Tootfinder

Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing
Ismaila Salihou Adamou, Elsa Dupraz, Reza Asvadi, Tad Matsumoto
https://arxiv.org/abs/2506.01747

Practical Short-Length Coding Schemes for Binary Distributed Hypothesis Testing
This paper addresses the design of practical shortlength coding schemes for Distributed Hypothesis Testing (DHT). While most prior work on DHT has focused on informationtheoretic analyses, deriving bounds on Type-II error exponents via achievability schemes based on quantization and quantizebinning, the practical implementation of DHT coding schemes has remained largely unexplored. Moreover, existing practical coding solutions for quantization and quantize-binning approaches were developed for …

This https://arxiv.org/abs/2309.03678 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system ba…

CityLens: Benchmarking Large Language-Vision Models for Urban Socioeconomic Sensing
Tianhui Liu, Jie Feng, Hetian Pang, Xin Zhang, Tianjian Ouyang, Zhiyuan Zhang, Yong Li
https://arxiv.org/abs/2506.00530

CityLens: Benchmarking Large Language-Vision Models for Urban Socioeconomic Sensing
Understanding urban socioeconomic conditions through visual data is a challenging yet essential task for sustainable urban development and policy planning. In this work, we introduce $\textbf{CityLens}$, a comprehensive benchmark designed to evaluate the capabilities of large language-vision models (LLVMs) in predicting socioeconomic indicators from satellite and street view imagery. We construct a multi-modal dataset covering a total of 17 globally distributed cities, spanning 6 key domains: e…

Is It Safe To Learn And Share? On Psychological Safety and Social Learning in (Agile) Communities of Practice
Christiaan Verwijs, Evelien Acun-Roos, Daniel Russo
https://arxiv.org/abs/2507.01065

Is It Safe To Learn And Share? On Psychological Safety and Social Learning in (Agile) Communities of Practice
As hybrid, distributed, and asynchronous work models become more prevalent, continuous learning in Agile Software Development (ASD) gains renewed importance. Communities of Practice (CoPs) are increasingly adopted to support social learning beyond formal education, often relying on virtual communication. Psychological safety, a prerequisite for effective learning, remains insufficiently understood in these settings. This mixed-methods study investigates psychological safety within Agile CoPs th…

Momentum-based Accelerated Algorithm for Distributed Optimization under Sector-Bound Nonlinearity
Mohammadreza Doostmohammadian, Hamid R. Rabiee
https://arxiv.org/abs/2506.22855

Momentum-based Accelerated Algorithm for Distributed Optimization under Sector-Bound Nonlinearity
Distributed optimization advances centralized machine learning methods by enabling parallel and decentralized learning processes over a network of computing nodes. This work provides an accelerated consensus-based distributed algorithm for locally non-convex optimization using the gradient-tracking technique. The proposed algorithm (i) improves the convergence rate by adding momentum towards the optimal state using the heavy-ball method, while (ii) addressing general sector-bound nonlinearities…

On Monday I started a new GitHub repo, to implement a distributed knowledge graph that knows about stuff in houses and how they are related. I used the Claude AI tools and Claude-flow agent framework to write all the code. Got a simplified thing running and cleaned up on Tuesday, built the full functionality on Wednesday and after a bit more testing and documentation wrapped up work before lunch on Thursday. It’s a lot of well tested and documented functionality.

Robust Distributed Phase Retrieval for Multi-View Compressive Networked Sensing With Outliers
Ming-Hsun Yang
https://arxiv.org/abs/2505.24651 https://

Robust Distributed Phase Retrieval for Multi-View Compressive Networked Sensing With Outliers
This work examines the multi-view compressive phase retrieval problem in a distributed sensor network, where each sensor device, limited by storage and sensing capabilities, can access only intensity measurements from an unknown part of the global sparse vector. The goal is to enable each sensor to recover its observable sparse signal when measurements are corrupted by outliers. To achieve reliable local signal recovery with limited data access, we propose a distributed reconstruction algorithm…

VeFIA: An Efficient Inference Auditing Framework for Vertical Federated Collaborative Software
Chung-ju Huang, Ziqi Zhang, Yinggui Wang, Binghui Wang, Tao Wei, Leye Wang
https://arxiv.org/abs/2507.02376

VeFIA: An Efficient Inference Auditing Framework for Vertical Federated Collaborative Software
Vertical Federated Learning (VFL) is a distributed AI software deployment mechanism for cross-silo collaboration without accessing participants' data. However, existing VFL work lacks a mechanism to audit the execution correctness of the inference software of the data party. To address this problem, we design a Vertical Federated Inference Auditing (VeFIA) framework. VeFIA helps the task party to audit whether the data party's inference software is executed as expected during large-scale infere…

Performance Measurements in the AI-Centric Computing Continuum Systems
Praveen Kumar Donta, Qiyang Zhang, Schahram Dustdar
https://arxiv.org/abs/2506.22884

Performance Measurements in the AI-Centric Computing Continuum Systems
Over the Eight decades, computing paradigms have shifted from large, centralized systems to compact, distributed architectures, leading to the rise of the Distributed Computing Continuum (DCC). In this model, multiple layers such as cloud, edge, Internet of Things (IoT), and mobile platforms work together to support a wide range of applications. Recently, the emergence of Generative AI and large language models has further intensified the demand for computational resources across this continuum…

Threshold Signatures for Central Bank Digital Currencies
Mostafa Abdelrahman, Filip Rezabek, Lars Hupel, Kilian Glas, Georg Carle
https://arxiv.org/abs/2506.23294

Threshold Signatures for Central Bank Digital Currencies
Digital signatures are crucial for securing Central Bank Digital Currencies (CBDCs) transactions. Like most forms of digital currencies, CBDC solutions rely on signatures for transaction authenticity and integrity, leading to major issues in the case of private key compromise. Our work explores threshold signature schemes (TSSs) in the context of CBDCs. TSSs allow distributed key management and signing, reducing the risk of a compromised key. We analyze CBDC-specific requirements, considering t…

The Instrumental Background of EP/FXT
Juan Zhang, Yong Chen, Shumei Jia, Haisheng Zhao, WeiWei Cui, Tianxiang Chen, Juan Wang, Hao Wang, Jin Wang, Chengkui Li, Xiaofan Zhao, Ju Guan, Dawei Han, Jingjing Xu, Liming Song, Hua Feng, Shuangnan Zhang, Weimin Yuan
https://arxiv.org/abs/2507.00510

The Instrumental Background of EP/FXT
The in-flight instrumental background of the Follow-up X-ray Telescope (FXT) onboard Einstein Probe (EP) mission is analysed in this work by utilizing observations collected during Performance Verification phase and subsequent dedicated filter wheel closed observations. The instrumental backgrounds of the two FXT modules are consistent with each other, with an average rate of $\sim 4\times10^{-2}$\,counts/s/keV at 0.5--10\,keV for each module. The background is nearly uniformly distributed acro…

Distributed Quantum Inner Product Estimation with Low-Depth Circuits
Congcong Zheng, Kun Wang, Xutao Yu, Ping Xu, Zaichen Zhang
https://arxiv.org/abs/2506.19574

Distributed Quantum Inner Product Estimation with Low-Depth Circuits
Distributed inner product estimation (DIPE) is a fundamental task in quantum information, aiming to estimate the inner product between two unknown quantum states prepared on distributed quantum platforms. Existing rigorous sample complexity analyses are limited to unitary $4$-designs, which present practical challenges for near-term quantum devices. This work addresses this challenge by exploring DIPE with low-depth quantum circuits. We first establish that DIPE with an arbitrary unitary $2$-de…

On Fine-Grained Distinct Element Estimation
Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas, David P. Woodruff, Samson Zhou
https://arxiv.org/abs/2506.22608 …

On Fine-Grained Distinct Element Estimation
We study the problem of distributed distinct element estimation, where $α$ servers each receive a subset of a universe $[n]$ and aim to compute a $(1+\varepsilon)$-approximation to the number of distinct elements using minimal communication. While prior work establishes a worst-case bound of $Θ\left(α\log n+\fracα{\varepsilon^2}\right)$ bits, these results rely on assumptions that may not hold in practice. We introduce a new parameterization based on the number $C = \fracβ{\varepsilon^2}$ …

Fixed-Point-Oriented Programming: A Concise and Elegant Paradigm
Yong Qi Foo, Brian Sze-Kai Cheong, Michael D. Adams
https://arxiv.org/abs/2507.21439 https://

Fixed-Point-Oriented Programming: A Concise and Elegant Paradigm
Fixed-Point-Oriented Programming (FPOP) is an emerging paradigm designed to streamline the implementation of problems involving self-referential computations. These include graph algorithms, static analysis, parsing, and distributed computing-domains that traditionally require complex and tricky-to-implement work-queue algorithms. Existing programming paradigms lack direct support for these inherently fixed-point computations, leading to inefficient and error-prone implementations. This white…

Data-Driven Stochastic Control via Non-i.i.d. Trajectories: Foundations and Guarantees
Abolfazl Lavaei
https://arxiv.org/abs/2507.23280 https://arxiv.org/p…

Data-Driven Stochastic Control via Non-i.i.d. Trajectories: Foundations and Guarantees
This work establishes a crucial step toward advancing data-driven trajectory-based methods for stochastic systems with unknown mathematical dynamics. In contrast to scenario-based approaches that rely on independent and identically distributed (i.i.d.) trajectories, this work develops a data-driven framework where each trajectory is gathered over a finite horizon and exhibits temporal dependence-referred to as a non-i.i.d. trajectory. To ensure safety of dynamical systems using such trajectorie…

Higher-order Topological States in Chiral Split Magnons of Honeycomb Altermagnets
Xuan Guo, Meng-Han Zhang, Dao-Xin Yao
https://arxiv.org/abs/2507.22996 https://

Higher-order Topological States in Chiral Split Magnons of Honeycomb Altermagnets
We theoretically explore higher-order topological magnons in collinear altermagnets, encompassing a dimensional hierarchy ranging from localized corner modes to propagating hinge excitations. By employing antiferromagnetic interlayer coupling in bosonic Bogoliubov-de Gennes (BdG) Hamiltonian, our work reveals anisotropic surface states and spatially distributed hinge modes within AA-type stacking configurations. We track the adiabatic evolution of Wannier centers to identify the bulk-polarizati…

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control. II: Non-Penalty Approach
Lechen Feng, Xun Li, Yuan-Hua Ni
https://arxiv.org/abs/2507.19895

Nonconvex Optimization Framework for Group-Sparse Feedback Linear-Quadratic Optimal Control. II: Non-Penalty Approach
This work is a companion paper of [8], where the distributed linear-quadratic problem with fixed communication topology (DFT-LQ) and the sparse feedback LQ problem (SF-LQ) are formulated into a nonsmooth and nonconvex optimization problem with affine constraints. Moreover, a penalty approach is considered in \cite{feng-part1}, and the PALM (proximal alternating linearized minimization) algorithm is studied with convergence and complexity analysis. In this paper, we aim to address the inherent d…

PRISM: Distributed Inference for Foundation Models at Edge
Muhammad Azlan Qazi, Alexandros Iosifidis, Qi Zhang
https://arxiv.org/abs/2507.12145 https://

PRISM: Distributed Inference for Foundation Models at Edge
Foundation models (FMs) have achieved remarkable success across a wide range of applications, from image classification to natural langurage processing, but pose significant challenges for deployment at edge. This has sparked growing interest in developing practical and efficient strategies for bringing foundation models to edge environments. In this work, we propose PRISM, a communication-efficient and compute-aware strategy for distributed Transformer inference on edge devices. Our method lev…

Heterogeneous Risk Management Using a Multi-Agent Framework for Supply Chain Disruption Response
Mingjie Bi, Juan-Alberto Estrada-Garcia, Dawn M. Tilbury, Siqian Shen, Kira Barton
https://arxiv.org/abs/2507.19049

Heterogeneous Risk Management Using a Multi-Agent Framework for Supply Chain Disruption Response
In the highly complex and stochastic global, supply chain environments, local enterprise agents seek distributed and dynamic strategies for agile responses to disruptions. Existing literature explores both centralized and distributed approaches, while most work neglects temporal dynamics and the heterogeneity of the risk management of individual agents. To address this gap, this letter presents a heterogeneous risk management mechanism to incorporate uncertainties and risk attitudes into agent …

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…

SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning
Chengcheng Zhu, Ye Li, Bosen Rao, Jiale Zhang, Yunlong Mao, Sheng Zhong
https://arxiv.org/abs/2506.20931

SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning
Federated Learning (FL) has emerged as a leading paradigm for privacy-preserving distributed machine learning, yet the distributed nature of FL introduces unique security challenges, notably the threat of backdoor attacks. Existing backdoor strategies predominantly rely on end-to-end label supervision, which, despite their efficacy, often results in detectable feature disentanglement and limited persistence. In this work, we propose a novel and stealthy backdoor attack framework, named SPA, whi…

Mapple: A Domain-Specific Language for Mapping Distributed Heterogeneous Parallel Programs
Anjiang Wei, Rohan Yadav, Hang Song, Wonchan Lee, Ke Wang, Alex Aiken
https://arxiv.org/abs/2507.17087

Mapple: A Domain-Specific Language for Mapping Distributed Heterogeneous Parallel Programs
Optimizing parallel programs for distributed heterogeneous systems remains a complex task, often requiring significant code modifications. Task-based programming systems improve modularity by separating performance decisions from core application logic, but their mapping interfaces are often too low-level. In this work, we introduce Mapple, a high-level, declarative programming interface for mapping distributed applications. Mapple provides transformation primitives to resolve dimensionality mi…

Third-Party Assessment of Mobile Performance in the 5G Era
ASM Rizvi, John Heidemann, David Plonka
https://arxiv.org/abs/2507.18834 https://arxiv.org/pdf/2…

Third-Party Assessment of Mobile Performance in the 5G Era
The web experience using mobile devices is important since a significant portion of the Internet traffic is initiated from mobile devices. In the era of 5G, users expect a high-performance data network to stream media content and for other latency-sensitive applications. In this paper, we characterize mobile experience in terms of latency, throughput, and stability measured from a commercial, globally-distributed CDN. Unlike prior work, CDN data provides a relatively neutral, carrier-agnostic p…

Stochastic Preconditioning for Neural Field Optimization
Selena Ling, Merlin Nimier-David, Alec Jacobson, Nicholas Sharp
https://arxiv.org/abs/2505.20473 h…

Stochastic Preconditioning for Neural Field Optimization
Neural fields are a highly effective representation across visual computing. This work observes that fitting these fields is greatly improved by incorporating spatial stochasticity during training, and that this simple technique can replace or even outperform custom-designed hierarchies and frequency space constructions. The approach is formalized as implicitly operating on a blurred version of the field, evaluated in-expectation by sampling with Gaussian-distributed offsets. Querying the blurr…

Optimizing Tensor Network Partitioning using Simulated Annealing
Manuel Geiger, Qunsheng Huang, Christian B. Mendl
https://arxiv.org/abs/2507.20667 https://

Optimizing Tensor Network Partitioning using Simulated Annealing
Tensor networks have proven to be a valuable tool, for instance, in the classical simulation of (strongly correlated) quantum systems. As the size of the systems increases, contracting larger tensor networks becomes computationally demanding. In this work, we study distributed memory architectures intended for high-performance computing implementations to solve this task. Efficiently distributing the contraction task across multiple nodes is critical, as both computational and memory costs are …

"Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity."
"LLM users also struggled to accurately quote their own work."
"Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels."

from my link log —
Idris 2: quantitative type theory in practice.
https://arxiv.org/abs/2104.00480
saved 2025-06-11 https://dotat…

Idris 2: Quantitative Type Theory in Practice
Dependent types allow us to express precisely what a function is intended to do. Recent work on Quantitative Type Theory (QTT) extends dependent type systems with linearity, also allowing precision in expressing when a function can run. This is promising, because it suggests the ability to design and reason about resource usage protocols, such as we might find in distributed and concurrent programming, where the state of a communication channel changes throughout program execution. As yet, howe…

Distributed Composite Optimization with Sub-Weibull Noises
Zhan Yu, Zhongjie Shi, Deming Yuan
https://arxiv.org/abs/2506.12901 https://

Distributed Composite Optimization with Sub-Weibull Noises
With the rapid development of multi-agent distributed optimization (MA-DO) theory over the past decade, the distributed stochastic gradient method (DSGM) occupies an important position. Although the theory of different DSGMs has been widely established, the main-stream results of existing work are still derived under the condition of light-tailed stochastic gradient noises. Increasing recent examples from various fields, indicate that, the light-tailed noise model is overly idealized in many pr…

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
Chang Sun, Zhiqiang Que, Vladimir Loncar, Wayne Luk, Maria Spiropulu
https://arxiv.org/abs/2507.04535

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
Neural networks with a latency requirement on the order of microseconds, like the ones used at the CERN Large Hadron Collider, are typically deployed on FPGAs fully unrolled and pipelined. A bottleneck for the deployment of such neural networks is area utilization, which is directly related to the required constant matrix-vector multiplication (CMVM) operations. In this work, we propose an efficient algorithm for implementing CMVM operations with distributed arithmetic (DA) on FPGAs that simult…

Distributed Computing From First Principles
Kenneth Odoh
https://arxiv.org/abs/2506.12959 https://arxiv.org/pdf/2506.12959

Distributed Computing From First Principles
This book on Distributed Computing aims to benefit a diverse audience, ranging from aspiring engineers, and seasoned researchers, to a wide range of professionals. Driven by my passion for making the core concepts of distributed computing accessible, this work is a significant undertaking designed to empower individuals from all backgrounds to gain valuable insight. Have you ever wondered how a typical distributed system works under the hood? Are you looking for a pedagogical guide with complet…

Efficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning
Jiexin Zhang, Shu Xu, Chunguo Li, Yongming Huang, Luxi Yang
https://arxiv.org/abs/2506.18560

Efficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning
Beamforming enhances signal strength and quality by focusing energy in specific directions. This capability is particularly crucial in cell-free integrated sensing and communication (ISAC) systems, where multiple distributed access points (APs) collaborate to provide both communication and sensing services. In this work, we first derive the distribution of joint target detection probabilities across multiple receiving APs under false alarm rate constraints, and then formulate the beam selection…

Shallow-depth GHZ state generation on NISQ devices
S. Siddardha Chelluri, Stephan Schuster, Sumeet, Riccardo Roma
https://arxiv.org/abs/2507.19145 https://

Shallow-depth GHZ state generation on NISQ devices
In this work, we focus on GHZ state generation under the practical constraint of limited qubit connectivity, a hallmark of current NISQ hardware. We study the GHZ state preparation across different connectivity graphs inspired by IBM and Google chip architectures, as well as random graphs that reflect distributed quantum systems. Our approach is a measurement-based protocol designed to utilize qubit connectivity constraints for the generation of GHZ states on NISQ devices. We benchmark this aga…

Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
Ze'ev Zukerman, Bassel Hamoud, Kfir Y. Levy
https://arxiv.org/abs/2507.05508

Beyond Communication Overhead: A Multilevel Monte Carlo Approach for Mitigating Compression Bias in Distributed Learning
Distributed learning methods have gained substantial momentum in recent years, with communication overhead often emerging as a critical bottleneck. Gradient compression techniques alleviate communication costs but involve an inherent trade-off between the empirical efficiency of biased compressors and the theoretical guarantees of unbiased compressors. In this work, we introduce a novel Multilevel Monte Carlo (MLMC) compression scheme that leverages biased compressors to construct statistically…

Failure modes of irregular ceramic foam under compression: Development of a new image based strut segmentation strategy
Vinit Vijay Deshpande, Romana Piat
https://arxiv.org/abs/2506.10860

Failure modes of irregular ceramic foam under compression: Development of a new image based strut segmentation strategy
The work investigates the failure modes of the microstructure of an irregular ceramic foam subjected to uniaxial compression loading. The foam material is manufactured using the direct foaming method and has polydispersed pores homogeneously distributed in the microstructure. The effective stress-strain curve and the macroscopic strength of the material differs significantly with the predictions of the Gibson-Ashby model that assumes a regular microstructure. The objective of this work is to de…

Truncated Cauchy Combination Test: a Robust and Powerful P-value Combination Method with Arbitrary Correlations
Bo Chen, Wei Xu, Xin Gao
https://arxiv.org/abs/2506.12489

Truncated Cauchy Combination Test: a Robust and Powerful P-value Combination Method with Arbitrary Correlations
Cauchy combination test has been widely used for combining correlated p-values, but it may fail to work under certain scenarios. We propose a truncated Cauchy combination test (TCCT) which focus on combining p-values with arbitrary correlations, and demonstrate that our proposed test solves the limitations of Cauchy combination test and always has higher power. We prove that the tail probability of our test statistic is asymptotically Cauchy distributed, so it is computationally effective to ac…

SZ Sequences: Binary-Based $(0, 2^q)$-Sequences
Abdalla G. M. Ahmed, Matt Pharr, Victor Ostromoukhov, Hui Huang
https://arxiv.org/abs/2505.20434 https://…

SZ Sequences: Binary-Based $(0, 2^q)$-Sequences
Low-discrepancy sequences have seen widespread adoption in computer graphics thanks to their superior convergence rates. Since rendering integrals often comprise products of lower-dimensional integrals, recent work has focused on developing sequences that are also well-distributed in lower-dimensional projections. To this end, we introduce a novel construction of binary-based (0, 4)-sequences; that is, progressive fully multi-stratified sequences of 4D points, and extend the idea to higher powe…

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis
Mia-Katrin Kvalsund, Mikkel Elle Lepper{\o}d
https://arxiv.org/abs/2507.12473

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis
While modern AI continues to advance, the biological brain remains the pinnacle of neural networks in its robustness, adaptability, and efficiency. This review explores an AI architectural path inspired by the brain's structure, particularly the minicolumn hypothesis, which views the neocortex as a distributed system of repeated modules - a structure we connect to collective intelligence (CI). Despite existing work, there is a lack of comprehensive reviews connecting the cortical column to the …

Conjugated Capabilities: Interrelations of Elementary Human Capabilities and Their Implication on Human-Machine Task Allocation and Capability Testing Procedures
Nils Mandischer, Larissa F\"uller, Torsten Alles, Frank Flemisch, Lars Mikelsons
https://arxiv.org/abs/2507.07560

Conjugated Capabilities: Interrelations of Elementary Human Capabilities and Their Implication on Human-Machine Task Allocation and Capability Testing Procedures
Human and automation capabilities are the foundation of every human-autonomy interaction and interaction pattern. Therefore, machines need to understand the capacity and performance of human doing, and adapt their own behavior, accordingly. In this work, we address the concept of conjugated capabilities, i.e. capabilities that are dependent or interrelated and between which effort can be distributed. These may be used to overcome human limitations, by shifting effort from a deficient to a conju…

Distributed Nash Equilibrium Seeking in Non-Monotone Games over the Simplex
Tatiana Tatarenko, S. Rasoul Etesami
https://arxiv.org/abs/2507.13244 https://

Distributed Nash Equilibrium Seeking in Non-Monotone Games over the Simplex
In this work, we present a novel characterization of approximate Nash equilibria in a class of convex games over the simplex. To achieve this, we regularize the utility functions using the Shannon entropy term, connect the solutions to the regularized game with the set of Nash equilibria, and formulate a multi-objective optimization problem to solve the regularized game. Based on the obtained properties of the stationary points in this optimization problem, we formulate two distributed heuristi…

Distributed Algorithms for Potential Problems
Alkida Balliu, Thomas Boudier, Francesco d'Amore, Dennis Olivetti, Gustav Schmid, Jukka Suomela
https://arxiv.org/abs/2507.12038 …

Distributed Algorithms for Potential Problems
In this work we present a fast distributed algorithm for local potential problems: these are graph problems where the task is to find a locally optimal solution where no node can unilaterally improve the utility in its local neighborhood by changing its own label. A simple example of such a problem is the task of finding a locally optimal cut, i.e., a cut where for each node at least half of its incident edges are cut edges. The distributed round complexity of locally optimal cut has been wide …

This https://arxiv.org/abs/2503.04091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Generalization in Federated Learning: A Conditional Mutual Information Framework
Federated learning (FL) is a widely adopted privacy-preserving distributed learning framework, yet its generalization performance remains less explored compared to centralized learning. In FL, the generalization error consists of two components: the out-of-sample gap, which measures the gap between the empirical and true risk for participating clients, and the participation gap, which quantifies the risk difference between participating and non-participating clients. In this work, we apply an i…

Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation
Yao Fehlis, Charles Crain, Aidan Jensen, Michael Watson, James Juhasz, Paul Mandel, Betty Liu, Shawn Mahon, Daren Wilson, Nick Lynch-Jonely, Ben Leedom, David Fuller
https://arxiv.org/abs/2507.17852

Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation
Building on the conceptual framework presented in our previous work on agentic AI for pharmaceutical research, this paper provides a comprehensive technical analysis of Tippy's multi-agent system implementation for drug discovery laboratory automation. We present a distributed microservices architecture featuring five specialized agents (Supervisor, Molecule, Lab, Analysis, and Report) that coordinate through OpenAI Agents SDK orchestration and access laboratory tools via the Model Context Prot…

This https://arxiv.org/abs/2501.01271 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Energy-and Spectral-Efficiency Trade-off in Distributed Massive-MIMO Networks
This paper investigates a fundamental yet under-explored trade-off between energy efficiency (EE) and spectral efficiency (SE) in distributed massive MIMO (D-mMIMO) systems. Unlike conventional EE-SE trade-off studies that primarily focus on transmission power, D-mMIMO systems introduce new energy consumption factors including fronthaul signaling and distributed signal processing, which are heavily influenced by AP-UE association. This work highlights the critical need for a system-level EE-SE …

Fast Distributed Nash Equilibrium Seeking in Monotone Games
Tatiana Tatarenko, Angelia Nedich
https://arxiv.org/abs/2507.11703 https://

Fast Distributed Nash Equilibrium Seeking in Monotone Games
This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with merely monotone and restricted strongly monotone pseudo-gradients. By leveraging the idea of the centralized operator extrapolation method presented in [5] to solve variational inequalities, we develop the algorithm converging to Nash equilibria in games, where players have no access to the full information but are able to communicate with neighbors over some communication graph. The convergenc…

New Limits on Distributed Quantum Advantage: Dequantizing Linear Programs
Alkida Balliu, Corinna Coupette, Antonio Cruciani, Francesco d'Amore, Massimo Equi, Henrik Lievonen, Augusto Modanese, Dennis Olivetti, Jukka Suomela
https://arxiv.org/abs/2506.07574

New Limits on Distributed Quantum Advantage: Dequantizing Linear Programs
In this work, we give two results that put new limits on distributed quantum advantage in the context of the LOCAL model of distributed computing. First, we show that there is no distributed quantum advantage for any linear program. Put otherwise, if there is a quantum-LOCAL algorithm $\mathcal{A}$ that finds an $α$-approximation of some linear optimization problem $Π$ in $T$ communication rounds, we can construct a classical, deterministic LOCAL algorithm $\mathcal{A}'$ that finds an $α$-ap…

Short note on phase retrievable weaving fusion frames
Animesh Bhandari
https://arxiv.org/abs/2506.08478 https://arxiv.org/pdf/2506.08…

Short note on phase retrievable weaving fusion frames
Fusion frames are extensively studied due to their effectiveness in recovering signals from large-scale data. They are applicable in distributed processing, wireless sensor networks, and packet encoding systems due to their robustness and redundancy. Motivated by the foundational work of Bemrose et al.\cite{Be16} and Balan\cite{Ba13}, this paper investigates the theoretical properties and characterizations of phase retrievable weaving fusion frames. These frames offer enhanced redundancy and st…

How to share Multipartite Entanglement in a Real-World Linear Network Connecting Two Metropoles
Janka Memmen, Anna Pappa
https://arxiv.org/abs/2507.17480 https://

How to share Multipartite Entanglement in a Real-World Linear Network Connecting Two Metropoles
The development of large-scale quantum communication networks necessitates the efficient distribution of quantum states to enable advanced cryptographic applications and distributed tasks. Multipartite entanglement is a key resource in many of these proposals, yet its generation is experimentally challenging, especially in noisy and lossy networks. While a substantial body of work focuses on the distribution of multi-partite entanglement in star-like topologies, practical implementations often …

Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks
Laksh Patel, Neel Shanbhag
https://arxiv.org/abs/2506.08024 https://

Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks
Distributed supply-chain optimization demands algorithms that can cope with unreliable communication, unbounded messaging delays, and geographically dispersed agents while still guaranteeing convergence with provable rates. In this work, we introduce DAPD-SCO (Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks), a fully asynchronous primal-dual scheme for network flow allocation over directed acyclic supply-chain graphs. Each edge agent independently updates its local f…

OLAF: Programmable Data Plane Acceleration for Asynchronous Distributed Reinforcement Learning
Nehal Baganal Krishna, Anam Tahir, Firas Khamis, Mina Tahmasbi Arashloo, Michael Zink, Amr Rizk
https://arxiv.org/abs/2507.05876

OLAF: Programmable Data Plane Acceleration for Asynchronous Distributed Reinforcement Learning
Asynchronous Distributed Reinforcement Learning (DRL) can suffer from degraded convergence when model updates become stale, often the result of network congestion and packet loss during large-scale training. This work introduces a network data-plane acceleration architecture that mitigates such staleness by enabling inline processing of DRL model updates as they traverse the accelerator engine. To this end, we design and prototype a novel queueing mechanism that opportunistically combines compa…

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz
https://arxiv.org/abs/2507.05532 …

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Inertial measurement units (IMUs) are central to wearable systems for activity recognition and pose estimation, but sensor placement remains largely guided by heuristics and convention. In this work, we introduce Where to Wear (W2W), a simulation-based framework for systematic exploration of IMU placement utility across the body. Using labeled motion capture data, W2W generates realistic synthetic IMU signals at 512 anatomically distributed surface patches, enabling high-resolution, task-specif…

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…

Shipwright: Proving liveness of distributed systems with Byzantine participants
Derek Leung, Nickolai Zeldovich, Frans Kaashoek
https://arxiv.org/abs/2507.14080

Shipwright: Proving liveness of distributed systems with Byzantine participants
Ensuring liveness in a decentralized system, such as PBFT, is critical, because there may not be any single administrator that can restart the system if it encounters a liveness bug. At the same time, liveness is challenging to achieve because any single participant could be malicious, and yet the overall system must make forward progress. While verification is a promising approach for ensuring the absence of bugs, no prior work has been able to verify liveness for an executable implementation …

Prediction-Augmented Mechanism Design for Weighted Facility Location
Yangguang Shi, Zhenyu Xue
https://arxiv.org/abs/2507.06509 https://

Prediction-Augmented Mechanism Design for Weighted Facility Location
Facility location is fundamental in operations research, mechanism design, and algorithmic game theory, with applications ranging from urban infrastructure planning to distributed systems. Recent research in this area has focused on augmenting classic strategyproof mechanisms with predictions to achieve an improved performance guarantee against the uncertainty under the strategic environment. Previous work has been devoted to address the trade-off obstacle of balancing the consistency (near-opt…

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
Samuel Ofosu Mensah, Kerol Djoumessi, Philipp Berens
https://arxiv.org/abs/2507.05852

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
Federated learning (FL) provides a promising paradigm for collaboratively training machine learning models across distributed data sources while maintaining privacy. Nevertheless, real-world FL often faces major challenges including communication overhead during the transfer of large model parameters and statistical heterogeneity, arising from non-identical independent data distributions across clients. In this work, we propose an FL framework that 1) provides inherent interpretations using pro…

Cross-Timeslot Optimization for Distributed GPU Inference Using Reinforcement Learning
Chengze Du, Zhiwei Yu, Heng Xu, Haojie Wang, Bo liu, Jialong Li
https://arxiv.org/abs/2507.10259

Cross-Timeslot Optimization for Distributed GPU Inference Using Reinforcement Learning
The rapid growth of large language model (LLM) services imposes increasing demands on distributed GPU inference infrastructure. Most existing scheduling systems rely on the current system state to make decisions, without considering how task demand and resource availability evolve over time. This lack of temporal awareness leads to inefficient GPU utilization, high task migration overhead, and poor system responsiveness under dynamic workloads. In this work, we identify the fundamental limitati…

Willchain: Decentralized, Privacy-Preserving, Self-Executing, Digital Wills
Jovonni L. PHarr
https://arxiv.org/abs/2507.03694 https://

Willchain: Decentralized, Privacy-Preserving, Self-Executing, Digital Wills
This work presents a novel decentralized protocol for digital estate planning that integrates advances distributed computing, and cryptography. The original proof-of-concept was constructed using purely solidity contracts. Since then, we have enhanced the implementation into a layer-1 protocol that uses modern interchain communication to connect several heterogeneous chain types. A key contribution of this research is the implementation of several modern cryptographic primitives to support vari…

Convergence and Robustness Bounds for Distributed Asynchronous Shortest-Path
Jared Miller, Mattia Bianchi, Florian D\"orfler
https://arxiv.org/abs/2507.07263

Convergence and Robustness Bounds for Distributed Asynchronous Shortest-Path
This work analyzes convergence times and robustness bounds for asynchronous distributed shortest-path computation. We focus on the Adaptive Bellman--Ford algorithm, a self-stabilizing method in which each agent updates its shortest-path estimate based only on the estimates of its neighbors and forgetting its previous estimate. In the asynchronous framework considered in this paper, agents are allowed to idle or encounter race conditions during their execution of the Adaptive Bellman--Ford algor…

Certification of High-Dimensional Entanglement Within the Resource Theory of Buscemi Nonlocality
Xian Shi
https://arxiv.org/abs/2506.07195 https://

Certification of High-Dimensional Entanglement Within the Resource Theory of Buscemi Nonlocality
The study on the certification of high degree entanglement, which is quantified in terms of Schmidt number, attracts much attention recently. Drawing inspiration from the work [PRX QUANTUM 2, 020301 (2021)], we first introduce resource theories of distributed measurements and teleportation instruments generated by a bipartite mixed state. Next we quantitatively characterized the relations among the convex resource theories of distributed measurements, teleportation instruments, and bipartite mi…

GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
Zhihao Xu, Srikar Chundury, Seongmin Kim, Amir Shehata, Xinyi Li, Ang Li, Tengfei Luo, Frank Mueller, In-Saeng Suh
https://arxiv.org/abs/2506.10531

GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems. In this work, we improve the scalability and efficiency of DQAOA through advanced problem decomposition and parallel execution using message passing on the Frontier CPU/GPU super…

Incentivised Orchestrated Training Architecture (IOTA): A Technical Primer for Release
Felix Quinque, Alan Aboudib, Szymon Fonau, Rodrigo Lopez Portillo Alcocer, Brian McCrindle, Steffen Cruz
https://arxiv.org/abs/2507.17766

Incentivised Orchestrated Training Architecture (IOTA): A Technical Primer for Release
In August 2024, Bittensor's Subnet 9 (SN9) demonstrated that a distributed network of incentivized, permissionless actors could each pretrain large language models (LLMs) ranging from 700 million to 14 billion parameters, while surpassing established baselines. While that work validated blockchain-based decentralized pretraining as viable, it contained core issues: (i) every miner had to fit an entire model locally, and (ii) "winner-takes-all" rewards encouraged model hoarding. Here we introd…

Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs
Sijia Geng, Thomas Lee, Dharik Mallapragada, Audun Botterud
https://arxiv.org/abs/2506.09388

Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs
Electrified transportation leads to a tighter integration between transportation and energy distribution systems. In this work, we develop scalable optimization models to co-design hydrogen and battery electric vehicle (EV) fleets, distributed energy resources, and fast-charging and hydrogen-fueling infrastructure to efficiently meet transportation demands. A novel integer-clustering formulation is used for optimizing fleet-level EV operation while maintaining accurate individual vehicle dispat…

Optimal Control of Thin-Film Flow on a Flexible Topography
S. Alrashidy, A. Kalogirou, D. Kalise, K. G. van der Zee
https://arxiv.org/abs/2506.15340 https:…

Optimal Control of Thin-Film Flow on a Flexible Topography
This work presents a mathematical model for the optimal control of thin-film flows over a flexible topography influenced by an external force. Our thin-film model allows for the rupture of films as well as the coalescence of bubbles. The objective is to find the optimal distributed force acting on the topography that minimises the differences between actual and desired thin-film profiles. A nonlinear lubrication equation governing the fluid dynamics and appropriate functional settings for this …

Building State Machine Replication Using Practical Network Synchrony
Yiliang Wan, Nitin Shivaraman, Akshaye Shenoi, Xiang Liu, Tao Luo, Jialin Li
https://arxiv.org/abs/2507.12792 …

Building State Machine Replication Using Practical Network Synchrony
Distributed systems, such as state machine replication, are critical infrastructures for modern applications. Practical distributed protocols make minimum assumptions about the underlying network: They typically assume a partially synchronous or fully asynchronous network model. In this work, we argue that modern data center systems can be designed to provide strong synchrony properties in the common case, where servers move in synchronous lock-step rounds. We prove this hypothesis by engineeri…

Relaying Quantum Information
Yigal Ilin, Uzi Pereg
https://arxiv.org/abs/2507.06770 https://arxiv.org/pdf/2507.06770

Relaying Quantum Information
Quantum relays are central to both quantum communication and distributed quantum computing, enabling long-distance transmission and modular architectures. Unlike classical repeaters, quantum repeaters preserve coherence without amplifying quantum information, relying on entanglement swapping and quantum error correction to overcome loss and decoherence. In this work, we investigate the transmission of quantum information via quantum relay channels. Our three-terminal relay model captures the tr…

Grey Rhino Warning: IPv6 is Becoming Fertile Ground for Reflection Amplification Attacks
Ling Hu, Tao Yang, Yu Pang, Bingnan Hou, Zhiping Cai, Bo Yu
https://arxiv.org/abs/2506.04768

Grey Rhino Warning: IPv6 is Becoming Fertile Ground for Reflection Amplification Attacks
Distributed Denial-of-Service (DDoS) attacks represent a cost-effective and potent threat to network stability. While extensively studied in IPv4 networks, DDoS implications in IPv6 remain underexplored. The vast IPv6 address space renders brute-force scanning and amplifier testing for all active addresses impractical. Innovatively, this work investigates AS-level vulnerabilities to reflection amplification attacks in IPv6. One prerequisite for amplification presence is that it is located in …

A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization
A. A. Vyguzov, F. S. Stonyakin
https://arxiv.org/abs/2507.05669

A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization
We study the Frank-Wolfe algorithm for constrained optimization problems with relatively smooth and relatively strongly convex objectives. Building upon our previous work, we propose a fully adaptive variant of the Frank-Wolfe method that dynamically adjusts the step size based on both the relative smoothness constant and the Triangle Scaling Exponent (TSE) of the Bregman divergence. Our method does not require prior knowledge of the function parameters and guarantees convergence using only loc…

Deployment of Containerized Simulations in an API-Driven Distributed Infrastructure
Tim Kraus, Axel Sauer, Ingo Feldner
https://arxiv.org/abs/2506.10642 ht…

Deployment of Containerized Simulations in an API-Driven Distributed Infrastructure
The increasingly dynamic market for embedded systems makes virtual prototypes an indispensable tool for hardware/software codesign. The broad acceptance of the methodology has led to a diverse range of solutions: from open-source, pure console-based simulators to highly capable commercial simulation tools. In this work we present SUNRISE, an infrastructure to provide users a unified approach to utilizing virtual prototyping solutions, facilitate access to various simulation technologies and boo…

Coordinated Fast Frequency Regulation in Dynamic Virtual Power Plants via Disturbance Estimation
Saif Ahmad, Seifeddine Ben Elghali, Hafiz Ahmed
https://arxiv.org/abs/2507.06713

Coordinated Fast Frequency Regulation in Dynamic Virtual Power Plants via Disturbance Estimation
In the context of dynamic virtual power plants (DVPPs), the integration of frequency containment reserve (FCR) and fast frequency control (FFC) enabled via local compensation of power imbalance represents a significant advancement in decentralized frequency regulation. However, they still have to cope with the limited power and energy capacities associated with commonly available storage solutions. This work combines a disturbance estimation based decentralized local control with distributed im…

Cooperative Gradient Coding
Shudi Weng, Ming Xiao, Chao Ren, Mikael Skoglund
https://arxiv.org/abs/2507.05230 https://arxiv.org/pdf/2…

Cooperative Gradient Coding
This work studies gradient coding (GC) in the context of distributed training problems with unreliable communication. We propose cooperative GC (CoGC), a novel gradient-sharing-based GC framework that leverages cooperative communication among clients. This approach ultimately eliminates the need for dataset replication, making it both communication- and computation-efficient and suitable for federated learning (FL). By employing the standard GC decoding mechanism, CoGC yields strictly binary ou…

Improved Byzantine Agreement under an Adaptive Adversary
Fabien Dufoulon, Gopal Pandurangan
https://arxiv.org/abs/2506.04919 https://…

Improved Byzantine Agreement under an Adaptive Adversary
Byzantine agreement is a fundamental problem in fault-tolerant distributed computing that has been studied intensively for the last four decades. Much of the research has focused on a static Byzantine adversary, where the adversary is constrained to choose the Byzantine nodes in advance of the protocol's execution. This work focuses on the harder case of an adaptive Byzantine adversary that can choose the Byzantine nodes \emph{adaptively} based on the protocol's execution. While efficient $O(\l…