LingoAI
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  • ✍️INTRODUCTION
    • About LingoAI
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    • LingoPod
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  • 1. Reward Release
  • Formula Setup
  • 2. LingoPod Rewards and Stabilization Reserve
  1. INTRODUCTION
  2. LINGOAI Token

Mining

1. Reward Release

First, the 60 million LINGOAI token will be allocated as a staking and mining pool for users, and will be linearly released. It will be allocated according to the following proportions:

Task
Percentage of total supply
Quantity (BillionοΌ‰

Data Contribution

30%

30

Cross-lingual LLM Development

10%

10

LingoPod

20%

20

30 billion tokens will be released over 20 years according to the following rules, while the remaining will be allocated for various tasks according to technical development . (The specific quality of the uploaded voice corpus will be managed by the community governance contract.)

We will use a decremental release model to release a total of 30 billion tokens. This model allows us to determine the monthly release amount, ensuring that the total token supply is gradually released over 20 years.

Formula Setup

Set the following parameters:

  • Total Supply = 30 billion tokens

  • Release Period months = 240 months

We will use an arithmetic sequence to set the monthly release amount. The formula is as follows::

Rn=R1β‹…(1βˆ’d)nβˆ’1R_n = R_1 \cdot (1 - d)^{n-1}Rn​=R1​⋅(1βˆ’d)nβˆ’1

Where:

  • R1​ is the initial monthly release amount

  • d is the monthly decrement amount

  • The first 12 months will release 12% of the entire reward.

Given:

  • Initial monthly release amount R1 = 134560470.49183798

  • the monthly decrement amount : d = 0.010000000149011612​

The release will be as shown in the figure below.

2. LingoPod Rewards and Stabilization Reserve

To mitigate the impact of future price fluctuations on reward earnings, we have established an innovative stabilization fund specifically designed to stabilize reward values.

  1. Parameters:

    • Daily fixed token release amount 𝑅

    • Daily token price 𝑃

    • Daily number of participants 𝑛

    • Daily number of participants ni for reward task i

    • m represents number of reward tasks

    • wi represents the weight of different tasks

    • n_ref and P_ref represent the the benchmark number of participants and he benchmark price respectively

    • Token amount in the reward pool 𝑆

    • πœ† is the difficulty factor for the number of participants

    • alpha is the smooth factor for the price impact

  2. Objectives:

    • Smooth the total value of daily rewards 𝑉, where 𝑉=𝑅⋅𝑃

    • Store a portion of tokens in the reward pool when the number of participants is low, for future use

  3. Function design: Calculation of the actual daily reward value 𝑉: 𝑉=𝑅⋅𝑃

    Adjustment when the number of participants is low, using ln⁑(𝑛) and the difficulty factor πœ†:

Rleft=Rt(1βˆ’(1βˆ’(A1+exp⁑(βˆ’Bβ‹…(nβˆ’C))))βˆ’Ξ±log⁑(PPref))R_{\text{left}} = R_t \left(1 - (1 - \left(\frac{A}{1 + \exp(-B \cdot (n - C))}\right)) - \alpha {\log(\frac{P}{P_{\text{ref}}}}) \right)Rleft​=Rt​(1βˆ’(1βˆ’(1+exp(βˆ’Bβ‹…(nβˆ’C))A​))βˆ’Ξ±log(Pref​P​))
neff=βˆ‘i=1mmwinin_{\text{eff}} = \sum_{i=1}^{m} mw_i n_ineff​=i=1βˆ‘m​mwi​ni​
Rpool=Rtβˆ’RreleaseR_{\text{pool}} = R_{\text{t}} - R_{\text{release}}Rpool​=Rtβ€‹βˆ’Rrelease​

The first part, 1βˆ’a1+exp⁑(βˆ’bβ‹…(nβˆ’c))1βˆ’1+exp(βˆ’bβ‹…(nβˆ’c))a​, ensures that when we set a benchmark number of people, for example, 1000, the amount of rewards drawn into the stabilization fund increases as the number of participants falls below this benchmark. Once the number of participants drops below a certain threshold, the reward extraction will stabilize and follow a standard rate. The latter part, Ξ±β‹…log⁑(P/Pref), ensures that when the price falls below the reference price, the stabilization fund will inject rewards into the pool, providing people with more incentives. Here is the example:

A: 0.9883333339127566, B: 0.02335101596185324, C: 135.56550803743016

n=10, y=0.9499999969308052

n=100, y=0.7000000006186333

n=1000, y=0.011666667779574591

n=5000, y=0.011666666087243427

n=10000, y=0.011666666087243427

n=50000, y=0.011666666087243427

n=100000, y=0.011666666087243427

Supplementing rewards from the pool to smooth the value:

Sβ€²=Rpool+SS' = R_{\text{pool}} + SSβ€²=Rpool​+S

where 𝑇 is a smoothing parameter that determines the rate at which rewards are supplemented from the pool.

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Last updated 1 month ago

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Figure 1