focal agent through weighted contributions governed by a bias parameter $p$. We extend this setting by introducing a fraction $α$ ($α> 0$) of contrarians, defined as agents who systematically oppose the prevailing influence irrespective of whether the group is unanimous or divided. Analytical mean-field calculations and M…

Resolving the Structural Duality of Graphene Grain Boundaries
Haojie Guo, Emiliano Ventura-Mac\'ias, Mariano D. Jim\'enez-S\'anchez, Nicoleta Nicoara, Pierre Mallet, Jean-Yves Veuillen, Vincent T. Renard, Antonio J. Mart\'inez-Galera, Pablo Pou, Julio G\'omez-Herrero, Rub\'en P\'erez, Iv\'an Brihuega
https://

Resolving the Structural Duality of Graphene Grain Boundaries
Grain boundaries (GBs) are ubiquitous in large-scale graphene samples, playing a crucial role in their overall performance. Due to their complexity, they are usually investigated as model structures, under the assumption of a fully relaxed interface. Here, we present cantilever-based non-contact atomic force microscopy (ncAFM) as a suitable technique to resolve, atom by atom, the complete structure of these linear defects. Our experimental findings reveal a richer scenario than expected, with t…

A Pseudo-Hermitian Hybrid Model at Finite Temperature: The Role of the Exceptional Points
Ignacio Fushimi (IFLP-UNLP, Argentina), Marta Reboiro (IFLP-UNLP, Argentina)
https://arxiv.org/abs/2510.08773

A Pseudo-Hermitian Hybrid Model at Finite Temperature: The Role of the Exceptional Points
We study a hybrid system formed by an ensemble of colour nitrogen-vacancy centres in diamond interacting with a superconducting flux-qubit at finite temperature. The presence of impurities in the system is modelled through pseudo-hermitian Hamiltonian, by introducing an asymmetry parameter in the interaction between the superconducting flux qubit and the ensemble of colour nitrogen-vacancy centres in diamond. We construct the exact grand partition function of the system, and from it we derive t…

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Behnoud Shafiezadeh, Amir Mashmool, Farshad Eshghi, Manoochehr Kelarestaghi
https://arxiv.org/abs/2509.06868

A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition
Automatic License-Plate Recognition (ALPR) plays a pivotal role in Intelligent Transportation Systems (ITS) as a fundamental element of Smart Cities. However, due to its high variability, ALPR faces challenging issues more efficiently addressed by deep learning techniques. In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step, coupled with the state-of-the-art You-Only-Look-Once (YOLO)v5 object detection architectures for License-Pl…

The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
Lucy D'Agostino McGowan
https://arxiv.org/abs/2510.11633 https://ar…

The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
This paper provides clear and practical guidance on the specification of imputation models when multiple imputation is used in conjunction with doubly robust estimation methods for causal inference. Through theoretical arguments and targeted simulations, we show that when a confounder has missing data the corresponding imputation model must include all variables used in either the propensity score model or the outcome model, and that these variables must appear in the same functional form as in…

From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
Chengrui Xiang, Tengfei Ma, Xiangzheng Fu, Yiping Liu, Bosheng Song, Xiangxiang Zeng
https://arxiv.org/abs/2510.12181

From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, …

Gravity Well Echo Chamber Modeling With An LLM-Based Confirmation Bias Model
Joseph Jackson, Georgiy Lapin, Jeremy E. Thompson
https://arxiv.org/abs/2509.03832 https://

Gravity Well Echo Chamber Modeling With An LLM-Based Confirmation Bias Model
Social media echo chambers play a central role in the spread of misinformation, yet existing models often overlook the influence of individual confirmation bias. An existing model of echo chambers is the "gravity well" model, which creates an analog between echo chambers and spatial gravity wells. We extend this established model by introducing a dynamic confirmation bias variable that adjusts the strength of pull based on a user's susceptibility to belief-reinforcing content. This variable is …

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
Yusheng Zheng, Wenxue Liu, Yunhong Che, Ferdinand Grimm, Jingyuan Zhao, Xiaosong Hu, Simona Onori, Remus Teodorescu, Gregory J. Offer
https://arxiv.org/abs/2509.10380…

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity
Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for resource-efficient and scalable development of accurate, robust, and adaptive internal temperature estimation algorithms by blending physics-based modeling with machine learning, in order to address the key challenges in data collection, model parameterization…

Hadamard-Riemannian Optimization for Margin-Variance Ensemble
Zexu Jin
https://arxiv.org/abs/2509.10189 https://arxiv.org/pdf/2509.10189

Hadamard-Riemannian Optimization for Margin-Variance Ensemble
Ensemble learning has been widely recognized as a pivotal technique for boosting predictive performance by combining multiple base models. Nevertheless, conventional margin-based ensemble methods predominantly focus on maximizing the expected margin while neglecting the critical role of margin variance, which inherently restricts the generalization capability of the model and heightens its vulnerability to overfitting, particularly in noisy or imbalanced datasets. Additionally, the conventional…

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Qiya Yang, Xiaoxi Liang, Zeping Xiao, Yingjie Deng, Yalong Wang, Yongqi Liu, Han Li
https://arxiv.org/abs/2510.10127

Breaking the Likelihood Trap: Consistent Generative Recommendation with Graph-structured Model
Reranking, as the final stage of recommender systems, demands real-time inference, accuracy, and diversity. It plays a crucial role in determining the final exposure, directly influencing user experience. Recently, generative reranking has gained increasing attention for its strong ability to model complex dependencies among items. However, most existing methods suffer from the "likelihood trap", where high-likelihood sequences are often perceived as low-quality by humans. These models tend to …

Fascinating results from modelling different charging strategies (dumb, smart, #V2G) for 15 million BEVs in Germany in 2030, this research by Adeline Guéret, Carlos Gaete-Morales and @….
At 50% V2G, the system costs are lower than in the situation without EVs.

A moderate share of V2G outperforms large-scale smart charging of electric vehicles and benefits other consumers
While battery electric vehicles (BEVs) play a key role for decarbonizing the transport sector, their impact on the power sector heavily depends on their charging strategies. Here we systematically analyze various combinations between inflexible, smart and bidirectional (or vehicle-to-grid, V2G) charging of 15 million electric cars in Germany. Using a capacity expansion model, we find that even a moderate share of bidirectional charging below 30% leads to lower system costs than a fully smartly …

The Role of Asteroseismology in Understanding Mass Loss in Red Giants
Sibel \"Ortel, Mutlu Y{\i}ld{\i}z
https://arxiv.org/abs/2510.02931 https://arxiv…

The Role of Asteroseismology in Understanding Mass Loss in Red Giants
Red giant stars play a key role in advancing our understanding of stellar mass loss. However, its initial mass and the amount of mass lost during this phase remain uncertain. In this study, we investigate the asteroseismic signatures of mass loss and the parameters that influence it. We examine six stars identified as red giant branch (RGB) stars in the APOKASC-2 catalog. Assuming these stars are on their first ascent of the RGB, we construct interior models. The resulting model ages are signif…

Why does your graph neural network fail on some graphs? Insights from exact generalisation error
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
https://arxiv.org/abs/2509.10337

Why does your graph neural network fail on some graphs? Insights from exact generalisation error
Graph Neural Networks (GNNs) are widely used in learning on graph-structured data, yet a principled understanding of why they succeed or fail remains elusive. While prior works have examined architectural limitations such as over-smoothing and over-squashing, these do not explain what enables GNNs to extract meaningful representations or why performance varies drastically between similar architectures. These questions are related to the role of generalisation: the ability of a model to make acc…

Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning
Guozhi Liu, Qi Mu, Tiansheng Huang, Xinhua Wang, Li Shen, Weiwei Lin, Zhang Li
https://arxiv.org/abs/2510.10085

Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning
Harmful fine-tuning issues present significant safety challenges for fine-tuning-as-a-service in large language models. Existing alignment-stage defenses, e.g., Vaccine, Repnoise, Booster, and T-Vaccine, mitigate harmful fine-tuning issues by enhancing the model's robustness during the alignment phase. While these methods have been proposed to mitigate the issue, they often overlook a critical upstream factor: the role of the original safety-alignment data. We observe that their defense perform…

Does This Look Familiar to You? Knowledge Analysis via Model Internal Representations
Sihyun Park
https://arxiv.org/abs/2509.07311 https://arxiv.org/pdf/25…

Does This Look Familiar to You? Knowledge Analysis via Model Internal Representations
Recent advances in large language models (LLMs) have been driven by pretraining, supervised fine tuning (SFT), and alignment tuning. Among these, SFT plays a crucial role in transforming a model 's general knowledge into structured responses tailored to specific tasks. However, there is no clearly established methodology for effective training data selection. Simply increasing the volume of data does not guarantee performance improvements, while preprocessing, sampling, and validation require s…

Coarsening model of chromosomal crossover placement
Marcel Ernst, Riccardo Rossetto, David Zwicker
https://arxiv.org/abs/2509.09521 https://arxiv.org/pdf/2…

Coarsening model of chromosomal crossover placement
Chromosomal crossovers play a crucial role in meiotic cell division, as they ensure proper chromosome segregation and increase genetic variability. Experiments have consistently revealed two key observations across species: (i) the number of crossovers per chromosome is typically small, but at least one, and (ii) crossovers on the same chromosome are subject to interference, i.e., they are more separated than expected by chance. These observations can be explained by a recently proposed coarsen…

Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
Hyun-kyu Ko, Youbin Kim, Jihyeon Park, Dongheok Park, Gyeongjin Kang, Wonjun Cho, Hyung Yi, Eunbyung Park
https://arxiv.org/abs/2510.00862

Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
State Space Models (SSMs)-most notably RNNs-have historically played a central role in sequential modeling. Although attention mechanisms such as Transformers have since dominated due to their ability to model global context, their quadratic complexity and limited scalability make them less suited for long sequences. Video super-resolution (VSR) methods have traditionally relied on recurrent architectures to propagate features across frames. However, such approaches suffer from well-known issue…

To break, or not to break: Symmetries in adaptive quantum simulations, a case study on the Schwinger model
Karunya Shailesh Shirali, Kyle Sherbert, Yanzhu Chen, Adrien Florio, Andreas Weichselbaum, Robert D. Pisarski, Sophia E. Economou
https://arxiv.org/abs/2510.03083

To break, or not to break: Symmetries in adaptive quantum simulations, a case study on the Schwinger model
We investigate the role of symmetries in constructing resource-efficient operator pools for adaptive variational quantum eigensolvers. In particular, we focus on the lattice Schwinger model, a discretized model of $1+1$ dimensional electrodynamics, which we use as a proxy for spin chains with a continuum limit. We present an extensive set of simulations comprising a total of $11$ different operator pools, which all systematically and independently break or preserve a combination of discrete tra…

Reaction rates with temperature-dependent cross sections: A quantum dynamical microscopic model for the neutron capture reaction on the $^{188}$Os target
N. Lightfoot, A. Diaz-Torres, P. Stevenson
https://arxiv.org/abs/2509.12404

Reaction rates with temperature-dependent cross sections: A quantum dynamical microscopic model for the neutron capture reaction on the $^{188}$Os target
The neutron capture process plays a vital role in creating the heavy elements in the universe. The environments involved in these processes are, in general, high in temperature and are characterized by two distinct reaction mechanisms: the slow and rapid neutron capture processes. In this work, the slow neutron capture process is described with the time-dependent coupled channels wave-packet (TDCCWP) method that uses both a many-body nuclear potential and an initial temperature-dependent state …

Controlling the Glass Transition through Active Fluctuating Interactions
Emir Sezik, Henry Alston, Thibault Bertrand
https://arxiv.org/abs/2509.07619 https://

Controlling the Glass Transition through Active Fluctuating Interactions
Fluctuating pairwise interactions are understood to drive fluid-like states in dense biological systems. These states find a broad range of functionalities, such as directing growth during morphogenesis and forming aggregates with heightened mechanical response. However, a tractable model capturing the role of microscopic fluctuating interactions in these structural transitions is crucially lacking. Here, we study a $p$-spin model with fluctuating pairwise couplings (of strength $D_a$ and persi…

BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Rocktim Jyoti Das, Harsh Singh, Diana Turmakhan, Muhammad Abdullah Sohail, Mingfei Han, Preslav Nakov, Fabio Pizzati, Ivan Laptev
https://arxiv.org/abs/2510.08572

BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Scaling data and models has played a pivotal role in the remarkable progress of computer vision and language. Inspired by these domains, recent efforts in robotics have similarly focused on scaling both data and model size to develop more generalizable and robust policies. However, unlike vision and language, robotics lacks access to internet-scale demonstrations across diverse robotic tasks and environments. As a result, the scale of existing datasets typically suffers from the need for manual…

Exploring Large Language Model as an Interactive Sports Coach: Lessons from a Single-Subject Half Marathon Preparation
Kichang Lee
https://arxiv.org/abs/2509.26593 https://

Exploring Large Language Model as an Interactive Sports Coach: Lessons from a Single-Subject Half Marathon Preparation
Large language models (LLMs) are emerging as everyday assistants, but their role as longitudinal virtual coaches is underexplored. This two-month single subject case study documents LLM guided half marathon preparation (July-September 2025). Using text based interactions and consumer app logs, the LLM acted as planner, explainer, and occasional motivator. Performance improved from sustaining 2 km at 7min 54sec per km to completing 21.1 km at 6min 30sec per km, with gains in cadence, pace HR cou…

Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective
Xu Shang, Yang Zheng
https://arxiv.org/abs/2509.09027 https://arxiv.org/p…

Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective
This paper explores the role of regularization in data-driven predictive control (DDPC) through the lens of convex relaxation. Using a bi-level optimization framework, we model system identification as an inner problem and predictive control as an outer problem. Within this framework, we show that several regularized DDPC formulations, including l1-norm penalties, projection-based regularizers, and a newly introduced causality-based regularizer, can be viewed as convex relaxations of their resp…

Energetic spectra from semi-implicit particle-in-cell simulations of magnetic reconnection
K. M. Schoeffler, F. Bacchini, K. Kormann, B. Eichmann, M. E. Innocenti
https://arxiv.org/abs/2509.11100

Energetic spectra from semi-implicit particle-in-cell simulations of magnetic reconnection
Astrophysical observations suggest that magnetic reconnection in relativistic plasmas plays an important role in the acceleration of energetic particles. Modeling this accurately requires numerical schemes capable of addressing large scales and realistic magnetic field configurations without sacrificing the kinetic description needed to model particle acceleration self-consistently. We demonstrate the computational advantage of the relativistic semi-implicit method (RelSIM), which allows for re…

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Zecheng Tang, Baibei Ji, Quantong Qiu, Haitian Wang, Xiaobo Liang, Juntao Li, Min Zhang
https://arxiv.org/abs/2510.06915

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g., LLM agent, it becomes indispensable to evaluate whether a model's responses are not only high-quality but also grounded in and consistent with the provided context. Yet, current RMs remain confined to short-context settings and primarily focus on response-level attributes (e.g., safety or helpfulness), while largel…

Likelihood-based inference for the Gompertz model with Poisson errors
Paolo Onorati, Sofia Ruiz-Suarez, Radu Craiu
https://arxiv.org/abs/2510.06787 https://

Likelihood-based inference for the Gompertz model with Poisson errors
Population dynamics models play an important role in a number of fields, such as actuarial science, demography, and ecology. Statistical inference for these models can be difficult when, in addition to the process' inherent stochasticity, one also needs to account for sampling error. Ignoring the latter can lead to biases in the estimation, which in turn can produce erroneous conclusions about the system's behavior. The Gompertz model is widely used to infer population size dynamics, but a full…

Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms
Yushang Zhao, Yike Peng, Li Zhang, Qianyi Sun, Zhihui Zhang, Yingying Zhuang
https://arxiv.org/abs/2509.04751

Multimodal Foundation Model-Driven User Interest Modeling and Behavior Analysis on Short Video Platforms
With the rapid expansion of user bases on short video platforms, personalized recommendation systems are playing an increasingly critical role in enhancing user experience and optimizing content distribution. Traditional interest modeling methods often rely on unimodal data, such as click logs or text labels, which limits their ability to fully capture user preferences in a complex multimodal content environment. To address this challenge, this paper proposes a multimodal foundation model-based…

Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration
Tengwei Song, Min Wu, Yuan Fang
https://arxiv.org/abs/2510.07035 https:/…

Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration
Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the model effectively and prevent it from collapsing into a single modality, posing limitations in scenarios where a certain modality is unavailabl…

Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking
Brittany Harbison, Samuel Taubman, Travis Taylor, Ashok. K. Goel
https://arxiv.org/abs/2509.09583

Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking
Social connection is a vital part of learning, yet online course environments present barriers to the organic formation of social groups. SAMI offers one solution by facilitating student connections, but its effectiveness is constrained by an incomplete Theory of Mind, limiting its ability to create an effective mental model of a student. One facet of this is its inability to intuit personality, which may influence the relevance of its recommendations. To explore this, we propose a personality …

Universal behaviors of the multi-time correlation functions of random processes with renewal: the step noise case (the random velocity of a L\'evy walk)
Marco Bianucci, Mauro Bologna, Daniele Lagomarsino-Oneto, Riccardo Mannella
https://arxiv.org/abs/2510.11747

Universal behaviors of the multi-time correlation functions of random processes with renewal: the step noise case (the random velocity of a Lévy walk)
Stochastic processes with renewal properties are powerful tools for modeling systems where memory effects and long-time correlations play a significant role. In this work, we study a broad class of renewal processes where a variable's value changes according to a prescribed Probability Density Function (PDF), $p(ξ)$, after random waiting times $θ$. This model is relevant across many fields, including classical chaos, nonlinear hydrodynamics, quantum dots, cold atom dynamics, biological motion…

In the Queue: Understanding How Reddit Moderators Use the Modqueue
Tanvi Bajpai, Eshwar Chandrasekharan
https://arxiv.org/abs/2509.07314 https://arxiv.org/…

In the Queue: Understanding How Reddit Moderators Use the Modqueue
On Reddit, the moderation queue (modqueue) is a primary interface for moderators to review reported content. Despite its central role in Reddit's community-reliant moderation model, little is known about how moderators actually use it in practice. To address this gap, we surveyed 110 moderators, who collectively oversee more than 400 unique subreddits, and asked them about their usage of the modqueue. Modqueue practices vary widely: some moderators approach it as a daily checklist, others as a …

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
Yang Liu, Chaoyu Yuan
https://arxiv.org/abs/2509.04315 https://arxiv.org/pdf/2509…

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
Uplift models play a critical role in modern marketing applications to help understand the incremental benefits of interventions and identify optimal targeting strategies. A variety of techniques exist for building uplift models, and it is essential to understand the model differences in the context of intended applications. The uplift curve is a widely adopted tool for assessing uplift model performance on the selection universe when observations are available for the entire population. Howeve…

Variational Quantum Eigensolver Models of Molecular Quantum Dot Cellular Automata
Nischal Binod Gautam, Enrique P. Blair
https://arxiv.org/abs/2510.12656 https://

Variational Quantum Eigensolver Models of Molecular Quantum Dot Cellular Automata
Molecular quantum-dot Cellular Automata (QCA) may provide low-power, high-speed computational hardware for processing classical information. Simulation and modeling play an important role in the design of QCA circuits because fully-coherent models of QCA scale exponentially with the number of devices, and such models are severely limited in size. For larger circuits, approximations become necessary. In the era of fault-tolerant quantum computation, however, it may become possible to model large…

EfficientDepth: A Fast and Detail-Preserving Monocular Depth Estimation Model
Andrii Litvynchuk, Ivan Livinsky, Anand Ravi, Nima Kalantari, Andrii Tsarov
https://arxiv.org/abs/2509.22527

EfficientDepth: A Fast and Detail-Preserving Monocular Depth Estimation Model
Monocular depth estimation (MDE) plays a pivotal role in various computer vision applications, such as robotics, augmented reality, and autonomous driving. Despite recent advancements, existing methods often fail to meet key requirements for 3D reconstruction and view synthesis, including geometric consistency, fine details, robustness to real-world challenges like reflective surfaces, and efficiency for edge devices. To address these challenges, we introduce a novel MDE system, called Efficien…

ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
Rui Dong, Wentao Ouyang, Xiangzheng Liu
https://arxiv.org/abs/2509.07594 https://

ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction
Click-through rate (CTR) prediction plays an important role in online advertising systems. On the one hand, traditional CTR prediction models capture the collaborative signals in tabular data via feature interaction modeling, but they lose semantics in text. On the other hand, Large Language Models (LLMs) excel in understanding the context and meaning behind text, but they face challenges in capturing collaborative signals and they have long inference latency. In this paper, we aim to leverage …

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Yiming Lin, Yuchen Niu, Shang Wang, Kaizhu Huang, Qiufeng Wang, Xiao-Bo Jin
https://arxiv.org/abs/2508.21816

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Specifically, given an input image, the model must first classify the main visual events (verb classification), then identify the participating entities and their semantic roles (semantic role labeling), and finally localize these entities in the image (semantic role localization). Existing methods treat verb cl…

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Qiyuan Liu, Hao Xu, Xuhong Chen, Wei Chen, Yee Whye Teh, Ning Miao
https://arxiv.org/abs/2510.01925

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune LLMs during reinforcement learning (RL) and help select the best answer from multiple candidates during inference. In this paper, we provide a systematic introduction to RMs, along with a comprehensive survey of their applications in LLM reasoning. We first review fundamental concepts of RMs, including their architectures, training methodologies, an…

Mitigating Overthinking through Reasoning Shaping
Feifan Song, Shaohang Wei, Bofei Gao, Yejie Wang, Wen Luo, Wei Li, Linli Yao, Weimin Xiong, Liang Chen, Tianyu Liu, Houfeng Wang
https://arxiv.org/abs/2510.09535

Mitigating Overthinking through Reasoning Shaping
Large reasoning models (LRMs) boosted by Reinforcement Learning from Verifier Reward (RLVR) have shown great power in problem solving, yet they often cause overthinking: excessive, meandering reasoning that inflates computational cost. Prior designs of penalization in RLVR manage to reduce token consumption while often harming model performance, which arises from the oversimplicity of token-level supervision. In this paper, we argue that the granularity of supervision plays a crucial role in ba…

Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
Om Dobariya, Akhil Kumar
https://arxiv.org/abs/2510.04950 https://ar…

Mind Your Tone: Investigating How Prompt Politeness Affects LLM Accuracy (short paper)
The wording of natural language prompts has been shown to influence the performance of large language models (LLMs), yet the role of politeness and tone remains underexplored. In this study, we investigate how varying levels of prompt politeness affect model accuracy on multiple-choice questions. We created a dataset of 50 base questions spanning mathematics, science, and history, each rewritten into five tone variants: Very Polite, Polite, Neutral, Rude, and Very Rude, yielding 250 unique prom…