Tootfinder

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
Faduo Liang, Yunfeng Yang, Shi-Lu Dai
https://arxiv.org/abs/2510.02885

Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently me…

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya
https://arxiv.org/abs/2510.02404

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its su…

Gini-based Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
Alexej Brauer, Paul Menzel
https://arxiv.org/abs/2510.04556 https://

Gini-based Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
In a dynamic landscape where portfolios and environments evolve, maintaining the accuracy of pricing models is critical. To the best of our knowledge, this is the first study to systematically examine concept drift in non-life insurance pricing. We (i) provide an overview of the relevant literature and commonly used methodologies, clarify the distinction between virtual drift and concept drift, and explain their implications for long-run model performance; (ii) review and formalize common perfo…

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Yu-Zhe Shi, Qiao Xu, Yanjia Li, Mingchen Liu, Huamin Qu, Lecheng Ruan, Qining Wang
https://arxiv.org/abs/2510.02679

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, part…

A Polynomial Space Lower Bound for Diameter Estimation in Dynamic Streams
Sanjeev Khanna, Ashwin Padaki, Krish Singal, Erik Waingarten
https://arxiv.org/abs/2510.04918 https://

A Polynomial Space Lower Bound for Diameter Estimation in Dynamic Streams
We study the space complexity of estimating the diameter of a subset of points in an arbitrary metric space in the dynamic (turnstile) streaming model. The input is given as a stream of updates to a frequency vector $x \in \mathbb{Z}_{\geq 0}^n$, where the support of $x$ defines a multiset of points in a fixed metric space $M = ([n], \mathsf{d})$. The goal is to estimate the diameter of this multiset, defined as $\max\{\mathsf{d}(i,j) : x_i, x_j > 0\}$, to a specified approximation factor while…

Dynamic Modeling and Control System Analysis for Continuous-Disc Filters in Pulp Mill Operations
Jose M. Campos-Salazar, Felipe Santander, Sebastian Larrain
https://arxiv.org/abs/2510.02385

Dynamic Modeling and Control System Analysis for Continuous-Disc Filters in Pulp Mill Operations
Vacuum disc filtration is critical in pulp mills for white liquor clarification and pulp washing, involving tightly coupled dynamics between rotational speed, vacuum pressure, slurry concentration, filtrate flow, and cake thickness. These nonlinear interactions are often regulated using empirical methods, lacking formal modeling and control. This article develops a dynamic, multivariable model of a continuous-disc filter (CD-filter) system based on first principles, simplified to a single repre…

Dynamic micromagnetism a la Ericksen-Leslie, allowing the Einstein-de Haas and Barnett effects
Amit Acharya, Siladitya Pal
https://arxiv.org/abs/2510.04393 https://

Dynamic micromagnetism a la Ericksen-Leslie, allowing the Einstein-de Haas and Barnett effects
A model of dissipative micromagnetics coupled to elasticity is developed, following the procedures of the Ericksen-Leslie theory of nematic liquid crystals allowing for angular momentum due to magnetization. An outcome is the Landau-Lifshitz-Gilbert theory coupled to material spin. A further power-less augmentation to the angular momentum of the theory with classical kinetic energy density is also considered, which allows for plausible approaches to model the Einstein-de Haas and Barnett effect…

Efficient Optimal Path Planning in Dynamic Environments Using Koopman MPC
Mohammad Abtahi, Navid Mojahed, Shima Nazari
https://arxiv.org/abs/2510.02584 https://

Efficient Optimal Path Planning in Dynamic Environments Using Koopman MPC
This paper presents a data-driven model predictive control framework for mobile robots navigating in dynamic environments, leveraging Koopman operator theory. Unlike the conventional Koopman-based approaches that focus on the linearization of system dynamics only, our work focuses on finding a global linear representation for the optimal path planning problem that includes both the nonlinear robot dynamics and collision-avoidance constraints. We deploy extended dynamic mode decomposition to ide…

Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech
Hieu-Nghia Huynh-Nguyen, Huynh Nguyen Dang, Ngoc-Son Nguyen, Van Nguyen
https://arxiv.org/abs/2510.02848

Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech
Zero-shot Text-to-Speech (TTS) has recently advanced significantly, enabling models to synthesize speech from text using short, limited-context prompts. These prompts serve as voice exemplars, allowing the model to mimic speaker identity, prosody, and other traits without extensive speaker-specific data. Although recent approaches incorporating language models, diffusion, and flow matching have proven their effectiveness in zero-shot TTS, they still encounter challenges such as unreliable synth…

Mask2IV: Interaction-Centric Video Generation via Mask Trajectories
Gen Li, Bo Zhao, Jianfei Yang, Laura Sevilla-Lara
https://arxiv.org/abs/2510.03135 https://

Mask2IV: Interaction-Centric Video Generation via Mask Trajectories
Generating interaction-centric videos, such as those depicting humans or robots interacting with objects, is crucial for embodied intelligence, as they provide rich and diverse visual priors for robot learning, manipulation policy training, and affordance reasoning. However, existing methods often struggle to model such complex and dynamic interactions. While recent studies show that masks can serve as effective control signals and enhance generation quality, obtaining dense and precise mask an…

Investigating mixed traffic dynamics of pedestrians and non-motorized vehicles at urban intersections: Observation experiments and modelling
Chaojia Yu, Kaixin Wang, Junle Li, Jingjie Wang
https://arxiv.org/abs/2510.04423

Investigating mixed traffic dynamics of pedestrians and non-motorized vehicles at urban intersections: Observation experiments and modelling
Urban intersections with mixed pedestrian and non-motorized vehicle traffic present complex safety challenges, yet traditional models fail to account for dynamic interactions arising from speed heterogeneity and collision anticipation. This study introduces the Time and Angle Based Social Force Model (TASFM), an enhanced framework extending the classical Social Force Model by integrating Time-to-Collision (TTC) metrics and velocity-angle-dependent tangential forces to simulate collision avoidan…

Hidden Actions and Hidden Information in Peer Review: A Dynamic Solution
Raphael Mu
https://arxiv.org/abs/2510.04906 https://arxiv.org/pdf/2510.04906

Hidden Actions and Hidden Information in Peer Review: A Dynamic Solution
We develop a simple model of the scientific peer review process, in which authors of varying ability invest to produce papers of varying quality, and journals evaluate papers based on a noisy signal, choosing to accept or reject each paper. We find that the first-best outcome is the limiting case as the evaluation technology is perfected, even though author type and effort are not known to the journal. Then, we consider the case where journals allow authors to challenge an initial rejection, an…

Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks
Alvaro Paz, Pauli Mustalahti, Mohammad Dastranj, Jouni Mattila
https://arxiv.org/abs/2510.02976

Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks
This paper presents a framework for real-time optimal controlling of a heavy-duty skid-steered mobile platform for trajectory tracking. The importance of accurate real-time performance of the controller lies in safety considerations of situations where the dynamic system under control is affected by uncertainties and disturbances, and the controller should compensate for such phenomena in order to provide stable performance. A multiple-shooting nonlinear model-predictive control framework is pr…

Model Falsification for Predicting Dynamical Responses of Uncertain Structural Systems
Subhayan De, Tianhao Yu, Patrick T. Brewick, Erik A. Johnson, Steven F. Wojtkiewicz
https://arxiv.org/abs/2510.02612

Model Falsification for Predicting Dynamical Responses of Uncertain Structural Systems
Accurate prediction of dynamical response of structural system depends on the correct modeling of that system. However, modeling becomes increasingly challenging when there are many candidate models available to describe the system behavior. Furthermore, uncertainties can be present even for the parameters of these model classes. The plausibility of each input-output model class of the structures with uncertain components can be determined by a Bayesian approach from measured dynamic responses …

An analysis of government subsidy policies in vaccine supply chain: Innovation, Production, or Consumption?
Ran Gu, Enhui Ding, Shigui Ma
https://arxiv.org/abs/2510.03661 https:…

An analysis of government subsidy policies in vaccine supply chain: Innovation, Production, or Consumption?
Vaccines play a crucial role in the prevention and control of infectious diseases. However, the vaccine supply chain faces numerous challenges that hinder its efficiency. To address these challenges and enhance public health outcomes, many governments provide subsidies to support the vaccine supply chain. This study analyzes a government-subsidized, three-tier vaccine supply chain within a continuous-time differential game framework. The model incorporates dynamic system equations that account …

Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments
Fangyu Liu, Peiwen Jiang, Wenjin Wang, Chao-Kai Wen, Shi Jin, Jun Zhang
https://arxiv.org/abs/2509.23590

Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments
Foundation model-based semantic transmission has recently shown great potential in wireless image communication. However, existing methods exhibit two major limitations: (i) they overlook the varying importance of semantic components for specific downstream tasks, and (ii) they insufficiently exploit wireless domain knowledge, resulting in limited robustness under dynamic channel conditions. To overcome these challenges, this paper proposes a foundation model-based adaptive semantic image trans…

DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao, Weipu Zhang, Jian Sun, Gao Huang, Jie Chen, Gang Wang
https://arxiv.org/abs/2509.24804 …

DyMoDreamer: World Modeling with Dynamic Modulation
A critical bottleneck in deep reinforcement learning (DRL) is sample inefficiency, as training high-performance agents often demands extensive environmental interactions. Model-based reinforcement learning (MBRL) mitigates this by building world models that simulate environmental dynamics and generate synthetic experience, improving sample efficiency. However, conventional world models process observations holistically, failing to decouple dynamic objects and temporal features from static backg…

Attack Detection in Dynamic Games with Quadratic Measurements
Muyan Jiang, Anil Aswani
https://arxiv.org/abs/2510.00241 https://arxiv.org/pdf/2510.00241

Attack Detection in Dynamic Games with Quadratic Measurements
This paper studies attack detection for discrete-time linear systems with stochastic process noise that produce both a vulnerable (i.e., attackable) linear measurement and a secured (i.e., unattackable) quadratic measurement. The motivating application of this model is a dynamic-game setting where the quadratic measurement is interpreted as a system-level utility or reward, and control inputs into the linear system are interpreted as control policies that, once applied, are known to all game pa…

Accuracy vs Performance: An abstraction model for deadline constrained offloading at the mobile-edge
Jamie Cotter, Ignacio Castineiras, Victor Cionca
https://arxiv.org/abs/2510.01885

Accuracy vs Performance: An abstraction model for deadline constrained offloading at the mobile-edge
In this paper, we present a solution for low-latency deadline-constrained DNN offloading on mobile edge devices. We design a scheduling algorithm with lightweight network state representation, considering device availability, communication on the network link, priority-aware pre-emption, and task deadlines. The scheduling algorithm aims to reduce latency by designing a resource availability representation, as well as a network discretisation and a dynamic bandwidth estimation mechanism. We impl…

Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination
Hassen Dhrif
https://arxiv.org/abs/2510.00326 https://…

Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination
The emergence of large language models has enabled sophisticated multi-agent systems, yet coordinating their reasoning capabilities through prompt engineering remains challenging. We present a theoretically-grounded framework for dynamic prompt orchestration that enhances reasoning across multiple specialized agents. This framework addresses three core challenges: logical consistency preservation during agent transitions, reasoning-aware prompt adaptation, and scalable coordination of distribut…

Choosing to Be Green: Advancing Green AI via Dynamic Model Selection
Emilio Cruciani, Roberto Verdecchia
https://arxiv.org/abs/2509.19996 https://arxiv.org…

Choosing to Be Green: Advancing Green AI via Dynamic Model Selection
Artificial Intelligence is increasingly pervasive across domains, with ever more complex models delivering impressive predictive performance. This fast technological advancement however comes at a concerning environmental cost, with state-of-the-art models - particularly deep neural networks and large language models - requiring substantial computational resources and energy. In this work, we present the intuition of Green AI dynamic model selection, an approach based on dynamic model selection…

Text Adversarial Attacks with Dynamic Outputs
Wenqiang Wang, Siyuan Liang, Xiao Yan, Xiaochun Cao
https://arxiv.org/abs/2509.22393 https://arxiv.org/pdf/25…

Text Adversarial Attacks with Dynamic Outputs
Text adversarial attack methods are typically designed for static scenarios with fixed numbers of output labels and a predefined label space, relying on extensive querying of the victim model (query-based attacks) or the surrogate model (transfer-based attacks). To address this gap, we introduce the Textual Dynamic Outputs Attack (TDOA) method, which employs a clustering-based surrogate model training approach to convert the dynamic-output scenario into a static single-output scenario. To impro…

To Remember, To Adapt, To Preempt: A Stable Continual Test-Time Adaptation Framework for Remote Physiological Measurement in Dynamic Domain Shifts
Shuyang Chu, Jingang Shi, Xu Cheng, Haoyu Chen, Xin Liu, Jian Xu, Guoying Zhao
https://arxiv.org/abs/2510.01282

To Remember, To Adapt, To Preempt: A Stable Continual Test-Time Adaptation Framework for Remote Physiological Measurement in Dynamic Domain Shifts
Remote photoplethysmography (rPPG) aims to extract non-contact physiological signals from facial videos and has shown great potential. However, existing rPPG approaches struggle to bridge the gap between source and target domains. Recent test-time adaptation (TTA) solutions typically optimize rPPG model for the incoming test videos using self-training loss under an unrealistic assumption that the target domain remains stationary. However, time-varying factors like weather and lighting in dynami…

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
Oussama Gabouj, Kamel Charaf, Ivan Zakazov, Nicolas Baldwin, Robert West
https://arxiv.org/abs/2510.01165

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
Large Language Models (LLMs) achieve strong performance across diverse tasks, but their effectiveness often depends on the quality of the provided context. Retrieval-Augmented Generation (RAG) enriches prompts with external information, but its reliance on static databases constrains adaptability and can result in irrelevant demonstrations. In this work, we propose a Generative Retrieval-Aligned Demonstrator (GRAD), a dynamic demonstration-based approach where an LLM model is trained to generat…

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Yukun Yang
https://arxiv.org/abs/2509.26145

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Depression is a major global public health challenge and its early identification is crucial. Social media data provides a new perspective for depression detection, but existing methods face limitations such as insufficient accuracy, insufficient utilization of time series features, and high annotation costs. To this end, this study proposes the LMILAtt model, which innovatively integrates Long Short-Term Memory autoencoders and attention mechanisms: firstly, the temporal dynamic features of us…

Dynamic Dual-level Defense Routing for Continual Adversarial Training
Wenxuan Wang, Chenglei Wang, Xuelin Qian
https://arxiv.org/abs/2509.21392 https://arx…

Dynamic Dual-level Defense Routing for Continual Adversarial Training
As adversarial attacks continue to evolve, defense models face the risk of recurrent vulnerabilities, underscoring the importance of continuous adversarial training (CAT). Existing CAT approaches typically balance decision boundaries by either data replay or optimization strategy to constrain shared model parameters. However, due to the diverse and aggressive nature of adversarial examples, these methods suffer from catastrophic forgetting of previous defense knowledge after continual learning.…

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
Maryam Babazadeh, Naim Bajcinca
https://arxiv.org/abs/2509.24598 https://arxi…

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
This paper presents a new method for achieving dynamic consensus in linear discrete-time homogeneous multi-agent systems (MAS) with marginally stable or unstable dynamics. The guarantee of consensus in this setting involves a set of constraints based on the graph's spectral properties, complicating the design of the coupling gains. This challenge intensifies for large-scale systems with diverse graph Laplacian spectra. The proposed approach reformulates the dynamic consensus problem with a pres…

Generalized Bayesian Inference for Dynamic Random Dot Product Graphs
Joshua Daniel Loyal
https://arxiv.org/abs/2509.19748 https://arxiv.org/pdf/2509.19748

Generalized Bayesian Inference for Dynamic Random Dot Product Graphs
The random dot product graph is a popular model for network data with extensions that accommodate dynamic (time-varying) networks. However, two significant deficiencies exist in the dynamic random dot product graph literature: (1) no coherent Bayesian way to update one's prior beliefs about the latent positions in dynamic random dot product graphs due to their complicated constraints, and (2) no approach to forecast future networks with meaningful uncertainty quantification. This work proposes …

Diffusion Modeling of the Three-Dimensional Magnetic Field in the Sun's Corona
Daniel E. da Silva, Michael Kirk, Nat Mathews, Andr\'es Mu\~noz-Jaramillo
https://arxiv.org/abs/2510.01441

Diffusion Modeling of the Three-Dimensional Magnetic Field in the Sun's Corona
In this work, we introduce a novel generative denoising diffusion model for synthesizing the Sun's three-dimensional coronal magnetic field, a complex and dynamic region characterized by evolving magnetic structures. Despite daily variability, these structures exhibit recurring patterns and long-term cyclic trends, presenting unique modeling challenges and opportunities at the intersection of physics and machine learning. Our generative approach employs an innovative architecture influenced by …

Universal critical dynamics near the chiral phase transition and the QCD critical point
Yunxin Ye, Johannes V. Roth, S\"oren Schlichting, Lorenz von Smekal
https://arxiv.org/abs/2509.26355

Universal critical dynamics near the chiral phase transition and the QCD critical point
We use a novel real-time formulation of the functional renormalization group (FRG) for dynamical systems with reversible mode couplings to study Model G and H, which are the conjectured dynamic universality classes of the two-flavor chiral phase transition and the QCD critical point, respectively. We compute the dynamic critical exponent in both models in spatial dimensions $2

Development of a Model Order Reduced Arbitrary Lagrangian Eulerian (MORALE) formulation for structures subjected to dynamic moving loads
Atul Anantheswar, Jannick Kehls, Ines Wollny, Tim Brepols, Stefanie Reese, Michael Kaliske
https://arxiv.org/abs/2509.20069

Development of a Model Order Reduced Arbitrary Lagrangian Eulerian (MORALE) formulation for structures subjected to dynamic moving loads
In recent developments, it has been demonstrated that the Arbitrary Lagrangian Eulerian (ALE) formulation can be utilized to improve computational efficiency, when simulating the response of structures subjected to moving loads. It is also well established in literature, that Model Order Reduction (MOR) techniques significantly enhance calculation speed. This contribution details the combination of both these tools into a novel Model Order Reduced Arbitrary Lagrangian Eulerian (MORALE) formulat…

Preference-Based Dynamic Ranking Structure Recognition
Nan Lu, Jian Shi, Xin-Yu Tian
https://arxiv.org/abs/2509.24493 https://arxiv.org/pdf/2509.24493

Preference-Based Dynamic Ranking Structure Recognition
Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic…

From Phenomenology to a Nonlinear Model of Dynamic Snap-Through of an Elastica
Chiraprabha Bhattacharyya, Ramsharan Rangarajan
https://arxiv.org/abs/2509.21328 https://

From Phenomenology to a Nonlinear Model of Dynamic Snap-Through of an Elastica
Rotating the clamped ends of a buckled elastica induces a snap-through instability. Predicting the limit point and determining the equilibria at the start and end of the snap are routine computations in the quasi-static setting. The instability itself, however, is dynamic, and quite violently so. We propose an energy-preserving nonlinear single degree of freedom model for this dynamic phenomenon in the case of a symmetrically deforming elastica. The model hinges on a surprising observation rela…

Age-structured stochastic populations under dynamic harvesters' behavior: well-posedness, asymptotic stability and numerically-amenable approximations
M. Isidora \'Avila-Thieme, Kerlyns Mart\'inez, H\'ector Olivero, Mauricio Tejo, Leonardo Videla
https://arxiv.org/abs/2509.22513

Age-structured stochastic populations under dynamic harvesters' behavior: well-posedness, asymptotic stability and numerically-amenable approximations
In this paper we study a model of age-structured ecological populations in continuous interaction with a community of harvesters. We propose an individual-based model for this feedback interactions and prove its convergence to a system of coupled stochastic differential equations. This limit process is complemented with socio-ecological and environmental factors. For the resulting system we establish well-posedness, positivity, and the non-attainability of extinction boundaries, together with…

Temporal-Aware Iterative Speech Model for Dementia Detection
Chukwuemeka Ugwu, Oluwafemi Oyeleke
https://arxiv.org/abs/2510.00030 https://arxiv.org/pdf/251…

Temporal-Aware Iterative Speech Model for Dementia Detection
Deep learning systems often struggle with processing long sequences, where computational complexity can become a bottleneck. Current methods for automated dementia detection using speech frequently rely on static, time-agnostic features or aggregated linguistic content, lacking the flexibility to model the subtle, progressive deterioration inherent in speech production. These approaches often miss the dynamic temporal patterns that are critical early indicators of cognitive decline. In this pap…

A geometric interpolation scheme for applying dynamic wetting to three-dimensional volume of fluid simulations
Yifan Han, Gerd Mutschke, Kerstin Eckert
https://arxiv.org/abs/2509.21076

A geometric interpolation scheme for applying dynamic wetting to three-dimensional volume of fluid simulations
This paper presents a three-dimensional framework for simulating dynamic wetting phenomena using the volume of fluid (VOF) method, implemented in Basilisk. A geometric interpolation scheme is developed to obtain an accurate and reliable value of the contact line velocity. To capture realistic wetting dynamics, a dynamic contact angle model is integrated that considers also contact angle hysteresis (CAH). The approach is validated against various experimental results, including droplet spreading…

Electromagnetics-Compliant Optimization of Dynamic Metasurface Antennas for Bistatic Sensing
Ioannis Gavras, George C. Alexandropoulos
https://arxiv.org/abs/2509.19801 https://

Electromagnetics-Compliant Optimization of Dynamic Metasurface Antennas for Bistatic Sensing
Dynamic Metasurface Antennas (DMAs) are recently attracting considerable research interests due to their potential to enable low-cost, reconfigurable, and highly scalable antenna array architectures for next generation wireless systems. However, most of the existing literature relies on idealized models for the DMA operation, often overlooking critical structural and physical constraints inherent to their constituent metamaterials. In this paper, leveraging a recently proposed model for this an…

Context-Aware Hybrid Routing in Bluetooth Mesh Networks Using Multi-Model Machine Learning and AODV Fallback
Md Sajid Islam, Tanvir Hasan
https://arxiv.org/abs/2509.21490 https:…

Context-Aware Hybrid Routing in Bluetooth Mesh Networks Using Multi-Model Machine Learning and AODV Fallback
Bluetooth-based mesh networks offer a promising infrastructure for offline communication in emergency and resource constrained scenarios. However, traditional routing strategies such as Ad hoc On-Demand Distance Vector (AODV) often degrade under congestion and dynamic topological changes. This study proposes a hybrid intelligent routing framework that augments AODV with supervised machine learning to improve next-hop selection under varied network constraints. The framework integrates four pred…

Toward a Realistic Encoding Model of Auditory Affective Understanding in the Brain
Guandong Pan, Yaqian Yang, Shi Chen, Xin Wang, Longzhao Liu, Hongwei Zheng, Shaoting Tang
https://arxiv.org/abs/2509.21381

Toward a Realistic Encoding Model of Auditory Affective Understanding in the Brain
In affective neuroscience and emotion-aware AI, understanding how complex auditory stimuli drive emotion arousal dynamics remains unresolved. This study introduces a computational framework to model the brain's encoding of naturalistic auditory inputs into dynamic behavioral/neural responses across three datasets (SEED, LIRIS, self-collected BAVE). Guided by neurobiological principles of parallel auditory hierarchy, we decompose audio into multilevel auditory features (through classical algorit…

Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting
Zinan Zheng, Yang Liu, Jia Li
https://arxiv.org/abs/2509.20911 https://

Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting
Graph neural networks have shown promising results in weather forecasting, which is critical for human activity such as agriculture planning and extreme weather preparation. However, most studies focus on finite and local areas for training, overlooking the influence of broader areas and limiting their ability to generalize effectively. Thus, in this work, we study global weather forecasting that is irregularly distributed and dynamically varying in practice, requiring the model to generalize t…

The Varieties of Schelling Model Experience
Marlyn Boke, Timothy Sorochkin, Jesse Anttila-Hughes, Alan O. Jamison
https://arxiv.org/abs/2509.14462 https://…

The Varieties of Schelling Model Experience
The Schelling model is a prototype for agent-based modeling in social systems. A comprehensive analysis of Schelling model rule variants is achieved by classification of the space of macroscopic outcomes via phase diagrams. Among 54 rule variants, only 3 phase diagram classes are found, characterized by the number of phase transitions. This classification scheme is found to be robust to the use of sociological and percolation-inspired measures of segregation. The statistical and dynamic drivers…

PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos
Ting-Hsuan Liao, Haowen Liu, Yiran Xu, Songwei Ge, Gengshan Yang, Jia-Bin Huang
https://arxiv.org/abs/2509.25183 h…

PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos
We present PAD3R, a method for reconstructing deformable 3D objects from casually captured, unposed monocular videos. Unlike existing approaches, PAD3R handles long video sequences featuring substantial object deformation, large-scale camera movement, and limited view coverage that typically challenge conventional systems. At its core, our approach trains a personalized, object-centric pose estimator, supervised by a pre-trained image-to-3D model. This guides the optimization of deformable 3D G…

Optimal Matching Strategies in Two-sided Markets: A Mean Field Approach
Erhan Bayraktar, Dantong Chu, Bohan Li, Ho Man Tai
https://arxiv.org/abs/2509.26531 https://

Optimal Matching Strategies in Two-sided Markets: A Mean Field Approach
This paper develops a mean field game framework for dynamic two-sided matching markets, extending existing matching theory by integrating micro-macro dynamics in two-sided environments. Unlike traditional matching models focusing on static equilibrium or unilateral optimization, our framework simultaneously captures dynamic interactions and strategic behaviors of both market sides, as well as the equilibrium. We model two types of agents who meet each other via Poisson processes and make simult…

StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions
Nicholas Kraabel, Jiangtao Liu, Yuchen Bian, Daniel Kifer, Chaopeng Shen
https://arxiv.org/abs/2509.17942

StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions
Stewarding natural resources, mitigating floods, droughts, wildfires, and landslides, and meeting growing demands require models that can predict climate-driven land-surface responses and human feedback with high accuracy. Traditional impact models, whether process-based, statistical, or machine learning, struggle with spatial generalization due to limited observations and concept drift. Recently proposed vision foundation models trained on satellite imagery demand massive compute and are ill-s…

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
Yixuan Han, Fan Ma, Ruijie Quan, Yi Yang
https://arxiv.org/abs/2509.22572 https://

Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time
Test-Time Scaling (TTS) enhances the reasoning ability of large language models (LLMs) by allocating additional computation during inference. However, existing approaches primarily rely on output-level sampling while overlooking the role of model architecture. In mainstream Mixture-of-Experts (MoE) LLMs, we observe that varying the number of activated experts yields complementary solution sets with stable accuracy, revealing a new and underexplored source of diversity. Motivated by this observa…

Multi-Agent Guided Policy Search for Non-Cooperative Dynamic Games
Jingqi Li, Gechen Qu, Jason J. Choi, Somayeh Sojoudi, Claire Tomlin
https://arxiv.org/abs/2509.24226 https://

Multi-Agent Guided Policy Search for Non-Cooperative Dynamic Games
Multi-agent reinforcement learning (MARL) optimizes strategic interactions in non-cooperative dynamic games, where agents have misaligned objectives. However, data-driven methods such as multi-agent policy gradients (MA-PG) often suffer from instability and limit-cycle behaviors. Prior stabilization techniques typically rely on entropy-based exploration, which slows learning and increases variance. We propose a model-based approach that incorporates approximate priors into the reward function a…

DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework
Rui Jia, Yuang Wei, Ruijia Li, Yuang-Hao Jiang, Xinyu Xie, Yaomin Shen, Min Zhang, Bo Jiang
https://arxiv.org/abs/2509.24821

DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework
While cognitive diagnosis (CD) effectively assesses students' knowledge mastery from structured test data, applying it to real-world teacher-student dialogues presents two fundamental challenges. Traditional CD models lack a suitable framework for handling dynamic, unstructured dialogues, and it's difficult to accurately extract diagnostic semantics from lengthy dialogues. To overcome these hurdles, we propose DiaCDM, an innovative model. We've adapted the initiation-response-evaluation (IRE) f…

Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach
Marta Cipriani, Marco Alf\`o, Mirko Signorelli
https://arxiv.org/abs/2509.18875 https://

Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach
Mixture cure models are widely used in survival analysis when a portion of patients is considered cured and is no longer at risk for the event of interest. In clinical settings, dynamic survival prediction is particularly important to refine prognosis by incorporating updated patient information over time. Landmarking methods have emerged as a flexible approach for this purpose, as they allow to summarize longitudinal covariates up to a given landmark time and to use these summaries in subseque…

Path-dependent, ESG-valued, option pricing in the Bachelier-Black-Scholes-Merton model
Bhathiya Divelgama, Nancy Asare Nyarko, W. Brent Lindquist, Svetlozar T. Rachev, Blessing Omotade
https://arxiv.org/abs/2509.18099

BugMagnifier: TON Transaction Simulator for Revealing Smart Contract Vulnerabilities
Yury Yanovich, Victoria Kovalevskaya, Maksim Egorov, Elizaveta Smirnova, Matvey Mishuris, Yash Madhwal, Kirill Ziborov, Vladimir Gorgadze, Subodh Sharma
https://arxiv.org/abs/2509.24444

BugMagnifier: TON Transaction Simulator for Revealing Smart Contract Vulnerabilities
The Open Network (TON) blockchain employs an asynchronous execution model that introduces unique security challenges for smart contracts, particularly race conditions arising from unpredictable message processing order. While previous work established vulnerability patterns through static analysis of audit reports, dynamic detection of temporal dependencies through systematic testing remains an open problem. We present BugMagnifier, a transaction simulation framework that systematically reveals…

DyKnow-RAG: Dynamic Knowledge Utilization Reinforcement Framework for Noisy Retrieval-Augmented Generation in E-commerce Search Relevance
Tingqiao Xu, Shaowei Yao, Chenhe Dong, Yiming Jin, Zerui Huang, Dan Ou, Haihong Tang
https://arxiv.org/abs/2510.11122

DyKnow-RAG: Dynamic Knowledge Utilization Reinforcement Framework for Noisy Retrieval-Augmented Generation in E-commerce Search Relevance
Accurately modeling query-item relevance drives e-commerce ranking, yet long-tail, knowledge-heavy, and fast-evolving queries exceed parametric LLM coverage. External context (reviews, attribute encyclopedias, UGC) can help but is noisy, and single-pass latency and cost forbid any clean-then-summarize step. The model must, per query, judge relevance and decide whether to use, partially use, or ignore the context. DyKnow-RAG is a dynamic noisy-RAG framework built on Group Relative Policy Optimiz…

Parallax: Efficient LLM Inference Service over Decentralized Environment
Chris Tong, Youhe Jiang, Gufeng Chen, Tianyi Zhao, Sibian Lu, Wenjie Qu, Eric Yang, Lynn Ai, Binhang Yuan
https://arxiv.org/abs/2509.26182

Parallax: Efficient LLM Inference Service over Decentralized Environment
Deploying a large language model (LLM) inference service remains costly because centralized serving depends on specialized GPU clusters and high-bandwidth interconnects in datacenters. An appealing alternative is to leverage collaborative decentralized GPU pools. However, heterogeneity in GPU and limited interconnected network bandwidth, along with potentially dynamic availability, make efficient scheduling the central challenge in this scenario. In this paper, we present Parallax, a decentrali…

Pack and Force Your Memory: Long-form and Consistent Video Generation
Xiaofei Wu, Guozhen Zhang, Zhiyong Xu, Yuan Zhou, Qinglin Lu, Xuming He
https://arxiv.org/abs/2510.01784 ht…

Pack and Force Your Memory: Long-form and Consistent Video Generation
Long-form video generation presents a dual challenge: models must capture long-range dependencies while preventing the error accumulation inherent in autoregressive decoding. To address these challenges, we make two contributions. First, for dynamic context modeling, we propose MemoryPack, a learnable context-retrieval mechanism that leverages both textual and image information as global guidance to jointly model short- and long-term dependencies, achieving minute-level temporal consistency. Th…

Equity Market Price Changes Are Predictable: A Natural Science Approach
Qingyuan Han
https://arxiv.org/abs/2510.01542 https://arxiv.org/pdf/2510.01542

Equity Market Price Changes Are Predictable: A Natural Science Approach
Equity markets have long been regarded as unpredictable, with intraday price movements treated as stochastic noise. This study challenges that view by introducing the Extended Samuelson Model (ESM), a natural science-based framework that captures the dynamic, causal processes underlying market behavior. ESM identifies peaks, troughs, and turning points across multiple timescales and demonstrates temporal compatibility: finer timeframes contain all signals of broader ones while offering sharper …

Earthquake modelling via Brownian motions on networks
Fausto Colantoni, Mirko D'Ovidio, Flavia Tavani
https://arxiv.org/abs/2509.21851 https://arxiv.or…

Earthquake modelling via Brownian motions on networks
We provide a general model for Brownian motions on metric graphs with interactions. In a general setting, for (sticky) Brownian propagations on edges, our model provides a characterization of lifetimes and holding times on vertices in terms of (jumping) Brownian accumulation of energy associated with that vertices. Propagation and accumulation are given by drifted Brownian motions subjected to non-local (also dynamic) boundary conditions. As the continuous (sticky) process approaches a vertex, …

Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation
Matteo Cardoni, Sam Leroux
https://arxiv.org/abs/2509.20269 https://

Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation
As deep neural networks are increasingly deployed in dynamic, real-world environments, relying on a single static model is often insufficient. Changes in input data distributions caused by sensor drift or lighting variations necessitate continual model adaptation. In this paper, we propose a hybrid training methodology that enables efficient on-device domain adaptation by combining the strengths of Backpropagation and Predictive Coding. The method begins with a deep neural network trained offli…

DSCC-HS: A Dynamic Self-Reinforcing Framework for Hallucination Suppression in Large Language Models
Xiao Zheng
https://arxiv.org/abs/2509.13702 https://ar…

DSCC-HS: A Dynamic Self-Reinforcing Framework for Hallucination Suppression in Large Language Models
Large Language Model (LLM) hallucination is a significant barrier to their reliable deployment. Current methods like Retrieval-Augmented Generation (RAG) are often reactive. We introduce **Dynamic Self-reinforcing Calibration for Hallucination Suppression (DSCC-HS)**, a novel, proactive framework that intervenes during autoregressive decoding. Inspired by dual-process cognitive theory, DSCC-HS uses a compact proxy model, trained in adversarial roles as a Factual Alignment Proxy (FAP) and a Hall…

Stability Analysis of An Integrated Multistage Stochastic Programming and Markov Decision Process Problem
Zhiyao Yang, Zhiping Chen, Huifu Xu
https://arxiv.org/abs/2509.22194 ht…

Stability Analysis of An Integrated Multistage Stochastic Programming and Markov Decision Process Problem
In this paper, we consider an integrated MSP-MDP framework which captures features of Markov decision process (MDP) and multistage stochastic programming (MSP). The integrated framework allows one to study a dynamic decision-making process that involves both transition of system states and dynamic change of the stochastic environment affected respectively by potential endogenous uncertainties and exogenous uncertainties. The integrated model differs from classical MDP models by taking into acco…

SEEC: Stable End-Effector Control with Model-Enhanced Residual Learning for Humanoid Loco-Manipulation
Jaehwi Jang, Zhuoheng Wang, Ziyi Zhou, Feiyang Wu, Ye Zhao
https://arxiv.org/abs/2509.21231

SEEC: Stable End-Effector Control with Model-Enhanced Residual Learning for Humanoid Loco-Manipulation
Arm end-effector stabilization is essential for humanoid loco-manipulation tasks, yet it remains challenging due to the high degrees of freedom and inherent dynamic instability of bipedal robot structures. Previous model-based controllers achieve precise end-effector control but rely on precise dynamics modeling and estimation, which often struggle to capture real-world factors (e.g., friction and backlash) and thus degrade in practice. On the other hand, learning-based methods can better mitig…

Developing a Dynamic Mobility Model for Backcasting Applications: A Case Study with Shared Autonomous Vehicles
Th\'eotime H\'eraud, Vinith Lakshmanan, Antonio Sciaretta
https://arxiv.org/abs/2509.17928

Developing a Dynamic Mobility Model for Backcasting Applications: A Case Study with Shared Autonomous Vehicles
This study proposes the application of a backcasting approach to a mobility model with the aim of defining an optimal decarbonization roadmap. The selected decision variable is the introduction of a fleet of shared autonomous vehicles. The mobility model developed is composed of six interconnected sub-models. After presenting each of these models in detail, a method is introduced to analyze the direct and indirect effects of the measure, and a necessary condition for the occurrence of an undesi…

A convergent adaptive finite element method for a phase-field model of dynamic fracture
Ram Manohar, S. M. Mallikarjuaniah
https://arxiv.org/abs/2510.05407 https://

A convergent adaptive finite element method for a phase-field model of dynamic fracture
We propose and analyze an adaptive finite element method for a phase-field model of dynamic brittle fracture. The model couples a second-order hyperbolic equation for elastodynamics with the Ambrosio-Tortorelli regularization of the Francfort-Marigo variational fracture energy, which circumvents the need for explicit crack tracking. Our numerical scheme combines a staggered time-stepping algorithm with a variational inequality formulation to strictly enforce the irreversibility of damage. The m…

A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network
Bilal Dalgic (Manisa Celal Bayar University, Turkey), Betul Sen (Manisa Celal Bayar University, Turkey), Muge Erel-Ozcevik (Manisa Celal Bayar University, Turkey)
https://arxiv.org/abs/2509.20068

A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network
Secure monitoring and dynamic control in an IIoT environment are major requirements for current development goals. We believe that dynamic, secure monitoring of the IIoT environment can be achieved through integration with the Software-Defined Network (SDN) and Digital Twin (DT) paradigms. The current literature lacks implementation details for SDN-based DT and time-aware intelligent model training for short-term anomaly detection against IIoT threats. Therefore, we have proposed a novel framew…

Bridge Micro-Deformation Monitoring Scheme with Integrated Sensing and Communications
Boxuan Sun, Hongliang Luo, Shaodan Ma, Feifei Gao
https://arxiv.org/abs/2509.17983 https://…

Bridge Micro-Deformation Monitoring Scheme with Integrated Sensing and Communications
In this paper, we propose a novel integrated sensing and communications (ISAC) scheme to perform bridge micro-deformation monitoring (BMDM) in complex environments. We first provide an excitation-bridge coupling model to represent the micro-deformation process of the bridge. Next, we design a novel frame structure for BMDM applications, and construct the OFDM echo channel model for basic scene of BMDM, including micro-deformation, dynamic objects, and static environment. Then, we develop a phas…

Secure and Efficient Access Control for Computer-Use Agents via Context Space
Haochen Gong, Chenxiao Li, Rui Chang, Wenbo Shen
https://arxiv.org/abs/2509.22256 https://

Secure and Efficient Access Control for Computer-Use Agents via Context Space
Large language model (LLM)-based computer-use agents represent a convergence of AI and OS capabilities, enabling natural language to control system- and application-level functions. However, due to LLMs' inherent uncertainty issues, granting agents control over computers poses significant security risks. When agent actions deviate from user intentions, they can cause irreversible consequences. Existing mitigation approaches, such as user confirmation and LLM-based dynamic action validation, sti…

Extending finite mixture models with skew-normal distributions and hidden Markov models for time series
Andrea Nigri, Marco Forti, Han Lin Shang
https://arxiv.org/abs/2509.19920

Extending finite mixture models with skew-normal distributions and hidden Markov models for time series
We introduce an extension of finite mixture models by incorporating skew-normal distributions within a Hidden Markov Model framework. By assuming a constant transition probability matrix and allowing emission distributions to vary according to hidden states, the proposed model effectively captures dynamic dependencies between variables. Through the estimation of state-specific parameters, including location, scale, and skewness, the proposed model enables the detection of structural changes, su…

Gyges: Dynamic Cross-Instance Parallelism Transformation for Efficient LLM Inference
Haoyu Chen, Xue Li, Kun Qian, Yu Guan, Jin Zhao, Xin Wang
https://arxiv.org/abs/2509.19729 h…

Gyges: Dynamic Cross-Instance Parallelism Transformation for Efficient LLM Inference
Efficiently processing the dynamics of requests, especially the context length variance, is important in Large Language Model (LLM) serving scenarios. However, there is an intrinsic trade-off: while leveraging parallelism strategies, such as Tensor Parallelism (TP), can coordinate multiple GPUs to accommodate larger context lengths, it inevitably results in degraded overall throughput. In this paper, we propose Cross-Instance Parallelism Transformation (Gyges), which adaptively adjusts the para…

Reconstruction of the Complete Vocal Tract Contour Through Acoustic to Articulatory Inversion Using Real-Time MRI Data
Sofiane Azzouz, Pierre-Andr\'e Vuissoz, Yves Laprie
https://arxiv.org/abs/2510.00914

Reconstruction of the Complete Vocal Tract Contour Through Acoustic to Articulatory Inversion Using Real-Time MRI Data
Acoustic to articulatory inversion has often been limited to a small part of the vocal tract because the data are generally EMA (ElectroMagnetic Articulography) data requiring sensors to be glued to easily accessible articulators. The presented acoustic to articulation model focuses on the inversion of the entire vocal tract from the glottis, the complete tongue, the velum, to the lips. It relies on a realtime dynamic MRI database of more than 3 hours of speech. The data are the denoised speech…

Blueprints of Trust: AI System Cards for End to End Transparency and Governance
Huzaifa Sidhpurwala, Emily Fox, Garth Mollett, Florencio Cano Gabarda, Roman Zhukov
https://arxiv.org/abs/2509.20394

Blueprints of Trust: AI System Cards for End to End Transparency and Governance
This paper introduces the Hazard-Aware System Card (HASC), a novel framework designed to enhance transparency and accountability in the development and deployment of AI systems. The HASC builds upon existing model card and system card concepts by integrating a comprehensive, dynamic record of an AI system's security and safety posture. The framework proposes a standardized system of identifiers, including a novel AI Safety Hazard (ASH) ID, to complement existing security identifiers like CVEs, …

CARGO: A Framework for Confidence-Aware Routing of Large Language Models
Amine Barrak, Yosr Fourati, Michael Olchawa, Emna Ksontini, Khalil Zoghlami
https://arxiv.org/abs/2509.14899

CARGO: A Framework for Confidence-Aware Routing of Large Language Models
As large language models (LLMs) proliferate in scale, specialization, and latency profiles, the challenge of routing user prompts to the most appropriate model has become increasingly critical for balancing performance and cost. We introduce CARGO (Category-Aware Routing with Gap-based Optimization), a lightweight, confidence-aware framework for dynamic LLM selection. CARGO employs a single embedding-based regressor trained on LLM-judged pairwise comparisons to predict model performance, with a…

Artificial Bottleneck Effect in Large Eddy Simulations
Mostafa Kamal, Perry L. Johnson
https://arxiv.org/abs/2509.18512 https://arxiv.org/pdf/2509.18512

DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation
Shenwei Kang, Xin Zhang, Wen Liu, Bin Li, Yujie Liu, Bo Gao
https://arxiv.org/abs/2509.17711

DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation
Human engagement estimation in conversational scenarios is essential for applications such as adaptive tutoring, remote healthcare assessment, and socially aware human--computer interaction. Engagement is a dynamic, multimodal signal conveyed by facial expressions, speech, gestures, and behavioral cues over time. In this work we introduce DA-Mamba, a dialogue-aware multimodal architecture that replaces attention-heavy dialogue encoders with Mamba-based selective state-space processing to achiev…

Clarke Differentials and the Envelope Theorem in Dynamic Programming
Yuhki Hosoya
https://arxiv.org/abs/2509.17103 https://arxiv.org/pdf/2509.17103

Clarke Differentials and the Envelope Theorem in Dynamic Programming
In this paper, we consider a deterministic dynamic programming model, and derive the envelope theorem using the Clarke differential. Compared with past research, the requirements for our result are weaker, and do not include differentiability, convexity, and boundedness.

A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
Di Jin, Jun Yang, Xiaobao Wang, Junwei Zhang, Shuqi Li, Dongxiao He
https://arxiv.org/abs/2509.11687

A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection
As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of …

CineWild: Balancing Art and Robotics for Ethical Wildlife Documentary Filmmaking
Pablo Pueyo, Fernando Caballero, Ana Cristina Murillo, Eduardo Montijano
https://arxiv.org/abs/2509.24921

CineWild: Balancing Art and Robotics for Ethical Wildlife Documentary Filmmaking
Drones, or unmanned aerial vehicles (UAVs), have become powerful tools across domains-from industry to the arts. In documentary filmmaking, they offer dynamic, otherwise unreachable perspectives, transforming how stories are told. Wildlife documentaries especially benefit, yet drones also raise ethical concerns: the risk of disturbing the animals they aim to capture. This paper introduces CineWild, an autonomous UAV framework that combines robotics, cinematography, and ethics. Built on model pr…

EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence
Chaoyin She, Ruifang Lu, Lida Chen, Wei Wang, Qinghua Huang
https://arxiv.org/abs/2509.14977

EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence
Ultrasound imaging has become the preferred imaging modality for early cancer screening due to its advantages of non-ionizing radiation, low cost, and real-time imaging capabilities. However, conventional ultrasound diagnosis heavily relies on physician expertise, presenting challenges of high subjectivity and low diagnostic efficiency. Vision-language models (VLMs) offer promising solutions for this issue, but existing general-purpose models demonstrate limited knowledge in ultrasound medical …

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

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

Adversarially Robust Assembly Language Model for Packed Executables Detection
Shijia Li, Jiang Ming, Lanqing Liu, Longwei Yang, Ni Zhang, Chunfu Jia
https://arxiv.org/abs/2509.15499

Adversarially Robust Assembly Language Model for Packed Executables Detection
Detecting packed executables is a critical component of large-scale malware analysis and antivirus engine workflows, as it identifies samples that warrant computationally intensive dynamic unpacking to reveal concealed malicious behavior. Traditionally, packer detection techniques have relied on empirical features, such as high entropy or specific binary patterns. However, these empirical, feature-based methods are increasingly vulnerable to evasion by adversarial samples or unknown packers (e.…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Tamir Shazman, Idan Lev-Yehudi, Ron Benchetit, Vadim Indelman
https://arxiv.org/abs/2509.10162 https://…

Online Robust Planning under Model Uncertainty: A Sample-Based Approach
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are widely adopted for their ability to approximate optimal actions using a generative model. However, in practical settings, the generative model is often learned from limited data, introducing approxi…

Guaranteed Robust Nonlinear MPC via Disturbance Feedback
Antoine P. Leeman, Johannes K\"ohler, Melanie N. Zeilinger
https://arxiv.org/abs/2509.18760 https://

Guaranteed Robust Nonlinear MPC via Disturbance Feedback
Robots must satisfy safety-critical state and input constraints despite disturbances and model mismatch. We introduce a robust model predictive control (RMPC) formulation that is fast, scalable, and compatible with real-time implementation. Our formulation guarantees robust constraint satisfaction, input-to-state stability (ISS) and recursive feasibility. The key idea is to decompose the uncertain nonlinear system into (i) a nominal nonlinear dynamic model, (ii) disturbance-feedback controllers…

A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
Runshi Tang, Runbing Zheng, Anru R. Zhang, Carey E. Priebe
https://arxiv.org/abs/2509.05221

A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
Dynamic multilayer networks are frequently used to describe the structure and temporal evolution of multiple relationships among common entities, with applications in fields such as sociology, economics, and neuroscience. However, exploration of analytical methods for these complex data structures remains limited. We propose a functional tensor-based model for dynamic multilayer networks, with the key feature of capturing the shared structure among common vertices across all layers, while simul…

A hybrid dynamic model and parameter estimation method for accurately simulating overhead cranes with friction
Jorge Vicente-Martinez (Universidad de Zaragoza), Edgar Ramirez-Laboreo (Universidad de Zaragoza)
https://arxiv.org/abs/2509.13330

A hybrid dynamic model and parameter estimation method for accurately simulating overhead cranes with friction
This paper presents a new approach to accurately simulating 3D overhead cranes with friction. Nonlinear friction dynamics have a significant impact on these systems, however, accurately modeling this phenomenon in simulations is a significant challenge. Traditional methods often rely on imprecise approximations of friction or require excessive computational times for reliable results. To address this, we present a hybrid dynamical model that features a trade-off between high-fidelity friction m…

Model-Based Lookahead Reinforcement Learning for in-hand manipulation
Alexandre Lopes, Catarina Barata, Plinio Moreno
https://arxiv.org/abs/2510.08884 https://

Model-Based Lookahead Reinforcement Learning for in-hand manipulation
In-Hand Manipulation, as many other dexterous tasks, remains a difficult challenge in robotics by combining complex dynamic systems with the capability to control and manoeuvre various objects using its actuators. This work presents the application of a previously developed hybrid Reinforcement Learning (RL) Framework to In-Hand Manipulation task, verifying that it is capable of improving the performance of the task. The model combines concepts of both Model-Free and Model-Based Reinforcement L…

The meaning of prompts and the prompts of meaning: Semiotic reflections and modelling
Martin Thellefsen, Amalia Nurma Dewi, Bent Sorensen
https://arxiv.org/abs/2509.14250 https:…

The meaning of prompts and the prompts of meaning: Semiotic reflections and modelling
This paper explores prompts and prompting in large language models (LLMs) as dynamic semiotic phenomena, drawing on Peirce's triadic model of signs, his nine sign types, and the Dynacom model of communication. The aim is to reconceptualize prompting not as a technical input mechanism but as a communicative and epistemic act involving an iterative process of sign formation, interpretation, and refinement. The theoretical foundation rests on Peirce's semiotics, particularly the interplay between …

Dynamic Factor Models with Forward-Looking Views
Anas Abdelhakmi, Andrew E. B. Lim
https://arxiv.org/abs/2509.11528 https://arxiv.org/pdf/2509.11528…

Dynamic Factor Models with Forward-Looking Views
Prediction models calibrated using historical data may forecast poorly if the dynamics of the present and future differ from observations in the past. For this reason, predictions can be improved if information like forward looking views about the state of the system are used to refine the forecast. We develop an approach for combining a dynamic factor model for risky asset prices calibrated on historical data, and noisy expert views of future values of the factors/covariates in the model, and …

(P)rior(D)yna(F)low: A Priori Dynamic Workflow Construction via Multi-Agent Collaboration
Yi Lin, Lujin Zhao, Yijie Shi
https://arxiv.org/abs/2509.14547 https://

(P)rior(D)yna(F)low: A Priori Dynamic Workflow Construction via Multi-Agent Collaboration
Recent studies have shown that carefully designed workflows coordinating large language models(LLMs) significantly enhance task-solving capabilities compared to using a single model. While an increasing number of works focus on autonomous workflow construction, most existing approaches rely solely on historical experience, leading to limitations in efficiency and adaptability. We argue that while historical experience is valuable, workflow construction should also flexibly respond to the unique…

SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
Xiao Zhou, Zengqi Peng, Jun Ma
https://arxiv.org/abs/2509.17850

SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
Accurate trajectory prediction of surrounding vehicles (SVs) is crucial for autonomous driving systems to avoid misguided decisions and potential accidents. However, achieving reliable predictions in highly dynamic and complex traffic scenarios remains a significant challenge. One of the key impediments lies in the limited effectiveness of current approaches to capture the multi-modal behaviors of drivers, which leads to predicted trajectories that deviate from actual future motions. To address…

SynchroRaMa : Lip-Synchronized and Emotion-Aware Talking Face Generation via Multi-Modal Emotion Embedding
Phyo Thet Yee, Dimitrios Kollias, Sudeepta Mishra, Abhinav Dhall
https://arxiv.org/abs/2509.19965

SynchroRaMa : Lip-Synchronized and Emotion-Aware Talking Face Generation via Multi-Modal Emotion Embedding
Audio-driven talking face generation has received growing interest, particularly for applications requiring expressive and natural human-avatar interaction. However, most existing emotion-aware methods rely on a single modality (either audio or image) for emotion embedding, limiting their ability to capture nuanced affective cues. Additionally, most methods condition on a single reference image, restricting the model's ability to represent dynamic changes in actions or attributes across time. T…

Trigger Where It Hurts: Unveiling Hidden Backdoors through Sensitivity with Sensitron
Gejian Zhao, Hanzhou Wu, Xinpeng Zhang
https://arxiv.org/abs/2509.19101 https://

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu
https://arxiv.org/abs/2510.09275

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) are fundamentally misaligned with real-world clinical practice. Most of them rely on static benchmarks derived from public medical exam items, which tend to overestimate model performance and ignore the difference between textbook cases and the ambiguous, varying conditions in the real world. Recent efforts toward dynamic evaluation offer a promi…

Eco-Conscious Customers Behavior in Capacitated Two-Echelon Location-Routing Models for Sustainable Last-Mile Delivery
Valentina Bonomi, Diana Jorge, Ana Barbosa-P\'ovoa
https://arxiv.org/abs/2509.22357

Eco-Conscious Customers Behavior in Capacitated Two-Echelon Location-Routing Models for Sustainable Last-Mile Delivery
This paper introduces a novel capacitated Two-Echelon Location-Routing Problem with Eco-conscious Customer Behavior (2E-LRP-ECB) aimed at enhancing the environmental sustainability of last-mile delivery (LMD) operations. The model jointly optimizes dynamic satellites location, vehicle routing, and customer delivery modes, explicitly accounting for (i) heterogeneous customer travel behaviors, (ii) heterogeneous fleet composition, and (iii) diverse emission profiles across both echelons. A piecew…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Yongliang Wang, Hamidreza Kasaei
https://arxiv.org/abs/2509.17381 https://…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Generating obstacle-free trajectories for robotic manipulators in unstructured and cluttered environments remains a significant challenge. Existing motion planning methods often require additional computational effort to generate the final trajectory by solving kinematic or dynamic equations. This paper highlights the strong potential of model-free reinforcement learning methods over model-based approaches for obstacle-free trajectory planning in joint space. We propose a fast trajectory planni…

Dynamic Group Detection using VLM-augmented Temporal Groupness Graph
Kaname Yokoyama, Chihiro Nakatani, Norimichi Ukita
https://arxiv.org/abs/2509.04758 https://

Dynamic Group Detection using VLM-augmented Temporal Groupness Graph
This paper proposes dynamic human group detection in videos. For detecting complex groups, not only the local appearance features of in-group members but also the global context of the scene are important. Such local and global appearance features in each frame are extracted using a Vision-Language Model (VLM) augmented for group detection in our method. For further improvement, the group structure should be consistent over time. While previous methods are stabilized on the assumption that grou…

Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding
Mingxiao Huo, Jiayi Zhang, Hewei Wang, Jinfeng Xu, Zheyu Chen, Huilin Tai, Yijun Chen
https://arxiv.org/abs/2509.11961

Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding
Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce Spec-LLaVA, a system that applies speculative decoding to accelerate VLMs without sacrificing output quality. Spec-LLaVA pairs a lightweight draft VLM with a large target model: the draft speculates future tokens, which the target verifies in parallel, allowing multiple tokens to be generated per step. To maximize eff…

ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Long Cui, Weiyun Wang, Jie Shao, Zichen Wen, Gen Luo, Linfeng Zhang, Yanting Zhang, Yu Qiao, Wenhai Wang
https://arxiv.org/abs/2510.12793

ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Visual Consistency Learning (ViCO), a novel training algorithm that enables the model to represent images of varying semantic complexities using different numbers of vision tokens. The key idea behind our method is to employ multiple MLP connectors, each with a different image compression ratio, to downsample the vision …

Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST
Monica Sekoyan, Nithin Rao Koluguri, Nune Tadevosyan, Piotr Zelasko, Travis Bartley, Nick Karpov, Jagadeesh Balam, Boris Ginsburg
https://arxiv.org/abs/2509.14128

Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST
This report introduces Canary-1B-v2, a fast, robust multilingual model for Automatic Speech Recognition (ASR) and Speech-to-Text Translation (AST). Built with a FastConformer encoder and Transformer decoder, it supports 25 languages primarily European. The model was trained on 1.7M hours of total data samples, including Granary and NeMo ASR Set 3.0, with non-speech audio added to reduce hallucinations for ASR and AST. We describe its two-stage pre-training and fine-tuning process with dynamic d…

RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking
Zhenguo Sun, Yibo Peng, Yuan Meng, Xukun Li, Bo-Sheng Huang, Zhenshan Bing, Xinlong Wang, Alois Knoll
https://arxiv.org/abs/2509.20717

RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking
Long-horizon, high-dynamic motion tracking on humanoids remains brittle because absolute joint commands cannot compensate model-plant mismatch, leading to error accumulation. We propose RobotDancing, a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies. The pipeline is end-to-end--training, sim-to-sim validation, and zero-shot sim-to-real--and uses a single-stage reinforcement learning (RL) setup with a unified observation, reward, and h…

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Xiangjun Zai, Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Wenjie Zhang
https://arxiv.org/abs/2510.12434

PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a structured form. However, existing KH-based RAG methods suffer from three major limitations: static retrieval planning, non-adaptive retrieval execution, and superficial use of KH structure and semantics, which constrain their ability to perform effective multi-hop question answering. To overcome these limitations, w…

F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
Qi Lv, Weijie Kong, Hao Li, Jia Zeng, Zherui Qiu, Delin Qu, Haoming Song, Qizhi Chen, Xiang Deng, Jiangmiao Pang
https://arxiv.org/abs/2509.06951

F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt reactive state-to-action mappings, often leading to short-sighted behaviors and poor robustness in dynamic scenes. In this paper, we introduce F1, a pretrained VLA framework which integrates the visual foresight generation into decision-making pipeline. F1 adopts a Mixture-of-Transformer architecture with dedicated modul…

FlightDiffusion: Revolutionising Autonomous Drone Training with Diffusion Models Generating FPV Video
Valerii Serpiva, Artem Lykov, Faryal Batool, Vladislav Kozlovskiy, Miguel Altamirano Cabrera, Dzmitry Tsetserukou
https://arxiv.org/abs/2509.14082

FlightDiffusion: Revolutionising Autonomous Drone Training with Diffusion Models Generating FPV Video
We present FlightDiffusion, a diffusion-model-based framework for training autonomous drones from first-person view (FPV) video. Our model generates realistic video sequences from a single frame, enriched with corresponding action spaces to enable reasoning-driven navigation in dynamic environments. Beyond direct policy learning, FlightDiffusion leverages its generative capabilities to synthesize diverse FPV trajectories and state-action pairs, facilitating the creation of large-scale training …

VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics
Girolamo Oddo, Roberto Nuca, Matteo Parsani
https://arxiv.org/abs/2510.07447 https://arx…

VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics
Developing a dynamic model for a high-performance vehicle is a complex problem that requires extensive structural information about the system under analysis. This information is often unavailable to those who did not design the vehicle and represents a typical issue in autonomous driving applications, which are frequently developed on top of existing vehicles; therefore, vehicle models are developed under conditions of information scarcity. This paper proposes a lightweight encoder-decoder mod…