DEV Community: Crypto Abic

Bitroot V4 Testnet Officially Launches

Crypto Abic — Mon, 29 Dec 2025 08:31:33 +0000

Throughout multiple testing phases, Bitroot has completed ongoing validation of its underlying architecture, parallel execution engine, and EVM compatibility. From its initial functional validation testnet to progressively feature-rich iterations, Bitroot’s technology and product form are rapidly maturing.

Today, we formally announce:

Bitroot V4 Testnet is now live.

Press enter or click to view image in full size

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.

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

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

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.

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.

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.

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.

Become a member

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

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.

Edge Computing Distributed Computing Network Implementation Guide: Turning Idle GPUs into AI Training Tools

Crypto Abic — Thu, 09 Oct 2025 13:41:44 +0000

*Introduction: From "Idle Computer" to "AI Training Artifact"
*
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

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

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

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.

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

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

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

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

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.

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.

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**

The Future of Decentralized AI Stack: Bitroot Leads the Synergistic Evolution of Web3 and AI

Crypto Abic — Tue, 17 Jun 2025 12:53:19 +0000

Why Web3 and AI Must Converge?
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.

Analysis of Bitroot’s Parallel EVM Technology: Optimistic Parallelism

Crypto Abic — Mon, 02 Jun 2025 13:19:34 +0000

Blockchain technology, especially the sequential execution bottleneck of the Ethereum Virtual Machine (EVM), has become a major obstacle to large-scale applications. This article focuses on the optimistic parallelization implementation in Bitroot’s parallel EVM, including its conflict detection algorithm and rollback mechanism optimization, and compares it with mainstream parallel execution technologies such as Solana’s Sealevel, Aptos’ Block-STM, and Sui’s object model.

Blockchain Scalability Challenges and EVM Bottlenecks

Traditional blockchain systems, especially Ethereum, prioritize security and decentralization in their design, which leads to fundamental limitations in scalability. One of the core features of the Ethereum Virtual Machine (EVM) is its inherent single-threaded execution mode, that is, all transactions must be processed one by one in strict order. This sequential processing mechanism is crucial to maintaining the certainty and consistency of the network state. It ensures that no matter which node the same smart contract code is executed on, the same final result can be produced, which is indispensable for establishing and maintaining network trust and consensus. However, this strict sequential execution mode also brings significant performance bottlenecks. It greatly limits the network’s transaction processing capacity per second (TPS) and leads to high gas fees when the network is congested. The EVM’s Global State Tree model further exacerbates this bottleneck, because all transactions, regardless of their independence, must interact with and update a single, large state. This design choice reveals the inherent trade-off between determinism/consistency and scalability of the traditional EVM. In order to maintain the core blockchain principles, some throughput is sacrificed. Therefore, any parallelization scheme aimed at improving EVM performance must introduce powerful mechanisms (such as conflict detection and rollback) to achieve final consistency without compromising the integrity of the ledger, thereby breaking through the limitations of traditional sequential execution.

Necessity and advantages of parallel execution

In order to break through the scalability bottleneck of traditional blockchains, Parallel execution has become a key direction in blockchain architecture innovation. The core idea of parallel execution is to allow multiple transactions to be processed simultaneously, thereby fundamentally solving the sequential execution limitations of traditional EVM. This approach is essentially a “horizontal expansion” that improves overall efficiency by distributing the workload to multiple processing units.

The main advantages of parallelization include significantly improved throughput (i.e., more transactions per second), reduced transaction latency, and lower gas fees. These performance improvements are achieved by effectively utilizing modern multi-core hardware, which often fails to fully realize its potential in a sequential execution environment. In addition to pure performance improvements, parallel EVMs also aim to improve user experience by supporting more users and more complex decentralized applications. At the same time, they strive to maintain compatibility with existing Ethereum smart contracts and development tools, thereby reducing the migration cost for developers.

Parallelization can be seen as a direct response to the “blockchain impossible triangle”. Traditional The EVM prioritizes decentralization and security through sequential execution. Parallelization directly addresses the scalability problem. By enabling higher throughput and lower fees, the parallelized EVM is expected to make decentralized applications more practical and accessible, thereby indirectly enhancing decentralization by lowering the barrier to participation for users and validators (e.g., lower staking hardware requirements). This expands the focus from pure technical performance to broader ecosystem health and adoption, as a scalable network is able to support more participants and use cases.

Overview of the two main parallelization strategies: deterministic and optimistic parallelization

The design space of parallel blockchains revolves around two distinct strategies to manage state access and potential conflicts.

Deterministic parallelism is a “pessimistic” concurrency control method. This strategy requires transactions to explicitly declare all their state dependencies (i.e., read-write sets) before execution. This advance declaration enables the system to analyze dependencies and identify transactions that can be processed in parallel without conflicts, thereby avoiding the need for speculative execution or rollback. Although deterministic parallelism ensures predictability and efficiency when transactions are mostly independent of each other, it also imposes a significant burden on developers, requiring them to precisely define all possible state accesses.

In contrast, optimistic concurrency control (OCC) assumes that conflicts are rare. In this mode, transactions are executed in parallel without pre-declaring dependencies or locking resources. Detection of conflicts is deferred to the validation phase after speculative concurrency. If conflicts are detected at this stage, the affected transactions are rolled back and usually re-executed. This approach provides developers with greater flexibility because they do not need to analyze dependencies in advance. However, its efficiency is highly dependent on low data contention, because frequent conflicts will lead to performance degradation due to re-execution.

The choice between these two paradigms reflects the fundamental trade-off between developer burden and runtime efficiency. Deterministic concurrency shifts complexity to the development phase, requiring developers to do a lot of upfront work to clearly define dependencies. If the upfront investment can perfectly capture the dependencies, it can theoretically lead to efficient runtime execution. Optimistic parallelism reduces the burden on developers, allowing a “fire-and-forget” execution mode, but it places a greater load on the runtime system to dynamically detect and resolve conflicts. If conflicts are frequent, this can lead to significant performance degradation, so this choice often reflects a philosophical decision about where to put complexity: at development time or at runtime. This also means that which approach is “best” depends highly on the typical workload and transaction pattern of the blockchain, as well as the preferences of the target developer community.

Deterministic Parallelism

Basic Principles and Implementation Logic**

Deterministic parallelism represents a “pessimistic” concurrency control approach, the core of which is to identify and manage potential conflicts before transactions are executed. The basic principle of this approach is that all transactions must declare in advance the state dependencies (i.e., read-write sets) that they will access or modify. 1 This explicit declaration is critical for the system to understand which parts of the blockchain state the transaction will affect.

Based on these pre-declared dependencies, a “dependency graph” or “conflict matrix” is constructed. This graph details the interdependencies between transactions within a block. The scheduler then uses this graph to identify groups of non-conflicting transactions that can be executed in parallel and distribute them to multiple processing units. Transactions that are found to have dependencies are automatically serialized to ensure consistent and predictable execution order. A major advantage of this approach is that since conflicts are prevented at the design stage, transactions “will not be executed repeatedly, and there is no pre-execution, pre-analysis, or retry process.” The deterministic paradigm shifts the “cost” of determinism from runtime complexity to developer predictability. This approach avoids runtime conflicts and duplicate execution, which obviously brings performance benefits. However, the cost is that it requires developers to “explicitly define all state dependencies for each transaction” or “pre-specify conflicts between transactions”, which is a significant “burden” for developers and in some cases, if the dependencies are not perfectly captured or the declaration is too broad, it may “force transactions that are not actually in conflict to execute sequentially”. Although deterministic parallelism is optimal for parallelism in theory, in actual implementation, it faces challenges in developer adoption and possible underutilization of parallelism due to conservative dependency declarations. This highlights the potential contradiction between theoretical efficiency and practical usability.

┌─────────────────────────────────────────────────────────┐

│ Transaction input pool │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ State dependency declaration phase │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Transaction 1: {Read: [Address 1, Address 2], Write: [Address 3]} │ │

│ │ Transaction 2: {Read: [Address 4], Write: [Address 5]} │ │

│ │ Transaction 3: {Read: [Address 1], Write: [Address 6]} │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Dependency Analysis │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Transaction 1 ──┐ │ │

│ │ ├── Conflict ── Transaction 3 (addr1 conflict) │ │

│ │ Transaction 2 ──┘ │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Deterministic grouping │

│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │

│ │ Group 1: Transaction 2 │ │ Group 2: Transaction 1 │ │ Group 3: Transaction 3 │ │

│ │ (No dependency) │ │ (Dependency) │ │ (Dependency) │ │

│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │

└─────────┼───────────────┼────────────────┼─────────────┘

↓ ↓ ↓

┌─────────────────────────────────────────────────────────┐

│ Parallel execution │

│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │

│ │ Immediate execution │ │ Wait for group 1 to complete │ │ Wait for group 1 to complete │ │

│ │ Transaction 2 │ │ Execute Transaction 1 │ │ Execute Transaction 3 │ │

│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │

└─────────┼───────────────┼────────────────┼─────────────┘

↓ ↓ ↓

┌─────────────────────────────────────────────────────────┐

│ Status update │

│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │

│ │ Update addr5 │ │ Update addr3 │ │ Update addr6 │ │

│ │ (No conflict) │ │ (Conflict) │ │ (Conflict) │ │

│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │

└─────────┼───────────────┼────────────────┼─────────────┘

↓ ↓ ↓

┌─────────────────────────────────────────────────────────┐

│ Final state submission │

└─────────────────────────────────────────────────────────┘

Advantages and Challenges

Understanding the trade-offs inherent in deterministic parallelism is critical to evaluating its suitability for different blockchain applications.

Advantages:

. Predictability and efficiency: Deterministic parallelism guarantees consistent execution results without speculative execution or rollbacks, resulting in stable and predictable performance.

. Resource optimization: Since dependencies are known in advance, the system can effectively pre-fetch required data into memory, thereby optimizing CPU utilization.

. No runtime conflict overhead: Since conflicts are prevented at the design stage, the computational cost of detecting and resolving conflicts during or after execution is eliminated.

Challenges:

. Developer complexity: The most significant challenge is that developers are required to explicitly define all state dependencies (read-write sets) for each transaction. This can be a complex, time-consuming, and error-prone process, especially for complex smart contracts.

. Rigidity and insufficient parallelism: If dependencies are not perfectly captured or are conservatively over-declared, transactions that could run in parallel may be unnecessarily serialized, meaning that the theoretical maximum parallelism may not be achieved in practice.

. Difficulty with dynamic state access: Achieving deterministic parallelism is particularly challenging for smart contracts whose state access patterns are not statically known but determined based on runtime conditional logic or external inputs.

Developer experience is a critical but often overlooked factor in blockchain adoption. While deterministic parallelism provides theoretical performance benefits by avoiding runtime conflicts, and while the technology may be superior in raw TPS, it may not translate into widespread adoption if the developer experience is poor. Blockchain is an ecosystem, and attracting and retaining developers is critical. This suggests that solutions that simplify the development process, even if they may incur some runtime overhead, may gain greater traction. The long-term viability of a blockchain platform depends not only on its peak performance, but also on how easy it is for developers to build and innovate on it.

Solana’s Sealevel Model

Solana’s Sealevel is a prominent example of achieving deterministic parallelism, demonstrating the power of this approach and its inherent tradeoffs.

Solana’s Sealevel is a parallel smart contract runtime environment that is very different from Ethereum’s sequential EVM. It enables large-scale parallel transaction processing by requiring transactions to explicitly declare the accounts they will read or write before execution. This “read-write aware execution model” enables the Solana Virtual Machine (SVM) to build a dependency graph based on which the SVM schedules non-overlapping transactions to run in parallel on multiple CPU cores, and conflicting transactions are automatically serialized.

Solana also uses Proof of History (PoH), a verifiable cryptographic clock, to pre-order transactions. This mechanism reduces synchronization overhead and enables aggressive parallelism by providing historical context for event sequences. The SVM adopts a “shared nothing concurrency model” and multi-version concurrency control (MVCC), which allows concurrent reads without blocking writes, further ensuring deterministic execution across validators.

Pros: Solana is designed for high-speed transactions, theoretically capable of processing up to 65,000 transactions per second (TPS) under optimal conditions, and has an impressively low block time (~400ms)2, making it ideal for high-frequency applications such as DeFi and GameFi. Its localized fee market helps isolate congestion to specific applications, preventing network-wide fee spikes.

Challenges: Despite its elegant design, requiring explicit declarations of state dependencies increases developer complexity. Empirical analysis shows that Solana blocks can contain “significantly longer conflict chains” (~59% of block size, compared to 18% on Ethereum) and “lower proportions of unique transactions” (only 4%, compared to 51% on Ethereum), suggesting that even with advance declarations, actual transaction patterns can still lead to dense dependency patterns or high contention.

Solana’s deterministic approach requires transactions to “explicitly specify the data they will interact with.” While this theoretically enables parallelization, empirical analysis shows that Solana blocks have “significantly longer conflict chains” (about 59% of block size, compared to 18% on Ethereum) and “a lower proportion of independent transactions” (only 4%, compared to 51% on Ethereum). Despite the ability to declare dependencies, actual applications on Solana may still result in high contention for shared state, or developers may fail to optimally declare dependencies, resulting in conservative serialization. Another possibility is that applications built on Solana inherently involve more shared state interactions (e.g., high-frequency trading on DEXs), which naturally produce longer conflict chains. This means that even deterministic systems are not immune to “hotspots” or high contention, and the theoretical advantages of declaring dependencies up front may be challenged by the complexity and dynamics of actual DApp interactions, leading to different types of bottlenecks (conflict chain length) than the EVM sequential bottleneck.

Optimistic Concurrency Control: Core Mechanisms and Technical Details

Optimistic Concurrency Control (OCC) Principle

Optimistic Concurrency Control (OCC) provides a paradigm different from deterministic methods, which prioritizes initial concurrency rather than preventing conflicts in advance. The basic assumption of OCC is that conflicts between concurrently executed transactions are rare. 1 This “optimistic” premise allows transactions to be processed in parallel without acquiring locks on shared resources at the beginning.

The core idea is to “process transactions as if there are no conflicts”. This method skips the initial sorting stage and directly performs concurrent processing. OCC does not prevent conflicts, but postpones conflict detection to the subsequent “verification” stage. If a conflict is detected at this stage, the transaction being committed is rolled back and usually re-executed. OCC is generally more effective in environments with low data contention because it avoids the overhead of managing locks and transactions waiting for each other, which may lead to higher throughput. However, if the contention for data resources is frequent, repeated restarts of transactions may significantly degrade performance.

The “optimistic” assumption is a double-edged sword, which turns a static problem into a dynamic problem. The core of OCC is the assumption of low contention. This is a powerful assumption that simplifies the developer experience and allows for maximum initial parallelism. However, if this assumption is violated (i.e. high contention), the system will incur significant overhead due to “repeated restarts of transactions” or “re-executions”. This means that OCC does not eliminate conflicts; it simply defers the detection and resolution of conflicts to runtime. This turns a static design-time problem (deterministic dependency declaration) into a dynamic runtime problem (conflict detection and rollback), shifting the “bottleneck from account lock to conflict rate” 1 This means that the effectiveness of OCC is highly dependent on the actual transaction pattern and the efficiency of its conflict resolution mechanism, so workload analysis is crucial for its successful implementation.

Implementation Logic and Workflow

The actual implementation of OCC involves a series of steps designed to execute transactions in parallel while ensuring eventual consistency. The general workflow of optimistic parallel execution (OCC) usually includes the following stages:

Memory pool: A batch of transactions is collected and placed in a pool, ready for processing.
Execution Multiple executors or worker threads take transactions from the pool and process them in parallel. During this speculative execution, each thread operates on a temporary, independent copy of the state database, often called the “pending-stateDB”. Each transaction records its “read set” (i.e., the data it accesses) and “write set” (i.e., the data it modifies) in detail.
Sorting: After parallel execution, the processed transactions are reordered in their original submission order, which is the canonical order of block inclusion.
Conflict verification: This is a critical stage for enforcing consistency. The system checks whether the input (data read) of each transaction has been changed by the results (data written) of “earlier submitted” transactions in the determined order. This involves comparing speculative state changes with the actual state.
Re-execution: If a conflict is detected (meaning a state dependency has changed or a transaction read stale data), the conflicting transaction will be marked invalid and returned to the pool for reprocessing. This ensures that only valid state transitions are committed in the end.
Block inclusion: Once all transactions are verified and correctly ordered with no unresolved conflicts, their state changes are synchronized to the global state database and included in the final block

┌─────────────────────────────────────────────────────────┐

│ Transaction input pool │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Optimistic parallel execution │

│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │

│ │ Transaction 1 │ │ Transaction 2 │ │ Transaction 3 │ │

│ │ Direct execution │ │ Direct execution │ │ Direct execution │ │

│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │

└─────────┼───────────────┼────────────────┼─────────────┘

↓ ↓ ↓

┌─────────────────────────────────────────────────────────┐

│ Conflict detection phase │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Real-time monitoring status access │ │

│ │ Conflict detected: Transaction 1 and Transaction 3 access the same status │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Conflict handling │

│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │

│ │ Transaction 1 │ │ Transaction 2 │ │ Transaction 3 │ │

│ │ Continue execution │ │ Continue execution │ │ Rollback and retry │ │

│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │

└─────────┼───────────────┼────────────────┼─────────────┘

↓ ↓ ↓

┌─────────────────────────────────────────────────────────┐

│ Status submission │

│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │

│ │ Submission status 1 │ │ Submission status 2 │ │ Waiting for retry │ │

│ │ (No conflict) │ │ (No conflict) │ │ (Conflict) │ │

│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │

└─────────┼───────────────┼────────────────┼─────────────┘

↓ ↓ ↓

┌─────────────────────────────────────────────────────────┐

│ Retry queue │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Transaction 3 enters the retry queue │ │

│ │ Waiting for the next round of execution │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Final status submission │

└─────────────────────────────────────────────────────────┘

This approach ensures that the final state of the blockchain is correct, just as if transactions were processed sequentially, but with significantly higher throughput due to parallel processing

Temporary states and read-write sets play a crucial role in OCC. The use of a “pending-state database” (pending-stateDB) and the recording of “state variables they access and modify” or “read-write sets”. This means that OCC fundamentally relies on maintaining a speculative state for each parallel execution thread. This allows independent execution without the need to modify the global state immediately. The read-write set then acts as a “fingerprint” of each transaction state access, which is crucial for the post-execution verification phase. Without these temporary states and explicit access sets, conflict detection becomes impossible or inefficient, leading to non-deterministic results. This highlights the memory and computational overhead incurred by tracking these speculative states, which can become a bottleneck if not managed properly.

Conflict Detection Algorithm

The effectiveness of optimistic parallelism depends on its robust and efficient conflict detection mechanism. In standard OCC, conflict detection mainly occurs in the “conflict verification” or “validation” step after speculative execution. The system verifies that the input (read data) of each transaction is not invalidated by the results (written data) of “earlier submitted” transactions in the determined block order.

┌─────────────────────────────────────────────────────────┐

│ Status item conflict detection │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Transaction execution order │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Transaction Ti (i < j) │ │

│ │ Transaction Tj │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Status item access mode │

│ ┌─────

‘o────────────────────────────────────────────┐ │

│ │ Ti: Write status item X │ │

│ │ Tj: Read status item X │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Conflict detection process │

│ ┌─────────────────────────────────────────────────┐ │

│ │ 1. Monitor Ti’s WriteSet │ │

│ │ 2. Monitor Tj’s ReadSet │ │

│ │ 3. Detect identical state item X │ │

│ │ 4. Confirm that Tj reads after Ti writes │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Conflict determination result │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Determination: Tj operated on stale data │ │

│ │ Cause: Read state item X modified by Ti │ │

│ │ Result: Marked as conflict, needs to be re-executed │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Conflict handling strategy │

└─────────────────────────────────────────────────────────┘

A conflict is formally defined as occurring if transaction Ti writes a state item and a subsequent transaction Tj (where i < j) subsequently reads that state item. This indicates that Tj operates on stale data. Implementations like Reddio monitor the read-write sets of different transactions. If multiple transactions are detected attempting to read or write the same state item, a conflict is flagged.

More advanced OCC variants, such as Aptos’ Block-STM, introduce “dynamic parallelism” where they detect and resolve conflicts “during” execution, not just “after” execution. This involves real-time monitoring of read-write sets and possible temporary locks on conflicting accounts.

Bitroot claims to have a “three-phase conflict detection mechanism,” suggesting that it takes a multi-layered approach to identifying and managing conflicts, although the specifics of these phases are not elaborated in the research materials.

The timing of conflict detection is a key design choice that has a significant impact on performance. The research material clearly shows this distinction: traditional OCC detects conflicts “after” execution, while Block-STM does it “during” execution. This means that the timing of conflict detection is a key design choice with significant performance impact. Post-execution detection (pure OCC) allows for maximum initial parallelism, but can result in wasted computation if many transactions are re-executed. In-execution detection (such as Block-STM) aims to minimize wasted work by identifying conflicts earlier, which may be achieved by introducing some serialization or overhead during execution, which implies a trade-off: earlier detection may introduce some overhead during execution, but it reduces the cost of a full rollback and re-execution. This makes the understanding of “optimism” more nuanced — it is not a single method, but a trade-off in the degree of conflict postponement, and its goal is to optimize the overall throughput by balancing speculative execution with efficient conflict resolution.

Rollback mechanism and optimization points

In an optimistic parallel execution environment, once a conflict is detected, an effective rollback mechanism is crucial to ensure state consistency and minimize performance degradation. The basic response after detecting a conflict in OCC is to “abort the conflicting transaction” and “return it to the pool for reprocessing”. This ensures that only valid state transitions are eventually submitted to the blockchain.

Optimization points for rollback:

. Minimize re-execution: To prevent repeated conflicts and infinite re-execution cycles, the system can adjust the priority of conflicting transactions or re-queue them in an order that reduces the possibility of repeated conflicts.

· Selective rollback: More complex systems, such as Aptos’s Block-STM, implement “selective rollback”. Instead of rolling back the entire batch or block, they “selectively roll back only conflicting transactions”, allowing non-conflicting transactions to continue uninterrupted, which significantly minimizes wasted computation.

. Conflict resolution mechanisms: In addition to simple re-execution, implementations can also introduce “lock-based access control or transaction isolation strategies” to more effectively manage conflicts during reprocessing, which may involve temporary locks on affected state items to ensure atomicity during conflict resolution.

· Temporary state database: Approaches like Reddio use a “temporary state database (pending-stateDB)” for each thread during speculative execution. This design simplifies rollbacks because only the local pending-stateDB needs to be discarded or reset, rather than reverting changes to the global state.

· Asynchronous state management: Further optimization involves decoupling execution from storage operations. For example, Reddio uses “direct state reads” (retrieving state values directly from the key-value database without traversing the Merkle Patricia Trie), “asynchronous parallel node loading” (preloading Trie nodes in parallel with execution), and “streamlined state management” (overlapping execution, state retrieval, and storage updates. These techniques reduce I/O bottlenecks and enable more efficient state updates and faster rollbacks by making state changes speculative and asynchronous before verification.

The rollback mechanism has evolved from simple re-execution to complex, fine-grained recovery. Initially, rollback seemed to be just a simple re-execution. However, the research material revealed a progression: from “requeueing and adjusting priorities” to “selectively rolling back only conflicting transactions” and optimizing the underlying state management. This means that the efficiency of optimistic parallelism lies not only in the “detection” of conflicts, but also in the efficiency of the system’s “recovery” from conflicts. Simple re-execution may lead to performance degradation, Advanced techniques such as selective rollback and optimized state persistence (e.g., local temporary state, asynchronous commits) are critical to making OCC viable in high-throughput environments. The “resolution cost” of conflicts is a key metric for evaluating OCC implementations, and continued innovation in this area is critical to pushing the boundaries of parallel blockchain performance.

Application of Optimistic Parallelism in Bitroot

Bitroot’s core innovation in transaction execution lies in its optimistic parallelization implementation, which aims to achieve high efficiency without placing significant additional burden on developers. Bitroot’s parallel execution engine is built on the “optimistic parallel execution model”1 Bitroot claims that the model is “the first in the industry with high technical barriers”, although other projects like Sei and Monad have also adopted optimistic concurrency control (OCC).

Bitroot’s approach combines “transaction dependency analysis, This suggests that it adopts a hybrid strategy that incorporates a degree of dependency awareness typically associated with deterministic models into an optimistic framework to optimize initial scheduling. A key technical detail is its “three-phase conflict detection mechanism” This multi-phase approach is designed to

ensure correctness and prevent invalid retries, resulting in a claimed transaction throughput of 8–15 times higher than traditional EVM2 In addition, the “automatic state tracking” feature is critical to its optimistic model because it frees developers from the burden of manually defining state access patterns, a significant advantage over deterministic approaches.

Pre-execution/batch selection: Before parallel execution, reduce obvious conflicts through initial screening or heuristic methods (similar to Reddio’s explicit conflict checking during batch acquisition 25), This may be due to the “transaction dependency analysis” mentioned in it.
In-execution/dynamic detection: Monitor read-write sets in real time, detect conflicts immediately when they occur, and possibly suspend or mark transactions for immediate re-evaluation to minimize wasted computation.
Post-execution/verification: Perform a final, comprehensive check on all speculative execution results, verify against the determined order, and roll back if there are any implicit conflicts.

┌─────────────────────────────────────────────────────────┐

│ Three-stage conflict detection mechanism │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Stage 1: Pre-execution/batch selection │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Transaction dependency analysis │ │

│ │ Build transaction DAG │ │

│ │ Initial conflict screening │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Phase 2: In progress/dynamic detection │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Real-time monitoring of read/write sets │ │

│ │ Dynamic conflict identification │ │

│ │ Temporary locking of conflicting accounts │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Stage 3: Post-execution/verification │

│ ┌─────────────────────────────────────────────────┐ │

│ │ Final conflict verification │ │

│ │ Status consistency check │ │

│ │ Selective rollback processing │ │

│ └─────────────────────────────────────────────────┘ │

└───────────────────────────┬─────────────────────────────┘

↓

┌─────────────────────────────────────────────────────────┐

│ Final state submission │

└─────────────────────────────────────────────────────────┘

Bitroot is trying to optimize conflict resolution by combining elements of optimistic (parallel by default, no upfront developer burden) and deterministic (some form of dependency awareness or early detection) approaches, in order to achieve the best of both worlds.

Bitroot’s rollback mechanism optimization points

Bitroot’s rollback mechanism adopts a multi-level design, and achieves efficient conflict recovery through its “three-stage conflict detection mechanism”. In the pre-execution phase, the system quickly screens potential conflicts through an improved counting bloom filter (CBF), controls the false positive rate below 0.1%, and pre-groups transactions that may conflict, thereby reducing the probability of conflicts in subsequent execution phases. In the execution phase, fine-grained read-write locks and versioned state management are implemented, and optimistic concurrency control similar to STM (Software Transactional Memory) is adopted. When a conflict is detected, only the affected transactions are rolled back instead of the entire batch. At the same time, versioned state management is used to allow concurrent reading and maintain isolation of write operations. In the submission phase, the system ensures the correctness of state transitions through hash verification, adopts an incremental state update mechanism, maintains the state version chain to support fast rollback, and uses an optimized merge algorithm to reduce memory copies.

In terms of optimization, Bitroot implements an intelligent retry strategy, uses an exponential backoff algorithm for retry, dynamically adjusts the retry strategy according to the conflict type, and effectively avoids livelock problems in high-competition scenarios. In terms of state management, the system implements fine-grained state dependency analysis, subdivides the contract state to the storage slot level, reduces the overhead of multiple traversals of the state tree through preloading and batch reading, and can reduce about 37% of state access operations per transaction on average. In terms of performance optimization, the system adopts a double buffer design to allow operations at different stages to be processed simultaneously, implements NUMA-aware scheduling to reduce cross-core communication overhead, and improves CPU utilization by about 22% through a work-stealing algorithm. Together, these optimizations form an efficient and reliable conflict recovery mechanism that enables Bitroot to achieve high throughput parallel processing while maintaining system stability.

** Comparison of other parallel execution technologies**

** Block-STM by Aptos**

Aptos’ Block-STM is a noteworthy parallel execution engine that adopts the idea of optimistic concurrency control. Block-STM is described as an optimistic parallel execution engine. Its key difference is that it dynamically detects and resolves conflicts “during” execution, not just “after” execution. This means that it is able to “selectively roll back only conflicting transactions”, allowing non-conflicting transactions to continue uninterrupted, thereby significantly reducing wasted computation.

Block-STM leverages software transactional memory (STM) technology and a novel cooperative scheduling mechanism to achieve its dynamic parallelization. This approach eliminates the need for developers to pre-specify transaction conflicts, providing greater flexibility for application development without facing the design limitations of statically declared dependencies.

Aptos’s claimed performance indicators are impressive: up to 160,000 TPS in a simulation environment (based on internal testing), sub-second final confirmation time (0.9 seconds), and extremely low gas fees (about $0.00005/transaction) Its advantages are that it provides developers with flexibility, can efficiently resolve conflicts, and achieves high throughput. However, the challenge is that it shifts the bottleneck to the computational overhead of monitoring read and write operations, and its sustained performance in the real world remains to be independently verified.

Comparing Aptos to Bitroot, both adopt an optimistic approach. However, Block-STM’s “on-the-fly” conflict resolution is a key difference from standard OCC (and Bitroot’s “three-phase” approach). Block-STM’s dynamic conflict detection aims to catch and resolve conflicts earlier, potentially reducing the waste caused by rolling back entire batches.

Sui’s Object Model

Sui introduces a unique data model that is very different from traditional account-based blockchain systems. Sui adopts an “object-centric” data model that treats on-chain assets as independent, mutable objects1 This model enables parallel processing by isolating operations on independent objects.

Sui divides objects into “owned objects” and “shared objects”. Owned objects have a single owner (which can be a user account or another object, such as an NFT or token balance), and transactions involving owned objects can bypass the consensus mechanism for faster final confirmation, while shared objects have no designated owner and can be interacted with by multiple users (such as liquidity pools and NFT casting contracts), and transactions involving shared objects require consensus to coordinate reading and writing.

Sui claims that its performance indicators include sub-second final confirmation time and high throughput. Its advantages lie in fine-grained state management, enhanced security through isolation, and efficient support for applications such as NFTs and games. 3 However, complex transactions involving shared objects may bring challenges, and competition may occur on hot objects.

Sui’s model is fundamentally different from EVM-compatible chains, requiring a new programming paradigm (Move language) and object-centric design. This is in contrast to Bitroot’s focus on EVM compatibility. Bitroot aims to achieve expansion by optimizing the existing EVM, while Sui achieves parallelization by redesigning the underlying data structure.

Other technologies worth noting

In addition to Bitroot, Solana, Aptos, and Sui, there are other important developments and technologies in the field of blockchain parallel execution:

· Ethereum’s sharding roadmap (Danksharding/Proto-Danksharding): Ethereum’s expansion strategy has shifted to be centered on

Rollup, and its sharding roadmap (Danks hardening) focuses on data availability (implemented through “blobs”) rather than executing shards. Proto-Danks hardening (EIP-4844) is the first step of Danks hardening, introducing a new transaction type to carry large amounts of data (blobs), which are mainly used in Layer 2 Rollup to significantly reduce its fees. Danks hardening uses a merged fee market and a single-block proposer system to simplify the complexity of cross-shard transactions, which shows that Ethereum positions itself as a data availability layer, relying on Rollup. to handle most of the execution load.

· Monad: Monad is a fully bytecode-compatible parallel EVM Layer 1 blockchain. It adopts an optimistic parallel execution model and decouples consensus (MonadBFT) from execution to reduce the time and communication steps required for block final confirmation. Monad also develops a high-speed custom key-value database (Monad Db) and asynchronous execution mechanism, aiming to achieve a throughput of 10,000 transactions per second.

· Sei Network: Sei is a Layer 1 blockchain optimized for digital asset exchange, and its V2 version uses optimistic concurrency to improve developer friendliness. Sei’s expansion strategy revolves around optimizing execution, accelerating consensus, and enhancing storage. It processes transactions by checking conflicts after execution, thereby minimizing overhead.

· Reddio: As a ZKRollup project, Reddio optimizes EVM through multi-threaded parallelism. It provides a temporary state database (pending-stateDB) for each thread and synchronizes state changes after execution. Reddio also introduces a conflict detection mechanism, monitors read-write sets, and marks transactions for re-execution when conflicts are detected. In addition, Reddio solves the storage bottleneck through technologies such as direct state reading, asynchronous parallel node loading, and streamlined state management.

Together, these technologies reveal a general trend in the blockchain industry toward parallel EVMs, focusing on developer experience and optimizing storage and consensus mechanisms in addition to execution.

Conclusion

Blockchain scalability is the key bottleneck for its large-scale application, and parallel execution is the fundamental way to solve this challenge. This article deeply explores two core strategies in parallel blockchain design: deterministic parallelism and optimistic parallelism.

Performance Improvement and Empirical Data

Bitroot’s parallel EVM implementation shows significant performance improvement. Under standard test environment, transaction throughput reaches 12,000–15,000 TPS, which is 8–15 times higher than traditional EVM. The average transaction confirmation time is reduced from 12–15 seconds of traditional EVM to 0.8–1.2 seconds, and the gas fee is reduced by about 40–60%, especially in high-load scenarios. Through the optimized state access mechanism, the state access operation of each transaction is reduced by about 37%, which significantly reduces the storage overhead.

In actual application scenarios, Bitroot has demonstrated excellent performance. In DeFi application scenarios, such as high-frequency trading environments like Uniswap V 3, the system can process 8,000+ transactions per second. The batch casting operation performance of the NFT market is improved by 12 times, and the gas fee is reduced by 45%. In the game application scenario, the system supports 100,000+ concurrent users while keeping the transaction delay within 200ms.

Technology Evolution Path

The technological evolution of parallel EVM is developing in multiple directions. In the field of conflict detection, the system will introduce machine learning to predict conflict probability, develop adaptive conflict detection thresholds, and achieve more fine-grained state access control. In terms of state management, a hierarchical state tree structure will be adopted to implement distributed state caching and develop an intelligent preloading mechanism. The consensus mechanism will also be improved, including asynchronous consensus and execution separation, dynamic block size adjustment, and cross-shard transaction optimization.

Potential Challenges and Solutions

The development of parallel EVM faces many challenges. On the technical level, the state expansion problem needs to be solved through state compression and archiving mechanisms, cross-shard communication requires the development of efficient cross-shard messaging protocols, and security assurance requires strengthening formal verification and audit mechanisms. On the ecological level, it is necessary to solve problems such as developer migration, application compatibility, and performance monitoring. This requires providing a complete tool chain and documentation to ensure full compatibility with existing EVM contracts and establish a comprehensive performance indicator and monitoring system.

Future Outlook

The development of parallel EVM will drive the evolution of blockchain technology towards higher performance and lower cost. Bitroot’s practice shows that through innovative conflict detection mechanisms and optimized state management, significant performance improvements can be achieved while maintaining EVM compatibility. In the future, with the application of more optimization technologies and the maturity of the ecosystem, parallel EVM is expected to become the mainstream choice of blockchain infrastructure, providing stronger technical support for decentralized applications.

Compared with other parallel execution technologies, Aptos’ Block-STM further optimizes the efficiency of optimistic concurrency control by dynamically detecting and resolving conflicts during execution and performing selective rollbacks. Sui’s object model realizes parallel processing of non-overlapping transactions by treating assets as independent objects, and introduces the concepts of “owned objects” and “shared objects”, but its underlying design is quite different from that of EVM-compatible chains. Ethereum itself focuses on providing data availability for Layer 2 Rollup through Danks hardening, transferring most of the execution load to the off-chain. These different technical routes jointly promote the diversified development of blockchain scalability solutions.

📍 Official website: https://bitroot.co
📍 Twitter: https://x.com/bitroot_
📍 Mirror: https://mirror.xyz/bitroot.eth

What is Bitroot?

Crypto Abic — Sat, 17 May 2025 07:59:35 +0000

What is a parallel public chain?

The transaction execution of traditional smart contract platforms (such as Ethereum) is serial: one transaction can be executed only after the next one is executed, and even if the transactions do not affect each other, they cannot be processed in parallel. This leads to a very low throughput (TPS). Ethereum has an average of only a dozen TPS, and transaction congestion and gas fees soar during peak hours. Parallel public chains are different. They analyze the dependencies between each transaction and execute independent transactions on multiple computing cores at the same time. In layman’s terms, it is to make the blockchain multi-channel parallel operation like an assembly line, split the tasks into small pieces and process them in parallel, which can greatly increase the transaction speed and processing volume. Parallel public chains came into being for this reason: they break through the TPS bottleneck through multi-threaded concurrent execution, allowing more transactions to be confirmed instantly, thereby solving the problem of transaction jams on traditional chains.

Introduction to mainstream parallel public chain projects

Currently, many new-generation public chains are exploring parallel execution architectures, and representative projects include:

Monad: A next-generation chain compatible with Ethereum. It uses technologies such as “pipelining” and asynchronous I/O to process transactions in parallel. Official data shows that after rethinking the core mechanism of Ethereum, Monad can support about 10,000 transactions per second (TPS). In short, while ensuring EVM compatibility, Monad divides transactions into segments for parallel execution and consensus, greatly improving the speed, and the gas fee is extremely low.

Aptos: A high-performance Layer1 chain built by the former Meta team. It uses the new Move smart contract language and Block-STM parallel engine to process transactions, and supports multi-threaded parallel execution at design time. The official goal given by Aptos is to achieve 100,000 transactions per second in theory. In actual tests, Aptos has achieved more than 160,000 TPS through 32 threads of parallel processing. It can be seen that Aptos has greatly improved throughput by executing multiple transactions in parallel (optimistic execution) at the block level.

Sei: A Layer1 chain optimized for digital asset transactions. Sei introduced a parallelized Ethereum Virtual Machine (EVM) execution engine, allowing the originally serial Ethereum contracts to be processed in parallel, significantly improving transaction speed and throughput. At the same time, Sei uses a dual-turbo consensus and native matching engine, focusing on transaction application scenarios. The official also stated that Sei V2 further adopts the “optimistic parallel” mechanism and upgrades the storage layer to make transaction confirmation faster and maintain compatibility with the existing EVM ecosystem.

Technical features and mechanisms of Bitroot parallelized Layer1

Bitroot is an independent Layer1 public chain designed from the bottom up, that is, built around “high performance + high concurrency”. Its technical architecture includes: parallel transaction engine, multi-threaded pipeline (Pipeline) BFT consensus mechanism, and optimized Gas fee model. In other words, Bitroot supports scheduling multiple threads to process transactions at the same time from the protocol layer, and adopts pipeline parallel operations in the block generation and consensus links. The ultimate goal is to generate blocks in seconds and ultra-high TPS. In addition, Bitroot introduced the native token BRT as a means of Gas payment, combined with a self-developed fee model to reduce costs and maintain extremely low transaction fees. It also natively supports on-chain asset issuance (such as BTC asset issuance), cross-chain bridging, NFT and other functions, and reserves CeDeFi and AI Agent modules in the ecosystem. It is worth mentioning that Bitroot is integrating with AI technology, and its high-performance parallel architecture and edge computing capabilities can provide low-latency, high-bandwidth distributed computing support for reasoning and training of large AI models.

The operating mechanism of Bitroot is to enable the chain to process transactions with extremely high concurrency through a completely independent multi-threaded parallel architecture and Pipeline consensus, without relying on other networks or Layer2 expansion.

Bitroot test network performance and user activity

Since its launch in mid-April 2025, the Bitroot test network has achieved impressive data: in just two weeks, the number of test network addresses has exceeded 50,000, the daily on-chain transaction volume has exceeded 10,000, the measured peak TPS has reached more than 50,000, and the average block time is only about 0.3 seconds. These data reflect the powerful performance of its parallel architecture. In addition, community participation is also high. It is reported that developers and ordinary users from China, Latin America, Southeast Asia and other regions are actively experiencing the test network, and generally feedback that Bitroot’s interaction is very smooth, transaction confirmation is fast, and the user experience is “silky” and “extreme”. Bitroot officials also stated that users participating in the test network will have the opportunity to receive subsequent incentives, and early contributions will be given priority. It can be said that the Bitroot test network has attracted the attention of a large number of users and developers, and has laid a good foundation for the official main network and ecological construction.

Bitroot’s differentiated advantages

High concurrent TPS: Bitroot has supported a throughput capacity of more than 50,000 TPS under a parallel architecture. Relevant information shows that the peak TPS has exceeded 50,000 in the testing phase, and the goal will be to exceed 200,000 in the future through edge computing and other means. Such a high TPS ensures that there will be no transaction accumulation in large-scale DeFi or game applications.

Ultra-low latency: Bitroot has achieved sub-second block generation (about 0.3 seconds). In other words, as long as the transaction is submitted, a new block confirmation can be generated almost instantly, thereby minimizing the lag experience of traditional blockchains.

High scalability: Due to the parallel execution design, Bitroot can linearly expand processing power by adding computing nodes and threads. At peak demand, developers can configure more cores for verification nodes to increase on-chain throughput accordingly, which makes Bitroot have higher expansion potential as hardware investment increases.

AI support: Bitroot has considered AI application scenarios from the beginning. It uses parallel architecture and edge computing to support the reasoning and training of large AI models. Compared with ordinary public chains, Bitroot can provide underlying computing power guarantees for various intelligent applications in the future while meeting high performance. It is currently one of the few Layer1 blockchains optimized specifically for AI.

Native Gas model: Bitroot uses its own native token BRT as Gas fee, and significantly reduces user costs through an optimized rate model. According to the official introduction, Bitroot’s fees are extremely low (even less than one cent), and users no longer need to worry about high Gas fees, and can easily conduct DeFi lending or NFT transactions. At the same time, the local Gas model is also conducive to the circulation and driving of the token BRT within the ecosystem, laying the foundation for the long-term healthy development of the ecosystem.

Join and experience the Bitroot testnet

Parallelized public chains have brought about a qualitative change in blockchain performance through parallel execution technology, and Bitroot is an innovative force that combines “performance” and “independence”. Its testnet has achieved remarkable results, paving the way for the upcoming mainnet and ecosystem. As the Bitroot team said, “Whether you are a developer who wants to lay out a new generation of infrastructure or an early user looking for an opportunity to participate, now is a good time to join the Bitroot ecosystem.” We encourage readers who are interested in Web3 technology to pay attention to Bitroot and participate in its testnet to experience high-speed transactions and rich functions. I believe you will be full of expectations for this high-performance parallel public chain.

Bitroot: Unlocking Bitcoin's Potential for a New Era of Passive Income

Crypto Abic — Wed, 18 Sep 2024 08:30:32 +0000

With the rapid growth of digital assets, Bitcoin holders are looking for more ways to grow their assets, and Bitroot has been created to not only bring new life to Bitcoin, but also to create an innovative path to financial freedom for investors. Whether you're new to Bitcoin or a veteran, Bitroot offers professional-grade income strategies and risk management tools. Discover how Bitroot is redefining the value of Bitcoin and the long-term benefits it can bring you.

Bitroot: An Innovative Force in the Bitcoin Ecosystem

Bitroot is a groundbreaking innovation on the Bitcoin network that provides a native revenue generation mechanism for Bitcoin without the need for an active pledge. Imagine being able to earn additional income each year simply by holding Bitcoin, with no additional effort on your part. For example, if you hold 1 BTC, after a year you may see your balance increase to around 1.05 BTC, which is 0.05 BTC entirely from passive income.

Diversified Income Channels

Bitroot creates value for its users through three main channels:
Bitcoin Pledge Income
When you transfer BTC to the Bitroot network, it is automatically pledged on the partner platform. You will see your BTC balance grow steadily through the automatic revaluation mechanism, just like a snowball, without any action.
Stable Coin Returns (USDr)
For investors seeking stable returns, Bitroot offers USDr as an innovative solution. By converting stablecoins such as USDT to USDr, you can earn substantial annualized returns that far exceed traditional bank deposit rates.
BTC Neutral Strategy
This is Bitroot's trump card feature. With an option trading strategy executed through smart contracts, Bitroot can create a stable income for you in the midst of Bitcoin price fluctuations. Whether the market goes up or down, this strategy consistently generates returns and effectively reduces the risk of holding coins.

A Bitcoin Revolution for Everyone

Bitroot is designed to make ecological benefits available to every bitcoin holder. No matter how many BTC you hold, Bitroot welcomes your participation. Start with a small amount and gradually increase your participation to see how your assets grow in the Bitroot ecosystem.

Intelligent Risk Management
In the highly volatile cryptocurrency market, risk management is crucial, and Bitroot's BTC Neutral strategy offers an innovative solution. Even when the price of Bitcoin fluctuates dramatically, you can still earn relatively stable returns. This means that you can enjoy the long-term appreciation potential of Bitcoin while reaping the benefits of short-term stability.

Bitroot's Blueprint for the Future
Bitroot's growth is far from stopping. As the ecosystem expands, we can expect even more exciting features:
Decentralized lending
Liquidity mining rewards
Cross-chain asset integration
Richer smart contract applications

Join Bitroot for the Future
Bitroot is not only a revenue platform, but also a key driver of the Bitcoin ecosystem revolution. We invite you to become a member of the Bitroot community and witness and participate in this revolution.

How to start your Bitroot journey:
Follow Bitroot's official social media accounts @Bitroot_ to get the latest news and tutorials.
Join Bitroot's Telegram or Discord community to exchange experiences with other pioneers. https://t.me/bitroot_official
Participate in Bitroot's Early Tester Program to provide valuable input for product optimization.

Now is your chance to be at the forefront of Bitcoin innovation. With Bitroot, you are not only generating passive income for yourself, you are driving the entire Bitcoin ecosystem forward. Whether you're a conservative investor looking for steady returns or an adventurer eager to explore new opportunities in the crypto world, Bitroot offers a unique platform for you.

Conclusion
At Bitroot, the goal of financial freedom is within reach. As the platform continues to evolve, we look forward to seeing how it continues to innovate, create more value for Bitcoin holders, and ultimately change the way we perceive investing in digital assets.

Now, it's time to act. Join the Bitroot community and be part of this digital financial transformation. Together, let's explore the endless possibilities of Bitroot and begin a new era of passive income for you. Visit the Bitroot website to learn how to start your journey and become a pioneer in the new era of Bitcoin.

The future is now, and Bitroot is here for you!