OEURO is a EUR-denominated clearing token designed for direct account-to-account settlement.It is implemented as a restricted ERC-20 token with 6 decimals and a deliberately narrow functional scope.Its primary purpose is clearing and settlement, rather than market trading or speculative use.Ordinary transfers are designed for direct EOA-to-EOA circulation.The reference implementation disables approve() and transferFrom().Ordinary transfers to smart-contract recipients are rejected. Minting and burning are explicit operations controlled by the manager.Authorized oracles may submit MINT or BURN requests but cannot directly alter token supply.Oracle requests remain valid for 24 hours and must be executed or rejected by the manager.External clearing references are protected against replay through single-use proof handling.The architecture therefore separates attestation authority from final settlement authority.OEURO uses a restricted recipient model for sensitive infrastructure addresses.The reference contract uses OpenZeppelin ERC-20 and Ownable2Step components.Ethereum Sepolia is used as the current public reference testnet environment.This Zenodo archive contains the Solidity source code, ABI, flattened contract and the OEURO technical white paper and audit reference.
PRE-GHR XXXIX v5.0 (2026-08-28) — release version closing all six objections of an adversarial pre-submission review. PRE-GHR XXXVIII gave the series its first formal definition of the irreducible governance residue δ0μ(P) := inf over admissible P' of ||residue(P')|| and proved a bit-level lower bound. This paper builds the property theory. The mathematics is a domain-order theory: every inequality follows from how the admissible domain D(P) behaves under enlargement or composition of protocols. We prove monotonicity of δ0μ in the erasure surface, an exact composition law δ0μ(P1 ∘ P2) = δ0μ(P1) + δ0μ(P2) − |T(P1) ∩ T(P2)| under explicit hypotheses (segment autonomy, joint attainability, cross-segment cleanliness), and positivity δ0μ(P) > 0 whenever T(P) is non-empty as a purely normative fact, with Landauer's principle confined to the physical interpretation. We then ask what a running system's audit ledgers can measure. The answer is stratified: the ordering structure is measurable in principle — conditional on a fixed normalization and full retention — while the absolute value is stated relative to a fixed code-point measure, and the aggregate-to-single-protocol bridge remains open. No interception statistic is claimed to equal δ0μ; where the wall cannot be built, the gap is marked, not papered over. Changes in v5.0 (six revision tickets, R01–R06, each closing one reviewer objection): R01 — Theorem 4 unilateralized: the safe direction (T(P) non-empty ⇒ δ0μ > 0) remains a theorem; the converse is demoted to Observation 4.1 under an explicit complete-erasure assumption. A witness-reading remark records that δ0μ is a minimum witness cardinality in the sense of why-provenance, inherited and not claimed as new. R02 — ledger counts restricted to lower witnesses only: the ordering claim is measurable solely under a fixed normalization and full retention, stated as an explicit condition rather than an implicit assumption. R03 — the uniform-sampling remark now carries an explicit finite-sample bound (Hoeffding's inequality in its standard form), two-sided: “holds in expectation” is no longer used as if it held for a sample. R04 — four empirical mappings corrected: schema-field disjointness is separated from retained-trace intersection; the approximate join reports both false-negative and false-positive error (the earlier “directionally safe, never over-counting” claim is withdrawn); the overlap-error direction is declared two-sided and governed by an error budget rather than assumed away; and the retention ratio is restated as an interception-event ratio in matched units. R05 — measure-relative notation throughout: bits and code points are two measures on one trace universe, so every ordering claim is stated at a fixed μ and changing μ defines a new quantity rather than restating the old one. R06 — subject classification reassessed and Related Work rebuilt. The paper contains no multiagent model and no coordinated-interaction result; the earlier cs.MA classification is withdrawn as unsupported by the technical content, and the classification adopted here is cs.CR primary with cs.DB cross-list. Related Work now separates the lineage the paper inherits from — linked timestamping and distributed witnesses (Haber & Stornetta 1991; Bayer, Haber & Stornetta 1993), split-view detection and the undefined gossip layer (Certificate Transparency, RFC 6962 / RFC 9162), existence-not-authenticity timestamping (OpenTimestamps), provenance and lineage (W3C PROV; Buneman, Khanna & Tan 2001; Cui, Widom & Wiener 2000), record linkage (Fellegi & Sunter 1969), trace semantics (Hoare 1978; Brookes, Hoare & Roscoe 1984), and measure and order (Halmos; Davey & Priestley) — from adjacent recent lines cited for comparison only. Where a construction of this paper rediscovers an existing one, priority is assigned to the source and no originality is claimed. Honesty notes. Citations to Hoeffding, Fellegi & Sunter, Halmos, Davey & Priestley and the CSP literature are made at the level of the standard statement of each framework only, pending full-text verification. Two candidate references were deliberately excluded because their primary sources could not be verified. Two gaps are inherited rather than closed: the hash-chain anchor has no consistency-proof comparison mechanism, and the anchor-propagation (gossip) layer is undefined in the source standard as well. Open questions Q5.1, Q5.3 and Q5.4 remain declared open.
Distributed consensus algorithms are fundamental to many modern systems, including blockchain networks, sensor networks, and cloud computing platforms. However, ensuring the correctness of these algorithms in the face of network failures, message delays, and other unpredictable events is a significant challenge. This paper proposes a novel approach to formally verify distributed consensus algorithms using temporal logic and model checking. We define the desired properties of the algorithm using temporal logic formulas, which express requirements such as safety (agreement) and liveness (eventual agreement). Subsequently, we employ model checking techniques to systematically explore the state space of the algorithm and determine whether it satisfies these temporal logic properties under various network conditions. The core idea is to provide a rigorous method for guaranteeing algorithm correctness and robustness, moving beyond traditional testing methods that often rely on exhaustive testing or probabilistic guarantees. The approach offers a quantifiable assurance level, crucial for deploying these algorithms in critical applications.
Decentralized networks, such as blockchain and peer-to-peer systems, rely on trust propagation mechanisms to maintain integrity and security. However, these mechanisms are often complex and prone to errors. This paper presents a novel approach to formally verify the correctness of trust propagation in these networks using temporal reachability analysis. We model the trust propagation process as a temporal system and employ model checking techniques to rigorously assess the system's behavior. The key contribution lies in providing a mathematical framework for guaranteeing that trust is propagated accurately and efficiently, addressing a critical gap in the current landscape of decentralized network security. This approach enables developers to confidently implement trust propagation algorithms, reducing the risk of vulnerabilities and enhancing network resilience. The paper details the methodology, provides a formal specification of the trust propagation process, and illustrates its application with a concrete example.
This paper proposes a novel decentralized blockchain verification system utilizing distributed Bayesian Networks (BNs). Traditional blockchain verification relies heavily on cryptographic proofs, which can be computationally intensive and susceptible to specialized attacks. Our approach offers a probabilistic and decentralized alternative. Each node maintains a Bayesian Network representing the blockchain's transaction graph, continuously updated with observed transactions. Consensus is achieved through iterative Bayesian inference and probabilistic agreement on the validity of new transactions. This system mitigates single points of failure, enhances security through probabilistic reasoning, and provides a more scalable verification process compared to traditional methods. The core claim is that a decentralized blockchain verification system can be built by leveraging distributed Bayesian Networks to model and verify transaction dependencies. The core mechanism involves continuous BN updates and consensus through iterative inference. This paper outlines the system architecture, the probabilistic inference process, and discusses potential applications and future research directions.
Trust is a fundamental element underpinning the successful operation of blockchain networks, yet it is frequently treated as an inherent characteristic rather than a subject of explicit investigation. This paper presents a novel formal model of trust within blockchain networks, leveraging game theory and network topology to provide a rigorous analytical framework. The model, denoted as (N, E, V, T), describes a network of nodes (N) connected by edges (E), each node possessing a valuation (V) and a trust threshold (T). Trust is modeled as a dynamic process influenced by node interactions, reputation, and network structure. The core contribution lies in defining the trust propagation mechanism, which can be expressed as: *Trust(i, j) = Trust(i, j) + α * (r(i, j) - T(i))* where: * *Trust(i, j)* represents the trust level between node *i* and node *j*. * *Trust(i, j)* represents the current trust level between node *i* and node *j*. * *α* is a trust propagation coefficient (0 ≤ *α* ≤ 1). * *r(i, j)* is the reputation score of node *j* as perceived by node *i*. * *T(i)* is the trust threshold of node *i*. This equation illustrates that trust between two nodes is influenced by the difference between the node's perceived reputation of the other node and its own trust threshold. The model allows for the simulation of various blockchain scenarios, including Byzantine fault tolerance, Sybil attacks, and collusion, providing valuable insights for designing robust and trustworthy blockchain systems. Furthermore, the model facilitates the exploration of trust-enhancing mechanisms, such as reputation systems, staking mechanisms, and consensus algorithms, by quantifying their impact on trust dynamics. The research contributes to a deeper understanding of the complexities of trust in distributed ledger technologies and offers a practical tool for improving their security and efficiency. ---