Dexy: A Simple Stablecoin Design Based on an Algorithmic Central Bank

Alexander Chepurnoy; Amitabh Saxena; Luca Gaetano D'Angelo

Home/Archive/arXiv

arXiv · 2610.03841

Dexy: A Simple Stablecoin Design Based on an Algorithmic Central Bank

Alexander Chepurnoy·Amitabh Saxena·Luca Gaetano D'Angelo

View original source ↗Read source document ↗Export metadata ↓

Abstract

We consider a new algorithmic stablecoin called Dexy consisting of three components: a central bank, a secondary market in the form of a customized CP-AMM liquidity pool, and a trusted oracle. The price of the stablecoin in the liquidity pool is stabilized, relative to the oracle price, via central bank interventions. Users mint Dexy stablecoins from the bank using basecoins, the base cryptocurrency asset.

Explore related subjects

cs.CE

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Alexander Chepurnoy, Amitabh Saxena, Luca Gaetano D'Angelo. 2026-10-06. Dexy: A Simple Stablecoin Design Based on an Algorithmic Central Bank. https://arxiv.org/abs/2610.03841

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Toward path-invariant embeddings for local distance source characterization

This work builds on recent advances in foundation models in the language and image domains to explore similar approaches for seismic source characterization. We rely on an architecture called Barlow Twins, borrowed from an understanding of the human visual cortical system and originally envisioned for the image domain and adapt it for learning path invariance in seismic event time series. Our model improves the performance on event characterization tasks such as source discrimination across catalogs by 10-12% and provides more reliable predictive uncertainty estimates. We suggest that dataset scale and diversity more than architecture may determine aspects of the current ceiling on performance. We leverage decision trees, linear models, and visualization to understanding the dependencies in learned representations.

cs.CE↗

arXiv2026

Procedural Generation of Conceptual 3D Ship Internal Arrangements for Design-Space Exploration

Ship internal arrangement is a complex spatial design problem in which competing functions within a fixed volume affect spatial relationships, mass distribution, trim, and stability. Exploring these trade-offs requires many layouts in a common format, yet public general-arrangement drawings are scarce, inconsistent, and two-dimensional. This study develops a common framework for generating conceptual 3D internal arrangements across six vessel families: bulk carriers, tankers, container vessels, offshore support vessels, patrol vessels, and yachts. Each arrangement uses paired voxel grids and topological graphs for spatial and connectivity analysis. The framework is evaluated through three experiments: (i) comparison of 50,000 generated arrangements with 23 reconstructed real references, (ii) envelope-constrained reconstruction of one reference per family, and (iii) response of generated layouts to requested function volumes. Generated design spaces broadly align with references within each family's sampling range. Searching alternative spatial subdivisions improves reconstruction across all families, with voxel accuracy reaching 75.5%. Fuel and ballast allocations respond near-proportionally to requested volume, while other functions depend on subdivision capacity, compartment eligibility, and discrete assignment. A fixed-hull offshore support vessel application shows that the same hull and requested programme can produce different adjacency and trim outcomes, with five nondominated concepts among 119 of 500 alternatives meeting stability and programme criteria. The results support layout design-space exploration across vessel families and changing requirements, with spatial organisation remaining important beyond volume compliance. The generator and conceptual general arrangement dataset are released publicly for reproducible analysis, benchmarking, and learning-based design studies.

cs.CE↗

arXiv2026

A dynamic pore-network model with discrete microbubble transport

We develop a hybrid pore-network model (PNM) for discrete microbubble transport coupled to connected two-phase flow in porous media. We apply the model to simulate flow in the porous transport layer of a rotating proton exchange membrane (PEM) electrolyser. At sufficiently large apparent gravitational forces, bubbles growing at the catalyst layer can detach at sizes smaller than the characteristic pore size and move through liquid-filled pores via buoyancy and advection. The model couples a dynamic two-phase PNM to a Lagrangian bubble tracker. Pore-scale Stokes simulations in square throats provide closures for the bubble buoyancy velocity, the hydraulic resistance of bubble-occupied throats, and the reciprocal coupling between liquid flow and bubble motion. Verification tests assess conservation, pressure-flow coupling, bubble growth, advection, merging, trapping, and transfer to the connected gas phase. The network simulations show that increasing apparent gravitational forces shifts a larger fraction of the produced gas toward discrete-bubble transport. The increase mostly comes from a reduced detachment radius, which decreases bubble size and lets them move more freely through the network.

cs.CE↗