Tootfinder

This https://arxiv.org/abs/2505.00237 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
This paper proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-shot multimodal motion prediction to anticipate motions of dynamic obstacles and model predictive control to incorporate these predictions into the motion planning process. Motion prediction is driven by an energy-based neural network that generates high-resolution, multi-step predictions in a single operation. The pred…

This https://arxiv.org/abs/2504.01759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Alpha-Beta HMM: Hidden Markov Model Filtering with Equal Exit Probabilities and a Step-Size Parameter
The hidden Markov model (HMM) provides a powerful framework for inference in time-varying environments, where the underlying state evolves according to a Markov chain. To address the optimal filtering problem in general dynamic settings, we propose the $αβ$-HMM algorithm, which simplifies the state transition model to a Markov chain with equal exit probabilities and introduces a step-size parameter to balance the influence of observational data and the model. By analyzing the algorithm's dyna…

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
Tao Yu, Simin Wang, Shunqing Zhang, Xiaojing Chen, Zi Xu, Xin Wang, Jiandong Li, Junyu Liu, Sihai Zhang
https://arxiv.org/abs/2506.03508

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or dat…

QuanUML: Towards A Modeling Language for Model-Driven Quantum Software Development
Xiaoyu Guo, Shinobu Saito, Jianjun Zhao
https://arxiv.org/abs/2506.04639

QuanUML: Towards A Modeling Language for Model-Driven Quantum Software Development
This paper introduces QuanUML, an extension of the Unified Modeling Language (UML) tailored for quantum software systems. QuanUML integrates quantum-specific constructs, such as qubits and quantum gates, into the UML framework, enabling the modeling of both quantum and hybrid quantum-classical systems. We apply QuanUML to Efficient Long-Range Entanglement using Dynamic Circuits and Shor's Algorithm, demonstrating its utility in designing and visualizing quantum algorithms. Our approach supports…

A robust approach to sigma point Kalman filtering
Shenglun Yi, Mattia Zorzi
https://arxiv.org/abs/2506.04815 https://arxiv.org/pdf/25…

A robust approach to sigma point Kalman filtering
In this paper, we address a robust nonlinear state estimation problem under model uncertainty by formulating a dynamic minimax game: one player designs the robust estimator, while the other selects the least favorable model from an ambiguity set of possible models centered around the nominal one. To characterize a closed-form expression for the conditional expectation characterizing the estimator, we approximate the center of this ambiguity set by means of a sigma point approximation. Furthermo…

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
Thomas Hazenberg, Yao Ma, Seyed Sahand Mohammadi Ziabari, Marijn van Rijswijk
https://arxiv.org/abs/2507.02698

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
This study investigates how Multi-Agent Reinforcement Learning (MARL) can improve dynamic pricing strategies in supply chains, particularly in contexts where traditional ERP systems rely on static, rule-based approaches that overlook strategic interactions among market actors. While recent research has applied reinforcement learning to pricing, most implementations remain single-agent and fail to model the interdependent nature of real-world supply chains. This study addresses that gap by evalu…

Exploring metrics for analyzing dynamic behavior in MPI programs via a coupled-oscillator model
Ayesha Afzal, Georg Hager, Gerhard Wellen
https://arxiv.org/abs/2506.02792

Exploring metrics for analyzing dynamic behavior in MPI programs via a coupled-oscillator model
We propose a novel, lightweight, and physically inspired approach to modeling the dynamics of parallel distributed-memory programs. Inspired by the Kuramoto model, we represent MPI processes as coupled oscillators with topology-aware interactions, custom coupling potentials, and stochastic noise. The resulting system of nonlinear ordinary differential equations opens a path to modeling key performance phenomena of parallel programs, including synchronization, delay propagation and decay, bottle…

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging
Christian Salomonsen, Luigi Tommaso Luppino, Fredrik Aspheim, Kristoffer Wickstr{\o}m, Elisabeth Wetzer, Michael Kampffmeyer, Rodrigo Berzaghi, Rune Sundset, Robert Jenssen, Samuel Kuttner
https:…

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging
Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer development research in small animal studies. Accurate kinetic modeling requires precise input function estimation, traditionally achieved via arterial blood sampling. However, arterial cannulation in small animals like mice, involves intricate, time-consuming, and terminal procedures, precluding longitudinal studies. This work proposes a non-invasive, fully convolutional deep learning-based approach…

This https://arxiv.org/abs/2408.11315 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Smoothing Variances Across Time: Adaptive Stochastic Volatility
We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We further enhance the mo…

This https://arxiv.org/abs/2506.02576 has been replaced.
link: https://scholar.google.com/scholar?q=a

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively …

This https://arxiv.org/abs/2505.20156 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters
Recent years have witnessed significant progress in audio-driven human animation. However, critical challenges remain in (i) generating highly dynamic videos while preserving character consistency, (ii) achieving precise emotion alignment between characters and audio, and (iii) enabling multi-character audio-driven animation. To address these challenges, we propose HunyuanVideo-Avatar, a multimodal diffusion transformer (MM-DiT)-based model capable of simultaneously generating dynamic, emotion-…

This https://arxiv.org/abs/2409.05141 has been replaced.
initial toot: https://mastoxiv.page/@a…

Modeling of the micro-focused Brillouin light scattering spectra
Although micro-focused Brillouin light scattering (BLS) has been used for more than twenty years, it lacks a complete theoretical description. This complicates the analysis of experimental data and significantly limits the information that can be obtained. To fill this knowledge gap, we have developed a semi-analytical model based on the mesoscopic continuous medium approach. The model consists of the following steps: calculation of the incident electric field and the dynamic susceptibility, ca…

This https://arxiv.org/abs/2406.07186 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Information Aggregation with Costly Information Acquisition
We study information aggregation in a dynamic trading model with partially informed traders. Ostrovsky [2012] showed that 'separable' securities aggregate information in all equilibria, however, separability is not robust to small changes in the traders' private information. To remedy this problem, we enhance the model by allowing traders to acquire signals with cost $κ$, in every period. We show that '$κ$ separable securities' aggregate information and, as the cost decreases, nearly all secu…

Static vs dynamic rough energy landscapes: Where is diffusion faster?
Dmitrii E. Makarov, Peter Sollich
https://arxiv.org/abs/2506.02195 https://

Static vs dynamic rough energy landscapes: Where is diffusion faster?
Molecules in dense environments, such as biological cells, are subjected to forces that fluctuate both in time and in space. While spatial fluctuations are captured by Lifson-Jackson-Zwanzig's model of "diffusion in a rough potential", and temporal fluctuations are often viewed as leading to additional friction effects, a unified view where the environment fluctuates both in time and in space is currently lacking. Here we introduce a discrete-state model of a landscape fluctuating both in time …

This https://arxiv.org/abs/2503.21369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Constraints on AvERA Cosmologies from Cosmic Chronometers and Type Ia Supernovae
We constrain AvERA cosmologies in comparison with the flat $Λ$CDM model using cosmic chronometer (CC) data and the Pantheon+ sample of type Ia supernovae (SNe Ia). The analysis includes fits to both CC and SN datasets using the \texttt{dynesty} dynamic nested sampling algorithm. For model comparison, we use the Bayesian model evidences and Anderson-Darling tests applied to the normalized residuals to assess consistency with a standard normal distribution. Best-fit parameters are derived within…

ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations
Anoushka Harit, Zhongtian Sun, Suncica Hadzidedic
https://arxiv.org/abs/2507.02014

ManifoldMind: Dynamic Hyperbolic Reasoning for Trustworthy Recommendations
We introduce ManifoldMind, a probabilistic geometric recommender system for exploratory reasoning over semantic hierarchies in hyperbolic space. Unlike prior methods with fixed curvature and rigid embeddings, ManifoldMind represents users, items, and tags as adaptive-curvature probabilistic spheres, enabling personalised uncertainty modeling and geometry-aware semantic exploration. A curvature-aware semantic kernel supports soft, multi-hop inference, allowing the model to explore diverse concep…

Analytical solution for dynamic evaporation of liquid in isothermal condition
Luiz Eduardo Czelusniak, Tim Niklas Bingert, Stephan Simonis, Alexander J. Wagner, Mathias J. Krause
https://arxiv.org/abs/2506.02270

Analytical solution for dynamic evaporation of liquid in isothermal condition
An analytical solution based on a diffuse interface model is presented for an isothermal evaporation problem under sub-saturation pressure. The macroscopic equations are derived from the free-energy method, widely recognized in the lattice Boltzmann literature, distinguishing our approach from conventional evaporation models that rely on jump conditions or pure kinetic theory. The interface behavior is fully described by differential equations, eliminating the need for assumptions such as local…

SPARSE-PIVOT: Dynamic correlation clustering for node insertions
Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovi\'c
https://arxiv.org/abs/2507.01830

SPARSE-PIVOT: Dynamic correlation clustering for node insertions
We present a new Correlation Clustering algorithm for a dynamic setting where nodes are added one at a time. In this model, proposed by Cohen-Addad, Lattanzi, Maggiori, and Parotsidis (ICML 2024), the algorithm uses database queries to access the input graph and updates the clustering as each new node is added. Our algorithm has the amortized update time of $O_ε(\log^{O(1)}(n))$. Its approximation factor is $20+\varepsilon$, which is a substantial improvement over the approximation factor of t…

This https://arxiv.org/abs/2501.00247 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Spectra of Magnetoroton and Chiral Graviton Modes of Fractional Chern Insulator
Employing the state-of-the-art time-dependent variational principle (TDVP) algorithm, we compute the spectra of charge-neutral excitations in the $ν=1/2$ (bosonic) \updated{ and $1/3$ (fermionic) fractional Chern insulator (FCI)} on the Haldane honeycomb lattice model. The magnetoroton visualized from the dynamic density structure factor acquires a minimum gap at finite momentum that can go soft with increasing interaction and give rise to a charge density wave (CDW) at the same wavevector. As…

This https://arxiv.org/abs/2505.04977 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

ChainMarks: Securing DNN Watermark with Cryptographic Chain
With the widespread deployment of deep neural network (DNN) models, dynamic watermarking techniques are being used to protect the intellectual property of model owners. However, recent studies have shown that existing watermarking schemes are vulnerable to watermark removal and ambiguity attacks. Besides, the vague criteria for determining watermark presence further increase the likelihood of such attacks. In this paper, we propose a secure DNN watermarking scheme named ChainMarks, which genera…

Model-theoretic Forcing in Transition Algebra
Go Hashimoto, Daniel G\u{a}in\u{a}
https://arxiv.org/abs/2506.22828 https://arxiv.org/p…

Model-theoretic Forcing in Transition Algebra
We study Löwenheim-Skolem and Omitting Types theorems in Transition Algebra, a logical system obtained by enhancing many sorted first-order logic with features from dynamic logic. The sentences we consider include compositions, unions, and transitive closures of transition relations, which are treated similarly to actions in dynamic logics to define necessity and possibility operators. We show that Upward Löwenheim-Skolem theorem, any form of compactness, and joint Robinson consistency proper…

Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
Swakshar Deb, Nian Wu, Frederick H. Epstein, Miaomiao Zhang
https://arxiv.org/abs/2507.02003

Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos.…

fftvis: A Non-Uniform Fast Fourier Transform Based Interferometric Visibility Simulator
Tyler A. Cox, Steven G. Murray, Aaron R. Parsons, Joshua S. Dillon, Kartik Mandar, Zachary E. Martinot, Robert Pascua, Piyanat Kittiwisit, James E. Aguirre
#toXiv_bot_toot

General Signal Model and Capacity Limit for Rydberg Quantum Information System
Jieao Zhu, Linglong Dai
https://arxiv.org/abs/2506.23455 https://

General Signal Model and Capacity Limit for Rydberg Quantum Information System
Rydberg atomic receivers represent a transformative approach to achieving high-sensitivity, broadband, and miniaturized radio frequency (RF) reception. However, existing static signal models for Rydberg atomic receivers rely on the steady-state assumption of atomic quantum states, which cannot fully describe the signal reception process of dynamic signals. To fill in this gap, in this paper, we present a general model to compute the dynamic signal response of Rydberg atomic receivers in closed …

Observer-Based Distributed Model Predictive Control for String-Stable Multi-vehicle Systems with Markovian Switching Topology
Wenwei Que, Yang Li, Lu Wang, Wentao Liu, Yougang Bian, Manjiang Hu, Yongfu Li
https://arxiv.org/abs/2507.02584

Observer-Based Distributed Model Predictive Control for String-Stable Multi-vehicle Systems with Markovian Switching Topology
Switching communication topologies can cause instability in vehicle platoons, as vehicle information may be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, capturing the dynamic characteristics of switching topologies and obtaining complete vehicle information for controller design while ensuring stability remains a significant challenge. In this st…

This https://arxiv.org/abs/2408.10868 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Inconsistency Removal of Reduced Bases in Parametric Model Order Reduction by Matrix Interpolation using Adaptive Sampling and Clustering
Parametric model order reduction by matrix interpolation allows for efficient prediction of the behavior of dynamic systems without requiring knowledge about the underlying parametric dependency. Within this approach, reduced models are first sampled and then made consistent with each other by transforming the underlying reduced bases. Finally, the transformed reduced operators can be interpolated to predict reduced models for queried parameter points. However, the accuracy of the predicted red…

Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing
Zhaoyuan Su, Tingfeng Lan, Zirui Wang, Juncheng Yang, Yue Cheng
https://arxiv.org/abs/2506.02006

Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing
Efficiently serving large language models (LLMs) under dynamic and bursty workloads remains a key challenge for real-world deployment. Existing serving frameworks and static model compression techniques fail to adapt to workload fluctuations, leading to either service-level objective (SLO) violations under full-precision serving or persistent accuracy degradation with static quantization. We present MorphServe, a dynamic, workload-aware LLM serving framework based on morphological adaptation. M…

SkinningGS: Editable Dynamic Human Scene Reconstruction Using Gaussian Splatting Based on a Skinning Model
Da Li, Donggang Jia, Markus Hadwiger, Ivan Viola
https://arxiv.org/abs/2506.21632

SkinningGS: Editable Dynamic Human Scene Reconstruction Using Gaussian Splatting Based on a Skinning Model
Reconstructing an interactive human avatar and the background from a monocular video of a dynamic human scene is highly challenging. In this work we adopt a strategy of point cloud decoupling and joint optimization to achieve the decoupled reconstruction of backgrounds and human bodies while preserving the interactivity of human motion. We introduce a position texture to subdivide the Skinned Multi-Person Linear (SMPL) body model's surface and grow the human point cloud. To capture fine details…

LongAnimation: Long Animation Generation with Dynamic Global-Local Memory
Nan Chen, Mengqi Huang, Yihao Meng, Zhendong Mao
https://arxiv.org/abs/2507.01945

LongAnimation: Long Animation Generation with Dynamic Global-Local Memory
Animation colorization is a crucial part of real animation industry production. Long animation colorization has high labor costs. Therefore, automated long animation colorization based on the video generation model has significant research value. Existing studies are limited to short-term colorization. These studies adopt a local paradigm, fusing overlapping features to achieve smooth transitions between local segments. However, the local paradigm neglects global information, failing to maintai…

Dynamic Programming Principle for Stochastic Control Problems on Riemannian Manifolds
Dingqian Gao, Qi L\"u
https://arxiv.org/abs/2507.01407 https://

Dynamic Programming Principle for Stochastic Control Problems on Riemannian Manifolds
In this paper, we first establish the dynamic programming principle for stochastic optimal control problems defined on compact Riemannian manifolds without boundary. Subsequently, we derive the associated Hamilton-Jacobi-Bellman (HJB) equation for the value function. We then prove the existence, uniqueness of viscosity solutions to the HJB equation, along with their continuous dependence on initial data and model parameters. Finally, under appropriate regularity conditions on the value function…

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
Danush Khanna, Aditya Kumar Guru, Srivarshinee Sridhar, Zidan Ahmed, Rubhav Bahirwani, Meetu Malhotra, Vinija Jain, Aman Chadha, Amitava Das, Kripabandhu Ghosh
https://arxiv.org/abs/2…

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
Inference accounts for the majority of latency and energy consumption in large language model (LLM) deployments, often exceeding 90% of total cost. While training-time efficiency has seen extensive progress, runtime optimization remains a key bottleneck, particularly under autoregressive decoding. Existing approaches -- such as pruning, quantization, early exits, and speculative decoding -- often require retraining, architectural changes, or disrupt decoding compatibility. We introduce QuickSil…

This https://arxiv.org/abs/2407.09802 has been replaced.
link: https://scholar.google.com/scholar?q=a

Long-time signatures of chaos in large atom-light frequency ratios Rabi model
As one of the famous effects in quantum Rabi model (QRM), Rabi oscillation may lead to the occurrence of quantum dynamics behaviors without classical dynamic counterparts, such as quantum collapse and revival effects. In this paper, we focus on studying the long-time quantum signatures of chaos in the large atom-light frequency ratios Rabi model. It is shown that the saturated values of the entanglement entropy for initial states located in chaotic sea are higher than that in the regular region…

This https://arxiv.org/abs/2505.03793 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
The proliferation of open-sourced Large Language Models (LLMs) and diverse downstream tasks necessitates efficient model selection, given the impracticality of fine-tuning all candidates due to computational constraints. Despite the recent advances in LLM selection, a fundamental research question largely remains nascent: how can we model the dynamic behaviors of LLMs during fine-tuning, thereby enhancing our understanding of their generalization performance across diverse downstream tasks? In …

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Ruijia Zhang, Zhengling Qi, Yue Wu, Xiangyu Zhang, Yanxun Xu
https://arxiv.org/abs/2506.20406

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
Dynamic treatment regimes (DTRs) provide a principled framework for optimizing sequential decision-making in domains where decisions must adapt over time in response to individual trajectories, such as healthcare, education, and digital interventions. However, existing statistical methods often rely on strong positivity assumptions and lack robustness under partial data coverage, while offline reinforcement learning approaches typically focus on average training performance, lack statistical gu…

Tunable spin-phonon polarons in a chiral molecular qubit framework
Aimei Zhou, Ruihao Bi, Zhenghan Zhang, Luming Yang, Xudong Tian, Denan Li, Mingshu Tan, Weibin Ni, Haozhou Sun, Jinkun Guo, Xinxing Zhao, Zhifu Shi, Wei Tong, Zhitao Zhang, Jin-Hu Dou, Feng Jin, Shi Liu, Mircea Dinca, Tijana Rajh, Jian Li, Wenjie Dou, Lei Sun
https://

Tunable spin-phonon polarons in a chiral molecular qubit framework
Chiral structures that produce asymmetric spin-phonon coupling can theoretically generate spin-phonon polarons -- quasiparticles exhibiting non-degenerate spin states with phonon displacements. However, direct experimental evidence has been lacking. Using a chiral molecular qubit framework embedding stable semiquinone-like radicals, we report spin dynamic signatures that clearly indicate the formation of spin-phonon polarons for the first time. Our non-adiabatic model reveals that these quasipa…

This https://arxiv.org/abs/2501.18564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: multitask interaction, generalization to unseen scenarios, and spatial memory. While significant progress has been made in robotic manipulation, existing approaches often fall short in generalization to complex environmental variations and addressing memory-dependent tasks. To bridge this gap, we introduce SAM2Act, a multi-view robotic transformer-based policy that leverages multi-reso…

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…

Deep Learning Enhanced Multivariate GARCH
Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, Chao Wang
https://arxiv.org/abs/2506.02796 https://…

Deep Learning Enhanced Multivariate GARCH
This paper introduces a novel multivariate volatility modeling framework, named Long Short-Term Memory enhanced BEKK (LSTM-BEKK), that integrates deep learning into multivariate GARCH processes. By combining the flexibility of recurrent neural networks with the econometric structure of BEKK models, our approach is designed to better capture nonlinear, dynamic, and high-dimensional dependence structures in financial return data. The proposed model addresses key limitations of traditional multiva…

Levy-driven temporally quenched dynamic critical behavior in directed percolation
Yanyang Wang, Yuxiang Yang, Wei Li
https://arxiv.org/abs/2507.01490 https…

Levy-driven temporally quenched dynamic critical behavior in directed percolation
Quenched disorder in absorbing phase transitions can disrupt the structure and symmetry of reaction-diffusion processes, affecting their phase transition characteristics and offering a more accurate mapping to real physical systems. In the directed percolation (DP) universality class, time quenching is implemented through the dynamic setting of transition probabilities. We developed a non-uniform distribution time quenching method in the (1+1)-dimensional DP model, where the random quenching fo…

This https://arxiv.org/abs/2503.19128 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

The Fragility of the Reverse Facilitation Effect in the Stroop Task: A Dynamic Neurocognitive Model
In typical Stroop experiments, participants perform better when the colors and words are congruent compared to when they are incongruent or neutral. Paradoxically, in some experimental conditions, neutral trials are faster than congruent trials. This phenomenon is known as Reverse Facilitation Effect (RFE). However, RFE has not been consistently replicated, leading to the so-called fragile results. There are some models that capture this effect, but they are not parsimonious. Here we employed o…

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents
Hang Su, Jun Luo, Chang Liu, Xiao Yang, Yichi Zhang, Yinpeng Dong, Jun Zhu
https://arxiv.org/abs/2506.23844

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents
Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents capable of perceiving, reasoning, and acting in dynamic, open-ended environments. These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities. While these capabilities significantly expand the functional scope of AI, they also introduce qualitatively novel security risks - such as memory poisoning, tool misuse, reward hacking, and emergent mi…

This https://arxiv.org/abs/2407.14318 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Some new results for the GHWS model
Here we outline some new results for the GHWS model which points to a discretization of parameter space into well differentiated collective dynamic states. We argue this can lead to basic processes in parameter space, starting with minimum modelling ingredients: a complex network with a disorder parameter and an excitable dynamics (cellular automata) on it.

A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems
Jonghan Lim, Ilya Kovalenko
https://arxiv.org/abs/2505.22814

A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems
Manufacturing environments are becoming more complex and unpredictable due to factors such as demand variations and shorter product lifespans. This complexity requires real-time decision-making and adaptation to disruptions. Traditional control approaches highlight the need for advanced control strategies capable of overcoming unforeseen challenges, as they demonstrate limitations in responsiveness within dynamic industrial settings. Multi-agent systems address these challenges through decentra…

Coverage of the unit cube by dynamic Boolean models
Hanna D\"oring, Lianne de Jonge, Xiaochuan Yang
https://arxiv.org/abs/2506.22286 https://

Coverage of the unit cube by dynamic Boolean models
Motivated by peer-to-peer telecommunication, we study a dynamic Boolean model. We define a Poisson number of random lines through the $(d-1)$-dimensional base of a $d$-dimensional unit cube and dilate them to define cylinders. Letting $ρ$ be the expected number of cylinders, the random variable of interest is the coverage radius $R_ρ$, which is the cylinder radius required to cover the $d$-dimensional unit cube. We show that $R_ρ^{d-1}$ is of the order $\log ρ/ ρ$ with high probability as …

A 3D Mobile Crowdsensing Framework for Sustainable Urban Digital Twins
Taku Yamazaki (Shibaura Institute of Technology), Kaito Watanabe (Shibaura Institute of Technology), Tatsuya Kase (Shibaura Institute of Technology), Kenta Hasegawa (Shibaura Institute of Technology), Koki Saida (Shibaura Institute of Technology), Takumi Miyoshi (Shibaura Institute of Technology)

A 3D Mobile Crowdsensing Framework for Sustainable Urban Digital Twins
In this article, we propose a 3D mobile crowdsensing (3D-MCS) framework aimed at sustainable urban digital twins (UDTs). The framework comprises four key mechanisms: (1) the 3D-MCS mechanism, consisting of active and passive models; (2) the Geohash-based spatial information management mechanism; (3) the dynamic point cloud integration mechanism for UDTs; and (4) the web-based real-time visualizer for 3D-MCS and UDTs. The active sensing model features a gamified 3D-MCS approach, where participan…

GigaAM: Efficient Self-Supervised Learner for Speech Recognition
Aleksandr Kutsakov, Alexandr Maximenko, Georgii Gospodinov, Pavel Bogomolov, Fyodor Minkin
https://arxiv.org/abs/2506.01192

GigaAM: Efficient Self-Supervised Learner for Speech Recognition
Self-Supervised Learning (SSL) has demonstrated strong performance in speech processing, particularly in automatic speech recognition. In this paper, we explore an SSL pretraining framework that leverages masked language modeling with targets derived from a speech recognition model. We also present chunkwise attention with dynamic chunk size sampling during pretraining to enable both full-context and streaming fine-tuning. Our experiments examine scaling with respect to model size and the amoun…

Estimating dynamic transmission rates with a Black-Karasinski process in stochastic SIHR models using particle MCMC
Avery Drennan, Jeffrey Covington, Dan Han, Andrew Attilio, Jaechoul Lee, Richard Posner, Eck Doerry, Joseph Mihaljevic, Ye Chen
https://arxiv.org/abs/2505.24127

Estimating dynamic transmission rates with a Black-Karasinski process in stochastic SIHR models using particle MCMC
Compartmental models are effective in modeling the spread of infectious pathogens, but have remaining weaknesses in fitting to real datasets exhibiting stochastic effects. We propose a stochastic SIHR model with a dynamic transmission rate, where the rate is modeled by the Black-Karasinski (BK) process - a mean-reverting stochastic process with a stable equilibrium distribution, making it well-suited for modeling long-term epidemic dynamics. To generate sample paths of the BK process and estima…

Towards Human-like Preference Profiling in Sequential Recommendation
Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang, Yanfang Ye, Soroush Vosoughi
https://arxiv.org/abs/2506.02261

Towards Human-like Preference Profiling in Sequential Recommendation
Sequential recommendation systems aspire to profile users by interpreting their interaction histories, echoing how humans make decisions by weighing experience, relative preference strength, and situational relevance. Yet, existing large language model (LLM)-based recommenders often fall short of mimicking the flexible, context-aware decision strategies humans exhibit, neglecting the structured, dynamic, and context-aware mechanisms fundamental to human behaviors. To bridge this gap, we propose…

QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
Xiangchen Li, Saeid Ghafouri, Bo Ji, Hans Vandierendonck, Deepu John, Dimitrios S. Nikolopoulos
https://arxiv.org/abs/2506.23934

QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
As machine learning inferences increasingly move to edge devices, adapting to diverse computational capabilities, hardware, and memory constraints becomes more critical. Instead of relying on a pre-trained model fixed for all future inference queries across diverse edge devices, we argue that planning an inference pattern with a request-specific model tailored to the device's computational capacity, accuracy requirements, and time constraints is more cost-efficient and robust to diverse scenari…

This https://arxiv.org/abs/2502.11647 has been replaced.
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DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing
Large Language Models (LLMs) are widely applied in decision making, but their deployment is threatened by jailbreak attacks, where adversarial users manipulate model behavior to bypass safety measures. Existing defense mechanisms, such as safety fine-tuning and model editing, either require extensive parameter modifications or lack precision, leading to performance degradation on general tasks, which is unsuitable to post-deployment safety alignment. To address these challenges, we propose DELM…

This https://arxiv.org/abs/2403.12093 has been replaced.
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Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games
Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices. We formulate this as a dynamic Stackelberg game: the government (leader) sets policies, and agents (followers) respond by optimizing their behavior over time. Unlike static models, this dynamic formulation captures temporal dependencies and strategic feedback critical to policy design. However, as the number of agents incre…

Jet precession in gamma-ray bursts: The roles of fallback accretion and disk dynamics
Yun-Peng Li, Da-Bin Lin, Guo-Yu Li, En-Wei Liang
https://arxiv.org/abs/2506.22043

Jet precession in gamma-ray bursts: The roles of fallback accretion and disk dynamics
The precession phenomenon of the jet in a gamma-ray burst (GRB) is a key probe of the physics of the central engine. Previous studies generally assumed a fixed precession period when analysing the temporal profiles in GRBs; however, the dynamic evolution of the fallback process and accretion disk can significantly affect the precession behaviour. In this work we present a jet precession model that incorporates the co-evolution of fallback accretion and the central black hole (BH). Our model dem…

An inverse-free fixed-time stable dynamical system and its forward-Euler discretization for solving generalized absolute value equations
Xuehua Li, Linjie Chen, Dongmei Yu, Cairong Chen, Deren Han
https://arxiv.org/abs/2507.00531

An inverse-free fixed-time stable dynamical system and its forward-Euler discretization for solving generalized absolute value equations
An inverse-free dynamical system is proposed to solve the generalized absolute value equation (GAVE) within a fixed time, where the time of convergence is finite and is uniformly bounded for all initial points. Moreover, an iterative method obtained by using the forward-Euler discretization of the proposed dynamic model are developed and sufficient conditions which guarantee that the discrete iteration globally converge to an arbitrarily small neighborhood of the unique solution of GAVE within …

Dynamic signature of the thermodynamic transition in a novel mean field system
Ehtesham Anwar, Ujjwal Kumar Nandi, Palak Patel, Sanket Kumawat, Sarika Maitra Bhattacharyya
https://arxiv.org/abs/2505.23371

Dynamic signature of the thermodynamic transition in a novel mean field system
Understanding the connection between thermodynamics and dynamics in glass-forming liquids remains a central challenge in condensed matter physics. In this study, we investigate a novel model system that enables a continuous crossover from a standard three dimensional liquid to a fully connected mean field like system by introducing pseudo neighbours. These pseudo neighbours enhance the effective connectivity of the system without altering its local structure. While their presence slows down the…

DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
Yuliang Huang, Imraj Singh, Thomas Joyce, Kris Thielemans, Jamie R. McClelland
https://arxiv.org/abs/2506.22280

DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
3D Cone-Beam CT (CBCT) is widely used in radiotherapy but suffers from motion artifacts due to breathing. A common clinical approach mitigates this by sorting projections into respiratory phases and reconstructing images per phase, but this does not account for breathing variability. Dynamic CBCT instead reconstructs images at each projection, capturing continuous motion without phase sorting. Recent advancements in 4D Gaussian Splatting (4DGS) offer powerful tools for modeling dynamic scenes, …

Dynamic Bayesian Item Response Model with Decomposition (D-BIRD): Modeling Cohort and Individual Learning Over Time
Hansol Lee, Jason B. Cho, David S. Matteson, Benjamin W. Domingue
https://arxiv.org/abs/2506.21723

Dynamic Bayesian Item Response Model with Decomposition (D-BIRD): Modeling Cohort and Individual Learning Over Time
We present D-BIRD, a Bayesian dynamic item response model for estimating student ability from sparse, longitudinal assessments. By decomposing ability into a cohort trend and individual trajectory, D-BIRD supports interpretable modeling of learning over time. We evaluate parameter recovery in simulation and demonstrate the model using real-world personalized learning data.

Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Sequential Auctions under Unknown Environments
Vincent Leon, S. Rasoul Etesami
https://arxiv.org/abs/2506.19038

Online Learning for Dynamic Vickrey-Clarke-Groves Mechanism in Sequential Auctions under Unknown Environments
We consider the problem of online dynamic mechanism design for sequential auctions in unknown environments, where the underlying market and, thus, the bidders' values vary over time as interactions between the seller and the bidders progress. We model the sequential auctions as an infinite-horizon average-reward Markov decision process (MDP), where the transition kernel and reward functions are unknown to the seller. In each round, the seller determines an allocation and a payment for each bidd…

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Haoxiang Luo, Yinqiu Liu, Ruichen Zhang, Jiacheng Wang, Gang Sun, Dusit Niyato, Hongfang Yu, Zehui Xiong, Xianbin Wang, Xuemin Shen
https://arxiv.org/abs/2507.00672

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, traditional AI models often fall short when dealing with complex, dynamic tasks that require advanced reasoning and multimodal data processing. This survey explores the integration of multi-LLMs (Large Language Models) to address this in edge computing, where multiple specialized LLMs collaborate to enhance task performance and adaptability …

Many LLMs Are More Utilitarian Than One
Anita Keshmirian, Razan Baltaji, Babak Hemmatian, Hadi Asghari, Lav R. Varshney
https://arxiv.org/abs/2507.00814 ht…

Many LLMs Are More Utilitarian Than One
Moral judgment is integral to large language model (LLM) alignment and social reasoning. As multi-agent systems gain prominence, it becomes crucial to understand how LLMs function collectively during collaboration, compared to individual agents. In human moral judgment, group deliberation leads to a utilitarian boost: a tendency to endorse norm violations that maximize benefits for the greatest number of people despite harms. We study whether a similar dynamic emerges in multi-agent LLM systems…

This https://arxiv.org/abs/2505.05204 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Quantum selection of order and dynamic properties of Kitaev-Heisenberg ferromagnet on a triangular lattice
Recent interest in monolayer materials motivated a search for two-dimensional ferromagnets with sizable spin-orbit coupling. Magnetic anisotropy of exchange Hamiltonian, induced by spin-orbit coupling, may not only stabilize long-range order, but also in turn can be a source of frustration and accidental degeneracy, which is the case for the Kitaev-Heisenberg model. Here we present an extensive study of ground state and excitations of ferromagnetic anisotropic-exchange Kitaev-Heisenberg model o…

SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging
Zijun Chen, Zhanpeng Zhou, Bo Zhang, Weinan Zhang, Xi Sun, Junchi Yan
https://arxiv.org/abs/2506.18135

SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging
Model merging has gained increasing attention due to its intriguing property: interpolating the parameters of different task-specific fine-tuned models leads to multi-task abilities. However, despite its empirical success, the underlying mechanisms of model merging remain poorly understood. In this work, we delve into the mechanism behind model merging from a representation perspective. Our analysis reveals that model merging achieves multi-task abilities through two key capabilities: i) distin…

Kwai Keye-VL Technical Report
Kwai Keye Team, Biao Yang, Bin Wen, Changyi Liu, Chenglong Chu, Chengru Song, Chongling Rao, Chuan Yi, Da Li, Dunju Zang, Fan Yang, Guorui Zhou, Hao Peng, Haojie Ding, Jiaming Huang, Jiangxia Cao, Jiankang Chen, Jingyun Hua, Jin Ouyang, Kaibing Chen, Kaiyu Jiang, Kaiyu Tang, Kun Gai, Shengnan Zhang, Siyang Mao, Sui Huang, Tianke Zhang, Tingting Gao, Wei Chen, Wei Yuan, Xiangyu Wu, Xiao Hu, Xingyu Lu, Yang Zhou, Yi-Fan Zhang, Yiping Yang, Yulong Chen, Zhenh…

Kwai Keye-VL Technical Report
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form videos, a dominant medium in today's digital landscape. To bridge this gap, we introduce \textbf{Kwai Keye-VL}, an 8-billion-parameter multimodal foundation model engineered for leading-edge performance in short-video understanding while maintaining robust general-purpose vision-language abilities. The development of Ke…

This https://arxiv.org/abs/2411.03502 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Adaptive Shock Compensation in the Multi-layer Network of Global Food Production and Trade
Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagation of the shocks. We analyze shock mitigation actions, such as increasing imports, boosting production, or substituting food items. Our find…

Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure
Bin Pi, Liang-Jian Deng, Minyu Feng, Matja\v{z} Perc, J\"urgen Kurths
https://arxiv.org/abs/2506.17925

Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure
Complex networks serve as abstract models for understanding real-world complex systems and provide frameworks for studying structured dynamical systems. This article addresses limitations in current studies on the exploration of individual birth-death and the development of community structures within dynamic systems. To bridge this gap, we propose a networked evolution model that includes the birth and death of individuals, incorporating reinforcement learning through games among individuals. …

This https://arxiv.org/abs/2505.17370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

FRIREN: Beyond Trajectories -- A Spectral Lens on Time
Long-term time-series forecasting (LTSF) models are often presented as general-purpose solutions that can be applied across domains, implicitly assuming that all data is pointwise predictable. Using chaotic systems such as Lorenz-63 as a case study, we argue that geometric structure - not pointwise prediction - is the right abstraction for a dynamic-agnostic foundational model. Minimizing the Wasserstein-2 distance (W2), which captures geometric changes, and providing a spectral view of dynamic…

Regularity of a bulk-surface Cahn-Hilliard model driven by Leray velocity fields
Andrea Giorgini, Patrik Knopf, Jonas Stange
https://arxiv.org/abs/2506.18617

Regularity of a bulk-surface Cahn-Hilliard model driven by Leray velocity fields
We consider a convective bulk-surface Cahn--Hilliard system with dynamic boundary conditions and singular potentials. For this model, well-posedness results concerning weak and strong solutions have already been established in the literature. However, they require the prescribed velocity fields to belong to function spaces with high time regularity. In this paper, we prove that the well-posedness of weak solutions holds true under more general regularity assumptions on the velocity fields. Next…

Driving factors of auditory category learning success
Nan Wang, Gangyi Feng
https://arxiv.org/abs/2506.01508 https://arxiv.org/pdf/25…

Driving factors of auditory category learning success
Our brain learns to update its mental model of the environment by abstracting sensory experiences for adaptation and survival. Learning to categorize sounds is one essential abstracting process for high-level human cognition, such as speech perception, but it is also challenging due to the variable nature of auditory signals and their dynamic contexts. To overcome these learning challenges and enhance learner performance, it is essential to identify the impact of learning-related factors in dev…

On the Study of Weighted Fractional Cumulative Residual Inaccuracy and its Dynamical Version with Applications
Aman Pandey, Chanchal Kundu
https://arxiv.org/abs/2506.22975

On the Study of Weighted Fractional Cumulative Residual Inaccuracy and its Dynamical Version with Applications
In recent years, there has been a growing interest in information measures that quantify inaccuracy and uncertainty in systems. In this paper, we introduce a novel concept called the Weighted Fractional Cumulative Residual Inaccuracy (WFCRI). We develop several fundamental properties of WFCRI and establish important bounds that reveal its analytical behavior. Further, we examine the behavior of WFCRI under a mixture hazard model. A dynamic version of WFCRI also proposed and studied its behavior…

Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates
Jian Sun, Bo Fu, Li Su
https://arxiv.org/abs/2506.22892 https:/…

Robust estimation of optimal dynamic treatment regimes with nonignorable missing covariates
Estimating optimal dynamic treatment regimes (DTRs) using observational data is often challenged by nonignorable missing covariates arsing from informative monitoring of patients in clinical practice. To address nonignorable missingness of pseudo-outcomes induced by nonignorable missing covariates, a weighted Q-learning approach using parametric Q-function models and a semiparametric missingness propensity model has recently been proposed. However, misspecification of parametric Q-functions at …

Data-Driven Dynamic Factor Modeling via Manifold Learning
Graeme Baker, Agostino Capponi, J. Antonio Sidaoui
https://arxiv.org/abs/2506.19945 https://

Data-Driven Dynamic Factor Modeling via Manifold Learning
We propose a data-driven dynamic factor framework where a response variable depends on a high-dimensional set of covariates, without imposing any parametric model on the joint dynamics. Leveraging Anisotropic Diffusion Maps, a nonlinear manifold learning technique introduced by Singer and Coifman, our framework uncovers the joint dynamics of the covariates and responses in a purely data-driven way. We approximate the embedding dynamics using linear diffusions, and exploit Kalman filtering to pr…

Dynamic allocation: extremes, tail dependence, and regime Shifts
Yin Luo, Sheng Wang, Javed Jussa
https://arxiv.org/abs/2506.12587 https://

Dynamic allocation: extremes, tail dependence, and regime Shifts
By capturing outliers, volatility clustering, and tail dependence in the asset return distribution, we build a sophisticated model to predict the downside risk of the global financial market. We further develop a dynamic regime switching model that can forecast real-time risk regime of the market. Our GARCH-DCC-Copula risk model can significantly improve both risk- and alpha-based global tactical asset allocation strategies. Our risk regime has strong predictive power of quantitative equity fac…

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
Prachi Jadhav, Hongwei Jin, Ewa Deelman, Prasanna Balaprakash
https://arxiv.org/abs/2506.02025

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including heuristic-based (e.g., First-Come-First-Served) or intensive optimization techniques, often lack adaptability to dynamic workloads and heterogeneous HPC systems. To address this, we propose a novel Large Language Model (LLM)-based scheduler using a ReAct-style framework (Reason +…

DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving
Dawood Wasif, Terrence J Moore, Chandan K Reddy, Jin-Hee Cho
https://arxiv.org/abs/2506.00819

DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving
End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vision-language-guided reinforcement learning (RL) methods introduce semantic feedback, they often rely on static prompts and fixed objectives, limiting adaptability to dynamic driving scenes. We present DriveMind, a unified semantic reward framework that integrates: (i) a contrastive Vision-Language Model (VLM) encoder…

Observation of terahertz spin-Hall magnetoresistance in insulating magnet YIG/Pt
P. Kuba\v{s}\v{c}\'ik, R. Schlitz, O. Gueckstock, O. Franke, M. Borchert, G. Jakob, K. Olejn\'ik, A. Farka\v{s}, Z. Ka\v{s}par, J. Jechumt\'al, M. Bu\v{s}ina, E. Schmoranzerov\'a, P. N\v{e}mec, M. Wolf, Y. Z. Wu, G. Woltersdorf, M. Kl\"aui, P. W. Brouwer, S. T. B. Goennenwein, T. Kampfrath, L. N\'advorn\'ik

Observation of terahertz spin-Hall magnetoresistance in insulating magnet YIG/Pt
We report on the observation of spin Hall magnetoresistance (SMR) in prototypical bilayers of ferrimagnetic yttrium iron garnet (YIG) and platinum in the frequency range from 0 THz to as high as 1.5 THz. The spectral composition of the effect exhibits a strong low-pass behavior, decreasing by approximately 75% from 0 THz to 0.2 THz and vanishing entirely at 1.5 THz. Using a dynamic magnetoresistive model, we can fully explain the spectral dependence by competition of transverse spin-torque (coh…

Coercivity Panorama of Dynamic Hysteresis
Miao Chen, Xiu-Hua Zhao, Yu-Han Ma
https://arxiv.org/abs/2506.24035 https://arxiv.org/pdf/2…

Coercivity Panorama of Dynamic Hysteresis
We study the stochastic $ϕ^4$ model under periodic driving by an external field $H$ at different scales of driving rate $v_H$, where the noise strength $σ$ quantifies the deviation of the system size from the thermodynamic limit. For large systems with small $σ$, we find the coercivity $H_c=H(\langleϕ\rangle=0)$ sequentially exhibits distinct behaviors with increasing $v_H$: $v_H$-scaling increase from zero, stable plateau ($v_H^0$), $v_H^{1/2}$-scaling increase, and abrupt decline to disap…

Captivity-Escape Games as a Means for Safety in Online Motion Generation
Christopher Bohn, Manuel Hess, S\"oren Hohmann
https://arxiv.org/abs/2506.01399

Captivity-Escape Games as a Means for Safety in Online Motion Generation
This paper presents a method that addresses the conservatism, computational effort, and limited numerical accuracy of existing frameworks and methods that ensure safety in online model-based motion generation, commonly referred to as fast and safe tracking. Computational limitations restrict online motion planning to low-fidelity models. However, planning with low-fidelity models compromises safety, as the dynamic feasibility of resulting reference trajectories is not ensured. This potentially …

The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking
Antonio Ojea
https://arxiv.org/abs/2506.23628 https://

The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking
Traditional Kubernetes networking struggles to meet the escalating demands of AI/ML and evolving Telco infrastructure. This paper introduces Kubernetes Network Drivers (KNDs), a transformative, modular, and declarative architecture designed to overcome current imperative provisioning and API limitations. KNDs integrate network resource management into Kubernetes' core by utilizing Dynamic Resource Allocation (DRA), Node Resource Interface (NRI) improvements, and upcoming OCI Runtime Specificati…

ACTLLM: Action Consistency Tuned Large Language Model
Jing Bi, Lianggong Bruce Wen, Zhang Liu, Chenliang Xu
https://arxiv.org/abs/2506.21250 https://

ACTLLM: Action Consistency Tuned Large Language Model
This paper introduces ACTLLM (Action Consistency Tuned Large Language Model), a novel approach for robot manipulation in dynamic environments. Traditional vision-based systems often struggle to learn visual representations that excel in both task execution and spatial reasoning, thereby limiting their adaptability in dynamic environments. ACTLLM addresses these challenges by harnessing language to craft structured scene descriptors, providing a uniform interface for both spatial understanding a…

Human sensory-musculoskeletal modeling and control of whole-body movements
Chenhui Zuo, Guohao Lin, Chen Zhang, Shanning Zhuang, Yanan Sui
https://arxiv.org/abs/2506.00071

Human sensory-musculoskeletal modeling and control of whole-body movements
Coordinated human movement depends on the integration of multisensory inputs, sensorimotor transformation, and motor execution, as well as sensory feedback resulting from body-environment interaction. Building dynamic models of the sensory-musculoskeletal system is essential for understanding movement control and investigating human behaviours. Here, we report a human sensory-musculoskeletal model, termed SMS-Human, that integrates precise anatomical representations of bones, joints, and muscle…

Securing AI Agents with Information-Flow Control
Manuel Costa, Boris K\"opf, Aashish Kolluri, Andrew Paverd, Mark Russinovich, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Santiago Zanella-B\'eguelin
https://arxiv.org/abs/2505.23643

Securing AI Agents with Information-Flow Control
As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of information-flow control (IFC) to provide security guarantees for AI agents. We present a formal model to reason about the security and expressiveness of agent planners. Using this model, we characterize the class of properties enforceable by dynamic taint-tracking and construct a taxonomy of tasks to evaluate security…

Development of a Photon-Counting Deadtime Noise Model that Extends Dynamic Range and Resolution in Atmospheric Lidar
Grant J. Kirchhoff, Matthew Hayman, Willem J. Marais, Jeffrey P. Thayer, Rory A. Barton-Grimley
https://arxiv.org/abs/2506.09253

Development of a Photon-Counting Deadtime Noise Model that Extends Dynamic Range and Resolution in Atmospheric Lidar
This work derives and validates a noise model that encapsulates deadtime of non-paralyzable detectors with random photon arrivals to enable advanced processing, like maximum-likelihood estimation, of high resolution atmospheric lidar profiles while accounting for deadtime bias. This estimator was validated across a wide dynamic range at high resolution (4 millimeters in range, 17 milliseconds in time). Experiments demonstrate that the noise model outperforms the current state-of-the-art for ver…

Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing
Burak Aydin, Emre Parmaksiz, Ronnie Sircar
https://arxiv.org/abs/2506.13088

Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing
We study the dynamic pricing of discrete goods over a finite selling horizon. One way to capture both the elastic and stochastic reaction of purchases to price is through a model where sellers control the intensity of a counting process, representing the number of sales thus far. The intensity describes the probabilistic likelihood of a sale, and is a decreasing function of the price a seller sets. A classical model for ticket pricing, which assumes a single seller and infinite time horizon, is…

Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model
Chong Li, Yingzhuo Deng, Jiajun Zhang, Chengqing Zong
https://arxiv.org/abs/2506.12388

Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model
The curse of multilinguality phenomenon is a fundamental problem of multilingual Large Language Models (LLMs), where the competition between massive languages results in inferior performance. It mainly comes from limited capacity and negative transfer between dissimilar languages. To address this issue, we propose a method to dynamically group and scale up the parameters of multilingual LLM while boosting positive transfer among similar languages. Specifically, the model is first tuned on monol…

Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n
Fei Yuhuan, Sun Xufei, Zang Ran, Wang Gengchen, Su Meng, Liu Fenghao
https://arxiv.org/abs/2507.00780

Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n
Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW mod…

Intraday Functional PCA Forecasting of Cryptocurrency Returns
Joann Jasiak, Cheng Zhong
https://arxiv.org/abs/2505.20508 https://arxi…

Intraday Functional PCA Forecasting of Cryptocurrency Returns
We study the Functional PCA (FPCA) forecasting method in application to functions of intraday returns on Bitcoin. We show that improved interval forecasts of future return functions are obtained when the conditional heteroscedasticity of return functions is taken into account. The Karhunen-Loeve (KL) dynamic factor model is introduced to bridge the functional and discrete time dynamic models. It offers a convenient framework for functional time series analysis. For intraday forecasting, we intr…

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Elia Trevisan, Khaled A. Mustafa, Godert Notten, Xinwei Wang, Javier Alonso-Mora
https://arxiv.org/abs/2506.21205

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents' predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenges, we propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI), a motion planner that incorporates uncertain future motions modelled with potentially non-Gaussian stochastic pre…

This https://arxiv.org/abs/2306.16904 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Endogenous Barriers to Learning
Building on the idea that lack of experience is a source of errors but that experience should reduce them, we model agents' behavior using a stochastic choice model (logit quantal response), leaving endogenous the accuracy of their choices. In some games, higher accuracy leads to unstable logit-response dynamics. Starting from the lowest possible accuracy, we define the barrier to learning as the maximum accuracy which keeps the logit-response dynamic stable (for all lower accuracies). This def…

SINDybrid: automatic generation of hybrid models for dynamic systems
Ulderico Di Caprio, M. Enis Leblebici
https://arxiv.org/abs/2506.12498 https://…

SINDybrid: automatic generation of hybrid models for dynamic systems
Hybrid modelling enhances the accuracy and predictive capability of dynamic models by integrating first principles with data-driven methods, effectively mitigating epistemic uncertainties inherent in mechanistic approaches. However, hybrid model construction remains complex, typically requiring expert knowledge to identify model epistemic uncertainty and select suitable machine-learning components to capture it. This complexity limits broader adoption in research and industry. We introduce SIND…

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta, Kai Zhao, Aysenur Inan, Kehui Yao, Jianpeng Xu, Praveen Kanumala, Jason Cho, Sushant Kumar
https://arxiv.org/abs/2506.21931

ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RAG-based approaches often rely on static retrieval heuristics and fail to capture nuanced user preferences in dynamic recommendation scenarios. In this work, we introduce ARAG, an Agentic Retrieval-Augmented Generation framework for Personalized Recommendation, which integrates a multi-agent collaboration mechanism int…

Online design of experiments by active learning for nonlinear system identification
Kui Xie, Alberto Bemporad
https://arxiv.org/abs/2506.21754 https://

Online design of experiments by active learning for nonlinear system identification
We investigate the use of active-learning (AL) strategies to generate the input excitation signal at runtime for system identification of linear and nonlinear autoregressive and state-space models. We adapt various existing AL approaches for static model regression to the dynamic context, coupling them with a Kalman filter to update the model parameters recursively, and also cope with the presence of input and output constraints. We show the increased sample efficiency of the proposed approache…

Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
Beining Wu, Jun Huang, Qiang Duan, Liang Dong, Zhipeng Cai
https://arxiv.org/abs/2507.00856

Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework
This paper aims to enhance the performance of Vehicular Platooning (VP) systems integrated with Wireless Federated Learning (WFL). In highly dynamic environments, vehicular platoons experience frequent communication changes and resource constraints, which significantly affect information exchange and learning model synchronization. To address these challenges, we first formulate WFL in VP as a joint optimization problem that simultaneously considers Age of Information (AoI) and Federated Learni…

Exact Time-Varying Turnpikes for Dynamic Operation of District Heating Networks
Max Rose, Hannes Gernandt, Timm Faulwasser, Johannes Schiffer
https://arxiv.org/abs/2506.21239

Exact Time-Varying Turnpikes for Dynamic Operation of District Heating Networks
District heating networks (DHNs) are crucial for decarbonizing the heating sector. Yet, their efficient and reliable operation requires the coordination of multiple heat producers and the consideration of future demands. Predictive and optimization-based control is commonly used to address this task, but existing results for DHNs do not account for time-varying problem aspects. Since the turnpike phenomenon can serve as a basis for model predictive control design and analysis, this paper examin…

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving
Heyang Huang, Cunchen Hu, Jiaqi Zhu, Ziyuan Gao, Liangliang Xu, Yizhou Shan, Yungang Bao, Sun Ninghui, Tianwei Zhang, Sa Wang
https://arxiv.org/abs/2506.13497

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving
The Text-to-Video (T2V) model aims to generate dynamic and expressive videos from textual prompts. The generation pipeline typically involves multiple modules, such as language encoder, Diffusion Transformer (DiT), and Variational Autoencoders (VAE). Existing serving systems often rely on monolithic model deployment, while overlooking the distinct characteristics of each module, leading to inefficient GPU utilization. In addition, DiT exhibits varying performance gains across different resoluti…

UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
Yuantao Wang, Haowei Yang, Wei Zhang, Shijian Lu
https://arxiv.org/abs/2506.12324 …

UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
Real-world object detection is a challenging task where the captured images/videos often suffer from complex degradations due to various adverse weather conditions such as rain, fog, snow, low-light, etc. Despite extensive prior efforts, most existing methods are designed for one specific type of adverse weather with constraints of poor generalization, under-utilization of visual features while handling various image degradations. Leveraging a theoretical analysis on how critical visual details…

Dynamic Hybrid Modeling: Incremental Identification and Model Predictive Control
Adrian Caspari, Thomas Bierweiler, Sarah Fadda, Daniel Labisch, Maarten Nauta, Franzisko Wagner, Merle Warmbold, Constantinos C. Pantelides
https://arxiv.org/abs/2506.18344

Dynamic Hybrid Modeling: Incremental Identification and Model Predictive Control
Mathematical models are crucial for optimizing and controlling chemical processes, yet they often face significant limitations in terms of computational time, algorithm complexity, and development costs. Hybrid models, which combine mechanistic models with data-driven models (i.e. models derived via the application of machine learning to experimental data), have emerged as a promising solution to these challenges. However, the identification of dynamic hybrid models remains difficult due to the…

Resonance Complexity Theory and the Architecture of Consciousness: A Field-Theoretic Model of Resonant Interference and Emergent Awareness
Michael Arnold Bruna
https://arxiv.org/abs/2505.20580

Resonance Complexity Theory and the Architecture of Consciousness: A Field-Theoretic Model of Resonant Interference and Emergent Awareness
This paper introduces Resonance Complexity Theory (RCT), which proposes that consciousness emerges from stable interference patterns of oscillatory neural activity. These patterns, shaped by recursive feedback and constructive interference, must exceed critical thresholds in complexity, coherence, gain, and fractal dimensionality to give rise to conscious experience. The resulting spatiotemporal attractors encode subjective awareness as dynamic resonance structures distributed across the neural…

A Dynamic Strategic Plan for Transition to Campus-Scale Clean Electricity Using Multi-Stage Stochastic Programming
Ahmet Emir \c{S}ener, Burak Kocuk, Tu\u{g}\c{c}e Y\"uksel
https://arxiv.org/abs/2506.16423

A Dynamic Strategic Plan for Transition to Campus-Scale Clean Electricity Using Multi-Stage Stochastic Programming
The transition to clean energy systems at large-scale campuses is a critical step toward achieving global decarbonization goals. However, this transition poses significant challenges, including substantial capital requirements, technological uncertainties, and the operational complexities of integrating renewable energy technologies. This study presents a dynamic strategic planning framework for campus-scale clean electricity transitions, utilizing a multi-stage stochastic programming model tha…

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Tianlin Zhang, Linzhu Yue, Hongbo Zhang, Lingwei Zhang, Xuanqi Zeng, Zhitao Song, Yun-Hui Liu
https://arxiv.org/abs/2506.14278

Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
Humanoid robots often face significant balance issues due to the motion of their heavy limbs. These challenges are particularly pronounced when attempting dynamic motion or operating in environments with irregular terrain. To address this challenge, this manuscript proposes a whole-body control framework for humanoid robots with heavy limbs, using a model-based approach that combines a kino-dynamics planner and a hierarchical optimization problem. The kino-dynamics planner is designed as a mode…

Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation
Nikhil Pawar, Guilherme Vieira Hollweg, Akhtar Hussain, Wencong Su, Van-Hai Bui
https://arxiv.org/abs/2506.14133

Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation
Accurate time series forecasting models are often compromised by data drift, where underlying data distributions change over time, leading to significant declines in prediction performance. To address this challenge, this study proposes an adaptive forecasting framework that integrates drift detection with targeted model retraining to compensate for drift effects. The framework utilizes the Pruned Exact Linear Time (PELT) algorithm to identify drift points within the feature space of time serie…

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Maximilian Du, Shuran Song
https://arxiv.org/abs/2506.13922 https://

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Deploying large, complex policies in the real world requires the ability to steer them to fit the needs of a situation. Most common steering approaches, like goal-conditioning, require training the robot policy with a distribution of test-time objectives in mind. To overcome this limitation, we present DynaGuide, a steering method for diffusion policies using guidance from an external dynamics model during the diffusion denoising process. DynaGuide separates the dynamics model from the base pol…