Tootfinder

Federated Learning based on Self-Evolving Gaussian Clustering
Miha O\v{z}bot, Igor \v{S}krjanc
https://arxiv.org/abs/2508.15393 https://arxiv.org/pdf/2508.…

Federated Learning based on Self-Evolving Gaussian Clustering
In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Federated Learning allows models to be trained locally on clients' devices, sharing only the model parameters with a central server instead of the data. Our method, implemented using PyTorch, was tested on clustering and classification tasks…

Convergence analysis of the dynamically regularized Lagrange multiplier method for the incompressible Navier-Stokes equations
Cao-Kha Doan, Thi-Thao-Phuong Hoang, Lili Ju, Rihui Lan
https://arxiv.org/abs/2508.14007

Convergence analysis of the dynamically regularized Lagrange multiplier method for the incompressible Navier-Stokes equations
This paper is concerned with temporal convergence analysis of the recently introduced Dynamically Regularized Lagrange Multiplier (DRLM) method for the incompressible Navier-Stokes equations. A key feature of the DRLM approach is the incorporation of the kinetic energy evolution through a quadratic dynamic equation involving a time-dependent Lagrange multiplier and a regularization parameter. We apply the backward Euler method with an explicit treatment of the nonlinear convection term and show…

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning
Yekun Zhu, Guang Chen, Chengjun Mao
https://arxiv.org/abs/2508.15507 https://arx…

Think in Blocks: Adaptive Reasoning from Direct Response to Deep Reasoning
Large Language Models (LLMs) with chains-of-thought have demonstrated strong performance on an increasing range of tasks, particularly those involving complex logical reasoning. However, excessively long chains can lead to overthinking, causing computational waste and slower responses. This raises a question: can LLMs dynamically adjust the length of their reasoning processes based on task complexity? To address this, we propose the Think in Blocks framework, which enables adaptive reasoning-fr…

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

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

Leveraging Multi-Instance GPUs through moldable task scheduling
Jorge Villarrubia, Luis Costero, Francisco D. Igual, Katzalin Olcoz
https://arxiv.org/abs/2507.13601

Leveraging Multi-Instance GPUs through moldable task scheduling
NVIDIA MIG (Multi-Instance GPU) allows partitioning a physical GPU into multiple logical instances with fully-isolated resources, which can be dynamically reconfigured. This work highlights the untapped potential of MIG through moldable task scheduling with dynamic reconfigurations. Specifically, we propose a makespan minimization problem for multi-task execution under MIG constraints. Our profiling shows that assuming monotonicity in task work with respect to resources is not viable, as is usu…

Enhancing Physical Layer Security in IoT-Based RF-FSO Integrated Networks: Multi-RIS Structures and their Impact on Secure Communication
Anika Tabassum Biva, Md. Ibrahim, A. S. M. Badrudduza, Imran Shafique Ansari
https://arxiv.org/abs/2509.15411

Enhancing Physical Layer Security in IoT-Based RF-FSO Integrated Networks: Multi-RIS Structures and their Impact on Secure Communication
Due to their ability to dynamically control the propagation environment, reconfigurable intelligent surfaces (RISs) offer a promising solution to address the challenges of $6$G wireless communication, especially in the context of Internet of Things (IoT) networks. This paper investigates a mixed communication model with multi-RIS-aided radio frequency (RF)-free space optics (FSO) to enhance the performance of IoT applications in complex environments. An eavesdropper is assumed to be present, at…

RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation
Simon Winther Albertsen, Hjalte Svaneborg Bj{\o}rnstrup, Mostafa Mehdipour Ghazi
https://arxiv.org/abs/2507.15524

RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation
Accurate segmentation is crucial for clinical applications, but existing models often assume fixed, high-resolution inputs and degrade significantly when faced with lower-resolution data in real-world scenarios. To address this limitation, we propose RARE-UNet, a resolution-aware multi-scale segmentation architecture that dynamically adapts its inference path to the spatial resolution of the input. Central to our design are multi-scale blocks integrated at multiple encoder depths, a resolution-…

From Wide Triples to UCXBs: Multimessenger Detection of Dynamically-formed Black Hole-White Dwarf Systems with LISA
Zeyuan Xuan, Cheyanne Shariat, Smadar Naoz
https://arxiv.org/abs/2508.13264

From Wide Triples to UCXBs: Multimessenger Detection of Dynamically-formed Black Hole-White Dwarf Systems with LISA
Ultracompact X-ray binaries (UCXBs) are a subclass of low-mass X-ray binaries characterized by tight orbits and degenerate donors, which pose significant challenges to our understanding of their formation. Recent discoveries of black hole (BH) candidates with main-sequence (MS) or red giant (RG) companions suggest that BH-white dwarf (BH-WD) binaries are common in the Galactic field. Motivated by these observations and the fact that most massive stars are born in triples, we show that wide BH-W…

Dynamic Transmission Line Switching Amidst Wildfire-Prone Weather Under Decision-Dependent Uncertainty
Juan-Alberto Estrada-Garcia, Ruiwei Jiang, Alexandre Moreira
https://arxiv.org/abs/2507.13611

Dynamic Transmission Line Switching Amidst Wildfire-Prone Weather Under Decision-Dependent Uncertainty
During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multi-stage optimization model that dynamically adjusts transmission grid topology in response to wildfire propagation, aiming to develop an optimal response policy. By accounting for…

An Algorithm for Computing the Exact Convex Hull in High-Dimensional Spaces
Qianwei Zhuang
https://arxiv.org/abs/2508.14407 https://arxiv.org/pdf/2508.1440…

An Algorithm for Computing the Exact Convex Hull in High-Dimensional Spaces
This study introduces a novel algorithm for computing the convex hull of a point set in high-dimensional Euclidean space. The method iteratively solves a sequence of dynamically updated quadratic programming (QP) problems for each point and leverages the solutions to provide theoretical guarantees for exact convex hull identification. Given $n$ points in an $m$-dimensional space, the algorithm achieves a best-case time complexity of $O(nm^p)$, where $p$ depends on the specific QP solver employe…

Revisiting the $\Lambda_c^ \to \Lambda \eta \pi^ $ and the roles of intermediate resonances
En Wang, Wen-Tao Lyu, Man-Yu Duan, Chu-Wen Xiao, Dian-Yong Chen, Ju-Jun Xie, Eulogio Oset
https://arxiv.org/abs/2507.13738

Revisiting the $Λ_c^+\to Ληπ^+$ and the roles of intermediate resonances
In this paper, we first review the theoretical and experimental studies of the process $Λ_c^+ \to π^+ ηΛ$. Motivated by the recent BESIII and Belle measurements, we have conducted a theoretical study of the process $Λ_c^+ \to π^+ ηΛ$, where the $a_0(980)$ and $Λ(1670)$ resonances are dynamically generated from the $S$-wave meson meson and meson baryon interaction, respectively. Our results give a reasonable description of the invariant mass distributions, which implies that the spin fl…

> _cobs_ext.cpython-313-wasm32-emscripten.so
> look inside
> ELF 64-bit LSB shared object, x86-64, version 1 (SYSV), dynamically linked
having a normal evening

Grid-Edge Energy-Flexible Technologies: A Comparative Analysis Across Generators, Loads, and Energy Storage Systems
Jesus Silva-Rodriguez, Tianxia Zhao, Ran Mo, Xingpeng Li
https://arxiv.org/abs/2508.14297

Grid-Edge Energy-Flexible Technologies: A Comparative Analysis Across Generators, Loads, and Energy Storage Systems
This review analysis presents a comprehensive exploration of energy flexibility in modern power systems. It examines the roles and mechanisms of flexible technologies across three main categories: generators, energy storage systems (ESS), and loads. Energy flexibility is defined as the ability to dynamically adjust supply and/or demand in response to grid conditions to maintain balance and stability. This is of particular importance to facilitate the integration of the growing variable renewabl…

Harnessing higher-dimensional fluctuations in an information engine
Antonio Patr\'on Castro, John Bechhoefer, David A. Sivak
https://arxiv.org/abs/2507.15503

Harnessing higher-dimensional fluctuations in an information engine
We study the optimal performance of an information engine consisting of an overdamped Brownian bead confined in a controllable, $d$-dimensional harmonic trap and additionally subjected to gravity. The trap's center is updated dynamically via a feedback protocol designed such that no external work is done by the trap on the bead, while maximizing the extraction of gravitational potential energy and achieving directed motion. We show that performance strikingly improves when thermal fluctuations …

Assessing the Benefits of Ground Vehicles as Moving Urban Base Stations
Laura Finarelli, Falko Dressler, Marco Ajmone Marsan, Gianluca Rizzo
https://arxiv.org/abs/2507.15423

Assessing the Benefits of Ground Vehicles as Moving Urban Base Stations
In the evolution towards 6G user-centric networking, the moving network (MN) paradigm can play an important role. In a MN, some small cell base stations (BS) are installed on top of vehicles, and enable a more dynamic, flexible and sustainable, network operation. By "following" the users movements and adapting dynamically to their requests, the MN paradigm enables a more efficient utilization of network resources, mitigating the need for dense small cell BS deployments at the cost of an increas…

Variations in Volatile-Driven Activity of Comet C/2017 K2 (PanSTARRS) Revealed by Long-Term Multi-Wavelength Observations
S. Hmiddouch, E. Jehin, M. Lippi, M. Vander Donckt, K. Aravind, D. Hutsem\'ekers, J. Manfroid, A. Jabiri, Y. Moulane, Z. Benkhaldoun
https://arxiv.org/abs/2507.13451

Variations in Volatile-Driven Activity of Comet C/2017 K2 (PanSTARRS) Revealed by Long-Term Multi-Wavelength Observations
Context. A comprehensive study of comets over a wide heliocentric distance range helps us understand the physical processes driving their activity and reveals compositional differences across dynamical groups. C/2017 K2 (PANSTARRS) is a Dynamically New Oort Cloud comet (DNC) that showed activity as far as 23.75 au and displayed a CO-rich coma at 6.72 au, making it a key object to investigate pre- and post-perihelion behavior. Aims. We aim to study the long-term activity evolution and chemical…

Origins and lifetimes of secular and tidal bars in simulated disc galaxies
Matthew Frosst, Danail Obreschkow, Aaron Ludlow, Amelia Fraser-McKelvie
https://arxiv.org/abs/2508.14626

Origins and lifetimes of secular and tidal bars in simulated disc galaxies
We investigate the formation of stellar bars in 307 Milky Way-mass disc galaxies in the TNG50 cosmological simulation. Most bars form rapidly in dynamically cold discs shortly after the central stellar mass exceeds that of dark matter. In these cases, bar formation is consistent with secular instabilities driven by the disc's self-gravity, which organises stellar orbits into a coherent bar structure. However, around 25 per cent of barred galaxies are dark matter dominated at the time of bar for…

HDBMS: A Context-Aware Hybrid Graph Traversal Algorithm for Efficient Information Discovery in Social Networks
Rowanda Ahmed, Belaynesh Chekol, Mahmoud Alsaleh
https://arxiv.org/abs/2508.14092

HDBMS: A Context-Aware Hybrid Graph Traversal Algorithm for Efficient Information Discovery in Social Networks
Graph-searching algorithms play a crucial role in various computational domains, enabling efficient exploration and pathfinding in structured data. Traditional approaches, such as Depth-First Search (DFS) and Breadth-First Search (BFS), follow rigid traversal patterns -- DFS explores branches exhaustively, while BFS expands level by level. In this paper, we propose the Hybrid Depth-Breadth Meaningful Search (HDBMS) algorithm, a novel graph traversal method that dynamically adapts its exploratio…

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
Michail Kavousanakis, Gianluca Fabiani, Anastasia Georgiou, Constantinos Siettos, Panagiotis Kevrekidis, Ioannis Kevrekidis
https://arxiv.org/abs/2509.15963

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
In the past, we have presented a systematic computational framework for analyzing self-similar and traveling wave dynamics in nonlinear partial differential equations (PDEs) by dynamically factoring out continuous symmetries such as translation and scaling. This is achieved through the use of time-dependent transformations -- what can be viewed as dynamic pinning conditions -- that render the symmetry-invariant solution stationary or slowly varying in rescaled coordinates. The transformation pr…

Surface Charge Relaxation Controls the Lifetime of Out-of-Equilibrium Colloidal Crystals
Laura Jansen, Thijs ter Rele, Marjolein Dijkstra
https://arxiv.org/abs/2507.14328

Surface Charge Relaxation Controls the Lifetime of Out-of-Equilibrium Colloidal Crystals
Interactions between charged colloidal particles are profoundly influenced by charge regulation and charge renormalization, rendering the effective potential highly sensitive to local particle density. In this work, we investigate how a dynamically evolving, density-dependent Yukawa interaction affects the stability of out-of-equilibrium colloidal structures. Motivated by a series of experiments where unexpectedly long-lived colloidal crystals have suggested the presence of like-charged attract…

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading
Chengwei Lou, Zekai Jin, Wei Tang, Guangfei Geng, Jin Yang, Lu Zhang
https://arxiv.org/abs/2507.14995

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading
Real-time peer-to-peer (P2P) electricity markets dynamically adapt to fluctuations in renewable energy and variations in demand, maximizing economic benefits through instantaneous price responses while enhancing grid flexibility. However, scaling expert guidance for massive personalized prosumers poses critical challenges, including diverse decision-making demands and lack of customized modeling frameworks. This paper proposed an integrated large language model-multi-agent reinforcement learnin…

AFABench: A Generic Framework for Benchmarking Active Feature Acquisition
Valter Sch\"utz, Han Wu, Reza Rezvan, Linus Aronsson, Morteza Haghir Chehreghani
https://arxiv.org/abs/2508.14734

AFABench: A Generic Framework for Benchmarking Active Feature Acquisition
In many real-world scenarios, acquiring all features of a data instance can be expensive or impractical due to monetary cost, latency, or privacy concerns. Active Feature Acquisition (AFA) addresses this challenge by dynamically selecting a subset of informative features for each data instance, trading predictive performance against acquisition cost. While numerous methods have been proposed for AFA, ranging from greedy information-theoretic strategies to non-myopic reinforcement learning appro…

The Distribution Shift Problem in Transportation Networks using Reinforcement Learning and AI
Federico Taschin, Abderrahmane Lazaraq, Ozan K. Tonguz, Inci Ozgunes
https://arxiv.org/abs/2509.15291

The Distribution Shift Problem in Transportation Networks using Reinforcement Learning and AI
The use of Machine Learning (ML) and Artificial Intelligence (AI) in smart transportation networks has increased significantly in the last few years. Among these ML and AI approaches, Reinforcement Learning (RL) has been shown to be a very promising approach by several authors. However, a problem with using Reinforcement Learning in Traffic Signal Control is the reliability of the trained RL agents due to the dynamically changing distribution of the input data with respect to the distribution o…

FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy
Yijin Chen, Wenqiang Xu, Zhenjun Yu, Tutian Tang, Yutong Li, Siqiong Yao, Cewu Lu
https://arxiv.org/abs/2508.14441

FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy
Dexterous in-hand manipulation is a long-standing challenge in robotics due to complex contact dynamics and partial observability. While humans synergize vision and touch for such tasks, robotic approaches often prioritize one modality, therefore limiting adaptability. This paper introduces Flow Before Imitation (FBI), a visuotactile imitation learning framework that dynamically fuses tactile interactions with visual observations through motion dynamics. Unlike prior static fusion methods, FBI …

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Soham Hans, Nikolos Gurney, Stacy Marsella, Sofia Hirschmann
https://arxiv.org/abs/2508.13240 https://

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adversaries is paramount. Traditional cyber defense strategies have largely focused on fortification, while some approaches attempt to anticipate attacker strategies by mapping them to cognitive vulnerabilities, yet they fall short in dynamically interpreting attacks in progress. In recognition of this gap, IARPA's ReSCIND…

Surfing on chemical waves: a simple yet dynamically rich two-sphere responsive gel swimmer
Joseph J. Webber, Thomas D. Montenegro-Johnson
https://arxiv.org/abs/2509.13850 https:…

Surfing on chemical waves: a simple yet dynamically rich two-sphere responsive gel swimmer
Self-oscillating gels are chemically-responsive hydrogels coupled to an oscillating chemical reaction of a stimulus solute. In response to the oscillating solute concentration field, responsive gels periodically swell and deswell, expelling their adsorbed water as they transition to a drier state, and reswelling once they return to a hydrophilic state. This volume phase transition occurs when the local stimulus concentration crosses a critical value, about which the hydrophilicity of the polyme…

Optimal Empirical Risk Minimization under Temporal Distribution Shifts
Yujin Jeong, Ramesh Johari, Dominik Rothenh\"ausler, Emily Fox
https://arxiv.org/abs/2507.13287

Optimal Empirical Risk Minimization under Temporal Distribution Shifts
Temporal distribution shifts pose a key challenge for machine learning models trained and deployed in dynamically evolving environments. This paper introduces RIDER (RIsk minimization under Dynamically Evolving Regimes) which derives optimally-weighted empirical risk minimization procedures under temporal distribution shifts. Our approach is theoretically grounded in the random distribution shift model, where random shifts arise as a superposition of numerous unpredictable changes in the data-g…

OpenAI debuts GPT‑5-Codex, a version of GPT‑5 optimized for agentic coding in Codex and says it spends its "thinking" time more dynamically than previous models (Maxwell Zeff/TechCrunch)
https://techcrunch.com/2025/09/15/openai-upgrades-codex-wi…

OpenAI upgrades Codex with a new version of GPT-5 | TechCrunch
OpenAI's AI coding agent, Codex, can now spend anywhere from a few seconds to several hours on a task, thanks to a new, customized version of GPT-5.

Unleashing the Potential of Multimodal LLMs for Zero-Shot Spatio-Temporal Video Grounding
Zaiquan Yang, Yuhao Liu, Gerhard Hancke, Rynson W. H. Lau
https://arxiv.org/abs/2509.15178

Unleashing the Potential of Multimodal LLMs for Zero-Shot Spatio-Temporal Video Grounding
Spatio-temporal video grounding (STVG) aims at localizing the spatio-temporal tube of a video, as specified by the input text query. In this paper, we utilize multimodal large language models (MLLMs) to explore a zero-shot solution in STVG. We reveal two key insights about MLLMs: (1) MLLMs tend to dynamically assign special tokens, referred to as \textit{grounding tokens}, for grounding the text query; and (2) MLLMs often suffer from suboptimal grounding due to the inability to fully integrate …

Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method
Suleyman Olcay Polat, Poli A. Nemkova, Mark V. Albert
https://arxiv.org/abs/2508.14783 https:/…

Synthetic Adaptive Guided Embeddings (SAGE): A Novel Knowledge Distillation Method
Model distillation enables the transfer of knowledge from large-scale models to compact student models, facilitating deployment in resource-constrained environments. However, conventional distillation approaches often suffer from computational overhead and limited generalization. We propose a novel adaptive distillation framework that dynamically augments training data in regions of high student model loss. Using UMAP-based dimensionality reduction and nearest neighbor sampling, our method iden…

$r$-process Heating Feedback on Disk Outflows from Neutron Star Mergers
Li-Ting Ma, Kuo-Chuan Pan, Meng-Ru Wu, Rodrigo Fern\'andez
https://arxiv.org/abs/2508.15288 https://

$r$-process Heating Feedback on Disk Outflows from Neutron Star Mergers
Neutron star mergers produce $r$-process elements, with yields that are sensitive to the kinematic and thermodynamic properties of the ejecta. These ejecta properties are potentially affected by dynamically-important feedback from $r$-process heating, which is usually not coupled to the hydrodynamics in post-merger simulations modeling the ejecta launching and expansion. The multi-messenger detection of GW170817 showed the importance of producing reliable ejecta predictions, to maximize the dia…

A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Jinyi Han, Xinyi Wang, Haiquan Zhao, Tingyun li, Zishang Jiang, Sihang Jiang, Jiaqing Liang, Xin Lin, Weikang Zhou, Zeye Sun, Fei Yu, Yanghua Xiao
https://arxiv.org/abs/2508.12903

A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most existing self-refinement methods rely on a reactive process with a fixed number of iterations, making it difficult to determine the optimal timing and content of refinement based on the evolving generation context. Inspired by the way humans dynamically refine their thoughts during execution, we propose ProActive Self-Re…

Energy-Efficient Routing Algorithm for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach
Parham Soltani, Mehrshad Eskandarpour, Amir Ahmadizad, Hossein Soleimani
https://arxiv.org/abs/2508.14679

Energy-Efficient Routing Algorithm for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach
Efficient energy management is essential in Wireless Sensor Networks (WSNs) to extend network lifetime and ensure reliable data transmission. This paper presents a novel method using reinforcement learning-based cluster-head selection and a hybrid multi-hop routing algorithm, which leverages Q-learning within a multi-agent system to dynamically adapt transmission paths based on the energy distribution across sensor nodes. Each sensor node is modeled as an autonomous agent that observes local st…

from my link log —
Portable dynamically linked packages on Linux.
https://brioche.dev/blog/portable-dynamically-linked-packages-on-linux/
saved 2025-03-18

A Vision-Based Shared-Control Teleoperation Scheme for Controlling the Robotic Arm of a Four-Legged Robot
Murilo Vinicius da Silva, Matheus Hipolito Carvalho, Juliano Negri, Thiago Segreto, Gustavo J. G. Lahr, Ricardo V. Godoy, Marcelo Becker
https://arxiv.org/abs/2508.14994

A Vision-Based Shared-Control Teleoperation Scheme for Controlling the Robotic Arm of a Four-Legged Robot
In hazardous and remote environments, robotic systems perform critical tasks demanding improved safety and efficiency. Among these, quadruped robots with manipulator arms offer mobility and versatility for complex operations. However, teleoperating quadruped robots is challenging due to the lack of integrated obstacle detection and intuitive control methods for the robotic arm, increasing collision risks in confined or dynamically changing workspaces. Teleoperation via joysticks or pads can be …

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework
Tram Thi Minh Tran, Judy Kay, Stewart Worrall, Marius Hoggenmueller, Callum Parker, Xinyan Yu, Julie Stephany Berrio Perez, Mao Shan, Martin Tomitsch
https://arxiv.org/abs/2508.12518

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework
External Human-Machine Interfaces (eHMIs) are key to facilitating interaction between autonomous vehicles and external road actors, yet most remain reactive and do not account for scalability and inclusivity. This paper introduces a conceptual design framework for adaptive eHMIs-interfaces that dynamically adjust communication as road actors vary and context shifts. Using the cyber-physical system as a structuring lens, the framework comprises three layers: Input (what the system detects), Proc…

Dynamically reconfigurable topological routing in nonlinear photonic systems
Stephan Wong, Simon Betzold, Sven Hofling, Alexander Cerjan
https://arxiv.org/abs/2509.12651 https:/…

Dynamically reconfigurable topological routing in nonlinear photonic systems
The propagation path of topologically protected states is bound to the interface between regions with different topology, and as such, the functionality of linear photonic devices leveraging these states is fixed during fabrication. Here, we propose a mechanism for dynamic control over a driven dissipative system's local topology, yielding reconfigurable topological interfaces and thus tunable paths for protected routing. We illustrate our approach in non-resonantly pumped polariton lattices, w…

Formal Verification of Neural Certificates Done Dynamically
Thomas A. Henzinger, Konstantin Kueffner, Emily Yu
https://arxiv.org/abs/2507.11987 https://

Formal Verification of Neural Certificates Done Dynamically
Neural certificates have emerged as a powerful tool in cyber-physical systems control, providing witnesses of correctness. These certificates, such as barrier functions, often learned alongside control policies, once verified, serve as mathematical proofs of system safety. However, traditional formal verification of their defining conditions typically faces scalability challenges due to exhaustive state-space exploration. To address this challenge, we propose a lightweight runtime monitoring fr…

Towards Efficient Hash Maps in Functional Array Languages
William Henrich Due, Martin Elsman, Troels Henriksen
https://arxiv.org/abs/2508.11443 https://arx…

Towards Efficient Hash Maps in Functional Array Languages
We present a systematic derivation of a data-parallel implementation of two-level, static and collision-free hash maps, by giving a functional formulation of the Fredman et al. construction, and then flattening it. We discuss the challenges of providing a flexible, polymorphic, and abstract interface to hash maps in a functional array language, with particular attention paid to the problem of dynamically sized keys, which we address by associating each hash map with an arbitrary context. The al…

Spatially and Dynamically Extended Molecular Gas in Stephan's Quintet Revealed by ALMA CO(1-0) Total Power Mapping
Fumiya Maeda, Shinya Komugi, Kazuyuki Muraoka, Misaki Yamamoto, Fumi Egusa, Kouji Ohta, Yoshihisa Asada, Asao Habe, Bunyo Hatsukade, Hiroyuki Kaneko, Masato Kobayashi, Kotaro Kohno, Ayu Konishi, Ren Matsusaka, Kana Morokuma-Matsui, Kouichiro Nakanishi, Tomoka Tosaki, Akiyoshi Tsujita

Spatially and Dynamically Extended Molecular Gas in Stephan's Quintet Revealed by ALMA CO(1-0) Total Power Mapping
We present ALMA Total Power CO(1-0) mapping of Stephan's Quintet (SQ), a prototypical compact galaxy group, with a uniform noise level at a spatial scale of ~25 kpc. These observations provide the first complete view of molecular gas across the whole system. Molecular gas is found to spread over a wide area (~120 x 80 kpc), mainly over the two main member galaxies (NGC7318B and 7319), but also in the shocked ridges between these galaxies, the tidal tail, and also in intergalactic regions north …

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Chi-Sheng Chen, En-Jui Kuo
https://arxiv.org/abs/2509.14163

Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step. The controller adopts a hybrid quantum--classical actor--critic architecture: a shallow variational quantum circuit (VQC) with ring entanglement generates policy features, which are mapped by a compact multil…

A Risk Manager for Intrusion Tolerant Systems: Enhancing HAL 9000 with New Scoring and Data Sources
Tadeu Freitas, Carlos Novo, In\^es Dutra, Jo\~ao Soares, Manuel Correia, Benham Shariati, Rolando Martins
https://arxiv.org/abs/2508.13364

A Risk Manager for Intrusion Tolerant Systems: Enhancing HAL 9000 with New Scoring and Data Sources
Intrusion Tolerant Systems (ITSs) have become increasingly critical due to the rise of multi-domain adversaries exploiting diverse attack surfaces. ITS architectures aim to tolerate intrusions, ensuring system compromise is prevented or mitigated even with adversary presence. Existing ITS solutions often employ Risk Managers leveraging public security intelligence to adjust system defenses dynamically against emerging threats. However, these approaches rely heavily on databases like NVD and Exp…

MIRAGE: KV Cache Optimization through Parameter Remapping for Multi-tenant LLM Serving
Ruihao Li, Shagnik Pal, Vineeth Narayan Pullu, Prasoon Sinha, Jeeho Ryoo, Lizy K. John, Neeraja J. Yadwadkar
https://arxiv.org/abs/2507.11507

MIRAGE: KV Cache Optimization through Parameter Remapping for Multi-tenant LLM Serving
KV cache accelerates LLM inference by avoiding redundant computation, at the expense of memory. To support larger KV caches, prior work extends GPU memory with CPU memory via CPU-offloading. This involves swapping KV cache between GPU and CPU memory. However, because the cache updates dynamically, such swapping incurs high CPU memory traffic. We make a key observation that model parameters remain constant during runtime, unlike the dynamically updated KV cache. Building on this, we introduce MI…

D$^2$-LIO: Enhanced Optimization for LiDAR-IMU Odometry Considering Directional Degeneracy
Guodong Yao, Hao Wang, Qing Chang
https://arxiv.org/abs/2508.14355 https://

D$^2$-LIO: Enhanced Optimization for LiDAR-IMU Odometry Considering Directional Degeneracy
LiDAR-inertial odometry (LIO) plays a vital role in achieving accurate localization and mapping, especially in complex environments. However, the presence of LiDAR feature degeneracy poses a major challenge to reliable state estimation. To overcome this issue, we propose an enhanced LIO framework that integrates adaptive outlier-tolerant correspondence with a scan-to-submap registration strategy. The core contribution lies in an adaptive outlier removal threshold, which dynamically adjusts base…

Inferring Attributed Grammars from Parser Implementations
Andreas Pointner, Josef Pichler, Herbert Pr\"ahofer
https://arxiv.org/abs/2507.13117 https:/…

Inferring Attributed Grammars from Parser Implementations
Software systems that process structured inputs often lack complete and up-to-date specifications, which specify the input syntax and the semantics of input processing. While grammar mining techniques have focused on recovering syntactic structures, the semantics of input processing remains largely unexplored. In this work, we introduce a novel approach for inferring attributed grammars from parser implementations. Given an input grammar, our technique dynamically analyzes the implementation of…

Embodied sensorimotor control: computational modeling of the neural control of movement
Muhammad Noman Almani, John Lazzari, Jeff Walker, Shreya Saxena
https://arxiv.org/abs/2509.14360

Embodied sensorimotor control: computational modeling of the neural control of movement
We review how sensorimotor control is dictated by interacting neural populations, optimal feedback mechanisms, and the biomechanics of bodies. First, we outline the distributed anatomical loops that shuttle sensorimotor signals between cortex, subcortical regions, and spinal cord. We then summarize evidence that neural population activity occupies low-dimensional, dynamically evolving manifolds during planning and execution of movements. Next, we summarize literature explaining motor behavior t…

Moderate deviation principle for the chi-square statistics
Zhenhong Yu, Yu Miao
https://arxiv.org/abs/2508.12380 https://arxiv.org/pdf/2508.12380

Moderate deviation principle for the chi-square statistics
In the present paper, we consider the Pearson chi-square statistic defined on a finite alphabet which is assumed to dynamically vary as the sample size increases, and establish its moderate deviation principle.

Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions
Yen-Ku Liu, Yun-Huei Pan, Pei-Fan Lu, Yun-Cheng Tsai, Samuel Yen-Chi Chen
https://arxiv.org/abs/2507.12835

Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions
Financial trading environments are characterized by high volatility, numerous macroeconomic signals, and dynamically shifting market regimes, where traditional reinforcement learning methods often fail to deliver breakthrough performance. In this study, we design a reinforcement learning framework tailored for financial systems by integrating quantum circuits. We compare (1) the performance of classical A3C versus quantum A3C algorithms, and (2) the impact of incorporating LSTM-based prediction…

Dynamically tunable hydrodynamic transport in boron nitride-encapsulated graphene
Akash Gugnani, Aniket Majumdar, Kenji Watanabe, Takashi Taniguchi, Arindam Ghosh
https://arxiv.org/abs/2508.10846

Dynamically tunable hydrodynamic transport in boron nitride-encapsulated graphene
Over the past decade, graphene has emerged as a promising candidate for exploring the viscous nature of electronic flow facilitated by the availability of extremely high-quality devices employing a graphene channel encapsulated within dielectric layers of hexagonal boron nitride (hBN). However, the level of disorder in such systems is mainly determined by the device fabrication protocols, making it impossible to obtain a tunability between the impurity-dominated and the viscous transport within…

My beloved waifu, Debichan, wholeheartedly approves of my gender-fluid, bisexual identity, only because I use Debian! 😉
#Waifu #Debichan #Debian

Quantifier Instantiations: To Mimic or To Revolt?
Jan Jakub\r{u}v, Mikol\'a\v{s} Janota
https://arxiv.org/abs/2508.13811 https://arxiv.org/pdf/2508.138…

Quantifier Instantiations: To Mimic or To Revolt?
Quantified formulas pose a significant challenge for Satisfiability Modulo Theories (SMT) solvers due to their inherent undecidability. Existing instantiation techniques, such as e-matching, syntax-guided, model-based, conflict-based, and enumerative methods, often complement each other. This paper introduces a novel instantiation approach that dynamically learns from these techniques during solving. By treating observed instantiations as samples from a latent language, we use probabilistic con…

VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving
Haichao Liu, Haoren Guo, Pei Liu, Benshan Ma, Yuxiang Zhang, Jun Ma, Tong Heng Lee
https://arxiv.org/abs/2507.15266

VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving
Scene understanding and risk-aware attentions are crucial for human drivers to make safe and effective driving decisions. To imitate this cognitive ability in urban autonomous driving while ensuring the transparency and interpretability, we propose a vision-language model (VLM)-enhanced unified decision-making and motion control framework, named VLM-UDMC. This framework incorporates scene reasoning and risk-aware insights into an upper-level slow system, which dynamically reconfigures the optim…

GenKOL: Modular Generative AI Framework For Scalable Virtual KOL Generation
Tan-Hiep To, Duy-Khang Nguyen, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
https://arxiv.org/abs/2509.14927

GenKOL: Modular Generative AI Framework For Scalable Virtual KOL Generation
Key Opinion Leader (KOL) play a crucial role in modern marketing by shaping consumer perceptions and enhancing brand credibility. However, collaborating with human KOLs often involves high costs and logistical challenges. To address this, we present GenKOL, an interactive system that empowers marketing professionals to efficiently generate high-quality virtual KOL images using generative AI. GenKOL enables users to dynamically compose promotional visuals through an intuitive interface that inte…

Competing color superconductivity and color Kondo effect in quark matter
Pradip Kattel, Abay Zhakenov, Natan Andrei
https://arxiv.org/abs/2507.11617 https:…

Competing color superconductivity and color Kondo effect in quark matter
The competition between bulk color superconductivity and the localized screening of a heavy quark impurity, analogous to the Kondo effect, leads to a rich spectrum of phenomena in dense quark matter. We investigate this competition at the edge of a superconducting quark bulk, where both the superconducting gap and the Kondo scale are dynamically generated in a tractable toy model. Utilizing the exact Bethe Ansatz method, we elucidate the resulting boundary physics. We identify distinct regimes …

Adaptive and Regime-Aware RL for Portfolio Optimization
Gabriel Nixon Raj
https://arxiv.org/abs/2509.14385 https://arxiv.org/pdf/2509.14385

Adaptive and Regime-Aware RL for Portfolio Optimization
This study proposes a regime-aware reinforcement learning framework for long-horizon portfolio optimization. Moving beyond traditional feedforward and GARCH-based models, we design realistic environments where agents dynamically reallocate capital in response to latent macroeconomic regime shifts. Agents receive hybrid observations and are trained using constrained reward functions that incorporate volatility penalties, capital resets, and tail-risk shocks. We benchmark multiple architectures, …

Coalitions on the Fly in Cooperative Games
Yao Zhang, Indrajit Saha, Zhaohong Sun, Makoto Yokoo
https://arxiv.org/abs/2507.11883 https://

Coalitions on the Fly in Cooperative Games
In this work, we examine a sequential setting of a cooperative game in which players arrive dynamically to form coalitions and complete tasks either together or individually, depending on the value created. Upon arrival, a new player as a decision maker faces two options: forming a new coalition or joining an existing one. We assume that players are greedy, i.e., they aim to maximize their rewards based on the information available at their arrival. The objective is to design an online value di…

Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System
Hoon Sagong, Heesu Kim, Hanbeen Hong
https://arxiv.org/abs/2509.12048 https://arxi…

Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System
Conventional autonomous trading systems struggle to balance computational efficiency and market responsiveness due to their fixed operating frequency. We propose Hi-DARTS, a hierarchical multi-agent reinforcement learning framework that addresses this trade-off. Hi-DARTS utilizes a meta-agent to analyze market volatility and dynamically activate specialized Time Frame Agents for high-frequency or low-frequency trading as needed. During back-testing on AAPL stock from January 2024 to May 2025, H…

At the Speed of the Heart: Evaluating Physiologically-Adaptive Visualizations for Supporting Engagement in Biking Exergaming in Virtual Reality
Oliver Hein, Sandra Wackerl, Changkun Ou, Florian Alt, Francesco Chiossi
https://arxiv.org/abs/2508.13095

At the Speed of the Heart: Evaluating Physiologically-Adaptive Visualizations for Supporting Engagement in Biking Exergaming in Virtual Reality
Many exergames face challenges in keeping users within safe and effective intensity levels during exercise. Meanwhile, although wearable devices continuously collect physiological data, this information is seldom leveraged for real-time adaptation or to encourage user reflection. We designed and evaluated a VR cycling simulator that dynamically adapts based on users' heart rate zones. First, we conducted a user study (N=50) comparing eight visualization designs to enhance engagement and exertio…

WANify: Gauging and Balancing Runtime WAN Bandwidth for Geo-distributed Data Analytics
Anshuman Das Mohapatra, Kwangsung Oh
https://arxiv.org/abs/2508.12961 https://

WANify: Gauging and Balancing Runtime WAN Bandwidth for Geo-distributed Data Analytics
Accurate wide area network (WAN) bandwidth (BW) is essential for geo-distributed data analytics (GDA) systems to make optimal decisions such as data and task placement to improve performance. Existing GDA systems, however, measure WAN BW statically and independently between data centers (DCs), while data transfer occurs dynamically and simultaneously among DCs during workload execution. Also, they use a single connection WAN BW that cannot capture actual WAN capacities between distant DCs. Such…

Triples as Links between Binary Black Hole Mergers, their Electromagnetic Counterparts, and Galactic Black Holes
Smadar Naoz, Zoltan Haiman, Eliot Quataert, Liz Holzknecht
https://arxiv.org/abs/2508.13270

Triples as Links between Binary Black Hole Mergers, their Electromagnetic Counterparts, and Galactic Black Holes
We propose a formation pathway linking black holes (BHs) observed in gravitational-wave (GW) mergers, wide BH-stellar systems uncovered by Gaia, and accreting low-mass X-ray binaries (LMXBs). In this scenario, a stellar-mass BH binary undergoes isolated binary evolution and merges while hosting a distant, dynamically unimportant tertiary stellar companion. The tertiary becomes relevant only after the merger, when the remnant BH receives a GW recoil kick. Depending on the kick velocity and syste…

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
Ashish Jha, Anh huy Phan, Razan Dibo, Valentin Leplat
https://arxiv.org/abs/2508.13653 https://

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling
Training modern neural networks on large datasets is computationally and environmentally costly. We introduce GRAFT, a scalable in-training subset selection method that (i) extracts a low-rank feature representation for each batch, (ii) applies a Fast MaxVol sampler to select a small, diverse subset that spans the batch's dominant subspace, and (iii) dynamically adjusts the subset size using a gradient-approximation criterion. By operating in low-rank subspaces and training on carefully chosen …

Crosslisted article(s) found for astro-ph.GA. https://arxiv.org/list/astro-ph.GA/new
[1/1]:
- From Wide Triples to UCXBs: Multimessenger Detection of Dynamically-formed Black Hole-White Dwarf...
Zeyuan Xuan, Cheyanne Shariat, Smadar Naoz

Enterprise AI Must Enforce Participant-Aware Access Control
Shashank Shreedhar Bhatt, Tanmay Rajore, Khushboo Aggarwal, Ganesh Ananthanarayanan, Ranveer Chandra, Nishanth Chandran, Suyash Choudhury, Divya Gupta, Emre Kiciman, Sumit Kumar Pandey, Srinath Setty, Rahul Sharma, Teijia Zhao
https://arxiv.org/abs/2509.14608

Enterprise AI Must Enforce Participant-Aware Access Control
Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data. While fine-tuning enhances performance by internalizing domain knowledge, it also introduces a critical security risk: leakage of confidential training data to unauthorized users. These risks are exacerbated when LLMs are combined with Retrieval-Augmented Generation (RAG) pipelines that dynamically fetch contextual docume…

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI
Ming Zeng, Xianbin Wang, Yuanwei Liu, Zhiguo Ding, George K. Karagiannidis, H. Vincent Poor
https://arxiv.org/abs/2507.12582

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI
Pinching-antenna technology has lately showcased its promising capability for reconfiguring wireless propagation environments, especially in high-frequency communication systems like millimeter-wave and terahertz bands. By dynamically placing the antenna over a dielectric waveguide, line-of-sight (LoS) connections can be made to significantly improve system performance. Although recent research have illustrated the advantages of pinching-antenna-assisted designs, they mainly presuppose complete…

Computational electro-optic frequency comb spectroscopy
J. J. Navarro-Alventosa, A. Aupart-Acosta, V. Dur\'an
https://arxiv.org/abs/2509.15030 https://…

Computational electro-optic frequency comb spectroscopy
Computational techniques have gained significant traction in photonics, enabling the co-design of hardware and data processing algorithms to drastically simplify optical system architectures and improve their performance. However, their application in optical frequency comb spectroscopy remains considerably underexplored. In this work, we introduce a non-interferometric approach to frequency comb spectroscopy based on dynamically tailored electro-optic modulation. The core of our method is a re…

PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Yulong Chen, Michael Sejr Schlichtkrull, Zhenyun Deng, David Corney, Nasim Asl, Joshua Salisbury, Andrew Dudfield, Andreas Vlachos
https://arxiv.org/abs/2509.11804

PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Political pledges reflect candidates' policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic, temporal and multi-document nature. To address this issue, we introduce \textsc{PledgeTracker}, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists…

Long-term monitoring of a dynamically new comet C/2020 V2 (ZTF)
Goldy Ahuja, K. Aravind, Shashikiran Ganesh, Said Hmiddouch, Mathieu Vander Donckt, Emmanuel Jehin, Devendra Sahu, T. Sivarani
https://arxiv.org/abs/2509.05902

Long-term monitoring of a dynamically new comet C/2020 V2 (ZTF)
Comet C/2020 V2 (ZTF) is categorized as a dynamically new long-period comet, making its first approach to the inner Solar system. We have observed this comet for around 32 months (from January 2022 to July 2024) at heliocentric distances from 5.41 au (pre-perihelion) to 5.26 au (post-perihelion) through various telescopes, employing photometric (60 epochs) and spectroscopic techniques (5 epochs). Using these observations, we derived the production rates of different molecules such as CN$(0-0)$,…

GView: A Survey of Binary Forensics via Visual, Semantic, and AI-Enhanced Analysis
Raul Zaharia (Al. I. Cuza University,Bitdefender), Drago\c{s} Gavrilu\c{t} (Al. I. Cuza University,Bitdefender), Gheorghi\c{t}\u{a} Mutu (Al. I. Cuza University,Bitdefender)
https://arxiv.org/abs/2509.13025

GView: A Survey of Binary Forensics via Visual, Semantic, and AI-Enhanced Analysis
Cybersecurity threats continue to become more sophisticated and diverse in their artifacts, boosting both their volume and complexity. To overcome those challenges, we present GView, an open-source forensic analysis framework with visual and AI-enhanced reasoning. It started with focus on the practical cybersecurity industry. It has evolved significantly, incorporating large language models (LLMs) to dynamically enhance reasoning and ease the forensic workflows. This paper surveys both the curr…

SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication
Ruijia Zhang, Xinyan Zhao, Ruixiang Wang, Sigen Chen, Guibin Zhang, An Zhang, Kun Wang, Qingsong Wen
https://arxiv.org/abs/2508.11733

SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication
LLM-based multi-agent systems exhibit strong collaborative capabilities but often suffer from redundant communication and excessive token overhead. Existing methods typically enhance efficiency through pretrained GNNs or greedy algorithms, but often isolate pre- and post-task optimization, lacking a unified strategy. To this end, we present SafeSieve, a progressive and adaptive multi-agent pruning algorithm that dynamically refines the inter-agent communication through a novel dual-mechanism. S…

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
Jos\'e Eduardo Zerna Torres, Marios Avgeris, Chrysa Papagianni, Gergely Pongr\'acz, Istv\'an G\'odor, Paola Grosso
https://arxiv.org/abs/2508.13806

Reinforcement Learning-based Adaptive Path Selection for Programmable Networks
This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 sw…

Dynamically emergent correlations in Brownian particles subject to simultaneous non-Poissonian resetting protocols
Gabriele de Mauro, Marco Biroli, Satya N. Majumdar, Gregory Schehr
https://arxiv.org/abs/2509.06658

Dynamically emergent correlations in Brownian particles subject to simultaneous non-Poissonian resetting protocols
We consider a one-dimensional gas of $N$ independent Brownian particles subject to simultaneous stochastic resetting, with inter-reset times drawn from a general waiting-time distribution $ψ(τ)$. This includes the well-known Poissonian case, where $ψ(τ)=re^{-rτ}$, and extends to more general classes of resetting, such as heavy-tailed and bounded distributions. We show that the simultaneous resetting generates correlations between particles dynamically. These correlations grow with time and…

EvolMathEval: Towards Evolvable Benchmarks for Mathematical Reasoning via Evolutionary Testing
Shengbo Wang, Mingwei Liu, Zike Li, Anji Li, Yanlin Wang, Xin Peng, Zibin Zheng
https://arxiv.org/abs/2508.13003

EvolMathEval: Towards Evolvable Benchmarks for Mathematical Reasoning via Evolutionary Testing
The rapid advancement of LLMs poses a significant challenge to existing mathematical reasoning benchmarks. These benchmarks commonly suffer from issues such as score saturation, temporal decay, and data contamination. To address this challenge, this paper introduces EvolMathEval, an automated mathematical benchmark generation and evolution framework based on evolutionary testing. By dynamically generating unique evaluation instances ab initio, the framework fundamentally eliminates the risk of …

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Senqiao Yang, Junyi Li, Xin Lai, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2507.13348

VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning
Recent advancements in vision-language models (VLMs) have improved performance by increasing the number of visual tokens, which are often significantly longer than text tokens. However, we observe that most real-world scenarios do not require such an extensive number of visual tokens. While the performance drops significantly in a small subset of OCR-related tasks, models still perform accurately in most other general VQA tasks with only 1/4 resolution. Therefore, we propose to dynamically proc…

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts
Cong Bai, Salish Maharjan, Zhaoyu Wang
https://arxiv.org/abs/2507.12569

Model Predictive Black Start for Dynamic Formation of DER-Led Microgrids with Inrush Current Impacts
Black start (BS) of the distribution system (DS) with high penetration of distributed energy resources (DERs) requires advanced control frameworks to ensure secure and efficient restoration. This paper proposes a model predictive black start (MPBS) framework incorporating an inrush current feasibility module to dynamically generate real-time feasible and optimal restoration sequences. Short-term forecasts of DER output and transmission grid (TG) availability are utilized to construct adaptive c…

Dynamically encircling an exceptional point through phase-tracked closed-loop control
Sen Zhang, Yangyu Huang, Lei Yu, Kaixuan He, Ning Zhou, Dingbang Xiao, Xuezhong Wu, Franco Nori, Hui Jing, Xin Zhou
https://arxiv.org/abs/2509.04940

Dynamically encircling an exceptional point through phase-tracked closed-loop control
The intricate complex eigenvalues of non-Hermitian Hamiltonians manifest as Riemann surfaces in control parameter spaces. At the exceptional points (EPs), the degeneracy of both eigenvalues and eigenvectors introduces noteworthy topological features, particularly during the encirclement of the EPs. Traditional methods for probing the state information on the Riemann surfaces involve static measurements; however, realizing continuous encircling remains a formidable challenge due to non-adiabatic…

AI-Driven Adaptive Air Transit Network with Modular Aerial Pods
Amir Shafiee, Alireza Yazdiani, Hanieh Rastegar, Rui Li, Rayan Karim, Aolei Cao, Ziyang Li, Xieqing Yu, Charlle Sy, Zhaoyao Bao, Xi Cheng, H. Oliver Gao
https://arxiv.org/abs/2509.12644

AI-Driven Adaptive Air Transit Network with Modular Aerial Pods
This paper presents an adaptive air transit network leveraging modular aerial pods and artificial intelligence (AI) to address urban mobility challenges. Passenger demand, forecasted from AI models, serves as input parameters for a Mixed-Integer Nonlinear Programming (MINLP) optimization model that dynamically adjusts pod dispatch schedules and train lengths in response to demand variations. The results reveal a complex interplay of factors, including demand levels, headway bounds, train config…

Dynamically Switchable Polarization Lasing between q-BIC and Bragg Resonance Modes
Hongyu Yuan, Jiaoyao Liu, Xiaolin Wang, Qianwen Jia, Jinwei Shi, Dahe Liu, Zhaona Wang
https://arxiv.org/abs/2508.09439

Dynamically Switchable Polarization Lasing between q-BIC and Bragg Resonance Modes
Quasi-bound states in the continuum (q-BICs) enable low-threshold lasing through high-Q cavity modes, yet their polarization tunability remains constrained by nanostructure-imposed cavity symmetries. By engineering a microcavity with an optimized duty cycle (0.34), we demonstrate a polarization-switchable distributed feedback (DFB) laser with controlled emission transitions between dual off-Γ q-BIC lasing and single Γ-point Bragg resonance (BR) lasing through switching pump polarization. The …

Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
Xia Cui
https://arxiv.org/abs/2507.11384 https://

Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss
This paper explores the application of a simple weighted loss function to Transformer-based models for multi-label emotion detection in SemEval-2025 Shared Task 11. Our approach addresses data imbalance by dynamically adjusting class weights, thereby enhancing performance on minority emotion classes without the computational burden of traditional resampling methods. We evaluate BERT, RoBERTa, and BART on the BRIGHTER dataset, using evaluation metrics such as Micro F1, Macro F1, ROC-AUC, Accurac…

WHU-STree: A Multi-modal Benchmark Dataset for Street Tree Inventory
Ruifei Ding, Zhe Chen, Wen Fan, Chen Long, Huijuan Xiao, Yelu Zeng, Zhen Dong, Bisheng Yang
https://arxiv.org/abs/2509.13172

WHU-STree: A Multi-modal Benchmark Dataset for Street Tree Inventory
Street trees are vital to urban livability, providing ecological and social benefits. Establishing a detailed, accurate, and dynamically updated street tree inventory has become essential for optimizing these multifunctional assets within space-constrained urban environments. Given that traditional field surveys are time-consuming and labor-intensive, automated surveys utilizing Mobile Mapping Systems (MMS) offer a more efficient solution. However, existing MMS-acquired tree datasets are limite…

Tao-Technology for Teen Mobile Use: Harmonizing Adaptation, Autonomy, and Reflection
Pengyu Zhu, Janghee Cho
https://arxiv.org/abs/2507.12204 https://

Tao-Technology for Teen Mobile Use: Harmonizing Adaptation, Autonomy, and Reflection
Adolescents' mobile technology use is often regulated through rigid control mechanisms that fail to account for their autonomy and natural usage patterns. Drawing on Taoist philosophy, particularly Wu Wei, Yin-Yang, and Zi Ran, this position paper proposes Tao-Technology, a self-organizing, adaptive regulatory framework. Integrating insights from Reflective Informatics and Information Ecologies, we explore how mobile technology can dynamically adjust to context while fostering self-reflection a…

Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime
Mojtaba Eshghie
https://arxiv.org/abs/2507.10590 https://

Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime
Language Model (LM) pipelines can dynamically refine their outputs against programmatic constraints. However, their effectiveness collapses when faced with competing soft constraints, leading to inefficient backtracking loops where satisfying one constraint violates another. We introduce Meta Self-Refining, a framework that equips LM pipelines with a meta-corrective layer to repair these competitions at runtime/inference-time. Our approach monitors the pipeline's execution history to detect osc…

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Yifei Li, Lingling Zhang, Hang Yan, Tianzhe Zhao, Zihan Ma, Muye Huang, Jun Liu
https://arxiv.org/abs/2508.11347

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several continual knowledge graph embedding (CKGE) methods have been developed to efficiently update KG embeddings to accommodate new facts while maintaining learned knowledge. As KGs grow at di…

CAD-Driven Co-Design for Flight-Ready Jet-Powered Humanoids
Punith Reddy Vanteddu, Davide Gorbani, Giuseppe L'Erario, Hosameldin Awadalla Omer Mohamed, Fabio Bergonti, Daniele Pucci
https://arxiv.org/abs/2509.14935

CAD-Driven Co-Design for Flight-Ready Jet-Powered Humanoids
This paper presents a CAD-driven co-design framework for optimizing jet-powered aerial humanoid robots to execute dynamically constrained trajectories. Starting from the iRonCub-Mk3 model, a Design of Experiments (DoE) approach is used to generate 5,000 geometrically varied and mechanically feasible designs by modifying limb dimensions, jet interface geometry (e.g., angle and offset), and overall mass distribution. Each model is constructed through CAD assemblies to ensure structural validity a…

Rethinking Reference Trajectories in Agile Drone Racing: A Unified Reference-Free Model-Based Controller via MPPI
Fangguo Zhao, Xin Guan, Shuo Li
https://arxiv.org/abs/2509.14726

Rethinking Reference Trajectories in Agile Drone Racing: A Unified Reference-Free Model-Based Controller via MPPI
While model-based controllers have demonstrated remarkable performance in autonomous drone racing, their performance is often constrained by the reliance on pre-computed reference trajectories. Conventional approaches, such as trajectory tracking, demand a dynamically feasible, full-state reference, whereas contouring control relaxes this requirement to a geometric path but still necessitates a reference. Recent advancements in reinforcement learning (RL) have revealed that many model-based con…

CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
Maosheng Qin, Renyu Zhu, Mingxuan Xia, Chenkai Chen, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang
https://arxiv.org/abs/2509.14030

CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources-including Large Language Models (LLMs), Small Language Models (SLMs), and human experts-they often focus narrowly on the labeling step itself. A critical gap remains in the holistic process control required to manage these sources dynamically, addressing complex scheduling and quality-cost trade-offs in a unified manner. Inspired by real-wor…

Multidimensional Nebular-Phase Calculations of Dynamically-Driven Double-Degenerate Double-Detonation Models for Type Ia Supernovae
J. M. Pollin, S. A. Sim, L. J. Shingles, R. Pakmor, F. P. Callan, C. E. Collins, F. K. Roepke, L. A. Kwok, A. Holas, S. Srivastav
https://arxiv.org/abs/2507.05000

Multidimensional Nebular-Phase Calculations of Dynamically-Driven Double-Degenerate Double-Detonation Models for Type Ia Supernovae
The dynamically-driven double-degenerate double-detonation model has emerged as a promising progenitor candidate for Type Ia supernovae. In this scenario, the primary white dwarf ignites due to dynamical interaction with a companion white dwarf, which may also undergo a detonation. Consequently, two scenarios exist: one in which the secondary survives and another in which both white dwarfs detonate. In either case, substantial departures from spherical symmetry are imprinted on the ejecta. Here…

BandFuzz: An ML-powered Collaborative Fuzzing Framework
Wenxuan Shi, Hongwei Li, Jiahao Yu, Xinqian Sun, Wenbo Guo, Xinyu Xing
https://arxiv.org/abs/2507.10845

BandFuzz: An ML-powered Collaborative Fuzzing Framework
Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative fuzzing relaxes the assumptions on target programs, providing constant and robust performance across various programs. Ideally, collaborative fuzzing should be a more promising direction toward generic fuzz…

The extended molecular gas of the Circinus galaxy and NGC 1097 as seen by APEX
Akhil Lasrado, Claudia Cicone, Axel Weiss
https://arxiv.org/abs/2508.10982 https://

The extended molecular gas of the Circinus galaxy and NGC 1097 as seen by APEX
The outer region of the interstellar medium (ISM) is witness to dynamically important events in a galaxy's evolutionary history such as outflows, inflows, tidal interactions, and mergers, as well as dynamical structures affecting its current evolution such as bars and spiral arms. Studying imprints of these processes in the diffuse, extended molecular gas is best achieved by a single dish telescope which can cover a large field of view with good sensitivity to large-scale structures. In this wo…

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation
Sangwoo Jeon, Juchul Shin, YeonJe Cho, Gyeong-Tae Kim, Seongwoo Kim
https://arxiv.org/abs/2508.11890

Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation
Modern autonomous drone missions increasingly require software frameworks capable of seamlessly integrating structured symbolic planning with adaptive reinforcement learning (RL). Although traditional rule-based architectures offer robust structured reasoning for drone autonomy, their capabilities fall short in dynamically complex operational environments that require adaptive symbolic planning. Symbolic RL (SRL), using the Planning Domain Definition Language (PDDL), explicitly integrates domai…

RegimeNAS: Regime-Aware Differentiable Architecture Search With Theoretical Guarantees for Financial Trading
Prathamesh Devadiga, Yashmitha Shailesh
https://arxiv.org/abs/2508.11338

RegimeNAS: Regime-Aware Differentiable Architecture Search With Theoretical Guarantees for Financial Trading
We introduce RegimeNAS, a novel differentiable architecture search framework specifically designed to enhance cryptocurrency trading performance by explicitly integrating market regime awareness. Addressing the limitations of static deep learning models in highly dynamic financial environments, RegimeNAS features three core innovations: (1) a theoretically grounded Bayesian search space optimizing architectures with provable convergence properties; (2) specialized, dynamically activated neural …

LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Reasoning
Jiaqi Wang, Binquan Ji, Haibo Luo, Yiyang Qi, Ruiting Li, Huiyan Wang, Yuantao Han, Cangyi Yang, jiaxu Zhang, Feiliang Ren
https://arxiv.org/abs/2509.12875

LTA-thinker: Latent Thought-Augmented Training Framework for Large Language Models on Complex Reasoning
Complex Reasoning in Large Language Models can be dynamically optimized using Test-Time Scaling (TTS) to mitigate Overthinking. Methods such as Coconut, SoftCoT and its variant are effective in continuous latent space inference, the core bottleneck still lies in the efficient generation and utilization of high-quality Latent Thought. Drawing from the theory of SoftCoT++ that a larger variance in the generated Latent Thought distribution more closely approximates the golden truth distribution, w…

Multi-CAP: A Multi-Robot Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments
Zongyuan Shen, Burhanuddin Shirose, Prasanna Sriganesh, Bhaskar Vundurthy, Howie Choset, Matthew Travers
https://arxiv.org/abs/2509.14941

Multi-CAP: A Multi-Robot Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments
Efficient coordination of multiple robots for coverage of large, unknown environments is a significant challenge that involves minimizing the total coverage path length while reducing inter-robot conflicts. In this paper, we introduce a Multi-robot Connectivity-Aware Planner (Multi-CAP), a hierarchical coverage path planning algorithm that facilitates multi-robot coordination through a novel connectivity-aware approach. The algorithm constructs and dynamically maintains an adjacency graph that …

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Zhanhong Jiang, Md Zahid Hasan, Nastaran Saadati, Aditya Balu, Chao Liu, Soumik Sarkar
https://arxiv.org/abs/2509.09485

Balancing Utility and Privacy: Dynamically Private SGD with Random Projection
Stochastic optimization is a pivotal enabler in modern machine learning, producing effective models for various tasks. However, several existing works have shown that model parameters and gradient information are susceptible to privacy leakage. Although Differentially Private SGD (DPSGD) addresses privacy concerns, its static noise mechanism impacts the error bounds for model performance. Additionally, with the exponential increase in model parameters, efficient learning of these models using s…

Demo: Secure Edge Server for Network Slicing and Resource Allocation in Open RAN
Adhwaa Alchaab, Ayman Younis, Dario Pompili
https://arxiv.org/abs/2507.11499

Demo: Secure Edge Server for Network Slicing and Resource Allocation in Open RAN
Next-Generation Radio Access Networks (NGRAN) aim to support diverse vertical applications with strict security, latency, and Service-Level Agreement (SLA) requirements. These demands introduce challenges in securing the infrastructure, allocating resources dynamically, and enabling real-time reconfiguration. This demo presents SnSRIC, a secure and intelligent network slicing framework that mitigates a range of Distributed Denial-of-Service (DDoS) attacks in Open RAN environments. SnSRIC incorp…

Planning with Dynamically Changing Domains
Mikhail Soutchanski, Yongmei Liu
https://arxiv.org/abs/2508.02697 https://arxiv.org/pdf/2508.02697

Planning with Dynamically Changing Domains
In classical planning and conformant planning, it is assumed that there are finitely many named objects given in advance, and only they can participate in actions and in fluents. This is the Domain Closure Assumption (DCA). However, there are practical planning problems where the set of objects changes dynamically as actions are performed; e.g., new objects can be created, old objects can be destroyed. We formulate the planning problem in first-order logic, assume an initial theory is a finite …

Mixture of Raytraced Experts
Andrea Perin, Giacomo Lagomarsini, Claudio Gallicchio, Giuseppe Nuti
https://arxiv.org/abs/2507.12419 https://

Mixture of Raytraced Experts
We introduce a Mixture of Raytraced Experts, a stacked Mixture of Experts (MoE) architecture which can dynamically select sequences of experts, producing computational graphs of variable width and depth. Existing MoE architectures generally require a fixed amount of computation for a given sample. Our approach, in contrast, yields predictions with increasing accuracy as the computation cycles through the experts' sequence. We train our model by iteratively sampling from a set of candidate exper…

Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Ryan M. Bena, Gilbert Bahati, Blake Werner, Ryan K. Cosner, Lizhi Yang, Aaron D. Ames
https://arxiv.org/abs/2508.11129

Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular, legged robots typically possess manipulable asymmetric geometries which must be considered during safety-critical trajectory planning. This work proposes a predictive safety filter: a nonlinear model predictive control (MPC) algorithm for online trajectory generation with geometry-aware safety constraints based on con…

Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks
Ngoc Duy Pham, Thusitha Dayaratne, Viet Vo, Shangqi Lai, Sharif Abuadbba, Hajime Suzuki, Xingliang Yuan, Carsten Rudolph
https://arxiv.org/abs/2507.12127

Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks
Advancements in wireless and mobile technologies, including 5G advanced and the envisioned 6G, are driving exponential growth in wireless devices. However, this rapid expansion exacerbates spectrum scarcity, posing a critical challenge. Dynamic spectrum allocation (DSA)--which relies on sensing and dynamically sharing spectrum--has emerged as an essential solution to address this issue. While machine learning (ML) models hold significant potential for improving spectrum sensing, their adoption …

Nominal Evaluation Of Automatic Multi-Sections Control Potential In Comparison To A Simpler One- Or Two-Sections Alternative With Predictive Spray Switching
Mogens Plessen
https://arxiv.org/abs/2508.11573

Nominal Evaluation Of Automatic Multi-Sections Control Potential In Comparison To A Simpler One- Or Two-Sections Alternative With Predictive Spray Switching
Automatic Section Control (ASC) is a long-standing trend for spraying in agriculture. It promises to minimise spray overlap areas. The core idea is to (i) switch off spray nozzles on areas that have already been sprayed, and (ii) to dynamically adjust nozzle flow rates along the boom bar that holds the spray nozzles when velocities of boom sections vary during turn maneuvers. ASC is not possible without sensors, in particular for accurate positioning data. Spraying and the movement of modern wi…

EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models
Yiqun Yao, Naitong Yu, Xiang Li, Xin Jiang, Xuezhi Fang, Wenjia Ma, Xuying Meng, Jing Li, Aixin Sun, Yequan Wang
https://arxiv.org/abs/2509.11914

EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models
We introduce EgoMem, the first lifelong memory agent tailored for full-duplex models that process real-time omnimodal streams. EgoMem enables real-time models to recognize multiple users directly from raw audiovisual streams, to provide personalized response, and to maintain long-term knowledge of users' facts, preferences, and social relationships extracted from audiovisual history. EgoMem operates with three asynchronous processes: (i) a retrieval process that dynamically identifies user via …