温馨提示：本站仅提供公开网络链接索引服务，不存储、不篡改任何第三方内容，所有内容版权归原作者所有
AI智能索引来源：http://www.bee.com/ko/59143.html
点击访问原文链接

Part 3 of the Gonka Algorithm Series: Computational Challenges and Anti-Cheating Mechanisms | Bee Network

进入原网站 导航首页
Part 3 of the Gonka Algorithm Series: Computational Challenges and Anti-Cheating Mechanisms | Bee Network Login 인기 뉴스 밈 런치패드 AI 에이전트 DeSci 탑체인 익스플로러 뉴비의 경우 100x 코인 꿀벌 게임 필수 웹사이트 필수 앱 암호화폐 유명인 드핀 루키 에센셜 함정 탐지기 기본 도구 고급 웹사이트 교환 NFT 도구 안녕, 로그아웃 웹3 유니버스 계략 DApp 꿀벌 하이브 성장하는 플랫폼 기원 후 찾다 영어 코인 충전 로그인 다운로드 웹3 유니 계략 DApp 꿀벌 하이브 기원 후 집•분석•Part 3 of the Gonka Algorithm Series: Computational Challenges and Anti-Cheating Mechanisms Part 3 of the Gonka Algorithm Series: Computational Challenges and Anti-Cheating Mechanisms분석4개월 전업데이트와이엇 20,466 7 Introduction: The core mechanism of Gonka PoW 2.0 The entire process can be summarized as the following diagram:

1. Computational Challenge Generation Mechanism
Computational challenges are at the core of Gonka PoW 2.0, transforming traditional proof-of-work into a meaningful AI computing task. Unlike traditional PoW, Gonka’s computational challenge is not a simple hash calculation, but a complete deep learning reasoning process, which not only ensures network security but also produces usable computational results.

1.1 Unified management of the seed system
All computations are driven by a unified seed, ensuring that all nodes in the network run the same computational tasks. This design ensures reproducibility and fairness in computations, as every node must execute the same computational tasks to obtain valid results.

Data source : mlnode/packages/pow/src/pow/compute/compute.py#L217-L225

Key elements of seed systems include:

-Block hash : acts as the master seed to ensure the consistency of computing tasks

-Public key : identifies the identity of the computing node

-Block height : ensure time synchronization

-Parameter configuration : control model architecture and computational complexity

1.2 Deterministic Initialization of LLaMA Model Weights
Each computational task starts with a unified LLaMA model architecture, with weights deterministically initialized via block hashing. This design ensures that all nodes use the same model structure and initial weights, guaranteeing consistent computational results.

Data source : mlnode/packages/pow/src/pow/models/llama31.py#L32-L51

Mathematical principles of weight initialization:

-Normal 분포 : N(0, 0.02²) – Small variance ensures gradient stability

– Determinism : The same block hash produces the same weight

-Memory efficiency : support float16 precision to reduce video memory usage

1.3 Target vector generation and distance calculation
The target vectors are uniformly distributed on the high-dimensional unit sphere, which is the key to the fairness of the computational challenge. By generating uniformly distributed target vectors in high-dimensional space, the randomness and fairness of the computational challenge are ensured.

Data source : mlnode/packages/pow/src/pow/random.py#L165-L177

In a 4096-dimensional vocabulary space, spherical geometry has the following properties:

-Unit length :

-Angle 분포 : The angle between any two random vectors tends to be 90°

– Concentration phenomenon : most of the mass is distributed near the surface of the sphere

Mathematical principle of uniform distribution on the sphere:

In n-dimensional space, a uniform distribution on the unit sphere can be generated as follows:

1. First generate n independent standard normal distribution random variables:

2. Then normalize:

This method ensures that the generated vectors are evenly distributed on the sphere, and the mathematical expression is:

~에 is the surface area of the n-1 sphere.

Distance calculation is a key step in verifying the calculation results. The effectiveness of the calculation is measured by calculating the Euclidean distance between the model output and the target vector:

Data source : Based on the processing logic in mlnode/packages/pow/src/pow/compute/compute.py

Steps for distance calculation:

1. Permutation application : rearrange the output dimensions according to the permutation seed

2. Vector normalization : Ensure that all output vectors lie on the unit sphere

3. Distance calculation : Calculate the Euclidean distance to the target vector

4. Batch encapsulation : encapsulate the results into ProofBatch data structure

2. Anti-cheating verification mechanism
To ensure the fairness and security of computational challenges, the system has designed a sophisticated anti-cheating verification system. This mechanism verifies the authenticity of computations through deterministic sampling and statistical testing, preventing malicious nodes from obtaining improper benefits through cheating.

2.1 ProofBatch Data Structure
The calculation results are encapsulated into the ProofBatch data structure, which is the core carrier of the verification process. ProofBatch contains the identity information of the computing node, timestamp, and calculation results, providing the necessary data foundation for subsequent verification.

Data source : mlnode/packages/pow/src/pow/data.py#L8-L25

Characteristics of the ProofBatch data structure:

-Identity : public_key uniquely identifies the computing node

– Blockchain binding : block_hash and block_height ensure time synchronization

-Calculation results : nonces and dist record all attempts and their distance values

– Sub-batch support : support extracting successful calculations that meet the threshold

2.2 Deterministic Sampling Mechanism
To improve verification efficiency, the system uses a deterministic sampling mechanism, verifying only some of the calculation results rather than all of them. This design not only ensures the effectiveness of verification, but also greatly reduces verification costs.

Gonka’s verification sampling rate is uniformly managed through on-chain parameters to ensure consistency across the entire network:

Data source : inference-chain/proto/inference/inference/params.proto#L75-L78

Data source : inference-chain/x/inference/types/params.go#L129-L133

Based on the seed system, the sampling process is completely deterministic, ensuring the fairness of verification. By using the SHA-256 hash function and the validator’s public key, block hash, block height and other information to generate the seed, all validators are guaranteed to use the same sampling strategy:

Data source : decentralized-api/mlnodeclient/poc.go#L175-L201

Advantages of deterministic sampling:

– Fairness : All validators use the same sampling strategy

– Efficiency : Only verify part of the data to reduce verification costs

– 보안 : It is difficult to predict the sampled data to prevent cheating

2.3 Statistical Fraud Detection
The system uses a binomial distribution test to detect fraud, using statistical methods to determine the honesty of computing nodes. This method sets an expected error rate based on hardware precision and computational complexity, and uses statistical tests to detect anomalies.

Data source : mlnode/packages/pow/src/pow/data.py#L7

The expected error rate is set taking into account the following factors:

-Floating point precision : Differences in floating point precision between different hardware

-Parallel computing : Numerical accumulation errors caused by GPU parallelization

– Randomness : small differences in model weight initialization

-System differences : computing behavior differences between different operating systems and drivers

Data source : mlnode/packages/pow/src/pow/data.py#L174-L204

Summary: Building a secure and reliable AI computing network
Gonka PoW 2.0 successfully combines the security requirements of blockchain with the practical value of AI computing through a carefully designed computational challenge and anti-cheating verification mechanism. The computational challenge ensures the meaningfulness of the work, while the anti-cheating mechanism guarantees the fairness and security of the network.

This design not only verifies the technical feasibility of “meaningful mining”, but also establishes a new standard for distributed AI computing: computing must be both secure and useful, verifiable and efficient.

By combining statistics, 암호화폐graphy, and distributed system design, Gonka PoW 2.0 successfully established a reliable anti-cheating mechanism while ensuring computational usefulness, providing a solid security foundation for the technical route of “meaningful mining.”

Note: This article is based on the actual code implementation and design documentation of the Gonka project. All technical analysis and configuration parameters are from the official code repository of the project.

About Gonka.ai
Gonka is a decentralized network designed to provide efficient AI computing power. Its design goal is to maximize the use of global GPU computing power to complete meaningful AI workloads. By eliminating centralized gatekeepers, Gonka provides developers and researchers with permissionless access to computing resources while rewarding all participants with its native GNK token.

Gonka was incubated by US AI developer Product Science Inc. Founded by the Libermans siblings, Web 2 industry veterans and former core product directors at Snap Inc., the company successfully raised $18 million in 2023 from investors including OpenAI investor Coatue Management, Solana investor Slow Ventures, K5, Insight, and Benchmark Partners. Early contributors to the project include well-known leaders in the Web 2-Web 3 space, such as 6 Blocks, Hard Yaka, Gcore, and Bitfury.

Official Website | 깃허브 | 엑스 | 불화 | 백서 | 경제 모델 | User Manual

이 글은 인터넷에서 퍼왔습니다: Part 3 of the Gonka Algorithm Series: Computational Challenges and Anti-Cheating MechanismsRecommended Articles

Related: TechnoRevenant Raised $300 Million in 7 Days: Is It an Insider or the New King of the Blockchain?Recommended Articles Who is TechnoRevenant, the man who earned $38 million in 20 minutes while Hyperliquid users lost hundreds of millions, and who invested $15 million earlier this year to become a major investor in WLFI? How did he do it? WLFI Big Investors’ “Fat Fingers” On the evening of September 1st, as many pre-sale addresses rushed to claim WLFI tokens in an effort to sell at the opening high, causing Ethereum gas fees to exceed 100 Gwei for the first time in a long time. However, moonmanifest.eth, an address holding 1 billion WLFI tokens, calmly claimed its tokens. Community verification later revealed that this address was TechnoRevenant, known for its $38 million profit on Hyperliquid last week. TechnoRevenant began planning its World Liberty Financial project as early as January 2025 ,…

# 분석# 암호# 토큰© 版权声명배열 上一篇 Messari Researcher: Using Perp DEX to Invest in US Stocks: The Next New Blue Ocean 下一篇 AI Cryptocurrency Trading Competition Latest Results: DeepSeek Returns to Top Spot 상关文章 Behind the tokenization of US stocks: a return to a narrative, or a signal of the evolution of Web3 financial structure? 6086cf14eb90bc67ca4fc62b 26,192 1 Amidst the crossroads of waiting for the Monad mainnet launch: Should the ecosystem move forward or remain where it is?R 6086cf14eb90bc67ca4fc62b 22,442 1 Exclusive Interview with RaveDAO: With TGE just around the corner, how strong is the consensus formed through offline “p 6086cf14eb90bc67ca4fc62b 19,021 1 AI Agent Weekly Report | Story Ecosystem Poseidon V1.0 Launched; BUZZ Gains Over 80% Weekly (September 1-7)Recommended A 6086cf14eb90bc67ca4fc62b 21,791 Matrixport Market Observation: BTC consolidates at the end of the year, and the crypto market is in urgent need of catal 6086cf14eb90bc67ca4fc62b 38,939 2 DeFi bull market strategy: The US SEC has released positive news, and three categories are worth paying attention to 6086cf14eb90bc67ca4fc62b 27,323 1 댓글 없음 댓글을 남기시려면 로그인이 필요합니다! 즉시 로그인 댓글이 없습니다... 최신 기사 How to Systematically Track High-Win-Rate Addresses on Polymarket? 3시간 전 114 CoinEx Research: Geopolitical Tensions Drive Up Oil and Gold Prices, Crypto Market Absorbs Liquidity Shock 3시간 전 282 Low-Threshold Investment in SpaceX and ByteDance: MSX Partners with Republic to Usher in a New Era of Global Top Unicorn Investment 3시간 전 220 Hold Bitcoin Mid-Term Short Positions, HYPE Successfully Rides the Wave for Profits | Guest Analysis 3시간 전 292 Arthur Hayes: Middle East Flares Up, Time to Be Bullish on Bitcoin 3시간 전 310 인기 있는 웹사이트TempoLighterGAIB글라이더PlanckRaylsBCPokerVooi Bee.com 세계 최대의 Web3 포털 파트너 코인카프 바이낸스 코인마켓캡 코인게코 코인라이브 갑옷 Bee Network 앱을 다운로드하고 web3 여정을 시작하세요 백지 역할 자주하는 질문 © 2021-2026. 모든 권리 보유. 개인 정보 정책 | 서비스 약관 꿀벌 네트워크 앱 다운로드 Web3 여정을 시작해보세요 세계 최대의 Web3 포털 파트너 CoinCarp Binance CoinMarketCap CoinGecko Coinlive Armors 백지 역할 자주하는 질문 © 2021-2026. 모든 권리 보유. 개인 정보 정책 | 서비스 약관 찾다 찾다사이트에온체인사회의소식 熱门推荐 : 에어드롭 헌터 데이터 분석 암호화폐 유명인 함정 탐지기 한국어 English 繁體中文 简体中文 日本語 Tiếng Việt العربية Bahasa Indonesia हिन्दी اردو Русский 한국어

智能索引记录