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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.