Tootfinder

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…

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…

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

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

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

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot

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…

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 …

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…

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…

Localist LLMs -- A Mathematical Framework for Dynamic Locality Control
Joachim Diederich
https://arxiv.org/abs/2510.09338 https://arxiv.org/pdf/2510.09338

Localist LLMs -- A Mathematical Framework for Dynamic Locality Control
We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining. This is achieved through group sparsity penalties on attention mechanisms, …

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…

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…

Combining Blotto Networks and Voter Models to Simulate Voter Behavior in Response to Competitive Election Spending
Renee Jerome
https://arxiv.org/abs/2510.09697 https://

Combining Blotto Networks and Voter Models to Simulate Voter Behavior in Response to Competitive Election Spending
In the past, the Voter Model has been explicitly used to model the impact of propaganda on a dynamic, interconnected population, and certain factors have been identified that influence the behavior of voters when under outside influence. The Blotto Game has also been explicitly used to study information wars between two opposing parties, whether in regards to a political issue or advertising war. Both the graph theory behind the Voter Model and the game theory aspects of the Blotto Game are rel…

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…

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…

Multi-modal Foundation Model for Cosmological Simulation Data
Bin Xia, Nesar Ramachandra, Azton I. Wells, Salman Habib, John Wise
https://arxiv.org/abs/2510.07684 https://

Multi-modal Foundation Model for Cosmological Simulation Data
We present a multi-modal foundation model for astrophysical galaxy data, designed to map between simulation- and observation-based galactic features. Our encoder-only transformer flexibly ingests scalar quantities (e.g., redshifts, galaxy masses) and vectors (e.g., star formation histories, spectra), supporting multi-task training that includes within-modality reconstruction and cross-modality prediction. With a dynamic masking strategy, the model can query arbitrary galaxy properties from part…

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…

Dynamic breaking of axial symmetry of acoustic waves in crystals as the origin of nonlinear inelasticity and chaos: Analytical model and MD simulations
Zbigniew Kozio{\l}
https://arxiv.org/abs/2510.04175

Dynamic breaking of axial symmetry of acoustic waves in crystals as the origin of nonlinear inelasticity and chaos: Analytical model and MD simulations
A Chain of Springs and Masses (CSM) model is used in the interpretation of molecular dynamics (MD) simulations of movement of atoms in FCC crystals, oriented like during typical simulations performed in studies of the dynamics of line dislocations. The proposed description is inspired by and supported by MD simulations. We find out that a force that is perpendicular to the direction of the applied external shear pressure and in the direction of line dislocations occurs within the bulk of crysta…

Nonlinear System Identification for Model-Based Control of Waked Wind Turbines
Sebastiano Randino, Lorenzo Schena, Nicolas Coudou, Emanuele Garone, Miguel Alfonso Mendez
https://arxiv.org/abs/2510.07336

Nonlinear System Identification for Model-Based Control of Waked Wind Turbines
This work presents a nonlinear system identification framework for modeling the power extraction dynamics of wind turbines, including both freestream and waked conditions. The approach models turbine dynamics using data-driven power coefficient maps expressed as combinations of compact radial basis functions and polynomial bases, parameterized in terms of tip-speed ratio and upstream conditions. These surrogate models are embedded in a first-order dynamic system suitable for model-based control…

Attention-Enhanced Reinforcement Learning for Dynamic Portfolio Optimization
Pei Xue, Yuanchun Ye
https://arxiv.org/abs/2510.06466 https://arxiv.org/pdf/25…

Attention-Enhanced Reinforcement Learning for Dynamic Portfolio Optimization
We develop a deep reinforcement learning framework for dynamic portfolio optimization that combines a Dirichlet policy with cross-sectional attention mechanisms. The Dirichlet formulation ensures that portfolio weights are always feasible, handles tradability constraints naturally, and provides a stable way to explore the allocation space. The model integrates per-asset temporal encoders with a global attention layer, allowing it to capture sector relationships, factor spillovers, and other cro…

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…

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
Jiaobiao Zhao, Kefeng Huang, Xiaoxiang Li, Mingxia Zhang, Peian Li, Jie Yang, Wenbo Liu, Yiming Zhao, Weidong Hu, Jianjun Ma
https://arxiv.org/abs/2510.09906

Experimental Characterization and Dynamic Modeling of THz Channels Under Fog Conditions
The terahertz (THz) band is a promising candidate for sixth-generation wireless networks, but its deploymen in outdoor environments is challenged by meteorological phenomena, particularly fog, which imposes variable and difficult-to-predict channel degradation. This article introduces dynamic channel model for the THz band explicitly driven by the time-evolving droplet size distribution (DSD) of fog, integrating real-time microphysical sensing to capture variations in the fog microstructure. Ex…

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…

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 Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Vishakha Lall, Yisi Liu
https://arxiv.org/abs/2510.08586 https://ar…

Dynamic Stress Detection: A Study of Temporal Progression Modelling of Stress in Speech
Detecting psychological stress from speech is critical in high-pressure settings. While prior work has leveraged acoustic features for stress detection, most treat stress as a static label. In this work, we model stress as a temporally evolving phenomenon influenced by historical emotional state. We propose a dynamic labelling strategy that derives fine-grained stress annotations from emotional labels and introduce cross-attention-based sequential models, a Unidirectional LSTM and a Transformer…

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang, Shaodong Chen, Yingyi Zhang, Chao Feng, Jiao Ran
https://arxiv.org/abs/2510.09358

Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models
Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods by incorporating multiple modalities of input information to produce a set of conclusive phrases. Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence and unseen scenarios. Additionally, we identify shortcomings in existing benchmarks that overestimate model capability due to significant overlap in training tests. In this work, we propose leveraging…

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…

Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Minmao Wang, Xingchen Liu, Shijie Yi, Likang Wu, Hongke Zhao, Fei Pan, Qingpeng Cai, Peng Jiang
https://arxiv.org/abs/2510.09167

Hierarchical Semantic RL: Tackling the Problem of Dynamic Action Space for RL-based Recommendations
Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcement learning (RL) to model long-term user value. However, these efforts face significant challenges due to the vast, dynamic action spaces inherent in recommendation, which hinder stable policy learning. To resolve this bottleneck, we introduce Hierarchical Semantic RL (HSRL), which reframes RL-based recommendation over…

PrediQL: Automated Testing of GraphQL APIs with LLMs
Shaolun Liu, Sina Marefat, Omar Tsai, Yu Chen, Zecheng Deng, Jia Wang, Mohammad A. Tayebi
https://arxiv.org/abs/2510.10407 h…

PrediQL: Automated Testing of GraphQL APIs with LLMs
GraphQL's flexible query model and nested data dependencies expose APIs to complex, context-dependent vulnerabilities that are difficult to uncover using conventional testing tools. Existing fuzzers either rely on random payload generation or rigid mutation heuristics, failing to adapt to the dynamic structures of GraphQL schemas and responses. We present PrediQL, the first retrieval-augmented, LLM-guided fuzzer for GraphQL APIs. PrediQL combines large language model reasoning with adaptive fee…

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…

Replaced article(s) found for physics.plasm-ph. https://arxiv.org/list/physics.plasm-ph/new
[1/1]:
- A Comprehensive Analytical Model of the Dynamic Z-Pinch
Alejandro Mesa Dame, Eric S. Lavine, David A. Hammer

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…

Anomalous Criticality of Absorbing State Transition toward Jamming
He-Da Wang, Bo Wang, Qun-Li Lei, Yu-Qiang Ma
https://arxiv.org/abs/2510.06641 https://ar…

Anomalous Criticality of Absorbing State Transition toward Jamming
The jamming transition is traditionally regarded as a geometric transition governed by static contact networks. Recently, dynamic phase transitions of athermal particles under periodic shear provide a new lens on this problem, leading to a conjecture that jamming transition corresponds to an absorbing-state transition within the Manna (conserved directed percolation) universality class. Here, by re-examing the biased random organization model, a minimal model for particles under periodic sheari…

Study of the stability of the fractional Stokes system from nonlinear optics around the zero equlibrium state
Mihai Ivan
https://arxiv.org/abs/2510.10130 https://

Study of the stability of the fractional Stokes system from nonlinear optics around the zero equlibrium state
The main purpose of this paper is to study the fractional-order system with Caputo derivative associated to single Stokes pulse. The dynamic behavior for this fractional model (called the fractional Stokes system) is investigated, including: the asymptotic stability around zero equilibrium state, the stabilization problem using appropriate linear controls and the numerical integration based on fractional Euler method.

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…

WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks
Jingbo Yang, Bairu Hou, Wei Wei, Shiyu Chang, Yujia Bao
https://arxiv.org/abs/2510.06587 https://

WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks
Large language model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that require long horizon navigation, large scale information extraction, and reasoning under constraints. We present WebDART, a general framework that enables a single LLM to handle such complex chores. WebDART (i) dynamically decomposes each objective into three focused subtasks: navigation, information extraction, and exe…

Zeeman: A Deep Learning Regional Atmospheric Chemistry Transport Model
Mijie Pang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Guoqiang Wang, Hong Liao, Wei Han
https://arxiv.org/abs/2510.06140

Zeeman: A Deep Learning Regional Atmospheric Chemistry Transport Model
Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric component - such as PM2.5 and ozone - the critical interactions among multiple pollutants and the combined influence of emissions and meteorology are often overlook. This study in…

On Dynamic Programming Theory for Leader-Follower Stochastic Games
Jilles Steeve Dibangoye, Thibaut Le Marre, Ocan Sankur, Fran\c{c}ois Schwarzentruber
https://arxiv.org/abs/2512.05667 https://arxiv.org/pdf/2512.05667 https://arxiv.org/html/2512.05667
arXiv:2512.05667v1 Announce Type: new
Abstract: Leader-follower general-sum stochastic games (LF-GSSGs) model sequential decision-making under asymmetric commitment, where a leader commits to a policy and a follower best responds, yielding a strong Stackelberg equilibrium (SSE) with leader-favourable tie-breaking. This paper introduces a dynamic programming (DP) framework that applies Bellman recursion over credible sets-state abstractions formally representing all rational follower best responses under partial leader commitments-to compute SSEs. We first prove that any LF-GSSG admits a lossless reduction to a Markov decision process (MDP) over credible sets. We further establish that synthesising an optimal memoryless deterministic leader policy is NP-hard, motivating the development of {\epsilon}-optimal DP algorithms with provable guarantees on leader exploitability. Experiments on standard mixed-motive benchmarks-including security games, resource allocation, and adversarial planning-demonstrate empirical gains in leader value and runtime scalability over state-of-the-art methods.
toXiv_bot_toot

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…

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…

Travel Bans vs. Social Distancing: A Mathematical Analysis
Christian Borgs, Karissa Huang, Geng Zhao
https://arxiv.org/abs/2510.08895 https://arxiv.org/pdf…

Travel Bans vs. Social Distancing: A Mathematical Analysis
As the world grows increasingly connected, infectious disease transmission and outbreaks become a pressing global concern for public health officials and policymakers. While policy interventions to contain and prevent the spread of disease have been proposed and implemented, there has been little rigorous quantitative analysis of the effectiveness of such interventions. In this paper, we study the susceptible-infected-recovered (SIR) infection process on a dynamic network model that models two …

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…

Frenkel anomaly on co-ordination numbers in liquid CO2 at 100 and 1000 bar studied by Monte Carlo simulation using Kihara potential model
Koji Kobashi
https://arxiv.org/abs/2510.11029

Frenkel anomaly on co-ordination numbers in liquid CO2 at 100 and 1000 bar studied by Monte Carlo simulation using Kihara potential model
An issue concerning the Frenkel line of liquid CO2 is that its location has not been unequivocally determined. So far, reliable Frenkel lines were identified from velocity autocorrelation functions (VAFs) computed by Molecular Dynamic simulations; however VAFs cannot be directly verified experimentally. By contrast, the co-ordination numbers (CNs) can be measured experimentally by X-ray and neutron scattering or computed by Monte Carlo (MC) simulation, and thus provide an alternative means of d…

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…

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…

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…

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…

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

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…

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…

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 …

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…

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…

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Pengcheng Li, Manzi Nan, Haojie Zhang, Junhuai Xu, Xilong Xiang, Yijie Wang, Yongjia Wang, Gaochan Yong, Tadaaki Isobe, Zhigang Xiao, Qingfeng Li
https://arxiv.org/abs/2510.12226

Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Revealing the neutron density distribution in the nucleus is one of the crucial tasks of nuclear physics. Within the framework of the ultrarelativistic quantum molecular dynamic model followed by a correlation afterburner program, we investigate the effects of the initial neutron density distribution on the charged-pion yield ratio $π^{-}/π^{+}$, the two-pion momentum correlation function, and the emission source dimension. It is found that the $π^{-}/π^{+}$ ratio is sensitive to the initia…

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 …

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…

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Minkyoung Cho, Ruben Ohana, Christian Jacobsen, Adityan Jothi, Min-Hung Chen, Z. Morley Mao, Ethem Can
https://arxiv.org/abs/2510.09561

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Current controllable diffusion models typically rely on fixed architectures that modify intermediate activations to inject guidance conditioned on a new modality. This approach uses a static conditioning strategy for a dynamic, multi-stage denoising process, limiting the model's ability to adapt its response as the generation evolves from coarse structure to fine detail. We introduce TC-LoRA (Temporally Modulated Conditional LoRA), a new paradigm that enables dynamic, context-aware control by c…

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
David Nguyen, Zulfiqar Zaidi, Kevin Karol, Jessica Hodgins, Zhaoming Xie
https://arxiv.org/abs/2510.08754

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and pred…

HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities
Xiaoxue Ren, Penghao Jiang, Kaixin Li, Zhiyong Huang, Xiaoning Du, Jiaojiao Jiang, Zhenchang Xing, Jiamou Sun, Terry Yue Zhuo
https://arxiv.org/abs/2510.12200

HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities
Web applications are prime targets for cyberattacks as gateways to critical services and sensitive data. Traditional penetration testing is costly and expertise-intensive, making it difficult to scale with the growing web ecosystem. While language model agents show promise in cybersecurity, modern web applications demand visual understanding, dynamic content handling, and multi-step interactions that only computer-use agents (CUAs) can perform. Yet, their ability to discover and exploit vulnera…

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…

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…

Toward Model Matching for Remotely Controlled Differential Drive Robotic Vehicles
Nikolaos D. Kouvakas, Fotis N. Koumboulis, Konstantinos G. Tzierakis, John Sigalas, Anastasios Dimakakos
https://arxiv.org/abs/2510.06081

Toward Model Matching for Remotely Controlled Differential Drive Robotic Vehicles
The problem of regulation of the orientation angle of a remotely controlled differential-drive mobile robot with actuator dynamics and network-induced delays is studied. Using a preinstalled two-layer nonlinear control scheme that decouples linear and angular velocities and regulates heading, a third, delay-dependent layer that achieves exact model matching from the orientation angle command to the orientation angle is introduced. The proposed outer loop controller is a delay dependent dynamic …

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…

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…

A Vision for Access Control in LLM-based Agent Systems
Xinfeng Li, Dong Huang, Jie Li, Hongyi Cai, Zhenhong Zhou, Wei Dong, XiaoFeng Wang, Yang Liu
https://arxiv.org/abs/2510.11108

A Vision for Access Control in LLM-based Agent Systems
The autonomy and contextual complexity of LLM-based agents render traditional access control (AC) mechanisms insufficient. Static, rule-based systems designed for predictable environments are fundamentally ill-equipped to manage the dynamic information flows inherent in agentic interactions. This position paper argues for a paradigm shift from binary access control to a more sophisticated model of information governance, positing that the core challenge is not merely about permission, but about…

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…

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…

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…

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…

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
Nico Bistolfi, Andreea Georgescu, Dave Hodson
https://arxiv.org/abs/2510.09494 …

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
As cloud infrastructure evolves to support dynamic and distributed workflows, accelerated now by AI-driven processes, the outdated model of standing permissions has become a critical vulnerability. Based on the Cloud Security Alliance (CSA) Top Threats to Cloud Computing Deep Dive 2025 Report, our analysis details how standing permissions cause catastrophic cloud breaches. While current security tools are addressing network and API security, the challenge of securing granular data access remain…

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…

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…

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…

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…

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…

Benders Decomposition for Passenger-Oriented Train Timetabling with Hybrid Periodicity
Zhiyuan Yao, Anita Sch\"obel, Lei Nie, Sven J\"ager
https://arxiv.org/abs/2511.09892 https://arxiv.org/pdf/2511.09892 https://arxiv.org/html/2511.09892
arXiv:2511.09892v1 Announce Type: new
Abstract: Periodic timetables are widely adopted in passenger railway operations due to their regular service patterns and well-coordinated train connections. However, fluctuations in passenger demand require varying train services across different periods, necessitating adjustments to the periodic timetable. This study addresses a hybrid periodic train timetabling problem, which enhances the flexibility and demand responsiveness of a given periodic timetable through schedule adjustments and aperiodic train insertions, taking into account the rolling stock circulation. Since timetable modifications may affect initial passenger routes, passenger routing is incorporated into the problem to guide planning decisions towards a passenger-oriented objective. Using a time-space network representation, the problem is formulated as a dynamic railway service network design model with resource constraints. To handle the complexity of real-world instances, we propose a decomposition-based algorithm integrating Benders decomposition and column generation, enhanced with multiple preprocessing and accelerating techniques. Numerical experiments demonstrate the effectiveness of the algorithm and highlight the advantage of hybrid periodic timetables in reducing passenger travel costs.
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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…

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…

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…

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…

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Denglin Cheng, Jiarong Kang, Xiaobin Xiong
https://arxiv.org/abs/2510.11539

Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Accurate state estimation is critical for legged and aerial robots operating in dynamic, uncertain environments. A key challenge lies in specifying process and measurement noise covariances, which are typically unknown or manually tuned. In this work, we introduce a bi-level optimization framework that jointly calibrates covariance matrices and kinematic parameters in an estimator-in-the-loop manner. The upper level treats noise covariances and model parameters as optimization variables, while …

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.…

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…

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…

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…

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, …

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Debojyoti Ghosh, Soumya K Ghosh, Adrijit Goswami
https://arxiv.org/abs/2510.05071

Neuroplastic Modular Framework: Cross-Domain Image Classification of Garbage and Industrial Surfaces
Efficient and accurate classification of waste and industrial surface defects is essential for ensuring sustainable waste management and maintaining high standards in quality control. This paper introduces the Neuroplastic Modular Classifier, a novel hybrid architecture designed for robust and adaptive image classification in dynamic environments. The model combines a ResNet-50 backbone for localized feature extraction with a Vision Transformer (ViT) to capture global semantic context. Addition…

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…

Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
Cheng Ouyang, Moeen Ul Islam, Dong Chen, Kaixiang Zhang, Zhaojian Li, Xiaobo Tan
https://arxiv.org/abs/2510.08953

Direct Data-Driven Predictive Control for a Three-dimensional Cable-Driven Soft Robotic Arm
Soft robots offer significant advantages in safety and adaptability, yet achieving precise and dynamic control remains a major challenge due to their inherently complex and nonlinear dynamics. Recently, Data-enabled Predictive Control (DeePC) has emerged as a promising model-free approach that bypasses explicit system identification by directly leveraging input-output data. While DeePC has shown success in other domains, its application to soft robots remains underexplored, particularly for thr…

Enhancing Automotive Security with a Hybrid Approach towards Universal Intrusion Detection System
Md Rezanur Islam, Mahdi Sahlabadi, Keunkyoung Kim, Kangbin Yim
https://arxiv.org/abs/2510.05824

Enhancing Automotive Security with a Hybrid Approach towards Universal Intrusion Detection System
Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System (IDS) is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data generated by vehicles regarding their model, driving style, test environment, and firmware update. To address this issue, a universal IDS has been developed that can be applied to all types of vehicles wit…

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Lars Ohnemus, Nils Hantke, Max Wei{\ss}er, Kai Furmans
https://arxiv.org/abs/2510.09483

FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
Dynamic Scene Graphs (DSGs) provide a structured representation of hierarchical, interconnected environments, but current approaches struggle to capture stochastic dynamics, partial observability, and multi-agent activity. These aspects are critical for embodied AI, where agents must act under uncertainty and delayed perception. We introduce FOGMACHINE , an open-source framework that fuses DSGs with discrete-event simulation to model object dynamics, agent observations, and interactions at scal…

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…

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…

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…

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…

Probabilistically-Safe Bipedal Navigation over Uncertain Terrain via Conformal Prediction and Contraction Analysis
Kasidit Muenprasitivej, Ye Zhao, Glen Chou
https://arxiv.org/abs/2510.07725

Probabilistically-Safe Bipedal Navigation over Uncertain Terrain via Conformal Prediction and Contraction Analysis
We address the challenge of enabling bipedal robots to traverse rough terrain by developing probabilistically safe planning and control strategies that ensure dynamic feasibility and centroidal robustness under terrain uncertainty. Specifically, we propose a high-level Model Predictive Control (MPC) navigation framework for a bipedal robot with a specified confidence level of safety that (i) enables safe traversal toward a desired goal location across a terrain map with uncertain elevations, an…

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…

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…

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…