🧠 LogicNet - Subnet 🤖

Introduction

Description

Our goal is to develop an open-source AI model capable of complex mathematics and detailed data analysis, enhanced by incentivized human feedback for continuous improvement.

• 📚 Albert Frontend App
• 📊 Miners/Validator Stats
• 📈 Grafana Dashboard
• 📚 Learn more about LogicNet
  • More about the roadmap
  • Info on our open-source specialized model
  • Custom model benchmarking against other models
  • RLHF feature video demo

Key Features

• 🚀 Advanced Computational Network: Incentivizing miners to enhance computational resources for complex AI/ML tasks.

• 💰 Incentive Mechanism:

  Updated Reward System:

  • Initial Score Calculation:

    • Each miner's response is evaluated to calculate an initial score using a weighted sum:
      • score = (0.2 * similarity_score) + (0.8 * correctness_score) - 0.1 * time_penalty
        • Similarity Score: Calculated based on the cosine similarity between the miner's reasoning and the self-generated ground truth answer.
        • Correctness Score: Determined by an LLM that assesses whether the miner's answer is correct based on the question and ground truth.
        • Time Penalty: Derived from the processing time of the response relative to the specified timeout.

  • Rank-Based Incentives:

    • Miners are ranked in descending order based on their initial scores.
    • Incentive rewards are assigned using a cubic function based on the rank:
      • incentive_reward = -1.038e-7 * rank³ + 6.214e-5 * rank² - 0.0129 * rank - 0.0118 + 1
      • This function scales rewards non-linearly to emphasize higher ranks, encouraging miners to provide higher-quality responses.
    • Reward Scaling:
      • The cubic function adjusts rewards so that top-ranked miners receive significantly higher rewards than lower-ranked ones.
      • Negative initial scores result in an incentive reward of zero.

  • Purpose of the New Incentive Mechanism:

    • Enhance Competition: By differentiating rewards based on rank, miners are motivated to outperform others.
    • Improve Quality: The emphasis on correctness and similarity encourages miners to provide accurate and relevant answers.
    • Address Flat Incentive Curve: The non-linear reward distribution resolves issues where miners previously received similar rewards despite varying performance levels.

• 🌟 Continuous Improvement: Expanding the math problem sets and categories to cover a broader range of topics.

Neurons Documentation

• 📖 Validator
• 📖 Miner