Tootfinder

DECAMP: Towards Scene-Consistent Multi-Agent Motion Prediction with Disentangled Context-Aware Pre-Training
Jianxin Shi, Zengqi Peng, Xiaolong Chen, Tianyu Wo, Jun Ma
https://arxiv.org/abs/2509.10426

DECAMP: Towards Scene-Consistent Multi-Agent Motion Prediction with Disentangled Context-Aware Pre-Training
Trajectory prediction is a critical component of autonomous driving, essential for ensuring both safety and efficiency on the road. However, traditional approaches often struggle with the scarcity of labeled data and exhibit suboptimal performance in multi-agent prediction scenarios. To address these challenges, we introduce a disentangled context-aware pre-training framework for multi-agent motion prediction, named DECAMP. Unlike existing methods that entangle representation learning with pret…

A Decentralized Microservice Scheduling Approach Using Service Mesh in Cloud-Edge Systems
Yangyang Wen, Paul Townend, Per-Olov \"Ostberg, Abel Souza, Cl\'ement Courageux-Sudan
https://arxiv.org/abs/2510.11189

A Decentralized Microservice Scheduling Approach Using Service Mesh in Cloud-Edge Systems
As microservice-based systems scale across the cloud-edge continuum, traditional centralized scheduling mechanisms increasingly struggle with latency, coordination overhead, and fault tolerance. This paper presents a new architectural direction: leveraging service mesh sidecar proxies as decentralized, in-situ schedulers to enable scalable, low-latency coordination in large-scale, cloud-native environments. We propose embedding lightweight, autonomous scheduling logic into each sidecar, allowin…

Morphogenetic mechanical metamaterials: Emerging tensor properties from self-organized structures
Thomas Froment\`eze, Philippe Michaud, Ali Hassny, Vincent Pateloup
https://arxiv.org/abs/2509.10277

Morphogenetic mechanical metamaterials: Emerging tensor properties from self-organized structures
Understanding how living organisms spontaneously develop complex functional structures inspires innovative approaches in engineering design. Here, we introduce a decentralized generative model based on morphogenesis to autonomously grow mechanical structures with controlled tensorial properties. By adapting Turing's reaction-diffusion concept through anisotropic diffusion, our approach enables the local emergence of microstructures exhibiting tailored stiffness and anisotropy, achieving target …

NetMoniAI: An Agentic AI Framework for Network Security & Monitoring
Pallavi Zambare, Venkata Nikhil Thanikella, Nikhil Padmanabh Kottur, Sree Akhil Akula, Ying Liu
https://arxiv.org/abs/2508.10052

NetMoniAI: An Agentic AI Framework for Network Security & Monitoring
In this paper, we present NetMoniAI, an agentic AI framework for automatic network monitoring and security that integrates decentralized analysis with lightweight centralized coordination. The framework consists of two layers: autonomous micro-agents at each node perform local traffic analysis and anomaly detection. A central controller then aggregates insights across nodes to detect coordinated attacks and maintain system-wide situational awareness. We evaluated NetMoniAI on a local micro-test…

Distributed Online Stochastic Convex-Concave Optimization: Dynamic Regret Analyses under Single and Multiple Consensus Steps
Wentao Zhang, Baoyong Zhang, Deming Yuan, Shengyuan Xu, Vincent K. N. Lau
https://arxiv.org/abs/2508.09411

Distributed Online Stochastic Convex-Concave Optimization: Dynamic Regret Analyses under Single and Multiple Consensus Steps
This paper considers the distributed online convex-concave optimization with constraint sets over a multiagent network, in which each agent autonomously generates a series of decision pairs through a designable mechanism to cooperatively minimize the global loss function. To this end, under no-Euclidean distance metrics, we propose a distributed online stochastic mirror descent convex-concave optimization algorithm with time-varying predictive mappings. Taking dynamic saddle point regret as a p…

The temporary impact of permanent hiring incentives: Evidence from Italy
Michele Cantarella, Maria Cristina Maurizio, Francesco Serti
https://arxiv.org/abs/2509.10193 https://…

The temporary impact of permanent hiring incentives: Evidence from Italy
This paper evaluates the short and medium-term effectiveness of hiring incentives aimed at promoting the permanent conversion of temporary contracts through social contribution exemptions. Using rich administrative data from Tuscany, providing detailed employment histories, we use difference in differences and regression discontinuity designs to exploit a unique change in eligibility criteria in 2018. We find that the incentives immediately increased the probability of conversion, with no evide…

When Competition Helps: Achieving Optimal Traffic Flow with Multiple Autonomous Planners
Ivan Geffner, Erez Karpas, Moshe Tennenholtz
https://arxiv.org/abs/2508.07145 https://…

When Competition Helps: Achieving Optimal Traffic Flow with Multiple Autonomous Planners
The inefficiency of selfish routing in congested networks is a classical problem in algorithmic game theory, often captured by the Price of Anarchy (i.e., the ratio between the social cost of decentralized decisions and that of a centrally optimized solution.) With the advent of autonomous vehicles, capable of receiving and executing centrally assigned routes, it is natural to ask whether their deployment can eliminate this inefficiency. At first glance, a central authority could simply compute…

Anarcho-syndicalism is the belief that workers, as the fundamental producers of social value, must seize control of the means of production through direct action and autonomous organization. It rejects both capitalist private ownership and state-controlled systems, arguing instead for a decentralized federation of worker-managed industries. From my perspective, anarcho-syndicalism is not merely an economic strategy but a revolutionary theory of social transformation, one that places the work…

A Minimal Model for Emergent Collective Behaviors in Autonomous Robotic Multi-Agent Systems
Hossein B. Jond
https://arxiv.org/abs/2508.08473 https://arxiv.…

A Minimal Model for Emergent Collective Behaviors in Autonomous Robotic Multi-Agent Systems
Collective behaviors such as swarming and flocking emerge from simple, decentralized interactions in biological systems. Existing models, such as Vicsek and Cucker-Smale, lack collision avoidance, whereas the Olfati-Saber model imposes rigid formations, limiting their applicability in swarm robotics. To address these limitations, this paper proposes a minimal yet expressive model that governs agent dynamics using relative positions, velocities, and local density, modulated by two tunable parame…

SwarmVLM: VLM-Guided Impedance Control for Autonomous Navigation of Heterogeneous Robots in Dynamic Warehousing
Malaika Zafar, Roohan Ahmed Khan, Faryal Batool, Yasheerah Yaqoot, Ziang Guo, Mikhail Litvinov, Aleksey Fedoseev, Dzmitry Tsetserukou
https://arxiv.org/abs/2508.07814

SwarmVLM: VLM-Guided Impedance Control for Autonomous Navigation of Heterogeneous Robots in Dynamic Warehousing
With the growing demand for efficient logistics, unmanned aerial vehicles (UAVs) are increasingly being paired with automated guided vehicles (AGVs). While UAVs offer the ability to navigate through dense environments and varying altitudes, they are limited by battery life, payload capacity, and flight duration, necessitating coordinated ground support. Focusing on heterogeneous navigation, SwarmVLM addresses these limitations by enabling semantic collaboration between UAVs and ground robots …