Shan Chang, Khuram Shahzad, Ali Nawaz Khan, Faisal Shahzad · 5 authors
Purpose This study aims to investigate the factors that drive and enhance sustainable supply chain management (SSCM) practices and performance, leveraging smart-contract-enabled green blockchain technology (BT). Specifically, it builds a research framework based on the dynamic capability view (DCV) theory to explore the interrelationships among green knowledge acquisition (GKA), generative AI (GAI), green BT, green organizational memory, and organizational environmental and operational performance. Design/methodology/approach A quantitative research approach was adopted. Cross-sectional survey data were collected via an online survey, yielding 318 valid responses from supply chain professionals in China. The hypothesized relationships within the proposed framework, grounded in DCV theory, were tested using Structural Equation Modeling via AMOS 24 and SPSS 23. Confirmatory factor analysis was conducted to validate the measurement model, and moderation effects were examined using mean-centered interaction terms within the SEM framework. Findings The analysis confirms the proposed framework’s validity and reveals several key findings: GKA positively impacts green BT. GAI significantly moderates the effect of GKA on green BT, strengthening the transformation of green knowledge into smart-contract-enabled blockchain capability. Green BT positively influences both environmental performance and operational performance. Green organizational memory acts as a crucial moderator, strengthening the positive relationships between green BT and both environmental and operational performance, amplifying the performance effects of green BT on both environmental and operational outcomes. Environmental performance ultimately contributes positively to operational performance. Practical implications Organizations in the supply chain sector can leverage these insights by prioritizing and maximizing GKA to effectively embed sustainability. Managers should promote the effective use of GAI in their processes, as it enhances the conversion of knowledge into tangible green BT implementations. Furthermore, maintaining a robust green organizational memory is recommended to maximize the performance returns from investments in green BT innovations. Originality/value This paper offers significant theoretical novelty by integrating GAI and green organizational memory as key dynamic capabilities within the DCV framework to examine green BT adoption in SSCM, a perspective previously underexplored. It is among the first to empirically confirm the moderating roles of both GAI and green organizational memory in the context of sustainable technology implementation, providing a unique model for achieving superior environmental and operational outcomes.
Anirban Dalui, Ritik Kumar Patra, Jatin Tiwary, Jeeva S
The voluntary carbon market is vital for getting private money to projects – worldwide – which cut emissions or take carbon from the air. However, current markets suffer from fragmentation, lack of transparency, manual verification, and risks of double-counting or fraudulent credits. These issues severely erode buyer trust and market efficiency. This study shows a blockchain system built to deal with these faults: verified carbon credits are made into ERC-20 tokens on the Ethereum blockchain. The system lets people trade without needing to trust each other, using Balancer Automated Market Maker – AMM – pools that have DAI stablecoins with them; and it ensures credits are removed from the market for good with a ‘buy-and-burn’ method, ending with ERC-721 Non-Fungible Tokens – NFTs – being given out as proof the emissions were offset. By including data linked to Inter-Planetary File System (IPFS), and signs from cryptographically checked people who confirmed things, the system stops double-counting, makes it easier to find where credits came from, and helps the market to trade more easily. Using what we’ve learned from carbon platforms which have used tokens, from 2020 to 2025, we use a fixed-effects Difference-in-Differences – DiD – test to find what effect the system has on how well prices work and how many trades there are. What we found is that using blockchain makes prices more steady when there’s a reasonable amount of trading, but doesn’t change trading volume much in the short run. These results show how blockchain can make carbon markets stronger, lower costs of trading, and help the Sustainable Development Goals – SDGs. What this study gives is useful to people who make policy, control things, and invest, who want to make digital carbon systems that can last, work well and grow.
Dr. Tejaswini S, Bhashitha Jain P L, Neil Chris Dsouza, Vibha Bhat
I. INTRODUCTION Cryptocurrency has rapidly transformed from a niche technological curiosity to a mainstream financial asset with millions of participants across the globe, and India is no exception to this trend. By 2022, India was estimated to have one of the largest populations of cryptocurrency holders in the world, with figures ranging from 10 to 20 crore users according to various industry reports. This explosive growth brought an urgent question to the forefront of Indian tax policy: how should gains and income arising from such digital assets be taxed, and how should compliance be ensured in a space that has historically operated in a regulatory grey zone? The term cryptocurrency, or more broadly Virtual Digital Asset (VDA), refers to any information, code, number, or token - other than Indian currency or foreign currency — generated through cryptographic means, providing a digital representation of value exchanged with or without consideration. Bitcoin, Ethereum, and a wide spectrum of altcoins and non-fungible tokens (NFTs) fall under this definition as formally adopted in India's Finance Act 2022. The Finance Act 2022 was a watershed moment in India's regulatory journey with digital assets. It introduced Section 115BBH into the Income Tax Act, 1961, which imposed a flat 30% tax on income arising from the transfer of VDAs, irrespective of the holding period or the taxpayer's income slab. Simultaneously, Section 194S mandated Tax Deducted at Source (TDS) at 1% on payments related to VDA transfers, placing the obligation on crypto exchanges and certain buyers to withhold tax at the point of transaction. These twin provisions created a definitive, if controversial, legal framework for cryptocurrency taxation in India.
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.
The Legendre Pseudorandom Function (PRF) is a highly efficient cryptographic primitive built upon the Legendre symbol, valued for its low multiplicative complexity in Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) protocols. While its security over prime fields $\mathbb{F}_p$ is well-documented, recent interest has shifted toward instantiations over extension fields $\mathbb{F}_{p^r}$. This paper presents the first comprehensive cryptanalysis of the single-degree Legendre PRF operating over $\mathbb{F}_{p^r}$. First, we analyze polynomial input encoding under a standard passive threat model (sequential additive counter queries). We demonstrate that while the absence of polynomial carry-overs causes an asynchronous "no-carry fracture" that neutralizes classical sliding-window collision attacks, the fracture itself is deterministically periodic. By introducing a novel "Differential Signature" bucketing technique, we prove that an adversary can systematically group fractured sequences by their structural shapes to bypass this defense, recovering the secret key in $\mathcal{O}(U \cdot p^r/M)$ operations, where $U$ is the unicity distance. Second, we evaluate the PRF under an active Chosen-Query threat model. We demonstrate that an adversary can circumvent the additive fracture by evaluating the PRF along a geometric sequence generated by a primitive polynomial. This structure invokes strict multiplicative homomorphism over $\mathbb{F}^*_{p^r}$, permitting a direct generalization of state-of-the-art table collision attacks to extract the key in $\mathcal{O}(p^r/M)$ operations. Finally, we establish the cryptographic boundaries of these attacks, formally proving the necessity of higher-degree key variants ($d \ge 2$) to achieve exponential security against structural reduction in extension fields.
Understanding how animals produce a versatile locomotor repertoire requires unraveling the interplay between higher centers, decentralized locomotor circuits, and sensory feedback. However, the principles governing their integration remain elusive. We investigated amphibious centipedes through stepwise neural lesions and neuromechanical modeling. Behavioral experiments revealed that while decentralized circuits autonomously generate coordination, the brain and subesophageal ganglion provide situational flexibility, such as modulating trunk undulation and initiating leg folding. Integrating these findings, our model demonstrated how higher centers selectively inhibit or release lower circuit dynamics. Simulations verified that varying only a few descending control parameters reproduces transitions between slow walking, fast walking, and swimming. This work may capture the essence of the locomotor circuitry that harnesses decentralized self-organization to coordinate the body’s large degrees of freedom.
Marc Dordal Carreras, Gloria Christina Heesen, Kohei Kawaguchi
Proof-of-stake deters attacks by keeping validator stake exposed to slashing and depreciation losses. Liquid staking lets operators obtain voting power using pooled stake while reducing their own exposure by selling liquid staking tokens (LSTs) and shifting uncovered slashing losses onto token holders. We study the security implications of this control-exposure wedge and the protocol design problem it creates. Competitive LST pricing can partly deter attack by lowering the resale value of claims when risk rises, but it cannot fully restore deterrence because liquid staking participants do not internalize ETH-wide depreciation losses. A fee-charging protocol prefers the no-attack regime because it maximizes total stake, yet collateral requirements alone do not generally make that outcome unique. Robust security may therefore require additional tools, including permissioned participation, screening or reserve capacity.
This white paper presents Execution Governance 2.1 (EG 2.1) as a doctrinal extension to Execution Governance 2.0 (EG 2.0). It addresses a structural governance gap in autonomous and execution-bound systems: action candidates may remain locally valid at execution time while gradually drifting away from the authority-anchored admissible structure from which their execution legitimacy originated. The paper defines this failure mode as continuity drift and argues that governance must not only preserve continuity, but also actively verify it over time. EG 2.1 introduces continuity verification as a distinct governance function, formalizes valid-looking drift as a governance failure class, and positions the execution boundary as a continuity-verifying pre-commit function that must verify structural consistency in addition to local execution-time validity. The paper explains how drift can re-enter governance under apparent validity, presents updated boundary logic and formal notation, and examines implications across AI systems, financial systems, physical AI and robotics, smart infrastructure, Web3/DAOs, and elderly care technology. It is presented as a conceptual governance architecture intended to support scholarly discussion, comparative analysis, and future research on accountable autonomy.
Web3 technologies, representing the next generation of a decentralised and user-centric Internet, offer innovative solutions to enhance adaptability, sustainability, and resilience in logistics systems aligned with the principles of Logistics 5.0. This study conducts a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, analysing peer-reviewed journal articles published between 2018 and 2024 and retrieved from Scopus, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The review specifically focuses on trucking logistics, a sector characterised by high fossil-fuel dependency, operational fragmentation, and significant environmental impact. The findings reveal that Artificial Intelligence and Internet of Things technologies dominate current implementations, mainly supporting fleet management, route optimisation, accident prevention, and risk assessment. In contrast, blockchain applications remain limited, and metaverse-based solutions are largely exploratory and confined to training scenarios. Key research gaps include the scarcity of integrated Web3 solutions, the limited consideration of human-centric Logistics 5.0 dimensions, and the lack of large-scale empirical validation in real-world trucking operations. Based on the analysis, this paper proposes a conceptual framework that maps Web3 technologies to trucking logistics areas, investment priorities, and Logistics 5.0 objectives, offering actionable guidance for Logistics Service Providers transitioning from Logistics 4.0 to Logistics 5.0.
Sreekanth Vp, Ronny Sebastian Roy, Surya Gopalakrishnan, Thanmaya S Krishna
The proliferation of cyber threats and the increasing digital vulnerability of students necessitate innovative educational approaches to cybersecurity awareness. Traditional methods of teaching cybersecurity often fail to engage learners effectively, resulting in poor knowledge retention and limited practical skills. This paper introduces Zyberix, an interactive educational game that leverages Web3 technologies to teach cybersecurity concepts through immersive gameplay and blockchain-based incentives. By integrating Unity game engine with Ethereum smart contracts, IPFS decentralized storage, and NFT rewards, Zyberix creates an engaging learning environment that simulates realistic cyber threats including phishing, malware, and social engineering attacks. The system rewards successful challenge completion with verifiable Non-Fungible Tokens (NFTs), providing both motivation and an introduction to blockchain technology. This research demonstrates how gamified experiential learning combined with Web3 technologies can significantly enhance cybersecurity education, addressing the critical gap between theoretical knowledge and practical application while fostering digital literacy among students.
Ali Akarma, Toqeer Ali Syed, Salman Jan, Hammad Muneer · 5 authors
The AI-based sensing and autonomous monitoring have become the main components of wildfire early detection, but current systems do not provide adaptive inter-agent coordination, structurally defined human control, and cryptographically verifiable responsibility. Purely autonomous alert dissemination in the context of safety critical disasters poses threats of false alarming, governance failure and lack of trust in the system. This paper provides a blockchain-based governance-conscious agentic AI architecture of trusted wildfire early warning. The monitoring of wildfires is modeled as a constrained partially observable Markov decision process (POMDP) that accounts for the detection latency, false alarms reduction and resource consumption with clear governance constraints. Hierarchical multi-agent coordination means dynamic risk-adaptive reallocation of unmanned aerial vehicles (UAVs). With risk-adaptive policies, a permissioned blockchain layer sets mandatory human-authorization as a state-transition invariant as a smart contract. We build formal assurances such as integrity of alerts, human control, non-repudiation and limited detection latency assumptions of Byzantine fault. Security analysis shows that it is resistant to alert injections, replays, and tampering attacks. High-fidelity simulation environment experimental evaluation of governance enforcement demonstrates that it presents limited operational overhead and decreases false public alerts and maintains adaptive detection performance. This work is a step towards a principled design paradigm of reliable AI systems by incorporating accountability into the agentic control loop of disaster intelligence systems that demand safety in their application.
Ali Akarma, Toqeer Ali Syed, Abdul Khadar Jilani, Salman Jan · 7 authors
Autonomous underwater vehicles (AUVs) and sensor nodes increasingly support decentralized sensing and coordination in the Internet of Underwater Things (IoUT), yet most deployments rely on static trust once authentication is established, leaving long-duration missions vulnerable to compromised or behaviorally deviating agents. In this paper, an interrogator based structure is presented that incorporates the idea of behavioral trust monitoring into underwater multi-agent operation without interfering with autonomy. Privileged interrogator module is a passive communication metadata analyzer that uses a lightweight transformer model to calculate dynamic trust scores, which are used to authorize the forwarding of mission critical data. Suspicious agents cause proportional monitoring and conditional restrictions, which allow fast containment and maintain network continuity. The evidence of trust is stored in a permissioned blockchain consortium which offers identity management which is not tampered and is decentralized without causing the overhead of public consensus mechanisms. Simulation based analysis shows that the evaluation of the result compares to a relative improvement of 21.7% in the detection accuracy compared to the static trust baselines with limited energy overhead. These findings suggest that behavior driven validation has the capability of reinforcing underwater coordination without compromising scalability and deployment.
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process. To instantiate this paradigm, we introduce LOCARD, the first agentic framework for blockchain forensics. LOCARD operationalizes this perspective through a Tri-Core Cognitive Architecture that decouples strategic planning, operational execution, and evaluative validation. Unlike generic LLM-based agents, it incorporates a Structured Belief State mechanism to enforce forensic rigor and guide exploration under explicit state constraints. To demonstrate the efficacy of the ABF paradigm, we apply LOCARD to the inherently complex domain of cross-chain transaction tracing. We introduce Thor25, a benchmark dataset comprising over 151k real-world cross-chain forensic records, and evaluate LOCARD on the Group-Transfer Tracing task for dismantling Sybil clusters. Validated against representative laundering sub-flows from the Bybit hack, LOCARD achieves high-fidelity tracing results, providing empirical evidence that modeling blockchain forensics as an autonomous agentic task is both viable and effective. These results establish a concrete foundation for future agentic approaches to large-scale blockchain forensic analysis. Code and dataset are publicly available at https://github.com/xhyumiracle/locard and https://github.com/xhyumiracle/thorchain-crosschain-data.
Blockchain and blockchain-inspired decentralized applications are on the rise thanks to their unique characteristics such as their decentralized nature, anonymity, and tamper-proof nature; however, blockchain transactions tend to experience long end-to-end latency, with a major contributor being the block creation step, which might block transaction processing. There are two approaches to ameliorate this overhead: speeding up the block creation process, or processing transactions before block creation finishes. In this project, we work towards designing a self-adaptive block creation process that automatically selects optimal configurations based on workload and hardware resources by defining mathematical models to predict transaction latency based on design and environmental parameters, developing measurement techniques to collect performance-related metrics in docker-hosted blockchain systems and observing trends to build intuition, and defining a mathematical model to predict transaction success rate under various key accessing patterns and block size configurations, validating it with simulation-based measurements.
Sarisht Wadhwa, Aviv Yaish, Fan Zhang, Kartik Nayak
Modern blockchains increasingly rely on parallel execution to improve throughput. We show several industry and academic transaction fee mechanisms (TFMs) struggle to simultaneously account for execution parallelism while remaining performant and fair. First, if parallelism affects fees, adversarial protocol manipulations that offset possible benefits to throughput by introducing fake transactions become rational: users can insert functionally useless parallel transactions solely to reduce fees, and schedulers can create useless sequential transactions to increase revenue. Execution contingency, a core feature of expressive programming languages, both exacerbates the aforementioned threats and introduces new ones: (1) users may overpay for unused resources, and (2) scheduler revenue is harmed when reserved scheduling slots go unused due to contingency. We introduce a framework for this challenging setting, and prove an impossibility, highlighting an inherent tension: both parallelism and contingency involve a trade-off between minimizing risks for users and schedulers, as favoring one comes at the expense of the other. To complete the picture, we introduce a fee mechanisms and prove that they achieve the boundaries of this trade-off. Our results provide rigorous foundations for evaluating designs advanced by notable blockchains, such as Sui and Monad.
This paper challenges the prevailing paradigm of large language models as the cognitive foundation of autonomous AI systems. Through empirical adversarial testing of Qwen3.6-Plus (April 2026) — the most capable publicly available agentic model at time of writing — we identify and document a fundamental epistemological limit in LLM-based autonomous governance: the observer-embeddedness ceiling. Every failure in the structured test suite shares the same root cause: the model cannot reason about the validity of its own observations when the observer is embedded in the system being observed. This ceiling is structural, consistent, and not addressable by increasing model scale. As an alternative, the paper presents a complete six-layer daemon architecture positioning deterministic process management as the foundation of autonomous intelligence, with LLMs relegated to boundary translation only. The architecture runs on commodity hardware (demonstrated on Apple M1 Max 64GB), operates at near-zero marginal cost per decision cycle (48× cost advantage over LLM-agent frameworks), and produces machine-native structured knowledge that accumulates permanently rather than being re-approximated each session. Key contributions:1. Empirical proof of the observer-embeddedness ceiling through 10 structured adversarial tests with full grading documentation2. Complete six-layer daemon architecture specification with reference implementation (Layers 1–6: process model, state persistence, decision functions, inter-daemon communication, governance, LLM integration)3. Machine-native knowledge architecture with six typed subsystems (State Store, Decision Store, Causal Graph, Contradiction Store, Pattern Store, Verified Truth Store)4. Progressive deployment model scaling from 500GB through 2TB, 10TB, and 96TB storage tiers, each enabling qualitatively distinct system capabilities5. Economic analysis demonstrating 48× cost reduction versus LLM-agent frameworks at operational maturity The theoretical foundation connects to the I=E×O framework and the Civilizational Library of Events (CLoE) concept developed in prior RRC-AI work. The paper argues that genuine machine intelligence emerges not from larger language models but from layered deterministic systems with precise state management, verified knowledge accumulation, and LLM involvement only at the human-language boundary. This work extends: Chalupka, R. (2025). The Theory of Integrated Intelligence. Zenodo. https://doi.org/10.5281/zenodo.17541664 Part of the RRC-AI Research Initiative. License: CC BY-NC-SA 4.0 International.
Local government forms the cornerstone of democratic decentralization and grassroots governance across political systems. This study presents a comparative functional analysis of local government models—namely the Anglo-Saxon, Continental (French), and Indian systems—focusing on key areas such as policing, education, finance, and governance structures. By examining variations in autonomy, administrative control, and fiscal capacity, the paper highlights how historical, constitutional, and political contexts shape the functioning of local institutions. The Anglo-Saxon model emphasizes decentralization and flexibility, the Continental model reflects centralized supervision and uniformity, while the Indian model represents a hybrid framework balancing constitutional recognition with fiscal dependence. The analysis underscores that while local governments are universally recognized as vital instruments of democratic governance, their effectiveness is contingent upon adequate financial empowerment, functional clarity, and institutional capacity. The study contributes to the broader discourse on decentralization, governance efficiency, and democratic deepening in the 21st century. Keywords Local Government; Comparative Politics; Decentralization; Anglo-Saxon Model; Continental Model; Indian Model; Fiscal Autonomy; Governance; Public Administration; Democratic Decentralization
A zero-knowledge proximity proof certifies geometric nearness but carries no commitment to an application context. In stateful geo-content systems, where drops can share coordinates, policies evolve, and content has persistent identity, this gap can permit proof transfer between application objects unless extra operational invariants are maintained. We present a systems-security analysis of this deployment problem: a taxonomy of context-binding vulnerabilities, a formal off-circuit verification model for a transcript-adversary that holds a recorded proof but cannot obtain fresh coordinates, an assumption comparison across five binding strategy classes, and a concrete instantiation, Zairn-ZKP, that embeds drop identity, policy version, and session context as public circuit inputs. Compared with a strong off-circuit alternative based on stored-digest server checking, in-proof binding reduces operational invariants from four to two and adds no measurable proving cost relative to the sound geo-only baseline (-0.12 ms median in our setup). It also removes a correctness pitfall we identify empirically: a plausible off-circuit implementation that omits one server-side check remains vulnerable to cross-drop transfer. Measurements across six network conditions, seven venues in four countries, and an epoch-window simulation indicate that same-epoch transfer is realistic in dense urban deployments unless per-request nonces are maintained. Across five platforms and seven binding strategies, the results support a deployable methodology for reducing assumption surfaces in stateful ZK-backed verification workflows.
Location-based systems that combine encrypted geographic search with zero-knowledge proximity proofs typically treat the two phases as independent. Under an honest-but-curious server, this leaves an authorization provenance gap: once session state is purged, no forensic procedure can attribute a proof to its originating search session, because the proof's public inputs encode no session-identifying information. We formalize this gap as the search-authorized proof (SAP) security notion and show via a concrete audit re-association attack that proof-external mechanisms, where authorization evidence remains outside the proof, cannot prevent forensic misattribution when the same drop parameters recur across sessions. Search-Bound Proximity Proofs (SBPP) realize the SAP requirements without modifying the ZKP circuit: session nonce, Merkle-root result-set commitment, and signed receipt are decomposed into independently auditable components, enabling property-level fault isolation in offline audit. Experiments on synthetic and real-world data (110,776 OpenStreetMap POIs) show sub-millisecond absolute overhead on a 125 ms Groth16 baseline.
Maritime shipping carries over 80% of global trade, yet cold-chain compliance verification forces a choice between disclosing sensitive telemetry and issuing unverifiable declarations. The EU's Digital Product Passport mandate requires verifiable provenance, but maritime IT systems lack a harmonized event model for interoperability. This thesis presents Ocean DPP, integrating EPCIS 2.0, oneM2M, IOTA anchoring, and Groth16 zero-knowledge proofs to verify compliance without revealing sensor data. Merkle-tree batching amortises on-chain cost, and sixteen experiments over 10,000+ events confirm 48 ms baseline latency, sub-10 ms proof verification, 37% scaling improvement, and zero message loss. The results demonstrate that privacy-preserving, standards-compliant DPPs are viable for maritime supply chains.
This Handbook presents Execution Governance as a unified cross-domain discipline for autonomous and execution-bound systems. It is not a collection of previously published white papers, but a foundational corpus that reorganizes and systematizes the author’s Execution Governance research into a single canonical reference framework. The Handbook is aligned with EG 2.0 v1.4 and treats structural admissibility, continuity preservation, commit validation, and constraint invariance during candidate formation as core governance requirements. It distinguishes structural continuity from present legitimacy and extends governance analysis from the execution boundary to the full formation-to-commit path. Organized into Foundations, Structural Architecture, Cross-Domain Applications, and Toward Standardization, the Handbook covers governance semantics, responsibility models, verifiable execution, maturity models, policy language primitives, and domain profiles across autonomous systems, Agentic AI, Physical AI, Smart City, Digital Nation, NetZero & ESG, Elderly Care Technology, Genuine News, Industry 6.0, and Web3. The application chapters are presented as domain profiles of a single governance doctrine rather than separate theories. This work is intended as a scholarly and professional reference for structural governance, pre-standardization discussion, academic curricula, industry adoption, regulatory alignment, and cross-domain execution-bound system design.
Tokenized assets increasingly operate across heterogeneous blockchain networks and off-chain ledgers, where a regulatory action (a freeze, a seizure, a confiscation) must take effect atomically and consistently across every domain holding the asset. We mechanize, in Isabelle/HOL, cross-domain state preservation as a functor: state machines are objects, structure-preserving synchronization maps are morphisms, and the category laws (identity, composition, associativity) hold as theorems. On this base we establish four results. Safety: a regulatory transition on one domain is faithfully reflected across all connected domains, with bidirectional roundtrip preservation, N-domain consistency, per-asset isolation, and terminal states preserved. Liveness: under f = 3f+1 shown to make that assumption inhabitable rather than vacuous. Convergence: from an arbitrary unlocked configuration, with no initial cross-chain consistency assumed, synchronization reaches a valid state in a bounded number of steps along a terminal-faithful recovery path. Hierarchy: a tower of synchronization-degree functors connected by natural transformations closed under composition, with a genuinely one-directional degree monotonicity. We couple the functor to Lochbihler and Maric's authenticated data structure at the global-state level, instantiated on a recursive model of the Canton transaction tree with a declared consensus-scope limit. The synchronization model is atomic; its lift to a partially synchronous network is future work. The application is a regulatory state transition model distilled from the RCP framework (arXiv:2603.29278). All ten Isabelle/HOL theory files build without sorry or oops and are submitted to the Archive of Formal Proofs.
Agentic AI rivals human capabilities across a wide range of domains. Looking ahead, it is foreseeable that AI agents will autonomously handle complex workflows and interactions. Early prototypes of this paradigm are emerging, e.g., OpenClaw and Moltbook, signaling a shift toward Agent-to-Agent (A2A) ecosystems. However, despite these promising blueprints, critical trust and security challenges remain, particularly in scenarios involving financial transactions. Ensuring secure and reliable payment mechanisms between unknown and untrusted agents is crucial to complete a fully functional and trustworthy A2A ecosystem. Although blockchain-based infrastructures provide a natural foundation for this setting, via programmable settlement, transparent accounting, and open interoperability, trust and security challenges have not yet been fully addressed. Hence, for the first time, we systematize blockchain-based A2A payments, e.g., X402, with a four-stage lifecycle: discovery, authorization, execution, and accounting. We categorize representative designs at each stage and identify key challenges, including weak intent binding, misuse under valid authorization, payment-service decoupling, and limited accountability. We highlight future directions for strengthening cross-stage consistency, enabling behavior-aware control, and supporting compositional payment workflows across agents and systems.