Jovay: Trustless Scalability for Mass Adoption

Abstract

Web3 is transitioning from a decentralized value transfer network into a foundational component of global financial infrastructure. This transformation is being accelerated by three key drivers: the tokenization of Real-World Assets (RWA), advances in blockchain scalability solutions, and the convergence of AI technologies. In response to this paradigm shift, Jovay is introduced as a high-performance, secure Ethereum Layer 2, engineered to serve as the technical backbone for next-generation digital finance and RWA integration.

To overcome the inherent scalability constraints of existing blockchain architectures, Jovay employs a fully pipelined, parallel execution engine. This engine breaks down transaction workflows into discrete execution units, enabling concurrent processing across transaction, block, and batch levels. As a result, the system achieves clusterscale throughput with reduced end-to-end latency—targeting performance levels suitable for large-scale financial applications. Jovay’s security model is based on a phased and heterogeneous validity proof mechanism. Initially, it leverages Trusted Execution Environments (TEE) to enable fast finality and scalable proving execution. Over time, the system will transition toward Zero-Knowledge Proofs (ZKP), which provide stronger cryptographic guarantees and reduced trust assumptions. These mechanisms are supported by robust on-chain attestation protocols and comprehensive third-party auditing. Built with modularity at its architectural core, Jovay decouples key components including execution, proof service, data availability, and settlement. This modular approach enables each component to scale independently, optimize resource allocation, and evolve in response to changing infrastructure requirements. Together, these innovations position Jovay as a foundational infrastructure layer for the secure, scalable, and smart tokenization of real-world assets, supporting the development of next-generation Web3 financial ecosystems.

1. Vision

Web3 is experiencing an evolutionary shift—from the Internet of Value toward a decentralized foundation for global financial infrastructure. Built upon cryptoeconomic primitives [1] and AI-driven economic operating systems, blockchain technology is enabling Web3 to emerge as a foundational layer for global finance. This transformation is driven by the deep integration of Real-World Assets (RWA) and on-chain intelligence, which together are redefining the architecture of the digital economy. Public blockchains such as Ethereum are evolving from value settlement layers into critical components of the global digital economy’s foundational infrastructure. This transformation is being driven by three core technological and economic pillars:

• Crypto-economic Primitives Revolution: The market capitalization of stablecoins has reached more than 200 billion [7], establishing its position as the economic anchor on blockchain. As highlighted in Keyrock’s report, tokenization has the potential to fundamentally reengineered the financial system [2]. According to World Economic Forum estimates, tokenization may account for 10% of global GDP by 2027 [3], with trillions of dollars in assets expected to transition onto blockchain networks during this period. Boston Consulting Group's forecast further indicates that securitized assets could reach $16 trillion by 2030 [4].
• Architectural Evolution: Blockchain networks continue to grapple with the scalability-decentralization-security trilemma. To overcome these constraints, the industry has explored various architectural decoupling strategies. Layer 1 scaling solutions—such as sharding [10]—seek to enhance throughput via protocol-level upgrades. For instance, sharding partitions the network into 64 parallel sub-chains, targeting a throughput exceeding 100K TPS. However, it faces challenges like cross-shard communication latency and high migration costs. In contrast, Layer 2 introduce an off-chain execution model with onchain data availability and finality, inheriting Ethereum’s security while significantly improving performance [5,6]. Notable approaches include Plasma, state channels, sidechains, and Rollups [11]. Among them, Rollups have emerged as the dominant scaling paradigm due to their balance between efficiency and security. They are primarily categorized into two types: Optimistic Rollups [12], which rely on fraud proofs, and Zero-Knowledge (ZK) Rollups [13], which use cryptographic validity proofs.
• Synergistic Integration Acceleration: The rapid advancement of artificial intelligence is catalyzing new synergies with Web3 technologies across multiple layers. At the infrastructure level, NEAR has proposed a privacypreserving, verifiable decentralized AI computing framework based on distributed AI networks [8]. At the protocol level, agent-based economic frameworks are forming intelligent transaction ecosystems [22], with emerging models built on Agent-to-Agent (A2A) protocols [23]. At the application level, RWA-integrated financial models enhanced by AI-driven risk control and pricing mechanisms are elevating the sophistication of digital financial systems. Meanwhile, AI-powered development tools and smart tokenization management products highlight the transformative potential of AI and Web3 convergence.

In response to this paradigm shift, Jovay has been designed as an Ethereum Layer 2. It leverages a parallel execution pipeline that decomposes transaction workflows into independent units for concurrent processing, supported by dynamic load balancing. This architecture enables cluster-scale performance while significantly reducing latency. Security is reinforced through a heterogeneous proof system combining Trusted Execution Environment (TEE) and Zero-Knowledge (ZK) proofs. An AIenhanced toolchain further supports end-to-end capabilities in smart contract development, debugging, performance tuning, and security auditing.

Jovay aims to build a high-performance, high-security Layer 2 network serving as the technological backbone of next-generation digital finance. From inception, performance and security have been prioritized. The project adopts a “pragmatismfirst” approach, aligning technical roadmaps with the scale and complexity of live on-chain assets.

Performance-wise, when evaluating Layer 2 capabilities, two key factors come into play: one is the intrinsic processing speed of the Layer 2 solution itself, and the other is the throughput bottleneck imposed by the underlying Layer 1 (Ethereum) for rollup data. In this context, we focus on the former — the performance of Layer 2 execution.

Currently, excluding potential limitations caused by Ethereum testnet rollup throughput, our single-node Layer 2 testnet achieves a throughput of 6,000–7,000 transactions per second (TPS). With multi-process concurrency during the testnet phase, this capacity is expected to scale to 20,000–30,000 TPS. Looking ahead, through clustered node expansion, Jovay aims to reach a total network capacity of 100,000 TPS within the next year.

We are also confident that ongoing upgrades to Ethereum will further improve Layer 1 throughput, making the ecosystem increasingly friendly to high-performance Layer 2 solutions.

In addition to overall system architecture, the performance of the smart contract execution engine remains a critical factor in achieving scalable and efficient blockchain operations. To address the requirements of deterministic execution in smart contract environments, we have designed and implemented a tiered lazy Just- In-Time (JIT) compilation engine, DeTerministic Virtual Machine (DTVM) , which has now been open-sourced1. The core JIT engine of DTVM is decoupled from the specific virtual machine implementation, enabling modular upgrades and cross-VM compatibility. Currently, we are adapting and optimizing this engine for Ethereum Virtual Machine (EVM) bytecode execution. Upon completion, this enhancement will serve as the next-generation execution engine for Jovay, significantly improving smart contract processing performance and throughput. We warmly welcome contributions and feedback from the open-source community to further refine and evolve this infrastructure.

Security-wise, beyond the architectural design of the TEE-ZK hybrid proof system, comprehensive code audits have already been completed. Critical system contracts and RPC interfaces have undergone auditing (conducted by Antgroup Skyward Lab [21]). As the testnet progresses, key subsystems—including sequencer, prover, and relayer modules—will undergo continuous audit cycles. A full-chain security evaluation is scheduled before mainnet launch. In the domain of zero-knowledge (ZK) proof systems, the ETHproof [23] competition hosted by the Ethereum Foundation has highlighted the growing adoption of RISC-V-based ZK virtual machines (ZKVMs) [24,25], largely due to their modular architecture, efficiency, and simplicity. From a cryptographic system design perspective, the community has made comprehensive improvements across multiple dimensions — from SNARKs [28] to STARKs [29], from Golidlock [26] to BabyBear [27], and from Plonky2 [26] to Plonky3 [27]. These efforts focus on enhancing both performance and security of underlying algorithms. Notably, proof protocols following the Sumcheck paradigm, when combined with advanced polynomial commitment schemes such as Basefold or WHIR [30], offer promising potential. They maintain the linear proof time complexity of Sumcheck while reducing computational overhead and improving proving efficiency. However, several key technical challenges remain:

• Trace Generation: Efficiently generating execution traces required by zkVMs during block processing remains computationally intensive and in need of optimization.
• Proof Composition: Developing efficient methods to integrate Sumcheck protocols with Polynomial Commitment Schemes (PCS), enabling multi-circuit aggregation and GPU-friendly acceleration, is still an active research direction.
• Security Assurance: As new algorithms rapidly emerge, the foundational security properties and implementation soundness of these constructions require rigorous formal analysis and validation — a task that demands collaborative effort from academia and industry alike.

To address these challenges, we are actively developing our own ZK proof system, which is expected to be publicly released by the end of this year. Worth highlighting, we have collaborated with academic researchers to conduct an in-depth study on the security of zk-SNARKs under the Generic Group Model (GGM). Our work reveals how group encoding length impacts protocol security, and introduces the first formalized framework for quantifying the relationship between encoding parameters and security guarantees. This result provides a theoretical foundation for standardizing GGM-based security arguments in zero-knowledge proofs. The related paper has been accepted at Asiacrypt 2024, a top-tier conference in cryptography [9].

2. Design Principles and Objectives

To address the core challenges of evolving next-generation financial infrastructure, modern Layer 2 architectures are increasingly embracing modularization as a foundational design principle. This approach enables flexible adaptation across performance, cost, and security dimensions, facilitating scalable and sustainable technological development. Jovay’s modular architecture is structured around five interdependent yet independently upgradable layers:

• Execution Layer Specialization: Efficient transaction processing hinges on execution engine optimization and robust parallel execution capabilities—key performance factors extensively explored in both Layer 1 and Layer 2 [15–18]. In Layer 2 environments with relatively centralized transaction coordination, effective strategies for transaction parallelism and scalable execution architectures are especially critical. Jovay implements an independent, modular execution layer to maximize throughput scalability—a design choice central to its architecture.

  Furthermore, ecosystem compatibility remains a top priority. Given the dominance of the Ethereum Virtual Machine (EVM), full EVM compatibility ensures seamless developer adoption by supporting both Solidity-based smart contract languages and standard transaction interfaces.

• Arbitration Layer Diversification: The arbitration layer ensures state correctness and dispute resolution in Layer 2 systems. Two dominant mechanisms exist: a) Fraud Proofs, which rely on interactive verification during a defined challenge period; b) Validity Proofs, which use verifiable computation to guarantee immediate correctness.

  Jovay selects validity proofs as its core arbitration mechanism due to their deterministic guarantees and fast finality—critical attributes for high-stakes financial transactions. Within this framework, verifiable computation can be implemented via either Zero-Knowledge Proofs (ZKPs) or Trusted Execution Environments (TEEs).

  TEEs offer low-latency execution and faster confirmation cycles through secure hardware isolation, but depend on trusted hardware providers. In contrast, ZKPs provide mathematically sound security with minimal trust assumptions, and recent advances in proof acceleration are rapidly improving their efficiency. Buterin introduced the hybrid verification model [20], which integrates zeroknowledge (ZK) proofs, Trusted Execution Environment (TEE), and Optimistic Rollup (OP) techniques. The use of multi-prover verification is expected to receive greater consideration in future developments.

• Data Availability Stratification: Traditional rollup solutions store transaction data directly on the Ethereum mainnet to ensure availability and security, but this practice incurs significant storage costs—particularly burdensome for highfrequency financial transactions. With the introduction of Proto-Danksharding (EIP-4844) [14], Ethereum now supports more cost-efficient data posting through blob transactions, substantially lowering Layer 2 operational overhead.

  Nevertheless, mission-critical transaction data still warrants on-chain preservation to maintain cryptographic guarantees. For transient data such as intermediate settlement states or short-term clearing records, Jovay will explore layered availability models that combine on-chain anchoring with off-chain distributed storage solutions in future. This stratified approach balances security and economic efficiency, enabling scalable growth without compromising integrity.

• Settlement Layer Independence: The settlement layer governs asset transfers and state finality between Layer 1 and Layer 2, forming the cornerstone of crosschain interoperability. Through modularization, it can be deployed independently while preserving cryptographic security and enabling near-instant finality. Jovay employs State Commitment Chains in conjunction with bridge contracts to enforce precise asset locking and unlocking mechanisms. These commitments serve as the primary source of truth for arbitration layer verification, ensuring consistent and tamper-proof reconciliation of Layer 2 states with the underlying Layer 1.

• Modularization Implementation: Building upon the above principles, Jovay establishes standardized, well-defined interfaces between modules to ensure composability and interoperability. Each component interacts through formalized protocols, enabling seamless integration and future extensibility. This modular structure not only simplifies maintenance but also allows for granular, targeted upgrades—such as hot-swapping execution engines or enhancing fraud detection logic—without disrupting the entire system. Such agility is essential for adapting to evolving regulatory landscapes, market dynamics, and technological innovations in global digital finance.

• AI Ecosystem Integration: Jovay proactively integrates artificial intelligence to enhance both developer experience and application intelligence. The rise of Large Language Models (LLMs) opens new frontiers for building smarter, more intuitive Web3 tools and services—from automated contract auditing to naturallanguage- driven transaction workflows. By embedding AI capabilities into its toolchain and runtime environment, Jovay aims to lower development barriers, improve risk modeling accuracy, and enable adaptive user interfaces. This fusion of AI and blockchain represents a strategic direction for the long-term evolution of Web3 infrastructure.

3. Architecture

3.1 Architecture Overview

In response to the evolving demands of Web3—particularly in high-performance, scalable, and secure financial infrastructure—Jovay is designed as a fully Ethereumnative Layer 2 network built on modular architecture principles. Its goal is to deliver a highly efficient and adaptable blockchain network.

Jovay leverages parallelized transaction execution to maximize throughput, implements diversified verification mechanisms (including ZKPs and TEEs) for robust security, and deeply integrates with AI ecosystems to support smart development and secure audit. Key technical features including:

• Throughput Scalability: The transaction lifecycle involves multiple stages— execution, proof generation, and data publication—each exhibiting distinct resource consumption patterns for computation, storage, and network bandwidth. Jovay's modular architecture decouples these functions, enabling independent performance scaling for execution and proof layers. This eliminates single-component bottlenecks, thereby achieving elastic system-wide throughput expansion.

• Hardware-Secured Proof System: Jovay employs validity proofs to enable immediate off-chain transaction validation, delivering faster finality and enhanced security. A phased migration strategy transitions from Trusted Execution Environment (TEE) to Zero-Knowledge Proofs (ZKP). Current Phase: TEE provides high scalability and rapid confirmation through execution environment authentication. Subsequent Phase: Transition to cryptographybased ZK proof systems. Research has been conducted on ZKP algorithm security within generic algebraic group models [9], supporting long-term security evolution.

• Deep AI Ecosystem Integration: Jovay's architecture inherently supports AI+Web3 convergence. Its verifiable computation foundation is under development, which will establish a trusted validation layer for on-chain AI Agent behaviors and AI model inference results. In future, this technical foundation will enable AI-driven automated trading strategies, intelligent risk assessment frameworks, and enhanced AI-assisted development experiences.

3.2 Detailed System Design

Grounded in the modularization principles outlined above, Jovay’s architecture is composed of specialized subsystems that operate independently yet interoperate seamlessly through well-defined interfaces. These modules form the foundational building blocks of its Layer 2 infrastructure (Fig. 1):

• Sequencer Subsystem: Central to transaction processing, the sequencer executes and batches transactions, generates blocks, and returns execution receipts. It ensures low-latency responsiveness during the initial phase of transaction handling, delivering real-time feedback to users and applications.

• Tracer Subsystem: This module records detailed transaction execution traces and constructs structured data formats for Simplified Payment Verification (SPV) and subsequent proof generation. Acting as an intermediary buffer, it bridges real-time sequencing with asynchronous batched proof computation, ensuring consistency and auditability.

Figure 1: Architecture of Jovay

• Bridge Subsystem: Responsible for cross-layer data anchoring and asset transfers, the bridge packages transaction data onto the Data Availability (DA) layer—currently Ethereum Layer 1—and submits cryptographic proofs for onchain verification. It facilitates bidirectional movement of assets and state updates between Layer 1 and Layer 2, supporting deposit, withdrawal, and cross-chain messaging functionalities.

• Prover Subsystem: The prover subsystem is responsible for generating cryptographically sound validity proofs for executed transaction batches. It supports dual verification mechanisms—Trusted Execution Environment (TEE)- based hardware validation and Zero-Knowledge Proof (ZKP) cryptographic protocols. In the current implementation phase, TEE ensures high-throughput proof generation and rapid finality. A roadmap-driven transition to ZKP-based validity proofs is underway to achieve trust-minimized security guarantees in future releases.

3.3 Exploring the Sequencer Subsystem: Parallelized Execution Technology

Jovay’s sequencer subsystem implements a multi-dimensional parallel execution architecture designed to maximize throughput while maintaining semantic consistency with serial execution semantics (Fig. 2).

• Compute-Storage Decoupling for Scalable Execution

  To address the inherent storage amplification issues in blockchain state management, Jovay adopts a compute-storage decoupled architecture. This design enables independent horizontal scaling of computation and storage units. Transactions are grouped and scheduled by an execution scheduler. They are then dispatched to distributed compute units for parallel execution. Post-execution results are concurrently written into distributed storage nodes. This dual-layer parallelism significantly improves hardware utilization, eliminating performance bottlenecks caused by single-CPU limitations and storage I/O constraints.

  

  Figure 2: Scalable Parallel Mechanism

• Asynchronous Multi-Stage Pipelining - Inter-Block Parallelism

  Through a multi-stage asynchronous pipelining architecture, Jovay achieves blocklevel concurrency across different processing stages. Each block's processing workflow is divided into multiple pipeline phases. These phases operate concurrently across multiple blocks, maximizing resource utilization. Block proposal intervals remain significantly shorter than individual block processing times, reducing userperceived transaction latency. This approach ensures high system throughput without compromising execution correctness.

• Adaptive Transaction Parallelism - Intra-Block Concurrency

  To optimize transaction-level concurrency within each block, Jovay employs a dynamic DAG-based scheduling mechanism. Incoming transactions are first validated and analyzed for read-write dependencies. A Directed Acyclic Graph (DAG) partitioning algorithm organizes transactions into dependency-free groups. These groups are assigned to distributed executors based on available resources for parallel execution. Transactions encountering conflicts during execution are dynamically rescheduled to maintain correctness. This mechanism guarantees deterministic equivalence with serial execution while achieving maximum intrablock parallelism.

3.4 Exploring the Prover Subsystem: Validity Proof Technology

Jovay’s prover subsystem integrates a hybrid validity proof engine that supports both Trusted Execution Environment (TEE)-based verification and cryptographic Zero- Knowledge Proofs (ZKPs). Currently, the system utilizes the TEE Prover to generate batched validity proofs with high efficiency and low latency (Fig. 3).

Core Characteristics of TEE Prover

• Verifiable Computation: Enables off-chain execution of transaction logic and state transitions, with on-chain verifiability via execution traces and SPV.

• Multi-Dimensional Parallelism: Achieves high throughput through pipelined parallelism at three levels: batch-level, chunk-level, and block-level.

• Heterogeneous TEE Support: Facilitates integration across diverse TEE hardware platforms through trust chain aggregation and clustered networking capabilities (under development).

• Cloud Deployment Neutrality: Ensures cross-cloud compatibility via Unified Attestation Service (UAS), which abstracts differences in hardware attestation formats across domestic and international environments (under development).

Input-Output Model The TEE Prover operates as a function 𝑓(𝑆, 𝑇), where:

𝑆: Initial world state before transaction batch execution

𝑇: Batch of transactions to be processed

𝑂𝑢𝑡𝑝𝑢𝑡: New world state 𝑆' and corresponding validity proof 𝑃

This proof is submitted to the on-chain Rollup contract for verification, ensuring secure and trustless settlement of L2 transactions on Ethereum L1.

Figure 3: Proving system based on TEE

Trust Chain Architecture Jovay's TEE Prover leverages a structured trust chain model to ensure end-to-end integrity and authenticity (Fig. 4):

1. Node Registration Phase

   Prior to initialization, all TEE Prover node measurements must be registered and publicly accessible on L1. This allows external entities to audit and verify node identities.

2. Node Initialization Verification

   During startup, the TEE Prover undergoes hierarchical hardware-level attestation: Hardware initialization, Secure enclave creation, Enclave quote generation. This process guarantees that only legitimate code runs inside trusted execution environments.

3. Node Enrollment Verification

   When registering with the Prover Controller (PC), nodes submit attestation reports for remote verification. While this serves as preliminary screening, final validation occurs on-chain.

4. Runtime On-Chain Verification

   During operation, the TEE Prover generates validity proofs and cryptographic signatures within enclaves. These attestation quotes are verified by on-chain Rollup contracts using dedicated TEE verification modules.

   

   Figure 4: Trust chain of proving system

TEE Verification Contracts Jovay implements a set of on-chain verification contracts to enforce the integrity and authenticity of TEE-generated proofs. These contracts perform the following critical functions:

1. Attestation Material Upload

   Administrators retrieve the latest attestation materials from Intel Platform Certification Service (PCS) and submit them to the Provisioning Certificate Service (PCCS) contract. Timestamps and digital signatures are verified to ensure material freshness and authenticity.

2. On-Chain Quote Attestation

   Submitted TEE attestation quotes are cryptographically verified using standardized attestation protocols. This step confirms that the quotes were generated within genuine TEE environments.

3. Measurement Consistency Validation

   The contract compares the mrsigner and mrenclave values embedded in the attestation quotes against the pre-registered node measurements stored on-chain. This ensures that only trusted TEE Prover nodes can participate in the network.

4. RWA Smart Tokenization on Jovay

4.1. RWA Standardized Phases

Blockchain technology facilitates the tokenization of real-world assets (RWA) through a structured five-phase lifecycle model: underlaying asset onboarding, asset preparation, asset tokenization, tokenized asset issuance (primary issuance), tokenized asset trading (secondary market and derivatives). This standardized framework ensures seamless integration of real-world assets into Web3 and decentralized finance (DeFi) ecosystems while preserving traceability, verifiability, and regulatory compliance. Jovay’s RWA infrastructure follows the five-phase processes for secure and trustless tokenization (Fig. 5).

1. Underlaying Asset Onboarding

   Real-world asset onboarding supports diverse types of assets such as new energy assets (e.g. EV charging equipment, photovoltaic equipment) and standard financial assets (e.g. bonds, bills, notes, trust). Taking new energy assets for example, due to their non-standard nature，hardware-software co-design solutions can be applied at the asset originator level to enhance trust, which benefits the subsequent tokenized asset issuance and trading processes. Secure IoT modules, software SDKs and proprietary chips can be employed to capture and authenticate real-time operational data. This data is then ingested into trusted asset management platforms for further processing.

2. Asset Preparation

   Key asset metadata—including underlaying asset detail information, ownership records, valuation models, and yield generation mechanisms—are formalized for on-chain representation. Original data sources are cryptographically anchored to the blockchain via Merkle proofs or ZK-based commitments, ensuring immutability and auditability. Besides, multiple assets could be package together to form a SPV-like asset package.

   

   Figure 5: RWA standardized phases

3. Asset Tokenization

   Tokenized asset portfolios are issued through verifiable smart contracts deployed on Jovay. These contracts encode automated revenue distribution logic and generate SPV-like tokenized asset certificates that represent fractional or full ownership rights.

4. Tokenized Asset Issuance (Primary Issuance)

   The tokenized assets are initially issued to distributors by the issuer, and subsequently issued or transferred to end investors. Meanwhile, the verified investment reports and audited statements can be delivered to the participant institutions and end investors.

5. Tokenized Asset Trading (Secondary Market and Derivatives)

   Secondary market activities such as redemptions, transfers, liquidity provisioning and decentralized finance (DeFi) derivatives like locking and reissuance/ borrowing/lending occur within regulated marketplaces governed by compliance frameworks. All financial transactions are enforced through programmable access controls and verified by on-chain settlement mechanisms, aligning with global supervisory standards.

4.2. Trusted Chain Architecture

Built upon the five-phase RWA lifecycle, the trusted architecture comprises six functional layers designed to ensure end-to-end integrity, confidentiality, and auditability: a). Asset Originator Layer: Proprietary IoT chipsets securely onboard physical asset data (e.g., from EV charging stations, solar panels). Trusted Execution Environments (TEEs) and Zero-Knowledge Proofs (ZKPs) establish verifiable data bridges with third-party stakeholders. b). Asset Management Layer: Layer2 network supports efficient and low-cost asset tokenization. Temporal modeling capabilities enable predictive analytics on data quality, risk indicators, and yield projections. c). Asset Issuance Layer: Smart contracts formalize processed asset portfolios into SPV-style tokenized structures. AI-powered modules assist in whitelist verification, pathway analysis, and visual report in. d). Asset Trading Layer: Regulatory-compliant mechanisms govern asset redemptions and transfers. For international investments, zkSQL-based verifiable reporting allows transaction validation without exposing sensitive data details. e). Data Services Layer: Manages model assets, executes offline/real-time data pipelines, and performs AI-assisted data quality assessments. Ensures trustworthy asset modeling and generates tamper-proof provenance records. f). Security Infrastructure Layer: End-to-end asset lifecycle protection is achieved through ZKP-based verification, privacy-preserving computation, and distributed key management systems.

Figure 6: Technical architecture of RWA application

4.3. RWA Workflow Example

4.3.1. Physical Assets

In renewable energy scenarios (e.g., EV charging stations, solar panels), IoT devices are securely connected via communication modules and customized chips. Operational data is collected into IoT asset management platforms. The Data Services Layer performs quality assurance, risk assessment, and yield optimization while anchoring static device data on-chain. The Asset Issuance Platform constructs token metadata (token basics, report hashes, role lists) based on asset packages and processed metrics. Security tokens are distributed to overseas investors through issuance channels, with verifiable investment reports where verifiable computation technologies (e.g. ZKP) could be applied to enhance trust.

Figure 7: RWA workflow example (physical assets)

4.3.2. Financial Assets

In financial assets scenarios, detailed information about financial assets, including net asset value (NAV) and yield metrics is typically verified and endorsed by financial institutions. Due to the standardized nature of these assets, blockchain oracle technology including both centralized oracle and decentralized oracle can be utilized to feed off-chain asset data into the blockchain. Other workflows include the tokenized asset issuance process via Asset Issuance Platform and then the tokenized assets are issued to distributor by issuer, and finally issued/transferred to end investors, followed by potential secondary market trading and the creation of derivative instruments.

Figure 7: RWA Workflow Example (Financial Assets)

5. Mission Forward

At its core, blockchain technology is not just about innovation — it is about empowerment. The evolution of Layer 2 represents a pivotal step toward fulfilling the original promise of decentralized systems: to create a more open, fair, and accessible digital future. As an Ethereum-native extension, Layer 2 delivers scalable, cost-efficient transactions without compromising the foundational principles of security and decentralization. It is not merely a technical advancement, but a mission-driven milestone — one that brings us closer to a world where Web3 is not a niche frontier, but a widely adopted, inclusive, and transformative force for all.

Jovay has redefined the boundaries of blockchain scalability through innovations in architecture and modular execution frameworks. Jovay delivers a robust foundation for mass-market Web3 applications, enabling seamless integration across decentralized finance (DeFi), NFTs, and real-world asset tokenization (RWA).

Beyond its role as a technical stack, Jovay serves as a foundational enabler of a more open, efficient, and trustless digital economy. By abstracting complexity into scalable execution layers, it empowers developers, institutions, and end-users to interact with decentralized systems at scale — paving the way for transparent governance, permissionless innovation, and programmable value transfer across borders.

As the blockchain ecosystem continues to mature, Jovay is committed to collaborating with global innovators, financial institutions, and regulatory stakeholders to shape the next generation of blockchain-driven industries. These partnerships aim to bridge traditional finance with decentralized infrastructure, fostering secure, compliant, and scalable solutions across asset tokenization, green finance, and institutional-grade DeFi protocols.

Together, we aim to materialize the vision of "Tokenizing the Future, Chaining the Value" — transforming real-world assets into programmable, interoperable digital entities while anchoring value creation within trustless systems.

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