Tootfinder

🌎 🌍 New agreement between GÉANT and EXA Infrastructure to deliver terabit expansion of R&E connectivity between #Europe and #NorthAmerica.
This strategic investment, procured under the GN5-IC1 project and planned in coordination with our community, with ESnet and with the ANA collaboration, …

GÉANT and EXA Infrastructure announce IRU agreement for terabit expansion of transatlantic R&E connectivity between Europe and North America | GÉANT CONNECT Online
GÉANT and digital infrastructure provider EXA Infrastructure are excited to announce the signature of an historical long-term agreement to deliver a major expansion of the transatlantic connectivity dedicated to Research and Education (R&E) collaboration between Europe and North America. Procured under GN5-IC1, the ambitious project planning and implementing GÉANT’s intercontinental connectivity investments, the new Indefeasible

Nonlinear Nonlocal Diffusion Equations for the Analysis of Continuous Coordination and Anti-Coordination Type Games
John S. McAlister, Nina H. Fefferman, Tadele A. Mengesha
https://arxiv.org/abs/2506.13929

Nonlinear Nonlocal Diffusion Equations for the Analysis of Continuous Coordination and Anti-Coordination Type Games
Coordination games with explicit spatial or relational structure are of interest to economists, ecologists, sociologists, and others studying emergent global properties in collective behavior. When assemblies of individuals seek to coordinate action with one another through myopic best response or other replicator dynamics, the resulting dynamical system can exhibit many rich behaviors. However, these behaviors have been studied only in the case where the number of players is countable and the …

A Coordination-Based Model for the Prediction of Surface Energies and the Shape of Metal Particles
Shyama Charan Mandal, Frank Abild-Pedersen
https://arxiv.org/abs/2506.15144

A Coordination-Based Model for the Prediction of Surface Energies and the Shape of Metal Particles
Surface energies of metal-based systems are important for determining the Wulff-constructed shapes of metal nanoparticles and understanding the stability. We have developed a coordination number-based model to predict the total energy of metal-based systems across a wide range of configurations. Our model has been tested against Density Functional Theory (DFT) calculations for late transition metals. This method enables on-the-fly surface energy predictions and allows for the Wulff construction…

Quantum Learning and Estimation for Distribution Networks and Energy Communities Coordination
Yingrui Zhuang, Lin Cheng, Yuji Cao, Tongxin Li, Ning Qi, Yan Xu, Yue Chen
https://arxiv.org/abs/2506.11730

Quantum Learning and Estimation for Distribution Networks and Energy Communities Coordination
Price signals from distribution networks (DNs) guide energy communities (ECs) to adjust energy usage, enabling effective coordination for reliable power system operation. However, this coordination faces significant challenges due to the limited availability of information (i.e., only the aggregated energy usage of ECs is available to DNs), and the high computational burden of accounting for uncertainties and the associated risks through numerous scenarios. To address these challenges, we propo…

Flexible Hardware-Enabled Guarantees for AI Compute
James Petrie, Onni Aarne, Nora Ammann, David Dalrymple
https://arxiv.org/abs/2506.15093 https://…

Flexible Hardware-Enabled Guarantees for AI Compute
As artificial intelligence systems become increasingly powerful, they pose growing risks to international security, creating urgent coordination challenges that current governance approaches struggle to address without compromising sensitive information or national security. We propose flexible hardware-enabled guarantees (flexHEGs), that could be integrated with AI accelerators to enable trustworthy, privacy-preserving verification and enforcement of claims about AI development. FlexHEGs consi…

Compendium Manager: a tool for coordination of workflow management instances for bulk data processing in Python
Richard J. Abdill, Ran Blekhman
https://arxiv.org/abs/2505.11385

Compendium Manager: a tool for coordination of workflow management instances for bulk data processing in Python
Compendium Manager is a command-line tool written in Python to automate the provisioning, launch, and evaluation of bioinformatics pipelines. Although workflow management tools such as Snakemake and Nextflow enable users to automate the processing of samples within a single sequencing project, integrating many datasets in bulk requires launching and monitoring hundreds or thousands of pipelines. We present the Compendium Manager, a lightweight command-line tool to enable launching and monitorin…

SyncTalk : High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting
Ziqiao Peng, Wentao Hu, Junyuan Ma, Xiangyu Zhu, Xiaomei Zhang, Hao Zhao, Hui Tian, Jun He, Hongyan Liu, Zhaoxin Fan
https://arxiv.org/abs/2506.14742

SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting
Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw, leading to unrealistic results. To address the critical issue of synchronization, identified as the ''devil'' in creating realistic talking heads, we introduce SyncTalk++, which…

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
Eman Alqudah, Ashfaq Khokhar
https://arxiv.org/abs/2506.14987 https://

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC
Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a CNN-based dynamic priority prediction mechanism for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach uses a Convolutional Neural Network and graph coloring to adaptively assign link priorities based on r…

GMT: General Motion Tracking for Humanoid Whole-Body Control
Zixuan Chen, Mazeyu Ji, Xuxin Cheng, Xuanbin Peng, Xue Bin Peng, Xiaolong Wang
https://arxiv.org/abs/2506.14770

GMT: General Motion Tracking for Humanoid Whole-Body Control
The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in t…

Adaptive Accompaniment with ReaLchords
Yusong Wu, Tim Cooijmans, Kyle Kastner, Adam Roberts, Ian Simon, Alexander Scarlatos, Chris Donahue, Cassie Tarakajian, Shayegan Omidshafiei, Aaron Courville, Pablo Samuel Castro, Natasha Jaques, Cheng-Zhi Anna Huang
https://arxiv.org/abs/2506.14723…

Adaptive Accompaniment with ReaLchords
Jamming requires coordination, anticipation, and collaborative creativity between musicians. Current generative models of music produce expressive output but are not able to generate in an \emph{online} manner, meaning simultaneously with other musicians (human or otherwise). We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody. We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for…

Harnessing the "Reactive Falling Effect" for rehabilitation and performance boosting
Paul-Emmanuel Sornette, Didier Sornette
https://arxiv.org/abs/2506.13959

Harnessing the "Reactive Falling Effect" for rehabilitation and performance boosting
We present Logic Workout, a novel training method based on radical dynamic instability combining rolling, deformation and spring instability on small fitballs. By exploiting the "reactive falling effect", it re-engages innate balance mechanisms and forces real-time motor corrections, leading to better joint control, coordination, and movement precision. Preliminary results from a cohort of 18 participants show complete resolution of chronic pain, enhanced functional mobility, and surprising imp…

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
Jiaming Yu, Le Liang, Hao Ye, Shi Jin
https://arxiv.org/abs/2506.14187

Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
High-density Wi-Fi deployments often result in significant co-channel interference, which degrades overall network performance. To address this issue, coordination of multi access points (APs) has been considered to enable coordinated spatial reuse (CSR) in next generation wireless local area networks. This paper tackles the challenge of downlink spatial reuse in Wi-Fi networks, specifically in scenarios involving overlapping basic service sets, by employing hierarchical multi-agent reinforceme…

Detecting Coordination on Short-Video Platforms: The Challenge of Multimodality and Complex Similarity on TikTok
Inga K. Wohlert, Davide Vega, Matteo Magnani, Alexandra Sergerberg
https://arxiv.org/abs/2506.05868

Detecting Coordination on Short-Video Platforms: The Challenge of Multimodality and Complex Similarity on TikTok
Research on online coordinated behaviour has predominantly focused on text-based social media platforms, where coordination manifests clearly through the frequent posting of identical hyperlinks or the frequent re-sharing of the same textual content by the same group of users. However, the rise of short-video platforms like TikTok introduces distinct challenges, by supporting integrated multimodality within posts and complex similarity between them. In this paper, we propose an approach to dete…

Collaborative Beamforming for Communication Applications Using a Two-Element Fully-Wireless Open-Loop Coherent Distributed Array
Jason M. Merlo, Jeffrey A. Nanzer
https://arxiv.org/abs/2506.13014

Collaborative Beamforming for Communication Applications Using a Two-Element Fully-Wireless Open-Loop Coherent Distributed Array
In this work we demonstrate a proof of concept of a fully-wireless two-node open-loop coherent distributed communication system and evaluate its performance by transmitting QPSK , 64-, and 256-QAM constellations at a symbol rate of 2 MBd over a 58 m link in an urban environment. The system is implemented in a distributed manner with on-node processing using software-defined radios (SDRs) and wireless internode communication to share coordination information and does not rely on external time or…

GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
Eman Alqudah, Ashfaq Khokhar
https://arxiv.org/abs/2506.15011 …

GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC
Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a Graph Convolutional Network (GCN) integrated with a Deep Q-Network (DQN) reinforcement learning framework for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach dynamically learns link priorities based on …

Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies
Guiyang Luo, Jinglin Li, Qixun Zhang, Zhiyong Feng, Quan Yuan, Yijing Lin, Hui Zhang, Nan Cheng, Ping Zhang
https://arxiv.org/abs/2506.08579

Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies
The low-altitude economy (LAE) is rapidly advancing toward intelligence, connectivity, and coordination, bringing new challenges in dynamic airspace management, unmanned aerial vehicle (UAV) operation, and security management. Existing systems remain fragmented and lack effective coordination. To bridge these gaps, we propose UTICN (Ubiquitous and Trusted Intelligent Cellular-native Network) for LAE, a unified cellular-native architecture that integrates multi-domain sensing, high-precision pos…

This https://arxiv.org/abs/2501.12227 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Multi-terminal Strong Coordination over Noisy Channels with Encoder Co-operation
We investigate the problem of strong coordination over a multiple-access channel (MAC) with cribbing encoders. In this configuration, two encoders observe independent and identically distributed (i.i.d.) samples of a source random variable each and encode the inputs to the MAC. The decoder which observes the output of the MAC together with side-information, must generate approximately i.i.d. samples of another random variable which is jointly distributed with the two sources and the side inform…

EconGym: A Scalable AI Testbed with Diverse Economic Tasks
Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, Haifeng Zhang
https://arxiv.org/abs/2506.12110

EconGym: A Scalable AI Testbed with Diverse Economic Tasks
Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce …

While I think it’s important to note the coordination, planning & moving parts that got those folks onto the beach on DDay, it’s also important to remind ourselves how much of a clusterfuck the whole thing was once in motion & what got them off the beach was individuals working together collectively

Just found @…'s 2019 blog post looking at the Prisoner's Dilemma as a way to describe Open Source sustainability,
https://dri.es/balancing-makers-and…

Series B, Episode 13 - Star One
DURKIM: Not computers. Computer. Singular. Very singular indeed. Our unbeatable control and coordination centre.
SERVALAN: No!
DURKIM: Servalan, by design or accident Star One is failing.
https://blake.torpidity.net/m/213/29 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration
Nicholas Vincent, Matthew Prewitt, Hanlin Li
https://arxiv.org/abs/2506.10272

Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration
This position paper argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, producers of information goods (such as journalists, researchers, and creative professionals) need to be able to collectively bargain with AI product builders in order to receive reasonable terms and a sustainable return on the informational value they contribute. We argue that without increased market coordination or collective bargaining on the side of th…

EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
Beichen Huang, Ran Cheng, Kay Chen Tan
https://arxiv.org/abs/2506.02049 https:…

EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
We introduce EvoGit, a decentralized multi-agent framework for collaborative software development driven by autonomous code evolution. EvoGit deploys a population of independent coding agents, each proposing edits to a shared codebase without centralized coordination, explicit message passing, or shared memory. Instead, all coordination emerges through a Git-based phylogenetic graph that tracks the full version lineage and enables agents to asynchronously read from and write to the evolving cod…

AgentFacts: Universal KYA Standard for Verified AI Agent Metadata & Deployment
Jared James Grogan
https://arxiv.org/abs/2506.13794 https://

AgentFacts: Universal KYA Standard for Verified AI Agent Metadata & Deployment
Enterprise AI deployment faces critical "Know Your Agent" (KYA) challenges where organizations must verify third-party agent capabilities and establish trust without standardized metadata or verification infrastructure. Current approaches rely on self-declared capabilities and custom integration processes that create trust gaps and coordination friction limiting confident enterprise adoption. This paper presents AgentFacts, a universal metadata standard that enables systematic agent verificatio…

Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
Boyuan Deng, Luca Rossini, Jin Wang, Weijie Wang, Nikolaos Tsagarakis
https://arxiv.org/abs/2506.05516

Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
Adaptive recovery from fall incidents are essential skills for the practical deployment of wheeled-legged robots, which uniquely combine the agility of legs with the speed of wheels for rapid recovery. However, traditional methods relying on preplanned recovery motions, simplified dynamics or sparse rewards often fail to produce robust recovery policies. This paper presents a learning-based framework integrating Episode-based Dynamic Reward Shaping and curriculum learning, which dynamically bal…

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams
Tauhid Tanjim, Jonathan St. George, Kevin Ching, Hee Rin Lee, Angelique Taylor
https://arxiv.org/abs/2506.08892

Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams
The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-se…

📣 Beim diesjährigen Culture Community Plenary 5
(24.–26.09.2025, Akademie der Wissenschaften und der Literatur | Mainz) bieten wir erneut beim Markt der Möglichkeiten ein Austauschformat vor Ort an, bei dem sich Projekte, Services von NFDI4Culture, aber auch Participants und Partner:innen des Konsortiums vorstellen können.
📨 Bis zum 15.07.2025 können Interessierte eine kurze Beschreibung des Posters an uns senden: coordination-office@nfdi4culture.de

🚨 New paper alert 🚨
Together with my co-authors, we compare two different diagnostic clinics of Moorfields Eye Hospital in London regarding their spatial designs and effective patient flows
We highlight the importance of line of sight relationships between diagnostic test stations to ease patient flow and coordination and suggest an ideal clinic configuration based on queuing models
Published

Lanes, clusters, sightlines: modelling patient flow in medical clinics | Buildings & Cities

This https://arxiv.org/abs/2504.19583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning
This paper proposes a parameter collaborative optimization algorithm for large language models, enhanced with graph spectral analysis. The goal is to improve both fine-tuning efficiency and structural awareness during training. In the proposed method, the parameters of a pre-trained language model are treated as nodes in a graph. A weighted graph is constructed, and Laplacian spectral decomposition is applied to enable frequency-domain modeling and structural representation of the parameter spa…

Multimodal Emotion Coupling via Speech-to-Facial and Bodily Gestures in Dyadic Interaction
Von Ralph Dane Marquez Herbuela, Yukie Nagai
https://arxiv.org/abs/2506.10010

Multimodal Emotion Coupling via Speech-to-Facial and Bodily Gestures in Dyadic Interaction
Human emotional expression emerges through coordinated vocal, facial, and gestural signals. While speech face alignment is well established, the broader dynamics linking emotionally expressive speech to regional facial and hand motion remains critical for gaining a deeper insight into how emotional and behavior cues are communicated in real interactions. Further modulating the coordination is the structure of conversational exchange like sequential turn taking, which creates stable temporal win…

Optimal Coordination of Flexible DERs in Local Energy and Flexibility Markets to Ensure Social Equity
Niloofar Pourghaderi, Milad Kabirifar, Payman Dehghanian
https://arxiv.org/abs/2506.02179

Optimal Coordination of Flexible DERs in Local Energy and Flexibility Markets to Ensure Social Equity
Local electricity markets offer a promising solution for integrating renewable energy sources and other distributed energy resources (DERs) into distribution networks. These markets enable the effective utilization of flexible resources by facilitating coordination among various agents. Beyond technical and economic considerations, addressing social equity within these local communities is critical and requires dedicated attention in market-clearing frameworks. This paper proposes a social equi…

Coexistence of static and dynamic local magnetic fields in an S = 3/2 honeycomb lattice antiferromagnet Co2Te3O8
J. Khatua, Suheon Lee, M. Pregelj, Samiul Sk, S. K. Panda, Bassam Hitti, Gerald D. Morris, I. da Silva, Kwang-Yong Choi, P. Khuntia
https://arxiv.org/abs/2506.09848

Coexistence of static and dynamic local magnetic fields in an S = 3/2 honeycomb lattice antiferromagnet Co2Te3O8
Two-dimensional honeycomb lattices, characterized by their low coordination numbers, provide a fertile platform for exploring various quantum phenomena due to the intricate interplay between competing magnetic interactions, spin-orbit coupling, and crystal electric fields. Beyond the widely studied Jeff= 1/2 honeycomb systems, S = 3/2 honeycomb lattices present a promising alternative route to realizing the classical spin liquid-like state within the spin-S Kitaev models. Herein, we present cry…

Just found @…'s 2019 blog post looking at the Prisoner's Dilemma as a way to describe Open Source sustainability,
https://dri.es/balancing-makers-and…

Enhancing Acoustic-to-Articulatory Speech Inversion by Incorporating Nasality
Saba Tabatabaee, Suzanne Boyce, Liran Oren, Mark Tiede, Carol Espy-Wilson
https://arxiv.org/abs/2506.09231

Enhancing Acoustic-to-Articulatory Speech Inversion by Incorporating Nasality
Speech is produced through the coordination of vocal tract constricting organs: lips, tongue, velum, and glottis. Previous works developed Speech Inversion (SI) systems to recover acoustic-to-articulatory mappings for lip and tongue constrictions, called oral tract variables (TVs), which were later enhanced by including source information (periodic and aperiodic energies, and F0 frequency) as proxies for glottal control. Comparison of the nasometric measures with high-speed nasopharyngoscopy sh…

This https://arxiv.org/abs/2302.12223 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Data Monetization through Strategic Coordination of Privately Informed Agents
We consider linear-quadratic games of incomplete information with Gaussian uncertainty, in which players' payoffs depend both on a privately observed type and an unknown but common state. A monopolist data platform observes the state, elicits the players' types, and sells information back to them via (possibly correlated) action recommendations. We fully characterize the class of all such implementable Gaussian mechanisms (where the joint distribution of actions and signals is multivariate norm…

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol
Haowen Xu, Yulin Sun, Jose Tupayachi, Olufemi Omitaomu, Sisi Zlatanov, Xueping Li
https://arxiv.org/abs/2506.13068

Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol
Optimizing urban freight logistics is critical for developing sustainable, low-carbon cities. Traditional methods often rely on manual coordination of simulation tools, optimization solvers, and expert-driven workflows, limiting their efficiency and scalability. This paper presents an agentic system architecture that leverages the model context protocol (MCP) to orchestrate multi-agent collaboration among scientific tools for autonomous, simulation-informed optimization in urban logistics. The …

This https://arxiv.org/abs/2505.05822 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Self-reorganization and Information Transfer in Massive Schools of Fish
The remarkable cohesion and coordination observed in moving animal groups and their collective responsiveness to threats are thought to be mediated by scale-free correlations, where changes in the behavior of one animal influence others in the group, regardless of the distance between them. But are these features independent of group size? Here, we investigate group cohesiveness and collective responsiveness in computational models of massive schools of fish of up to 50,000 individuals. We show…

London Blue Light Collaboration Evaluation: A Comparative Analysis of Spatio temporal Patterns on Emergency Services by London Ambulance Service and London Fire Brigade
Fangyuan Li, Yijing Li, Luke Edward Rogerson
https://arxiv.org/abs/2506.06011

London Blue Light Collaboration Evaluation: A Comparative Analysis of Spatio temporal Patterns on Emergency Services by London Ambulance Service and London Fire Brigade
With rising demand for emergency services, the London Ambulance Service, LAS, and the London Fire Brigade, LFB, face growing challenges in resource coordination. This study investigates the temporal and spatial similarities in their service demands to assess potential for routine cross-agency collaboration. Time series analysis revealed aligned demand peaks in summer, on Fridays, during daytime hours, and were highly sensitive to high temperature weather conditions. Bivariate mapping and Moran …

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
Moshi Wei, Sparks Li
https://arxiv.org/abs/2506.09335 https://…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
The Intelligent System of Emergent Knowledge (ISEK) establishes a decentralized network where human and artificial intelligence agents collaborate as peers, forming a self-organizing cognitive ecosystem. Built on Web3 infrastructure, ISEK combines three fundamental principles: (1) a decentralized multi-agent architecture resistant to censorship, (2) symbiotic AI-human collaboration with equal participation rights, and (3) resilient self-adaptation through distributed consensus mechanisms. The…

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation
Tiyasa Mitra, Ritika Borkar, Nidhi Bhatia, Ramon Matas, Shivam Raj, Dheevatsa Mudigere, Ritchie Zhao, Maximilian Golub, Arpan Dutta, Sailaja Madduri, Dharmesh Jani, Brian Pharris, Bita Darvish Rouhani
https://arxiv.org/abs/2506.05508

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation
As inference scales to multi-node deployments, disaggregation - splitting inference into distinct phases - offers a promising path to improving the throughput-interactivity Pareto frontier. Despite growing enthusiasm and a surge of open-source efforts, practical deployment of disaggregated serving remains limited due to the complexity of the optimization search space and system-level coordination. In this paper, we present the first systematic study of disaggregated inference at scale, evaluati…

CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks
Yixuan Li, Yutang Lin, Jieming Cui, Tengyu Liu, Wei Liang, Yixin Zhu, Siyuan Huang
https://arxiv.org/abs/2506.08931

CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks
Humanoid teleoperation plays a vital role in demonstrating and collecting data for complex humanoid-scene interactions. However, current teleoperation systems face critical limitations: they decouple upper- and lower-body control to maintain stability, restricting natural coordination, and operate open-loop without real-time position feedback, leading to accumulated drift. The fundamental challenge is achieving precise, coordinated whole-body teleoperation over extended durations while maintain…

This https://arxiv.org/abs/2503.17613 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Coordinated Shirking in Technology Adoption
This paper studies a model of technology adoption: a manager tries to induce a group of workers to exert costly effort to vet a new technology before they choose whether to use it. The manager finds it too costly to simultaneously replace large groups of unproductive workers, so they shirk when coordination is possible. Widely applicable technology expands productive possibilities but also provides an opportunity for coordinated shirking, and can thus lead to widespread production failure. Furt…

This https://arxiv.org/abs/2502.14847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Red-Teaming LLM Multi-Agent Systems via Communication Attacks
Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling sophisticated agent collaboration through message-based communications. While the communication framework is crucial for agent coordination, it also introduces a critical yet unexplored security vulnerability. In this work, we introduce Agent-in-the-Middle (AiTM), a novel attack that exploits the fundamental communication mechanisms in LLM-MAS by intercepting and manipulat…

Joint Routing and Control Optimization in VANET
Chen Huang, Dingxuan Wang, Ronghui Hou
https://arxiv.org/abs/2506.08038 https://arxiv…

Joint Routing and Control Optimization in VANET
In this paper, we introduce DynaRoute, an adaptive joint optimization framework for dynamic vehicular networks that simultaneously addresses platoon control and data transmission through trajectory-aware routing and safety-constrained vehicle coordination. DynaRoute guarantees continuous vehicle movement via platoon safety control with optimizing transmission paths through real-time trajectory prediction and ensuring reliable data. Our solution achieves three key objectives: (1) maintaining pla…

This https://arxiv.org/abs/2503.14353 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework
The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-agent coordination. We propose a novel, principled framework for the analysis of DGD and diffusion for strongly convex, smooth objectives, and arbitrary undirected topologies, using contraction mappings coupled with a result called the mean Hessian theorem (MHT). The use of these tools yields tight convergence bounds, b…

CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects
Huaijin Pi, Zhi Cen, Zhiyang Dou, Taku Komura
https://arxiv.org/abs/2505.21437

CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects
Synthesizing whole-body manipulation of articulated objects, including body motion, hand motion, and object motion, is a critical yet challenging task with broad applications in virtual humans and robotics. The core challenges are twofold. First, achieving realistic whole-body motion requires tight coordination between the hands and the rest of the body, as their movements are interdependent during manipulation. Second, articulated object manipulation typically involves high degrees of freedom …

HiBerNAC: Hierarchical Brain-emulated Robotic Neural Agent Collective for Disentangling Complex Manipulation
Hongjun Wu, Heng Zhang, Pengsong Zhang, Jin Wang, Cong Wang
https://arxiv.org/abs/2506.08296

HiBerNAC: Hierarchical Brain-emulated Robotic Neural Agent Collective for Disentangling Complex Manipulation
Recent advances in multimodal vision-language-action (VLA) models have revolutionized traditional robot learning, enabling systems to interpret vision, language, and action in unified frameworks for complex task planning. However, mastering complex manipulation tasks remains an open challenge, constrained by limitations in persistent contextual memory, multi-agent coordination under uncertainty, and dynamic long-horizon planning across variable sequences. To address this challenge, we propose \…

Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
Yun Hua, Haosheng Chen, Shiqin Wang, Wenhao Li, Xiangfeng Wang, Jun Luo
https://arxiv.org/abs/2506.07388

Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
Large Language Models (LLMs) show strong collaborative performance in multi-agent systems with predefined roles and workflows. However, in open-ended environments lacking coordination rules, agents tend to act in self-interested ways. The central challenge in achieving coordination lies in credit assignment -- fairly evaluating each agent's contribution and designing pricing mechanisms that align their heterogeneous goals. This problem is critical as LLMs increasingly participate in complex hum…

Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Armin Lederer, Erfaun Noorani, Andreas Krause
https://arxiv.org/abs/2506.07347

Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Ensuring safety in multi-agent systems is a significant challenge, particularly in settings where centralized coordination is impractical. In this work, we propose a novel risk-sensitive safety filter for discrete-time multi-agent systems with uncertain dynamics that leverages control barrier functions (CBFs) defined through value functions. Our approach relies on centralized risk-sensitive safety conditions based on exponential risk operators to ensure robustness against model uncertainties. W…

R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
Yuante Li, Xu Yang, Xiao Yang, Minrui Xu, Xisen Wang, Weiqing Liu, Jiang Bian
https://arxiv.org/abs/2505.15155

R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large language models and multi-agent systems, current quantitative research pipelines suffer from limited automation, weak interpretability, and fragmented coordination across key components such as factor mining and model innovation. In this paper, we propose R&D-Agent for Quantitative Finance, in short RD-Agent(Q), the fi…

From Turbulence to Tranquility: AI-Driven Low-Altitude Network
K\"ur\c{s}at Tekb{\i}y{\i}k, Amir Hossein Fahim Raouf, \.Ismail G\"uven\c{c}, Mingzhe Chen, G\"une\c{s} Karabulut Kurt, Antoine Lesage-Landry
https://arxiv.org/abs/2506.01378

From Turbulence to Tranquility: AI-Driven Low-Altitude Network
Low Altitude Economy (LAE) networks own transformative potential in urban mobility, emergency response, and aerial logistics. However, these networks face significant challenges in spectrum management, interference mitigation, and real-time coordination across dynamic and resource-constrained environments. After addressing these challenges, this study explores three core elements for enabling intelligent LAE networks as follows machine learning-based spectrum sensing and coexistence, artificial…

This https://arxiv.org/abs/2504.14305 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning
Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches that mimic whole-body motions often neglect the distinct roles of upper and lower body. This oversight leads to computationally intensive policy learning and frequently causes robot instability and falls during real-world execution. To address these issues, we propose Adversarial Locomotion and Motion Imitation (ALMI…

This https://arxiv.org/abs/2505.15965 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Analyzing the Impact of Accent on English Speech: Acoustic and Articulatory Perspectives
Advancements in AI-driven speech-based applications have transformed diverse industries ranging from healthcare to customer service. However, the increasing prevalence of non-native accented speech in global interactions poses significant challenges for speech-processing systems, which are often trained on datasets dominated by native speech. This study investigates accented English speech through articulatory and acoustic analysis, identifying simpler coordination patterns and higher average p…

This https://arxiv.org/abs/2504.00587 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
The rapid advancement of large language models (LLMs) has enabled the development of multi-agent systems where multiple LLM-based agents collaborate on complex tasks. However, existing systems often rely on centralized coordination, leading to scalability bottlenecks, reduced adaptability, and single points of failure. Privacy and proprietary knowledge concerns further hinder cross-organizational collaboration, resulting in siloed expertise. We propose AgentNet, a decentralized, Retrieval-Augme…

This https://arxiv.org/abs/2505.13455 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Exploring Emotional Synchrony in Dyadic Interactions: The Role of Speech Conditions in Facial and Vocal Affective Alignment
Understanding how humans express and synchronize emotions across multiple communication channels particularly facial expressions and speech has significant implications for emotion recognition systems and human computer interaction. Motivated by the notion that non-overlapping speech promotes clearer emotional coordination, while overlapping speech disrupts synchrony, this study examines how these conversational dynamics shape the spatial and temporal alignment of arousal and valence across fac…

This https://arxiv.org/abs/2505.04364 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Benchmarking LLMs' Swarm intelligence
Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) when operating under strict swarm-like constraints-limited local perception and communication-remains largely unexplored. Existing benchmarks often do not fully capture the unique challenges of decentralized coordination when agents operate with incomplete spatio-temporal information. To bridge this gap, we introduce SwarmBench, a novel benchmark designed …

Sequence Modeling for N-Agent Ad Hoc Teamwork
Caroline Wang, Di Yang Shi, Elad Liebman, Ishan Durugkar, Arrasy Rahman, Peter Stone
https://arxiv.org/abs/2506.05527

Sequence Modeling for N-Agent Ad Hoc Teamwork
N-agent ad hoc teamwork (NAHT) is a newly introduced challenge in multi-agent reinforcement learning, where controlled subteams of varying sizes must dynamically collaborate with varying numbers and types of unknown teammates without pre-coordination. The existing learning algorithm (POAM) considers only independent learning for its flexibility in dealing with a changing number of agents. However, independent learning fails to fully capture the inter-agent dynamics essential for effective colla…

This https://arxiv.org/abs/2505.13455 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Exploring Emotional Synchrony in Dyadic Interactions: The Role of Speech Conditions in Facial and Vocal Affective Alignment
Understanding how humans express and synchronize emotions across multiple communication channels particularly facial expressions and speech has significant implications for emotion recognition systems and human computer interaction. Motivated by the notion that non-overlapping speech promotes clearer emotional coordination, while overlapping speech disrupts synchrony, this study examines how these conversational dynamics shape the spatial and temporal alignment of arousal and valence across fac…

Towards Language-Augmented Multi-Agent Deep Reinforcement Learning
Maxime Toquebiau, Jae-Yun Jun, Fa\"iz Benamar, Nicolas Bredeche
https://arxiv.org/abs/2506.05236

Towards Language-Augmented Multi-Agent Deep Reinforcement Learning
Communication is a fundamental aspect of coordinated behavior in multi-agent reinforcement learning. Yet, most prior works in this field have focused on emergent communication protocols developed from scratch, often resulting in inefficient or non-interpretable systems. Inspired by the role of language in natural intelligence, we investigate how grounding agents in a human-defined language can improve learning and coordination of multiple embodied agents. We propose a framework in which agents …

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Harsh Goel, Mohammad Omama, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Sandeep Chinchali
https://arxiv.org/abs/2505.24265

R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has achieved significant progress in large-scale traffic control, autonomous vehicles, and robotics. Drawing inspiration from biological systems where roles naturally emerge to enable coordination, role-based MARL methods have been proposed to enhance cooperation learning for complex tasks. However, existing methods exclusively derive roles from an agent's past experience during training, neglecting their influence on its future trajectories. This paper…

This https://arxiv.org/abs/2410.08948 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Emergent social conventions and collective bias in LLM populations
Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong coll…

This https://arxiv.org/abs/2408.09955 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs
LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent, a large-scale autonomous LLM-based multi-agent system. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communic…