Blockchain Papers

Simon Curty, Hans-Georg Fill

No abstract is available for this record.

ELVIS GERARDIN KONJOH SELABI

Smart contracts deployed on blockchain platforms are immutable once deployed, making correctness and security critical concerns that have led to substantial financial losses due to vulnerabilities. A significant proportion of these vulnerabilities stem from human-written code rather than blockchain infrastructure or cryptographic primitives. This observation motivates a paradigm shift from manual code development to model-driven approaches that generate semantically correct smart contracts from formal specifications. This thesis presents EDAM (Enhanced Data-Aware Machines), a behavioural model for specifying smart contracts that balances expressiveness with tractability. The framework extends traditional data-aware finite state machines [3] with essential features for a wide range of smart contract applications: dynamic role-based access control enabling runtime role assignment and revocation, participant management supporting unbounded and varying participants, and explicit modelling of inter-contract interactions through call tries with success and failure handling. The formal semantics of EDAM are grounded in established techniques from behavioural type theory, process calculi, and finite state machine theory, enabling rigorous reasoning about contract behaviour. The thesis contributes a comprehensive toolchain that integrates modelling, code genera- tion, test generation, and validation in a unified methodology. We develop a code generation engine that automatically produces Solidity smart contracts from EDAM specifications. The generated code faithfully implements the formal model, ensuring that the behaviour established at the model level is preserved in the executable code. The code generation process uses an intermediate JavaScript Object Notation (JSON) representation, which enables platform-agnostic code generation with ongoing extensions to support additional blockchain platforms such as Aptos. We present an automated test generation methodology that produces executable test suites from EDAM specifications. The approach combines symbolic trace generation using the formal semantics implemented in OCaml with randomized exploration of the Finite State Machine (FSM) network, enabling concrete trace derivation through random value assignment and Satisfiability Modulo Theories (SMT) constraint solving. This methodology systematically explores the state space to generate traces that exercise transitions, guards, and role constraints, producing executable test suites for standard testing frameworks such as Hardhat. The process is fully automated and can be integrated into the development workflow. Our evaluation demonstrates the expressiveness and practicality of the approach through a diverse benchmark of smart contracts, including contracts from the Azure repository [148], standard token contracts (Ethereum Request for Comments 20 (Token Standard) (ERC20)), Decentralized Finance (DeFi) protocols (Automated Market Makers (AMMs)), and multi- coordinator systems. The evaluation demonstrates expressiveness through the modelling of essential features, showing that the approach is able to model a wide range of smart contract features. The validation methodology employs a multi-faceted approach that combines code coverage analysis, mutation testing to validate the correctness of the generated code and the effectiveness of the test suites, and cross-validation by applying the generated tests to other established implementations. This cross-validation approach shows that our generated test suites are applicable to validate existing smart contract implementations, providing evidence of the quality and correctness of both the generated code and the testing methodology. The results empirically indicate that our model-driven approach produces contracts and test suites that preserve the structure and semantics of the formal model and can be applied to validate existing smart contract implementations. Unlike existing approaches that address isolated phases of the development lifecycle, EDAM provides an integrated toolchain that ensures consistency between specifications, vii generated code, and test suites. The framework shows that behavioural types provide a solid foundation for smart contract modelling and verification, enabling the development of unified frameworks that integrate modelling, code generation, test generation, and val- idation. Although our implementation targets blockchain platforms, the methodology is platform-agnostic and may generalise to other service-oriented and distributed architectures. The results show that model-driven approaches can produce high-quality smart contracts and comprehensive test suites, contributing to the advancement of secure smart contract development practices.

Tawfiq Hasanin, Rayan Mosli, Sahar Jambi

Faculty governance in higher education depends on transparent participation, reliable quorum enforcement, accountable record keeping, and strict alignment with institutional regulations. Conventional departmental council processes provide formal authority and academic deliberation, but they often rely on manual documentation, fragmented records, and procedural enforcement that is difficult to verify after the fact. This work presents an integrated hybrid Decentralized Autonomous Organization (DAO) framework for faculty governance that combines regulatory alignment analysis, a working smart-contract prototype, and scenario-based simulation. The framework is designed for university departmental councils and is structured across three layers: off-chain community governance, on-chain protocol governance, and off-chain execution governance. It expands prior conceptual work by incorporating governance dimensions related to roles, incentives, membership, communication, decision-making, identity, auditability, conflict-of-interest handling, and institutional ratification. The evaluation simulates 1488 proposals across twelve scenarios covering four faculty sizes (15, 30, 50, and 100 members) and three adoption levels (low, moderate, and high). Scenario results indicate that adoption intensity is the dominant driver of governance performance: mean participation increases from about 33% under low usage to about 85% under high usage, quorum achievement rises from about 6% to about 96%, and execution rises from about 19% to about 70%. Relative to a modeled conventional workflow baseline, the DAO-supported process reduces decision-cycle time by about 76%, improves audit completeness by about 30%, and increases traceability from about 0.63 to 1.00. The results indicate that DAO-assisted faculty governance can strengthen transparency, procedural consistency, and auditability while preserving legally mandated university authority, but its practical value depends on sustained participation, privacy safeguards, cost control, and clearly defined hybrid control points.

Mojtaba Eshghie, Wolfgang Ahrendt, Cyrille Artho, Thomas Hildebrandt · 5 authors

Abstract Smart contracts manage blockchain assets and embody business processes. Yet, mainstream languages lack explicit support for process concepts such as roles, action dependencies, and time constraints, leading to increased implementation complexity and analysis challenges. To address this, we use Dynamic Condition Response (DCR) graphs, a formal business process modeling language, to formalize the semantics of smart contract business logic. Modeling smart contracts in DCR graphs involves translating their underlying behavioral logic into a declarative visual model using DCR’s explicit constructs for events, roles, data, time, and inter-event relationships. Furthermore, we systematically model 15 common high-level smart contract design patterns , representing recurring solutions to business logic-level problems. These formalizations reduce ambiguity compared to informal descriptions and serve as language-independent specifications. We demonstrate the modeling process through three complete smart contract case studies that combine six design patterns. Our modeling methodology, formalizations, and correspondence between smart contract semantics and DCR graphs enable future automated analysis and verification.

Yepeng Ding, Ahmed Twabi, Junwei Yu, Lingfeng Zhang · 6 authors

Smart contract auditing remains challenging because vulnerabilities often emerge only under complex execution conditions, cross-transaction interactions, and environment-dependent assumptions. Existing analysis techniques, including static analysis, symbolic execution, fuzzing, and recent LLM-assisted approaches, each provide useful but incomplete coverage, and monolithic auditing pipelines often struggle to balance search breadth, reproducibility, and reporting reliability. This paper presents SEMA, a self-evolving multi-agent auditing framework for smart contracts that formulates auditing as a resource-bounded discovery of concrete counterexamples under replay-certified reporting semantics. SEMA combines heterogeneous specialized agents, an orchestrator, a shared artifact-centric knowledge base, and a replay-based referee. During auditing, agents generate and consume reusable artifacts, such as candidate invariants, refuted hypotheses, transaction templates, and coverage cues, allowing the shared search state to evolve across rounds without modifying the analyzers themselves. To ensure reporting reliability, findings are accepted only when the referee can replay the candidate scenario under a pinned execution configuration and confirm violation of an executable security property. We further evaluate SEMA on an annotated smart contract benchmark under a fixed 300 s budget per contract. The full system achieves 0.9469 instance recall, 0.9441 success rate, and 0.9445 macro-average category recall on the retained executable subset, outperforming both symbolic-only and fuzzing-only baselines, as well as multi-agent ablations that disable dynamic knowledge evolution or cross-agent artifact reuse.

Luca Barbaro

Across a wide range of modern process-aware systems, behavioral knowledge is captured and used in multiple shapes. The same system may be described at design time through process modeling, observed at runtime through execution data, and acted upon through decision-making components that adapt to changing operational conditions. As a result, process representations must remain consistent across heterogeneous tasks, supporting verification, diagnostics, and optimization, without the need to redefine them from scratch every time the viewpoint changes. Meeting these requirements calls for representations that are expressive enough to capture complex behavioral relations, while remaining susceptible to analytical reasoning, quantitative assessment, and integration with data-driven techniques. These challenges arise in a variety of domains, including business processes, service- oriented architectures, and autonomous and learning-based systems. Among these landscapes, Business Process Management (BPM) provides a particularly illustrative domain in which the value of behavioral process knowledge becomes both operational and measurable. Business information systems generate large amounts of execution data that records how processes unfold in practice. This has fostered the development of process mining techniques, which aim to discover process representations from observed execution traces in the form of event logs and to assess whether observed behavior matches expected behavior. Consequently, process representations should not be regarded as mere design-time artifacts. Instead, they enable organizations to obtain a complete view of their operations, identify inefficiencies and potential deviations, and systematically improve the way work is performed. Indeed, behavioral insights directly translate into measurable performance gains, affecting key performance indicators (KPIs), namely throughput time, resource utilization, regulation compliance, and operational costs. Business process modeling (hereafter process modeling) has traditionally relied on imperative (or procedural) representations (e.g., Workflow net or BPMN) that explicitly define the allowed execution paths of a system. While these models provide precise operational semantics and support automated execution, their explicit nature may restrict analysis and diagnosis in settings characterized by variability, flexibility, or partial observability. Conversely, declarative specifications have received increasing interest, as they suggest a significantly different way of representing processes, describing behavior in terms of constraints that executions must satisfy, offering a more flexible representation that is particularly suited to highly dynamic environments. These constraints are typically expressed as temporal rules grounded in logics such as Linear Temporal Logic on Finite Traces (LTLf), stating what must hold in a process rather than prescribing how it is achieved. As a result, they can serve as an internal behavioral layer that can be reused across multiple analysis and reasoning tasks. Despite their complementary strengths, imperative and declarative paradigms are often treated as separate approaches, adjusting their adoption across different analytical and reasoning tasks. Similar process representation challenges arise in service-based and cyber-physical settings, as well as in autonomous and learning-based systems, where agents must continuously adapt their behavior while still respecting safety and compliance constraints. In these scenarios, behavioral knowledge configures as a basis for monitoring and assessing whether observed or learned policies remain consistent with expected temporal and causal relations. This thesis investigates the use of declarative temporal specifications as a unifying abstraction span the full lifecycle of behavioral reasoning in process-aware systems. The main research objective guiding the work is stated as follows: RO: Declarative Specifications as a Unifying Behavioral Abstraction Investigate the use of declarative temporal specifications as a unifying abstraction for representing, verifying, andexploiting behavioral knowledge in process-aware systems. With this goal in mind, we set three research questions across process representations, verification, and decision making. RQ1: Process Representation Paradigms How can a declarative process specification be systematically derived from an imperative process model, preserving behavioral equivalence between the two representations? First, we discuss the relationship between imperative and declarative process representations in the business processes domain, showing how a declarative specification can be systematically derived from an imperative model while preserving behavioral equivalence. To this end, we introduce a systematic method to synthesize declarative specifications from safe and sound Workflow nets, together with formal guarantees ensuring that the two representations capture exactly the same behavior. This result enables principled transitions between paradigms, allowing analysts to select the most suitable representation depending on the task at hand. RQ2: Conformance Measurement How can the satisfaction of declarative process specifications be quantitatively assessed over observed executions? Once a process has been modeled, the growing availability of execution data makes it natural to quantify how closely observed traces adhere to the normative representation. This is the goal of conformance checking, which measures the degree of correspondence between a model (or specification) and recorded executions. Traditional conformance checking verification techniques for declarative specifications often assess individual rules in isolation, overlooking their mutual interplay and offering primarily binary satisfaction outcomes. We address this gap by proposing a probabilistic framework to quantify the satisfaction of declarative process specifications over event logs. We introduce interestingness measures that characterize the degree to which LTLf declarative constraints are supported by observed behavior. These measures enable behavioral diagnostics and provide a foundation for detecting deviations, changes, and emerging patterns in process executions. RQ3: Decentralized Data Sources How can declarative specifications be checked against execution data that is distributed across multiple independent sources? Capturing specification-level insights becomes even more relevant when execution data are distributed across independent sources, each party observing only a partial view of the process, while a global assessment must be derived from distributed evidence. In these inter-organizational settings, traditional conformance checking approaches are hindered by confidentiality and governance constraints that prevent centralizing raw event logs. To overcome this limitation, we propose CONFINE, a secrecy-preserving framework that enables process mining and declarative conformance checking without disclosing sensitive execution data between collaborating organizations. CONFINE executes mining and checking algorithms inside trusted execution environments (TEEs), where event logs are transmitted and processed under attested confidentiality guarantees. The architecture supports a symmetric collaboration model in which each party can provide execution data and perform joint analyses, enabling behavioral insight to cross organizational boundaries while preserving data sovereignty.

Atul Gupta, Divakar Yadav, Raghuraj Singh Suryavanshi

This study develops a formally grounded verification framework for blockchain consensus mechanisms and smart contract behavior using Event-B and the Rodin platform. Unlike prior approaches that rely primarily on simulation or case-based validation of isolated contracts, this work integrates Finite State Machine (FSM) abstraction, invariant-driven proof, refinement modeling, and temporal logic verification to analyze Proof of Work (PoW), Proof of Stake (PoS), and mechanisms for double-spending prevention. Solidity smart contracts are abstracted into FSMs and encoded as Event-B machines, enabling the formal specification of state transitions and safety constraints. Safety properties-including transaction uniqueness, state consistency, access control enforcement, and ledger invariant preservation-are verified through automatically generated proof obligations in Rodin. A total of 312 proof obligations were generated, of which 287 (92%) were automatically discharged, and 25 were proven interactively, resulting in complete invariant coverage. Liveness properties were specified in Computation Tree Logic (CTL) and validated via model checking, confirming deadlock freedom and eventual validator selection under PoS conditions. Double-spending prevention was formally enforced using state-consistent ledger modeling, where uniqueness constraints were proven across all reachable states. Protocol-level consensus logic for PoW and PoS was refined across three abstraction levels, ensuring block integrity and validator correctness through stepwise refinement. The results demonstrate that machine-checked proofs provide verifiable correctness guarantees beyond simulation-based evaluation, establishing a rigorous and reproducible verification pipeline that enhances correctness assurance and protocol-level robustness in blockchain systems.

Harshit Arora, Varun Singh, Abhinav Rajeev Kumar, Nanjappan Manikandan

Abstract We introduce Koan, a system for compiling natural language DeFi requests into executable safety-validated directed acyclic graphs (DAGs). Assembling correct multi-step DeFi workflows requires sequencing irrevocable on-chain transactions across heterogeneous protocols, demanding flexible intent understanding and strict execution discipline simultaneously - a combination no existing tool provides. Koan addresses this in two phases. Phase 1 translates user intent into a typed graph via an LLM with deterministic fallback heuristics. Phase 2 validates that graph, injects missing safety nodes, and executes with dependency-aware scheduling. We evaluated on 1,000 prompts across 9 DeFi categories. Intent-to-workflow correctness reached 82.4%; DAG validity 93.6%. The Safety Injector raised price-impact check coverage from 41.2% to 98.4%, and 7.3% of all workflows were aborted by injected checks identifying excessive risk. Workflow authoring averaged 2.4 min versus 46.8 min for manual scripting (a 20x speedup), and compiled flows achieved 97% execution success with 18% gas savings on matched DEX routes under testnet conditions. Keywords Blockchain systems, decentralized finance, intent compilation, large language models, workflow orchestration.

Sathya Krishnasamy

Background: Healthcare organizations face unprecedented challenges in maintaining process compliance due to increasingly federated data and systems topologies, coupled with complex state, federal, and jurisdictional regulatory compliance and verification requirements. The emergence of distributed ledger technology (DLT) and artificial intelligence presents both transformative opportunities and significant compliance challenges. These emerging technologies enable computing paradigms that shift toward data locality models where computational models meet the data rather than moving sensitive patient information across organizational boundaries. This computational approach offers innovative pathways to mitigate data breach risks, while simultaneously introducing new verification complexities as the underlying technologies continue to advance: healthcare entities must cryptographically prove that operations performed on locally-held data were executed according to approved specifications while enabling selective disclosure capabilities across entity lines. However, traditional verification mechanisms lack the cryptographic guarantees necessary for these privacy-preserving, multi-entity healthcare workflows, creating substantial risks in clinical decision-making, patient privacy, and regulatory adherence. Objective: This paper introduces the ZK-PRET Business Process Prover framework that integrates Object Management Group (OMG) business process standards with zero-knowledge cryptographic verification to enable privacy-preserving healthcare process compliance across distributed systems. Methods: We developed a multi-layer architecture combining formal business process modeling, zero-knowledge proof generation, and regulatory compliance verification. The framework extends established OMG standards with cryptographic verification capabilities to achieve verifiable compliance, privacy preservation, and regulatory accountability. Implementation testing was conducted in synthetic data environments designed to represent real-world healthcare scenarios.¹ These environments enable comprehensive modeling and testing of multi-entity process orchestration patterns while maintaining privacy protections essential for healthcare research and development. All scenarios, clinical examples, and process expressions presented in this paper utilize synthetic data to ensure no real patient data, clinical records, or identifiable health information was used. Results: The ZK-PRET Business Process Prover framework demonstrates practical applicability across many healthcare domains including treatment planning, telemedicine coordination, healthcare administration, consumer health services, multi-entity clinical trials, and supply chain management. Implementation results demonstrate cryptographic verification capabilities that enable mathematical prevention of regulatory violations rather than post-hoc detection. The results demonstrate configurable privacy preservation through zero-knowledge verification and consistent proof sizes suitable for modeling complex orchestrations, while leveraging already widely used Web 2 process models, suitable for multiple runtime deployment topologies. Conclusions: Zero-knowledge healthcare process verification represents a foundational technology for regulatory compliance in distributed healthcare systems. While agentic AI systems present important opportunities for automation, the underlying requirement for verifiable process compliance through cryptographic means brings broader challenges. ZK-PRET Business Process Prover addresses these challenges in healthcare transformative flows, enabling safer deployment of autonomous systems while maintaining regulatory standards.

Omer Aziz, Muhammad Shoaib Farooq, Adel Khelifi, Abdullah Khalid Omer

<title>Abstract</title> The digital preservation of cultural heritage has advanced considerably through blockchain technologies , offering secure, immutable, and decentralized management of museum records. However, traditional blockchain frameworks suffer from scalability limitations, leading to high transaction costs, network congestion, and inefficient resource utilization—particularly in virtual museum environments with dynamic user activity. To address these challenges, we propose VERAONET (Virtual Ecosystem for Rewards and Archaeological Operations Network), a novel Layer-2 blockchain framework designed specifically for digital heritage applications. VERAONET introduces a pluggable consensus architecture, enabling dynamic switching between Proof of Work (PoW), Adjustable PoW (APoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS) mechanisms in response to real-time network conditions. The framework was evaluated in two experimental environments: a Ganache-based local simulation (10–500 users) and a Geth virtual testbed (50–10,000 users). Results indicate that PoS and DPoS significantly outperformed PoW and APoW under high-load conditions, reducing latency, gas consumption, and chain bloat. Conversely, PoW and APoW proved effective in low-traffic, high-security scenarios. The adaptive consensus algorithm enhanced transaction finality and resource efficiency, demonstrating strong suitability for dynamic virtual museum ecosystems. VERAONET thus provides a scalable, sustainable, and cost-effective blockchain infrastructure tailored for cultural heritage preservation. Future work will focus on integrating AI-driven optimizations, supporting cross-chain interoperability, and embedding decentralized governance models.

Ashwaq Khan

Customer lifecycle decisions in enterprises are often fragmented across marketing, finance, customer experience, and operations, resulting in inconsistent actions and suboptimal outcomes. This paper introduces Multi-Agent Decision Intelligence (MADI), a framework that models enterprise functions as autonomous yet coordinated agents aligned through shared objectives. Using centralized training with decentralized execution, agents negotiate actions that balance lifetime value growth, churn reduction, customer experience, and cost-to-serve efficiency under operational and regulatory constraints. Experimental results on industrial and semi-synthetic datasets demonstrate consistent improvements over siloed optimization, centralized reinforcement learning, and heuristic baselines. A pilot deployment within a Saudi enterprise further confirms practical feasibility, improved crossfunctional alignment, and reduced decision latency. MADI provides an enterprise-ready blueprint for coordinated AI-driven decisionmaking in complex organizational environments.

John Ceri

Computational research depends on the ability to independently reproduce results, yet modern workflows are fragile: they drift across environments, depend on undocumented assumptions, and often fail silently. ValiChord provides a decentralised, agent‑centric infrastructure for independent reproducibility validation. Validators re‑execute workflows in diverse environments, generate cryptographically signed attestations, and contribute structured detector evidence that captures environment drift, dependency skew, execution variability, and workflow fragility. A commit–reveal protocol preserves validator independence, while Harmony Records synthesise divergent outcomes without collapsing them into binary judgements. ValiChord validates computation, not data provenance, and is explicit about this boundary: it strengthens the computational layer of scientific integrity without claiming to detect data fabrication. The system is built on Holochain, not blockchain, ensuring tamper‑evident provenance without global ledgers, tokens, or consensus mechanisms. Reference implementation and detector suite: https://github.com/topeuph-ai/ValiChord

Thomas Twenhoeven, Kai Hoberg, Moritz Petersen

Purpose Sharing information is crucial for the success of supply chains. However, sharing information requires a careful balance between privacy and transparency. This study aims to explore the potential of zero-knowledge proofs (ZKPs) to improve this balance by enabling partial information sharing. Design/methodology/approach The authors apply a three-stage methodology to inductively generate a set of use cases for ZKPs in SCM. The authors expand and validate this set of use cases through a series of interviews and analyze the technology based on the use cases and further insights generated in the interviews. Findings The authors find that ZKPs can provide trust and privacy, increase speed and reduce costs across supply chain functions and relationships. The authors identify the two mechanisms responsible for these benefits and theorize on the relationship between the novel type of trust provided by ZKPs and interpersonal trust. Research limitations/implications This explorative study shows that ZKPs have the potential to make a substantial impact on SCM. They increase the attractiveness of information sharing and enable transactional relationships where more strategic relationships were previously required. However, their implementation and reliance on accurate input data require further investigation. Originality/value The authors expand existing literature on partial information sharing by investigating the partial sharing of one individual item of information. In doing so, the authors explore a novel technology with unique characteristics relevant to SCM. To the authors’ knowledge, they conduct the first study regarding ZKPs in SCM.

Viana Ricardo Luz Holanda

This project is not abandoned. It is frozen. Reason for freeze: The work entered an infinite refinement loop. The architecture evolved, but external peer review, validation feedback, or community contribution did not materialize. Continuing alone without structural feedback ceased to be research and became exhaustion. What is SDIA? SDIA — Semantic Domain Integration Architecture — is the governing umbrella of the DEIP ecosystem. It is not a product, not a platform, not a vendor pattern. It is an architectural invariant: business domain semantics govern every layer of the integration stack simultaneously — gateway routing, runtime resolution, orchestration, event channels, and data contracts. The governing principle: 👉 The domain is the primary key. Always. Across every layer. Regardless of technology. What This Document Establishes This document is the comprehensive prior art record for the SDIA ecosystem. It establishes formal protection across: 4 core components — GDCR · DDCR · ODCP · DCEP 1 forward declaration — DCBP (Domain-Centric Data Pattern, discovered March 23, 2026, Warsaw) 50 named architectural variants 90 control-plane and metadata routing variants 11 domain application patterns — Kubernetes · Multi-Cloud · Event-Driven · AI/LLM · Industrial IoT · Service Mesh · Data Mesh · GraphQL Federation · Zero Trust · Semantic Versioning · Blockchain/Web3 Complete mathematical model — f(k) → v — deterministic, O(1), fail-fast, language-agnostic, platform-agnostic Full algorithmic prior art — Phantom v12 reference implementation (JavaScript) + cross-language ports (Lua · Java · C# · Python) The Mathematical Core At its foundation, SDIA routing is defined as: f(k) → v Where k = routing key constructed from semantic domain components, and v = resolved backend endpoint. Properties: Deterministic — same input = same output, always, in any language, any platform O(1) complexity — independent of metadata store size, domain count, or platform Fail-fast — unregistered keys rejected at ~0.1ms, zero backend exposure Total over governed space — only explicitly registered combinations resolve Invariant under infrastructure change — engine never changes, metadata evolves Validated Results ~2,067,904 requests processed 100% routing accuracy · zero routing failures Sub-4ms resolution latency · 99.99% uptime 8 enterprise platforms · 5 programming languages · 13 configurations 42 IoT sensors · 4 environments Platforms: SAP BTP APIM · AWS API Gateway · Azure APIM · Kong Gateway · Kong on Kubernetes · Netflix Zuul · Industrial IoT (Mosquitto + Node-RED) · Kubernetes + Istio Ecosystem Architecture Layer Component Role Gateway GDCR Semantic facade — 1 proxy per domain, not per system Runtime DDCR 7-stage deterministic resolution engine Orchestration ODCP Domain-centric package, iFlow, and credential governance Events DCEP Domain-centric event channel governance Data DCBP Domain-centric data contracts and data product routing Umbrella SDIA Unifying semantic addressing paradigm across all layers What SDIA Protects Any implementation — regardless of vendor, product name, platform, or programming language — that: Uses domain-centric routing as the primary organizational principle Employs metadata-driven resolution satisfying f(k) → v Implements semantic URL patterns: /domain/entity/action/target Enforces domain boundaries as security, governance, or semantic perimeters Decouples consumer addresses from backend implementation details ...constitutes a derivative application of the SDIA prior art established February 6, 2026. Prior Art Chain February 6, 2026 — Wayback Machine (Marco Zero · first public disclosure) February 7, 2026 — Medium (first formal publication) February–March 2026 — Zenodo (5 DOI-published specifications) March 2026 — IP.com Prior Art Database · IPCOM000277630D–000277633D March 2026 — USPTO Trademark Applications · 99680660 (GDCR) · 99691792 (DDCR) Version History Version Status DOI v2.0 ✅ CURRENT zenodo.org/records/18877636 v1.0 ⚠️ Superseded zenodo.org/records/18877636 Links Repository: github.com/rhviana/deip SDIA Extension (this document): https://zenodo.org/records/18877636 DEIP Source of Truth: https://doi.org/10.5281/zenodo.19004802 Citation (v2.0) APA: Viana, R. L. H. (2026). SDIA — Semantic Domain Integration Architecture: Complete Extensions, Variants & Prior Art Documentation — Version 2.0. Zenodo. https://zenodo.org/records/18877636 Author Ricardo Luz Holanda Viana Enterprise Integration Architect | Creator of DEIP Ecosystem | SAP BTP Integration Suite Expert | SAP Press Author Warsaw, Poland · March 2026 · ORCID: 0009-0009-9549-5862 "Technology changes by the quarter. Business processes last for decades. The domain never lies."

Chris Liu, Peter Bodorik, Dawn Jutla

The adoption of blockchain-based smart contracts for the trading of goods and services promises greater transparency, automation, and trustlessness, but also raises challenges related to payment integration and modularity. While business analysts (BAs) can express business logic and control flow using BPMN and decision rules using DMN, payment tasks that involve concrete transfers (on-chain, off-chain, cross-chain, or hybrid) require careful implementation by developers due to platform-specific constraints and semantic richness. To address this separation of concerns, we introduce a methodology within the context of the smart contract-as-a-service (SCaaS) approach that supports (1) identifying and mapping generic payment tasks in BPMN to pre-deployed payment smart contracts, (2) augmenting BPMN models with matching payment fragments from a pattern repository, and (3) automatically transforming the augmented models into smart contracts that invoke the appropriate payment services. Our approach builds on prior work in automated BPMN-to-smart contract transformation using Discrete Event–Hierarchical State Machine (DE-HSM) multi-modal modeling to capture process semantics and nested transactions, while enabling payment service reuse, extensibility, and the separation of concerns. We illustrate this methodology via representative use cases spanning conventional, DeFi, and cross-chain payments, and discuss the implications for modular contract deployment and maintainability.

Damaris Jepkurui Kangogo, László Gönczy, Imre Kocsis

No abstract is available for this record.

Choonhwa Lee, Yibo Zhang, Dani Mertens, Eunsam Kim

Decentralized workflows supported by provenance data aim to combine into a platform with high trustworthiness, transparency, and accountability. Despite the huge potential of the approach, one limitation of the system architecture is that it lacks access to external data, limiting the functionality and potential use cases of the workflow. To address this limitation, we extend an existing decentralized workflow with a provenance bridge, enabling the workflow to access external provenance-supported data without damaging the trustworthiness of the system. The introduction of this bridge effectively broadens the range of use cases for the decentralized workflow by opening it to various external provenance-supported data sources. This article presents the architectural design of the provenance bridge-enabled workflow system, and discusses our prototype implementation effort along with evaluation results. Specifically, we have designed and implemented the architectural framework of the provenance bridge that expands the provenance domain boundary by incorporating support for provenance model translation and oracle-based external data access. The proposed approach lays a solid foundation for a wider adoption of smart contract-based workflow systems by effectively bridging the gap between different provenance domains.

Balaram Tripathy

Enterprises are rapidly shifting from human-interpreted dashboards to Autonomous Analytical Entities (AAE) that execute decisions directly on production systems. This transition introduces a new failure mode—Agentic Divergence—where decentralized agents act on misaligned, drifted, or out-of-scope data products and metadata, leading to high-impact errors at scale. This paper proposes the Autonomous Analytical Coherence (AAC) framework, centered on an Analytical Control Plane (ACP) that inserts a mandatory, machine-enforced governance layer between AAEs and decentralized data products. The ACP mandates Agentic Data Contracts (ADC) as runtime dependencies and enforces Kullback–Leibler (KL) divergence-based drift checks within Trusted Execution Environments (TEE) to safeguard both analytical coherence and data sovereignty. Simulation-based experiments across finance and logistics workloads indicate that AAC reduces erroneous autonomous transactions by 77% compared with uncoordinated agent deployments, with only a 25 ms median increase in latency. These results demonstrate that treating governance as a runtime dependency is a practical path toward safe, high-stakes autonomous analytics in enterprise data meshes.

Tudor Christian Balan, Wolfram Pfeifer, Adele Veschetti, Massimo Bartoletti · 5 authors

Unintended behavior in smart contracts can lead to major financial losses. Due to the immutable nature of blockchains, it is of utmost importance to ensure the functional correctness of smart contracts before deployment. Formal verification is a powerful technology for such critical applications, as it can show the absence of errors. Current approaches focus on verifying programs on specific blockchains, such as the Ethereum Virtual Machine (EVM). Consequently, the SmartML smart contract modeling language was developed to design smart contracts independently of any particular blockchain. In this work, we present a novel approach for formally verifying SmartML contracts via an automatic translation to Java Card and the Java Modeling Language (JML). We extend SmartML with SmartJML, a JML-like specification language, and describe how SmartML and SmartJML can be automatically translated into Java Card and JML. With this, the established deductive verification tool KeY can be used for conducting proofs on the generated Java Card program. The faithfulness of our translation ensures that the obtained guarantees hold for the original SmartML models. In addition to the theoretical work, we provide a prototypical implementation of the automatic translation and evaluate it with a case study of an escrow.

Subhasis Thakur

No abstract is available for this record.

Peng, Zifan, Zheng, Jingyi, Liu, Yule, Jia, Huaiyu · 11 authors

Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread "blind signing" (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new user-centered NLP task for Ethereum transaction understanding, and construct a dataset of 187 complex Ethereum transactions annotated with transaction-level summaries and token flow-level semantic labels. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.

Oscar Medina, Brenda Meloni, Ana Strub, Marelo Martín Marciszack

Event patterns are used to model interactions between smart contracts and their environment, improving the efficiency, security, and flexibility of applications based on blockchain technologies. Event pattern-driven smart contract modelling is an emerging trend. Defining a new pattern or selecting a pre-existing one from a limited catalog is a fundamental activity for any analysis model that utilizes event patterns. This study presents a proposal to manage a catalog of event patterns that can be applied to the conceptual modelling of smart contracts and incorporates machine learning techniques to optimize pattern selection. To test this proposal, a prototype application called PatCat (Pattern Catalogue) was developed, using a decentralized electronic voting application as a case study. The incorporation of patterns at the beginning of the modelling process simplifies and clarifies the elicitation of requirements, among other benefits, while the use of machine learning accelerates the description of the problem situation. Consequently, a specialized application for managing a catalog of event patterns, supported by machine learning techniques, proves useful in standardizing and streamlining smart contract modelling tasks.

Sathya Krishnasamy

Abstract Background: Healthcare organizations face unprecedented challenges in maintaining process compliance due to increasingly federated data and systems topologies, coupled with complex state, federal, and jurisdictional regulatory compliance and verification requirements. The emergence of distributed ledger technology (DLT) and artificial intelligence presents both transformative opportunities and significant compliance challenges. These emerging technologies enable computing paradigms that shift toward data locality models where computational models meet the data rather than moving sensitive patient information across organizational boundaries. This computational approach offers innovative pathways to mitigate data breach risks, while simultaneously introducing new verification complexities as the underlying technologies continue to advance: healthcare entities must cryptographically prove that operations performed on locally-held data were executed according to approved specifications while enabling selective disclosure capabilities across entity lines. However, traditional verification mechanisms lack the cryptographic guarantees necessary for these privacy-preserving, multi-entity healthcare workflows, creating substantial risks in clinical decision-making, patient privacy, and regulatory adherence. Objective: This paper introduces the ZK-PRET Business Process Prover framework that integrates Object Management Group (OMG) business process standards with zero-knowledge cryptographic verification to enable privacy-preserving healthcare process compliance across distributed systems. Methods: We developed a multi-layer architecture combining formal business process modeling, zero-knowledge proof generation, and regulatory compliance verification. The framework extends established OMG standards with cryptographic verification capabilities to achieve verifiable compliance, privacy preservation, and regulatory accountability. Implementation testing was conducted in synthetic data environments designed to represent real-world healthcare scenarios.¹ These environments enable comprehensive modeling and testing of multi-entity process orchestration patterns while maintaining privacy protections essential for healthcare research and development. All scenarios, clinical examples, and process expressions presented in this paper utilize synthetic data to ensure no real patient data, clinical records, or identifiable health information was used. Results: The ZK-PRET Business Process Prover framework demonstrates practical applicability across many healthcare domains including treatment planning, telemedicine coordination, healthcare administration, consumer health services, multi-entity clinical trials, and supply chain management. Implementation results demonstrate cryptographic verification capabilities that enable mathematical prevention of regulatory violations rather than post-hoc detection. The results demonstrate configurable privacy preservation through zero-knowledge verification and consistent proof sizes suitable for modeling complex orchestrations, while leveraging already widely used Web 2 process models, suitable for multiple runtime deployment topologies. Conclusions: Zero-knowledge healthcare process verification represents a foundational technology for regulatory compliance in distributed healthcare systems. While agentic AI systems present important opportunities for automation, the underlying requirement for verifiable process compliance through cryptographic means brings broader challenges. ZK-PRET Business Process Prover addresses these challenges in healthcare transformative flows, enabling safer deployment of autonomous systems while maintaining regulatory standards.

Sizheng Fan, Tian Min

Recent advances in large language models (LLMs) have enabled the emergence of intelligent agents capable of performing complex multi-step tasks across various domains. In parallel, the growth of Web3 has introduced a decentralized web infrastructure, yet remains largely inaccessible to non-technical users due to operational complexity, fragmented information, and security risks. In this article, we present Web3Agent , an AI agent system that integrates LLM-based interaction with blockchain environments to enable language-driven on-chain operations. Web3Agent automatically decomposes user instructions into structured workflows, dynamically queries blockchain data and APIs, and performs multi-step operations such as asset transfers, token swaps, and smart contract execution. Web3Agent incorporates real-time inspection, error handling, and interaction transparency across its operation log, and flow visualization components. We evaluate the system and perform ablation study with customized dataset in a simulated environment, demonstrating its feasibility in orchestrating complex Web3 tasks and highlighting implications for agent-based abstraction in decentralized systems.