Tootfinder

BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
Sike Xiang, Shuang Chen, Amir Atapour-Abarghouei
https://arxiv.org/abs/2509.08715 https:…

BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
As multimodal large language models (MLLMs) advance, their large-scale architectures pose challenges for deployment in resource-constrained environments. In the age of large models, where energy efficiency, computational scalability and environmental sustainability are paramount, the development of lightweight and high-performance models is critical for real-world applications. As such, we propose a lightweight MLLM framework for end-to-end visual question answering. Our proposed approach centr…

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Siyi Lu, Run Liu, Dongsheng Yang, Lei He
https://arxiv.org/abs/2508.06074

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Autonomous driving systems face significant challenges in perceiving complex environments and making real-time decisions. Traditional modular approaches, while offering interpretability, suffer from error propagation and coordination issues, whereas end-to-end learning systems can simplify the design but face computational bottlenecks. This paper presents a novel approach to autonomous driving using deep reinforcement learning (DRL) that integrates bird's-eye view (BEV) perception for enhanced …

Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates
Zixin Zhang, James Avtges, Todd D. Murphey
https://arxiv.org/abs/2509.08241 https://

Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates
Data-driven control methods need to be sample-efficient and lightweight, especially when data acquisition and computational resources are limited -- such as during learning on hardware. Most modern data-driven methods require large datasets and struggle with real-time updates of models, limiting their performance in dynamic environments. Koopman theory formally represents nonlinear systems as linear models over observables, and Koopman representations can be determined from data in an optimizat…

Towards Efficient and Secure Cloud Control Systems: Advances, Challenges, and Future Directions
Yasir Ali, Tayyab Manzoor, Huan Yang, Asif Ali, Yuanqing Xia
https://arxiv.org/abs/2509.09299

Towards Efficient and Secure Cloud Control Systems: Advances, Challenges, and Future Directions
Networked Control Systems (NCSs) have been instrumental in realizing fully connected and responsive intelligent environments within the context of real-time virtual control and management. However, traditional NCSs face considerable challenges in handling the vast amounts of data generated by large-scale control applications, particularly in terms of data acquisition, storage, and computational processing. To address these challenges, the emergence of cloud computing and advancements in control…

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
Sajid Mannan, Vaibhav Bihani, Carmelo Gonzales, Kin Long Kelvin Lee, Nitya Nand Gosvami, Sayan Ranu, Santiago Miret, N M Anoop Krishnan
https://arxiv.org/abs/2508.05762

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evaluation has been limited to computational benchmarks that may not reflect real-world performance. Here, we present UniFFBench, a comprehensive framework for evaluating UMLFFs against experimental measurements of ~1,500 carefully curated mineral structures spanning diverse chemical environments, bonding types, structural …

One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
Yuan Pu, Yazhe Niu, Jia Tang, Junyu Xiong, Shuai Hu, Hongsheng Li
https://arxiv.org/abs/2509.07945

One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
In heterogeneous multi-task learning, tasks not only exhibit diverse observation and action spaces but also vary substantially in intrinsic difficulty. While conventional multi-task world models like UniZero excel in single-task settings, we find that when handling large-scale heterogeneous environments, gradient conflicts and the loss of model plasticity often constrain their sample and computational efficiency. In this work, we address these challenges from two perspectives: the single learni…

FlexEmu: Towards Flexible MCU Peripheral Emulation (Extended Version)
Chongqing Lei, Zhen Ling, Xiangyu Xu, Shaofeng Li, Guangchi Liu, Kai Dong, Junzhou Luo
https://arxiv.org/abs/2509.07615

FlexEmu: Towards Flexible MCU Peripheral Emulation (Extended Version)
Microcontroller units (MCUs) are widely used in embedded devices due to their low power consumption and cost-effectiveness. MCU firmware controls these devices and is vital to the security of embedded systems. However, performing dynamic security analyses for MCU firmware has remained challenging due to the lack of usable execution environments -- existing dynamic analyses cannot run on physical devices (e.g., insufficient computational resources), while building emulators is costly due to the …

On the interaction of fish and marine hydrokinetic turbines: Insights gained through experimental and computational observations
Hossein Seyedzadeh, Mehrshad Gholami Anjiraki, Guglielmo Sonnino Sorisio, Catherine Wilson, Fotis Sotiropoulos, Ali Khosronejad
https://arxiv.org/abs/2508.04558

On the interaction of fish and marine hydrokinetic turbines: Insights gained through experimental and computational observations
Tidal and riverine hydrokinetic turbines offer promising solutions for renewable energy generation in aquatic environments. However, their ecological impact, especially on fish behavior, warrants a thorough investigation. This study presents an integrated experimental and computational analysis of fish turbine interactions, combining laboratory observations with high fidelity large eddy simulations. The simulations capture essential flow features, including wake asymmetry, vortex shedding, and …

Hardware Acceleration in Portable MRIs: State of the Art and Future Prospects
Omar Al Habsi, Safa Mohammed Sali, Anis Meribout, Mahmoud Meribout, Saif Almazrouei, Mohamed Seghier
https://arxiv.org/abs/2509.06365

Hardware Acceleration in Portable MRIs: State of the Art and Future Prospects
There is a growing interest in portable MRI (pMRI) systems for point-of-care imaging, particularly in remote or resource-constrained environments. However, the computational complexity of pMRI, especially in image reconstruction and machine learning (ML) algorithms for enhanced imaging, presents significant challenges. Such challenges can be potentially addressed by harnessing hardware application solutions, though there is little focus in the current pMRI literature on hardware acceleration. T…

Genesis: A Spiking Neuromorphic Accelerator With On-chip Continual Learning
Vedant Karia, Abdullah Zyarah, Dhireesha Kudithipudi
https://arxiv.org/abs/2509.05858 https://…

Genesis: A Spiking Neuromorphic Accelerator With On-chip Continual Learning
Continual learning, the ability to acquire and transfer knowledge through a models lifetime, is critical for artificial agents that interact in real-world environments. Biological brains inherently demonstrate these capabilities while operating within limited energy and resource budgets. Achieving continual learning capability in artificial systems considerably increases memory and computational demands, and even more so when deploying on platforms with limited resources. In this work, Genesis,…

STADI: Fine-Grained Step-Patch Diffusion Parallelism for Heterogeneous GPUs
Han Liang, Jiahui Zhou, Zicheng Zhou, Xiaoxi Zhang, Xu Chen
https://arxiv.org/abs/2509.04719 https://…

STADI: Fine-Grained Step-Patch Diffusion Parallelism for Heterogeneous GPUs
The escalating adoption of diffusion models for applications such as image generation demands efficient parallel inference techniques to manage their substantial computational cost. However, existing diffusion parallelism inference schemes often underutilize resources in heterogeneous multi-GPU environments, where varying hardware capabilities or background tasks cause workload imbalance. This paper introduces Spatio-Temporal Adaptive Diffusion Inference (STADI), a novel framework to accelerate…

Real-time Photorealistic Mapping for Situational Awareness in Robot Teleoperation
Ian Page (GIPSA-lab), Pierre Susbielle (GIPSA-lab), Olivier Aycard (GIPSA-lab), Pierre-Brice Wieber
https://arxiv.org/abs/2509.06433

Real-time Photorealistic Mapping for Situational Awareness in Robot Teleoperation
Achieving efficient remote teleoperation is particularly challenging in unknown environments, as the teleoperator must rapidly build an understanding of the site's layout. Online 3D mapping is a proven strategy to tackle this challenge, as it enables the teleoperator to progressively explore the site from multiple perspectives. However, traditional online map-based teleoperation systems struggle to generate visually accurate 3D maps in real-time due to the high computational cost involved, lead…

Grid-like Error-Correcting Codes for Matrix Multiplication with Better Correcting Capability
Hao Shi, Zhengyi Jiang, Zhongyi Huang, Bo Bai, Gong Zhang, Hanxu Hou
https://arxiv.org/abs/2508.04355

Grid-like Error-Correcting Codes for Matrix Multiplication with Better Correcting Capability
Matrix multiplication over the real field constitutes a foundational operation in the training of deep learning models, serving as a computational cornerstone for both forward and backward propagation processes. However, the presence of silent data corruption (SDC) in large-scale distributed training environments poses a significant threat to model convergence and predictive accuracy, particularly when such errors manifest during matrix multiplication. Due to their transient and non-intrusive n…

Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
Zijian Wang, Beizhen Zhao, Hao Wang
https://arxiv.org/abs/2508.04965 https://arxiv.org/pd…

Perceive-Sample-Compress: Towards Real-Time 3D Gaussian Splatting
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated remarkable capabilities in real-time and photorealistic novel view synthesis. However, traditional 3DGS representations often struggle with large-scale scene management and efficient storage, particularly when dealing with complex environments or limited computational resources. To address these limitations, we introduce a novel perceive-sample-compress framework for 3D Gaussian Splatting. Specifically, we propose a scene percept…

Reasoning under uncertainty in the game of Cops and Robbers
Dazhu Li, Sujata Ghosh, Fenrong Liu
https://arxiv.org/abs/2508.00004 https://arxiv.org/pdf/2508…

Reasoning under uncertainty in the game of Cops and Robbers
The game of Cops and Robbers is an important model for studying computational queries in pursuit-evasion environments, among others. As recent logical explorations have shown, its structure exhibits appealing analogies with modal logic. In this paper, we enrich the game with a setting in which players may have imperfect information. We propose a new formal framework, Epistemic Logic of Cops and Robbers (ELCR), to make the core notions of the game precise, for instance, players' positions, obser…

Language and Experience: A Computational Model of Social Learning in Complex Tasks
C\'edric Colas, Tracey Mills, Ben Prystawski, Michael Henry Tessler, Noah Goodman, Jacob Andreas, Joshua Tenenbaum
https://arxiv.org/abs/2509.00074

Language and Experience: A Computational Model of Social Learning in Complex Tasks
The ability to combine linguistic guidance from others with direct experience is central to human development, enabling safe and rapid learning in new environments. How do people integrate these two sources of knowledge, and how might AI systems? We present a computational framework that models social learning as joint probabilistic inference over structured, executable world models given sensorimotor and linguistic data. We make this possible by turning a pretrained language model into a proba…

Interactive High-Performance Visualization for Astronomy and Cosmology
Eva Sciacca, Nicola Tuccari, Umer Arshad, Fabio Pitari, Giuseppa Muscianisi, Emiliano Tramontana
https://arxiv.org/abs/2510.04665 …

Interactive High-Performance Visualization for Astronomy and Cosmology
The exponential growth of data in Astrophysics and Cosmology demands scalable computational tools and intuitive interfaces for analysis and visualization. In this work, we present an innovative integration of the VisIVO scientific visualization framework with the InterActive Computing (IAC) service at Cineca, enabling interactive, high-performance visual workflows directly within HPC environments. Through seamless integration into Jupyter-based science gateways, users can now access GPU-enabled…

Accelerated Convolutive Transfer Function-Based Multichannel NMF Using Iterative Source Steering
Xuemai Xie, Xianrui Wang, Liyuan Zhang, Yichen Yang, Shoji Makino
https://arxiv.org/abs/2510.02382

Accelerated Convolutive Transfer Function-Based Multichannel NMF Using Iterative Source Steering
Among numerous blind source separation (BSS) methods, convolutive transfer function-based multichannel non-negative matrix factorization (CTF-MNMF) has demonstrated strong performance in highly reverberant environments by modeling multi-frame correlations of delayed source signals. However, its practical deployment is hindered by the high computational cost associated with the iterative projection (IP) update rule, which requires matrix inversion for each source. To address this issue, we propo…

Microstructure sensitive recurrent neural network surrogate model of crystal plasticity
Michael D. Atkinson, Michael D. White, Adam J. Plowman, Pratheek Shanthraj
https://arxiv.org/abs/2510.06904

Microstructure sensitive recurrent neural network surrogate model of crystal plasticity
The development of next-generation structural materials for harsh environments requires rapid assessment of mechanical performance and its dependence on microstructure. While full-field crystal plasticity (CP) models provide detailed insights, the high computational cost limits their use with uncertainty quantification workflows and in component-scale simulation. Surrogate models based on recurrent neural networks (RNNs) have shown promise in reproducing history-dependent mechanical behaviour b…

Relative Localization of UAV Swarms in GNSS-Denied Conditions
Guangyu Lei, Yuqi Ping, Tianhao Liang, Huahao Ding, Tingting Zhang
https://arxiv.org/abs/2509.04412 https://…

Relative Localization of UAV Swarms in GNSS-Denied Conditions
Relative localization of unmanned aerial vehicle (UAV) swarms in global navigation satellite system (GNSS) denied environments is essential for emergency rescue and battlefield reconnaissance. Existing methods suffer from significant localization errors among UAVs due to packet loss and high computational complexity in large swarms. This paper proposes a clustering-based framework where the UAVs simultaneously use communication signals for channel estimation and ranging. Firstly, the spectral c…

Towards an Embodied Composition Framework for Organizing Immersive Computational Notebooks
Sungwon In, Eric Krokos, Kirsten Whitley, Chris North, Yalong Yang
https://arxiv.org/abs/2509.13291

Towards an Embodied Composition Framework for Organizing Immersive Computational Notebooks
As immersive technologies evolve, immersive computational notebooks offer new opportunities for interacting with code, data, and outputs. However, scaling these environments remains a challenge, particularly when analysts manually arrange large numbers of cells to maintain both execution logic and visual coherence. To address this, we introduce an embodied composition framework, facilitating organizational processes in the context of immersive computational notebooks. To evaluate the effectiven…

V.I.P. : Iterative Online Preference Distillation for Efficient Video Diffusion Models
Jisoo Kim, Wooseok Seo, Junwan Kim, Seungho Park, Sooyeon Park, Youngjae Yu
https://arxiv.org/abs/2508.03254

V.I.P. : Iterative Online Preference Distillation for Efficient Video Diffusion Models
With growing interest in deploying text-to-video (T2V) models in resource-constrained environments, reducing their high computational cost has become crucial, leading to extensive research on pruning and knowledge distillation methods while maintaining performance. However, existing distillation methods primarily rely on supervised fine-tuning (SFT), which often leads to mode collapse as pruned models with reduced capacity fail to directly match the teacher's outputs, ultimately resulting in de…

ControlMed: Adding Reasoning Control to Medical Language Model
Sung-Min Lee, Siyoon Lee, Juyeon Kim, Kyungmin Roh
https://arxiv.org/abs/2507.22545 https://…

ControlMed: Adding Reasoning Control to Medical Language Model
Reasoning Large Language Models (LLMs) with enhanced accuracy and explainability are increasingly being adopted in the medical domain, as the life-critical nature of clinical decision-making demands reliable support. Despite these advancements, existing reasoning LLMs often generate unnecessarily lengthy reasoning processes, leading to significant computational overhead and response latency. These limitations hinder their practical deployment in real-world clinical environments. To address thes…

SDC-Based Model Predictive Control: Enhancing Computational Feasibility for Safety-Critical Quadrotor Control
Saber Omidi
https://arxiv.org/abs/2509.24208 https://

SDC-Based Model Predictive Control: Enhancing Computational Feasibility for Safety-Critical Quadrotor Control
Nonlinear Model Predictive Control (NMPC) is widely used for controlling high-speed robotic systems such as quadrotors. However, its significant computational demands often hinder real-time feasibility and reliability, particularly in environments requiring robust obstacle avoidance. This paper proposes a novel SDC-Based Model Predictive Control (MPC) framework, which preserves the high-precision performance of NMPC while substantially reducing computational complexity by over 30%. By reformula…

Meta-learning ecological priors from large language models explains human learning and decision making
Akshay K. Jagadish, Mirko Thalmann, Julian Coda-Forno, Marcel Binz, Eric Schulz
https://arxiv.org/abs/2509.00116

Meta-learning ecological priors from large language models explains human learning and decision making
Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remains an open question whether learning and decision making can be explained as a principled adaptation to the statistical structure of real-world tasks. We introduce ecologically rational analysis, a computational framework that unifies the normative foundations of rational analysis with ecological grounding. Leveraging large language models to generate ecologically valid cognitive tasks at scale, and using…

Fair Resource Allocation for Fleet Intelligence
Oguzhan Baser, Kaan Kale, Po-han Li, Sandeep Chinchali
https://arxiv.org/abs/2509.03353 https://arxiv.org/p…

Fair Resource Allocation for Fleet Intelligence
Resource allocation is crucial for the performance optimization of cloud-assisted multi-agent intelligence. Traditional methods often overlook agents' diverse computational capabilities and complex operating environments, leading to inefficient and unfair resource distribution. To address this, we open-sourced Fair-Synergy, an algorithmic framework that utilizes the concave relationship between the agents' accuracy and the system resources to ensure fair resource allocation across fleet intelli…

TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
Shuo Sha, Anupam Bhakta, Zhenyuan Jiang, Kevin Qiu, Ishaan Mahajan, Gabriel Bravo, Brian Plancher
https://arxiv.org/abs/2510.04839

TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environmental interactions, and system wear. Traditional methods such as Recursive Least Squares (RLS) and the Kalman Filter (KF) often struggle to track abrupt parameter shifts or incur high computational costs, limiting their effectiveness in dynamic environments and for computationally constrained robotic systems. As such, we introduce TAG-K, a lightweigh…

Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning
Yutong Wang, Pengliang Ji, Kaixin Li, Baolong Bi, Tao Feng, Guillaume Sartoretti
https://arxiv.org/abs/2508.03018

Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning
Large Language Reasoning Models have demonstrated remarkable success on static tasks, yet their application to multi-round agentic planning in interactive environments faces two fundamental challenges. First, the intractable credit assignment problem renders conventional reinforcement learning ineffective in sparse-reward settings. Second, the computational overhead of verbose, step-by-step reasoning histories is prohibitive. To address these challenges, we propose BPO, a three-stage framework …

Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
Gilberto Cunha, Alexandra Ram\^oa, Andr\'e Sequeira, Michael de Oliveira, Lu\'is Barbosa
https://arxiv.org/abs/2507.18606 …

Hybrid quantum-classical algorithm for near-optimal planning in POMDPs
Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks. Recent advances demonstrate that inference on sparse Bayesian networks can be accelerated using quantum rejection sampling combined with amplitude amplification, leading to a computational speedup in estimating acceptance probabilities.\\ Building on this resul…

Securing IoT Devices in Smart Cities: A Review of Proposed Solutions
Andr\'es F. Betancur-L\'opez
https://arxiv.org/abs/2510.01445 https://arxiv.or…

Securing IoT Devices in Smart Cities: A Review of Proposed Solutions
Privacy and security in Smart Cities remain at constant risk due to the vulnerabilities introduced by Internet of Things (IoT) devices. The limited computational resources of these devices make them especially susceptible to attacks, while their widespread adoption increases the potential impact of security breaches. This article presents a review of security proposals aimed at protecting IoT devices in Smart City environments. The review was conducted by analyzing recent literature on device-l…

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Safa Mohammed Sali, Mahmoud Meribout, Ashiyana Abdul Majeed
https://arxiv.org/abs/2509.04162

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Transformers and vision-language models (VLMs) have emerged as dominant architectures in computer vision and multimodal AI, offering state-of-the-art performance in tasks such as image classification, object detection, visual question answering, and caption generation. However, their high computational complexity, large memory footprints, and irregular data access patterns present significant challenges for deployment in latency- and power-constrained environments. Field-programmable gate array…

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Ricardo Hidalgo Arag\'on, Jes\'us M. Gonz\'alez-Barahona, Gregorio Robles
https://arxiv.org/abs/2507.17314

How Do Code Smells Affect Skill Growth in Scratch Novice Programmers?
Context. Code smells, which are recurring anomalies in design or style, have been extensively researched in professional code. However, their significance in block-based projects created by novices is still largely unknown. Block-based environments such as Scratch offer a unique, data-rich setting to examine how emergent design problems intersect with the cultivation of computational-thinking (CT) skills. Objective. This research explores the connection between CT proficiency and design-level c…

Investigating solid-state CH3OH formation with chemical modelling
K. -Y. Huang, E. M\'endez-Robayo, S. Viti, Mario -A. Higuera -G
https://arxiv.org/abs/2509.22203 https://…

Investigating solid-state CH3OH formation with chemical modelling
Context. Recent Monte Carlo simulations and laboratory studies of interstellar ices have proposed an alternative pathway involving the radical-molecule H-atom abstraction reaction in the overall mechanism of methanol (CH3OH) formation in dark molecular clouds. Aims. A computational study was conducted to investigate the contribution of the radical-molecule H-atom abstraction route in CH3OH formation in interstellar ices, both in non-shocked and shocked environments, and to examine how the physi…

Accelerating a Linear Programming Algorithm on AMD GPUs
Xiyan Hu, Titus Parker, Connor Phillips, Yifa Yu
https://arxiv.org/abs/2508.16806 https://arxiv.org…

Accelerating a Linear Programming Algorithm on AMD GPUs
Linear Programming (LP) is a foundational optimization technique with widespread applications in finance, energy trading, and supply chain logistics. However, traditional Central Processing Unit (CPU)-based LP solvers often struggle to meet the latency and scalability demands of dynamic, high-dimensional industrial environments, creating a significant computational challenge. This project addresses these limitations by accelerating linear programming on AMD Graphics Processing Units (GPUs), lev…

Quantum Annealing Hyperparameter Analysis for Optimal Sensor Placement in Production Environments
Nico Kraus, Marvin Erdmann, Alexander Kuzmany, Daniel Porawski, Jonas Stein
https://arxiv.org/abs/2507.16584

Quantum Annealing Hyperparameter Analysis for Optimal Sensor Placement in Production Environments
To increase efficiency in automotive manufacturing, newly produced vehicles can move autonomously from the production line to the distribution area. This requires an optimal placement of sensors to ensure full coverage while minimizing the number of sensors used. The underlying optimization problem poses a computational challenge due to its large-scale nature. Currently, classical solvers rely on heuristics, often yielding non-optimal solutions for large instances, resulting in suboptimal senso…

Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning
Siyi Zhao, Wei Wang, Yanmin Qian
https://arxiv.org/abs/2509.21833 https://

Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning
Recent advancements in automatic speech recognition (ASR) have achieved notable progress, whereas robustness in noisy environments remains challenging. While speech enhancement (SE) front-ends are widely used to mitigate noise as a preprocessing step for ASR, they often introduce computational non-negligible overhead. This paper proposes optimizations to reduce SE computational costs without compromising ASR performance. Our approach integrates layer-wise frame resampling and progressive sub-ba…

Human vs. AI Safety Perception? Decoding Human Safety Perception with Eye-Tracking Systems, Street View Images, and Explainable AI
Yuhao Kang, Junda Chen, Liu Liu, Kshitij Sharmad, Martina Mazzarello, Simone Mora, Fabio Duarte, Carlo Ratti
https://arxiv.org/abs/2509.25457

Human vs. AI Safety Perception? Decoding Human Safety Perception with Eye-Tracking Systems, Street View Images, and Explainable AI
The way residents perceive safety plays an important role in how they use public spaces. Studies have combined large-scale street view images and advanced computer vision techniques to measure the perception of safety of urban environments. Despite their success, such studies have often overlooked the specific environmental visual factors that draw human attention and trigger people's feelings of safety perceptions. In this study, we introduce a computational framework that enriches the existin…

Accurate Machine-Learning Description for SiC in Extreme Environments
Jintong Wu, Zhuang Shao, Junlei Zhao, Flyura Djurabekova, Kai Nordlund, Fredric Granberg, Qingmin Zhang, and Jesper Byggm\"astar
https://arxiv.org/abs/2510.01827

Accurate Machine-Learning Description for SiC in Extreme Environments
Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies, including both ab initio and classical atomistic approaches. In this work, we develop a computationally efficient and general-purpose machine-learned interatomic potential (ML-IAP) capable of multimillion-atom molecular dynamics (MD) simulations over microsecond …

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
Zhiyu Wang, Mohammad Goudarzi, Mingming Gong, Rajkumar Buyya
https://arxiv.org/abs/2508.21328

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
The rapid proliferation of Internet of Things (IoT) applications across heterogeneous Cloud-Edge-IoT environments presents significant challenges in distributed scheduling optimization. Existing approaches face issues, including fixed neural network architectures that are incompatible with computational heterogeneity, non-Independent and Identically Distributed (non-IID) data distributions across IoT scheduling domains, and insufficient cross-domain collaboration mechanisms. This paper proposes…

Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning
Thanh Thi Nguyen, Quoc Viet Hung Nguyen, Jonathan Kua, Imran Razzak, Dung Nguyen, Saeid Nahavandi
https://arxiv.org/abs/2508.20688

Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning
Enabling multiple autonomous machines to perform reliably requires the development of efficient cooperative control algorithms. This paper presents a survey of algorithms that have been developed for controlling and coordinating autonomous machines in complex environments. We especially focus on task allocation methods using computational intelligence (CI) and deep reinforcement learning (RL). The advantages and disadvantages of the surveyed methods are analysed thoroughly. We also propose and …

Tensor Networks for Liquids in Heterogeneous Systems
Zachary A. Johnson, Luciano G. Silvestri, Pierson Guthrey, Michael S. Murillo
https://arxiv.org/abs/2507.19352 https://

Tensor Networks for Liquids in Heterogeneous Systems
Many-body correlations in strongly coupled liquids and plasmas are critical for many applications in nanofluids, biology, and fusion-related plasma physics, but their description in fully heterogeneous environments remains challenging due to the high-dimensional equations involved. Recently, tensor network decompositions have emerged as powerful tools for tackling such equations by reducing memory usage and computational complexity. In this paper, we solve for equilibrium density and density-de…

Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach
Lu Liu, Chi Xie, Xi Xiong
https://arxiv.org/abs/2508.19203 https://

Optimizing Highway Traffic Flow in Mixed Autonomy: A Multiagent Truncated Rollout Approach
The development of connected and autonomous vehicles (CAVs) offers substantial opportunities to enhance traffic efficiency. However, in mixed autonomy environments where CAVs coexist with human-driven vehicles (HDVs), achieving efficient coordination among CAVs remains challenging due to heterogeneous driving behaviors. To address this, this paper proposes a multiagent truncated rollout approach that enhances CAV speed coordination to improve highway throughput while reducing computational over…

Towards Production-Worthy Simulation for Autonomous Cyber Operations
Konur Tholl, Mariam El Mezouar, Ranwa Al Mallah
https://arxiv.org/abs/2508.19278 https://

Towards Production-Worthy Simulation for Autonomous Cyber Operations
Simulated environments have proven invaluable in Autonomous Cyber Operations (ACO) where Reinforcement Learning (RL) agents can be trained without the computational overhead of emulation. These environments must accurately represent cybersecurity scenarios while producing the necessary signals to support RL training. In this study, we present a framework where we first extend CybORG's Cage Challenge 2 environment by implementing three new actions: Patch, Isolate, and Unisolate, to better repres…

Revealing the Power of Post-Training for Small Language Models via Knowledge Distillation
Miao Rang, Zhenni Bi, Hang Zhou, Hanting Chen, An Xiao, Tianyu Guo, Kai Han, Xinghao Chen, Yunhe Wang
https://arxiv.org/abs/2509.26497

Revealing the Power of Post-Training for Small Language Models via Knowledge Distillation
The rapid advancement of large language models (LLMs) has significantly advanced the capabilities of artificial intelligence across various domains. However, their massive scale and high computational costs render them unsuitable for direct deployment in resource-constrained edge environments. This creates a critical need for high-performance small models that can operate efficiently at the edge. Yet, after pre-training alone, these smaller models often fail to meet the performance requirements…

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Ebrahim Abu-Helalah, Jordi Serra, Jordi Perez-Romero
https://arxiv.org/abs/2507.14199

On Splitting Lightweight Semantic Image Segmentation for Wireless Communications
Semantic communication represents a promising technique towards reducing communication costs, especially when dealing with image segmentation, but it still lacks a balance between computational efficiency and bandwidth requirements while maintaining high image segmentation accuracy, particularly in resource-limited environments and changing channel conditions. On the other hand, the more complex and larger semantic image segmentation models become, the more stressed the devices are when process…

MAGIC-MASK: Multi-Agent Guided Inter-Agent Collaboration with Mask-Based Explainability for Reinforcement Learning
Maisha Maliha, Dean Hougen
https://arxiv.org/abs/2510.00274 ht…

MAGIC-MASK: Multi-Agent Guided Inter-Agent Collaboration with Mask-Based Explainability for Reinforcement Learning
Understanding the decision-making process of Deep Reinforcement Learning agents remains a key challenge for deploying these systems in safety-critical and multi-agent environments. While prior explainability methods like StateMask, have advanced the identification of critical states, they remain limited by computational cost, exploration coverage, and lack of adaptation to multi-agent settings. To overcome these limitations, we propose a mathematically grounded framework, MAGIC-MASK (Multi-Agen…

Low Resource Audio Codec Challenge Baseline Systems
Yusuf Ziya Isik, Rafa{\l} {\L}aganowski
https://arxiv.org/abs/2510.00264 https://arxiv.org/pdf/2510.002…

Low Resource Audio Codec Challenge Baseline Systems
The Low-Resource Audio Codec (LRAC) Challenge aims to advance neural audio coding for deployment in resource-constrained environments. The first edition focuses on low-resource neural speech codecs that must operate reliably under everyday noise and reverberation, while satisfying strict constraints on computational complexity, latency, and bitrate. Track 1 targets transparency codecs, which aim to preserve the perceptual transparency of input speech under mild noise and reverberation. Track 2 …

TikTok Rewards Divisive Political Messaging During the 2025 German Federal Election
Kirill Solovev, Chiara Drolsbach, Emma Demirel, Nicolas Pr\"ollochs
https://arxiv.org/abs/2509.10336

TikTok Rewards Divisive Political Messaging During the 2025 German Federal Election
Short-form video platforms like TikTok reshape how politicians communicate and have become important tools for electoral campaigning. Yet it remains unclear what kinds of political messages gain traction in these fast-paced, algorithmically curated environments, which are particularly popular among younger audiences. In this study, we use computational content analysis to analyze a comprehensive dataset of N=25,292 TikTok videos posted by German politicians in the run-up to the 2025 German fede…

Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing
Alice Zhang, Callihan Bertley, Dawei Liang, Edison Thomaz
https://arxiv.org/abs/2507.12002

Detecting In-Person Conversations in Noisy Real-World Environments with Smartwatch Audio and Motion Sensing
Social interactions play a crucial role in shaping human behavior, relationships, and societies. It encompasses various forms of communication, such as verbal conversation, non-verbal gestures, facial expressions, and body language. In this work, we develop a novel computational approach to detect a foundational aspect of human social interactions, in-person verbal conversations, by leveraging audio and inertial data captured with a commodity smartwatch in acoustically-challenging scenarios. To…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
Sai Teja Reddy Adapala
https://arxiv.org/abs/2509.19517 https://arxiv.org/…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
The scaling of Large Language Models (LLMs) has exposed a critical gap between their performance on static benchmarks and their fragility in dynamic, information-rich environments. While models excel at isolated tasks, the computational limits that govern their reasoning under cognitive load remain poorly understood. In this work, we introduce a formal theory of computational cognitive load, positing that extraneous, task-irrelevant information (Context Saturation) and interference from task-sw…

First Practical Experiences Integrating Quantum Computers with HPC Resources: A Case Study With a 20-qubit Superconducting Quantum Computer
Eric Mansfield, Stefan Seegerer, Panu Vesanen, Jorge Echavarria, Burak Mete, Muhammad Nufail Farooqi, Laura Schulz
https://arxiv.org/abs/2509.12949

First Practical Experiences Integrating Quantum Computers with HPC Resources: A Case Study With a 20-qubit Superconducting Quantum Computer
Incorporating Quantum Computers into High Performance Computing (HPC) environments (commonly referred to as HPC+QC integration) marks a pivotal step in advancing computational capabilities for scientific research. Here we report the integration of a superconducting 20-qubit quantum computer into the HPC infrastructure at Leibniz Supercomputing Centre (LRZ), one of the first practical implementations of its kind. This yielded four key lessons: (1) quantum computers have stricter facility require…

Population-Aligned Persona Generation for LLM-based Social Simulation
Zhengyu Hu, Zheyuan Xiao, Max Xiong, Yuxuan Lei, Tianfu Wang, Jianxun Lian, Kaize Ding, Ziang Xiao, Nicholas Jing Yuan, Xing Xie
https://arxiv.org/abs/2509.10127

Population-Aligned Persona Generation for LLM-based Social Simulation
Recent advances in large language models (LLMs) have enabled human-like social simulations at unprecedented scale and fidelity, offering new opportunities for computational social science. A key challenge, however, is the construction of persona sets that authentically represent the diversity and distribution of real-world populations. Most existing LLM-based social simulation studies focus primarily on designing agentic frameworks and simulation environments, often overlooking the complexities…

Real-time 3D Visualization of Radiance Fields on Light Field Displays
Jonghyun Kim, Cheng Sun, Michael Stengel, Matthew Chan, Andrew Russell, Jaehyun Jung, Wil Braithwaite, Shalini De Mello, David Luebke
https://arxiv.org/abs/2508.18540

Real-time 3D Visualization of Radiance Fields on Light Field Displays
Radiance fields have revolutionized photo-realistic 3D scene visualization by enabling high-fidelity reconstruction of complex environments, making them an ideal match for light field displays. However, integrating these technologies presents significant computational challenges, as light field displays require multiple high-resolution renderings from slightly shifted viewpoints, while radiance fields rely on computationally intensive volume rendering. In this paper, we propose a unified and ef…

GSFusion:Globally Optimized LiDAR-Inertial-Visual Mapping for Gaussian Splatting
Jaeseok Park, Chanoh Park, Minsu Kim, Soohwan Kim
https://arxiv.org/abs/2507.23273 https://

GSFusion:Globally Optimized LiDAR-Inertial-Visual Mapping for Gaussian Splatting
While 3D Gaussian Splatting (3DGS) has revolutionized photorealistic mapping, conventional approaches based on camera sensor, even RGB-D, suffer from fundamental limitations such as high computational load, failure in environments with poor texture or illumination, and short operational ranges. LiDAR emerges as a robust alternative, but its integration with 3DGS introduces new challenges, such as the need for exceptional global alignment for photorealistic quality and prolonged optimization tim…

Minimizing AoI in Mobile Edge Computing: Nested Index Policy with Preemptive and Non-preemptive Structure
Ning Yang, Yibo Liu, Shuo Chen, Meng Zhang, Haijun Zhang
https://arxiv.org/abs/2508.20564

Minimizing AoI in Mobile Edge Computing: Nested Index Policy with Preemptive and Non-preemptive Structure
Mobile Edge Computing (MEC) leverages computational heterogeneity between mobile devices and edge nodes to enable real-time applications requiring high information freshness. The Age-of-Information (AoI) metric serves as a crucial evaluator of information timeliness in such systems. Addressing AoI minimization in multi-user MEC environments presents significant challenges due to stochastic computing times. In this paper, we consider multiple users offloading tasks to heterogeneous edge servers …

Cloud Native System for LLM Inference Serving
Minxian Xu, Junhan Liao, Jingfeng Wu, Yiyuan He, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.18007 https://

Cloud Native System for LLM Inference Serving
Large Language Models (LLMs) are revolutionizing numerous industries, but their substantial computational demands create challenges for efficient deployment, particularly in cloud environments. Traditional approaches to inference serving often struggle with resource inefficiencies, leading to high operational costs, latency issues, and limited scalability. This article explores how Cloud Native technologies, such as containerization, microservices, and dynamic scheduling, can fundamentally impr…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
Sai Teja Reddy Adapala
https://arxiv.org/abs/2509.19517 https://arxiv.org/…

Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
The scaling of Large Language Models (LLMs) has exposed a critical gap between their performance on static benchmarks and their fragility in dynamic, information-rich environments. While models excel at isolated tasks, the computational limits that govern their reasoning under cognitive load remain poorly understood. In this work, we introduce a formal theory of computational cognitive load, positing that extraneous, task-irrelevant information (Context Saturation) and interference from task-sw…

Deep Hierarchical Learning with Nested Subspace Networks
Paulius Rauba, Mihaela van der Schaar
https://arxiv.org/abs/2509.17874 https://arxiv.org/pdf/2509.…

Deep Hierarchical Learning with Nested Subspace Networks
Large neural networks are typically trained for a fixed computational budget, creating a rigid trade-off between performance and efficiency that is ill-suited for deployment in resource-constrained or dynamic environments. Existing approaches to this problem present a difficult choice: training a discrete collection of specialist models is computationally prohibitive, while dynamic methods like slimmable networks often lack the flexibility to be applied to large, pre-trained foundation models. …

A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection
Yong Zhang, Cunjian Chen, Qiang Gao, Yi Wang, Bin Fang
https://arxiv.org/abs/2508.16397

A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection
Real-time surface defect detection is critical for maintaining product quality and production efficiency in the steel manufacturing industry. Despite promising accuracy, existing deep learning methods often suffer from high computational complexity and slow inference speeds, which limit their deployment in resource-constrained industrial environments. Recent lightweight approaches adopt multibranch architectures based on depthwise separable convolution (DSConv) to capture multiscale contextual …

Optimization for Massive 3D-RIS Deployment: A Generative Diffusion Model-Based Approach
Kaining Wang, Bo Yang, Zhiwen Yu, Xuelin Cao, M\'erouane Debbah, Chau Yuen
https://arxiv.org/abs/2509.11969

Optimization for Massive 3D-RIS Deployment: A Generative Diffusion Model-Based Approach
Reconfigurable Intelligent Surfaces (RISs) transform the wireless environment by modifying the amplitude, phase, and polarization of incoming waves, significantly improving coverage performance. Notably, optimizing the deployment of RISs becomes vital, but existing optimization methods face challenges such as high computational complexity, limited adaptability to changing environments, and a tendency to converge on local optima. In this paper, we propose to optimize the deployment of large-scal…

Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs
Chang-Hun Ji, SiWoon Song, Youn-Hee Han, SungTae Moon
https://arxiv.org/abs/2507.21506

Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs
A drone trajectory planner should be able to dynamically adjust the safety-efficiency trade-off according to varying mission requirements in unknown environments. Although traditional polynomial-based planners offer computational efficiency and smooth trajectory generation, they require expert knowledge to tune multiple parameters to adjust this trade-off. Moreover, even with careful tuning, the resulting adjustment may fail to achieve the desired trade-off. Similarly, although reinforcement le…

Mo-Re-W Alloys for High Temperature Applications: Phase Stability, Elasticity, and Thermal Property Insights via Multi-Cell Monte Carlo and Machine Learning
Tyler D. Dole\v{z}al, Nick A. Valverde, Jodie Yuwono, Ryan Kemnitz
https://arxiv.org/abs/2507.20085

Mo-Re-W Alloys for High Temperature Applications: Phase Stability, Elasticity, and Thermal Property Insights via Multi-Cell Monte Carlo and Machine Learning
The increasing demand for materials capable of withstanding high temperatures and harsh environments necessitates the discovery of advanced alloys. This study introduces a computational routine to predict solid-state phase stability and calculates elastic constants to determine high temperature viability. With it, machine learning models were trained on 1,014 Mo-Re-W structures to enable a large compilation of elastic and thermal properties over the complete Mo-Re-W compositional domain with ex…

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Wei Fan, JinYi Yoon, Xiaochang Li, Huajie Shao, Bo Ji
https://arxiv.org/abs/2507.17228

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models. While SL reduces computational overhead on edge devices, it encounters significant challenges in heterogeneous environments where devices vary in computing resources, communication capabilities, environmental conditions, and privacy requirements. Although recent studie…

MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Zheming Yang, Qi Guo, Yunqing Hu, Chang Zhao, Chang Zhang, Jian Zhao, Wen Ji
https://arxiv.org/abs/2509.16995

MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Multimodal large language models (MLLMs) enable powerful cross-modal inference but impose significant computational and latency burdens, posing severe challenges for deployment in resource-constrained environments. In this paper, we propose MoA-Off, an adaptive heterogeneous modality-aware offloading framework with edge-cloud collaboration for efficient MLLM inference. MoA-Off introduces a lightweight heterogeneous modality-aware module that estimates the complexity of heterogeneous inputs thro…

A Lightweight Crowd Model for Robot Social Navigation
Maryam Kazemi Eskeri, Thomas Wiedemann, Ville Kyrki, Dominik Baumann, Tomasz Piotr Kucner
https://arxiv.org/abs/2508.19595 …

A Lightweight Crowd Model for Robot Social Navigation
Robots operating in human-populated environments must navigate safely and efficiently while minimizing social disruption. Achieving this requires estimating crowd movement to avoid congested areas in real-time. Traditional microscopic models struggle to scale in dense crowds due to high computational cost, while existing macroscopic crowd prediction models tend to be either overly simplistic or computationally intensive. In this work, we propose a lightweight, real-time macroscopic crowd predic…

ASCMamba: Multimodal Time-Frequency Mamba for Acoustic Scene Classification
Bochao Sun, Dong Wang, Han Yin
https://arxiv.org/abs/2508.15632 https://arxiv.o…

ASCMamba: Multimodal Time-Frequency Mamba for Acoustic Scene Classification
Acoustic Scene Classification (ASC) is a fundamental problem in computational audition, which seeks to classify environments based on the distinctive acoustic features. In the ASC task of the APSIPA ASC 2025 Grand Challenge, the organizers introduce a multimodal ASC task. Unlike traditional ASC systems that rely solely on audio inputs, this challenge provides additional textual information as inputs, including the location where the audio is recorded and the time of recording. In this paper, we…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Jingsong Xia, Yue Yin, Xiuhan Li
https://arxiv.org/abs/2508.11532 htt…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture. Through structural optimization and loss function design, the proposed method enhances feature extraction capability and classification performance while redu…

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…

Safety filtering of robotic manipulation under environment uncertainty: a computational approach
Anna Johansson, Daniel Lindmark, Viktor Wiberg, Martin Servin
https://arxiv.org/abs/2509.12674

Safety filtering of robotic manipulation under environment uncertainty: a computational approach
Robotic manipulation in dynamic and unstructured environments requires safety mechanisms that exploit what is known and what is uncertain about the world. Existing safety filters often assume full observability, limiting their applicability in real-world tasks. We propose a physics-based safety filtering scheme that leverages high-fidelity simulation to assess control policies under uncertainty in world parameters. The method combines dense rollout with nominal parameters and parallelizable spa…

CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
Muqing Li, Ning Li, Xin Yuan, Wenchao Xu, Quan Chen, Song Guo, Haijun Zhang
https://arxiv.org/abs/2508.09208

CoMoE: Collaborative Optimization of Expert Aggregation and Offloading for MoE-based LLMs at Edge
The proliferation of large language models (LLMs) has driven the adoption of Mixture-of-Experts (MoE) architectures as a promising solution to scale model capacity while controlling computational costs. However, deploying MoE models in resource-constrained mobile edge computing environments presents significant challenges due to their large memory footprint and dynamic expert activation patterns. To address these challenges, we propose a novel dynamic resource-aware collaborative optimization f…

From Logic to Language: A Trust Index for Problem Solving with LLMs
Tehseen Rug, Felix B\"ohmer, Tessa Pfattheicher
https://arxiv.org/abs/2507.16028 h…

From Logic to Language: A Trust Index for Problem Solving with LLMs
Classical computation, grounded in formal, logical systems, has been the engine of technological progress for decades, excelling at problems that can be described with unambiguous rules. This paradigm, however, leaves a vast ocean of human problems -- those characterized by ambiguity, dynamic environments, and subjective context -- largely untouched. The advent of Large Language Models (LLMs) represents a fundamental shift, enabling computational systems to engage with this previously inaccessi…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Xin Chen, Rui Huang, Longbin Tang, Lin Zhao
https://arxiv.org/abs/2509.17340 https:/…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Agile mapless navigation in cluttered 3D environments poses significant challenges for autonomous drones. Conventional mapping-planning-control pipelines incur high computational cost and propagate estimation errors. We present AERO-MPPI, a fully GPU-accelerated framework that unifies perception and planning through an anchor-guided ensemble of Model Predictive Path Integral (MPPI) optimizers. Specifically, we design a multi-resolution LiDAR point-cloud representation that rapidly extracts spat…

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

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

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Minze Li, Wei Zhao, Ran Chen, Mingqiang Wei
https://arxiv.org/abs/2507.13647 …

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Real-time trajectory planning for unmanned aerial vehicles (UAVs) in dynamic environments remains a key challenge due to high computational demands and the need for fast, adaptive responses. Traditional Particle Swarm Optimization (PSO) methods, while effective for offline planning, often struggle with premature convergence and latency in real-time scenarios. To overcome these limitations, we propose PE-PSO, an enhanced PSO-based online trajectory planner. The method introduces a persistent exp…

Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
Yude Li, Zhexuan Zhou, Huizhe Li, Youmin Gong, Jie Mei
https://arxiv.org/abs/2509.13816

Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
Robust autonomous navigation for Autonomous Aerial Vehicles (AAVs) in complex environments is a critical capability. However, modern end-to-end navigation faces a key challenge: the high-frequency control loop needed for agile flight conflicts with low-frequency perception streams, which are limited by sensor update rates and significant computational cost. This mismatch forces conventional synchronous models into undesirably low control rates. To resolve this, we propose an asynchronous reinfo…

No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terrain
Mohitvishnu S. Gadde, Pranay Dugar, Ashish Malik, Alan Fern
https://arxiv.org/abs/2508.11929

No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terrain
Effective bipedal locomotion in dynamic environments, such as cluttered indoor spaces or uneven terrain, requires agile and adaptive movement in all directions. This necessitates omnidirectional terrain sensing and a controller capable of processing such input. We present a learning framework for vision-based omnidirectional bipedal locomotion, enabling seamless movement using depth images. A key challenge is the high computational cost of rendering omnidirectional depth images in simulation, m…

How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots
Renyuan Liu, Haoting Zhou, Chuankai Fang, Qinbing Fu
https://arxiv.org/abs/2509.13827 https://

How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots
Anyone who has tried to swat a fly has likely been frustrated by its remarkable agility.This ability stems from its visual neural perception system, particularly the collision-selective neurons within its small brain.For autonomous robots operating in complex and unfamiliar environments, achieving similar agility is highly desirable but often constrained by the trade-off between computational cost and performance.In this context, insect-inspired intelligence offers a parsimonious route to low-p…