arXiv subjects

Ethan Buchman

Publications and source records attributed to Ethan Buchman.

5 recordsLinked to original sources

arXiv2026

Deepening the Secondary Market: Integrating Trade Credit into Market Clearing with the Cycles Protocol

Current post-trade clearing systems rely almost exclusively on cash or cash-like collateral, leaving vast reserves of short-term liquidity embedded in trade credit outside formal settlement infrastructures. A key barrier to integrating this liquidity is the near-universal dependence of clearing services on novation, which imposes institutional overhead that restricts accessibility and limits the range of obligations that can be brought into settlement. This paper introduces the Cycles Protocol: a distributed, multilateral clearing mechanism based on double-entry accounting and atomic cycle execution that maximizes balance sheet compression. Unlike novation-based clearing, Cycles does not redistribute counterparty risk; it can thus be applied generally to existing financial networks, without any change in counterparty relations, allowing it to complement existing clearing systems and Central Counterparties (CCPs). By representing commitments as edges on a unified directed graph, Cycles surfaces liquidity hiding within existing network structure. We focus here on two applications of Cycles to deepening secondary market liquidity: first, as a compression layer between existing clearing participants and CCPs; and second, as a means to incorporate the liquidity of the trade credit network into formal settlement, extending market clearing beyond financial obligations and into real-economy financing.

q-fin.GN↗

arXiv2025

Cycles Protocol: A Peer-to-Peer Electronic Clearing System

For centuries, financial institutions have responded to liquidity challenges by forming closed, centralized clearing clubs with strict rules and membership that allow them to collaborate on using the least money to discharge the most debt. As closed clubs, much of the general public has been excluded from participation. But the vast majority of private sector actors consists of micro or small firms that are vulnerable to late payments and generally ineligible for bank loans. This low liquidity environment often results in gridlock and leads to insolvency, and it disproportionately impacts small enterprises and communities. On the other hand, blockchain communities have developed open, decentralized settlement systems, along with a proliferation of store of value assets and new lending protocols, allowing anyone to permissionlessly transact and access credit. However, these protocols remain used primarily for speculative purposes, and so far have fallen short of the large-scale positive impact on the real economy prophesied by their promoters. We address these challenges by introducing Cycles, an open, decentralized clearing, settlement, and issuance protocol. Cycles is designed to enable firms to overcome payment inefficiencies, to reduce their working capital costs, and to leverage diverse assets and liquidity sources, including cryptocurrencies, stablecoins, and lending protocols, in service of clearing more debt with less money. Cycles solves real world liquidity challenges through a privacy-preserving multilateral settlement platform based on a graph optimization algorithm. The design is based on a core insight: liquidity resides within cycles in the payment network's structure and can be accessed via settlement flows optimized to reduce debt.

cs.CE↗

arXiv2020

A Tendermint Light Client

In Tendermint blockchains, the proof-of-stake mechanism and the underlying consensus algorithm entail a dynamic fault model that implies that the active validators (nodes that sign blocks) may change over time, and a quorum of these validators is assumed to be correct only for a limited period of time (called trusting period). The changes of the validator set are under control of the blockchain application, and are committed in every block. In order to check what is the state of the blockchain application at some height h, one needs to know the validator set at that height so that one can verify the corresponding digital signatures and hashes. A naive way of determining the validator set for height h requires one to: (i) download all blocks before h, (ii) verify blocks by checking digital signatures and hashes and (iii) execute the corresponding transactions so the changes in the validator sets are reproduced. This can potentially be very slow and computationally and data intensive. In this paper we formalize the dynamic fault model imposed by Tendermint, and describe a light client protocol that allows to check the state of the blockchain application that, in realistic settings, reduces significantly the amount of data needed to be downloaded, and the number of required computationally expensive signature verification operations. In addition to mathematical proofs, we have formalized the light client protocol in TLA+, and checked safety and liveness with the APALACHE model checker.

cs.DC↗

arXiv2019

The latest gossip on BFT consensus

The paper presents Tendermint, a new protocol for ordering events in a distributed network under adversarial conditions. More commonly known as Byzantine Fault Tolerant (BFT) consensus or atomic broadcast, the problem has attracted significant attention in recent years due to the widespread success of blockchain-based digital currencies, such as Bitcoin and Ethereum, which successfully solved the problem in a public setting without a central authority. Tendermint modernizes classic academic work on the subject and simplifies the design of the BFT algorithm by relying on a peer-to-peer gossip protocol among nodes.

cs.DC↗

arXiv2015

Understanding Minimum Probability Flow for RBMs Under Various Kinds of Dynamics

Energy-based models are popular in machine learning due to the elegance of their formulation and their relationship to statistical physics. Among these, the Restricted Boltzmann Machine (RBM), and its staple training algorithm contrastive divergence (CD), have been the prototype for some recent advancements in the unsupervised training of deep neural networks. However, CD has limited theoretical motivation, and can in some cases produce undesirable behavior. Here, we investigate the performance of Minimum Probability Flow (MPF) learning for training RBMs. Unlike CD, with its focus on approximating an intractable partition function via Gibbs sampling, MPF proposes a tractable, consistent, objective function defined in terms of a Taylor expansion of the KL divergence with respect to sampling dynamics. Here we propose a more general form for the sampling dynamics in MPF, and explore the consequences of different choices for these dynamics for training RBMs. Experimental results show MPF outperforming CD for various RBM configurations.

cs.LG↗