Today, Bitroot officially releases two core testnet products:
the native DEX (CrusSwap) and the Cross-Chain Bridge, marking an important milestone in the v4 testnet phase and the gradual formation of Bitroot’s on-chain economic loop.

This update is not a concept demo, but a functional, composable infrastructure layer designed to be tested under real usage scenarios.

From Parallel Architecture to Real Applications

Bitroot is built as a high-performance, parallelized Layer1, fully EVM-compatible, with the goal of solving execution bottlenecks that limit DeFi scalability.

With the launch of the DEX and cross-chain bridge, Bitroot enters a new phase:

• from pure performance testing

• to real asset flow, real liquidity movement, and real contract interaction

This is a critical step in validating whether parallel execution can sustain complex DeFi behavior under load.

CrusSwap: Native DEX on Bitroot Testnet

The Bitroot testnet DEX, CrusSwap, is now live:

🔗 https://crus.finance/#/home

CrusSwap allows users to:

• swap assets directly on Bitroot

• add and manage liquidity

• observe execution latency and confirmation speed in practice

Thanks to Bitroot’s parallelized EVM design, transaction execution remains smooth even during concurrent interactions. This provides a realistic testing environment for:

• high-frequency trading

• arbitrage strategies

• liquidity-intensive DeFi protocols

Rather than optimizing for short-term incentives, this phase focuses on stress-testing execution logic and user experience.

Cross-Chain Bridge: Connecting Bitroot with External Liquidity

To support cross-ecosystem testing, Bitroot has also launched its Cross-Chain Bridge:

🔗 https://dev-bridge.bitroot.co

The bridge currently enables asset transfers between Bitroot and BSC, allowing developers and users to test:

• cross-chain asset security

• liquidity inflow and outflow

• interaction between Bitroot-native DeFi and external ecosystems

Test Assets & Contract Addresses

ETK (Bitroot):
0xada4b7fe868d334c92eb8bdaec555b85905c5fa5

ETK (BSC):
0xe1F62a5615E51B80e1d83f4E4d51a3F969fa2589

TTA:
0x4446FB54FC94F78e1589EA07E20EF6AE8a771Ce8

This setup allows the community to simulate real-world cross-chain flows and identify edge cases early in the testnet stage.

Why This Update Matters

The release of the DEX and bridge represents more than feature expansion.

It validates three key directions of Bitroot’s roadmap:

1. Parallel execution must work under real DeFi conditions, not just benchmarks

2. Cross-chain interaction must be practical and observable, not theoretical

3. EVM compatibility should translate into zero-friction usage, not hidden complexity

Only by exposing the system to real usage patterns can performance, security, and stability be meaningfully evaluated.

v4 Testnet: Focus on Feedback and Iteration

This release is part of Bitroot’s ongoing v4 testnet iteration.

The current phase emphasizes:

• execution correctness under concurrency

• bridge security and asset consistency

• UX feedback from real users and developers

Community feedback will directly influence subsequent optimization and upgrades.

What’s Next

With the DEX and cross-chain bridge now live, Bitroot is gradually forming:

• a functional DeFi base layer

• a cross-chain liquidity entry point

• a realistic testing environment for developers

More testnet products and ecosystem integrations are already in preparation.

Join the Testing Phase

Bitroot invites developers, traders, and infrastructure builders to:

• interact with the DEX

• test cross-chain flows

• provide feedback on performance and UX

This stage is about building a solid Layer1 foundation, not short-term hype.

Parallel execution is no longer just a whitepaper concept — it’s now open for real-world testing.

Today, we formally announce:

Bitroot V4 Testnet is now live.

This represents Bitroot's final testnet deployment prior to mainnet launch, offering the closest approximation to the actual mainnet environment.

Unlike previous V1/V2/V3 testnets, V4 Testnet transcends mere technical validation upgrades. It constitutes a comprehensive system-level restructuring and integration test geared towards mainnet readiness.

I. What is Bitroot V4 Testnet?

Bitroot V4 Testnet operates under mainnet assumptions as the final testing iteration. Its core objectives have shifted from ‘functional viability’ to:

System stability and long-term operational capability

Product completeness and authentic user experience

Interoperability and consistency across all ecosystem modules

In essence:

V4 Testnet represents a comprehensive dress rehearsal for Bitroot's mainnet architecture, rather than an experimental trial.

At this stage, Bitroot will cease introducing frequent radical experimental features, instead prioritising stability, uniformity, and sustainability as core design principles.

II. Key Differences Between This V4 Testnet and the Previous Three

Compared to prior testnets, Bitroot V4 Testnet represents a comprehensive upgrade from the ground up. The focus extends beyond the chain itself, encompassing the entire product layer and interaction layer.

1. Completely Upgraded Block Explorer

Redesigned information architecture and data presentation logic

Enhanced clarity in transaction, block, contract, and address query experiences

Data synchronisation and indexing methods closer to mainnet environments

2. Cross-Chain Bridge System with New UI/UX

Complete restructuring of decentralised transaction workflows

Clarified transaction and cross-chain management logic

Designed for real-world user scenarios, not merely testing

3. Concurrent Execution Engine and Consensus Layer Synergistic Upgrade

V4 Testnet conducts the first systematic validation of Bitroot's parallel execution architecture under near-mainnet parameters, focusing on:

Stability and throughput performance of parallel transaction scheduling under high concurrency

Coherence between parallel execution and block production workflows

Deterministic behaviour of block generation and finality confirmation under complex loads

Synchronisation efficiency and self-recovery capabilities of nodes during abnormal states (disconnections, reconnections, state inconsistencies)

The core objective of this phase is to validate whether parallelised execution can maintain stable collaboration with the consensus layer under long-term operational conditions.

4. EVM execution environment and gas mechanism upgrades

Within the V4 Testnet, Bitroot's EVM execution environment enters mainnet-level compatibility and security testing:

Verification of full compatibility for mainstream EVM smart contracts on Bitroot

Execution stability under high-complexity contracts and multi-contract interaction scenarios

Consistency and predictability of gas metering rules under parallel execution conditions

Stability and boundary testing of the gas fee model across varying load intervals

The focus of this phase is not merely ‘EVM support’, but verifying:

Whether EVM maintains mainnet-grade security and determinism within a parallel execution architecture.

5. System-Level Upgrade Validation for Cross-Chain Infrastructure

The cross-chain module is no longer tested as an isolated feature on V4 Testnet, but integrated into collaborative validation of the underlying system:

Integrity of cross-chain asset flows between Bitroot and other public chains

Security and stability of cross-chain bridges under high-frequency, continuous usage scenarios

Verification of asset security and rollback mechanisms under extreme conditions (abnormal interruptions, state inconsistencies)

Consistency testing between cross-chain state and Bitroot on-chain state synchronisation

The objective of this phase is to ensure the cross-chain system does not become a source of systemic risk under mainnet conditions.

6. Synergistic Operation Testing of Ecosystem Applications and Underlying Systems

V4 Testnet incorporates deployed ecosystem applications into comprehensive system load testing, including but not limited to:

Real-world trading behaviour testing for decentralised exchanges (DEXs)

Complete business processes including liquidity provision, withdrawal, and LP management

On-chain performance under concurrent multi-DApp operation

Real-time availability and consistency of on-chain data for block explorers and third-party tools

III. Core Focus Areas for V4 Testnet Testing

During the Bitroot V4 Testnet phase, testing will primarily focus on the following aspects:

• Stability of the parallelised EVM public chain under real-world usage intensity

• Long-term performance of transaction execution, state updates, and fee models

• Collaborative operation of multi-product systems within the same public chain environment

• User experience and usability issues encountered during authentic operational pathways

This constitutes not merely a technical test, but a comprehensive evaluation of both product and systems engineering capabilities.

IV. Bitroot V4 Testnet Network Information

The following details the complete test environment for Bitroot V4 Testnet:

🔗 Mainnet Connection Information

Name: Bitroot Testnet

RPC

https://dev-rpc.bitroot.co

Chain ID:

15881

Native Token:

BRT

Test Token Claim (Faucet)

https://devnet.bitroot.co/faucet

Block Explorer

https://devnet.bitroot.co/

V. Phased Rollout: Bitroot V4 Testnet Testing

Bitroot V4 Testnet represents the final comprehensive test network prior to mainnet launch. Unlike previous testnets, this iteration will progress through a phased rollout and incremental validation approach, ensuring each core functionality undergoes thorough verification in real-world usage scenarios.

Through a more granular and extended testing cycle, we aim to empower the community, developers, and node participants to identify issues more deeply, provide feedback, and collaboratively refine the protocol and infrastructure.

Testing Phase Schedule

Phase One｜Foundational Network and Account Interaction Testing

Opening Date: 26 December

This phase primarily focuses on validating the foundational capabilities of the Bitroot mainnet, including:

Adding the Bitroot V4 Testnet network (RPC / ChainID)

Wallet integration and network recognition

Basic functionality usage of the browser (Explorer)

Native asset (BRT) transfer testing between accounts

Transaction packaging, confirmation, and on-chain data traceability

During this phase, users may freely add the Bitroot chain, familiarise themselves with fundamental operational workflows, and validate network stability under genuine user interactions.

Phase Two｜Cross-Chain Bridge Testing

Projected: One week following Phase One completion

Upon establishing stable foundational chain operations, official cross-chain bridge testing will commence, prioritising verification of:

Bitroot ↔ Cross-chain asset flows between other test networks

Transaction latency, stability, and failure handling logic for cross-chain transactions

Security under high concurrency and continuous cross-chain scenarios

Asset consistency and rollback mechanisms under extreme conditions

This phase constitutes a critical preparatory step prior to mainnet launch, laying the foundation for Bitroot's multi-chain ecosystem.

Phase Three｜DEX and DeFi Component Testing

Expected: Commences following completion of cross-chain testing

Subsequently, decentralised exchange and liquidity-related testing modules will be progressively released, including:

DEX order matching and price execution

Liquidity provisioning / removal (LP)

Parallel execution performance under high-frequency trading scenarios

Stability of contract invocations, gas fees, and state updates

This will serve as a concentrated validation of Bitroot's parallel EVM execution capabilities and suitability for real-world DeFi scenarios.

VI. Who Should Participate in Bitroot V4 Testnet?

This testnet is open to all users wishing to preview the Bitroot mainnet architecture, including but not limited to:

Developers: Testing contract deployment, invocation, and execution performance

Ecosystem projects: Pre-adapting products to mainnet-grade environments

Community users: Experiencing complete trading, cross-chain, and interaction workflows

Every genuine operation and piece of feedback will directly assist Bitroot in further validating the chain's practicality and security prior to mainnet launch.

VII. From V4 Testnet to Mainnet

The launch of Bitroot V4 Testnet signifies that Bitroot has formally entered the final phase preceding its mainnet release.

Following the stable operation of the V4 testnet and the fulfilment of its established objectives, Bitroot will progressively advance:

Final confirmation of mainnet parameters and system configurations

Preparations for migrating ecosystem products to the mainnet environment

Publication of the mainnet launch schedule

End

The enduring value of blockchain is never determined by the speed of deployment, but rather by the reliability of its foundational architecture and its capacity to withstand the tests of time and scale.

Bitroot V4 Testnet represents the ultimate validation of system stability and constitutes the most crucial step before Bitroot's advancement to mainnet.

We invite you to join the Bitroot V4 Testnet, to collectively witness and participate in the arrival of the Bitroot mainnet.

Over the past fortnight, the Bitroot team traversed Hangzhou, Taizhou, Quanzhou, Shenzhen, Kunming and Zunyi, engaging in in-depth discussions with developers, researchers, community members and industry partners. Together, they explored development pathways for parallelised EVM public chains, AI-driven innovation, and the long-term construction of blockchain ecosystems. This journey represented not merely a series of offline exchange events, but a profound connection forging technical consensus and community alignment.

About Bitroot

Bitroot is the world's first parallelised AI infrastructure public chain, dedicated to building the next-generation high-performance AI computing network. Through its groundbreaking parallelised EVM and BFT consensus mechanism, Bitroot achieves transaction processing capabilities exceeding 100,000 TPS and an ultra-fast confirmation speed of 0.3 seconds, fundamentally resolving the performance bottlenecks of traditional blockchains.

Simultaneously, Bitroot natively integrates an AI support layer, providing an efficient, verifiable, and low-cost on-chain runtime environment for AI agents, large model training, and GPU computing networks. This drives the scalable deployment of decentralised intelligent applications.

As a high-performance, parallelised EVM-AI public chain, Bitroot seeks to foster face-to-face engagement with developers, community members, and ecosystem partners. This aims to deepen understanding of the project's core philosophy: building next-generation high-performance public chain infrastructure through a parallelised architecture and AI-driven intelligent execution engine.

The China Tour is not only about showcasing Bitroot's technological achievements, but also about listening to the community, gathering industry feedback, and building global consensus together. Chinese developers hold a pivotal position within the global Web3 ecosystem. Through this China Tour, Bitroot aims to re-establish the virtuous cycle of ‘technology-community-consensus’, laying a solid foundation for the future global ecosystem.

Bitroot China Tour: Recap of Each Stop

Hangzhou Stop｜Consensus Takes Flight: The Future of Parallelised EVM As the inaugural stop on Bitroot's China Tour, Hangzhou proved an ideal starting point with its open innovation ecosystem and vibrant academic atmosphere.

During this event, the Bitroot team shared the project's research achievements in multi-threaded architecture, cross-chain interoperability mechanisms, and parallel computing engine design.

Guests from academia and the developer community engaged in lively discussions centred on the convergence of AI and blockchain. The Hangzhou stop marked not only the commencement of technological exploration but also the launch of shared understanding.

Taizhou Station｜Co-building the Ecosystem: Unlocking Development Potential

Bitroot's second stop brought us to Taizhou. The event centred on community co-creation and developer ecosystem development, featuring not only technical presentations but also open roundtable discussions covering topics such as mainnet development progress, testnet performance, and ecosystem incentive mechanisms.

The Bitroot team reached preliminary cooperation agreements with multiple local teams on-site, laying the groundwork for future ecosystem expansion within China.

Quanzhou Station｜Ecosystem Implementation: Innovative Applications and Testing Outcomes The third stop of Bitroot's tour—Quanzhou Station—focused discussions on the practical implementation of the Bitroot ecosystem, encompassing innovative applications such as DeFi, GameFi, and AI Agents.

The Bitroot team announced that the testnet had surpassed 300,000+ addresses and provided a detailed overview of the launched DEX and cross-chain bridge versions.

The Quanzhou event not only showcased the technological advancements of Bitroot but also demonstrated its expanding influence within the local developer ecosystem.

Shenzhen Station｜Industry Focus: Dialogue Between Technology and Policy

The Shenzhen Station continued the community's fervour. The event welcomed several distinguished guests from government departments, industry associations, and research institutions, who engaged in in-depth discussions centred on ‘The Convergence of Blockchain and the Digital Economy’.

The Bitroot team systematically presented the project's security model, achievements in parallel architecture optimisation, mainnet progress, and audit plans.

Guests acknowledged Bitroot's practical innovations in architecture and compliance exploration, engaging in constructive discussions on synergising blockchain industry development with regulatory frameworks.

Kunming Station｜AI Consensus: The Future of Intelligent Public Blockchains

Under the theme ‘Innovative Consensus, Building the Future Together’, the Kunming Station event featured in-depth discussions between the Bitroot team and local Web3 entrepreneurs and technical communities.

The event highlighted the practical application of parallelised EVM architecture within AI and financial scenarios, while sharing updates on the Bitroot AI Agent initiative and progress towards the Bitroot mainnet launch. This Kunming gathering provided developers with a deeper understanding of Bitroot's strategic roadmap for AI integration.

Zunyi Station｜Building the Future Together: From Technology to Ecosystem Implementation

As the final stop on Bitroot's China Tour, the Zunyi Station concluded successfully under the theme ‘The Future of Parallel Public Blockchains and Ecosystem Development’.

The event reviewed significant achievements during the testnet phase, including optimised cross-chain bridge performance, enhanced DEX functionality, and validated stability of the parallel execution layer. Community members actively engaged in discussions, offering numerous constructive suggestions regarding ecosystem incentives post-mainnet launch.

The successful conclusion of the Zunyi Station brought the Bitroot China Tour to a perfect close, symbolising the further consolidation of consensus within the Bitroot community.

Through these six events, the Bitroot team has gained a profound appreciation for the enthusiasm, professionalism and creativity of the Chinese community. Whether during developer exchanges or informal discussions among community members, all participants demonstrated a keen focus on foundational innovations within public blockchains.

Numerous developers expressed keen interest in parallelised architecture, viewing it as a crucial avenue for overcoming EVM performance limitations. Community representatives also offered practical suggestions regarding node operation, ecosystem incentives, and smart contract security – feedback that will directly inform Bitroot's subsequent technical iterations.

More significantly, the China tour has enabled Bitroot to establish a stronger local network and foundational ecosystem partnerships. Multiple institutions, node partners, and community organisations have expressed their intent to join the Bitroot ecosystem, with some developer teams already commencing tests to deploy applications on Bitroot. The transformation of technical exchanges into collaborative intentions and community interactions into joint ecosystem development represents precisely the outcomes Bitroot aspired to achieve.

💡 Bitroot Technology Showcase: The Journey from Concept to Implementation

At each stop of Bitroot's China Tour, alongside exchanges on ideas and industry trends, the technology demonstrations proved among the most anticipated segments for the community. The Bitroot team presented the project's latest developments at the roadshow, offering attendees a tangible glimpse of the performance and potential of Bitroot as a next-generation parallel public chain through testnet product demonstrations.

This event served not merely as a face-to-face exchange, but as a comprehensive showcase of Bitroot's phased achievements. The team demonstrated the latest versions of the following core modules:

🔹 Bitroot Testnet Explorer

The Bitroot Testnet Explorer (test-scan.bitroot.co) is now fully operational, supporting asset queries, transaction tracking, block browsing, and node status monitoring.

The browser features a clean, intuitive interface and stable performance, providing users and developers with a convenient channel for visualising on-chain data.

It not only showcases Bitroot's mature design in data traceability and transparency but also lays a robust infrastructure foundation for the future mainnet ecosystem.

🔹 Bitroot Cross-Chain Bridge

The cross-chain bridge (bridge.bitroot.co) constitutes a vital component of the Bitroot ecosystem, facilitating bidirectional asset transfers between the Bitroot and BSC networks.

At the event, the team demonstrated the entire process of cross-chain transfers from the Bitroot chain to the BSC testnet. The entire operation was completed in mere seconds, with swift and stable transaction confirmations. This cross-chain system employs a lightweight verification mechanism and a distributed signature scheme to ensure the security and efficiency of the asset transfer process.

🔹 Crusswap DEX — Bitroot's Native Decentralised Exchange

Crusswap (test-dex.bitroot.co) is a native DEX deployed on the Bitroot testnet, supporting multiple trading pairs including BRT/USDC.

During the roadshow demonstration, participants experienced the full operational workflow encompassing swift swaps, liquidity provision, and withdrawal. Transaction confirmations proved exceptionally rapid with virtually no latency.

Crusswap fully validates Bitroot's performance advantages under its parallelised EVM architecture and high-throughput consensus mechanism, marking the initial formation of financial applications within the Bitroot ecosystem.

🔹 Developer Hub

The Bitroot Developer Hub (bitroot.gitbook.io/developer-hub) provides ecosystem builders with comprehensive technical documentation and API specifications.

Currently available resources include RPC information, token deployment guides, smart contract interaction manuals, and node configuration documentation, enabling developers to seamlessly execute end-to-end operations from wallet integration to smart contract deployment.

This not only demonstrates Bitroot's openness and scalability but also provides robust support for future application implementations across DApps, DeFi, AI, and RWA domains.

Future Outlook: From Consensus to Co-Creation

From its inception, Bitroot's vision has extended beyond merely constructing a public chain to building an ‘intelligence-driven open ecosystem’.

Moving forward, Bitroot will continue advancing security audits and mainnet launch preparations, while enhancing the stability of its cross-chain bridge, DEX, and parallel execution engine. Concurrently, the team will prioritise refining the AI Agent framework, RWA asset support, and developer toolchain.

By integrating AI-powered decision-making into on-chain execution logic, Bitroot aspires to become a cornerstone infrastructure of the AI-driven era—enabling intelligent agents to interact securely and trustworthily with smart contracts, thereby infusing computational intelligence into the decentralised world.

At the ecosystem level, Bitroot will deepen collaborations with global developers, node partners, and community organisations, establishing a multilingual global community and educational framework. The conclusion of the China tour marks not an endpoint, but the commencement of Bitroot's global expansion. Future engagements will extend to Southeast Asia, Europe, and Latin America, facilitating face-to-face exchanges with developers to share the technical philosophy and practical achievements of parallel public chains.

Bitroot believes the future of public chains lies in open innovation and authentic value connections. As AI converges with Real-World Assets (RWA) and blockchain's foundational performance advances, a new era of intelligent economics is emerging. At this pivotal juncture, Bitroot is building a decentralised world for tomorrow – rooted in technology, bridged by consensus, and driven by intelligence.

Conclusion The successful conclusion of Bitroot's China Tour marks not only a milestone in our journey but also the beginning of a new chapter.

We have witnessed the strength of China's developers, the cohesion of our community, and the immense potential of the blockchain industry.

Moving forward, Bitroot will continue to walk alongside every partner who believes in technology and innovation, jointly propelling the public chain ecosystem towards greater intelligence, openness, and globalisation.

The Bitroot China Tour has concluded, but Bitroot's future has only just begun.

Imagine your home gaming rig, your office's underutilised servers, or even that dust-gathering NAS device becoming computational nodes capable of training ChatGPT-level large models. This isn't science fiction—it's an unfolding technological revolution.

Much like Uber transformed idle cars into shared transport tools, edge computing is now converting hundreds of millions of idle devices worldwide into a distributed AI training network. Today, we'll demystify how this ‘computing power sharing economy’ operates in accessible terms.

==============================================================
Core questions answered: Three key questions

Question 1: How is computing power split implemented?

Living metaphor: breaking down a big house into smaller rooms

Imagine you're renovating a large villa, but each worker can only handle one small room. You need to break down the entire renovation task into:

• The plumber is responsible for the pipes and circuits

• The mason is responsible for the walls and floors

• The carpenter is responsible for doors, Windows and furniture

• The painter is responsible for painting and decorating

The same goes for computing power splitting in edge computing:

Entry-level explanation: Take a large AI model (say, 100 billion parameters) and break it into many small pieces. Each device is only

responsible for training a small part of the model, like a jigsaw puzzle, and then put all the pieces together to form the complete model.

Technological advancement:

Professional technical details:

1 .ZeRO Style Parameter Sharding Mechanism:

• Shard the model parameters into different GPUs by dimension

• Each GPU stores only 1lN of parameters, and the required parameters are loaded dynamically

• Parameter sharing is implemented through the parameter server mode

2. Split Learning Model Split:

• According to the network layer split model, the first half is on the client and the second half is on the server

• Protect data privacy while implementing distributed training

• Information is passed through the middle layer to avoid leakage of raw data

3. Federal Data Sharding:

• Each node is trained with local data and only gradient updates are uploaded

• Privacy is protected by secure aggregation algorithms

• Supporting asynchronous updates and fault tolerance

Problem 2: How to achieve distributed computing power?

Beginner's explanation:

• Task release: like issuing a taxi demand

• Resource matching: The system finds the most appropriate device

• Task execution: The device starts "accepting orders" training

• Results collection: Summary of training results

Upward class design:

Details of professional technical implementation: 1 .Intelligent Task Scheduling Algorithm:

• Based on the device capability scoring system (GPU model, video memory, network bandwidth, latency, reputation score)

• Support dynamic load balancing and task migration

• Implement priority queues and resource reservation mechanisms

2. Communication protocol optimization:

• Web RTC DataChannels: Solves NAT traversal problem and supports browser participation

• gRPC over TLS: efficient inter-service communication with support for streaming

• Asynchronous aggregation: reduces network wait time and improves overall efficiency

3. Resource management mechanism:

• Real-time monitoring of equipment status and performance indicators

• Adjust task allocation strategy dynamically

• Intelligent load balancing and failover

Question 3: What if the GPU drops midway? Will the data be lost?

Can the task continue? A life analogy: The backup doctor in surgery

Just as hospitals have backup doctors during surgery, distributed training has multiple safeguards:

Beginner's explanation:

• Checkpoint save: Save your progress regularly, just like a game save

• Multiple backup copies: Important tasks are handled simultaneously across multiple devices.

• Automatic recovery: Tasks continue automatically after the device comes back online.

Inclusive error tolerance mechanism:

Details of professional technical implementation:

1 Design of checkpoint mechanism:

• Incremental checkpoints: only save the changed parts, reducing storage overhead

• Distributed checkpoints: Split the checkpoints into multiple nodes

• Encrypted storage: Ensure the security of checkpoint data

• Versioning: Support for multiple version rollback and recovery

2. Redundant execution strategy:

• Multi-replica critical tasks: Important tasks are performed in parallel on 3-5 nodes

• Voting mechanism: Verify the correctness of results by majority vote

• Malicious node detection: identification and isolation of abnormal behavior nodes

• Dynamic adjustment: Adjust the number of copies according to network conditions

3. Fault recovery mechanism:

• Automatic detection: real-time monitoring of node status and network connections

• Task migration: Seamlessly transfer tasks to other available nodes

• State recovery: Recovery of training status from the most recent checkpoint

• Data consistency: Ensure that the restored data state is correct

4. Data security:

• Encrypted transmission: All data is encrypted

• Distributed backup: Data is backed up and stored on multiple nodes

• Blockchain records: Key operations are recorded on the blockchain

• Access control: strict permission management and identity authentication

==============================================================

Technology enables deep analysis

Core algorithm: Make distributed training more efficient

1. Communication optimization: Reduce the time to "wait for data"

Problem analysis: How to reduce communication overhead when the bandwidth of home network is limited?

Technical solutions:

Implementation details:

• Gradient compression: only transmit important gradient updates, reducing communication by 90%

• Asynchronous aggregation: aggregates completed updates without waiting for all nodes

• Local aggregation: Aggregation within nodes in the same region, then uploaded to the central hub.

2. Memory optimization: Let ordinary GPU also train large models

Problem analysis: How to train large models with insufficient video memory on a single card?

Technical solutions:

Implementation details:

• Parameter sharding: Distributing model parameters across multiple cards, with each card storing only 1/N.

• Activated computation: Trading time for space by recalculating activation values on demand.

• CPU offloading: Put some parameters in memory and load them when the GPU needs them.

3. Secure aggregation: Protect privacy while enabling collaboration

Problem analysis: How to collaborate in training without data leakage?

Technical solutions:

Implementation details:

• Differential privacy: adding noise to protect privacy and control the loss of accuracy

• Secure multi-party computation: encrypted aggregated gradients, mathematically ensuring privacy security.

• Federated learning: data stays local, only model parameters are shared.

==============================================================

Real-world application scenario: Let technology

truly serve life scenario 1: Home AI assistant training

User story: Sam wants to train an AI assistant that can understand his family dialect.

Technical implementation process:

Value embodiment:

• Privacy protection: Dialect data will not be uploaded to the cloud

• Cost reduction: No need to rent expensive cloud servers

• Personalization: The model is specially adapted to the language habits of Sam's family.

Scenario 2: Enterprise data security training

User story: A bank needs to train a risk control model, but the data cannot be exported from the bank's

Technical implementation process:

Value embodiment:

• Compliance: meet financial data security requirements

• Efficiency: Multiple servers train in parallel

• Traceability: The training process is fully auditable.

Scenario 3: Scientific research collaboration and innovation

User story: Collaborative research on new drug technology in many laboratories around the world.

Technical implementation process:

Value embodiment:

• Knowledge sharing: accelerating scientific progress

• Privacy: protection of trade secrets

• Cost allocation: reduce R&D costs

==============================================================

Technical challenges and solutions

Challenge 1: Network instability

Problem description: The home network is often disconnected, which affects the training progress

Solution architecture:

Technical detail ：

• Breakpoint continuation: Regularly save training status and support recovery from any point

• Task migration: automatically detect network status and seamlessly switch nodes

• Asynchronous training: Improves fault tolerance by not waiting for all nodes to synchronize

• Smart reconnect: automatically detect network recovery and rejoin the training challenge

2: device performance differences

Problem description: GPU performance varies greatly between different devices

Solution architecture:

Technical detail ：

• Intelligent scheduling: Assign tasks according to the capability score of the device

• Load balancing: dynamically adjust task allocation to avoid performance bottlenecks

• Heterogeneous training: adapt to different hardware configurations and make full use of resources

• Dynamic adjustment: real-time monitoring of performance, adjusting training strategies

Challenge 3: safety risks

Problem description: Malicious nodes may disrupt the training process

Solution architecture:

Technical detail ：

• Results verification: multi-node cross-validation, detection of abnormal results

• Credit system: record the historical performance of nodes and establish a trust mechanism

• Encryption communication: end-to-end encryption to protect data transmission security

• Access control: strict access control to prevent unauthorized access

==============================================================

Future outlook: A new era of computing power democratization

Technology development trends

2024-2026: Infrastructure improvements

2026-2028: Application scenarios explode

2028-2030: Ecological maturity

Social influence

Economic level:

• Create new employment opportunities

• Lower the threshold for AI application

• Promote the optimised allocation of computing resources

Societal level：

• Protecting personal privacy

• Promoting the democratisation of technology

• Narrowing the digital divide

Technical level：

• Accelerate the development of AI technology

• Promote the adoption of edge computing

• Foster cross-disciplinary collaboration

=============================================================

Conclusion: Let everyone participate in the AI revolution

The edge computing distributed computing network isn't just a technological upgrade—it's a social revolution reshaping the power dynamics of computing. Just as the internet empowered everyone to become content creators, edge computing is now enabling anyone to become an AI trainer.

For ordinary users: Your idle devices can create value and participate in the AI revolution For developers: Lower costs and more possibilities for innovation For enterprises: Protect data security and improve training efficiency For society: Democratization of computing power and universal access to technology

By combining technological idealism with engineering pragmatism, we are building a more open, fair, and efficient computing future where everyone can participate in and benefit from it.

==============================================================

" Technology should not be the privilege of a few, but a tool that everyone can understand and use. Edge computing makes AI training go from the cloud to the edge, from monopoly to democracy, from expensive to universal. "

--Bitroot Technical Team

In this era of rapid technological development, the integration of artificial intelligence and blockchain technology shows enormous potential, but at the same time, this combination also brings significant performance bottlenecks. AI models, particularly deep learning systems, have extremely high demands on computational resources, with their training processes requiring massive processing power, which undoubtedly places a heavy burden on nodes within blockchain networks. Additionally, AI processing often involves massive datasets, and when these data need to be transmitted within the distributed architecture of a blockchain, it may lead to significant delays, further exacerbating performance challenges.

Traditional blockchain systems have inherent design limitations that make them ill-suited for the demands of the AI era. For example, early blockchains like Bitcoin can only process approximately 7 transactions per second, while Ethereum can handle around 30 transactions per second. This contrasts sharply with centralised systems like Visa, which can process up to 24,000 transactions per second. Low transaction throughput severely limits the practicality of blockchain in large-scale applications, especially in scenarios requiring high-frequency interactions such as smart contracts and financial transactions. Additionally, blockchain faces data storage limitations, consensus mechanism bottlenecks (such as the high energy consumption and computational requirements of Proof of Work (PoW), and issues related to centralisation and validator selection in Proof of Stake (PoS)), as well as network congestion and high latency, among other scalability challenges. These performance constraints make it difficult for traditional blockchain to effectively support the stringent requirements of AI algorithms for real-time decision-making and high throughput.

Currently, decentralised AI networks lag significantly behind centralised AI infrastructure in terms of speed, scale, and efficiency. These decentralised systems face significant challenges in terms of throughput, latency, computational scalability (particularly the massive demand for GPU clusters by large language models), and network scalability due to requirements for specialised hardware or technical expertise. . The current situation shows that while blockchain offers decentralisation, transparency, and immutability—highly attractive characteristics for AI—its inherent design, such as sequential execution and consensus overhead, limits its performance. Centralised AI can leverage large-scale, coordinated computing resources (such as GPU and TPU clusters), which decentralised networks struggle to achieve due to coordination overhead and consensus latency. This inherent contradiction forms the deep-seated ‘scalability paradox’ faced by the integration of AI and blockchain:

While blockchain's decentralised nature provides AI with data integrity and trust, its performance bottlenecks hinder large-scale application.

Bitroot's core innovation—the Pipeline BFT consensus mechanism and multi-engine parallel EVM architecture—was designed to address this fundamental challenge. This paper aims to deeply analyse the principles of Pipeline BFT and its key role in Bitroot's high-performance parallel EVM, explaining how Bitroot achieves true high throughput, ultra-low latency, and extremely low gas fees through a collaborative architecture while maintaining its decentralised characteristics, laying the foundation for the next generation of blockchain infrastructure.

Overview of the Pipeline BFT Consensus MechanismFundamental Principles

The Pipeline BFT consensus mechanism is an extension of the classic Practical Byzantine Fault Tolerance (PBFT) algorithm. PBFT, as a widely adopted consensus mechanism in distributed systems, ensures system safety and liveness even in the presence of arbitrary failures (including malicious behaviour). It can tolerate up to f Byzantine fault nodes, provided that the total number of nodes in the network, n, satisfies the condition n = 3f + 1.

The traditional PBFT consensus process typically consists of five phases: 1. Request 2. Pre-prepare 3. Prepare 4. Commit 5. Reply

However, the communication complexity of classic PBFT is O(n^2), which becomes a significant performance bottleneck as the number of nodes increases. Pipeline BFT introduces a pipeline design, dividing the consensus process into four core phases:

Propose → Prevote → Precommit → Commit. The pipeline design allows the system to process transactions from different rounds in parallel, significantly reducing communication overhead and improving throughput.

Pipeline BFT Pipeline Parallel Processing Architecture Diagram:

The pipeline structure endows Pipeline BFT with multi-height parallel processing capabilities. This means that the system no longer needs to wait for a block to complete consensus and final confirmation before starting the consensus process for the next block, but can instead process multiple blocks or ‘heights’ of consensus simultaneously. Parallel processing effectively addresses the leader bottleneck issue in classical BFT, significantly improving consensus efficiency. The paradigm shift from sequential to parallel processing represents a direct breakthrough in scalability bottlenecks at the consensus layer for Pipeline BFT.

Technical Details

In Pipeline BFT, the state space and message space are clearly defined to ensure the system provides correct security and liveness guarantees when facing Byzantine faults. BFT protocols typically detail their fault models, such as the types of malicious behaviour they can tolerate and network models (e.g., synchronous, semi-synchronous, or asynchronous), and prove their safety (all honest nodes reach consensus) and liveness (requests will eventually be processed) properties based on these models. Messages transmitted during the consensus process, such as pre-preparation, preparation, and commit messages, all include message digests and the sender's digital signature to ensure message integrity and authenticity, preventing tampering and forgery.

Pipeline BFT achieves high-level isolation and state machine instance parallelism by processing multiple levels in parallel. This means that for different blocks, their consensus processes can proceed independently without interfering with each other. This effectively runs multiple state machine replica instances simultaneously, with each instance responsible for advancing the state of its corresponding block. This design enables the system to utilise resources more efficiently and handle more concurrent requests.

To further optimise performance and reduce communication overhead, Pipeline BFT adopts the BLS signature aggregation mechanism. BLS signatures allow multiple independent validator signatures to be aggregated into a single compact signature. The BLS signature aggregation feature significantly reduces storage and bandwidth consumption, making it particularly efficient for large networks with a large number of validators, thereby accelerating the final confirmation time of blocks. BLS signature aggregation directly addresses the challenges of PBFT-like protocols.

BLS signature aggregation advantage comparison chart:

BLS signature aggregation effectively reduces the main bottleneck of communication complexity, enabling large-scale expansion while maintaining decentralised characteristics.

In terms of performance parameter settings, Bitroot's Pipeline BFT consensus mechanism aims to achieve excellent performance. According to test data, Bitroot can achieve high throughput while reducing block confirmation time to sub-second levels. Additionally, the system has designed a robust view change strategy to ensure that, in the event of a primary node failure, a new leader can be swiftly and smoothly elected to maintain network activity and continuous operation.

Security and Liveness Proof As a Byzantine fault-tolerant consensus algorithm, Pipeline BFT is designed to provide formalised security and liveness proofs. Security ensures that at any given point in time, all honest nodes agree on the same transaction sequence, thereby preventing double-spending attacks and state divergence. Liveness ensures that transactions submitted by clients will ultimately be processed and committed to the blockchain, even in the presence of a certain number of malicious nodes.

In the context of multi-level consensus, ensuring security isolation is critical. Although the system processes multiple blocks in parallel, it must strictly ensure that the consensus processes between different blocks do not interfere with each other, leading to state inconsistencies or security vulnerabilities. This means that sophisticated mechanisms are needed to manage potential conflicts in concurrent operations and ensure the correctness of the final state.

Implementing parallelisation often introduces challenges in maintaining consistency and security. The design of Pipeline BFT must strictly ensure that concurrent block processing does not compromise the fundamental security and liveness properties of the BFT protocol. This makes conflict resolution and state management critical in parallel consensus, serving as the key to ensuring system stability and reliability.

Security and Liveness Guarantee Mechanisms: Core Security Principles: Pipeline BFT ensures that all honest nodes reach a fully consistent decision on blocks of the same height, preventing double-spending and state forks. This is achieved through the following mechanisms:

1. 2f+1 threshold verification mechanism: Each consensus phase requires confirmation from at least 2f+1 nodes, ensuring that honest nodes maintain an absolute majority in a network with up to f malicious nodes.

2. Phase Locking Mechanism: Nodes ‘lock’ specific blocks during the pre-commit phase, preventing changes to votes in subsequent rounds and ensuring consistency.

3. Signature Verification and Tamper-Proofing: All messages include BLS aggregate signatures to ensure message authenticity and integrity.

Activity Guarantee Mechanism: The system ensures that transactions are ultimately processed and confirmed when network conditions permit:

1. VRF Fair Election: Leaders are elected using a verifiable random function to avoid leader monopolisation and censorship.

2. Timeout and View Change: When a leader fails, the system automatically triggers a view change to elect a new leader and continue progressing.

3. Network Synchronisation Assumption: Under the assumption of eventual network synchronisation, the system can continue to progress without stalling Special Considerations for Concurrency Safety: Pipeline BFT maintains overall safety while achieving multi-level concurrency through the following mechanisms:. Level Independence: Consensus processes at different levels are completely isolated to prevent mutual interference . Dependency Management: Ensures that block submission order aligns with height sequence, maintaining the global order of the blockchain. State Consistency Check: Verifiable Timestamps (VTS) ensure that the final state remains consistent across all nodes

Parallelism Security Guarantees In multi-level parallel consensus, Bitroot employs the following mechanisms to ensure security:

1. Height isolation: Consensus instances at different heights are completely independent to prevent state confusion

2. Dependency verification: Ensures that blocks at height h+1 can only be committed after height h is determined

3. Global ordering consistency: Ensures that all nodes reach consensus on the final block sequence through VTS Pipeline BFT and parallel EVM collaboration mechanism

Multi-Engine Concurrent Execution Model The sequential execution mode of the traditional EVM (Ethereum Virtual Machine) is the core cause of its performance bottleneck. In sequential mode, transactions must be processed one after another, especially when state conflicts occur, leading to network congestion and severely limiting the overall scalability of the blockchain.

To overcome this limitation, Bitroot has introduced a multi-engine parallel execution model, with the core being the native parallel execution engine. Bitroot's architecture adopts a ‘mainchain (consensus layer) + execution chain (execution layer)’ separated design, with a modular architecture laying the foundation for parallelisation.

Bitroot's parallel scheduler uses a dynamic transaction scheduling mechanism to analyse dependencies between transactions and construct a conflict graph. This enables the system to identify and execute transactions that do not conflict with each other in parallel, such as token transfers and NFT minting, while transactions involving the same account are processed sequentially to maintain consistency. The self-adaptive concurrent scheduling mechanism achieves a fundamental paradigm shift from traditional sequential EVM execution to concurrent execution, significantly improving throughput and the utilisation rate of underlying hardware resources, and overcoming the inherent limitations of single-threaded processing.

Concurrent block consensus + transaction scheduling The deep synergy between the Pipeline BFT consensus mechanism and the parallel EVM is the key to Bitroot's high performance. Pipeline BFT supports multi-level consensus concurrent execution, meaning multiple consensus instances can run in parallel, each reaching consensus on a series of blocks. This design effectively distributes workload across different consensus instances, significantly enhancing the throughput capacity of the consensus layer.

At the execution layer, Bitroot's parallel EVM closely collaborates with the Dependency Graph Scheduler (D-TGA) to achieve high transaction parallelism. pEVM's smart transaction grouping algorithm can reduce inter-group conflict rates to extremely low levels (e.g., below 5%). It achieves this by analysing transaction read/write sets and dependency relationships to construct precise conflict graphs, thereby enabling a large number of non-conflicting transactions to be executed in parallel. For example, token transfers between different users or minting operations not involving the same NFT can be processed simultaneously, while transactions involving the same account are serialised as necessary to ensure state correctness.

Parallel Block Consensus + Transaction Scheduling Architecture Diagram:

The close collaboration between the consensus layer and the execution layer enables Bitroot to significantly increase block generation rates and reduce transaction latency. The parallel ‘slots’ provided by Pipeline BFT can be efficiently filled by the parallel EVM, thereby achieving deep streamlining and parallel processing from consensus to execution. The synergistic effects ultimately manifest in exceptional performance metrics.

State Consistency and Version Management

Maintaining state consistency in a parallel execution environment is a complex challenge. Bitroot employs optimistic concurrency control (OCC) and version snapshot mechanisms to support concurrent submission of streamlined blocks. Optimistic Concurrency Control allows transactions to execute based on an initial estimate of the state, without the need to pre-lock resources. If a conflict occurs, the system will use a rollback mechanism. Multi-Version Concurrency Control (MVCC) or state snapshots are used to manage different versions of the state, ensuring that read operations never wait for write operations, and vice versa, thereby maximising concurrency.

The system carefully detects conflicts to ensure that the output of any transaction is not altered by the results of previously committed dependent transactions. If a conflict is detected, the system minimises the rollback scope to the minimum, re-executing or rolling back only the affected transactions, thereby minimising overhead. The mechanism further optimises state access paths by efficiently managing state changes and using incremental commit snapshots, reducing storage and processing overhead.

State Consistency and Version Management Mechanism Diagram:

Optimistic Concurrency Control (OCC) and Multi-Version Concurrency Control (MVCC) are complex engineering techniques that strike a delicate balance between the advantages of parallel processing and the demand for strong consistency. The core challenge lies in maximising concurrent execution while minimising the frequency and impact of rollback operations, which is critical for the performance of real-world systems.

Deterministic ordering and global timestamp system

In a multi-threaded concurrent execution environment, ensuring that the final global order of transactions is deterministic and consistent across all nodes is critical. Bitroot addresses this issue by introducing Verifiable Timestamps (VTS) and combining them with a Pipeline BFT election mechanism using Verifiable Random Functions (VRF).

Verifiable timestamps are tamper-proof time records that rely on a decentralised network to create and verify records. Combined with Verifiable Random Functions (VRF) for leader election, VRF can generate verifiable, unpredictable random values, ensuring the fairness and tamper-proof nature of the leader selection process. This helps establish a fair and robust global time reference system.

Verifiable timestamp and VRF election process diagram:

By combining deterministic ordering, verifiable timestamps, and VRF election mechanisms, Bitroot ensures that despite concurrent processing, the final state of the blockchain remains consistent and can effectively resist malicious manipulation or censorship. VRF, as a cryptographic primitive, provides provably fair and unpredictable randomness. Bitroot integrates it into the leader election of Pipeline BFT, enhancing the trustworthiness and decentralisation of the consensus process, which is crucial for public blockchains. It reinforces the core principles of trustlessness and transparency in Web3.

Parallel EVM applications in AI scenarios

Optimisation of EVM for AI applications

AI tasks, particularly deep learning and large language models (LLMs), are highly computationally intensive and have complex state dependencies. They require significant processing power, typically GPUs or TPUs, frequent state updates, and access to large datasets. Traditional sequential EVMs cannot efficiently provide the parallel resources needed for AI model training and inference, limiting blockchain applications in the AI field.

There is a symbiotic relationship between AI and blockchain. Blockchain provides AI with data integrity, transparency, and immutability, addressing issues of data bias and credibility. In turn, AI can optimise blockchain operations, such as managing transaction loads and enhancing security. This mutually beneficial relationship makes Bitroot's architecture crucial for building decentralised AI applications.

The role of Pipeline BFT

Pipeline BFT plays a critical role in AI scenarios. Its sub-second block confirmation time enables on-chain AI models to receive and submit state updates at a higher frequency, thereby achieving more frequent model iterations and real-time response capabilities for AI applications.

Additionally, Pipeline BFT's parallel consensus capability supports high-frequency model iteration tasks. AI model training and inference typically require a large number of small, fast updates or computations.

Pipeline BFT can process multiple blocks in parallel, perfectly aligning with high-frequency, high concurrency requirements. For AI applications that require real-time decision-making (e.g., automated agents, dynamic DeFi protocols), low latency and high-frequency updates are critical. Pipeline BFT directly addresses this issue, making decentralised AI more feasible in demanding use cases.

Parallel EVM functionality

Bitroot's multi-engine parallel EVM supports concurrent invocations of AI microservices. This means it can simultaneously process multiple AI-related smart contract invocations, effectively supporting distributed AI agents or AI microservice architectures to meet the multi-threaded invocation requirements of AI applications.

Bitroot's AI native support aims to expand the EVM instruction set to support AI agent calls and verifiable computation caching. Bitroot's EVM attempts to include dedicated AI instruction sets such as TENSOR_OP or ATTENTION. Integrating such instruction sets would significantly enhance the execution efficiency of AI-specific computations. Current EVM opcodes are general-purpose, while precompiled contracts (Precompiles) are an effective way to natively implement complex cryptographic or computational operations. The evolution of EVM towards AI-specific functionality, whether through expanding opcodes, precompiled contracts, or optimising data structures, is crucial for building a truly ‘AI-native’ blockchain. This will make the EVM a more efficient AI computing foundation, transcending its role as a general-purpose smart contract platform.

For large-scale AI tasks, Bitroot's architecture supports a hybrid on-chain/off-chain execution model, where computationally intensive components (such as model training and large-scale inference) can be efficiently processed off-chain, while critical verification, settlement, and data integrity are maintained on-chain. This hybrid mode allows AI models to fully leverage the massive off-chain computational resources while benefiting from blockchain's security, transparency, and immutability.

Comparison with other consensus mechanisms

In the field of blockchain consensus mechanisms, Pipeline BFT differs significantly from traditional solutions such as PBFT, HotStuff, and Tendermint, particularly in terms of latency, throughput (TPS), energy consumption, scalability, and fault tolerance:.

PBFT: The classic PBFT protocol has O(n^2) communication overhead, leading to a sharp decline in performance as the number of nodes increases. Its throughput is relatively low; for example, systems similar to Ethereum achieve approximately 30 TPS. PBFT can tolerate

f Byzantine faults among 3f+1 nodes..

HotStuff: HotStuff optimises communication complexity to linear O(n) in normal operation mode and introduces optimistic responsiveness, enabling faster and smoother view changes.

.  Tendermint: The Tendermint protocol has a mandatory waiting delay after view changes, which affects its performance under dynamic network conditions..

Pipeline BFT (Bitroot): Bitroot's Pipeline BFT significantly reduces communication overhead through streamlining and BLS signature aggregation mechanisms.

Traditional BFT protocols typically use a serial processing model, while Bitroot's Pipeline BFT and its multi-engine parallel execution architecture achieve non-linear improvements in parallel processing capabilities and transaction throughput. Multi-BFT protocols, similar in concept to Bitroot's Pipeline BFT, allow multiple consensus instances to run in parallel, effectively distributing workload and achieving significant throughput improvements. For example, in a network with 128 replicas, the ISS protocol achieves throughput improvements of 37 times and 56 times compared to PBFT and HotStuff, respectively. Bitroot's dynamic transaction scheduler and conflict graph mechanism further support the parallel execution of non-conflicting transactions, greatly improving overall throughput. This structural transformation fundamentally changes the way consensus and execution are handled, thereby breaking through the long-standing performance bottlenecks of traditional blockchains.

Adaptability to AI Applications

Bitroot Pipeline BFT+ Concurrent EVM demonstrates the following advantages in AI training/inference scenarios:.

Throughput: Bitroot's TPS is several orders of magnitude higher than traditional EVM chains, directly supporting the high-frequency interactions required by AI applications. This is critical for AI applications that require real-time decision-making.

. GPU Utilisation: Parallel EVM can process transactions concurrently, with its self-adaptive scheduling mechanism designed to optimise the utilisation of underlying hardware resources, including GPU/TPU, which are critical for AI computing.

.  Cost: Bitroot's ultra-low transaction fees make high-frequency AI micro-transactions economically viable, effectively addressing the ‘dust payment’ issue required by AI agents.

Bitroot's architectural choices are not merely general performance improvements but are specifically tailored to the demands of AI workloads. This provides unique advantages for building decentralised AI applications, potentially enabling use cases that are currently difficult to achieve on other chains due to performance or cost constraints.

Summary

The Pipeline BFT consensus mechanism is the core engine driving Bitroot's performance leap. Its streamlined, multi-level parallel consensus design fundamentally enhances blockchain throughput and response speed. By closely coordinating with a multi-engine EVM scheduler, Bitroot has built a powerful and efficient decentralised AI computing platform.

The collaborative architecture not only solves the performance bottlenecks faced by traditional blockchains in the AI era, but also lays a solid foundation for the future development of decentralised AI.

Bitroot's architecture provides ample room for further innovation:

Cross-chain consensus: Bitroot has robust cross-chain bridge technology (IMCP), supporting the free circulation of multiple mainstream assets such as BTC, ETH, and BSC. Future development may involve more complex cross-chain consensus mechanisms to achieve deeper interoperability.

Heterogeneous execution engine integration: In addition to the EVM, the platform may integrate other specialised execution environments in the future to accommodate diverse computational needs, such as specific AI models or WebAssembly (WASM).

Evolution of AI-native consensus mechanisms: AI demonstrates significant potential in optimising consensus mechanisms. Future developments may see AI algorithms directly participating in the optimisation of consensus processes, such as through dynamic resource allocation, predictive analysis of transaction flows, or enhanced security audits. This will pave the way for the emergence of true AI-native consensus mechanisms.

Bitroot's strategic positioning makes it a pioneer in the decentralised AI infrastructure field, dedicated to addressing core technical challenges. The integration of AI and blockchain is not merely about performance; it is about building a more fair, transparent, and secure digital future, where AI's power is democratised and governed by the community rather than centralised entities. The infrastructure Bitroot is building is the key pillar to realising this vision.

The "Revolution of Intent" in Human-Computer Interaction

Human-computer interaction has undergone two fundamental transformations, each reshaping the digital landscape. The first was the "Usability Revolution" from DOS to graphical user interfaces (GUIs), which solved the core problem of users being able to "use" computers. By introducing visual elements like icons, windows, and menus, GUIs enabled the proliferation of Office software, games, and laid the groundwork for complex interactions.

The second transformation was the "Context Revolution" from GUIs to mobile devices, addressing the demand for "anytime, anywhere" access. This gave rise to mobile applications like WeChat and TikTok, with gestures like swiping becoming universal digital languages.

We now stand at the brink of the third revolution: the "Revolution of Intent". Its core lies in enabling computers to "understand you better"—AI systems that predict and anticipate users' deeper needs and intentions, not just execute explicit commands. This marks a paradigm shift from "explicit instructions" to "implicit understanding and prediction".

AI is no longer just a tool for task execution but is evolving into a predictive intelligence layer that permeates all digital interactions. For instance, intent-driven AI networks can anticipate and adapt to user needs, optimize resource utilization, and create entirely new value streams. In telecommunications, intent-based automation allows networks to dynamically allocate resources in real time, adapting to changing demands and conditions to deliver smoother user experiences. This capability is critical for managing complexity in dynamic environments like 5G, where efficient resource allocation ensures seamless performance.

This deeper understanding of user intent is critical for the widespread application and value creation of AI. Therefore, the integrity, privacy, and control over the underlying infrastructure supporting AI have become particularly crucial.

However, this "Revolution of Intent" introduces a layer of complexity. While natural language interfaces represent the highest level of abstraction—users simply need to express their intent—the challenges of "prompt engineering" indicate that conveying precise intentions to AI systems may require a new form of technical literacy. This reveals a latent contradiction: AI aims to simplify user interaction, but achieving ideal outcomes often demands that users deeply understand how to "dialogue" with these complex systems. To truly build trust and ensure AI systems can be effectively guided and controlled, users must be able to "peer into their inner workings," comprehend and direct their decision-making processes. This emphasizes that AI systems must not only be "intelligent" but also "interpretable" and "controllable," especially as they transition from mere prediction to autonomous action.

The "Revolution of Intent" imposes fundamental requirements on the underlying infrastructure. If AI's demand for massive data and computational resources remains under centralized control, it will trigger severe privacy concerns and lead to monopolies over the interpretation of user intent. As a ubiquitous "predictive intelligence layer," AI's architecture must prioritize integrity, privacy, and control. This intrinsic demand for robust, private, and controllable infrastructure—combined with AI's ability to adapt to emerging capabilities, understand contextual nuances, and bridge the gap between user expression and actual needs—naturally drives the shift toward decentralized models. Decentralization ensures this "intent layer" cannot be monopolized by a few entities, resists censorship, and protects user privacy through data localization. Thus, the "Revolution of Intent" is not merely a technological advancement in AI; it profoundly drives the evolution of AI's foundational architecture toward decentralization, safeguarding user sovereignty and preventing centralized monopolies over intent interpretation.

The "Revolution of Intent" in AI and the Decentralization Pursuit of Web3

In today’s technological era, AI and Web3 are undoubtedly two of the most disruptive frontier technologies. AI, by simulating human learning, thinking, and reasoning capabilities, is profoundly transforming industries such as healthcare, finance, education, and supply chain management. Meanwhile, Web3 represents a suite of technologies aimed at decentralizing the internet, centered around blockchain, decentralized applications (dApps), and smart contracts. Web3’s fundamental principles emphasize digital ownership, transparency, and trust, striving to build a user-centric digital experience that enhances security and grants users greater control over their data and assets.

The convergence of AI and Web3 is widely regarded as the key to unlocking a decentralized future. This integration creates a powerful synergistic effect: AI enhances Web3’s functionality, while Web3 addresses AI’s inherent centralization concerns and limitations, creating a mutually beneficial outcome.

Key Benefits of AI-Web3 Convergence:

Enhanced Security: AI identifies patterns in massive datasets to detect vulnerabilities and anomalies, strengthening Web3 network security; Blockchain’s immutability further provides AI systems with a secure, tamper-proof environment.

**Improved User Experience：**AI-powered decentralized applications (dApps) are emerging, offering users novel experiences. AI-driven personalization delivers hyper-customized interactions aligned with user needs and expectations, boosting satisfaction and engagement in Web3 applications.

Automation and Efficiency: AI simplifies complex processes in the Web3 ecosystem. Integrated with smart contracts, AI-driven automation autonomously handles transactions, identity verification, and operational tasks, reducing reliance on intermediaries and lowering operational costs.

Advanced Data Analytics: Web3 generates and stores vast amounts of data on blockchain networks. AI is critical for extracting actionable insights, enabling data-driven decision-making, real-time network performance monitoring, and proactive threat detection to ensure security.

This convergence is not merely a simple technological overlay but a deeper symbiotic relationship, where AI’s analytical capabilities and automation enhance Web3’s security, efficiency, and user experience. Meanwhile, Web3’s decentralized nature, transparency, and minimal-trust characteristics directly address AI’s inherent centralization risks and ethical concerns. This mutual reinforcement demonstrates that no single technology can independently realize its full transformative potential; they are interdependent, co-constructing a truly decentralized, intelligent, and equitable digital future. Bitroot’s full-stack approach is built on this understanding, aiming to achieve seamless deep integration across layers, creating synergies rather than fragmented components.

The fusion of these two technologies is inevitable yet faces profound intrinsic contradictions and challenges.

Earlier sections outlined compelling reasons driving AI and Web3 toward inevitable convergence. However, this powerful integration is not without inherent friction points and deep-seated contradictions. The foundational philosophies underpinning these technologies—“AI’s historical trend toward centralization and control” versus “Web3’s fundamental pursuit of decentralization and individual sovereignty”—reveal deeply rooted internal conflicts. These fundamental differences are often overlooked or inadequately addressed by piecemeal solutions, constituting major challenges that current technological paradigms struggle to reconcile.

The core contradiction of this fusion lies in the "control paradox". AI’s "Revolution of Intent" promises unprecedented understanding and predictive power, which inherently implies significant influence or control over user experiences, information flows, and even final outcomes. Historically, such control has been centralized. Web3, by design, seeks to decentralize control, granting individuals direct ownership and autonomy over their data, digital assets, and online interactions. Thus, the core contradiction of Web3-AI fusion is how to effectively integrate a technology (AI) reliant on centralized data aggregation and control with another (Web3) explicitly designed to dismantle such centralization. If AI becomes overly powerful and centralized within Web3 frameworks, it undermines the core spirit of decentralization. Conversely, if Web3 imposes excessive constraints on AI in the name of decentralization, it risks inadvertently stifling AI’s transformative potential and broad applicability. Bitroot’s solution carefully navigates this profound paradox. Its ultimate success hinges on whether it can genuinely democratize AI’s power, ensuring widespread distribution of benefits through community governance rather than repackaging centralized AI within a blockchain shell. By embedding governance, accountability, and user-defined constraints at the protocol layer, Bitroot directly addresses this challenge, aligning AI’s capabilities with Web3’s decentralization principles.

This document will delve into these intrinsic contradictions and practical limitations, revealing the profound "dual dilemma" that necessitates Bitroot’s novel, holistic approach.

Core Challenges of Web3-AI Integration (The Dual Dilemma)

These critical barriers can be categorized into two major domains: the pervasive centralization issues plaguing the AI industry and the inherent technical and economic limitations of current Web3 infrastructure. This "dual dilemma" represents the fundamental problems Bitroot's innovative solutions aim to address.

The Centralization Crisis in AI:

The high degree of centralization in AI development, deployment, and control directly conflicts with Web3’s core principles, posing significant obstacles to achieving a truly decentralized intelligent future.

Problem 1: Monopolization of Compute, Data, and Models

The current AI landscape is dominated by a few corporations, primarily cloud giants like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. These entities maintain monopolistic control over the massive computational resources (especially high-performance GPUs) and vast datasets required to develop and deploy cutting-edge AI models. This concentration of power makes it extremely difficult for independent developers, startups, or academic labs to afford or access the GPU compute power needed for large-scale AI training and inference.

This de facto monopoly not only stifles innovation by creating high-cost barriers but also limits the diversity of perspectives and methodologies integrated into AI development. Furthermore, acquiring high-quality, ethically-sourced data has become a critical bottleneck for many companies, highlighting the scarcity and control issues surrounding this key component of AI. The centralization of compute and data is not merely an economic obstacle—it represents a profound barrier to "AI democratization". The concentration of resources and control determines who benefits from AI advancements and raises serious ethical concerns. It risks creating a future governed by profit-driven algorithms rather than systems serving humanity’s collective well-being.

Problem 2: The "Black Box" Problem and Trust Deficit

Centralized AI systems, particularly complex deep learning models, face a critical challenge known as the "black box problem".These models often operate without revealing their internal reasoning processes, making it impossible for users to understand how conclusions are reached. This inherent lack of transparency severely undermines trust in AI model outputs, as users cannot verify decisions or comprehend the underlying trade-offs.

The Clever Hans Effect exemplifies this issue: models may arrive at correct conclusions for entirely wrong reasons. This opacity makes it difficult to diagnose and adjust system behavior when models produce inaccurate, biased, or harmful outputs.

Moreover, the "black box" nature introduces significant security vulnerabilities. For example, generative AI models are susceptible to prompt injection and data poisoning attacks, which can covertly alter model behavior without user detection. This "black box" problem is not just a technical hurdle—it represents a fundamental ethical and regulatory challenge. Even with advances in explainable AI (XAI), many methods provide only post-hoc approximate explanations rather than true interpretability. Critically, transparency alone does not guarantee fairness or ethical alignment. This highlights a deep trust deficit. Decentralized, verifiable AI aims to address this by relying on verifiable processes rather than blind trust.

Problem 3: Unfair Value Distribution and Inadequate Incentives

In the current centralized AI paradigm, a handful of large corporations control the vast majority of AI resources. Meanwhile, individuals contributing valuable compute power or data often receive little or no compensation. As one critique aptly states, private entities "take everything, sell it back to you"—a fundamentally unfair dynamic. This centralized control actively hinders small businesses, independent researchers, and open-source projects from competing on equal footing, stifling broader innovation and limiting diversity in AI development. The lack of clear, fair incentive structures discourages widespread participation and contribution to the AI ecosystem. This unfair value distribution under centralized AI significantly weakens the motivation for broader participation and diverse resource contributions, ultimately limiting the collective intelligence and diverse inputs that could accelerate AI progress. This economic imbalance directly impacts the speed, direction, and accessibility of AI innovation, often prioritizing corporate interests over collective welfare and open collaboration.

The Capability Limits of Web3:

Existing blockchain infrastructure suffers from inherent technical and economic limitations, hindering its ability to support the complexity, high performance, and cost-efficiency required for advanced AI applications. These limitations form the second critical dimension of the "dual dilemma" in Web3-AI integration.

Problem 1: Performance Bottlenecks (Low TPS, High Latency) Cannot Support Complex AI Computations

Traditional public chains, exemplified by Ethereum, face severe performance constraints:

Low Throughput: Ethereum Layer 1 handles only 15–30 transactions per second (TPS).

High Latency: Sequential transaction execution causes network congestion and high fees.

This limitation stems from strict transaction order-execution design principles—each operation must be processed sequentially. It leads to network congestion and high fees, rendering it unsuitable for high-frequency applications.

Complex AI computations—especially those involving real-time analytics, large-scale model training, or rapid inference—demand throughput and latency levels far exceeding what current blockchain architectures natively provide. The inability to handle high-frequency interactions fundamentally blocks AI integration into decentralized application (dApp) core functionalities.

Many existing blockchains are designed around sequential execution and rigid consensus mechanisms, imposing strict scalability ceilings. This is not merely an inconvenience but a hard technical limit, preventing Web3 from transcending niche use cases to support general-purpose, data-intensive AI workloads. Without fundamental architectural shifts, Web3’s performance limitations will remain a bottleneck for meaningful AI integration.

Problem 2: High On-Chain Computation Costs

Deploying and running complex computations on public chains incurs high transaction fees ("gas fees"), which fluctuate based on network congestion and computational complexity.

●Bitcoin’s Proof-of-Work (PoW) Energy Drain: Bitcoin’s consensus mechanism consumes vast computational power and energy, directly driving up transaction costs and environmental impact.

●Private/Consortium Chain Costs: Even private/consortium chains face high setup and ongoing maintenance expenses. Smart contract upgrades or new feature implementation further inflate total expenditures.

Current economic models on many public chains make compute-intensive AI operations prohibitively expensive for widespread adoption. This cost barrier, combined with performance limits, pushes heavy AI workloads off-chain. This reintroduces the centralization risks Web3 aims to eliminate, creating a dilemma: the benefits of decentralization are undermined by economic impracticality.

Key Challenge: Design a system where critical verifiable components remain on-chain, while intensive computations are processed efficiently and verifiably off-chain.

Problem 3: Paradigm Mismatch (AI’s Probabilism vs. Blockchain’s Determinism)

AI and blockchain differ fundamentally in philosophy and technical design:

AI’s Probabilistic Nature: Modern AI models, particularly those based on machine learning and deep learning, are inherently probabilistic. They model uncertainty and generate results based on likelihoods, often incorporating elements of randomness. This means that, under identical input conditions, probabilistic AI systems may produce slightly different outputs. These models excel at handling complex, uncertain environments such as speech recognition or predictive analytics.

Blockchain’s Deterministic Nature: In contrast, blockchain technology is fundamentally deterministic. Given a specific set of inputs, smart contracts or transactions on a blockchain will always yield the same, predictable, and verifiable output. This absolute determinism serves as the cornerstone of blockchain’s trustless, immutable, and auditable nature, making it highly suitable for rule-based tasks like financial transaction processing.

The inherent technical and philosophical differences between blockchain and AI represent profound barriers to achieving genuine fusion. Blockchain’s determinism is its core strength in establishing trust and immutability, yet it directly conflicts with AI’s probabilistic, adaptive, and often nonlinear nature. The challenge extends beyond merely connecting these paradigms—it demands the construction of a system capable of harmonizing them. How can probabilistic AI outputs be reliably, verifiably, and immutably recorded or applied on a deterministic blockchain without compromising AI’s inherent characteristics or damaging blockchain’s core integrity? This requires complex design involving interfaces, verification layers, and potentially new cryptographic primitives.

Attempts to integrate AI with Web3 often fail to resolve the above fundamental contradictions and limitations. Many existing solutions either merely wrap centralized AI services in crypto tokens, failing to achieve true decentralization, or struggle to overcome the inherent performance, cost, and trust issues of centralized AI and traditional blockchain infrastructure. These piecemeal approaches cannot deliver the comprehensive benefits promised by genuine fusion.

Therefore, a comprehensive, end-to-end "decentralized AI stack" is inevitable. This stack must address all layers of the technical architecture: from the underlying technical architecture (computing, storage) to higher-level components such as models, data management, and application layers. Such an integrated stack aims to fundamentally redistribute power, effectively alleviating widespread privacy concerns, improving fairness in access and participation, and significantly enhancing the overall accessibility of high-level AI capabilities.

A truly decentralized AI approach seeks to reduce single points of failure, enhance data privacy by distributing information across numerous nodes rather than centralized servers, and democratize cutting-edge technologies to promote collaborative AI development, while ensuring strong security, scalability, and genuine inclusivity across the entire ecosystem.

The challenges faced by Web3-AI integration are not isolated, but rather interconnected and systemic. For example, high on-chain costs push AI computations off-chain, reintroducing centralization and black-box risks. Similarly, AI’s probabilistic nature conflicts with blockchain’s determinism, requiring new verification layers—which themselves demand high-performance infrastructure. Therefore, solving computational issues without addressing data provenance, or resolving performance bottlenecks without tackling privacy concerns, will leave critical vulnerabilities or fundamental limitations. The necessity of building a "complete decentralized AI stack" is thus not merely a design choice, but a strategic imperative driven by the interconnected nature of these challenges. Bitroot aims to build a comprehensive full-stack solution, demonstrating its deep recognition that these problems are systemic in nature and require systematic and integrated responses. This positions Bitroot to become a leader in defining the next generation of decentralized intelligent architectures, as its success will prove that it is feasible to address these complex, intertwined challenges in a coherent and unified manner.

Bitroot’s Architectural Blueprint: Five Core Innovations to Address Fundamental Challenges

In the previous sections, we have thoroughly explored the inevitability of Web3-AI integration and the profound challenges it faces, including AI’s centralization dilemma and Web3’s own capability boundaries. These challenges are not isolated but deeply interconnected, forming the "dual dilemma" that hinders the development of a decentralized intelligent future. Bitroot addresses these systemic issues with a comprehensive and innovative full-stack solution. This section details Bitroot’s five core technological innovations and demonstrates how they work synergistically to build a high-performance, high-privacy, high-trust decentralized AI ecosystem.

Innovation 1: "Parallelized EVM" to Solve Web3 Performance Bottlenecks

Challenge: Low TPS and High Latency in Traditional Public Chains Cannot Support Complex AI Computations

The Ethereum Virtual Machine (EVM), as the execution environment for Ethereum and many compatible Layer-1 and Layer-2 blockchains, has a core limitation: sequential transaction execution. Each transaction must be processed strictly in order, resulting in inherently low transactions per second (TPS) (e.g., Ethereum Layer 1 typically operates at 15–30 TPS) and causing network congestion and high gas fees. While high-performance blockchains like Solana claim higher TPS (e.g., 65,000 TPS) through innovative consensus mechanisms and architecture, many EVM-compatible chains still face these fundamental scalability issues. This performance deficit is a critical barrier for AI applications, especially those requiring real-time analytics, complex model inference, or autonomous agent operations, which demand extremely high transaction throughput and minimal latency.

Bitroot’s Solution: Design and Implementation of a High-Performance Parallel EVM Engine with Optimized Pipelined BFT Consensus

Bitroot’s core innovation at the execution layer is the design and implementation of a parallel EVM. This concept fundamentally solves the sequential execution bottleneck of traditional EVMs. By executing multiple transactions concurrently, the parallel EVM aims to deliver significantly higher throughput, utilize underlying hardware resources more efficiently (via multi-threading), and ultimately improve user experience on the blockchain by supporting larger-scale users and applications.

The Parallel EVM Workflow Typically Includes:

1.Transaction Pooling: Group transactions into a pool for processing.

2.Parallel Execution: Multiple executors simultaneously extract and process transactions from the pool, recording the state variables accessed and modified by each transaction.

3.Ordering: Transactions are reordered to their original submission sequence.

4.Conflict Validation: The system rigorously checks for conflicts, ensuring that no transaction’s inputs have been altered by the committed results of previously executed, dependent transactions.

5.Re-execution (if needed): If state dependency conflicts are detected, conflicting transactions are returned to the pool for re-execution to ensure data integrity.

As a complement to the parallel EVM, Bitroot integrates an optimized pipelined BFT consensus mechanism. Pipelined BFT algorithms (e.g., HotShot) aim to drastically reduce the time and communication steps required for block finalization. They process transactions across different rounds in parallel using a non-leader pipelined framework. In pipelined BFT consensus, each newly proposed block (e.g., block n) includes the quorum certificate (QC) or timeout certificate (TC) of the previous block (n-1). QC represents a majority "agree" vote confirming consensus, while TC represents a majority "disagree" or "timeout" vote. This continuous pipelined validation process simplifies block finalization. This mechanism not only significantly improves throughput but also enhances consensus efficiency by minimizing communication overhead in the network. It also helps stabilize network throughput and maintain network liveness by preventing certain types of attacks.

Exponential TPS Improvement via Transaction Parallelism:

Bitroot’s parallel EVM directly addresses fundamental throughput limitations by concurrently processing multiple transactions. This architectural shift enables TPS improvements by orders of magnitude compared to traditional sequential EVMs. This capability is crucial for AI applications that inherently generate large volumes of data and require rapid, high-frequency processing.

Dramatically Reduced Transaction Confirmation Time via Consensus Pipelining:

The optimized pipelined BFT consensus mechanism significantly reduces transaction confirmation latency. It achieves this by simplifying the block finalization process and minimizing communication overhead typically associated with distributed consensus protocols. This ensures near-real-time responsiveness, critical for dynamic, AI-driven decentralized applications.

High-Performance Infrastructure for Large-Scale AI-Powered dApps:

The combination of the parallel EVM and optimized pipelined BFT consensus creates a robust, high-performance foundational layer. This infrastructure is specifically designed to support the computational and transactional demands of large-scale AI-powered decentralized applications, effectively overcoming the long-standing limitations of Web3 in deep AI integration.

Innovation 2: "Decentralized AI Compute Network" to Break Compute Monopolies

Challenge: AI Compute Power is Highly Centralized Among Cloud Giants, Leading to High Costs and Stifled Innovation

Current AI compute power is highly concentrated among a few cloud giants, such as AWS, GCP, and Azure. These centralized entities control the vast majority of high-performance GPU resources, making AI training and inference prohibitively expensive for startups, independent developers, and research institutions. This monopoly not only creates high cost barriers but also stifles innovation and limits the diversity of AI development.

Bitroot’s Solution: Build a Decentralized AI Compute Network Composed of Distributed and Edge Compute Nodes

Bitroot directly challenges this centralization by building a decentralized AI compute network that aggregates idle GPU resources globally, including distributed compute and edge computing nodes. For example, projects like Nosana demonstrate how developers can leverage decentralized GPU networks for AI model training and inference, while GPU owners rent out their hardware. This model utilizes underutilized global resources, significantly lowering AI compute costs. Edge computing is particularly important, as it pushes data processing closer to data generation points, reducing reliance on centralized data centers and lowering latency and bandwidth requirements while enhancing data sovereignty and privacy protection.

Aggregate Idle GPU Resources Globally via Economic Incentives:

Bitroot uses token economics and other incentive mechanisms to encourage individuals and organizations worldwide to contribute their idle GPU compute power. This transforms underutilized resources into usable computational capacity and provides fair economic returns to contributors, directly addressing the issue of unfair value distribution in centralized AI.

Dramatically Reduce AI Training and Inference Costs, Democratizing Compute Power:

By aggregating large-scale distributed compute power, Bitroot offers AI training and inference services at a fraction of the cost of traditional cloud services. This breaks the monopoly of a few giants over compute power, making AI development and applications more accessible and democratic, thus fostering broader innovation.

Provide an Open, Censorship-Resistant Compute Infrastructure:

The decentralized compute network does not rely on any single entity, offering inherent censorship resistance and high resilience. Even if some nodes go offline, the network can continue operating, ensuring continuous AI service availability. This open infrastructure provides a broader space for AI innovation and aligns with Web3’s decentralized spirit. This approach directly challenges the cost barriers and access restrictions imposed by centralized cloud providers. It democratizes computing power by lowering costs for broader participants, including startups and independent developers, and fosters innovation. The distributed nature of the network inherently provides censorship resistance and resilience, as computing no longer depends on a single control point. This also aligns with the broader movement toward sustainable AI by leveraging more energy-efficient, localized processing nodes and reducing reliance on large, energy-intensive data centers, delivering environmental benefits.

Innovation 3: "Web3 Paradigm" for Decentralized, Verifiable Large Model Training

Challenge: Traditional Large Model Training is Opaque, Unverifiable, and Lacks Quantifiable Contributions

Traditional AI large model training is often a "black box": data sources, versions, and processing methods are opaque, leading to potential biases, quality issues, or lack of trustworthiness. Additionally, the training process lacks verifiability, making it difficult to ensure integrity and tamper-proofing. More importantly, in centralized models, contributors (e.g., data or compute providers) cannot be fairly quantified or incentivized, leading to unfair value distribution and insufficient innovation incentives.

Bitroot’s Solution: Deeply Integrate Web3 Features into AI Training

Bitroot constructs a decentralized, transparent, and verifiable large model training paradigm by embedding Web3’s core features into every stage of AI training.

How Web3 Enhances AI):

Data Transparency and Traceability: Training data sources, versions, processing pipelines, and ownership information are recorded on-chain, creating immutable digital footprints. This data provenance mechanism answers critical questions like "When was the data created?", "Who created it?", and "Why was it created?", ensuring data integrity and enabling audits to detect anomalies or biases. This is crucial for building trust in AI model outputs.

Verifiable Processes: Bitroot combines advanced cryptographic techniques like zero-knowledge proofs (ZKPs) to verify key checkpoints in the AI training process. This means that even without exposing raw training data or model internals, cryptographic proofs can validate the correctness, integrity, and tamper-proof nature of the training process. This fundamentally solves the AI "black box" problem and enhances trust in model behavior.

Decentralized Collaborative Training: Bitroot uses token economics to incentivize global participants to securely train AI models collaboratively. Contributors (whether providing compute power or data) are quantified and recorded on-chain, with earnings fairly distributed based on their contributions and model performance. This incentive mechanism promotes an open, inclusive AI development ecosystem, overcoming innovation stagnation and unfair value distribution in centralized models.

Innovation 4: "Privacy-Enhancing Technology Stack" to Build Trust Foundations

Challenge: How to Protect Data Privacy, Model IP, and Computational Integrity in Open AI Networks

In open decentralized networks, AI computations face multiple privacy and security challenges:

·Sensitive training data or inference inputs may be exposed.

·AI model intellectual property (IP) may be stolen.

·Computational integrity is difficult to guarantee, risking tampering or inaccurate results. Traditional encryption methods often require data decryption before computation, exposing sensitive information.

Bitroot’s Solution: Integrating Zero-Knowledge Proofs (ZKP), Multi-Party Computation (MPC), and Trusted Execution Environments (TEE) into a "Defense-in-Depth" Architecture

Bitroot constructs a multi-layered "defense-in-depth" architecture by integrating three leading privacy-enhancing technologies—Zero-Knowledge Proofs (ZKP), Multi-Party Computation (MPC), and Trusted Execution Environments (TEE)—to comprehensively protect data privacy, model IP, and computational integrity in AI systems.

ZKP：

Zero-Knowledge Proofs (ZKPs) allow one party (the prover) to prove to another party (the verifier) that a statement is true without revealing any additional information.

·In Bitroot’s architecture, ZKPs are used for publicly verifiable computation results. This means AI computations can be cryptographically proven correct without exposing input data or model details.

·This directly addresses the AI "black box" issue. Users can verify that AI outputs are derived from correct computational logic without needing to trust the internal workings of the model.

MPC：

Multi-Party Computation (MPC) enables multiple parties to jointly compute a function without revealing their individual raw input data.

·Bitroot leverages MPC to enable collaborative computation across multiple data sources. For example, AI models can be trained or inferences performed without pooling original sensitive datasets.

·This is vital for scenarios requiring data aggregation from multiple owners (e.g., healthcare, finance) while strictly preserving privacy. It effectively prevents data leaks and misuse by ensuring no party gains access to others’ raw inputs.

TEE：

Trusted Execution Environments (TEEs) are hardware-level security zones that create isolated memory and computation spaces within the CPU. These protect data and code from being stolen or tampered with by the host system.

·Bitroot uses TEEs to provide hardware-level isolation for AI model training and inference. This ensures AI model parameters and sensitive input data remain protected during computation, even if the underlying operating system or cloud provider is compromised.

·The combination of TEE with ZKP and MPC is particularly powerful:

• ·TEE acts as a secure host for executing MPC workflows, preventing tampering during collaborative computations.

• ·TEE ensures the integrity of ZKP production, preventing adversarial manipulation of proofs. This integration significantly enhances overall system security by adding hardware-enforced trust layers.

ZKP, MPC, and TEE integration represents a sophisticated, multi-layered privacy and security approach that directly addresses critical trust issues arising when AI processes sensitive data in decentralized environments. ZKP is crucial for proving the correctness of AI computations (inference or training) without exposing proprietary models or private input data, thereby enabling verifiable AI while protecting intellectual property. This directly solves the "black-box" problem by allowing result validation without revealing "how it was done." MPC enables multiple parties to collaboratively train or perform inference on combined datasets without exposing their respective raw data to each other or centralized authorities. This is vital for secure industry collaboration (e.g., healthcare, finance) requiring data from multiple owners while strictly preserving privacy, and for building robust models. TEE provides hardware-level guarantees of execution integrity and data confidentiality, ensuring that even if the host system is compromised, sensitive data and AI models within the TEE remain protected during computation, preventing unauthorized access or modification. This "defense-in-depth" strategy is critical for high-risk AI applications (e.g., healthcare, finance) where data integrity and privacy are paramount, and helps establish foundational trust in decentralized AI systems. The complementary nature of these technologies—where TEE protects MPC protocols and ZKP generation—further enhances their combined effectiveness.

Innovation 5: "Controllable AI Smart Contracts" to Govern On-Chain AI Agents

Challenge: How to Safely Empower AI Agents to Control and Operate On-Chain Assets Without Risking Loss or Malicious Behavior

As AI agents increasingly operate in Web3 ecosystems (e.g., DeFi strategy optimization or supply chain automation), a core challenge is safely granting autonomous AI entities direct control over on-chain assets. Due to their autonomy and complexity, AI agents risk unintended decisions, malicious behavior, or systemic instability. Traditional centralized control cannot resolve trust and accountability issues in decentralized environments.

Bitroot’s Solution: Design a Security Framework for AI-Smart Contract Interactions

Bitroot ensures controllability, verifiability, and accountability of AI agents through a comprehensive security framework:

Permissioning and Proving Mechanism: Every on-chain operation of AI agents must be accompanied by verifiable proofs (e.g., TEE remote attestation or ZKP) and strictly validated by smart contracts. These proofs cryptographically verify the AI agent’s identity, whether its actions comply with predefined rules, and whether its decisions are based on trusted model versions and weights—without exposing its internal logic. This provides a transparent and auditable on-chain record of the AI agent’s behavior, ensuring compliance with expected outcomes and effectively preventing fraud or unauthorized operations.

Economic Incentives and Penalties: Bitroot introduces a staking mechanism requiring AI agents to lock a certain amount of tokens before executing on-chain tasks. The agent’s behavior is directly tied to its reputation and economic stakes. If an AI agent is found to engage in malicious behavior, violate protocol rules, or cause systemic losses, its staked tokens will be slashed. This mechanism incentivizes benign behavior through direct economic consequences and provides a compensation mechanism for potential errors or malicious actions, thereby enforcing accountability in trustless environments.

Governance and Control: Through a decentralized autonomous organization (DAO) governance model, the Bitroot community can restrict and upgrade AI agents’ functionalities, permissions, and callable smart contract scopes. Community members participate in decision-making via voting, jointly defining the agents’ behavioral rules, risk thresholds, and upgrade paths. This decentralized governance ensures AI agent evolution aligns with community values and interests, avoiding unilateral control by centralized entities and embedding human collective oversight into autonomous AI systems.

The security framework for AI agents' on-chain operations directly addresses critical challenges in ensuring accountability for autonomous AI and preventing accidental or malicious behavior. The requirement for verifiable proofs (e.g., ZKP or TEE proofs) for every on-chain action provides a cryptographic audit trail, ensuring AI agents operate within predefined parameters and that their actions can be publicly verified without exposing proprietary logic. This is crucial for establishing trust in AI agents, especially when they are granted greater autonomy and control over digital assets or critical decisions. The implementation of economic incentives and penalty mechanisms—particularly token staking and slashing—aligns AI agents' behavior with the network's interests. By requiring agents to stake tokens and penalizing misconduct through slashing, Bitroot creates direct economic consequences for undesirable actions, thereby enforcing accountability in trustless environments. Additionally, the integration of DAO governance empowers the community to collectively define, restrict, and upgrade AI agents' functionalities and permissions. This decentralized control mechanism ensures AI agents evolve in alignment with community values and prevents centralized entities from unilaterally dictating their behavior. By embedding human oversight into autonomous AI systems through collective governance, this comprehensive approach transforms AI agents from potential liabilities into trusted autonomous participants within the Web3 ecosystem.

Synergy and Ecosystem Vision

Bitroot does not simply stack AI and Web3 technologies but constructs a closed-loop ecosystem where AI and Web3 mutually reinforce and co-evolve. This design philosophy deeply recognizes that the challenges of Web3-AI integration are systemic and require systemic solutions. By addressing core issues—compute monopolies, trust gaps, performance bottlenecks, high costs, and agent loss of control—at the architectural level, Bitroot lays a solid foundation for the future of decentralized intelligence.

Empowerment 1: Trustworthy Collaboration and Value Networks:

Bitroot’s decentralized AI compute network and verifiable large-model training incentivize global idle compute providers and data contributors through token economics. This mechanism ensures contributors can receive fair rewards and participate in joint ownership and governance of AI models. This automated economy and on-chain rights management mechanism fundamentally resolves unfair value distribution and insufficient innovation incentives in centralized AI, building a collaboration network based on trust and equitable returns. In this network, AI model development is no longer exclusive to tech giants but driven by the global community, aggregating broader wisdom and resources.

Empowerment 2: Democratized Compute Power and Censorship Resistance:

Bitroot’s parallelized EVM and decentralized AI compute network jointly achieve compute democratization and censorship resistance. By aggregating global idle GPU resources, Bitroot significantly reduces AI training and inference costs, making compute capabilities no longer a privilege of cloud giants. Meanwhile, its distributed training/inference network and economic incentive mechanisms ensure openness and censorship resistance of AI infrastructure. This means AI applications can operate in environments free from single-entity control, effectively avoiding centralized censorship and single-point failure risks. This enhanced compute accessibility provides equal AI development and deployment opportunities for innovators worldwide.

Empowerment 3: Transparent, Auditable Execution Environment:

Bitroot’s decentralized, verifiable large-model training and privacy-enhancing technology stack jointly build a transparent, auditable AI execution environment. Through on-chain data provenance, zero-knowledge proofs (ZKP) for training process and computation result validation, and Trusted Execution Environment (TEE) hardware guarantees for computational integrity, Bitroot solves AI’s "black-box" problem and trust deficits. Users can publicly verify the origin of AI models, training processes, and computational correctness without exposing sensitive data or model details. This verifiable computation chain establishes unprecedented trust for AI applications in high-risk domains like finance and healthcare.

These three empowerments together demonstrate that Bitroot’s full-stack architecture creates a self-reinforcing cycle. Democratized compute access and fair value distribution incentivize participation, leading to more diverse data and models. Transparency and verifiability establish trust, which in turn encourages broader adoption and collaboration. This continuous feedback loop ensures AI and Web3 mutually enhance each other, forming a more robust, equitable, and intelligent decentralized ecosystem.

Bitroot’s full-stack technology stack not only solves existing challenges but will also catalyze an unprecedented new intelligent application ecosystem, profoundly transforming how we interact with the digital world.

Empowerment 1: Enhanced Intelligence and Efficiency

AI for DeFi Strategy Optimization: Based on Bitroot’s high-performance infrastructure and controllable AI smart contracts, AI agents can achieve smarter and more efficient strategy optimization in decentralized finance (DeFi). These AI agents analyze on-chain data, market prices, and external information in real time, autonomously executing complex tasks like arbitrage, liquidity mining yield optimization, risk management, and portfolio rebalancing. They identify market trends and opportunities invisible to traditional methods, improving DeFi protocol efficiency and user returns.

Smart Contract Auditing: Bitroot’s AI capabilities enable automated auditing of smart contracts, significantly enhancing Web3 application security and reliability. AI-driven audit tools rapidly detect vulnerabilities, logic errors, and potential risks in smart contract code—even issuing warnings before deployment. This drastically reduces manual auditing time and costs while effectively preventing fund losses and trust crises caused by contract vulnerabilities.

Empowerment 2: Revolutionary User Experience

AI Agents Empowering DApp Interactions: Bitroot’s controllable AI smart contracts allow AI agents to autonomously execute complex tasks directly within DApps, providing highly personalized experiences based on user behavior and preferences. For example, AI agents act as personal assistants, simplifying complex DApp workflows, offering customized recommendations, and even representing users in on-chain decisions and transactions. This significantly lowers Web3 application barriers, boosting user satisfaction and engagement.

AIGC Empowering DApp Interactions: Combined with Bitroot’s decentralized compute network and verifiable training, AI-generated content (AIGC) will revolutionize DApps. Users can leverage AIGC tools in decentralized environments to create art, music, 3D models, and interactive experiences, ensuring ownership and copyright protection on-chain. AIGC will dramatically enrich DApp content ecosystems, enhancing user creativity and immersive experiences. For instance, in metaverse and gaming DApps, AI can generate personalized content in real time, amplifying user interaction and participation.

Empowerment 3: Stronger Data Insights

AI-Driven Decentralized Oracles: Bitroot’s tech stack empowers next-generation AI-driven decentralized oracles. These oracles use AI algorithms to aggregate data from multiple off-chain sources, performing real-time analysis, anomaly detection, credibility validation, and predictive modeling. They filter out erroneous or biased data and transmit high-quality, standardized off-chain data to on-chain systems, providing smart contracts and DApps with more accurate and reliable external insights. This will greatly enhance demand for external data insights in fields like DeFi, insurance, and supply chain management.

These applications highlight Bitroot’s transformative potential across domains. The combination of AI agent on-chain integration and verifiable computing enables applications to achieve unprecedented autonomy, security, and trust levels, driving decentralized finance, gaming, and content creation from simple dApps toward truly intelligent decentralized systems.

By integrating parallelized EVM, decentralized AI compute networks, verifiable large-model training, privacy-enhancing technologies, and controllable AI smart contracts, Bitroot systematically addresses core challenges at the intersection of Web3 and AI—performance bottlenecks, compute monopolies, transparency gaps, privacy, and security. These innovations synergistically build an open, fair, and intelligent decentralized ecosystem, laying a solid foundation for the digital world’s future.

About Bitroot

Bitroot is a decentralised infrastructure platform focused on building a high-performance, low-latency, low-cost blockchain ecosystem. We are committed to building an independent parallelised public chain to solve the pain points of existing public chains in terms of transaction throughput, confirmation speed and fees.

At the same time, Bitroot provides on-chain products such as asset issuance on BTC chain, CeDeFi service and cross-chain bridge to promote the development of digital assets, decentralised finance (DeFi) and digital economy.

Bitroot Parallelised Public Chain Core Advantages and Problem Solving

With its self-developed parallel processing architecture and efficient consensus mechanism, Bitroot Parallelisation Public Chain has the following core advantages:

High throughput and low latency: By processing transactions in parallel, the transaction processing volume per second (TPS) is significantly increased and sub-second block confirmation is achieved.

Low Transaction Costs: Optimising the Gas model effectively reduces transaction costs, allowing users to enjoy more economical on-chain operations.

Security and Stability: Advanced consensus algorithms (e.g. improved Pipeline BFT), multi-signature and smart contract auditing are adopted to ensure network security and data consistency.

Completely independent Layer1 public chain: different from Layer2 solution, Bitroot public chain runs independently, with higher flexibility and application scalability.

These technological innovations effectively solve the problems of performance bottlenecks, high transaction costs and excessive latency of traditional public chains, providing solid support for DeFi, NFT, enterprise applications and cross-chain interoperability.

What can you do on the Bitroot testbed now?

We invite developers and users to explore and build the Bitroot Testbed with us. Here are some of the exciting activities you can get involved in:

Builders can

Deploy Smart Contracts: Try deploying smart contracts on the Bitroot Testbed and test the performance of our public chain.

Develop unique dApps: Unleash your creativity by building unique apps with powerful Bitroot features!

Users can

Experience DApps: Try out some of the DApps powered by Bitroot technology and provide valuable feedback to help us improve. Here are some of the dApps currently available:

@CrusSwap (DEX)
@ALLINDOGE_Alpha (AI)

More DApps are being finalised and will be available to users soon.

Marketing and Promotion : Writing research articles, technical explanations, tutorial guides; posting relevant content on Twitter, WeChat, Telegram, Reddit, etc.; organising community events, AMAs, online discussions, offline Meetups; recommending new users to use Bitroot products.

Participate in test network activities: A series of test activities will be held after the launch, accompanied by some incentives to reward the community for their support of Bitroot.

Testnet information and process

Testnet Information: https://bitroot.gitbook.io/bitroot/product-manual-and-user-guide/bitroot-test-network-information

Claim your test tokens: https://bitroot.gitbook.io/bitroot/product-manual-and-user-guide/bitroot-testnet-test-coin-claim

Add Bitroot RPC information: https://bitroot.gitbook.io/bitroot/product-manual-and-user-guide/add-bitroot-network-information

DEX Trading: https://bitroot.gitbook.io/bitroot/product-manual-and-user-guide/swap

Add/reduce liquidity: https://bitroot.gitbook.io/bitroot/product-manual-and-user-guide/adding-liquidity-and-remove-liquidity

What's next?

Our journey has just begun and we have many plans for the future.

Improving our product based on user feedback: We value the insights and experiences of our TestNet users. Your feedback is critical to improving our product.

Create and maintain an ecosystem page: Our goal is to feature the coolest dApps and showcase the creativity of our community in a dedicated section of the upcoming Bitroot website.

Publishing a point system for beta network activities: Users of our beta network will earn points for community interactions, on-chain tasks, referrals, etc., thus contributing to the rapid development of the Bitroot ecosystem.

Eco-Incentive Programme: In order to encourage innovation and community participation, we will be hosting a series of hackathons, which will provide developers with the opportunity to build on our platform, demonstrating the versatility and potential of our public chain.

In addition, we will be launching ecological incentives designed to reward projects that make positive contributions to the Bitroot ecosystem. By offering these incentives, we aim to foster a vibrant and dynamic community of developers and users.

Expectations and Thanks

The launch of the Bitroot Parallelisation Public Chain Test Network is an important milestone in our journey towards a new era of high-performance blockchain. We invite users and developers around the world to participate in the test, give us their feedback, and work together to drive the Bitroot ecosystem to continue to evolve and thrive.

Please stay tuned to our official channels for the latest progress and event details!

Follow Bitroot to keep up with the protocol and ecosystem progress:

Website | Twitter | Discord | Mirror |Telegram

The RootStar Programme will be open to members of the global community, and through a multi-dimensional incentive mechanism, it will identify and support those ‘RootStars’ who truly drive Bitroot forward!

I. Bitroot RootStar Foundation (transparent operation)

Funding Pool Management

Historical ambassador rewards and official promotion funds are injected into the foundation in batches, with a recommended reserve of ≥$50,000 equivalent BRT for the first period.
Transparent mechanism: Monthly disclosure of fund usage details in social media (e.g. 60% studio subsidy, 20% KOL cooperation, etc.).

Community Supervision Committee

Elect 5 people from the ambassadors with top rankings in terms of coin holdings and contributions to form a monitoring group to vote on major expenditures.

II. Studio Programme (Reinforcement of landedness)

Upgraded application requirements:

Basic requirement: hold 20 BRT + fixed venue (need to submit a photo of the venue with watermark).

Tiered reward system:

🌟 Basic standard (1 venue/month): 1,000 USD equivalent BRT

🌟 🌟 Premium Studios (3 shows/month + 100 new people in the community): additional $300-$500 equivalent BRT

Additional benefit: Premium studios receive official co-branding (e.g. ‘Bitroot × [city name] Innovation Centre’).

Implementation of SOPs:
Standardised official toolkit: PPT template, promotional video footage, offline event checklist.

Mandatory checklist: the video must include [number of participants + live interactive session] to prove authenticity.

III. Bitroot Business School (systematic knowledge output)

Lecturer training programme

Certification system: Lecturers who pass the assessment are awarded NFT certificates, which are divided into three levels: junior/senior/chief (with incentives of US$800/1200/2000/month respectively).

Course Development: Compulsory course: ‘Bitroot Technical Architecture’ ‘BRT Economic Model Practical Course: ‘How to Organise an Effective Campaign’

IV. Online community operations

Upgrade requirements:

Group activity assessment: at least 3 interactive topic discussions per week (official topic library provided), eliminating zombie groups.

Fission Reward: For every group member who invites 5 people and holds ≥1 BRT, the group owner will be rewarded with $10 equivalent BRT.

Unified logo system:

All communities are required to use the officially designed unified avatar + number (e.g. Bitroot Chinese Community-No.008).

V. KOL Affiliate Programme

Tiered co-operation:

Head KOL (fans>200,000): customised content (e.g. in-depth project interviews), rewarded with a combination of BRT + cash.

Waist and tail KOL: Provide light cooperation templates such as ‘BRT Challenge’, and settle the payment according to the conversion data.

Community boosting mechanism:

Set up ‘heat task’: when KOL content is released, community members can unlock additional drop pools by forwarding it more than 500 times.

VI. New - Data Tracking and Feedback

All promotions are required to use official tracking links/UTM parameters, and TOP3 contributors are selected each month for additional rewards (e.g. AMA opportunities with founders).

🪐 Whether you're a builder or a believer. Bitroot is looking forward to working with you to become a ‘Root Star’ and shine the light of Web3!

However, shortly after the birth of Bitcoin, people began to realise the potential of smart contracts and wanted to introduce their functionality to the Bitcoin network. However, implementing smart contracts on Bitcoin faces a number of challenges and limitations due to the fact that Bitcoin's design goals and security model are different from those of smart contract platforms such as Ether.

One of the most important limitations is the simplicity of Bitcoin's scripting language. Bitcoin uses a scripting language called Bitcoin Script to define validation conditions for transactions, but it is very limited in its ability to implement complex smart contract logic. This makes it difficult to write and execute complex smart contracts on Bitcoin.

In addition, Bitcoin's security and stability may pose some challenges for implementing a complete smart contract system. The design goal of Bitcoin is to be a decentralised digital currency that focuses on security and attack resistance. To achieve these goals, Bitcoin employs relatively conservative design choices that limit certain features and scalability. This conservative design has made the Bitcoin network more stable, but it has also limited its development in smart contracts.

Overall, Bitcoin has some historical legacy issues with smart contracts, mainly due to its design goals, scripting language limitations, and security and stability concerns. Nonetheless, there are still projects and solutions that attempt to extend smart contract functionality on the Bitcoin network, but due to the inherent functionality shortcomings of Bitcoin scripting, there have been no projects that have made a splash.

OP_CAT is coming back soon!

Today, however, there has been a resurgence in the expansion of Bitcoin's smart contracts. op_cat was initially part of Bitcoin's official command set, but due to the prudence of Satoshi Nakamoto, the opcode was moved out of the Bitcoin protocol in August 2010. op_cat allows for combinatorial concatenation of multiple byte strings of UTXO unlocking scripts, which can increase the programmability of BTC's mainnet, the scalability of its procedures, and the computational complexity of on-chain verification. scalability, and computational complexity of on-chain verification.

OP_CAT is already live on the Bitcoin Signet testnet on 1 May 2024

The reintroduction of OP_CAT provides Bitcoin with a powerful tool through which so-called smart contracts, which set pre-specified conditions for specific Bitcoin outputs, can be implemented. This not only provides a new approach to scaling, but also supports many other innovative approaches that rely on smart contracts. In addition, it signals that Bitcoin is more than just a payment network, but can also be a versatile and scalable computing platform.

The return of OP_CAT will bring major changes to Bitcoin:

It can enable and emulate restriction clauses (covenants), changing the dynamics of scripts and providing more useful features for the Bitcoin network. Similar to the 2021 Taproot upgrade, OP_CAT can create entirely new layers of functionality.

It enables and enhances higher level security features such as vaults and even a return feature that will return all assets to a "safe address" if a malicious actor obtains your seed key before the actor sends them away from you. Providing users with a higher level of protection against theft.

It is possible to create wallet permission lists and other dynamic limit output scripts, as well as a new form of wills/trusts for sending bitcoins to heirs.

It creates Layer2 and bridging mechanisms that connect other chains.

Relying on the return of OP_CAT, the basics are in place to run smart contracts on Bitcoin.

Bitroot: The Transformer of Bitcoin Smart Contracts

The conditions are now in place for Bitcoin to introduce smart contracts, and Bitroot is an enabler, a change agent, and the Bitroot project consists of three core components: the Bitroot Protocol, the BRVM, and the Bitroot Layer2, which work in tandem to give Bitcoin the ability to run a wide range of innovative applications.

Bitroot Protocol

The Bitroot Protocol is a native programmable asset issuance protocol based on Bitcoin.The various existing Bitcoin asset issuance protocols, which generally suffer from complex protocol implementations, poor user experience, and rubbish Unspent Transaction Outputs (UTXO) Ordinals are "engraved" on a particular Satoshi by embedding metadata into the transaction's Witness Data field that is "inscribed" on a particular satoshi. However, the Ordinals protocol has the undesirable consequence of the proliferation of UTXO.Runes homogenised token protocol based on UTXO has a more sensible technical architecture than Ordinals. It does not support NFT asset issuance, and the long font size of runes is not conducive to propagation and exchange listing.

Bitroot Protoco is a simple and flexible protocol that enables the minting and transfer of native assets on Bitcoin, and allows for the issuance of BRT20-compliant tokens and NFTs through the Bitroot protocol, which is positioned as a data layer on Bitcoin that can store arbitrary data in Bitcoin transactions (which can be deposited into the Bitcoin blockchain). Maintains a robust abstraction layer between the Bitcoin UTXO system and the Bitroot system. Provides more operability for asset innovation and applications on the Bitcoin chain.

But just issuing native assets on Bitcoin is not enough. Bitroot's vision is to enrich the BitcoinFI ecosystem by introducing smart contracts to Bitcoin, thereby leveraging Bitcoin's trillion-dollar liquidity. Therefore, Bitroot has developed and implemented BRVM, which makes Bitcoin interoperable with Bitroot Layer2, enabling two-way verification based on zero-knowledge proofs.

BRVM

BRVM is a concrete implementation of a computational paradigm expressing Turing-complete Bitcoin smart contracts, and can be thought of as a 'Bitcoin Virtual Machine'. Unlike the Ethernet Virtual Machine (EVM), BRVM does not perform operations on the chain, but rather verifies them, similar to the principle of Optimistic Rollups. Using this mechanism, any computable function can be verified on Bitcoin. As such, the BRVM is the infrastructure that connects BTC to the rest of Layer2.

The core principle of BRVM is to translate EVM/WASM/JavaScript opcodes into Bitcoin Script opcodes, which is a virtual machine that uses logic gate circuits as a kind of intermediate form between EVM opcodes and Bitcoin Script opcodes. With BRVM, instructions that would otherwise be processed on EVM/WASM can be processed directly on the Bitcoin chain.

The Bitroot protocol solves the problem of executing smart contracts on the Bitcoin blockchain through BRVM, but using a combination of logic gate circuits to directly express certain extremely complex transaction processing flow may generate a huge amount of work, which is a huge test for Bitcoin. Therefore, Bitroot will perform smart contract calculations in a Layer2 manner, and then verify the calculation results through BRVM, thus extending the computational power of Bitcoin smart contracts.

Bitroot Layer2

Bitroot Layer2 is a Layer 2 solution for Bitcoin that inherits the security of Bitcoin. In Bitroot, we build a layered virtual machine technology derived from the BRVM solution. This technology supports arbitrary types of computational operations through zero-knowledge proofs and optimistic execution mechanisms, and verifies the validity of Layer2 computations in the Bitcoin network. BRVM also enables us to support any front-end type of smart contract (e.g., smart contracts implemented in EVM).

The core significance of the birth of Bitroot Layer2 is to help BRVM solve the problem of needing a lot of computing power. Eco-users can perform all kinds of interactions in Layer2, such as DeFi, Metaverse, Chain Tour, DEX, and all kinds of derivatives transactions, and the computation of these applications will be done in Layer2, and then verified in the Bitcoin network via BRVM. This ensures the high performance required by the various products, and also ensures that the results of the transactions are validated by the Bitcoin network.

In a nutshell, Bitroot is not just an asset issuance protocol or Layer2, it's more of a turnkey solution for introducing smart contracts to the Bitcoin ecosystem. Starting from solving the problem of issuing Bitcoin native assets, to empowering Bitcoin native assets and introducing smart contract support, it was discovered that even if Bitcoin could run smart contracts, the weak performance of the Bitcoin network would not be able to support the large number of transactions.

Therefore, Bitroot lays the foundation for the entire Bitcoin smart contract scaling solution by deriving a high-performance Layer2 that supports bi-directional zero-knowledge proofs, and in the future, Bitroot is bound to pry trillions of dollars of liquidity on the Bitcoin chain through the introduction of smart contracts and become the vanguard of Bitcoin smart contracts.

A New Era for Bitcoin

Bitroot is driving a major change in the bitcoin ecosystem by introducing smart contracts to the bitcoin network. This will revolutionise the Bitcoin ecosystem, giving it a whole new look and a broader range of applications.

In summary, Bitroot's three core components each play a key role.

The Bitroot Protocol is an asset issuance protocol that allows for the issuance of BRT20-standard tokens and NFTs on Bitcoin, opening up new possibilities for crypto assets.

BRVM is a "Bitcoin Virtual Machine" that translates smart contract opcodes, such as ethereum virtual machines, into Bitcoin scripts to run and verify smart contracts on the Bitcoin chain.

Bitroot Layer2 is a high-performance Layer 2 network supporting bi-directional zero-knowledge proofs, capable of efficiently handling complex smart contract computations and obtaining validation on the Bitcoin network via BRVM.

Through the organic combination of these three links, Bitroot enables Bitcoin for the first time to have the ability to run a variety of DeFi, meta-universe, Chain Tour, DEX and other smart contract applications, expanding Bitcoin from a single digital currency to a multi-functional computing platform. This not only releases trillions of dollars of bitcoin's liquidity, but will also lead to a large number of innovative applications that will greatly enrich the bitcoin ecosystem. It can be said that Bitroot is opening a new era of smart contracts for Bitcoin.

With Bitcoin's recent halving event and the approval of a Bitcoin exchange-traded fund (ETF), Bitcoin has once again hit a record high of $73,000, once again showing its strong appeal as an investment asset. At the same time, emerging assets in the Bitcoin ecosystem such as Ordinals and Runes have also drawn public attention to the potential applications of Bitcoin. These developments not only open up new possibilities for the future of Bitcoin, but also unprecedented opportunities for investors and market participants, heralding the opening of a trillion-dollar market in the Bitcoin market.

BitrootBRT20 is a root protocol based on the Bitcoin blockchain. It is another innovative Bitcoin blockchain asset issuance protocol after Ordinals and Runes. It mainly uses the transaction and block log of Bitcoin to achieve enhanced blockchain functions, such as asset issuance, decentralized transactions and smart contracts.

This investment research article aims to evaluate Bitroot's technical characteristics, market potential, competitive environment and the main risks it faces, and provide decision support for potential investors.

Technical overview: Bitroot's working principle and function

Bitroot is a Bitcoin-based blockchain meta-protocol BRT20 that utilizes the Bitcoin blockchain to store and transmit additional data, extending the functionality of Bitcoin. By embedding root data in Bitcoin transactions, Bitroot can support complex financial instruments and smart contracts without modifying Bitcoin's core protocol. Here are the technical details and how it works:

1. Transaction Embedding and OP _ RETURN

Bitroot uses Bitcoin's OP _ RETURN operation code to embed data in ordinary Bitcoin transactions. OP _ RETURN is a scripted operation code used to include data in a bitcoin transaction without affecting the validity of the transaction. This approach allows users to insert up to 80 bytes of data into the output of a transaction, which is considered unexpandable (i.e. "burned") by the Bitcoin network, guaranteeing that this data will not affect the money supply of Bitcoin.

Here is an example of using Python and the bitcoinlib library (which is a popular library for handling bitcoin-related operations) to create a bitcoin transaction that contains the output of OP _ RETURN. First, you need to install the bitcoinlib library. If it is not already installed, you can do so with the following command:

You can then use the following code to construct a Bitcoin transaction containing OP _ RETURN data:

2. Asset Issuance and Trading

Users can issue and trade custom digital assets by creating bitcoin transactions in a specific format. For example, to issue a new asset, a user needs to build a bitcoin transaction that contains special metadata that defines the asset's name, total amount, and other attributes. Once such transactions are confirmed by the Bitcoin network and added to the blockchain, the nodes of the Bitroot protocol parse the data and record the corresponding asset creation and distribution in their internal database.

First, let's say you've installed a fictional "Bitroot" Python library. You can install it with the following command:

Next, use the following Python script to simulate issuing a new asset and embed smart contract logic in the transaction:

1. Smart contract function

Bitroot's smart contract implementation is different from existing EVMs because it is built on top of the Bitcoin blockchain. Bitroot uses a simplified programming language called "Script," which is part of the Bitcoin transaction footbed language, and extends it to support smart contracts. Here are the basic ways Bitroot implements smart contracts

1. Bitcoin-based scripting language

The execution of Bitroot smart contracts relies on Bitcoin's foot language. The scripting language was originally designed to be non-Turing-complete, meaning that it could not perform all computational tasks and was mainly used for simple conditions such as signature verification. Bitroot extends its capabilities by adding additional operation codes and capabilities to support more complex smart contracts.

2.Embed Data in Transactions

Bitroot's smart contract data is embedded through the OP _ RETURN output in bitcoin transactions. OP _ RETURN allows data to be added to Bitcoin transactions that are not expendable to the Bitcoin network, but can be parsed and executed by Bitroot nodes. The logics and change of state of the smart contract can therefore be encoded and stored in this data.

1. Analysis and Execution of Smart Contracts

When the Bitcoin network confirms transactions containing OP _ RETURN data, the Bitroot node will parse the data in those transactions and perform the corresponding actions based on the embedded smart contract code. This includes the transfer of assets, enforcement of contractual terms, etc. Each bitroot node verifies and performs these operations independently to maintain network consistency.

1. Limitations and Functions

While Bitroot enhances the capabilities of smart contracts in this way, its capabilities are limited by the language itself. For example, due to the non-Turing-complete nature of Bitcoin scripts, certain complex computations and conditional logic may be difficult to implement. Bitroot's smart contracts are therefore better suited for applications that do not require complex logic, such as simple financial agreements and asset management.

1. Future expansion

The Bitroot community is already exploring more ways to extend the capabilities of smart contracts, including possible protocol upgrades and integration with other blockchain technologies to provide broader application support.

4.Bitroot wallets and software tools

To enable users to interact with and take advantage of the functionality of the Bitroot protocol, the developer community has provided a range of tools and wallet applications. These tools include graphical interface applications for asset management, trading, and smart contract interactions, as well as APIs that support automation and integration.

1. Dual code base and security model

Bitroot runs on two codebases: Bitcoin Core and Bitroot's own software. This means that Bitroot transactions must be confirmed by the Bitcoin network. Because Bitroot operates on the Bitcoin blockchain, it inherits the security and decentralization features of Bitcoin, but also assumes the potential technical limitations and risks of Bitcoin.

conclusion

In this way, Bitroot extends its functionality with existing Bitcoin infrastructure, allowing users to develop new financial tools and services while keeping Bitcoin core stable and secure. This design fully reflects the adaptability and innovation of blockchain technology, making Bitroot an effective means to achieve high-level functions in the Bitcoin ecosystem.

Market Positioning and Application: Strategic Significance and Practical Use of Bitroot

Bitroot positions itself as a root protocol BRT20 built on top of the Bitcoin blockchain, aiming to enrich its range of applications by extending Bitcoin's basic functionality. This expansion is primarily achieved by enabling Bitcoin to support complex financial instruments, smart contracts, and decentralized applications (DApps). The following details about Bitroot's market positioning and various application scenarios:

1. Digital asset issuance

Bitroot allows users to issue custom digital assets on the Bitcoin blockchain, which can include tokens, collections, or any form of digital representation. These assets can represent physical items, intellectual property, stocks, debt, or any other type of contract.

1. Decentralized financial services (DeFi)

Bitroot uses its meta protocol, BRT20, to support the creation of various decentralized financial services, such as exchanges, lending platforms, and derivatives markets. By using assets and smart contracts issued by Bitroot, users can trade and borrow without a centralized intermediary, reducing costs and increasing efficiency.

1. Smart Contracts and Automated Tradin

Bitroot supports smart contracts, which allow developers to write self-executing contract code to handle complex financial transactions and other types of contract logic. This capability allows users to automate the execution of agreements and contracts without involving traditional legal and financial intermediaries.

1. Games and Virtual Goods

Bitroot offers a new level of functionality in the Bitcoin ecosystem, as developers can leverage its protocol to create and trade virtual goods and characters in games. For example, in-game items can be issued and traded as Bitroot assets, and transactions between players can be conducted directly on the blockchain, ensuring transparency and security of transactions.

5.Art and Intellectual Property

Artists and creators can issue digital assets associated with their work through Bitroot, which can be used as proof of original work, as well as for distribution of copyright and proceeds. This approach provides a new means of financing and marketing for artists, as well as a way for buyers to verify authenticity.

1. Fundraising and crowdfunding

Bitroot can also be used for crowdfunding and fundraising campaigns, where project sponsors can issue tokens as a means of raising funds. These tokens may represent shares in projects, rights to future earnings, or pre-orders for products and services.

Conclusion

Through these applications, Bitroot not only enhances the functionality of Bitcoin, but also opens up new opportunities for various industries and sectors. Its market positioning highlights the versatility of blockchain technology and its potential to disrupt traditional financial and business models. Despite technical and market acceptance challenges, Bitroot's diverse applications demonstrate its long-term potential as a platform for blockchain innovation.

Competitive Analysis: Bitroot's Place in the Bitcoin Ecosystem

Bitroot, BRT20, a root protocol of the Bitcoin blockchain, while uniquely positioned in its market segment, is also facing competition from multiple fronts, especially from other blockchain platforms and tier 2 solutions. Here's a detailed analysis of the state of the Bitroot competition:

1. Comparison with Ethereum

Ethereum is the most direct competitor, offering comprehensive smart contract capabilities and a large developer community. Ethereum's smart contracts are Turing-complete, meaning they are more complex and powerful than what Bitroot offers. Ethereum also supports a wide range of decentralized applications (DApps), which gives it a distinct advantage in attracting developers and users.

2.Comparison with other side chains and layers

Sidechains and Layer 2 resolution solutions such as Lightning Network, RGB and Merlin provide fast and low-cost transactions. These solutions reduce blockchain congestion by creating off-chain transaction channels, while Bitroot's operations still rely on the main chain processing of the Bitcoin blockchain. This may be at a disadvantage in terms of processing speed and transaction cost, but it is reported that Bitroot is also actively developing Bitcoin Layer 2, which only supports high PTS and low Gas, which may make Bitroot become the second tier of Bitcoin.

2. Competition with its Bitcoin asset issuance protocol

Emerging asset issuance protocols such as ordinals protocol, runes and other non-programmable NFT assets, Bitroot provides a mechanism to extend its functionality on the Bitcoin blockchain, allowing users to create and trade multiple types of custom digital assets and execute complex smart contracts. This allows Bitroot to go beyond simple digital collections to support applications ranging from financial derivatives to in-game assets. And as an extension of the Bitcoin blockchain, Bitroot inherits Bitcoin's high security and decentralized characteristics. This provides a solid foundation of security for transactions and assets on Bitroot, protecting them from centralized risks and cyber attacks.

3. Features and Differentiation

Despite fierce competition, Bitroot is unique in its seamless integration with Bitcoin. By running smart contracts on Bitcoin's existing blockchain, Bitroot benefits from Bitcoin's security and decentralized features, which many other platforms cannot offer. In addition, Bitroot's asset creation and trading capabilities allow the use of Bitcoin's infrastructure without the need to create a new blockchain or token.

4. Legal and regulatory challenges

At present, Bitcoin's anti-censorship has attracted attention, but most of the Bitcoin ecological second layer or sidechains do not have anti-censorship ability. While bitroot assets are stored on the Bitcoin chain, and the Bitcoin layer 2 network will use Bitcoin addresses and synchronize information bilaterally, naturally meeting compliance requirements, Bitroot may be in a strong position to attract large enterprises and financial institutions that are concerned about compliance.

Conclusion

The existence and development of Bitroot has brought important impacts and new opportunities to the Bitcoin ecosystem. As RBT20, a root protocol running on the Bitcoin blockchain, Bitroot plays a key role in expanding the basic functionality and application scenarios of Bitcoin. Here is a look at the main impact of Bitroot on Bitcoin and the opportunities it brings:

Influence

Improved functionality of Bitcoin:

By embedding additional data into Bitcoin transactions, Bitroot makes Bitcoin not just a monetary system, but a versatile platform that can support complex transactions and smart contracts. This expansion of functionality increases the diversity and utility of the Bitcoin system.

Increase the practical value of bitcoin:

By supporting the issuance and trading of digital assets, Bitroot enhances the capabilities of the Bitcoin blockchain as a digital asset registry and management platform, which expands the range of applications of Bitcoin and thus its overall value.

Promoting Bitcoin innovation:

The emergence of Bitroot has inspired the community and developers to explore new blockchain applications without modifying the Bitcoin core protocol, and has promoted the innovation and development of Bitcoin technology and ecology.

The emergence of Bitroot has inspired the community and developers to explore new blockchain applications without modifying the Bitcoin core protocol, and has promoted the innovation and development of Bitcoin technology and ecology.

opportunity

New business models and markets:

Bitroot makes it possible to implement a variety of business applications on the Bitcoin blockchain, from game assets to art to complex financial derivatives. This opens up new revenue streams and business opportunities for businesses and individuals, such as the creation and trading of digital art and the development of in-game economic systems.

Enhanced Bitcoin's competitiveness:

While other blockchain platforms such as Ethereum are rolling out support for smart contracts and diverse applications, Bitroot provides a mechanism for Bitcoin to support similar functionality, thereby maintaining its influence and relevance in the highly competitive blockchain market.

Engaging the broader user and developer community:

By providing new features and applications, Bitroot attracts a group of developers and users who seek to develop and experiment with new applications on the Bitcoin platform, thereby enhancing the vitality and innovation of the Bitcoin community.

Bitroot brings important technical extensions and new market opportunities to Bitcoin, which not only improves the functionality and utility of Bitcoin, but also supports the continued innovation and development of the Bitcoin ecosystem. By enabling Bitcoin to support a wider range of applications and services, Bitroot helps maintain Bitcoin's competitiveness and leadership in the growing digital currency and blockchain technology market, and is expected to be the golden key to unlocking the Bitcoin trillion market.

Open source protocol: bitroot is an open source protocol, which means that its code is publicly available and anyone can review, modify or run their own nodes, which increases transparency and reduces the risk of a single point of failure.

User-controlled assets: The tokens issued through bitroot are fully controlled by the user, who owns the private key, representing full control over the asset. This is in contrast to traditional banking systems or centralized institutions that manage assets.

License-free participation: Anyone can use bitroot to create and trade tokens without having to obtain approval from any centralized authority. This is in line with the principles of decentralization and permissionless participation, where everyone has an equal opportunity to participate and innovate.

Security of a decentralized network: bitroot benefits from the decentralization of the bitcoin network in terms of security. The Bitcoin network is maintained by thousands of nodes around the world, making any attempt to attack or manipulate the network extremely difficult and expensive.

bitroot uses the bitcoin blockchain, which provides a decentralized feature that makes token issuance and management both secure and transparent, in line with the core principles of Decentralized Finance (DeFi).

Smart Contracts Are Also the Future of the Bitcoin Ecosystem

The importance of smart contracts in the Bitcoin ecosystem needs no further elaboration, as evidenced by the current popularity of the Bitcoin app market.

The use of tokens issued by bitroot is a cornerstone of the bitcoin ecosystem for the operation of smart contracts.

1.ERC-20-like tokens bring the possibility of DEFI development to Bitcoin.

Ether owes half of its prosperity to the development of DEFI. The on-chain financial protocols are the driving force behind the ecosystem, liquidity is the blood that feeds the ethereum ecosystem, and bitcoin, with a market capitalization three times that of ethereum, has the potential to be even bigger. Pledging, locking, releasing, signing, voting, and governance are some of the technological breakthroughs bitroot has realized on the Bitcoin network. As a user of the Bitcoin network, bitroot allows you to index your assets, monitor market data, and manage your assets in an intelligent and convenient way. Based on this, Bitcoin Network smart contracts have taken a big step forward in providing the foundation for DEFI.

2.With support for the latest ERC-404 protocol, the Bitcoin ecosystem will have a liquid NFT.

ERC-404 is an emerging ethereum token standard that combines the ERC-20 and ERC-721 standards for a token that encapsulates the uniqueness of NFTs with the substitutability and liquidity associated with ERC-20 tokens. The use of bitroot can directly issue ERC-404, shaping a "symbiotic relationship between tokens", which not only can be applied to projects such as RWA, DID, MEME, etc., but also from the perspective of NFT, the release of liquidity in the game, art and all aspects of the more imaginative space.

3.bitroot provides smart contract co-construction and builds bitcoin ecological prosperity.

bitroot extends the programmability of tokens and the ways in which they can be used. By using bitroot to build smart contracts on other chains, we can co-create more creative protocols, increase the functionality of the protocols, and together we can build a thriving bitcoin ecosystem. With the emergence of coin-issuing protocols such as Ordinals and BRC20, Bitcoin has taken a new turn from being just a store-of-value currency to a financial ecosystem, from a zero-interest asset to an interest-bearing asset. This has led to a proliferation of liquidity and second tier solutions for the Bitcoin ecosystem, with infrastructure often being the most important constraint. bitroot has adopted a bottom-up model, solving the problem of asset issuance on the Bitcoin network before building the Bitcoin application protocols that will continue to empower the Bitcoin ecosystem.