Proof of Useful Work (PoUW)
Proof of Useful Work (PoUW) serves as a cornerstone validation mechanism in the Cortensor network. While Proof of Inference (PoI) focuses on embedding vector distances to ensure consistency and reliability of AI outputs, PoUW evaluates the quality, relevance, and correctness of results through pre-defined prompts and templates. Together, these complementary systems ensure a robust decentralized framework for validating tasks and outputs.
How PoUW Works
Task Submission Users submit tasks to the network, and miners generate AI inference outputs based on the requirements. These outputs are evaluated using model-specific prompts to verify their quality and adherence to the task specifications.
Validation by Validators
Prompt-Based Assessment: Validators use pre-defined prompts and templates tailored to specific tasks or models. These prompts are designed to assess key metrics such as relevance, accuracy, and usefulness.
Scoring: Validators assign scores to outputs, typically on a numerical scale (e.g., 1-10), based on task-specific criteria provided in the prompts.
Collaborative Validation: Validators pool their scores, cross-referencing results to achieve a consensus on the quality and validity of outputs. This collaborative approach mitigates individual bias and ensures fairness.
Integration with NodeReputation Validator scores are fed into the NodeReputation system, influencing miner rankings and incentivizing consistent performance. Reliable miners are rewarded, while those producing subpar outputs see a decline in their reputation scores.
How PoUW Differs from PoI
PoUW: Focuses on output validation through task-specific prompts and templates, ensuring the relevance and quality of the task completion. Validators actively use models to perform evaluations.
PoI: Primarily measures embedding vector distances between outputs from different nodes. This process ensures consistency and reliability when the same input is processed across multiple nodes, identifying variations and confirming results.
Together, PoUW and PoI form a robust dual-layer validation mechanism:
PoI ensures output consistency across decentralized nodes.
PoUW ensures output relevance and quality using task-specific evaluations.
Efficient Sampling for Validation
To maintain scalability, the Cortensor network employs task sampling:
Only a subset of tasks is selected for validation.
Validators focus on these tasks to balance efficiency with thoroughness.
Sampling ensures the network can scale while maintaining high validation accuracy.
Relation to Other Modules
NodeStats: Captures validation performance, integrating it into long-term metrics for miner and validator behavior.
Cognitive Module: Oversees mining processes and validation orchestration, ensuring the system aligns with PoUW and PoI standards.
Session Module: Manages user task submissions and validation outcomes, providing seamless integration for miners, validators, and users.
Key Benefits of PoUW
Task-Specific Validation: Pre-defined prompts ensure outputs align with task requirements.
Decentralized Trust: Validators operate independently to provide unbiased evaluations.
Scalability: Sampling ensures efficient validation without compromising quality.
Fair Rewards: Integration with NodeReputation incentivizes meaningful contributions and reliable outputs.
Future Enhancements
Dynamic Prompt Systems: Expanding templates for more complex and diverse AI tasks.
Enhanced Validator Incentives: Rewarding accuracy and consistency in validation efforts.
Real-Time Feedback: Providing immediate insights into validation processes for users and miners.
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