Tootfinder

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Sheng-Bin Duan, Fu-Chao Xie, Wen-Kai Wang, Si-Cheng Wang, Ling-Yun Li, Tian Tu, Zeng-Guang Hou
https://arxiv.org/abs/2506.20966…

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Vision-language-action (VLA) models extend vision-language models (VLM) by integrating action generation modules for robotic manipulation. Leveraging strengths of VLM in vision perception and instruction understanding, VLA models exhibit promising generalization across diverse manipulation tasks. However, applications demanding high precision and accuracy reveal performance gaps without further adaptation. Evidence from multiple domains highlights the critical role of post-training to align fou…

Data Efficacy for Language Model Training
Yalun Dai, Yangyu Huang, Xin Zhang, Wenshan Wu, Chong Li, Wenhui Lu, Shijie Cao, Li Dong, Scarlett Li
https://arxiv.org/abs/2506.21545 https://arxiv.org/pdf/2506.21545 https://arxiv.org/html/2506.21545
arXiv:2506.21545v1 Announce Type: new
Abstract: Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or optimal subset of training data. Techniques such as data filtering, sampling, and selection play a crucial role in this area. To complement it, we define Data Efficacy, which focuses on maximizing performance by optimizing the organization of training data and remains relatively underexplored. This work introduces a general paradigm, DELT, for considering data efficacy in LM training, which highlights the significance of training data organization. DELT comprises three components: Data Scoring, Data Selection, and Data Ordering. Among these components, we design Learnability-Quality Scoring (LQS), as a new instance of Data Scoring, which considers both the learnability and quality of each data sample from the gradient consistency perspective. We also devise Folding Ordering (FO), as a novel instance of Data Ordering, which addresses issues such as model forgetting and data distribution bias. Comprehensive experiments validate the data efficacy in LM training, which demonstrates the following: Firstly, various instances of the proposed DELT enhance LM performance to varying degrees without increasing the data scale and model size. Secondly, among these instances, the combination of our proposed LQS for data scoring and Folding for data ordering achieves the most significant improvement. Lastly, data efficacy can be achieved together with data efficiency by applying data selection. Therefore, we believe that data efficacy is a promising foundational area in LM training.
toXiv_bot_toot

Slip-flow theory for thermo-osmosis based on a kinetic model with near-wall potential
Tetsuro Tsuji, Koichiro Takita, Satoshi Taguchi
https://arxiv.org/abs/2506.20229

Slip-flow theory for thermo-osmosis based on a kinetic model with near-wall potential
In this paper, thermal-slip coefficients in slip boundary conditions of the Stokes equation are derived using the generalized slip-flow theory, with special interest in the role of near-wall potential in micro- and nanoscale flows. As the model of fluids and fluid-solid interaction, we employ the model Boltzmann equation for dilute gases and the diffuse-reflection boundaries with near-wall potential, respectively. It is found that, when the mean free path of gas molecules and the effective rang…

U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs
Racheal Mukisa, Arvind K. Bansal
https://arxiv.org/abs/2506.20689

U-R-VEDA: Integrating UNET, Residual Links, Edge and Dual Attention, and Vision Transformer for Accurate Semantic Segmentation of CMRs
Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links…

Continuous spin superparticle model
I. L. Buchbinder, S. A. Fedoruk
https://arxiv.org/abs/2506.19709 https://arxiv.org/pdf/2506.19709…

Continuous spin superparticle model
We construct a new model of a particle propagating in $4D$, ${\cal N}=1$ superspace that describes the dynamics of a continuous spin irreducible representation of the Poincaré supergroup. The model is characterized by two-component Weyl spinor additional even variables playing the role of extra coordinates. A canonical formulation, specific local fermionic $κ$-symmetry, and a compete system of bosonic and fermionic constraints are derived. All bosonic constrains are first-class, while fermion…

Model Reduction of Homogeneous Polynomial Dynamical Systems via Tensor Decomposition
Xin Mao, Can Chen
https://arxiv.org/abs/2506.19165 https://

Model Reduction of Homogeneous Polynomial Dynamical Systems via Tensor Decomposition
Model reduction plays a critical role in system control, with established methods such as balanced truncation widely used for linear systems. However, extending these methods to nonlinear settings, particularly polynomial dynamical systems that are often used to model higher-order interactions in physics, biology, and ecology, remains a significant challenge. In this article, we develop a novel model reduction method for homogeneous polynomial dynamical systems (HPDSs) with linear input and out…

This https://arxiv.org/abs/2503.03711 has been replaced.
initial toot: https://mastoxiv.page/@a…

The shear Alfvén continuum of quasisymmetric stellarators
The shear Alfvén wave (SAW) continuum plays a critical role in the stability of energetic particle-driven Alfvén eigenmodes. We develop a theoretical framework to analyze the SAW continuum in three-dimensional quasisymmetric magnetic fields, focusing on its implications for stellarator design. By employing a near-axis model and degenerate perturbation theory, the continuum equation is solved, highlighting unique features in 3D configurations, such as the interactions between spectral gaps. Nu…

This https://arxiv.org/abs/2404.04146 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Nonlocally coupled moisture model for convective self-aggregation
Clouds play a central role in climate physics by interacting with precipitation, radiation, and circulation. Despite being a fundamental issue in convective organization, the self-aggregation of clouds lacks a theoretical explanation due to its complexity. In this study, we introduce an idealized mathematical model where the system's state is represented solely by the vertically integrated water vapor content of atmospheric columns under the weak temperature gradient approximation. By analyzing…

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Pengyu Han, Zeyi Liu, Shijin Chen, Dongliang Zou, Xiao He
https://arxiv.org/abs/2506.17740

Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions across different operating conditions degrades model performance when a model trained under one condition is applied to others. With the recent advancements in deep learning, transfer learning has been introduced to the fault diagnosis field as a paradigm for addressing multi-condition fault diagnosis. Among these me…

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Zofia Pizo\'n, Shinji Kimijima, Grzegorz Brus
https://arxiv.org/abs/2506.17224

Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming
Hydrogen's role is growing as an energy carrier, increasing the need for efficient production, with methane steam reforming being the most widely used technique. This process is crucial for applications like fuel cells, where hydrogen is converted into electricity, pushing for reactor miniaturization and optimized process control through numerical simulations. Existing models typically address either kinetic or equilibrium regimes, limiting their applicability. Here we show a surrogate model ca…

Mott metal-insulator transition in a modified periodic Anderson model: Insights from entanglement entropy and role of short-range spatial correlations
Ankur Majumder, Sudeshna Sen
https://arxiv.org/abs/2506.16533

Mott metal-insulator transition in a modified periodic Anderson model: Insights from entanglement entropy and role of short-range spatial correlations
The Mott-Hubbard metal-insulator transition is a paradigmatic phenomenon where Coulomb interactions between electrons drive a metal-insulator phase transition. It has been extensively studied within the Hubbard model, where a quantum critical transition occurs at a finite temperature second-order critical point. This work investigates the Mott metal-insulator transition in a modified periodic Anderson model that may be viewed as a three-orbital lattice model including an interacting, localized …

Constraining parameters of spinor field dark energy: An alternative to $\Lambda$CDM model under the spherically symmetric FLRW space-time
Bijan Saha, Mahendra Goray
https://arxiv.org/abs/2506.16323

Constraining parameters of spinor field dark energy: An alternative to $Λ$CDM model under the spherically symmetric FLRW space-time
This study constrains the cosmological parameters within the scope of a spherically symmetric FLRW cosmological model, the role of a nonlinear spinor field in the universe's evolution. To test this approach, we incorporate the recent Cosmic chronometers, Supernova, and Sloan Digital Sky Survey data. It is found that if spherical coordinates give the FLRW model, the energy-momentum tensor (EMT) of the spinor field possesses nontrivial non-diagonal components. These non-diagonal components of EMT…

Role of bubble positioning in force induced melting of DNA
Bidisha Mukherjee, Amit Raj Singh, Garima Mishra
https://arxiv.org/abs/2506.18821 https://

Role of bubble positioning in force induced melting of DNA
We investigate the role of bubble positioning in the force-induced melting of double-stranded DNA using two distinct approaches: Brownian Dynamics simulations and the Gaussian Network Model. We isolate the effect of bubble positioning by using DNA molecules with 50% AT - 50% GC base-pair composition which ensures constant enthalpy. Our results reveal that it is not just the sequence itself, but its specific arrangement that influences DNA stability. We examine two types of DNA sequences contain…

Xray2Xray: World Model from Chest X-rays with Volumetric Context
Zefan Yang, Xinrui Song, Xuanang Xu, Yongyi Shi, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
https://arxiv.org/abs/2506.19055

Xray2Xray: World Model from Chest X-rays with Volumetric Context
Chest X-rays (CXRs) are the most widely used medical imaging modality and play a pivotal role in diagnosing diseases. However, as 2D projection images, CXRs are limited by structural superposition, which constrains their effectiveness in precise disease diagnosis and risk prediction. To address the limitations of 2D CXRs, this study introduces Xray2Xray, a novel World Model that learns latent representations encoding 3D structural information from chest X-rays. Xray2Xray captures the latent rep…

Collaborative Editable Model
Kaiwen Tang, Aitong Wu, Yao Lu, Guangda Sun
https://arxiv.org/abs/2506.14146 https://arxiv.org/pdf/2506.…

Collaborative Editable Model
Vertical-domain large language models (LLMs) play a crucial role in specialized scenarios such as finance, healthcare, and law; however, their training often relies on large-scale annotated data and substantial computational resources, impeding rapid development and continuous iteration. To address these challenges, we introduce the Collaborative Editable Model (CoEM), which constructs a candidate knowledge pool from user-contributed domain snippets, leverages interactive user-model dialogues c…

Modeling the uncertainty on the covariance matrix for probabilistic forecast reconciliation
Chiara Carrara, Lorenzo Zambon, Dario Azzimonti, Giorgio Corani
https://arxiv.org/abs/2506.19554

Modeling the uncertainty on the covariance matrix for probabilistic forecast reconciliation
In forecast reconciliation, the covariance matrix of the base forecasts errors plays a crucial role. Typically, this matrix is estimated, and then treated as known. In contrast, we propose a Bayesian reconciliation model that explicitly accounts for the uncertainty in the covariance matrix. We choose an Inverse-Wishart prior, which leads to a multivariate-t reconciled predictive distribution and allows a completely analytical derivation. Empirical experiments demonstrate that this approach impr…

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Masudul Hasan Masud Bhuiyan, Gianluca De Stefano, Giancarlo Pellegrino, Cristian-Alexandru Staicu
https://arxiv.org/abs/2506.18191

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Static analysis plays a key role in finding bugs, including security issues. A critical step in static analysis is building accurate call graphs that model function calls in a program. However, due to hard-to-analyze language features, existing call graph construction algorithms for JavaScript are neither sound nor complete. Prior work shows that even advanced solutions produce false edges and miss valid ones. In this work, we assist these tools by identifying missed call edges. Our main idea i…

The dominant role of jetting in micron- and sub-micron sea spray produced by bubble bursting: a revised model and comparison with measurements
Alfonso M. Ganan-Calvo, Miguel A. Herrada, Jose M. Lopez-Herrera
https://arxiv.org/abs/2506.17836

The dominant role of jetting in micron- and sub-micron sea spray produced by bubble bursting: a revised model and comparison with measurements
The primary physical mechanism governing the production of sub-micron sea spray aerosols (SSA) -- specifically the competition between film and jet droplets from bubble bursting -- has remained a subject of intense debate. This work presents a revised, first-principles model that establishes the overwhelming dominance of jetting for producing micron and sub-micron aerosols. Our approach first rules out the film droplet mechanism as a primary contributor for this size range by demonstrating thro…

Validity of generalized Gibbs ensemble in a random matrix model with a global $\mathbb{Z}_2$-symmetry
Adway Kumar Das
https://arxiv.org/abs/2506.16176 http…

Validity of generalized Gibbs ensemble in a random matrix model with a global $\mathbb{Z}_2$-symmetry
$\mathbb{Z}_2$ symmetry is ubiquitous in quantum mechanical systems where it drives various phase transitions and emergent physics. To understand the role of $\mathbb{Z}_2$ symmetry in the thermalization of a local observable in a disordered system, we consider random symmetric centrosymmetric (SC) matrices where the exchange symmetry is conserved. Such a conservation law splits the Hilbert space into decoupled subspaces such that the energy spectrum of a SC matrix is a superposition of two pur…

Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
Anna Hamberger, Sebastian Murgul, Jochen Schmidt, Michael Heizmann
https://arxiv.org/abs/2506.14223

Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges…

Exploring the effect of spatial scales in studying urban mobility pattern
Hoai Nguyen Huynh
https://arxiv.org/abs/2506.16762 https://…

Exploring the effect of spatial scales in studying urban mobility pattern
Urban mobility plays a crucial role in the functioning of cities, influencing economic activity, accessibility, and quality of life. However, the effectiveness of analytical models in understanding urban mobility patterns can be significantly affected by the spatial scales employed in the analysis. This paper explores the impact of spatial scales on the performance of the gravity model in explaining urban mobility patterns using public transport flow data in Singapore. The model is evaluated ac…

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman, Jean Lahoud, Mustansar Fiaz, Hisham Cholakkal
https://arxiv.org/abs/2506.12208

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreove…

Dynamics of Multiphase Carbon in the Turbulent Circumgalactic Medium
Yue Hu, Evan Scannapieco, Edward Buie II, Siyao Xu, Samuel T Sebastian, Om Biswal
https://arxiv.org/abs/2506.17554

Dynamics of Multiphase Carbon in the Turbulent Circumgalactic Medium
The circumgalactic medium (CGM) plays a crucial role in regulating material and energy exchange between galaxies and their environments. The best means of observing this medium is through absorption-line spectroscopy, but we have yet to develop a consistent physical model that fully explains these results. Here we investigate the impact of turbulence and non-equilibrium chemistry on the properties of the CGM, using three-dimensional hydrodynamic simulations that include the impact of an ionizin…

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang, Kowei Shih, Dannier Li, Chengrui Zhou
https://arxiv.org/abs/2506.11421

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined s…

Coriolis force acting on near-surface horizontal flows during simulations of flux emergence produces a tilt angle consistent with Joy's law on the Sun
William Roland-Batty, Hannah Schunker, Robert H. Cameron, Damien Przybylski, Laurent Gizon, David I. Pontin
https://arxiv.org/abs/2506.15935

Coriolis force acting on near-surface horizontal flows during simulations of flux emergence produces a tilt angle consistent with Joy's law on the Sun
Joy's law describes the tilt of bipolar active regions on the Sun away from an east-west orientation, where the flux of the polarity concentrated at the prograde side tends to be closer to the equator than the polarity on the retrograde side. Joy's law is attributed to the Coriolis force because of the observed increase in tilt angle at higher latitudes. This tilt plays a crucial role in some solar dynamo models. Our goal is to model the effects of the Coriolis force on a flux tube as it rises …

A simulation model investigation of neutron-oxygen inelastic scattering and subsequent nucleus deexcitation based on experimental data
Y. Hino, Y. Ashida, T. Tano, Y. Koshio
https://arxiv.org/abs/2506.14388

A simulation model investigation of neutron-oxygen inelastic scattering and subsequent nucleus deexcitation based on experimental data
The hadron interaction model for light nuclei plays an important role in understanding the event topology of neutrino-nucleus interaction in a large-scale neutrino detector. In the Super-Kamiokande experiment, the neutral current quasi-elastic interaction induced by atmospheric neutrinos introduces 68-82% uncertainty in the search for diffuse supernova neutrino background. This uncertainty stems from the mismodeling of the interaction between oxygen and fast neutrons generated by the neutral cu…

The Impact of $\Omega_{m0}$ Prior Bias on Cosmological Parameter Estimation: Reconciling DESI DR2 BAO and Pantheon SNe Data Combination Results
Seokcheon Lee
https://arxiv.org/abs/2506.16022

The Impact of $Ω_{m0}$ Prior Bias on Cosmological Parameter Estimation: Reconciling DESI DR2 BAO and Pantheon+ SNe Data Combination Results
Recent cosmological parameter analyses combining DESI DR2 Baryon Acoustic Oscillation (BAO) data with external probes, such as Pantheon+ Supernovae (SNe) observations, have reported deviations of the dark energy equation-of-state parameters ($\oo, \oa$) from the standard $Λ$CDM model predictions ($\oo=-1, \oa=0$). A notable aspect of these results is the role of $\Omo$ prior information from SNe, which is known to exhibit tension with BAO-only constraints. In this study, we rigorously investig…

On the existence of consistent adversarial attacks in high-dimensional linear classification
Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro
https://arxiv.org/abs/2506.12454

On the existence of consistent adversarial attacks in high-dimensional linear classification
What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a central role. We introduce a new error metric that precisely capture this distinction, quantifying model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth l…

The role of small-angle electron-electron scattering in transverse magnetic focusing experiment
Dmitry A. Egorov, Dmitriy A. Pokhabov, Evgeny Yu. Zhdanov, Andrey A. Shevyrin, Askhat K. Bakarov, Alexander A. Shklyaev, Arthur G. Pogosov
https://arxiv.org/abs/2506.09432

The role of small-angle electron-electron scattering in transverse magnetic focusing experiment
We demonstrate the crucial role of small-angle scattering in transverse magnetic focusing (TMF) in ballistic GaAs/AlGaAs heterostructures. Measurements in various samples show that the role significantly depends on their geometry. We propose a phenomenological model parameterizing this dependence with the angular acceptance of the detecting contact. This model is consistent with the diversity of experimental data and therefore enables accurate extraction of the key characteristic of inter-elect…

Price equilibria with positive margins in loyal-strategic markets with discrete prices
Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, Priyank Sinha
https://arxiv.org/abs/2506.17239

Price equilibria with positive margins in loyal-strategic markets with discrete prices
In competitive supply chains (SCs), pricing decisions are crucial, as they directly impact market share and profitability. Traditional SC models often assume continuous pricing for mathematical convenience, overlooking the practical reality of discrete price increments driven by currency constraints. Additionally, customer behavior, influenced by loyalty and strategic considerations, plays a significant role in purchasing decisions. To address these gaps, this study examines a SC model involvin…

Role of topotactic hydrogen in Superconductivity of Infinite-layer Nickelate NdNiO$_{2}$: A first-principles and variational Monte Carlo study
Manoj Gupta, Arun Kumar Maurya, Amal Medhi, Tanusri Saha Dasgupta
https://arxiv.org/abs/2506.13399

Role of topotactic hydrogen in Superconductivity of Infinite-layer Nickelate NdNiO$_{2}$: A first-principles and variational Monte Carlo study
Employing combination of first-principles calculations, low-energy model construction, and variational Monte Carlo solution of the ab-initio derived Hubbard model, we study the effect of hydrogenation in the electronic structure and superconducting properties of infinite-layer nickelate, NdNiO$_2$. We find that the introduction of hydrogen at the apical oxygen vacancy position strongly influences the Wannier function corresponding to the effective interstitial orbital at the Ni site bound to th…

This https://arxiv.org/abs/2504.09546 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

A simulation-heuristics dual-process model for intuitive physics
The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time. While mental simulation accurately captured human judgments in simpler scenarios, a linear heuristic model better matched human predictions when simu…

Quantifying Flow State Dynamics: A Prefrontal Cortex EEG-Based Model Validation Study. Unveiling the Prefrontal Cortex's Role in Flow State Experience: An Empirical EEG Analysis
Gianluca Rosso, Raffaella Ricci, Lorenzo Pia, Giovanni Rebaudo, Michele Guindani, Alberto Marocchino, Giorgio De Pieri, Andrea Filippo Rosso
https://

Quantifying Flow State Dynamics: A Prefrontal Cortex EEG-Based Model Validation Study. Unveiling the Prefrontal Cortex's Role in Flow State Experience: An Empirical EEG Analysis
This article aims to explore the optimization of mental performance through the analysis of metrics associated with the psychological state known as flow. Several clinical studies have shown a correlation between the mental state of flow (characterized by deep and relaxed concentration and high psychophysical efficiency) and brain activity measured through electroencephalography (EEG). This study confirms such a correlation, focusing in particular on the sports field, where the flow state tends…

Dark energy and QCD instanton vacuum in Friedmann-Lemaitre-Robertson-Walker universe
M. M. Musakhanov
https://arxiv.org/abs/2506.13128 https://

Dark energy and QCD instanton vacuum in Friedmann-Lemaitre-Robertson-Walker universe
The standard model of the universe, lambdaCDM, is based on the Friedmann-Lemaitre-Robertson-Walker metric with flat 3-dim coordinate space and Friedmann equations. The cosmological constant lambda is providing the cancellations of the matter fields contributions in the flat space. The dynamical dark energy is appearing on over of the vacuum energy of matter fields at the flat space. Within the Standard Model the gluon Yang-Mills (YM) fields are playing the special role since specific properties…

Hatevolution: What Static Benchmarks Don't Tell Us
Chiara Di Bonaventura, Barbara McGillivray, Yulan He, Albert Mero\~no-Pe\~nuela
https://arxiv.org/abs/2506.12148

Hatevolution: What Static Benchmarks Don't Tell Us
Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speec…

This https://arxiv.org/abs/2505.01199 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning
Medical audio signals, such as heart and lung sounds, play a crucial role in clinical diagnosis. However, analyzing these signals remains challenging: traditional methods rely on handcrafted features or supervised deep learning models that demand extensive labeled datasets, limiting their scalability and applicability. To address these issues, we propose CaReAQA, an audio-language model that integrates a foundation audio model with the reasoning capabilities of large language models, enabling c…

The Role of Connection Density in an Adaptive Network with Chaotic Units
Ramiro Pl\"uss, Pablo Mart\'in Gleiser
https://arxiv.org/abs/2505.11437 h…

The Role of Connection Density in an Adaptive Network with Chaotic Units
We investigate the role of connection density in an adaptive network model of chaotic units that dynamically rewire based on their internal states and local coherence. By systematically varying the network's connectivity density, we uncover distinct dynamical regimes and structural transitions, revealing mechanisms of spontaneous modularity, dynamical segregation, and integration. We find that at higher densities, the network exhibits both local clustering and global synchrony. Additionally, we…

Joint ASR and Speaker Role Tagging with Serialized Output Training
Anfeng Xu, Tiantian Feng, Shrikanth Narayanan
https://arxiv.org/abs/2506.10349 https://

Joint ASR and Speaker Role Tagging with Serialized Output Training
Automatic Speech Recognition systems have made significant progress with large-scale pre-trained models. However, most current systems focus solely on transcribing the speech without identifying speaker roles, a function that is critical for conversational AI. In this work, we investigate the use of serialized output training (SOT) for joint ASR and speaker role tagging. By augmenting Whisper with role-specific tokens and fine-tuning it with SOT, we enable the model to generate role-aware trans…

How universal is the mean-field universality class for percolation in complex networks?
Lorenzo Cirigliano
https://arxiv.org/abs/2506.17175 https://…

How universal is the mean-field universality class for percolation in complex networks?
Clustering and degree correlations are ubiquitous in real-world complex networks. Yet, understanding their role in critical phenomena remains a challenge for theoretical studies. Here, we provide the exact solution of site percolation in a model for strongly clustered random graphs, with many overlapping loops and heterogeneous degree distribution. We systematically compare the exact solution with mean-field predictions obtained from a treelike random rewiring of the network, which preserves on…

Evolutionary model for energy trading in community microgrids using Hawk-Dove strategies
Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Ionut Anghel
https://arxiv.org/abs/2506.06325

Evolutionary model for energy trading in community microgrids using Hawk-Dove strategies
This paper proposes a decentralized model of energy cooperation between microgrids, in which decisions are made locally, at the level of the microgrid community. Each microgrid is modeled as an autonomous agent that adopts a Hawk or Dove strategy, depending on the level of energy stored in the battery and its role in the energy trading process. The interactions between selling and buying microgrids are modeled through an evolutionary algorithm. An individual in the algorithm population is repre…

Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Anarya Ray
https://arxiv.org/abs/2506.05657

Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations. However, realistic simulations are costly to implement for a dense grid of initial conditions. Normalizing flows can emulate the distribution functions of a simulated population of binary parameters and thereby enable empirical constraints on the astrophysical initial conditions and branching fractions of variou…

MobileHolo: A Lightweight Complex-Valued Deformable CNN for High-Quality Computer-Generated Hologram
Xie Shuyang, Zhou Jie, Xu Bo, Wang Jun, Xu Renjing
https://arxiv.org/abs/2506.14542

MobileHolo: A Lightweight Complex-Valued Deformable CNN for High-Quality Computer-Generated Hologram
Holographic displays have significant potential in virtual reality and augmented reality owing to their ability to provide all the depth cues. Deep learning-based methods play an important role in computer-generated holograms (CGH). During the diffraction process, each pixel exerts an influence on the reconstructed image. However, previous works face challenges in capturing sufficient information to accurately model this process, primarily due to the inadequacy of their effective receptive fiel…

When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Junghyun Min, Xiulin Yang, Shira Wein
https://arxiv.org/abs/2506.14613 https://

When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Natural Language Inference (NLI) relies heavily on adequately parsing the semantic content of the premise and hypothesis. In this work, we investigate whether adding semantic information in the form of an Abstract Meaning Representation (AMR) helps pretrained language models better generalize in NLI. Our experiments integrating AMR into NLI in both fine-tuning and prompting settings show that the presence of AMR in fine-tuning hinders model generalization while prompting with AMR leads to sligh…

The Dynamic Role of Aerosol and Exudate Transport in the Diffusion of Lung Infection in Respiratory Infectious Diseases (taking SARS-CoV-2 as an example): A Hypothesis Model
Shi Qiru
https://arxiv.org/abs/2505.24057

The Dynamic Role of Aerosol and Exudate Transport in the Diffusion of Lung Infection in Respiratory Infectious Diseases (taking SARS-CoV-2 as an example): A Hypothesis Model
This paper proposes a hypothetical model for the dual role of respiratory aerosols and inflammatory exudates in the dynamics and progression of SARS-CoV-2 lung infection. Starting from a new paradigm in infectious disease transmission, we reflect on the often-overlooked role of physical transmission media within the host individual. The hypothesis posits that tiny aerosols (including those inhaled externally and those self-generated and re-inhaled by the host) play a crucial role in the initial…

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Huong Nguyen, Hong-Tri Nguyen, Praveen Kumar Donta, Susanna Pirttikangas, Lauri Lov\'en
https://arxiv.org/abs/2506.10607

Graph-based Gossiping for Communication Efficiency in Decentralized Federated Learning
Federated learning has emerged as a privacy-preserving technique for collaborative model training across heterogeneously distributed silos. Yet, its reliance on a single central server introduces potential bottlenecks and risks of single-point failure. Decentralizing the server, often referred to as decentralized learning, addresses this problem by distributing the server role across nodes within the network. One drawback regarding this pure decentralization is it introduces communication ineff…

AGNI: A radiative-convective model for lava planet atmospheres
Harrison Nicholls, Raymond Pierrehumbert, Tim Lichtenberg
https://arxiv.org/abs/2506.00091 h…

AGNI: A radiative-convective model for lava planet atmospheres
It is important that we are able to accurately model the atmospheres of (exo)planets. This is because atmospheres play a central role in setting a planet's thermochemical environment at a given point in time, and also in regulating how it evolves over geological timescales. Additionally, it is primarily by observation of their atmospheres that we are able to characterise exoplanets. There is particular demand for accurate models in the context of so-called lava worlds: planets with molten inter…

The Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
Weipeng Jiang, Xiaoyu Zhang, Xiaofei Xie, Jiongchi Yu, Yuhan Zhi, Shiqing Ma, Chao Shen
https://arxiv.org/abs/2506.12320

The Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
Large Language Model (LLM) libraries have emerged as the foundational infrastructure powering today's AI revolution, serving as the backbone for LLM deployment, inference optimization, fine-tuning, and production serving across diverse applications. Despite their critical role in the LLM ecosystem, these libraries face frequent quality issues and bugs that threaten the reliability of AI systems built upon them. To address this knowledge gap, we present the first comprehensive empirical investig…

Aggregation of vortex structures in 2D: the blob-wave system and its role in zonal flows
Franco Flandoli, Matteo Palmieri, Milo Viviani
https://arxiv.org/abs/2505.22700

Aggregation of vortex structures in 2D: the blob-wave system and its role in zonal flows
We give a rigorous mathematical result, supported by accurate numerical simulations, of the aggregation of a concentrated vortex blob with an underlying non constant vorticity field: the blob moves in the direction of the gradient of the field. It is a unique example of Lagrangian explanation of aggregation of vortex structures of the same sign in 2D inviscid fluids. A conceptual model of zonal flow maintenance and modulation based on this idea is then developed, which produces shapes in accord…

System Prompt Extraction Attacks and Defenses in Large Language Models
Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
https://arxiv.org/abs/2505.23817 https…

System Prompt Extraction Attacks and Defenses in Large Language Models
The system prompt in Large Language Models (LLMs) plays a pivotal role in guiding model behavior and response generation. Often containing private configuration details, user roles, and operational instructions, the system prompt has become an emerging attack target. Recent studies have shown that LLM system prompts are highly susceptible to extraction attacks through meticulously designed queries, raising significant privacy and security concerns. Despite the growing threat, there is a lack of…

Memory-Driven Bounded Confidence Opinion Dynamics: A Hegselmann-Krause Model Based on Fractional-Order Methods
Meiru Jiang, Wei Su, Guojian Ren, Yongguang Yu
https://arxiv.org/abs/2506.04701

Memory-Driven Bounded Confidence Opinion Dynamics: A Hegselmann-Krause Model Based on Fractional-Order Methods
Memory effects play a crucial role in social interactions and decision-making processes. This paper proposes a novel fractional-order bounded confidence opinion dynamics model to characterize the memory effects in system states. Building upon the Hegselmann-Krause framework and fractional-order difference, a comprehensive model is established that captures the persistent influence of historical information. Through rigorous theoretical analysis, the fundamental properties including convergence …

Mathematical Modeling of Kelvin-Helmholtz Instability at Tangential Discontinuities in Partially Ionized Plasma: Application to Heliopause Dynamics
A. V. Titova, S. D. Korolkov, V. V. Izmodenov
https://arxiv.org/abs/2506.14343

Mathematical Modeling of Kelvin-Helmholtz Instability at Tangential Discontinuities in Partially Ionized Plasma: Application to Heliopause Dynamics
The stability of the heliopause, the tangential discontinuity separating the solar wind from the interstellar medium, is influenced by various processes, including the Kelvin-Helmholtz instability. This study investigates the role of charge exchange collisions between protons and hydrogen (H) atoms in reduction of the Kelvin-Helmholtz growth rate at the heliopause. Using a two-dimensional gasdynamic model with the inclusion of H atoms, we perform numerical simulations of the plasma flow near th…

Matter symmetries in supersymmetric standard models from non-invertible selection rules
Tatsuo Kobayashi, Hironobu Mita, Hajime Otsuka, Riku Sakuma
https://arxiv.org/abs/2506.10241

Matter symmetries in supersymmetric standard models from non-invertible selection rules
We discuss phenomenological implications of non-invertible selection rules in the framework of the supersymmetric standard model. We find that a remnant $\mathbb{Z}_2$ symmetry of fusion algebras which holds at all-loop order plays the role of $R$-parity, forbidding baryon and lepton violating operators. In addition, a combination of Standard Model gauge symmetry and the non-invertible selection rules lead to baryon triality and proton hexality that can protect the proton from decay. In general…

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Yifan Hao, Xingyuan Pan, Hanning Zhang, Chenlu Ye, Rui Pan, Tong Zhang
https://arxiv.org/abs/2506.01901

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Supervised fine-tuning (SFT) on domain-specific data is the dominant approach for adapting foundation models to specialized tasks. However, it has been observed that SFT models tend to forget knowledge acquired during pretraining. In vision models, ensembling a pretrained model with its fine-tuned counterpart has been shown to mitigate this issue. In this work, we demonstrate that the same holds for language models, and, more strikingly, we observe an overadaptation phenomenon: the ensemble mod…

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Vu Thi Huong, Ida Litzel, Thorsten Koch
https://arxiv.org/abs/2506.04045

Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives
Fuzzy clustering, which allows an article to belong to multiple clusters with soft membership degrees, plays a vital role in analyzing publication data. This problem can be formulated as a constrained optimization model, where the goal is to minimize the discrepancy between the similarity observed from data and the similarity derived from a predicted distribution. While this approach benefits from leveraging state-of-the-art optimization algorithms, tailoring them to work with real, massive dat…

Towards Structure-aware Model for Multi-modal Knowledge Graph Completion
Linyu Li, Zhi Jin, Yichi Zhang, Dongming Jin, Chengfeng Dou, Yuanpeng He, Xuan Zhang, Haiyan Zhao
https://arxiv.org/abs/2505.21973

Towards Structure-aware Model for Multi-modal Knowledge Graph Completion
Knowledge graphs (KGs) play a key role in promoting various multimedia and AI applications. However, with the explosive growth of multi-modal information, traditional knowledge graph completion (KGC) models cannot be directly applied. This has attracted a large number of researchers to study multi-modal knowledge graph completion (MMKGC). Since MMKG extends KG to the visual and textual domains, MMKGC faces two main challenges: (1) how to deal with the fine-grained modality information interacti…

The exchange-correlation dipole moment dispersion method
Kyle R Bryenton, Erin R Johnson
https://arxiv.org/abs/2506.02352 https://arx…

The exchange-correlation dipole moment dispersion method
Density-functional theory (DFT) has become the workhorse of modern computational chemistry, with dispersion corrections such as the exchange-hole dipole moment (XDM) model playing a key role in high-accuracy modelling of large-scale systems. Here, we introduce a new physics-guided XDM variant, termed the exchange-correlation dipole moment (XCDM) model, which supplements XDM with same- and opposite-spin dynamical correlation terms, substantially improving accuracy for molecular $C_6$ dispersion …

This https://arxiv.org/abs/2505.17282 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Attention with Trained Embeddings Provably Selects Important Tokens
Token embeddings play a crucial role in language modeling but, despite this practical relevance, their theoretical understanding remains limited. Our paper addresses the gap by characterizing the structure of embeddings obtained via gradient descent. Specifically, we consider a one-layer softmax attention model with a linear head for binary classification, i.e., $\texttt{Softmax}( p^\top E_X^\top ) E_X v = \frac{ \sum_{i=1}^T \exp(p^\top E_{x_i}) E_{x_i}^\top v}{\sum_{j=1}^T \exp(p^\top E_{x_{j…

This https://arxiv.org/abs/2307.14984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents
Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$y…

This https://arxiv.org/abs/2410.03874 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A differentiable N-body code for transit timing and dynamical modelling -- II. Photodynamics
Exoplanet transits contain substantial information about the architecture of a system. By fitting transit light curves we can extract dynamical parameters and place constraints on the properties of the planets and their host star. Having a well-defined probabilistic model plays a crucial role in making robust measurements of these parameters, and the ability to differentiate the model provides access to more robust inference tools. Gradient-based inference methods can allow for more rapid and a…

This https://arxiv.org/abs/2310.00468 has been replaced.
link: https://scholar.google.com/scholar?q=a

When Should a Leader Act Suboptimally? The Role of Inferability in Repeated Stackelberg Games
When interacting with other decision-making agents in non-adversarial scenarios, it is critical for an autonomous agent to have inferable behavior: The agent's actions must convey their intention and strategy. We model the inferability problem using Stackelberg games with observations where a leader and a follower repeatedly interact. During the interactions, the leader uses a fixed mixed strategy. The follower does not know the leader's strategy and dynamically reacts to the statistically infe…

Assessing Future Wind Energy Potential under Climate Change: The Critical Role of Multi-Model Ensembles in Robustness Assessment
Andrea Lira-Loarca, Francesco Ferrari, Andrea Mazzino
https://arxiv.org/abs/2505.24463

Assessing Future Wind Energy Potential under Climate Change: The Critical Role of Multi-Model Ensembles in Robustness Assessment
Accurate projections of wind energy potential under climate change are critical for effective long-term energy planning. While previous studies have highlighted the value of multi-model ensembles, they often fall short in capturing the full spectrum of uncertainties and temporal dynamics relevant to wind resource reliability. This paper presents one of the most comprehensive assessments to date, leveraging a large ensemble of 21 high-resolution RCM-GCM combinations from the EURO-CORDEX initiati…

Consciousness via MIPT?
Alexander Gorsky
https://arxiv.org/abs/2506.04875 https://arxiv.org/pdf/2506.04875

Consciousness via MIPT?
The measurement-induced phase transition (MIPT) is a recently formulated phenomenon in out-of-equilibrium systems. The competition between unitary evolutions and measurement-induced non-unitaries leads to the transition between the entangled and disentangled phases at some critical measurement rate. We conjecture that self-organized MIPT plays a key role in the generative model of cognitive networks and the formation of the state of consciousness in the "newborn-adult" transition. To this aim, …

Nanodroplets Condensation on Solid Surfaces
Matteo Teodori, Dario Abbondanza, Mirko Gallo, Carlo Massimo Casciola
https://arxiv.org/abs/2506.12565 https://…

Nanodroplets Condensation on Solid Surfaces
This paper deals with the condensation of liquid droplets on hydrophobic and hydrophilic surfaces. A stochastic mesoscale model based on the theory of fluctuating hydrodynamics and the thermodynamics of a diffuse interface approach shows how direct simulation of the vapour-liquid transition from the nucleation process to droplet hydrodynamics can be achieved. Such simulations explain the role of wettability in filmwise and dropwise condensation regimes and the main limitations of classical nucl…

Transport Theory and Correlation Measurements: Coming to Terms on Emission Sources
Pierre Nzabahimana, Pawel Danielewicz, Giuseppe Verde
https://arxiv.org/abs/2506.01271

Transport Theory and Correlation Measurements: Coming to Terms on Emission Sources
Two-particle correlations play a pivotal role in understanding the space-time characteristics of particle emission in Heavy-ion collisions. These characteristics are typically represented by a relative emission source and can be obtained using transport model simulations such as the Boltzmann- Uehling-Uhlenbeck (BUU) transport model. In this paper, we utilize the BUU transport model to simulate the p-p source. Subsequently, we integrate this source and the p-p kernel within the KP formula to ca…

This https://arxiv.org/abs/2503.15391 has been replaced.
initial toot: https://mastoxiv.page/@a…

Modeling crystal defects using defect-informed neural networks
Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges for traditional ML models. Here, we introduce Defect-Informed Equivariant Graph Neural Network (DefiNet), a model specifically designed to accurately capture defect-related interactions and geometric configura…

This https://arxiv.org/abs/2406.19157 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

How to build your latent Markov model -- the role of time and space
Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of model formulations, the inconsistent use of terminology, and the various inferential approaches and software packages can be overwhelming to practitioners, especially when they are new to this area. With this review-like paper, we thus aim to provide guidance for both statisticians and practitioners working with latent…

This https://arxiv.org/abs/2405.16643 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

The Hamilton-Jacobi-Bellman Equation in Economic Dynamics with a Non-Smooth Fiscal Policy
We consider a class of economic growth models that includes the classical Ramsey--Cass--Koopmans capital accumulation model and verify that, under several assumptions, the value function of the model is the unique viscosity solution to the Hamilton--Jacobi--Bellman equation. Moreover, we discuss a solution method for these models using differential inclusion, where the subdifferential of the value function plays an important role. Next, we present an assumption under which the value function is…

On the role of non-linear latent features in bipartite generative neural networks
Tony Bonnaire, Giovanni Catania, Aur\'elien Decelle, Beatriz Seoane
https://arxiv.org/abs/2506.10552

On the role of non-linear latent features in bipartite generative neural networks
We investigate the phase diagram and memory retrieval capabilities of bipartite energy-based neural networks, namely Restricted Boltzmann Machines (RBMs), as a function of the prior distribution imposed on their hidden units - including binary, multi-state, and ReLU-like activations. Drawing connections to the Hopfield model and employing analytical tools from statistical physics of disordered systems, we explore how the architectural choices and activation functions shape the thermodynamic pro…

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Francesco Giannino, Annalisa Iuorio, Cinzia Soresina
https://arxiv.org/abs/2506.03981

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Many mathematical models describing vegetation patterns are based on biomass--water interactions, due to the impact of this limited resource in arid and semi-arid environments. However, in recent years, a novel biological factor called autotoxicity has proved to play a key role in vegetation spatiotemporal dynamics, particularly by inhibiting biomass growth and increasing its natural mortality rate. In a standard reaction-diffusion framework, biomass-toxicity dynamics alone are unable to suppor…

This https://arxiv.org/abs/2411.06564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Distributionally Robust Adaptive Beamforming
As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model or the data size of snapshots is limited, the performance of beamformers significantly degrades. In this article, we comprehensively study the conceptual system, theoretical analysis, and algorithmic design for robust beamforming against uncertainties in the assumed snapshot or…

Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model
Haibin Wu, Yuxuan Hu, Ruchao Fan, Xiaofei Wang, Kenichi Kumatani, Bo Ren, Jianwei Yu, Heng Lu, Lijuan Wang, Yao Qian, Jinyu Li
https://arxiv.org/abs/2506.04518

Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model
Speech language models (Speech LMs) enable end-to-end speech-text modelling within a single model, offering a promising direction for spoken dialogue systems. The choice of speech-text jointly decoding paradigm plays a critical role in performance, efficiency, and alignment quality. In this work, we systematically compare representative joint speech-text decoding strategies-including the interleaved, and parallel generation paradigms-under a controlled experimental setup using the same base lan…

A First Look at Bugs in LLM Inference Engines
Mugeng Liu, Siqi Zhong, Weichen Bi, Yixuan Zhang, Zhiyang Chen, Zhenpeng Chen, Xuanzhe Liu, Yun Ma
https://arxiv.org/abs/2506.09713

A First Look at Bugs in LLM Inference Engines
Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this…

This https://arxiv.org/abs/2505.09999 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Practical workloads play an essential role in this process: they are critical for motivating and benchmarking serving techniques and systems. However, the existing understanding of real-world LLM serving workloads is limited due to the lack of a comprehensive workload characterization. Prior analyses remain insufficient …

Functional data decomposition reveals unexpectedly strong soil moisture-precipitation coupling over the Great Plains
Yifu Gao, Runze Li, Efi Foufoula-Georgiou, Jasper A. Vrugt
https://arxiv.org/abs/2506.13939

Functional data decomposition reveals unexpectedly strong soil moisture-precipitation coupling over the Great Plains
Soil moisture-precipitation coupling (SMPC) plays a critical role in Earth's water and energy cycles but remains difficult to quantify due to synoptic-scale variability and the complex interplay of land-atmosphere processes. Here, we apply high-dimensional model representation (HDMR) to functionally decompose the structural, correlative, and cooperative contributions of key land-atmosphere variables to precipitation. Benchmark tests confirm that HDMR overcomes limitations of commonly used corre…

Topological Freeze-out by Semi-Annihilation
Joe Davighi, Serah Moldovsky, Hitoshi Murayama, Christiane Scherb, Nudzeim Selimovic
https://arxiv.org/abs/2506.05468

Topological Freeze-out by Semi-Annihilation
We point out that a QCD-like dark sector can be coupled to the Standard Model by gauging the topological Skyrme current, which measures the dark baryon number in the infrared, to give a technically natural model for dark matter. This coupling allows for a semi-annihilation process $χχ\rightarrow χX_μ$, where $X_μ$ is the gauge boson mediator and $χ$ a dark pion field, which plays the dominant role in setting the dark matter relic abundance. The topological interaction is purely $p$-wave a…

Determination of Effect of the Movement of a Finite, Dip-slip Fault in Viscoelastic Half-space of Fractional Burger Rheology
Pabita Mahato (Mondal), Seema Sarkar (Mondal)
https://arxiv.org/abs/2506.03977

Determination of Effect of the Movement of a Finite, Dip-slip Fault in Viscoelastic Half-space of Fractional Burger Rheology
The seismically active regions often correlate with fault lines, and the movement of these faults plays a crucial role in defining how stress is stored or released in these areas. To investigate the deformation and accumulation/release of stress and strain in seismically active regions during the aseismic period, a mathematical model has been developed by considering a finite, creeping dip-slip fault inclined in the viscoelastic half-space of a fractional Burger rheology. Laplace transformation…

This https://arxiv.org/abs/2109.08659 has been replaced.
link: https://scholar.google.com/scholar?q=a

Thermalization in asymmetric harmonic chains
The symmetry of the interparticle interaction potential (IIP) plays a critical role in determining the thermodynamic and transport properties of solids. This study investigates the isolated effect of IIP asymmetry on thermalization. Asymmetry and nonlinearity are typically intertwined. To isolate the effect of asymmetry, we introduce a one-dimensional asymmetric harmonic (AH) model whose IIP possesses asymmetry but no nonlinearity, evidenced by energy-independent vibrational frequencies. Extens…

A Gaussian process approach for rapid evaluation of skin tension
Matt Nagle, Hannah Conroy Broderick, Christelle Vedel, Michel Destrade, Michael Fop, Aisling Ni Annaidh
https://arxiv.org/abs/2506.05118

A Gaussian process approach for rapid evaluation of skin tension
Skin tension plays a pivotal role in clinical settings, it affects scarring, wound healing and skin necrosis. Despite its importance, there is no widely accepted method for assessing in vivo skin tension or its natural pre-stretch. This study aims to utilise modern machine learning (ML) methods to develop a model that uses non-invasive measurements of surface wave speed to predict clinically useful skin properties such as stress and natural pre-stretch. A large dataset consisting of simulated w…

This https://arxiv.org/abs/2503.09117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models
Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. However, the pursuit of complete unlearning often comes with substantial costs due to its compromises in their general functionality, leading to a notorious trade-off between unlearning and retention. In examining the update process for unlearning dynamically, we find gradients hold essential information for revealing this …

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
https://arxiv.org/abs/2506.09942 …

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). …

This https://arxiv.org/abs/2404.14500 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The SAGA Survey. V. Modeling Satellite Systems around Milky Way-mass Galaxies with Updated UniverseMachine
Environment plays a critical role in shaping the assembly of low-mass galaxies. Here, we use the UniverseMachine (UM) galaxy-halo connection framework and the Data Release 3 of the Satellites Around Galactic Analogs (SAGA) Survey to place dwarf galaxy star formation and quenching into a cosmological context. UM is a data-driven forward model that flexibly parameterizes galaxy star formation rates (SFR) using only halo mass and assembly history. We add a new quenching model to UM, tailored for g…

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Kumud Tripathi, Chowdam Venkata Kumar, Pankaj Wasnik
https://arxiv.org/abs/2506.01365

Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Voice Activity Detection (VAD) plays a key role in speech processing, often utilizing hand-crafted or neural features. This study examines the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) and pre-trained model (PTM) features, including wav2vec 2.0, HuBERT, WavLM, UniSpeech, MMS, and Whisper. We propose FusionVAD, a unified framework that combines both feature types using three fusion strategies: concatenation, addition, and cross-attention (CA). Experimental results reveal that …

A Standing-Wave Model for the Low-Frequency Dynamics of a Turbulent Separation Bubble
Lukas M. Fuchs, Ben Steinfurth, Jakob G. R. von Saldern, Julien Weiss, Kilian Oberleithner
https://arxiv.org/abs/2505.24723

A Standing-Wave Model for the Low-Frequency Dynamics of a Turbulent Separation Bubble
This study investigates the low-frequency dynamics of a turbulent separation bubble (TSB) forming over a backward-facing ramp, with a focus on very-large-scale coherent structures associated with the so-called 'breathing motion'. Using time-resolved particle image velocimetry (PIV) in both streamwise and spanwise planes, we examine the role of sidewall confinement, an aspect largely overlooked in previous research. Spectral proper orthogonal decomposition (SPOD) of the streamwise velocity field…

Testing Hypotheses of Covariate Effects on Topics of Discourse
Gabriel Phelan, David A. Campbell
https://arxiv.org/abs/2506.05570 https://

Testing Hypotheses of Covariate Effects on Topics of Discourse
We introduce an approach to topic modelling with document-level covariates that remains tractable in the face of large text corpora. This is achieved by de-emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are of interest. We propose combining a convex formulation of non-negative matrix factorization with standard regression techniques as a fast-to-compute and…

Non-Equilibrium Origin of Native Ring Anisotropy in Amorphous Systems
Zihang Wang, Dirk Bouwmeester
https://arxiv.org/abs/2506.08491 https://

Non-Equilibrium Origin of Native Ring Anisotropy in Amorphous Systems
Native ring structures within amorphous networks play a critical role in determining structural and optical properties, in part due to their ability to host dopants such as rare earth ions in silicate systems. In this work, we demonstrate that the universal features of structural anisotropy in amorphous networks can be efficiently simulated using a model based on stochastically deformed, edge sharing N member native ring structures. This model isolates and characterizes the structural anisotrop…

The Category Implementation Role Model Award goes to #SchleswigHolstein
The award is handed over by @… from #DNIP
#nextcloudsummit25

This https://arxiv.org/abs/2502.08425 has been replaced.
initial toot: https://mastoxiv.page/@a…

Interlayer interactions in $\text{La}_3\text{Ni}_2\text{O}_7$ under pressure: from $s^{\pm}$ to $d_{xy}$-wave superconductivity
We investigate the role of \emph{interlayer} interaction terms in the competition between different superconducting gap symmetries in the bilayer nickelate $\text{La}_3\text{Ni}_2\text{O}_7$ under high pressure. We study a two-layer, two-orbital electron model that encompasses both intra- and interlayer Coulomb interaction terms within the matrix random-phase approximation. We find that interlayer interactions favor a $d_{xy}$-wave superconducting pairing symmetry over the $s^{\pm}$-wave symmet…

This https://arxiv.org/abs/2501.16162 has been replaced.
initial toot: https://mastoxiv.page/@a…

Quantum Transport with Spin Orbit Coupling: New Developments in TranSIESTA
We present the implementation of spinor quantum transport within the non-equilibrium Green's function (NEGF) code TranSIESTA based on Density Functional Theory (DFT). First-principles methods play an essential role in molecular and material modelling, and the DFT+NEGF approach has become a widely-used tool for quantum transport simulation. Exisiting (open source) DFT-based quantum transport codes either model non-equilibrium/finite-bias cases in an approximate way or rely on the collinear spin …

Probing quasiparticle excitations in a doped Mott insulator via Friedel oscillations
Anurag Banerjee, Emile Pangburn, Catherine P\'epin, Cristina Bena
https://arxiv.org/abs/2505.24300

Probing quasiparticle excitations in a doped Mott insulator via Friedel oscillations
In this work, we investigate impurity-induced Friedel oscillations in the doped two-dimensional Hubbard model, focusing on the role of holon and doublon excitations. We show that weak impurities, due to the non-fermionic nature of the underlying quasiparticles, induce Friedel oscillations whose behavior is consistent with an effective non-interacting theory for these quasiparticles, and whose wavevector reflects the violation of Luttinger's theorem. At larger impurity strength, the system trans…

Generative AI and Organizational Structure in the Knowledge Economy
Fasheng Xu, Jing Hou, Wei Chen, Karen Xie
https://arxiv.org/abs/2506.00532 https://

Generative AI and Organizational Structure in the Knowledge Economy
The adoption of GenAI is fundamentally reshaping organizations in the knowledge economy. GenAI can significantly enhance workers' problem-solving abilities and productivity, yet it also presents a major reliability challenge: hallucinations, or errors presented as plausible outputs. This study develops a theoretical model to examine GenAI's impact on organizational structure and the role of human-in-the-loop oversight. Our findings indicate that successful GenAI adoption hinges primarily on mai…

This https://arxiv.org/abs/2412.13807 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

The role of accreted and in situ populations in shaping the stellar halos of low-mass galaxies
The stellar halos of dwarf galaxies are becoming an object of interest in the extragalactic community due to their detection in some recent observations. Additionally, new cosmological simulations of very high resolution were performed, allowing their study. These stellar halos could help shed light on our understanding of the assembly of dwarf galaxies and their evolution, and allow us to test the hierarchical model for the formation of structures at small scales. We aim to characterise the st…

This https://arxiv.org/abs/2412.07901 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Homophily Within and Across Groups
Homophily-the tendency to connect with similar others-plays a central role in shaping network structure and function. It is often treated as a uniform, global parameter, independent from other structural features. Here, we propose a maximum-entropy framework that decomposes homophily into contributions within and across groups, with the stochastic block model emerging as a special case. Our exponential-family formulation, parameterized by group size, fits real-world social networks well and all…

This https://arxiv.org/abs/2504.05003 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nuc…

The role of the screening potential in the deuteron-deuteron thermonuclear reaction rates
The deuteron-deuteron (D-D) thermonuclear reaction rates in metallic environments (considering the electron screening effects) is calculated using the S-factor functions which were obtained by fitting to low-energy data on D-D reactions. For this purpose, a fitted S-factor model based on the NACRE compilation is employed. This limited the energy range of Big Bang nucleosynthesis (BBN) for the $ ^{2}\textrm{H}\left(d,p\right) ^{3}\textrm{H}$ and $^{2} \textrm{H} \left(d,n\right) ^{3}\t…

This https://arxiv.org/abs/2505.20007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model
Emotion plays a fundamental role in human interaction, and therefore systems capable of identifying emotions in speech are crucial in the context of human-computer interaction. Speech emotion recognition (SER) is a challenging problem, particularly in natural speech and when the available data is imbalanced across emotions. This paper presents our proposed system in the context of the 2025 Speech Emotion Recognition in Naturalistic Conditions Challenge. Our proposed architecture leverages cross…

Dynamics of thin film flows on a vertical fibre with vapor absorption
Souradip Chattopadhyay, Zihao Yu, Y. Sungtaek Ju, Hangjie Ji
https://arxiv.org/abs/2505.22379

Dynamics of thin film flows on a vertical fibre with vapor absorption
Water vapor capture through free surface flows plays a crucial role in various industrial applications, such as liquid desiccant air conditioning systems, water harvesting, and dewatering. This paper studies the dynamics of a silicone liquid sorbent (also known as water-absorbing silicone oil) flowing down a vertical cylindrical fibre while absorbing water vapor. We propose a one-sided thin-film-type model for these dynamics, where the governing equations form a coupled system of nonlinear four…

Family of Multilayer Graphene Superconductors with Tunable Chirality: Momentum-Space Vortices Forged in the Berry-Ring of Fire
Adarsh S. Patri, Marcel Franz
https://arxiv.org/abs/2505.21616

Family of Multilayer Graphene Superconductors with Tunable Chirality: Momentum-Space Vortices Forged in the Berry-Ring of Fire
Recent experiments in rhombohedrally-stacked multilayer graphene heterostructures have reported signatures of chiral superconductivity, emerging from a spin and valley-polarized normal state with broken time-reversal symmetry and an associated anomalous Hall effect. These findings bring into focus the role of the electronic Bloch wavefunction and the quantum geometric tensor in determining the superconducting pairing channel. In this work, we examine superconducting instabilities of a model of …