Blockchain Papers

nithin nagineni

No abstract is available for this record.

Matěj Hůlek

Zero-knowledge důkazy (ZKP) umožňují dokazovateli přesvědčit ověřovatele o správnosti tvrzení, aniž by odhalil podkladový svědek. V posledních letech se ZKP stal klíčovou součástí aplikací chránících soukromí i škálovatelných blockchainových systémů, od stručných rollupů založených na SNARK až po transparentní konstrukce STARK založené na hašovacích funkcích a důkazy vykonání v systémech zkVM/zkEVM. Tato diplomová práce zkoumá hlavní moderní rodiny ZKP, včetně pairing-based zk-SNARKů, Plonkish protokolů, zk-STARKů, Bulletproofs a novějších hash-based přístupů k proximity testingu, jako je WHIR, se zaměřením na jejich kryptografické stavební bloky, bezpečnostní předpoklady a praktické kompromisy. Práce dále mapuje existující benchmarkingové přístupy a identifikuje běžné problémy, které komplikují spravedlivé porovnání, včetně nekonzistentních benchmarkových úloh, heterogenní volby parametrů a nereprodukovatelných běhových \\ prostředí. Na základě této analýzy práce vymezuje klíčové hodnoticí metriky a navrhuje sjednocený benchmarkingový framework typu host-agent s kontejnerizovanými provery, explicitními run manifesty, standardizovanými kontrakty výsledků a podporou jak pro CPU, tak pro GPU výpočetní cesty napříč systémy založenými na obvodech i zkVM. S využitím tohoto frameworku práce hodnotí reprezentativní implementace na sadě sémanticky porovnatelných primárních benchmarkových úloh a sekundárních benchmarkových úloh orientovaných na specializaci. Výsledky ukazují, že neexistuje jeden univerzálně nejlepší proof system: transparentní frameworky založené na obvodech, jako jsou Plonky3 a Winterfell, dosahují u primárních benchmarkových úloh nejlepších výsledků z hlediska doby generování důkazu a paměťových nároků, pairing-based systémy jako Groth16 a ICICLE poskytují nejmenší důkazy a nejrychlejší verifikaci pro scénáře omezené možnostmi ověřovatele nebo pro on-chain nasazení a systémy zkVM, jako jsou RISC Zero a SP1, směňují vyšší spotřebu paměti a větší důkazy za lepší programovatelnost a snazší integraci obecně použitelných výpočtů. Experimenty dále ukazují, že akcelerace pomocí GPU je nejpřínosnější u dostatečně velkých benchmarkových úloh, zatímco inicializační režie a limity VRAM její přínos u menších případů snižují. Celkově práce přispívá jak praktickým jednotným benchmarkingovým frameworkem, tak experimentálně podloženými doporučeními pro volbu ZKP přístupů v různých podmínkách nasazení, hardwaru, důvěryhodnostních modelů a bezpečnostních omezení.

Michal Vaňo

Federatívne učenie (FL) umožňuje spoločné trénovanie modelu bez priameho zdieľania údajov, ale často sa spolieha na silné predpoklady o čestnom správaní klienta a servera. To je dôvod, prečo štandardné FL protokoly poskytujú iba obmedzenú záruku ohľadom výpočtov na strane klienta, integrity odoslaných informácii, alebo ohľadom správnosti agregácie na strane servera. Táto diplomová práca skúma použitie systémov s nulovými znalosťami (ZKP) spolu s podpornými metódami na vytvorenie dôvery v FL prostredí. V tejto práci sa po úvode k FL a ZKP ďalej skúma prehľad existujúcich ZKP nástrojov v prostredí FL. Na základe tejto analýzy je vytvorená kategorizácia existujúcich prístupov FL založených na ZKP, ktorá je postavená najmä na cieľoch daného systému. Na základe identifikovaných možností zlepšenia práca navrhuje overiteľný protokol váženej agregácie. V tomto protokole je každý prijatý príspevok previazaný s autorizovanou váhou, prípustnou skrytou aktualizáciou, konzistentným váženým vstupom a výslednou aktualizáciou modelu, ktorú je možné verejne overiť prepočítaním. Tento protokol bol implementovaný ako prototyp s plne funkčnými kryptografickými komponentami. Následne je tento protokol vyhodnotený.

Yury Cristian Martínez Évora

No abstract is available for this record.

Vedang Ratan Vatsa

This study analyzes 128,286 academic papers tagged as blockchain or cryptocurrency research by OpenAlex's machine-learning concept classifier, published between 2013 and mid-2026. A broader keyword search across paper abstracts identifies 1,938,409 publications that mention Web3-related terms. The analysis measures keyword frequency, temporal trajectories, growth rates, citation distributions, geographic concentration, institutional output, and open access rates. Key findings include 117x growth in annual blockchain publications between 2013 and 2025, the rise of zero-knowledge proofs as the fastest-growing cryptographic primitive (2.1x growth, 2025-2026 vs. 2022-2023), DeFi research experiencing a 74x increase from 2019 to 2025, NFT research peaking in 2023 before declining, China and India leading global output with 13.5% and 13.3% of all papers respectively, and 43.5% of all papers receiving zero citations.

Supriya Khadka, Sanchari Das

Public distributed ledgers enforce integrity through radical transparency, creating tension with data minimization principles required for regulatory compliance. While Zero-Knowledge Proofs (ZKPs) offer a theoretical privacy solution, existing constructions often overlook adversarial constraints in smart contract environments. Specifically, the asynchronous decoupling of off-chain proof generation from on-chain submission introduces front-running and proof-reuse risks in public mempools. In this work, we formalize Selective Disclosure Authorization Schemes (SDAS), a cryptographic primitive for granular and revocable compliance checks on public ledgers without revealing the underlying witness. We define a security model for SDAS, introducing Ledger-Bound Attribute Unlinkability and Context-Aware Sender Binding to capture how valid proofs remain bound to their intended authorization context. To validate sender binding, we present ZK-Compliance, an Ethereum-based instantiation that operationalizes a user-controlled "Grant, Verify, Revoke" lifecycle. We implement the sender-binding component using a 14-constraint Circom circuit that anchors the zero-knowledge proof to the executing on-chain sender address. Our Sepolia evaluation confirms practical viability: browser-based proof generation executes in under 200 ms, and on-chain verification costs 240,512 gas, neutralizing proof reuse by different callers while preserving strict attribute privacy.

Nuha Omran Abokhdair, Ali Alissawi Ahmed AlQudairi

Decentralized storage networks increasingly rely on blockchain-based verification to ensure data integrity without centralized control; however, proof-intensive workloads introduce significant latency and on-chain cost overhead. This paper presents a lifecycle-based comparative analysis of major zero-knowledge proof (ZKP) models used in decentralized storage, focusing on zk-SNARK frameworks and transparent zk-STARK constructions. A multi-layer evaluation framework is introduced, aligning performance analysis with the core stages of the proof lifecycle: generation, aggregation, and on-chain verification. Building on this analysis, the paper proposes a hybrid architecture that combines parallel STARK-based proof generation with recursive SNARK-based compression, reducing on-chain verification complexity to near-constant. A Filecoin-inspired case study, supported by a quasi-empirical performance model, demonstrates that the proposed hybrid approach significantly reduces verification latency and data overhead while mitigating the linear growth of verification costs. The results indicate that hybrid ZKP architectures offer a scalable and economically viable solution for decentralized storage systems and large-scale blockchain networks. Keywords: Zero-knowledge proofs, zk-SNARKs, zk-STARKs, recursive aggregation, decentralized storage, verifiable cryptography, scalability, gas cost.

Loso Judijanto

This study presents a comprehensive bibliometric analysis of blockchain identity management research published between 2010 and 2025, aiming to map its intellectual structure, thematic evolution, and global collaboration patterns. Using data retrieved from the Scopus database and analyzed with VOSviewer, the study applies network visualization, overlay visualization, density mapping, citation analysis, and co-authorship analysis to uncover dominant research streams and emerging frontiers. The results reveal that the field is conceptually centered on blockchain-based authentication and decentralized identity management systems, with increasing scholarly attention toward privacy-preserving mechanisms such as zero-knowledge proofs, anonymity, and data protection. Thematic evolution indicates a clear transition from foundational infrastructure-oriented studies to application-driven and regulatory-sensitive research domains, including e-government, IoT, healthcare, and digital governance. Collaboration analysis highlights the leading role of China and India, supported by strong transcontinental linkages with the United States and European countries, reflecting a globally interconnected yet regionally concentrated research landscape. By systematically mapping publication trends, thematic clusters, and collaboration networks, this study provides a structured knowledge base that supports future theoretical development, guides practical implementation, and informs policy formulation in blockchain-based digital identity ecosystems.

Deny Prasetyo, Siska Narulita, Ahmad Jurnaidi Wahidin, Rosalina Yani Widiastuti · 7 authors

This study introduces a trust centric machine learning framework designed to improve decision making reliability and security in decentralized digital service ecosystems. Traditional machine learning models often focus on accuracy and efficiency but fail to address the challenges of trust and security in decentralized environments. In contrast, the proposed framework integrates dynamic trust indicators and employs Federated Learning (FL) to ensure privacy while enhancing decision making performance. The framework also incorporates Zero Knowledge Proofp based Verifiable Machine Learning (ZKP-VML), which ensures transparency and security without compromising sensitive data. Through continuous real time trust assessments, the framework adapts to changing conditions, improving the accuracy and reliability of decisions in environments where participants may not fully trust each other. The application of this framework in autonomous vehicles and IoT networks demonstrated its ability to make robust, secure decisions, even in complex and uncertain scenarios. The framework’s ability to incorporate both trust and security into its decision making processes sets it apart from traditional models, which typically do not address the trustworthiness of data or participants. This research highlights the importance of integrating trust and security into machine learning models, particularly in decentralized systems, and offers a robust solution to trust management challenges. However, challenges such as scalability and computational efficiency remain, and future work should focus on enhancing these aspects, along with exploring the framework's applicability in other decentralized domains like finance or supply chain management. The integration of privacy preserving technologies and improvements in adversarial robustness are also potential areas for future research.

Ilyes Tarik MAZARI

This comprehensive technical survey presents integration architectures for the Y.I.N. (Your Information Never leaves your control) Nine Pillars framework across 200+ commercial platforms spanning artificial intelligence (100+ LLM providers including OpenAI, Anthropic, Google DeepMind, Meta, Microsoft, Mistral AI, Baidu, Alibaba, Tencent), healthcare (50+ providers including Epic Systems, Tempus, PathAI), finance (40+ institutions including JPMorgan Chase, Goldman Sachs, BlackRock), autonomous vehicles (20+ companies including Waymo, Tesla, Cruise), telecommunications (25+ carriers including AT&T, China Mobile, Deutsche Telekom), and energy (20+ companies including Siemens Energy, NextEra) across 25+ countries. The Y.I.N. Nine Pillars architecture provides end-to-end privacy protection through: (1) Data Privacy (Differential Privacy), (2) Computation Privacy (Homomorphic Encryption), (3) Storage Privacy (Encryption at Rest), (4) Transmission Privacy (TLS 1.3), (5) Access Control (Zero-Knowledge Proofs), (6) Audit Trail (Merkle Trees), (7) Deletion Rights (Cryptographic Erasure), (8) Quantum Resistance (Lattice-based Cryptography), and (9) Token Licensing (Cryptographic Payment Enforcement). The Ninth Pillar token licensing system, covered by U.S. Patent Application 63/949,361 (filed December 28, 2025), provides cryptographic enforcement of usage rights by integrating token-derived blinding factors into homomorphic encryption operations, making computational correctness mathematically dependent on valid authorization. The system achieves 99.37% accuracy with valid tokens versus 50.7% with invalid tokens (t=147.3, p<10^-50), with security proven under CDH hardness (2^128 operations) and Ring-LWE assumptions. Integration schematics are provided for regulatory compliance with HIPAA (healthcare), SOX/DORA (finance), GDPR/EU AI Act (European Union), CCPA (California), PIPL (China), ISO 27001, NERC CIP (energy), and 15+ other frameworks. Extension directions are documented for community research including TEE hybrid architectures, MPC integration, VDF token lifetimes, key-homomorphic PRFs, flexible validation policies, hardware attestation, ABE capabilities, off-chain settlement, and DID/VC integration. Organizations seeking to implement these integration patterns may obtain licenses for individual pillars, sector packages, or the complete Nine Pillars system from the patent holder. Patent Notice: The Y.I.N. Nine Pillars architecture and Ninth Pillar token licensing system are covered by U.S. Patent Applications 63/949,361 (Ninth Pillar, filed December 28, 2025), 63/923,348 (QFED-MAZARI Quantum Extensions), 19/399,646 (Core Y.I.N. Architecture), 19/403,244 (Hardware Implementation), and 19/417,196 (SQL Database Integration), comprising 430+ claims across 15 patent applications.

Marwa Ali Hamdan AL-Jabri, Nafisa Abul Ghafoor Othman AL-Ansari

Access control is an important part of cybersecurity in distributed systems since conventional centralized mechanisms are not always sufficient. Due to blockchain, individuals have begun to employ decentralized access control models as they are capable of enhancing transparency, auditing and defending against fraud. At the reason of this report, we survey various blockchain-based access control systems, paying special attention to their architectures, confirmation mechanisms, identity models and policy enforcement mechanisms. We categorize the current literature into various groups based on their platforms (e.g. Ethereum, Hyperledger, Fabric), control mechanisms (e.g. RBAC, ABAC and capability-based) and whether they introduce additional privacy-tools such as zero-knowledge proofs and decentralized identifiers. The paper analyzes and describes the key gaps in current frameworks in terms of scalability, interoperability and computing expenses. Then, the shortcomings of the current research are pointed out so that they could guide future efforts in the field of blockchain-based access control systems.

Mohankumar S D, J V N Lakshmi, Shashidhara D

Investigations of cybercrime today require forensic architectures that natively traverse multiple blockchains with ease while protecting and scaling evidence processing. Although blockchains support tamper- evident logs, their original single-chain architecture limits cross-platform interoperability and forensic scaling. Recent developments overcome these limitations such as zero-knowledge proofs supporting private but verifiable evidence verification, sharding architectures splitting state without compromising latency, and AI-based anomaly detectors identifying subtle tampering. But challenges remains like zero- knowledge proofs are computationally expensive, sharding poses intricate state-consistency problems and AI models need to be retrained constantly, incurring operational burden. Future research needs to make these pieces work for real- time, large-scale forensic applications by designing light-weight zero-knowledge constructs, self-tuning shard governance systems and compact AI with incremental-update threads. Integrating such abilities into single frameworks will offer privacy, scalability and security, supporting forensic processes for which courts will give credit in various, changing block-chain environments.

Sungmin Lee, Kichang Lee, Gyeongmin Han, JeongGil Ko

Many location-based services rely on a point-in-polygon test ( PiP ), checking whether a point or a trajectory lies inside a geographic zone. Since geometric operations are expensive in zero-knowledge proofs, privately performing the PiP test is challenging. In this paper, we answer the research questions of how different ways of encoding zones affect accuracy and proof cost by exploiting grid-based lookup tables under a fixed STARK execution model. Beyond a Boolean grid-based baseline that marks cells as in- or outside, we explore a distance-aware encoding approach that stores how far each cell is from a zone boundary and uses interpolation to reason within a cell. % Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids with only a moderate verification overhead, making zone encoding the key lever for efficient zero-knowledge spatial checks. Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids (max. % 60%p 60%p accuracy gain) with only a moderate verification overhead (approximately 1.4×), making zone encoding the key lever for efficient zero-knowledge spatial checks.

Abdullah Ayub Khan, Asif Ali Laghari, Hamad Almansour, Teerath Kumar · 7 authors

Wearable health technology has revolutionized remote monitoring and personalized healthcare by allowing real-time surveillance of patient health measurements and vital signs. However, their widespread acceptance is hampered by issues with security, privacy preservation, data protection, and interoperability. Blockchain Technology (BT), in particular Zero-Knowledge Proofs (ZKPs) and smart contracts, present a viable way to enhance the privacy, provenance, and integrity of wearable health data. This paper proposes a BT-enabled system that guarantees decentralized, unforged data management, transparency, immutability, and dynamic traceability for wearable health devices, particularly smartwatches with biosensors. To evaluate the effectiveness of the proposed work, the main performance-related metrics-latency, throughput, computational overhead, security robustness, and scalability-are looked at. The experiment's simulated findings show that BT integration is effective, with a 99.33% improvement in data integrity and protection. Automated access control protocols demonstrate data protection by utilizing smart contracts, and ZKPs guarantee verifiable data exchanges without jeopardizing patient privacy. These results demonstrate improved interoperability, decreased processing time, and increased security in comparison to comparable cutting-edge centralized platforms.

Aarti Punia, Preeti Gulia, Nasib Singh Gill, Deepak Kaushik · 7 authors

An effective healthcare data system must safeguard individual privacy, foster public trust, and enhance societal resilience. To achieve this, access to critical health information must be provided in an ethical, secure, and reliable manner. This paper proposes a blockchain-based healthcare management framework designed to improve security, privacy, and transparency in healthcare administration. The architecture incorporates smart contracts, multi-signature wallets, and zero-knowledge proofs (ZKPs) to securely facilitate key operations such as patient registration, policy updates, and medical device management on a decentralized platform. Multi-signature wallets require authorization from multiple stakeholders for sensitive transactions, while ZKPs enable identity or access verification without disclosing confidential information. A built-in performance monitoring module collects key metrics, including transaction latency, gas consumption, and block time, which are visualized using JavaScript. Overall, the proposed system offers a secure, transparent, and privacy-preserving approach to decentralized healthcare management.

Ilyes Tarik MAZARI, Yanis Mazari, Ilyan Mazari

We present the Mazari Bidirectional Architecture, a cryptographic framework providing end-to-end privacy guarantees for computation systems employing homomorphic encryption (HE), differential privacy (DP), and zero-knowledge proofs (ZK). The architecture comprises two complementary orderings: Y.I.N. Mazari Ordering (DP→ZK→HE) for secure data submission and Y.A.N.G. Mazari Ordering (VERIFY→DISAGGREGATE→DECRYPT→DENOISE) for secure result retrieval. Through exhaustive permutation analysis, we prove that among all possible orderings for each direction, exactly one ordering in each direction is secure—and these orderings are mathematical duals. The Y.I.N. ordering prevents information leakage during data submission, while the Y.A.N.G. ordering prevents timing side-channel attacks during result retrieval. This paper provides comprehensive coverage of all implementation variations, security proofs, and robustness analysis to establish technical foundations for privacy-preserving computation research.

Mohamad Al-Zawahreh

Current commercial Large Language Model (LLM) architectures enforce a "server-side memory" paradigm, where user cognitive state is stored, managed, and monetized by the provider. This centralization creates two critical vulnerabilities: the economic inefficiency of "token inflation" (re-processing redundant context) and the epistemological risk of "rented cognition" (lack of user sovereignty over identity). This paper proposes a disruptive architectural shift: Remember Me AI, formally defined as the Client-Side Narrative Protocol (CSNP). By integrating Cross-Session Narrative Memory (CSNM) with a novel Semantic Compression Layer and Distributed Local Storage, we demonstrate a mechanism to reduce context token costs by approximately 40x while maintaining longitudinal coherence. We argue that this architecture commoditizes the inference layer, forcing a market transition from "Memory-as-a-Service" to "Compute-as-a-Commodity." This restores epistemological sovereignty to the user and neutralizes the lock-in mechanisms of hyperscale providers. The protocol includes Merkle-CRDT synchronization for multi-device consistency, Zero-Knowledge Safety Proofs for regulatory compliance, and a Polyglot Transpiler to ensure interoperability across proprietary model endpoints.

Steven Antwan

The fast digitalization of contemporary society has changed the data into a valuable resource, and it has been the key to the innovation in the financial sector, healthcare, politics, and industries, and it has also increased risks both in terms of misusing it, stealing it, and using it. Information security through maintaining confidentiality, integrity and availability of information has thus become a pre-requisite to trust in digital infrastructures. The present paper gives a detailed discussion of how cryptography, cybersecurity, and data privacy come into convergence and have a central role to play in protecting the digital ecosystems. Basic cryptographic primitives such as symmetric and asymmetric encryption, hash functions as well as digital signatures are discussed as the foundation of secure communication. With these, more complex privacy-sensitive technology like homomorphic encryption, zero-knowledge proofs, and differential privacy is discussed as technology that could offer the opportunity to perform safe computation and share data without jeopardizing the privacy of individuals.The paper also explores the disruptive potential of quantum computing, specifically how it can render the popular public-key systems insecure by figuring out ways to break them, e.g. the Shor algorithm, and assesses the new paradigm of post-quantum cryptography as a reaction to this existential risk. The examples are discussed within various fields such as secure communication schemes, data-at-rest security, cloud computing, and the Internet of things (IoT), e.g., in which cryptographic efficiency and versatility are most crucial. It is a synthesis of these factors that the paper highlights that cryptography is not only a technical protection but it is a cornerstone enabling resiliency, trust, and privacy-by-design in the digital era. This paper then ends with a discussion on the challenges that still need to be tackled, including scalability, usability and compliance with regulations, and how future research will be needed to define the future of secure and privacy-preserving technologies in the increasingly interconnected world.

Kichang Lee, Sungmin Lee, Jaeho Jin, JeongGil Ko

Modern cloud-based AI training relies on extensive telemetry and logs to ensure accountability. While these audit trails enable retrospective inspection, they struggle to address the inherent non-determinism of deep learning. Stochastic operations, such as dropout, create an ambiguity surface where attackers can mask malicious manipulations as natural random variance, granting them plausible deniability. Consequently, existing logging mechanisms cannot verify whether stochastic values were generated and applied honestly without exposing sensitive training data. To close this integrity gap, we introduce Verifiable Dropout, a privacy-preserving mechanism based on zero-knowledge proofs. We treat stochasticity not as an excuse but as a verifiable claim. Our approach binds dropout masks to a deterministic, cryptographically verifiable seed and proves the correct execution of the dropout operation. This design enables users to audit the integrity of stochastic training steps post-hoc, ensuring that randomness was neither biased nor cherry-picked, while strictly preserving the confidentiality of the model and data.

UmmeAmmara Qureshi, Bhumika Doshi, Aditya More, Kashyap Joshi · 5 authors

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs...

Elif Nur Kucur, Tolga Büyüktanır, Muharrem Ugurelli, Kazım Yıldız

Privacy-preserving machine learning (PPML) constitutes a core element of responsible AI by supporting model training and inference without exposing sensitive information. This survey presents a comprehensive examination of the major cryptographic PPML techniques and introduces a unified taxonomy covering technical models, verification criteria, and evaluation dimensions. The study consolidates findings from both survey and experimental works using structured comparison tables and emphasizes that recent research increasingly adopts hybrid and verifiable PPML designs. In addition, we map PPML applications across domains such as healthcare, finance, Internet of Things (IoT), and edge systems, indicating that cryptographic approaches are progressively transitioning from theoretical constructs to deployable solutions. Finally, the survey outlines emerging trends—including the growth of zero-knowledge proofs (ZKPs)-based verification and domain-specific hybrid architectures—and identifies practical considerations that shape PPML adoption in real systems.

U. A. Vishniakou, YiWei Xia

This article examines the integration of blockchain, distributed file system, and virtualization technologies (including Virtual SAN, VSAN) to improve data storage privacy and efficiency. The limitations of traditional centralized storage models are presented, such as vulnerability to tampering, limited flexibility in access control, difficulty in auditing, and low resource efficiency. A concept for an integrated data storage system based on blockchain smart contracts is proposed. This system incorporates data encryption, implementation of an access control strategy based on blockchain attributes, the use of virtualization optimized through VSAN, distributed key management, and privacy-enhancing technologies (zero-knowledge proof, trusted computing environment). The proposed architecture provides flexible access control, efficient data processing, and a high level of security in a public or private blockchain environment.

Sinka Gao, Guoqiang Li

Abstract In the realm of blockchains, synchronization challenges are two-folded. First, smart contracts from different blockchains cannot communicate with each other, making it hard to establish a trustworthy communication channel to share and maintain a universal state between each other. Second, transactions on different blockchains can hardly be ordered. Hence interference is expected. We need a novel way to handle interference. Traditional solutions involving third parties have safety and liveness issues and thus compromise between safety, permissionless, and liveness. ZK Multi-Blockchain Aggregatoris a multi-blockchain execution layer that leverages the power of zero-knowledge proof to minimize the trust base of multi-blockchain communication, which does not compromise safety, liveness, permissionless, and atomicity. In contrast to traditional blockchain bridges performing transactions on different blockchains separately and using a relay system to enforce the order of transactions and prevent interference, our method uses an entirely new approach, such that for each multi-blockchain transaction, it simulates the multi-blockchain transaction in its aggregator chain. Our aggregator uses zero-knowledge proofs of the simulation to convince involved blockchains to update their local state accordingly. On top of this layer, rich applications over multi-blockchains can run safely and efficiently.

Shinichi Nagata

Decision-OS V5 Revised (SiriusA2) addresses a practical AI safety problem: how human oversight can prevent irreversible decisions from being executed under pressure, confusion, coercion, or panic. It is designed for safety-critical, non-medical decision support settings where a user may be authenticated, yet the execution path may still be unsafe. The framework proposes a human-in-the-loop confirmation layer for irreversible risk. Instead of allowing a valid user action to move directly into execution, SiriusA2 routes protected actions through auditable confirmation states such as Request, Observe, Hold, Approve, Reject, Stop Candidate, Execute, and Revoke. The core mechanism is a trajectory-aware duress_score. This score is not an intent classifier, diagnosis, truthfulness score, or autonomous veto. It is an operational control-routing signal used to detect when a valid execution path deviates from an ordinary decision trajectory and approaches irreversible harm. SiriusA2 preserves human final consent through two-step confirmation, an explicit revoke path, optional family multisig, and a Zero-Knowledge approval layer (310/320) that verifies authorization without exposing personally identifiable information. The duress_score does not replace ZK approval or multisig; it routes actions into the confirmation path, while ZK qualification and multisig provide independent authorization conditions before irreversible execution. The revised manuscript integrates the SiriusA Adoption Gate into the main paper. The gate provides a Hold-first confirmation path for irreversible, externally pressured, unusually urgent, or high-stake actions: Request → Observe/Hold → Approve/Reject → Execute/Revoke. A score-based Stop Candidate does not automatically become Stop or Freeze. Stop or Freeze requires an independent non-score condition, such as verified revoke input, ZK-qualified m-of-k approval, policy-defined guardian confirmation, or an emergency protocol condition. If no such condition is available, SiriusA intentionally prefers continued Hold and evidence preservation over an AI-only execution veto. This release also clarifies cold-start behavior, causal bridge support, baseline maturity, corrected event terms, disclosure boundaries for calibration-sensitive parameters, and non-PII audit requirements. Safety is operationalized through auditable state transitions, non-PII KPIs, five-line gate outputs, evidence packaging (ZIP + SHA256), and explicit prohibitions on automatic transmission, payment, or reporting. A minimal proof-of-concept gate exists as a public runtime artifact, demonstrating PASS / DELAY / BLOCK routing, conservative severity merging, evidence union, pre-execution checking, and auditable JSON outputs. Deployment-level validation, calibration, and domain-specific robustness remain future work. Gateway / series index:https://github.com/shin4141/decision-os-paper Recommended read order:V5 Revised (SiriusA2) → V6 (PIC) → V8 (v2)Optional: V7 (AGI definition) Related repositories:- V5 Policy Pack / specification and adoption materials: https://github.com/shin4141/paper-public- Gate engine / MMAR-L0: https://github.com/shin4141/mmar-l0-core- SiriusA core runtime: https://github.com/shin4141/siriusA-core SSOT:GitHub repository “decision-os-paper”.This PDF corresponds to the revised SiriusA2 release candidate committed to the SSOT repository. Release note:This revised release integrates the trajectory-aware duress_score definition, SiriusA Adoption Gate, ZK qualification layer, family multisig, independent non-score Stop conditions, non-PII audit structure, V4-compatible escalation interface, and proof-of-concept gate positioning into the main paper. Transparency / Author’s Note:https://github.com/shin4141/decision-os-paper/blob/main/AUTHORS_NOTE.md