Tootfinder

Mazzaroth: A High-Throughput DAG Consensus with State Root
Haohan Li
https://arxiv.org/abs/2506.01960 https://arxiv.org/pdf/2506.0196…

Mazzaroth: A High-Throughput DAG Consensus with State Root
Nakamoto Consensus achieves a decentralized ledger through a single-chain blockchain, assuming a maximum network delay, which limits block generation speed, resulting in low throughput. \cite{pg2018} (PG) enhances throughput using a blockDAG structure, but its probabilistic confirmation restricts smart contract applications. To address this, Mazzaroth proposes a Pow-based blockDAG consensus, employing a linear ordering algorithm to compute the \cite{eth} and achieve state finality, thereby supp…

On dual-rate consensus under transmission delays
David Umsonst, Mina Ferizbegovic
https://arxiv.org/abs/2506.02840 https://arxiv.org/…

On dual-rate consensus under transmission delays
In this paper, we investigate the problem of dual-rate consensus under transmission delays, where the control updates happen at a faster rate than the measurements being received. We assume that the measurements are delayed by a fixed delay and show that for all delays and rates, the system reaches a consensus if and only if the communication graph of the agents is connected and the control gain is chosen in a specific interval. Based on these results we dive deeper into the convergence propert…

Comparison of Rochester with Buffalo shows why cities should remove, not underground, their bad highways.
Draws one good lesson & one bad one:
Yes, plan in advance for what you want to do.
No, don't try to "build consensus". Build support. Consensus-seeking doesn't avoid "paralysis by analysis," it creates it.

Rochester gave up on a highway and filled it in. Should Baltimore?
Rochester gave up lanes of highway traffic to turn a trench into green space — and wants to do more.

This https://arxiv.org/abs/2504.13703 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Consensus-aware Contrastive Learning for Group Recommendation
Group recommendation aims to provide personalized item suggestions to a group of users by reflecting their collective preferences. A fundamental challenge in this task is deriving a consensus that adequately represents the diverse interests of individual group members. Despite advancements made by deep learning-based models, existing approaches still struggle in two main areas: (1) Capturing consensus in small-group settings, which are more prevalent in real-world applications, and (2) Balancin…

A localized consensus-based sampling algorithm
Arne Bouillon, Alexander Bodard, Panagiotis Patrinos, Dirk Nuyens, Giovanni Samaey
https://arxiv.org/abs/2505.24861

A localized consensus-based sampling algorithm
We develop a novel interacting-particle method for sampling from non-Gaussian distributions. As a first step, we propose a new way to derive the consensus-based sampling (CBS) algorithm, starting from ensemble-preconditioned Langevin diffusions. We approximate the target potential by its Moreau envelope, such that the gradient in the Langevin equation can be replaced by a proximal operator. We then approximate the proximal operator by a weighted mean, and finally assume that the initial and tar…

Explaining Sustained Blockchain Decentralization with Quasi-Experiments: Resource Flexibility of Consensus Mechanisms
Harang Ju, Madhav Kumar, Ehsan Valavi, Sinan Aral
https://arxiv.org/abs/2505.24663

Explaining Sustained Blockchain Decentralization with Quasi-Experiments: Resource Flexibility of Consensus Mechanisms
Decentralization is a fundamental design element of the Web3 economy. Blockchains and distributed consensus mechanisms are touted as fault-tolerant, attack-resistant, and collusion-proof because they are decentralized. Recent analyses, however, find some blockchains are decentralized, others are centralized, and that there are trends towards both centralization and decentralization in the blockchain economy. Despite the importance and variability of decentralization across blockchains, we still…

This https://arxiv.org/abs/2506.03830 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Construction of Urban Greenland Resources Collaborative Management Platform
Nowadays, environmental protection has become a global consensus. At the same time, with the rapid development of science and technology, urbanisation has become a phenomenon that has become the norm. Therefore, the urban greening management system is an essential component in protecting the urban environment. The system utilises a transparent management process known as" monitoring - early warning - response - optimisation," which enhances the tracking of greening resources, streamlines mainte…

This https://arxiv.org/abs/2311.05009 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes
We present a consensus-based framework that unifies phase space exploration with posterior-residual-based adaptive sampling for surrogate construction in high-dimensional energy landscapes. Unlike standard approximation tasks where sampling points can be freely queried, physical systems with complex energy landscapes such as molecular dynamics (MD) do not have direct access to arbitrary sampling regions due to the physical constraints and energy barriers; the surrogate construction further reli…

This https://arxiv.org/abs/2202.12453 has been replaced.
link: https://scholar.google.com/scholar?q=a

Social Learning under Platform Influence: Consensus and Persistent Disagreement
Individuals increasingly rely on social networking platforms to form opinions. However, these platforms typically aim to maximize engagement, which may not align with social good. In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on their neighbors' opinions and on the content shown to them by the platform. We focus on a stochastic block model with two blocks, where the initial opinions of the individuals in dif…

This https://arxiv.org/abs/2504.02712 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

TeleMoM: Consensus-Driven Telecom Intelligence via Mixture of Models
Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Telecom) due to technical complexity, specialized terminology, and rapidly evolving knowledge. Traditional methods, such as scaling model parameters or retraining on domain-specific corpora, are computationally expensive and yield diminishing returns, while existing approaches like retrieval-augmented generation, mixture of experts, and fine-tuning struggle with accuracy, efficiency, and coor…

Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence
Kordel K. France, Rohith Peddi, Nik Dennler, Ovidiu Daescu
https://arxiv.org/abs/2506.00398

Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence
Despite extraordinary progress in artificial intelligence (AI), modern systems remain incomplete representations of human cognition. Vision, audition, and language have received disproportionate attention due to well-defined benchmarks, standardized datasets, and consensus-driven scientific foundations. In contrast, olfaction - a high-bandwidth, evolutionarily critical sense - has been largely overlooked. This omission presents a foundational gap in the construction of truly embodied and ethica…

Code-Switching and Syntax: A Large-Scale Experiment
Igor Sterner, Simone Teufel
https://arxiv.org/abs/2506.01846 https://arxiv.org/pd…

Code-Switching and Syntax: A Large-Scale Experiment
The theoretical code-switching (CS) literature provides numerous pointwise investigations that aim to explain patterns in CS, i.e. why bilinguals switch language in certain positions in a sentence more often than in others. A resulting consensus is that CS can be explained by the syntax of the contributing languages. There is however no large-scale, multi-language, cross-phenomena experiment that tests this claim. When designing such an experiment, we need to make sure that the system that is p…

This https://arxiv.org/abs/2405.15927 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Application based Evaluation of an Efficient Spike-Encoder, "Spiketrum"
Spike-based encoders represent information as sequences of spikes or pulses, which are transmitted between neurons. A prevailing consensus suggests that spike-based approaches demonstrate exceptional capabilities in capturing the temporal dynamics of neural activity and have the potential to provide energy-efficient solutions for low-power applications. The Spiketrum encoder efficiently compresses input data using spike trains or code sets (for non-spiking applications) and is adaptable to both…

This https://arxiv.org/abs/2503.15680 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Spatial dependence of the break in the energy spectrum of cosmic rays in the new anisotropic diffusion approach
At present, there is no consensus on whether the spectral break in the cosmic ray flux of all elements around 4 PeV is a general characteristic of the Milky Way or is determined by a combination of factors that significantly affect the energy position of the knee. We argue that considering the anisotropic propagation of cosmic rays within a realistically modeled Galactic magnetic field enables an accurate description of the spectral break without invoking a source-related cutoff energy, and nat…

Has anyone tried to advance the idea of GLOP-like #IPv6 prefix assignments?
There are probably at least as many arguments against such a proposal than there would be to advance it, but is often useful to say we asked that question and came to a consensus in why or why not.
The reasons for might include using it for private address space or just to have globally autonomous assignments o…

This https://arxiv.org/abs/2411.07161 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

RoundTable: Investigating Group Decision-Making Mechanism in Multi-Agent Collaboration
Effective group decision-making is critical in Multi-Agent Systems (MAS). Yet, how different mechanisms for reaching consensus impact collaboration quality and efficiency remains understudied. We conduct a systematic study on group decision-making mechanisms in a decentralized setting. Through controlled experiments, we analyze how different voting rules affect decision quality and efficiency in a multi-round collaboration. Results reveal that majority voting often cause inefficient collaborati…

Sensor Fusion Methods for Gaussian Mixture Models
Ishan Paranjape, Islam Hussein, Jeremy Murray-Krezan, Sean Phillips, Suman Chakravorty
https://arxiv.org/abs/2506.00383

Sensor Fusion Methods for Gaussian Mixture Models
Consensus is a popular technique for distributed state estimation. This formulation allows networks of connected agents or sensors to exchange information about the distribution of a set of targets with their immediate neighbors without the need of a centralized node or layer. We present decentralized consensus-based fusion techniques for a system whose target prior estimates are a weighted mixture of Gaussian probability density functions (PDFs) for the following cases: 1) in which all agents …

This https://arxiv.org/abs/2502.08465 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Morpheus Consensus: Excelling on trails and autobahns
Recent research in consensus has often focussed on protocols for State-Machine-Replication (SMR) that can handle high throughputs. Such state-of-the-art protocols (generally DAG-based) induce undue overhead when the needed throughput is low, or else exhibit unnecessarily-poor latency and communication complexity during periods of low throughput. Here we present Morpheus Consensus, which naturally morphs from a quiescent low-throughput leaderless blockchain protocol to a high-throughput leader…

Get me out of this hole: a profile likelihood approach to identifying and avoiding inferior local optima in choice models
Stephane Hess, David Bunch, Andrew Daly
https://arxiv.org/abs/2506.02722

Get me out of this hole: a profile likelihood approach to identifying and avoiding inferior local optima in choice models
Choice modellers routinely acknowledge the risk of convergence to inferior local optima when using structures other than a simple linear-in-parameters logit model. At the same time, there is no consensus on appropriate mechanisms for addressing this issue. Most analysts seem to ignore the problem, while others try a set of different starting values, or put their faith in what they believe to be more robust estimation approaches. This paper puts forward the use of a profile likelihood approach t…

Construction of Urban Greenland Resources Collaborative Management Platform
Dongyang Lyu, Xiaoqi Li, Zongwei Li
https://arxiv.org/abs/2506.03830 https://…

Construction of Urban Greenland Resources Collaborative Management Platform
Nowadays, environmental protection has become a global consensus. At the same time, with the rapid development of science and technology, urbanisation has become a phenomenon that has become the norm. Therefore, the urban greening management system is an essential component in protecting the urban environment. The system utilises a transparent management process known as" monitoring - early warning - response - optimisation," which enhances the tracking of greening resources, streamlines mainte…

This https://arxiv.org/abs/2502.17982 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Kinetic variable-sample methods for stochastic optimization problems
We discuss kinetic-based particle optimization methods and variable-sample strategies for problems where the cost function represents the expected value of a random mapping. Kinetic-based optimization methods rely on a consensus mechanism targeting the global minimizer, and they exploit tools of kinetic theory to establish a rigorous framework for proving convergence to that minimizer. Variable-sample strategies replace the expected value by an approximation at each iteration of the optimizatio…

This https://arxiv.org/abs/2505.21356 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Towards Robust Automated Perceptual Voice Quality Assessment with Deep Learning
Objective: Perceptual voice quality assessment plays a critical role in diagnosing and monitoring voice disorders by providing standardized evaluation of vocal function. Traditionally, this process relies on expert raters utilizing standard scales, such as the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) and Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS). However, these metrics are inherently subjective and susceptible to inter-rater variability, motivating the need f…

Proof of Work With External Utilities
Yogev Bar-On, Ilan Komargodski, Omri Weinstein
https://arxiv.org/abs/2505.21685 https://arxiv.o…

Proof of Work With External Utilities
Proof-of-Work (PoW) consensus is traditionally analyzed under the assumption that all miners incur similar costs per unit of computational effort. In reality, costs vary due to factors such as regional electricity cost differences and access to specialized hardware. These variations in mining costs become even more pronounced in the emerging paradigm of \emph{Proof-of-Useful-Work} (PoUW), where miners can earn additional \emph{external} rewards by performing beneficial computations, such as Art…

This https://arxiv.org/abs/2502.12660 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Collective Intelligence in Dynamic Networks
We revisit DeGroot learning to examine the robustness of social learning in dynamic networks -- networks that evolve randomly over time. Dynamics have double-edged effects depending on social structure: while they can foster consensus and boost collective intelligence in "sparse" networks, they can have adverse effects such as slowing down the speed of learning and causing long-run disagreement in "well-connected" networks. Collective intelligence arises in dynamic networks when average influen…

The Families that Stay Together: A Network Analysis of Dynastic Power in Philippine Politics
Rafael Acuna, Aldie Alejandro, Robert Leung
https://arxiv.org/abs/2505.21280

The Families that Stay Together: A Network Analysis of Dynastic Power in Philippine Politics
Dynasties have long dominated Philippine politics. Despite the theoretical consensus that dynastic rule erodes democratic accountability, there is limited empirical evidence establishing dynasties' true impact on development. A key challenge has been developing robust metrics for characterizing dynasties that facilitate meaningful comparisons across geographies and election cycles. Using election data from 2004 to 2022, we leverage methods from graph theory to develop four indicators to investi…