Tootfinder

Stochastic forest transition model dynamics and parameter estimation via deep learning
Satoshi Kumabe, Tianyu Song, Ton Viet Ta
https://arxiv.org/abs/2507.21486 https://

Stochastic forest transition model dynamics and parameter estimation via deep learning
Forest transitions, characterized by dynamic shifts between forest, agricultural, and abandoned lands, are complex phenomena. This study developed a stochastic differential equation model to capture the intricate dynamics of these transitions. We established the existence of global positive solutions for the model and conducted numerical analyses to assess the impact of model parameters on deforestation incentives. To address the challenge of parameter estimation, we proposed a novel deep learn…

Time-continuous strongly conservative space-time finite element methods for the dynamic Biot model
Johannes Kraus, Maria Lymbery, Kevin Osthues
https://arxiv.org/abs/2507.19955 …

Time-continuous strongly conservative space-time finite element methods for the dynamic Biot model
We consider the dynamic Biot model (see [Biot, M. A. J. Appl. Phys. 33, 1482--1498 (1962)]) describing the interaction between fluid flow and solid deformation including wave propagation phenomena in both the liquid and solid phases of a saturated porous medium. This model couples a hyperbolic equation for momentum balance to a second-order in time dynamic Darcy law and a parabolic equation for the balance of mass and is here considered in three-field formulation with the displacement of the el…

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, …

NMPCM: Nonlinear Model Predictive Control on Resource-Constrained Microcontrollers
Van Chung Nguyen, Pratik Walunj, Chuong Le, An Duy Nguyen, Hung Manh La
https://arxiv.org/abs/2507.21259

NMPCM: Nonlinear Model Predictive Control on Resource-Constrained Microcontrollers
Nonlinear Model Predictive Control (NMPC) is a powerful approach for controlling highly dynamic robotic systems, as it accounts for system dynamics and optimizes control inputs at each step. However, its high computational complexity makes implementation on resource-constrained microcontrollers impractical. While recent studies have demonstrated the feasibility of Model Predictive Control (MPC) with linearized dynamics on microcontrollers, applying full NMPC remains a significant challenge. Thi…

This https://arxiv.org/abs/2502.11647 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

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…

Fundamental diagram constrained dynamic optimal transport via proximal splitting methods
Anqi Dong, Karl Henrik Johansson, Johan Karlsson
https://arxiv.org/abs/2507.20717 https:…

Fundamental diagram constrained dynamic optimal transport via proximal splitting methods
Optimal transport has recently been brought forward as a tool for modeling and efficiently solving a variety of flow problems, such as origin-destination problems and multi-commodity flow problems. Although the framework has shown to be effective for many large scale flow problems, the formulations typically lack dynamic properties used in common traffic models, such as the Lighthill-Whitham-Richards model. In this work, we propose an optimal transport framework that includes dynamic constraint…

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…

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…

A lightweight numerical model for predictive control of borehole thermal energy storages
Johannes van Randenborgh, Steffen Daniel, Moritz Schulze Darup
https://arxiv.org/abs/2507.20974

A lightweight numerical model for predictive control of borehole thermal energy storages
Borehole thermal energy storage (BTES) can reduce the operation of fossil fuel-based heating, ventilation, and air conditioning systems for buildings. With BTES, thermal energy is stored via a borehole heat exchanger in the ground. Model predictive control (MPC) may maximize the use of BTES by achieving a dynamic interaction between the building and BTES. However, modeling BTES for MPC is challenging, and a trade-off between model accuracy and an easy-to-solve optimal control problem (OCP) must…

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…

AdaDPCC: Adaptive Rate Control and Rate-Distortion-Complexity Optimization for Dynamic Point Cloud Compression
Chenhao Zhang, Wei Gao
https://arxiv.org/abs/2508.20741 https://…

AdaDPCC: Adaptive Rate Control and Rate-Distortion-Complexity Optimization for Dynamic Point Cloud Compression
Dynamic point cloud compression (DPCC) is crucial in applications like autonomous driving and AR/VR. Current compression methods face challenges with complexity management and rate control. This paper introduces a novel dynamic coding framework that supports variable bitrate and computational complexities. Our approach includes a slimmable framework with multiple coding routes, allowing for efficient Rate-Distortion-Complexity Optimization (RDCO) within a single model. To address data sparsity …

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…

Real-Time Distributed Optical Fiber Vibration Recognition via Extreme Lightweight Model and Cross-Domain Distillation
Zhongyao Luo, Hao Wu, Zhao Ge, Ming Tang
https://arxiv.org/abs/2507.20587

Real-Time Distributed Optical Fiber Vibration Recognition via Extreme Lightweight Model and Cross-Domain Distillation
Distributed optical fiber vibration sensing (DVS) systems offer a promising solution for large-scale monitoring and intrusion event recognition. However, their practical deployment remains hindered by two major challenges: degradation of recognition accuracy in dynamic conditions, and the computational bottleneck of real-time processing for mass sensing data. This paper presents a new solution to these challenges, through a FPGA-accelerated extreme lightweight model along with a newly proposed …

A two-state generalisation of the strong collision model
Ola Kenji Forslund
https://arxiv.org/abs/2508.20727 https://arxiv.org/pdf/2508.20727

A two-state generalisation of the strong collision model
Muon spin relaxation is a powerful technique for probing static and dynamic local magnetic fields. The strong collision model, based on a Gaussian-Markovian process, is commonly used to account for dynamical effects. Yet, it remains limited in describing systems where the local field undergoes discrete state changes. To address this, I introduce a generalized two-state strong collision model that explicitly incorporates transitions between distinct local field environments during fluctuations. …

DRR-MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN)
Fatemeh Roshanzadeh, Hamid Barati, Ali Barati
https://arxiv.org/abs/2508.20272

DRR-MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN)
Named Data Networking (NDN) represents a transformative shift in network architecture, prioritizing content names over host addresses to enhance data dissemination. Efficient queue and resource management are critical to NDN performance, especially under dynamic and high-traffic conditions. This paper introduces DRR-MDPF, a novel hybrid strategy that integrates the Markov Decision Process Forwarding (MDPF) model with the Deficit Round Robin (DRR) algorithm. MDPF enables routers to intelligently…

Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations
Manuel V. Loureiro, Steven Derby, Aleksei Medvedev, Alejandro Ariza-Casabona, Gonzalo Fiz Pontiveros, Tri Kurniawan Wijaya
https://arxiv.org/abs/2508.20945

Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations
Recently, autoregressive recommendation models (ARMs), such as Meta's HSTU model, have emerged as a major breakthrough over traditional Deep Learning Recommendation Models (DLRMs), exhibiting the highly sought-after scaling law behaviour. However, when applied to multi-domain scenarios, the transformer architecture's attention maps become a computational bottleneck, as they attend to all items across every domain. To tackle this challenge, systems must efficiently balance inter and intra-domain…

A Simulation-Based Conceptual Model for Tokenized Recycling: Integrating Blockchain, Market Dynamics, and Behavioral Economics
Atta Ul Mustafa
https://arxiv.org/abs/2507.19901 h…

A Simulation-Based Conceptual Model for Tokenized Recycling: Integrating Blockchain, Market Dynamics, and Behavioral Economics
This study develops a conceptual simulation model for a tokenized recycling incentive system that integrates blockchain infrastructure, market-driven pricing, behavioral economics, and carbon credit mechanisms. The model aims to address the limitations of traditional recycling systems, which often rely on static government subsidies and fail to generate sustained public participation. By introducing dynamic token values linked to real-world supply and demand conditions, as well as incorporating…

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 …

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes
Yunpeng Mei, Hongjie Cao, Yinqiu Xia, Wei Xiao, Zhaohan Feng, Gang Wang, Jie Chen
https://arxiv.org/abs/2508.20547

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes
Real-time interactive grasp synthesis for dynamic objects remains challenging as existing methods fail to achieve low-latency inference while maintaining promptability. To bridge this gap, we propose SPGrasp (spatiotemporal prompt-driven dynamic grasp synthesis), a novel framework extending segment anything model v2 (SAMv2) for video stream grasp estimation. Our core innovation integrates user prompts with spatiotemporal context, enabling real-time interaction with end-to-end latency as low as …

Exploring the Stratified Space Structure of an RL Game with the Volume Growth Transform
Justin Curry, Brennan Lagasse, Ngoc B. Lam, Gregory Cox, David Rosenbluth, Alberto Speranzon
https://arxiv.org/abs/2507.22010

Exploring the Stratified Space Structure of an RL Game with the Volume Growth Transform
In this work, we explore the structure of the embedding space of a transformer model trained for playing a particular reinforcement learning (RL) game. Specifically, we investigate how a transformer-based Proximal Policy Optimization (PPO) model embeds visual inputs in a simple environment where an agent must collect "coins" while avoiding dynamic obstacles consisting of "spotlights." By adapting Robinson et al.'s study of the volume growth transform for LLMs to the RL setting, we find that the…

Weak decaying collective-excitation approximation for Yukawa one-component plasmas
Ilnaz I. Fairushin, Anatolii V. Mokshin
https://arxiv.org/abs/2507.21525 https://

Weak decaying collective-excitation approximation for Yukawa one-component plasmas
In this paper, the theoretical model of weak decaying collective excitations characteristic of many-particle systems with long-range interaction potentials is developed using the example of one-component strongly coupled Yukawa plasmas. The proposed model is based on the self-consistent relaxation theory of collective dynamics and covers spatial scales from extended hydrodynamics to scales related to the mean interparticle distance. The theoretical model reproduces the dynamic structure factor …

Controllability of a One-Dimensional Dynamic Debonding Model
Nicola De Nitti, Arick Shao
https://arxiv.org/abs/2508.19755 https://arxiv.org/pdf/2508.19755

Controllability of a One-Dimensional Dynamic Debonding Model
We investigate a one-dimensional dynamic debonding model, introduced by Freund (1990), in which the wave equation is coupled with a Griffith criterion governing the propagation of the fracture. In particular, we study the boundary controllability of the system to a prescribed target state. Our main results provide precise characterizations of the reachable target states, in both \( C^{ 0, 1 } \) and \( C^1 \) regularity settings, and construct exact controls toward these target states.

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…

Ensemble-Based Estimation of Alzheimer's Disease Incidence from Dynamic Population Reconstructions
Giulia Bertaglia, Elisa Iacomini, Alex Viguerie
https://arxiv.org/abs/2507.20179

Ensemble-Based Estimation of Alzheimer's Disease Incidence from Dynamic Population Reconstructions
We present a two-stage methodology for reconstructing Alzheimer's disease (AD) incidence over time using ensemble Kalman inversion (EKI) applied to mortality data. In the first stage, we use EKI to infer temporal trends in all-cause and Alzheimer's-specific mortality by fitting an age-structured demographic model to observed death counts. This yields posterior estimates of evolving population structure and age-specific AD mortality rates. In the second stage, we apply a back-calculation procedu…

Competition and Attraction Improve Model Fusion
Jo\~ao Abrantes, Robert Tjarko Lange, Yujin Tang
https://arxiv.org/abs/2508.16204 https://arxiv.org/pdf/250…

Competition and Attraction Improve Model Fusion
Model merging is a powerful technique for integrating the specialized knowledge of multiple machine learning models into a single model. However, existing methods require manually partitioning model parameters into fixed groups for merging, which restricts the exploration of potential combinations and limits performance. To overcome these limitations, we propose Model Merging of Natural Niches (M2N2), an evolutionary algorithm with three key features: (1) dynamic adjustment of merging boundarie…

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…

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.

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen
https://arxiv.org/abs/2507.21743

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which c…

DMPC-Swarm: Distributed Model Predictive Control on Nano UAV swarms
Alexander Gr\"afe, Joram Eickhoff, Marco Zimmerling, Sebastian Trimpe
https://arxiv.org/abs/2508.20553 h…

DMPC-Swarm: Distributed Model Predictive Control on Nano UAV swarms
Swarms of unmanned aerial vehicles (UAVs) are increasingly becoming vital to our society, undertaking tasks such as search and rescue, surveillance and delivery. A special variant of Distributed Model Predictive Control (DMPC) has emerged as a promising approach for the safe management of these swarms by combining the scalability of distributed computation with dynamic swarm motion control. In this DMPC method, multiple agents solve local optimization problems with coupled anti-collision constr…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Backward Joint Model for the Joint Dynamic Prediction of Time-to-Event and Longitudinal Data: Bas...
Wenhao Li, Shikun Wang, Zhe Yin, Brad C. Astor, Wei Yang, Tom H. Greene, Liang Li

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…

Density-dependent transport coefficients in two-dimensional cellular aggregates
Subhadip Chakraborti, Vasily Zaburdaev
https://arxiv.org/abs/2507.19919 https://

Density-dependent transport coefficients in two-dimensional cellular aggregates
The large-scale collective behavior of biological systems can be characterized by macroscopic transport, which arises from the non-equilibrium microscopic interactions among individual constituents. A prominent example is the formation of dynamic aggregates by motile eukaryotic cells or bacteria mediated by active contractile forces. In this work, we develop the two-dimensional fluctuating hydrodynamics theory based on the microscopic dynamics of a model system of aggregation by \textit{Neisser…

Efficient Reduced Order Modeling Based on HODMD to Predict Intraventricular Flow Dynamics
Eneko Lazpita, Jesus Garicano-Mena, Soledad Le Clainche
https://arxiv.org/abs/2507.21716

Efficient Reduced Order Modeling Based on HODMD to Predict Intraventricular Flow Dynamics
Accurate and efficient modeling of cardiac blood flow is crucial for advancing data-driven tools in cardiovascular research and clinical applications. Recently, the accuracy and availability of computational fluid dynamics (CFD) methodologies for simulating intraventricular flow have increased. However, these methods remain complex and computationally costly. This study presents a reduced order model (ROM) based on higher order dynamic mode decomposition (HODMD). The proposed approach enables a…

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach
Marcelo C. Medeiros, Jeronymo M. Pinro
https://arxiv.org/abs/2508.20795 https://

Time Series Embedding and Combination of Forecasts: A Reinforcement Learning Approach
The forecasting combination puzzle is a well-known phenomenon in forecasting literature, stressing the challenge of outperforming the simple average when aggregating forecasts from diverse methods. This study proposes a Reinforcement Learning - based framework as a dynamic model selection approach to address this puzzle. Our framework is evaluated through extensive forecasting exercises using simulated and real data. Specifically, we analyze the M4 Competition dataset and the Survey of Professi…

A Dynamic, Signals-Based Reinterpretation of Microeconomic Theory
Sarang Shah
https://arxiv.org/abs/2508.20268 https://arxiv.org/pdf/2508.20268

A Dynamic, Signals-Based Reinterpretation of Microeconomic Theory
Economics has long been a science of static equilibria, in which time is a second-order rather than first-order concern. Without time, economic modelers may neglect or obscure the role of time-dependent phenomena, e.g. path-dependency, and limit their ability to compare agnostically the model results with empirical observations. In this article, I outline a dynamic, signals-based recipe for building microeconomic models from traditional static models. I demonstrate this recipe using a classic "…

TokenVerse : Towards Flexible Multitask Learning with Dynamic Task Activation
Shashi Kumar, Srikanth Madikeri, Esa\'u Villatoro-Tello, Sergio Burdisso, Pradeep Rangappa, Andr\'es Carofilis, Petr Motlicek, Karthik Pandia, Shankar Venkatesan, Kadri Hacio\u{g}lu, Andreas Stolcke
https://arxiv.org/abs/2508.19856

TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation
Token-based multitasking frameworks like TokenVerse require all training utterances to have labels for all tasks, hindering their ability to leverage partially annotated datasets and scale effectively. We propose TokenVerse++, which introduces learnable vectors in the acoustic embedding space of the XLSR-Transducer ASR model for dynamic task activation. This core mechanism enables training with utterances labeled for only a subset of tasks, a key advantage over TokenVerse. We demonstrate this b…

A Dynamic Approach to Collaborative Document Writing
Avital Finanser, Nimrod Talmon
https://arxiv.org/abs/2508.17489 https://arxiv.org/pdf/2508.17489

A Dynamic Approach to Collaborative Document Writing
We introduce a model for collaborative text aggregation in which an agent community coauthors a document, modeled as an unordered collection of paragraphs, using a dynamic mechanism: agents propose paragraphs and vote on those suggested by others. We formalize the setting and explore its realizations, concentrating on voting mechanisms that aggregate votes into a single, dynamic document. We focus on two desiderata: the eventual stability of the process and its expected social welfare. Followin…

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units
Chao Hao, Zezheng Wang, Yanhua Huang, Ruiwen Xu, Wenzhe Niu, Xin Liu, Zitong Yu
https://arxiv.org/abs/2508.18763 …

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units
This paper investigates the enhancement of reasoning capabilities in language models through token-level multi-model collaboration. Our approach selects the optimal tokens from the next token distributions provided by multiple models to perform autoregressive reasoning. Contrary to the assumption that more models yield better results, we introduce a distribution distance-based dynamic selection strategy (DDS) to optimize the multi-model collaboration process. To address the critical challenge o…

A General Molecular-Scale Dynamic Memristor Model Based on Non-equilibrium Charge Transport Kinetics and Its Information Processing Capability in Reservoir Computing
Yueqi Chen, Xuan Ji, Xi Yu
https://arxiv.org/abs/2508.18014

A General Molecular-Scale Dynamic Memristor Model Based on Non-equilibrium Charge Transport Kinetics and Its Information Processing Capability in Reservoir Computing
Non-equilibrium molecular-scale dynamics, where fast electron transport couples with slow chemical state evolution, underpins the complex behaviors of molecular memristors, yet a general model linking these dynamics to neuromorphic computing remains elusive. We introduce a dynamic memristor model that integrates Landauer and Marcus electron transport theories with the kinetics of slow processes, such as proton/ion migration or conformational changes. This framework reproduces experimental condu…

Combined machine learning for stock selection strategy based on dynamic weighting methods
Lin Cai (Department of Statistics, Columbia University, New York, USA), Zhiyang He (Department of Engineering and Informatics, University of Sussex, Brighton, UK), Caiya Zhang (Department of Statistics and Data Science, Hangzhou City University, Hangzhou, China)
https://…

Combined machine learning for stock selection strategy based on dynamic weighting methods
This paper proposes a novel stock selection strategy framework based on combined machine learning algorithms. Two types of weighting methods for three representative machine learning algorithms are developed to predict the returns of the stock selection strategy. One is static weighting based on model evaluation metrics, the other is dynamic weighting based on Information Coefficients (IC). Using CSI 300 index data, we empirically evaluate the strategy' s backtested performance and model predic…

A Solvable Molecular Switch Model for Stable Temporal Information Processing
H. I. Nurdin, C. A. Nijhuis
https://arxiv.org/abs/2508.15451 https://arxiv.org…

A Solvable Molecular Switch Model for Stable Temporal Information Processing
This paper studies an input-driven one-state differential equation model initially developed for an experimentally demonstrated dynamic molecular switch that switches like synapses in the brain do. The linear-in-the-state and nonlinear-in-the-input model is exactly solvable, and it is shown that it also possesses mathematical properties of convergence and fading memory that enable stable processing of time-varying inputs by nonlinear dynamical systems. Thus, the model exhibits the co-existence …

Yume: An Interactive World Generation Model
Xiaofeng Mao, Shaoheng Lin, Zhen Li, Chuanhao Li, Wenshuo Peng, Tong He, Jiangmiao Pang, Mingmin Chi, Yu Qiao, Kaipeng Zhang
https://arxiv.org/abs/2507.17744

Yume: An Interactive World Generation Model
Yume aims to use images, text, or videos to create an interactive, realistic, and dynamic world, which allows exploration and control using peripheral devices or neural signals. In this report, we present a preview version of \method, which creates a dynamic world from an input image and allows exploration of the world using keyboard actions. To achieve this high-fidelity and interactive video world generation, we introduce a well-designed framework, which consists of four main components, incl…

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…

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah, Maryam Hafeez, Syed Ali Zaidi, Hamed Ahmadi
https://arxiv.org/abs/2507.21698

EcoFL: Resource Allocation for Energy-Efficient Federated Learning in Multi-RAT ORAN Networks
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication ef…

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…

A Nonlinear MPC Framework for Loco-Manipulation of Quadrupedal Robots with Non-Negligible Manipulator Dynamics
Ruturaj Sambhus, Kapi Ketan Mehta, Ali MirMohammad Sadeghi, Basit Muhammad Imran, Jeeseop Kim, Taizoon Chunawala, Vittorio Pastore, Sujith Vijayan, Kaveh Akbari Hamed
https://arxiv.org/abs/2507.22042…

A Nonlinear MPC Framework for Loco-Manipulation of Quadrupedal Robots with Non-Negligible Manipulator Dynamics
Model predictive control (MPC) combined with reduced-order template models has emerged as a powerful tool for trajectory optimization in dynamic legged locomotion. However, loco-manipulation tasks performed by legged robots introduce additional complexity, necessitating computationally efficient MPC algorithms capable of handling high-degree-of-freedom (DoF) models. This letter presents a computationally efficient nonlinear MPC (NMPC) framework tailored for loco-manipulation tasks of quadrupeda…

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…

Dynamic correlations in Calogero-Sutherland model
G. Lleopart Motis, D. M. Gangardt, M. Pustilnik, G. E. Astrakharchik
https://arxiv.org/abs/2507.17397 https://

Dynamic correlations in Calogero-Sutherland model
The Calogero-Sutherland model represents a paradigmatic example of an integrable quantum system with applications ranging from cold atoms to random matrix theory. Combining sum rules with the Monte Carlo technique, we introduce a stochastic method that allows one to compute the dynamic structure factor and obtain an exact description of excitations beyond the conventional Luttinger liquid regime. We explore a broad range of interaction regimes, including weak interactions, where a Bogoliubov-ty…

Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs
Meriem Mastouri, Emna Ksontini, Wael Kessentini
https://arxiv.org/abs/2507.16044

Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs
Large Language Models (LLMs) are evolving from passive text generators into active agents that invoke external tools. To support this shift, scalable protocols for tool integration are essential. The Model Context Protocol (MCP), introduced by Anthropic in 2024, offers a schema-driven standard for dynamic tool discovery and invocation. Yet, building MCP servers remains manual and repetitive, requiring developers to write glue code, handle authentication, and configure schemas by hand-replicatin…

Competition and Attraction Improve Model Fusion
Jo\~ao Abrantes, Robert Tjarko Lange, Yujin Tang
https://arxiv.org/abs/2508.16204 https://arxiv.org/pdf/250…

Competition and Attraction Improve Model Fusion
Model merging is a powerful technique for integrating the specialized knowledge of multiple machine learning models into a single model. However, existing methods require manually partitioning model parameters into fixed groups for merging, which restricts the exploration of potential combinations and limits performance. To overcome these limitations, we propose Model Merging of Natural Niches (M2N2), an evolutionary algorithm with three key features: (1) dynamic adjustment of merging boundarie…

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…

Human Feedback Driven Dynamic Speech Emotion Recognition
Ilya Fedorov, Dmitry Korobchenko
https://arxiv.org/abs/2508.14920 https://arxiv.org/pdf/2508.14920…

Human Feedback Driven Dynamic Speech Emotion Recognition
This work proposes to explore a new area of dynamic speech emotion recognition. Unlike traditional methods, we assume that each audio track is associated with a sequence of emotions active at different moments in time. The study particularly focuses on the animation of emotional 3D avatars. We propose a multi-stage method that includes the training of a classical speech emotion recognition model, synthetic generation of emotional sequences, and further model improvement based on human feedback.…

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. …

Training oscillator Ising machines to assign the dynamic stability of their equilibrium points
Yi Cheng, Zongli Lin
https://arxiv.org/abs/2507.14386 https:…

Training oscillator Ising machines to assign the dynamic stability of their equilibrium points
We propose a neural network model, which, with appropriate assignment of the stability of its equilibrium points (EPs), achieves Hopfield-like associative memory. The oscillator Ising machine (OIM) is an ideal candidates for such a model, as all its $0/π$ binary EPs are structurally stable with their dynamic stability tunable by the coupling weights. Traditional Hopfield-based models store the desired patterns by designing the coupling weights between neurons. The design of coupling weights sh…

Negative Shanshui: Real-time Interactive Ink Painting Synthesis
Aven-Le Zhou
https://arxiv.org/abs/2508.16612 https://arxiv.org/pdf/2508.16612

Negative Shanshui: Real-time Interactive Ink Painting Synthesis
This paper presents Negative Shanshui, a real-time interactive AI synthesis approach that reinterprets classical Chinese landscape ink painting, i.e., shanshui, to engage with ecological crises in the Anthropocene. Negative Shanshui optimizes a fine-tuned Stable Diffusion model for real-time inferences and integrates it with gaze-driven inpainting, frame interpolation; it enables dynamic morphing animations in response to the viewer's gaze and presents as an interactive virtual reality (VR) exp…

A Stochastic Compartmental Model of Suicide Risk Dynamics in U.S. Veterans
Anna Singley, Carrie Manore, Hannah Highlander, Ben McMahon
https://arxiv.org/abs/2508.18553 https://

A Stochastic Compartmental Model of Suicide Risk Dynamics in U.S. Veterans
We present a stochastic differential equation model of suicidal progression in U.S. veterans, simulating transitions across mental health states under dynamic stress and covariate influence. Transition rates are modulated by an Ornstein-Uhlenbeck stress process and clinical features derived from retrospective case-control data. Simulations reveal profile-dependent tipping behavior, with risk-loaded individuals exhibiting persistent ideation and attempt states. Area-under-the-curve and phase pla…

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…

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…

Querying Graph-Relational Data
Michael J. Sullivan, Zhibo Chen, Elvis Pranskevichus, Robert J. Simmons, Victor Petrovykh, Alja\v{z} Mur Er\v{z}en, Yury Selivanov
https://arxiv.org/abs/2507.16089

Querying Graph-Relational Data
For applications that store structured data in relational databases, there is an impedance mismatch between the flat representations encouraged by relational data models and the deeply nested information that applications expect to receive. In this work, we present the graph-relational database model, which provides a flexible, compositional, and strongly-typed solution to this "object-relational mismatch." We formally define the graph-relational database model and present a static and dynamic …

Spatiotemporal Maps for Dynamic MRI Reconstruction
Rodrigo A. Lobos, Xiaokai Wang, Rex T. L. Fung, Yongli He, David Frey, Dinank Gupta, Zhongming Liu, Jeffrey A. Fessler, Douglas C. Noll
https://arxiv.org/abs/2507.14429

Spatiotemporal Maps for Dynamic MRI Reconstruction
The partially separable functions (PSF) model is commonly adopted in dynamic MRI reconstruction, as is the underlying signal model in many reconstruction methods including the ones relying on low-rank assumptions. Even though the PSF model offers a parsimonious representation of the dynamic MRI signal in several applications, its representation capabilities tend to decrease in scenarios where voxels present different temporal/spectral characteristics at different spatial locations. In this work…

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…

Endogenous Quality in Social Learning
Georgy Lukyanov, Konstantin Shamruk, Ekaterina Logina
https://arxiv.org/abs/2508.20539 https://arxiv.org/pdf/2508.205…

Endogenous Quality in Social Learning
We study a dynamic reputation model with a fixed posted price where only purchases are public. A long-lived seller chooses costly quality; each buyer observes the purchase history and a private signal. Under a Markov selection, beliefs split into two cascades - where actions are unresponsive and investment is zero - and an interior region where the seller invests. The policy is inverse-U in reputation and produces two patterns: Early Resolution (rapid absorption at the optimistic cascade) and D…

Modal Logic for Stratified Becoming: Actualization Beyond Possible Worlds
Alexandre Le Nepvou
https://arxiv.org/abs/2506.17276 https://

Modal Logic for Stratified Becoming: Actualization Beyond Possible Worlds
This article develops a novel framework for modal logic based on the idea of stratified actualization, rather than the classical model of global possible worlds. Traditional Kripke semantics treat modal operators as quantification over fully determinate alternatives, neglecting the local, dynamic, and often asymmetric nature of actualization processes. We propose a system Stratified Actualization Logic (SAL) in which modalities are indexed by levels of ontological stability, interpreted as admi…

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…

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…

FARM: Frame-Accelerated Augmentation and Residual Mixture-of-Experts for Physics-Based High-Dynamic Humanoid Control
Tan Jing, Shiting Chen, Yangfan Li, Weisheng Xu, Renjing Xu
https://arxiv.org/abs/2508.19926

FARM: Frame-Accelerated Augmentation and Residual Mixture-of-Experts for Physics-Based High-Dynamic Humanoid Control
Unified physics-based humanoid controllers are pivotal for robotics and character animation, yet models that excel on gentle, everyday motions still stumble on explosive actions, hampering real-world deployment. We bridge this gap with FARM (Frame-Accelerated Augmentation and Residual Mixture-of-Experts), an end-to-end framework composed of frame-accelerated augmentation, a robust base controller, and a residual mixture-of-experts (MoE). Frame-accelerated augmentation exposes the model to high-…

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
Abhinav Singh, Petros Koumoutsakos
https://arxiv.org/abs/2507.19298 https://arxi…

Controlling Topological Defects in Polar Fluids via Reinforcement Learning
Topological defects in active polar fluids exhibit complex dynamics driven by internally generated stresses, reflecting the deep interplay between topology, flow, and non-equilibrium hydrodynamics. Feedback control offers a powerful means to guide such systems, enabling transitions between dynamic states. We investigated closed-loop steering of integer-charged defects in a confined active fluid by modulating the spatial profile of activity. Using a continuum hydrodynamic model, we show that loc…

A New Perspective of the Meese-Rogoff Puzzle: Application of Sparse Dynamic Shrinkage
Zheng Fan, Worapree Maneesoonthorn, Yong Song
https://arxiv.org/abs/2507.14408

A New Perspective of the Meese-Rogoff Puzzle: Application of Sparse Dynamic Shrinkage
We propose the Markov Switching Dynamic Shrinkage process (MSDSP), nesting the Dynamic Shrinkage Process (DSP) of Kowal et al. (2019). We revisit the Meese-Rogoff puzzle (Meese and Rogoff, 1983a,b, 1988) by applying the MSDSP to the economic models deemed inferior to the random walk model for exchange rate predictions. The flexibility of the MSDSP model captures the possibility of zero coefficients (sparsity), constant coefficient (dynamic shrinkage), as well as sudden and gradual parameter mov…

Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks
Chen Shang, Jiadong Yu, Dinh Thai Hoang
https://arxiv.org/abs/2508.19566 https://…

Energy-Efficient Learning-Based Beamforming for ISAC-Enabled V2X Networks
This work proposes an energy-efficient, learning-based beamforming scheme for integrated sensing and communication (ISAC)-enabled V2X networks. Specifically, we first model the dynamic and uncertain nature of V2X environments as a Markov Decision Process. This formulation allows the roadside unit to generate beamforming decisions based solely on current sensing information, thereby eliminating the need for frequent pilot transmissions and extensive channel state information acquisition. We then…

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Ruiqi Wu, Yuang Yao, Tengfei Ma, Chenran Zhang, Na Su, Tao Zhou, Geng Chen, Wen Fan, Yi Zhou
https://arxiv.org/abs/2508.16129

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities with reinforcement learning paradigm. Although several multimodal reasoning models have been explored in the medical domain, most of them focus exclusively on basic reasoning, which refers to shallow inference based on visual feature matching. However, real-world clinical diagnosis extends beyond basic reasoning, demanding reasoning processes that integrate heterogeneous clinical information (such…

Continual Neural Topic Model
Charu Karakkaparambil James, Waleed Mustafa, Marius Kloft, Sophie Fellenz
https://arxiv.org/abs/2508.15612 https://arxiv.org/p…

Continual Neural Topic Model
In continual learning, our aim is to learn a new task without forgetting what was learned previously. In topic models, this translates to learning new topic models without forgetting previously learned topics. Previous work either considered Dynamic Topic Models (DTMs), which learn the evolution of topics based on the entire training corpus at once, or Online Topic Models, which are updated continuously based on new data but do not have long-term memory. To fill this gap, we propose the Continu…

A Modular Residual Learning Framework to Enhance Model-Based Approach for Robust Locomotion
Min-Gyu Kim, Dongyun Kang, Hajun Kim, Hae-Won Park
https://arxiv.org/abs/2507.18138 h…

A Modular Residual Learning Framework to Enhance Model-Based Approach for Robust Locomotion
This paper presents a novel approach that combines the advantages of both model-based and learning-based frameworks to achieve robust locomotion. The residual modules are integrated with each corresponding part of the model-based framework, a footstep planner and dynamic model designed using heuristics, to complement performance degradation caused by a model mismatch. By utilizing a modular structure and selecting the appropriate learning-based method for each residual module, our framework dem…

Functional limit theorems for edge counts in dynamic random connection hypergraphs
Christian Hirsch, Benedikt Jahnel, P\'eter Juh\'asz
https://arxiv.org/abs/2507.16270

Functional limit theorems for edge counts in dynamic random connection hypergraphs
We introduce a dynamic random hypergraph model constructed from a bipartite graph. In this model, both vertex sets of the bipartite graph are generated by marked Poisson point processes. Vertices of both vertex sets are equipped with marks representing their weight that influence their connection radii. Additionally, we also assign the vertices of the first vertex set a birth-death process with exponential lifetimes and the vertices of the second vertex set a time instant representing the occur…

Replaced article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- Backward Joint Model for the Joint Dynamic Prediction of Time-to-Event and Longitudinal Data: Bas...
Wenhao Li, Shikun Wang, Zhe Yin, Brad C. Astor, Wei Yang, Tom H. Greene, Liang Li

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…

BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
Jianmin Hu, Minxian Xu, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.17133 https://

BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
In recent years, the Mixture-of-Experts (MoE) architecture has been widely applied to large language models (LLMs), providing a promising solution that activates only a subset of the model's parameters during computation, thereby reducing overall memory requirements and allowing for faster inference compared to dense models. Despite these advantages, existing systems still face issues of low efficiency due to static model placement and lack of dynamic workloads adaptation. This leads to subopti…

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…

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…

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Ruiqi Wu, Yuang Yao, Tengfei Ma, Chenran Zhang, Na Su, Tao Zhou, Geng Chen, Wen Fan, Yi Zhou
https://arxiv.org/abs/2508.16129

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities with reinforcement learning paradigm. Although several multimodal reasoning models have been explored in the medical domain, most of them focus exclusively on basic reasoning, which refers to shallow inference based on visual feature matching. However, real-world clinical diagnosis extends beyond basic reasoning, demanding reasoning processes that integrate heterogeneous clinical information (such…

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…

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…

Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Tristan S. W. Stevens, Ois\'in Nolan, Ruud J. G. van Sloun
https://arxiv.org/abs/2508.17326 https://

Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded by haze arising from multipath reverberations, particularly in difficult-to-image patients. In this work, we propose a semantic-guided, diffusion-based dehazing algorithm developed for the MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method integrates a pixel-wise noise model, der…

Cooperative SGD with Dynamic Mixing Matrices
Soumya Sarkar, Shweta Jain
https://arxiv.org/abs/2508.14565 https://arxiv.org/pdf/2508.14565

Cooperative SGD with Dynamic Mixing Matrices
One of the most common methods to train machine learning algorithms today is the stochastic gradient descent (SGD). In a distributed setting, SGD-based algorithms have been shown to converge theoretically under specific circumstances. A substantial number of works in the distributed SGD setting assume a fixed topology for the edge devices. These papers also assume that the contribution of nodes to the global model is uniform. However, experiments have shown that such assumptions are suboptimal …

A Unified Transformer Architecture for Low-Latency and Scalable Wireless Signal Processing
Yuto Kawai, Rajeev Koodli
https://arxiv.org/abs/2508.17960 https://

A Unified Transformer Architecture for Low-Latency and Scalable Wireless Signal Processing
We propose a unified Transformer-based architecture for wireless signal processing tasks, offering a low-latency, task-adaptive alternative to conventional receiver pipelines. Unlike traditional modular designs, our model integrates channel estimation, interpolation, and demapping into a single, compact attention-driven architecture designed for real-time deployment. The model's structure allows dynamic adaptation to diverse output formats by simply modifying the final projection layer, enablin…

Dynamic correlations in a polar fluid: confronting stochastic density functional theory to simulations
Sleeba Varghese, Pierre Illien, Benjamin Rotenberg
https://arxiv.org/abs/2507.16239

Dynamic correlations in a polar fluid: confronting stochastic density functional theory to simulations
Understanding the dynamic behavior of polar fluids is essential for modeling complex systems such as electrolytes and biological media. In this work, we develop and apply a Stochastic Density Functional Theory (SDFT) framework to describe the polarization dynamics in the Stockmayer fluid, a prototypical model of dipolar liquids consisting of Lennard-Jones particles with embedded point dipoles. Starting from the overdamped Langevin dynamics of dipolar particles, we derive analytical expressions …

DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
Qingyun Sun, Jiaqi Yuan, Shan He, Xiao Guan, Haonan Yuan, Xingcheng Fu, Jianxin Li, Philip S. Yu
https://arxiv.org/abs/2507.13396

DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoning
Graph Retrieval-Augmented Generation has emerged as a powerful paradigm for grounding large language models with external structured knowledge. However, existing Graph RAG methods struggle with temporal reasoning, due to their inability to model the evolving structure and order of real-world events. In this work, we introduce DyG-RAG, a novel event-centric dynamic graph retrieval-augmented generation framework designed to capture and reason over temporal knowledge embedded in unstructured text.…

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Takashi Hayakawa, Satoshi Asai
https://arxiv.org/abs/2507.17686 …

Debiased maximum-likelihood estimators for hazard ratios under machine-learning adjustment
Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the indefinite baseline hazard of the model fails to identify temporal change in the risk set composition due to treatment assignment and unobserved factors among multiple, contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose ab…

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…

DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation
Yitong Li, Raoul Grasman
https://arxiv.org/abs/2507.13957 …

DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation
The modern recommender systems are facing an increasing challenge of modelling and predicting the dynamic and context-rich user preferences. Traditional collaborative filtering and content-based methods often struggle to capture the temporal patternings and evolving user intentions. While Large Language Models (LLMs) have gained gradual attention in recent years, by their strong semantic understanding and reasoning abilities, they are not inherently designed to model chronologically evolving us…

Resilience Through Escalation: A Graph-Based PACE Architecture for Satellite Threat Response
Anouar Boumeftah, Sarah McKenzie-Picot, Peter Klimas, Gunes Karabulut Kurt
https://arxiv.org/abs/2506.20882

Resilience Through Escalation: A Graph-Based PACE Architecture for Satellite Threat Response
Satellite systems increasingly face operational risks from jamming, cyberattacks, and electromagnetic disruptions. Traditional redundancy strategies often fail against dynamic, multi-vector threats. This paper introduces a resilience-by-design framework grounded in the PACE (Primary, Alternate, Contingency, Emergency) methodology, originally developed for tactical communications in military operations, adapting it to satellite systems through a layered state-transition model informed by threat …

FlowVLA: Thinking in Motion with a Visual Chain of Thought
Zhide Zhong, Haodong Yan, Junfeng Li, Xiangchen Liu, Xin Gong, Wenxuan Song, Jiayi Chen, Haoang Li
https://arxiv.org/abs/2508.18269

FlowVLA: Thinking in Motion with a Visual Chain of Thought
Many Vision-Language-Action (VLA) models rely on an internal world model trained via next-frame prediction. This approach, however, struggles with physical reasoning as it entangles static appearance with dynamic motion, often resulting in implausible visual forecasts and inefficient policy learning. To address these limitations, we introduce the Visual Chain of Thought (Visual CoT): a pre-training framework that encourages a model to reason about how a scene evolves before predicting what it w…

Policy Optimization in the Linear Quadratic Gaussian Problem: A Frequency Domain Perspective
Haoran Li, Xun Li, Yuan-Hua Ni, Xuebo Zhang
https://arxiv.org/abs/2508.17252 https:/…

Policy Optimization in the Linear Quadratic Gaussian Problem: A Frequency Domain Perspective
The Linear Quadratic Gaussian (LQG) problem is a classic and widely studied model in optimal control, providing a fundamental framework for designing controllers for linear systems subject to process and observation noises. In recent years, researchers have increasingly focused on directly parameterizing dynamic controllers and optimizing the LQG cost over the resulting parameterized set. However, this parameterization typically gives rise to a highly non-convex optimization landscape for the r…

A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources
Hamid Varmazyari, Masoud H. Nazari
https://arxiv.org/abs/2508.18500

A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources
This paper presents a machine-learning based Stochastic Hybrid System (SHS) modeling framework to detect contingencies in active distribution networks populated with inverter-based resources (IBRs). In particular, this framework allows detecting unobservable contingencies, which cannot be identified by normal sensing systems. First, a state-space SHS model combining conventional and IRB-based resources is introduced to formulate the dynamic interaction between continuous states of distribution …

Stochastically Structured Reservoir Computers for Financial and Economic System Identification
Lendy Banegas, Fredy Vides
https://arxiv.org/abs/2507.17115 https://

Stochastically Structured Reservoir Computers for Financial and Economic System Identification
This paper introduces a methodology for identifying and simulating financial and economic systems using stochastically structured reservoir computers (SSRCs). The proposed framework leverages structure-preserving embeddings and graph-informed coupling matrices to model inter-agent dynamics with enhanced interpretability. A constrained optimization scheme ensures that the learned models satisfy both stochastic and structural constraints. Two empirical case studies, a dynamic behavioral model of …

Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters
Yingfan Zhou, Philip Sanderink, Sigurd Jager Lemming, Cheng Fang
https://arxiv.org/abs/2508.13303

Diff-MSM: Differentiable MusculoSkeletal Model for Simultaneous Identification of Human Muscle and Bone Parameters
High-fidelity personalized human musculoskeletal models are crucial for simulating realistic behavior of physically coupled human-robot interactive systems and verifying their safety-critical applications in simulations before actual deployment, such as human-robot co-transportation and rehabilitation through robotic exoskeletons. Identifying subject-specific Hill-type muscle model parameters and bone dynamic parameters is essential for a personalized musculoskeletal model, but very challenging…

Fast RLS Identification Leveraging the Linearized System Sparsity: Predictive Cost Adaptive Control for Quadrotors
Tam W. Nguyen
https://arxiv.org/abs/2508.17577 https://…

Fast RLS Identification Leveraging the Linearized System Sparsity: Predictive Cost Adaptive Control for Quadrotors
This paper presents a centralized predictive cost adaptive control (PCAC) strategy for the position and attitude control of quadrotors. PCAC is an optimal, prediction-based control method that uses recursive least squares (RLS) to identify model parameters online, enabling adaptability in dynamic environments. Addressing challenges with black-box approaches in systems with complex couplings and fast dynamics, this study leverages the unique sparsity of quadrotor models linearized around hover p…

Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory
Johann Licher, Max Bartholdt, Henrik Krauss, Tim-Lukas Habich, Thomas Seel, Moritz Schappler
https://arxiv.org/abs/2508.12681

Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory
Dynamic control of soft continuum robots (SCRs) holds great potential for expanding their applications, but remains a challenging problem due to the high computational demands of accurate dynamic models. While data-driven approaches like Koopman-operator-based methods have been proposed, they typically lack adaptability and cannot capture the full robot shape, limiting their applicability. This work introduces a real-time-capable nonlinear model-predictive control (MPC) framework for SCRs based…

DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models
Yuxiao Zhu, Junfeng Chen, Xintong Zhang, Meng Guo, Zhongkui Li
https://arxiv.org/abs/2508.14387

DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models
Online coordination of multi-robot systems in open and unknown environments faces significant challenges, particularly when semantic features detected during operation dynamically trigger new tasks. Recent large language model (LLMs)-based approaches for scene reasoning and planning primarily focus on one-shot, end-to-end solutions in known environments, lacking both dynamic adaptation capabilities for online operation and explainability in the processes of planning. To address these issues, a …

Implementation and evaluation of a prediction algorithm for an autonomous vehicle
Marco Leon Rapp
https://arxiv.org/abs/2508.12312 https://arxiv.org/pdf/25…

Implementation and evaluation of a prediction algorithm for an autonomous vehicle
This paper presents a prediction algorithm that estimates the vehicle trajectory every five milliseconds for an autonomous vehicle. A kinematic and a dynamic bicycle model are compared, with the dynamic model exhibiting superior accuracy at higher speeds. Vehicle parameters such as mass, center of gravity, moment of inertia, and cornering stiffness are determined experimentally. For cornering stiffness, a novel measurement procedure using optical position tracking is introduced. The model is in…